Practical Application of Smart Cities Tools and Strategies

### **Chapter 9**

## Building Smart System by Applied Deep Learning and Spatial Indoor Agent Based Model for a New Adaptation University Learning Process Post Covid-19

*Adipandang Yudono, Sapto Wibowo, Christia Meidiana, Surjono Surjono, Irnia Nurika, Erryana Martati and Yan Akhbar Pamungkas*

#### **Abstract**

The impact of COVID-19 implied various restrictions on people's mobility, especially for the higher education communities, by implementing the Learning from Home approach. This approach has altered the behavior of a human on a daily basis for a year long. Subsequently, the global vaccination program has been the advent of a "New Normal" approach as it reenables the direct human interactions by following health protocols to abide such as social distancing. This study investigated the pedestrian flow in the Department of Urban and Regional Planning (DURP) lecture building, Brawijaya University, and predicted the potential crowd spots using the Integrated Agent-Based Model (ABM), Computer Vision, and the Geographical Information System on an Indoor scale. Additionally, alternative designs of pedestrian flow were proposed to prevent crowds from occurring. The results showed the East and West entrance paths of the DURP building have high traffic, so the proper response is to organize the Southside door as an alternative entrance for pedestrian access. Moreover, the opening of the south gate could reduce the crowd spots on the 2nd Floor of the DURP lecture building.

**Keywords:** pedestrian flow, social distancing, new normal, agent-based modeling, computer vision, geographical information system

#### **1. Introduction**

The rapid development of science on a global scale has affected the potential evolution of 3 groups of traditional scientific branches formal science, social science, and natural science [1–4]. Nowadays, these scientific branches are associating with each other, thus forming new clusters, such as Social Sciences, Humanities,

Arts for People and The Economic (SHAPE), and the Science-Technology-Engineering-Math (STEM). STEM cluster is formed by the combination of Formal Sciences (Mathematics and Statistics) with Natural Sciences (Biology, Physics, and Chemistry) [5]. In contrast, the SHAPE cluster is formed by the social life linked to scientific fields consisting of politics, psychology, and sociology [6, 7].

The merging of scientific clusters in regards to addressing the global issues related to human life still has some discrepancies. The gaps are still present between the natural science from the STEM cluster and the social science field from the SHAPE cluster. Therefore, the development of a new curriculum consisting of the combination of STEM and SHAPE clusters is proposed, namely Humanitarian Engineering (HE), through the ENHANCE project composed of the works of researchers from the Warwick University of U.K., along with the academics from Greece, Bangladesh, Vietnam, and Indonesia.

In the traditional engineering academic texts, it is challenging to find the term HE thus, some researchers have been trying to define it as the development of science progresses. Passino [8] stated that humanitarian engineering is an approach to constructing technologies that could assist the engineering in helping people, while VanderSteen [9] identified HE as a tool to solve social issues. Moreover, Hill and Miles [10] recognized HE as the solution to social problems by investigating the achievement of sustainability in developing countries. Therefore, this study regards HE as a scientific field that focuses on addressing complex humanitarian matters through the perspective of the SHAPE cluster using a STEM approach to propose smart, equitable, and harmonious solutions.

The purpose of this paper is to analyze the humanitarian engineering field through the micro-scale of the planning field by re-designing the pedestrian flow inside a lecture building concerning the new normal learning process to prevent the higher risk of COVID-19 transmission by avoiding the potential crowd spots using Integrated Agent-Based Model (ABM), Computer Vision, and the Geographical Information System on an Indoor scale.

#### **2. Methods**

Replace the entirety of this text with the main body of your chapter. The body is where the author explains experiments, presents and interprets data of one's research. Authors are free to decide how the main body will be structured. However, you are required to have at least one heading. Please ensure that either British or American English is used consistently in your chapter.

This study aims to analyze the lecture building of the Department of Urban and Regional Planning, Brawijaya University, along with its surrounding environment. The descriptive and evaluative analysis is used to investigate the pedestrian's flow, namely Integrated Agent-Based Model (ABM), Computer Vision, and the Geographical Information System on an Indoor scale. The descriptive statistical method is used to investigate the characteristic of the pedestrian consisting of movement, speed, and density. Moreover, evaluative analysis is taken to calculate the density of pedestrian traffic by utilizing the time series data of pedestrians' peak volume during each working hour/day.

Decision making which utilizes images acquired from sensors is known as Computer Vision (CV) [11–13]. The purpose of the CV is to construct an intelligent

#### *Building Smart System by Applied Deep Learning and Spatial Indoor Agent Based Model… DOI: http://dx.doi.org/10.5772/intechopen.106508*

machine with "see" ability. The Agent-Based Model (ABM) is known as an individual-centric and decentralized approach, whereas the modeler is tasked to pinpoint the agent or active entity (in this case, the person), characterize their behavior, detects agents in a specific environment, create connections in between, and establishes the simulation. Moreover, Geographical Information System (GIS) on an indoor scale is defined as a complete mapping system to make the disconnected project data practical, operate complex artificial environments, track indoor devices, evaluate space allocation in confined spaces, and recognize and react to the real-time events.

The detection of the pedestrian can be achieved by using various computer vision methods. One of the classical human detection methods was invented by the Voila-Jones algorithm, which aims to recognize human faces with a fast detection rate at the cost of low accuracy [14]. It is also revealed that the accuracy drops even more for non-frontal faces [15].

In regards to the development of human detection, the Histogram of Oriented Gradient (GOD) was later proposed in junction with linear Support Vector Machine (SVM), which offers an accuracy rate of up to 89% [16]. In addition, the drawback of the HOG algorithm is required expensive computational resources to operate [17].

Nevertheless, the rapid development of CV human detection through neural network algorithms utilizes the classical methods' base concept, namely, You Only Look Once (YOLO) [18]. Single Shot Detection (SSD) [19], and Faster Region-based Convolutional Neural Network) (Faster R-CNN) [20]. Compared with classical image processing methods, improved robustness and reliability are expected from AI-based human detection [21].

The detection of the pedestrian in this study utilizes the You Only Look Once (YOLO) method. The YOLO algorithm was considered to be used regarding its reliability with a fast detection rate [22]. YOLO has been recognized to be able to outperform HOG-SVM. Therefore, it's been widely used for many purposes, such as operating autonomous cars [23].

YOLO implementation was accessible on DARKNET (open source neural network). The idea of YOLO is to see the whole image once and later passes through the neural network once it immediately detects actual objects, thus later known as the name of the method, YOLO, from the abbreviation of You Only Look Once. The purpose of YOLO is to perform real-time object detection. A localizer or repurpose classifier is used by the detection system. A model is utilized for an image at various locations and scales. The highest-rated area image will be regarded as a detection.

YOLO utilizes the Artificial Neural Network (ANN) approach for object detection in an image by dividing the image into regions and predicting each bounding and probability box from each region. The bounding boxes are later compared with each expected probability. In addition to that, there are several advantages of YOLO compared to a classifier-oriented system. YOLO can carry out the test of the entire image with predictions informed globally on the image. YOLO is also several times faster than the Region Convolutional Neural Network (R-CNN) due to its ability to synthesize the neural network for making predictions than the R-CNN, which needs thousands of images to operate.

In order to represent the actual condition of pedestrian traffic and other geographic features, the ABM is assisted by spatial and geographic visualization data. Several ABM and GIS integration applications have been recognized at the macro scale, such as in the region and urban areas. Hartmann and Zerjav [24] revealed that the assimilation between ABM and GIS is proven effective in planning the optimum health service location concerning the nature of the urban population. The ABM and GIS are generally used for building simulation modeling, such as to estimate the impact of resource investment decisions concerning the health costs, population development of an area, and burden and spread of disease. Nonetheless, the integration of ABM at the microscale with GIS at the indoor scale is still limited. Therefore, to fill this gap, this study aimed to incorporate the main idea of CV as the novelty of the research.

#### **3. Research results**

This chapter has three sections: dataset development, human object detection process, and Agent-Based Model and Geographical Information System Indoor. The linkage between these sections will be investigated in the context of research steps for collecting the pedestrian behavior inside the DURP lecture building to rearrange pedestrian traffic to reduce the COVID-19 transmission.

#### **3.1 Dataset development**

Datasets collection is the first step before training the YOLO algorithm to recognize the object. The data collection can be conducted through video recording and later exported as images or downloading related pictures from the internet. **Figure 1** describes the collection of the dataset before being used to train the YOLO algorithm.

There are several criteria considered in data processing testing experiments, which consist of:


A camera with a VGA resolution (320 × 240 pixels) and frame rate of ±20 frames/ second was used to record the video through the Open Camera application. Static exposure value (camera's sensitivity to light) conditions were used as the camera

*Building Smart System by Applied Deep Learning and Spatial Indoor Agent Based Model… DOI: http://dx.doi.org/10.5772/intechopen.106508*

settings. The bright condition was recorded during the daytime outside the room and indoors with lights on, while the dark condition was recorded during the evening, and indoor lights were off. The recorded video has a duration of ±10 seconds, and pedestrians walk five meters away from the camera position. The different conditions for recording the video are being considered to investigate the lighting effects, environment (indoor or outdoor), and the presence of other objects than humans, such as shadows, would affect the detection results.

#### **3.2 Human object detection process**

The selection of human objects was conducted through blob analysis in MATLAB by examining the size of each object. The purpose of the blob analysis function is to determine the area of a human object based on the minimum and maximum blob area. The elimination of objects with a size of fewer than 10,000 pixels and larger than 980 pixels was considered to eliminate non-human objects. The other stages were conducted, including extraction of video frames, normalization of images, background subtraction, morphological operations, and object detection. Subsequently,


#### **Table 1.**

*Human object detection testing.*

the images extracted from the video frames were normalized. The image was later reconstructed in the form of opening, closing, and filling operations through a background subtraction approach and morphological operations. Specific values are determined at each stage for human object detection.

The conducted test results based on 30 videos were reported in the form of "correct" and "incorrect" information. The test could be considered appropriate if the system detection results match the manual calculation results and vice versa for inappropriate information results. **Table 1** explains the human object detection testing, which revealed 11 incorrect tests out of 30 tests conducted. There were two tests with incorrect information from 10 video tests with bright lighting effects. On the contrary, the number of tests with incorrect information was higher in dark lighting effects, with nine out of 10 tests. These results revealed that the system could detect human (pedestrian) objects better in bright lighting than in darker lighting due to the distraction from the shadows, which were later misinterpreted as human objects. The other distractions that could affect the detection are light bias and other moving objects such as smoke.

The tests during dark conditions revealed that the poor lighting and unstable camera sensor caused the lower detection capability and exceeded the number of detected objects from the manual calculation. Therefore, the DURP lecture building was set with bright conditions (lights on) at the later stage of pedestrian data collection through video recording, as described in **Figure 2**.

#### **3.3 Agen based model and geographical information systems indoor integration process**

The agent-based model (ABM) is considered a computational technique that aims to reinforce the analysis of the artificial environment utilized by interfacing the agents in nontrivial ways. The response from every agent is demonstrated separately. Agents who act with agents and later respond to their ongoing case as a set of attitude rules are subsequently derived from the principal theory actions and connections within a definitive framework [25].

When the agent initiates communication with the Geographic Information System (GIS) by sending a "seek to migration" message to the GIS, the evaluation of topological connection and geographic coordinates provides the agent the attitude of being authorized. Subsequently, the GIS responds by updates (renewing the GIS database and related graphical demonstration) or returns a message to the agent mentioning why the migration failed to perform, such as the area is as of now involved, or no permission could be given for the development [26].

*Building Smart System by Applied Deep Learning and Spatial Indoor Agent Based Model… DOI: http://dx.doi.org/10.5772/intechopen.106508*

#### **Figure 2.**

*Video recording of pedestrian detection and tracking on 1st and 2nd floor of the DURP building.*

#### **4. Discussions**

#### **4.1 Observed pedestrian traffic on the 1st floor of the DURP building under normal conditions**

The investigation of pedestrian traffic was done through CCTV recording and later processed with CV to conduct the tracking analysis. It is revealed that the pedestrian traffic on the 1st Floor of the DURP lecture building was concentrated in the corridor connecting the West and East Gate with a very high-density category (1.5). On the contrary, the lowest level of pedestrian density, with values ranging from 0.1 to 0.25, was revealed on the rotation path of the DURP building from the north to south, then turned west towards the Faculty of Engineering Administration building as it is described on **Figure 3**. This route was chosen as the most preferred path by the existing pedestrians because the intersections could connect to many other possible directions.

Another dense pedestrian traffic was recorded in the Plaza area and North-South corridor of the DURP building, representing the traffic from U.B.'s academic community. This was caused by the path connecting the main rooms on the 1st Floor of the DURP building. Considering if the offline teaching and learning process takes place,

**Figure 3.** *Observed pedestrian traffic on the 1st Floor of the DURP Building.*

then the daily movements could reach 600 based on the number of active students from four grades (2017 to 2020 grade and the average number of students per class is 30 people). Therefore, this situation has the potential for COVID-19 transmission through direct contact, thus worsening the pandemic situation over the university.

#### **4.2 Re-designing pedestrian traffic on the 1st floor of the DURP building for minimizing crowd spots**

In response to the observed pedestrian traffic and density in the DURP lecture building, scenarios are proposed to reduce potential physical contact. Opening the south gate with restrictions on the number of the academic community allowed up to 360 from 600 people. Therefore, this proposed scenario could help minimize the COVID-19 virus transmission through potential physical contact.

In addition to the proposed scenario, offline lecture classes are designed for students from grade 1 (batch 2020) and grade 2 (batch 2019), whereas grades 3 and 4 (batch 2018 and 2017) are proposed for online learning procedures. Finally, the simulation of integration of ABM and GIS Indoor scale to re-design pedestrian traffic on the 1st floor could significantly decrease pedestrian traffic and density, as explained in **Figure 4**.

### **4.3 Observed pedestrian traffic on the 2nd floor of the DURP building under normal conditions**

Based on the observed pedestrian traffic on the 2nd floor of the DURP building, the stairway and plaza in front of the stairs and the route to the DURP library are to be considered to be traversed, as explained in **Figure 5**.

It is revealed that the plaza in front of the stairs, the stairway, and the DURP library are the most preferred route by pedestrians because there are no classrooms available on the 2nd floor, only consists of lecturer's room, library, and the academic's meeting rooms. Therefore, a low magnitude of pedestrian traffic was observed through the North-South corridor area. Furthermore, a high magnitude of pedestrian traffic is expected on the 3rd floor since it primarily consists of classrooms and a computer laboratory.

*Building Smart System by Applied Deep Learning and Spatial Indoor Agent Based Model… DOI: http://dx.doi.org/10.5772/intechopen.106508*

#### **Figure 4.**

*Re-designing the access for minimizing pedestrian traffic on the 1st floor of the DURP building.*

**Figure 5.** *Observed pedestrian traffic on the 2nd floor of the DURP building.*

#### **4.4 Re-designing pedestrian traffic on the 2nd floor of the durp building for minimizing crowd spots**

According to the observed pedestrian traffic on the 2nd floor, scenarios are proposed to re-design the traffic. The restriction of the number of the academic community and the opening of the east gate could produce a significant decrease of pedestrian traffic, especially in the plaza on the north and the staircase corridor, as explained in **Figure 6**.

The proposed scenario could help reduce the pedestrian traffic in the staircase corridor at the north, since there is no classroom on the 2nd floor of the DURP building. Thus, dividing the entrance to the 2nd floor, which formerly could only be accessed through the staircase, could help reduce the pedestrian traffic and later could help on minimizing the potential transmission of COVID-19.

#### **Figure 6.**

*Re-designing the access to minimize pedestrian traffic on the 2nd floor of the DURP building.*

#### **5. Conclusion**

The relatively new scientific field of Humanitarian Engineering (HE) could potentially fill the gaps between STEM and SHAPE clusters. HE aims to investigate humanitarian trends and issues mainly studied in the social science field from the SHAPE cluster and later proposed the solution through engineering perspective from natural science and formal science from the STEM cluster. HE approaches are considered in this paper, referring to the latest social issues concerning the COVID-19 pandemic. Human activities such as school shopping and work are expected to return to normal once the global vaccination program has been completed. On the contrary, there were no optimal results reported from the studies related to the impact of global vaccination. Therefore the 'new normal' approach is proposed while maintaining the health protocols, in which avoiding crowd spots is part of the protocols.

It is revealed that the HE approach by studying pedestrian traffic in the DURP lecture building through CV and ABM-GIS Indoor simulation could helps on minimizing the crowd spots. The north and west entrance paths on the 1st Floor were observed with a high magnitude of pedestrian traffic. Therefore, the east side door opening could be an alternative for new accessibility for pedestrians. A similar approach applies to the 2nd Floor by opening the east gate could help minimize crowd spots.

The restrictions on the number of academic communities entering the DURP building are considering the need for empirical and site visits to case studies for grades 1 and 2. In addition, grades 3 and 4 have fewer classes to attend, and the learning patterns of senior students are emphasized critical thinking through the exploration of literature studies outside the classes.

#### **Acknowledgements**

This study was conducted by developing Humanitarian Engineering Curriculum under ENHANCE Project, funded by Erasmus+ with partners from Warwick University (U.K.), University of West Attica (Greece), Universitas Brawijaya (Indonesia), Institut Teknologi Bandung (Indonesia), Universitas Gadjah Mada (Indonesia), Bangladesh University of Engineering and Technology (Bangladesh),

*Building Smart System by Applied Deep Learning and Spatial Indoor Agent Based Model… DOI: http://dx.doi.org/10.5772/intechopen.106508*

University of Dhaka (Bangladesh), Ho Chi Minh City University of Transport (Vietnam), and Ho Chi Minh City University of Technology (Vietnam). Furthermore, this research combined with Universitas Brawijaya's research under the Faculty of Engineering, Universitas Brawijaya's non-tax revenue (PNPB) research scheme. Therefore, at the end of this writing, the researchers would thank Erasmus+ and the Faculty of Engineering—Universitas Brawijaya.

### **Author details**

Adipandang Yudono1,2\*, Sapto Wibowo3 , Christia Meidiana1 , Surjono Surjono1 , Irnia Nurika4 , Erryana Martati<sup>5</sup> and Yan Akhbar Pamungkas<sup>6</sup>

1 Department of Urban and Regional Planning, Brawijaya University, Indonesia

2 Center of Geospatial Information Systems and Data Science, Brawijaya University, Indonesia

3 Department of Electronic Engineering, State Polytechnic of Malang, Indonesia

4 Department of Agro Industrial Technology, Brawijaya University, Indonesia

5 Department of Agricultural Product Technology, Brawijaya University, Indonesia

6 Center of Geospatial Information Systems and Data Science, Brawijaya University, Indonesia

\*Address all correspondence to: adipandang@ub.ac.id

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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## The Role of Aggregators in Smart Grids

*Lasse Berntzen and Qian Meng*

#### **Abstract**

Sustainable smart cities need to focus on energy production and use. By installing solar panels, prosumers may contribute to the energy production in the city. The use of solar panels is particularly relevant to free-standing residential buildings. Prosumers may also trade flexibility, the ability to shift energy use to periods when the total energy consumption is lower. Prosumers may also store energy for future sale or consumption. An aggregator is a new role connecting prosumers with energy providers. The aggregator negotiates terms, provides flexibility on behalf of its prosumers, and may even provide energy storage capabilities. This chapter describes the evolving role of aggregators and their possible business models. The aggregators will contribute to smarter energy production and use in smart cities.

**Keywords:** aggregator, flexibility, prosumer, smart cities, smart energy, smart grids, sustainability

#### **1. Introduction**

In the ERA-NET Smart Multi-layer Aggregator project [1], the University of South-Eastern Norway (USN) was leading a work package on emerging business models in the energy sector [2] and also performed research on the adoption of new technology to achieve more flexibility within electricity grids [3]. Before this project, we had been researching electronic government, refocusing on smart cities since 2015 [4]. During the ERA-NET project, we soon saw the emerging role of the aggregator as a new actor in the energy market. Smart energy is closely connected to smart cities. Improving energy efficiency is one of the obvious ways of being smart. Smart energy has become more important with energy shortage and increasing energy prices.

The utilization of energy in a city is a complex process. A modern city needs to fulfill the demand for energy for purposes of commerce, household, infrastructure, transport, etc. Sustainable energy, especially solar energy produced in households and other buildings, has changed the current energy market and plays a significant role in the energy landscape for a smart city [5]. Solar power is primarily used for electric energy generation, but a small fraction of the solar power is used for thermal energy.

Statistics published by the International Renewable Energy Agency (IRENA) [6] show that in the last ten years, worldwide solar energy generation capacity increased with a steady high annual growth both for solar photovoltaic and solar thermal; even in the year 2020 when COVID-19 struck the world heavily. The change is shown

#### **Figure 1.**

*Global growth in solar energy capacity.*

in **Figure 1**. Solar photovoltaic energy dominates renewable capacity expansion, accounting for around 100 GW installation capacity growth in 2018−2020.

The next section discusses smart energy as one application domain of smart cities. The third section discusses smart grids and the emerging role of prosumers. The fourth section focuses on the role of aggregators. Section five brings in the topic of electric vehicles in smart cities, and finally, the sixth section provides a conclusion.

#### **2. Smart cities and smart energy**

The smart city is a concept with no unified definition [7], but technology plays an important role. The main objective is to improve the quality of life for its citizens through better service provision, reduced environmental footprint, and improved participation. In most cases, smart cities are materialized through projects within application areas [8]. Such application areas can be smart traffic [9], smart parking [10], smart public transport [11], smart waste handling [12], smart safety and security [13], and smart energy, as shown in **Figure 2**.

Smart energy is a concept where information and communication technologies are used to achieve the process of using devices for energy efficiency. Smart energy is about reducing energy use but also introducing new renewable energy sources.

Smart energy relies on smart grids to improve energy efficiency mainly by adopting smart meters (SM) that allow almost real-time tracking of power consumption. SM can also monitor and control the electric power consumption of appliances. In addition, SM can measure power production from solar panels and power transmission from electric vehicles. During the shift of grids toward smart grids, SM enable customers to use electric power more efficiently, but also contribute to energy pricing based on current demand in the market. This will incentivize customers to plan their consumption and thereby contribute to increased flexibility.

Calvillo, Sánchez, and Villar [14] propose a comprehensive smart city model that includes all energy-related activities while keeping the size and complexity of the model manageable. Such a model is highly desirable to successfully meet the

#### **Figure 2.**

*The smart city and some application domains.*

increasing energy needs of present and future cities. They propose five main energyrelated activities that have been called intervention areas:


All these areas are related to each other but contribute to the energy system in different ways: generation provides energy, while storage helps in securing its availability; infrastructure involves the distribution of energy and user interfaces; facilities and transport are the main final consumers of energy, as they need it to operate.

#### **3. Smart grids and prosumers**

The energy distribution normally follows a two or three-layer model, where the top level is the transmission system operator (TSO). In Norway, Statnett is the transmission system operator. Statnett is a state enterprise owned by the Norwegian state through the Ministry of Petroleum and Energy. The mission of Statnett is to secure the Norwegian power supply through operations, monitoring, and preparedness. Statnett also plays an essential role in realizing Norway's climate objectives [15].

The distribution system operators connect the customers to the grid. In 2018 Norway had 124 distribution system operators, with the ten largest having two-thirds of the customers and 60% of the total energy deliveries [16]. **Figure 3** shows the organization of the electricity grid.

**Figure 3.** *Electricity flow in the smart grid.*

**Figure 4.** *Smart grid.*

A smart grid is a local electricity grid enabling a two-way flow of electricity and data, including various operation and energy measures, such as SM, smart appliances, renewable energy resources, and energy-efficient resources [17].

**Figure 4** shows a smart grid consisting of four households with installed photovoltaic panels and SM. The SM communicate with a smart meter data collection point through wireless technology or power line communication. The households are examples of prosumers since they can both produce and consume electric energy.

The most common production comes from solar panels, but prosumers can also generate electricity from wind and geothermal wells. Renewable energy is dependent upon environmental conditions. These conditions vary with the hours of the day and the weather.

In the context of smart cities, prosumers are not restricted to households. All buildings, including apartment blocks, office buildings, and shopping malls, can be prosumers. As long as they have open areas exposed to the sun, they may produce

#### *The Role of Aggregators in Smart Grids DOI: http://dx.doi.org/10.5772/intechopen.106860*

energy. Also, geothermal energy may be an option since geothermal energy can be used both for heating and cooling purposes.

Flexibility is when a building or household can change consumption patterns based on the situation in the energy market. Flexibility can be precious to the DSO to handle possible peaks.

Load shifting and peak shaving are two important techniques to improve energy use [18]. The consumption of electricity varies throughout the day. In Norway, we have one peak in the morning and one in the afternoon. The peak in the morning is mainly caused by electric water heaters kicking in after morning showers. The afternoon peak happens when most are coming home from work, cooking dinner, etc. Load shifting can simply be explained as moving the load to other time periods. One example is to spread the load from water heaters. Another example is to charge electric vehicles during the night.

**Figure 5** shows load shifting in practice. The goal is to keep the consumption at a maximum of 5.5 units throughout the day. The morning peak, from 07:00 to 10:00, is above this level. By controlling water heating, some of the load can be shifted to later in the day. A total of 8.5 units need to be moved. From 12:00 until 16:00, the consumption is lower than 5.5 units, so the spare capacity can be used for water heaters and other appliances that have been put into flexibility mode. Note that the limit of 5.5 units is not fully utilized from 13:00 to 16:00. A new peak appears from 17:00 to 19:00. For this period, a total of 3.5 units need to be shifted to later in the evening,

Peak shaving has to do with storage. If electricity can be stored somewhere in the facilities, this stored energy can be used to shave the peaks. Tesla has introduced its power wall as a household battery storage system [19]. Storage can also be centralized, as described in the next section. Load shifting and peak shaving are important since the grid needs to be dimensioned to handle the peaks.

**Figure 5.** *Load shifting.*

#### **4. The role of aggregators**

A prosumer can produce and sell surplus energy in the energy market. In Norway, a prosumer can sign an agreement (energy customer plus agreement) with an energy provider. The distribution system operator is obliged to facilitate energy transfer to and from the customer. The prosumer cannot produce more than 100 kWh per hour. If production exceeds this limit, the prosumer must seek a license as an energy producer [20]. The tariffs for selling energy to the market are generally not beneficial. A prosumer will seek to use its own produced energy before selling to the market.

To facilitate collaboration among a group of prosumers, an aggregator is necessary. The EU 2019/944 Electricity Directive defines aggregation as a "function performed by a natural or legal person who combines multiple customer loads or generated electricity for sale, purchase, or auction in any electricity market" [21].

The role of aggregators and their function has become a hot topic for the reason of being a significant part of the European power market. The European framework assigns aggregators a fundamental role in energy market liberalization and distributed energy resources (DER) integration toward carbon-neutral energy systems. The aggregators cannot only participate in the demand response activity and wholesale market bidding but also contribute to maximizing economic efficiency and fostering cross-zonal trading, considering, in particular, the overall system efficiency [22].

As the number of prosumers grows, the business opportunities for a new energy ecosystem actor, the aggregator, emerge. As earlier mentioned, flexibility may be important to shift or shave peaks caused by differences in consumption during the day. The aggregator is a business entity that can aggregate energy from a group of prosumers. A higher volume benefits the aggregator when negotiating with the distribution system operators and energy providers. The aggregator can also provide services, such as settlements, storage, etc.

#### **4.1 Smart-MLA**

The Smart-MLA project [1] made a prototype for a multi-layer aggregator, as shown in **Figure 6**.

The main goal of the ERA-NET Smart Multi-Layer Aggregator project (Smart-MLA) was to demonstrate how an aggregator could improve energy efficiency through flexibility [2]. On the lowest layer, the community aggregator simply collects information from a household to optimize energy use. The primary impact is the reduction of energy bills, but a secondary effect is increased customer awareness related to their energy use.

On the second layer, the aggregator will start controlling appliances. The community aggregator collects usage patterns and constraints from the household. One such constraint could be the charging of an electric vehicle. The car should be fully charged at 7 am. The community aggregator can then decide when to charge the car as long as the constraint is met. The aggregator will consider the future hour-by-hour energy prices and information about the weather to predict output from photovoltaic panels.

On the third layer, the aggregator does not only optimize the flexibility obtained by controlling appliances but also uses this flexibility to negotiate with the market. The aggregator uses the combined flexibility of its prosumers to improve the market position. The aggregator handles settlements with its prosumers and the energy provider.

While the project demonstrated the opportunities of the multi-layer aggregator model, there are still barriers to overcome. First, there are regulatory issues that need to be handled. The aggregator needs to be established as part of the energy ecosystem. Using Norway as a selected case, the current regulatory environment does not recognize the aggregator as a market actor. A prosumer can sell energy to an energy provider within the limit of 100 MWh per hour. An aggregator will easily exceed this limit and has to be licensed as an energy provider.

The second obstacle is the lack of trust in the energy market. As part of the project, the University of South-Eastern Norway surveyed early adopters of smart home technology [3]. The results showed that the early adopters wanted full control of their energy production and consumption and were unlikely to transfer control to an external entity. Based on the results, we discussed possible remedies from organizational models where the prosumers own the aggregator as a cooperative. Regulatory measures include self-regulation to make the energy market more transparent to achieve the necessary trust among the prosumers. Also, technology can be used to increase trust. Our research also pointed to blockchain technology as a possibility to achieve full transparency about pricing and settlements.

#### **4.2 Aggregator business opportunities**

The main electricity market stakeholders include the power generators, transmission system operators, distribution system operators, prosumers, and aggregators. In the transition to green energy, there are great business opportunities for aggregators, which can be categorized as follows [2]:

#### *4.2.1 Energy efficiency services provider*

The aggregator offers the customers an energy-saving plan by installing highefficiency equipment. The aggregator can monitor and control the equipment to participate in the demand response in the power market. E.g., at the peak of the power consumption, the aggregator helps users to reduce their demand and consume electricity later when the power price is low.

#### *4.2.2 Information value-added services provider*

The aggregator provides their consumers a value-added service through IoT and big data technologies that provide data and analysis for real-time electricity prices, electricity demand and consumption at a household, and power distributed generation nearby. Then the prosumers can take control of their electricity consumption in real-time and decide when to sell their own generated power at a peak in the grid.

#### *4.2.3 Integrated energy services provider*

With the demand for green transition and access to various smart terminals like electric vehicles, charging stations, smart home appliances, and distributed energy generation, the aggregator can develop the business to cooperate with other service suppliers (like heating) to deliver integrated energy services, optimize the integrated energy solution to maximize the benefit for the users. With many assets and a wide range of businesses, the aggregator may behave in the dual role of an energy supplier and an energy service provider.

#### *4.2.4 Extended services provider for zero-emission*

For the EU zero-emission target, many countries have implemented policies and measures to replace fossil fuel cars with electric cars. In practice, Norway's electric vehicle policy has proven effective by reducing taxes and fees for electric vehicles while fossil fuel cars are heavily taxed. Thus electric vehicles have become much cheaper than fossil fuel cars. As a result, by the end of 2021, there were 460,734 electric cars registered among a total of 2,893,987 private passenger cars [23]. This clearly shows how incentives shape consumer choice by a combination of taxes and rewards. The aggregator can then expand their customer channels through cooperation with the electrical vehicle sellers and benefit prosumers with their energy-saving, information, and integrated energy services.

#### **4.3 The aggregator as a storage provider**

In the smart grid, the intermittent and random output of solar energy has brought challenges to the balance of demand, supply, and grid stability. As to prosumers, solar energy is stored for self-consumption in most cases. While from the perspective of energy efficiency and management efficiency, storing energy by the aggregator will be a more feasible solution [24], as shown in **Figure 7**.

In storage service, prosumers store energy mostly for self-consumption. Even if they make a profit out of the outrage of storing produced energy in the battery and selling energy at peak time to maximize their own profit, this could be inefficient when taking many prosumers as a system. Scale effect also works with aggregation of many prosumers than respectively. For prosumers, it is not only the cost of batteries but also the additional hardware to handle the charging and discharging of the batteries and the installation cost that need to be considered when investing in battery

**Figure 7.** *Energy storage by the aggregator.*

storage. If an aggregator supplies a storage service, the aggregator could use a larger facility and not be overly concerned about the compactness of the installation [25].

In addition to achieving the outrage goal, aggregators storing electricity is also a key mechanism for supplying electricity reliably, increasing security and economic value, and decreasing carbon dioxide emissions. Aggregator storage also plays a significant role in keeping a balance between supply and demand, avoiding electric fluctuations, contributing to the stability of the low voltage DSO grid, and making the DSO grid system more efficient, especially for the weak low voltage grid in Norway [24].

#### **5. Electric Vehicles in the Smart City – Norway as a Case Study**

Electro-mobility is an important exponent of smart city strategies. Considerable investments in electric vehicles are being made worldwide, and supporting infrastructure not only offers the potential to reduce road transport emissions but also unlock other smart city opportunities. This includes new solutions for mobility, energy use, public services, residential and commercial buildings, wider urban systems, citizen engagement, and behavior change. Accelerating the adoption of electric vehicles, and realizing the associated smart city benefits, requires coordinated action among all stakeholders [26].

One case for smart energy use in smart cities is the adoption of electric vehicles. Electric vehicles reduce the environmental footprint by reducing CO2 emissions and other air pollutants. Norway has the highest adoption rate of electric vehicles worldwide. This result is due to the Norwegian government's determination and effective measures.

The Norwegian Parliament has decided on a national goal that all new cars sold by 2025 should be zero-emission (electric or hydrogen). By February 2022, there were more than 470.000 registered battery electric cars (BEVs) in Norway. Battery electric vehicles held a 64 % market share in 2021. The transition speed is closely related to policy instruments and a wide range of incentives [27].

Five years ago, Oslo, the Norwegian capital, had some serious problems with air pollution caused by certain meteorological conditions. In January 2017, Oslo was closed for diesel cars for a short period. The city council considered raising traffic tolls on days with high pollution levels. The uptake of electric vehicles has significantly reduced the pollution problems seen earlier.

When electric vehicles are considered to contribute to smart cities for energy storage and green transition. Tesla Powerwall is the pioneer with its battery based on lithium iron phosphate (LiFePO4) chemistry. With the development of green energy, battery technology is also undergoing a significant transformation. According to BloombergNEF's research, lithium-ion battery pack prices were above \$1,200 per kilowatt-hour in 2010 and fell 89% to \$132/kWh in 2021 [28].

Therefore, soon, electric vehicles are expected to meet the EU zero-emissions goal, serving as one part of smart energy and a role of energy storage in the smart grid.

#### **6. Conclusion**

Smart energy is an important part of smart cities. Smart cities need to be energy efficient. The role of prosumers refers to buildings and households that can produce renewable energy. The aggregator is a new role in the energy ecosystem. The aggregator can represent a group of prosumers dealing with the energy market. The aggregator may also offer additional services to help its prosumers achieve more energy efficiency. While fulfilling the balance between the energy demand and supply, especially for load shifting and peak shaving, energy storage is an important component. Prosumer storage is efficient for self-consumption mode, but from the perspective of scale effect for many prosumers, storage provided by an aggregator is more feasible and sustainable. New business opportunities for the aggregators have been identified, and aggregators will play a significant role under the EU framework to achieve the green transition goals. Electric vehicles will also contribute to smart traffic and smart energy when their worldwide adoption increases.

The function and role of aggregators in a smart city need more investigation, such as the social acceptance of aggregators in the energy market, the interaction and collaboration with other stakeholders, and creating business models for aggregators. The fundamental role of aggregators in the European power market and distributed energy resources will become clearer.

#### **Acknowledgements**

This work was supported by the Manu Net scheme Grant number MNET20/ NMCS-3779 and funded through the Research Council of Norway Grant number 322500 with the project title "Cloud-based analysis and diagnosis platform for photovoltaic (PV) prosumers." It builds on results from the ERA-Net Smart Grids Plus scheme Grant number 89029 with the project title "Multi-layer aggregator solutions to facilitate optimum demand response and grid flexibility," funded through the Research Council of Norway Grant number 295750.

*The Role of Aggregators in Smart Grids DOI: http://dx.doi.org/10.5772/intechopen.106860*

#### **Author details**

Lasse Berntzen\* and Qian Meng University of South-Eastern Norway, Horten, Norway

\*Address all correspondence to: lasse.berntzen@usn.no

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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#### **Chapter 11**

## Heat Pump to Increase the Efficiency of a Geothermal Heating System in the City of Călimănești - Case Study

*Laurentiu Constantin Lipan and Sorin Dimitriu*

#### **Abstract**

Romania is one of the European countries that has a rich geothermal potential, the main uses of this resource being the spaces heating and thermal baths. Recently, concerns about increasing energy efficiency and limiting greenhouse gas emissions, have determined an increased attention to heating systems using geothermal water. These will play an important role in the future in developing sustainable energies and reducing the use of fossil fuels. The authors present a case study on a heating system in the city of Călimănești, initially using liquid fuel, which was modernized by using geothermal hot water for the preparation of the thermal agent. As geothermal water, with an initial temperature of 95°C, cannot be cooled below 50°C, the authors considered the possibility of using a heat pump to fully exploit the thermal potential of geothermal water, increase the capacity of the system and increase its energy efficiency. The implementation of the heat pump in the district heating system and the results expected to be obtained are discussed. It was considered that the heat pump works in parallel with the heating system, obtaining an increase in its capacity by approx. 60% and an increase in energy efficiency by approx. 30%.

**Keywords:** district heating, geothermal heat pump, geothermal heating system, energy efficiency, heat recovery, use of geothermal water

#### **1. Introduction**

Geothermal energy can be defined as the energy inside the earth, which generates geological phenomena on a planetary scale. The term used comes from the Greek words "geo" (earth) and "thermos" (heat) and is used nowadays to define that part of the earth's energy that can be recovered and used by man to generate heat and power. Geothermal energy is characterized as a renewable and sustainable energy. The character of renewable is given by its continuous production inside the earth. During the operation of natural geothermal systems, the regeneration of geothermal energy takes place by heating the geothermal water to the same time scale at which it is extracted for use. In the usual case of dry hot rocks and hot water aquifers in sedimentary

basins, energy recharging is done through a slow process of thermal conduction. The sustainability of a resource depends on its initial quantity, regeneration rate and consumption rate. Obviously, consumption can be sustained if the resource is regenerated faster than it is depleted. In this context, sustainable development involves the use of the resource so that through continuous regeneration it will allow its use by future generations. The degree of sustainability of geothermal energy is still high because, compared to the volume of resources and fast regeneration rate, humanity out of convenience, uses only a small part.

Natural geothermal springs have been used for heating and thermal baths since ancient times. Archeological discoveries suggest that the earliest uses of geothermal energy took place over 10,000 years ago in North America, where autochthone populations have used the hot springs in this area for both practical and spiritual purposes. The finding that hot mineral baths ameliorate or even cure some diseases, led to the consideration of these springs as sacred, endowed by gods with magical healing powers [1].

Evidence of this has also been found in the peoples of ancient Greece and Roman Empire. At the same time, evidence was also discovered that attested to more commonplace uses: heating of living spaces, hot baths and activities related to food preparation. Evidence that geothermal energy was used for heating dates to the first century (AD), being found in the Roman city of Pompeii. However, concerns about such uses of geothermal energy were initially limited to locations where hot geothermal water was naturally accessible in the form of springs [2].

Nowadays, the direct use of geothermal energy is reported and documented in at least 88 countries. The estimated capacity to be currently installed in these countries is approx. 108,000 MW, and the annual energy consumption of approx. 1,021,000 TJ (284,000 GWh/year), being oriented towards: geothermal heat pumps (58.8%), swimming pools and thermal baths (18%), direct space heating (16%), greenhouse heating (3.5%), uses in industrial processes (1.6%), aquaculture and fish farming (1.3%), drying of cereals (0.35%), other uses (0.45%). The highest consumptions were reported in order in China, USA, Sweden, Turkey, and Japan [3].

According to data reported at the World Geothermal Congress in 2020 (WGC 2020), only in recent years, 2015-2020, there has been an increase in the amounts of energy used from geothermal sources by approx. 27%. Concerns about the production of electricity in ORC installations that take heat from geothermal waters have also intensified, with five countries installing production capacity for the first time: Belgium (0.8 MW), Chile (48 MW), Croatia (16.5 MW), Honduras (35 MW) and Hungary (3 MW). Approximately 2650 wells were drilled in 42 countries and approx. \$ 22.3 billion has been invested in geothermal projects [4].

Geothermal energy resources are located over a wide range of depths and can be in the form of hot water, steam, or hot rocks. Hot water aquifers with temperatures between 60°C and 100°C are the most suitable applications for space heating and agricultural systems. For these aquifers to be commercially interesting, they must be located at depths up to 2000 - 3000 m and have a temperature of at least 60°C. Areas with hot water under pressure with higher temperatures and areas with hot rocks are suitable as a source for electricity generation [5].

Traditionally, the heating of homes, office buildings and commercial spaces has been done with the help of local heat sources: stoves, fireplaces and hot water boilers using different types of fuels. These systems not only have low energy efficiency but are also powerful generators of carbon dioxide as well as various polluting suspensions. For urban areas with high population density, all studies conducted at national

*Heat Pump to Increase the Efficiency of a Geothermal Heating System in the City… DOI: http://dx.doi.org/10.5772/intechopen.107252*

and international level have led to the conclusion that from the point of view of energy efficiency and environmental protection, district heating (DH) systems are advantageous [6].

The multiple and obvious advantages of district heating are: high energy efficiency; the possibility of using several types of fuels; use of residual energy resulting from industrial processes (hot water, steam); use of renewable resources (solar energy, geothermal water, biomass, biofuels, household waste and other combustible waste); simple operation by the consumer, which is not involved in fuel supply activities, maintenance and operation supervision; consumer safety, compared to individual sources; reduced pollution, by placing thermal energy sources outside the living area and achieving a low level of pollutant emissions and greenhouse gases; the possibility of applying local investment policies in the field of energy efficiency and improving the quality of the environment.

Despite all these major advantages, compared to the alternative of individual heating, the consumer connected to a DH system also faces a certain degree of limitation of thermal comfort, determined by how the system can respond to variable loads or to operate economically under load limitation. Nevertheless, the DH system solution provides the necessary heating and hot water at prices less than or equal to those offered by individual alternative solutions. District heating is a suitable solution for all sizes of networks, from a few buildings to the neighborhood or city level. This has strengthened of DH system position in many European countries in recent years [7, 8].

In the current conditions in which the amplification of global warming imposes firm and hard measures for limiting the greenhouse gas emissions by restricting the use of traditional fuels, the integration of geothermal resources in DH systems is a major requirement. Geothermal heating systems not only reduce or eliminate carbon dioxide emissions but, by eliminating the consumption of conventional fuels, provide consumers with heat at much lower prices, especially in the current context of the gas and oil crisis. Geothermal heat resources have been used in district heating systems for many years in Iceland and France. Other such installations appear in Germany, Hungary, Italy, Romania, Belgium, and the United Kingdom. During 2014, 30 PJ of heat were supplied from geothermal sources worldwide, of which 7.3 PJ in the European Union. However, these reserves appear to be somewhat underestimated, according to reports from the European GeoDH project. Given that about a quarter of the European Union's population lives in urban areas where geothermal energy could be extracted and used, future geothermal heating systems appear as an efficient solution to the current problems of thermal energy supply [9].

#### **2. Geothermal heating system in the city of Călimănești, Romania**

Romania has an important potential of geothermal energy sources, on its territory being identified several areas in which the geothermal potential is estimated to allow economic applications. They are located on an extensive area in western plain of Romania, in the middle of country, on the Olt River Valley north of Râmnicu Vâlcea, in the northern part of Bucharest city and south of Brăila city. **Figure 1** shows the locations of these geothermal reservoirs in Romania. Geological surveys conducted before 1990 showed that the known geothermal potential in Romania is about 10PJ/ year, of which only about two thirds is exploited. The approximately 80 functional wells can produce an annual amount of thermal energy of about 7PJ [10].

**Figure 1.** *The distribution of geothermal reservoirs on the Romanian territory [10].*

In Romania, the temperature of "low enthalpy" geothermal sources, with exploitation by drilling-extraction is between 25°C and 60°C, and 60–125°C for the "mesothermal". The economic drilling and extraction limit for geothermal waters was agreed for the depth of 3300 m and was reached in some areas of Romania, such as the Bucharest North - Otopeni geothermal basin and certain perimeters in Snagov and Balotești localities. Romania was ranked as the third country in Europe, after Greece and Italy, for its very high geothermal potential [11].

More than 80% of the wells are exploited artesian, 18 wells require anti-scaling chemical treatment, and 6 are used for reinjection. The main direct uses of the geothermal energy are space and district heating; bathing; greenhouse heating; industrial process heat; fish farming and animal husbandry [11].

This chapter presents a case study. The authors present a solution to increase the energy efficiency of the district heating system of the city of Călimănești, which works having as main source of heat the geothermal water extracted from the aquifer of Călimănesti – Căciulata - Cozia perimeter, located on the Olt River Valley. In this area, the geothermal water is provided by three drillings having more than 3000 m in depths, located on the right side of the Olt River, at about 1-2 km one from each other as presented in the **Figure 2**. The three existing drillings stand out deposits of medium enthalpy geothermal water (the temperature at the exit of the well is 92 … 95°C). The available flow volume of the three wells is 50.4 l/s, equivalent to a thermal potential of 13.2 MW, when the geothermal water is cooled to 30°C [12].

The drilling located in vicinity of Căciulata and Cozia localities (borehole 1008 and 1006) are used only for local heating needs. The geothermal water feeds a group of hotels and SPA treatment units, for heating, domestic hot water supply and thermal pools. The high thermal potential of the geothermal water leads to its direct

*Heat Pump to Increase the Efficiency of a Geothermal Heating System in the City… DOI: http://dx.doi.org/10.5772/intechopen.107252*

**Figure 2.** *Olt Valley working geothermal perimeter [12].*

exploitation. In the cold season, the geothermal water (having a temperature of 92 … 95°C) is cooled in a plate heat exchanger, producing the thermal agent (TA) for the district heating system. A second heat exchanger produces domestic hot water (DHW). The geothermal water, cooled in the two heat exchangers, feeds the thermal pool, after that being discharged in the Olt River at a temperature of about 30°C. In the warm season, the mass flow extracted is reduced, only the heat exchanger for domestic hot water and thermal pool being in use.

The third drilling is situated at 1,2 km from Călimăneşti, providing a volume flow of 18 l/s at the same temperature values 92 … 95°C. This locality, beside the tourists which are staying in hotels, has about 8600 permanent habitants; 15% of the habitants are living in apartments connected to a centralized system for thermal energy supply. In the cold season of 2019-2020, 461 apartments and residential houses, 9 public institutions and 47 economic operators were branched to DH system, which must ensure a thermal need of about 3500 kW for heating and about 500 kW for DHW supply (for the conventional climatic parameters) [13]. The DH system was initially designed with three thermal units, equipped with hot water boilers using light liquid fuel. The geothermal water from the nearby well was initially used only for the thermal energy supply of the SPA treatment units and thermal pools. The project of the DH system feeding with geothermal energy was started in 2002 with internal financing and was later supported by European funds. Initially, the project included all the three wells (1006, 1008 and 1009) to provide the centralized heating of Călimănesti town. Later, it was utilized only the available water from the well 1009, situated in vicinity of town. The available volume flow is of 18 l/s, from which about 8 l/s is utilized by a SPA center and a hotel; the rest of volume flow (about 10 l/s) being used in the DH system of Călimănesti. To use the geothermal water into the DH system, a geothermal heating station was built just near the geothermal well. The

geothermal water produces, by using plate heat exchangers, the primary thermal fluid for the DH system, having a temperature of about 85°C. This primary thermal fluid serves to partially cover the heating demand and to completely cover the DHW preparation.

**Figure 3** shows the drilling 1009 and the geothermal station that prepares the heating agent for the DH system. The geothermal water from the Olt Valley aquifer has a high combustible gas content, containing over 90% methane. As a result, before being introduced into the heat exchangers of the geothermal station, it is degassed, the collected gases being discharged into the atmosphere. **Figure 4** shows the actual operating diagram of the geothermal station.

The primary thermal agent from the DH system of Călimănești town is returned to the geothermal station with a temperature around 40°C. For this reason, the geothermal water extracted from the drilling well, with a temperature of 95°C, cannot be cooled, in the heat exchangers of geothermal station, to a temperature below of about 50°C. Because the exploitation of geothermal aquifer is artesian, this water is discharged directly into the Olt River after a cooling until 30°C, from reason of aquatic environment protection. The cooling and discharge into the Olt River of the waste geothermal water, represents a heat loss of approx. 1/3 of its full thermal potential. The authors examined the possibility of recovering this heat loss by implementing in the thermal agent preparation circuit a heat pump that uses as heat source the waste geothermal water discharged from the geothermal station, cooling it from 50–30°C. In this way, the entire thermal potential of geothermal water is used, while also increasing the capacity of the DH system by growing the flow of thermal agent produced. Waste geothermal water can be discharged directly into the Olt River, without any negative impact on the aquatic environment. The functional diagram of the geothermal station, coupled with a mechanical vapor compression heat pump, is presented in **Figure 5**.

**Figure 3.** *The borehole 1009 and geothermal station (source: photo of the authors).*

*Heat Pump to Increase the Efficiency of a Geothermal Heating System in the City… DOI: http://dx.doi.org/10.5772/intechopen.107252*

#### **Figure 4.**

*The actual operating diagram of the geothermal station [12]. GB – Geothermal borehole; DT – Degassing tank; PU – Pumping units; DTP – Distribution thermal points; PHEH – Plate heat exchanger for heating; PHEW – Plate heat exchanger for DHW.*

#### **Figure 5.**

*The functional diagram of the geothermal station with heat pump (source: the drawing of authors). CPcompressor; EXV – Expansion valve; CD – Condenser; EV – Evaporator.*

#### **3. The geothermal heat pump**

The idea of introducing a heat pump in the functional diagram of the geothermal station was to connect it in parallel with the heat exchangers that prepare the heat sent to the network of the DH system, supplementing its flow. The flow of geothermal water available from the wellbore is 10 l/s, which allows the coverage of a thermal load of 1820 kW, the geothermal water being cooled only to 50°C. Using the heat pump for further cooling of the geothermal water until a temperature of 30°C, an additional thermal power of 1069 kW can be obtained, estimating a value of the heat pump COP of 4,5. This result shows that the capacity of the district heating system is increased by about 60%. For geothermal wastewater to be used as a heat source, if it is cooled to 30°C, the refrigerant vaporization temperature must be around 25°C. As the temperature of the agent supplied to the heating system by the geothermal station is 85°C, the refrigerant used by the heat pump must have a condensation temperature of at least 90°C. Consequently, it is necessary to use high temperature working agents with a critical temperature above 100°C. In addition to this requirement, the refrigerant used must have an environmental impact in accordance with the provisions of the EU Regulation on fluorinated greenhouse gases and have thermodynamic properties to achieve the highest possible coefficient of performance.

Most of the agents shown in **Table 1** are "wet agents", for which the end of dry saturated vapors compression, from the evaporator, falls within the saturation domain. To avoid this phenomenon, a superheat of the vapors must be introduced at the compressor suction, of at least 10 degrees. The cooling of the heat pump condenser being carried out with the returned agent from the DH system, having a temperature of 45°C, a subcooling of refrigerant until a temperature around 60°C, is possible. **Figure 6** shows how to perform the processes of the condensate subcooling and superheating of cold vapors at the compressor suction. The cooling is carried out either in a separate heat exchanger or in the final part of the condenser and the superheating of the vapors, by introducing an internal regenerative heat exchanger in the operation scheme of the heat pump.

Subcooling of the condensate has the effect of increasing the specific thermal load of the condenser, respectively of the energy efficiency (*COP*). With the agents presented in **Table 1**, the authors performed the analysis of the thermodynamic cycle of the heat pump, to choose the most suitable agent for the imposed operating conditions:


*Note: HFC = hydrofluorocarbons; HC = Hydrocarbons; HFO = Hydrofluoro-olefins. \*Azeotropic blend R1234ze/R227ea (91,1%/8,9%).*

#### **Table 1.**

*The properties of a few agents used for high temperature heat pumps [14].*

*Heat Pump to Increase the Efficiency of a Geothermal Heating System in the City… DOI: http://dx.doi.org/10.5772/intechopen.107252*

#### **Figure 6.**

*The operating diagram of the heat pump installation (source: the drawing of authors). EV – Evaporator; IHE – Internal heat exchanger; CP – Compressor; CD – Condenser; SR – Subcooler; EXV – Expansion valve; DTP – Distribution thermal point.*


For all the considered agents, according to these conditions, the condensation temperature was chosen *tc* ¼ 90°*C:*, and the vaporization temperature *tv* ¼ 25°*C*

The thermodynamic cycle, in the p-h diagram, is presented in **Figure 7**.

Saturation pressures corresponding to these temperatures depend on the agent considered:

$$p\_v = p\_{sat}(t\_v); \quad p\_c = p\_{sat}(t\_c) \tag{1}$$

The superheating of the vapors sucked by the compressor is carried out with the help of the hot condensate having a temperature of 60°C, by means of the regenerative heat exchanger. Since in this heat exchanger, the flow of the hot fluid is equal to that of the cold fluid, the thermal balance of the appliance has the form:

$$h\_4 - h\_5 = h\_1 - h\_7 \tag{2}$$

specifying the enthalpy of condensate at the inlet to the expansion valve.

The state parameters in the characteristic points of the operating scheme in **Figure 6**, respectively of the thermodynamic cycle in the p-h diagram in **Figure 7**, were determined using the EES software. The **Table 2** shows the calculation algorithm of the state parameters of the cycle.

According to the thermodynamic cycle in **Figure 7**, the specific energy parameters of the installation are:

#### **Figure 7.**

*The thermodynamic cycle of the heat pump installation, in p-h diagram (source: generated in EES software by authors).*


#### **Table 2.**

*Algorithm for determining the state parameters in EES software.*

specific thermal load of the condenser:

$$\left|q\_c\right| = h\_2 - h\_4 \text{ [kJ/kg]} \tag{3}$$

*Heat Pump to Increase the Efficiency of a Geothermal Heating System in the City… DOI: http://dx.doi.org/10.5772/intechopen.107252*

specific cooling power of the evaporator:

$$q\_v = h\_{\overline{\prime}} - h\_6 \text{ [kJ/kg]} \tag{4}$$

specific mechanical compression work:

$$\left| l\_{cp} \right| = h\_2 - h\_1 \left[ \mathbf{k} \mathbf{j} / \mathbf{k} \mathbf{g} \right] \tag{5}$$

The heat flow taken up by the heat pump evaporator depends on the available geothermal water flow *V*\_ *gw* [m3 /s] and the temperature up to which the water can be cooled:

$$
\dot{Q}\_w = \dot{V}\_{\text{gw}} \rho\_{\text{gw}} \left( h\_{\text{gw1}} - h\_{\text{gw2}} \right) \text{ [kW]} \tag{6}
$$

where *ρgw* [kg/m*<sup>3</sup>* ], the density of waste geothermal water, was considered at its average temperature *tgw* ¼ 0*:*5 *tgw*<sup>1</sup> þ *tgw*<sup>2</sup> � �. The flow of refrigerant is determined by the heat flow recovered from waste geothermal water:

$$
\dot{m} = \frac{\dot{Q}\_v}{q\_v} \left[ \text{kg/s} \right] \tag{7}
$$

and establishes the heat flow given to the condenser and respectively the theoretical power required to drive the compressor:

$$\left| \dot{Q}\_{\varepsilon} \right| = \dot{m} \left| q\_{\varepsilon} \right| \left[ \text{kW} \right] \tag{8}$$

$$P\_{cp} = \dot{m} \left| l\_{cp} \right| \left[ \text{kW} \right] \tag{9}$$

resulting in COP (energy efficiency)

$$\text{COP} = \frac{|\dot{Q}\_c|}{P\_{cp}} \tag{10}$$

The efficiency of the Carnot cycle carried out between the two heat sources of the thermodynamic cycle, the hot source, and the cold source, has the expression:

$$\text{COP}\_C = \frac{T\_{HS}}{T\_{HS} - T\_{CS}} \tag{11}$$

wherein the temperatures of the two heat sources, *THS* and *TCS*, are the mean thermodynamic temperatures of the hot water produced at the condenser and respectively of the geothermal water from which the heat is extracted at the evaporator.

The mechanical work of the reversible Carnot cycle has the significance of the minimum mechanical work necessary to achieve heat transfer from cold to hot source. In the case of a real irreversible cycle, carried out between the same heat sources, the internal and external irreversibility, determine a destruction of the available energy, determining the increase of the mechanical work consumption. The most crucial parameter for the thermodynamic evaluation of a cycle, from this point of view, is the exergetic efficiency (carnotic efficiency):

$$\eta\_{\rm ex} = \frac{|l\_C|}{|l\_{\rm cycle}|} = \mathbf{1} - \left(\sum \overline{\pi}\_{\rm ir, int} + \sum \overline{\pi}\_{\rm ir, ext}\right) = \frac{\rm COP}{\rm COP\_C} \tag{12}$$

wherein P*πir*,*int* is the sum of the rates of the losses caused by the internal irreversibility of the cycle and P*πir*,*ext*, the sum of the rates of the losses caused by its external irreversibility.

To determine which of the working agents shown in **Table 1** is more suitable for this heat pump installation, in terms of properties and performance, were determined the state parameters for each point of the cycle in **Figure 7**, according to the calculation algorithm presented in **Table 2**. The calculus was carried out in the same initial conditions for each refrigerant. The drive power of the compressor and the heat flow yielded to the condenser were also calculated, resulting in the performance coefficient of the installation. The reference Carnot cycle was considered between the average thermodynamic temperatures of the geothermal water in the evaporator and respectively of the thermal agent in the condenser.

The results obtained are presented in **Table 3** and in **Figure 8**.

All refrigerants that have been analyzed are in the category of those that do not affect the ozone layer, with zero ODP potential. In terms of performance, R245fa, R600 and R1233ze(E) refrigerants for which energy efficiency (COP) has the highest values are noted (**Figure 8**).

R245fa is a colorless, one-component fluid of class HFC (1,1,1,3,3-Pentafluoropropane, C3H3F5) which may replace the use of HCFC R123 and R11. R245fa is among the HFC refrigerants that do not deplete the ozone layer (ODP = 0) but have a significant global warming potential (GWP = 1030). R245fa is used primarily as a blowing and insulation agent for plastic foam insulation, but also as an industrial air conditioning refrigerant, heat recovery systems and high energy recovery systems. HFC-245fa is listed as non-toxic and non-flammable, but exposure to high levels of R245fa can lead to heart sensitization and eye irritation. It falls into safety class B1 [15].


*Note: Operating parameters, same for all refrigerants:*

*Condensation temperature: 90 °C.*

*Vaporization temperature: 25°C.*

*Superheating at the compressor suction (*Δ*tsh): 10°.*

*Thermodynamic mean temperature of the geothermal water: 40.55°C.*

*Thermodynamic mean temperature of the thermal agent: 64.55°C.*

*Heat flow taken from the evaporator: 830.60 kW.*

*Efficiency of the reference Carnot cycle: 13.84.*

#### **Table 3.**

*The performances of the heat pump, for different refrigerants.*

*Heat Pump to Increase the Efficiency of a Geothermal Heating System in the City… DOI: http://dx.doi.org/10.5772/intechopen.107252*

#### **Figure 8.**

*The COP of the heat pump installation, for different agents (source: results from Table 3).*

R600 refrigerant is a natural refrigerant of class HC (butane, C4H10) suitable for many refrigeration applications. R600 refrigerant is environmentally friendly and efficient. R600 is a commonly used refrigerant with low environmental impact and good thermodynamic performance. The R600 has low power consumption in refrigeration applications and is compatible with many different lubricants. The R600 is suitable for a variety of tasks in industrial, commercial, and household refrigeration, such as kitchen refrigerators and professional freezers and desks, as well as refrigeration and freezing appliances. R600 is classified as a highly flammable class A3 refrigerant and is subject to strict regulations regarding the amount of agent charged into installations [16].

R1233zd(E) refrigerant is a recent agent, chemically a hydrochlorofluoroolefin (HFO, CF3-CH=CClH). Although this refrigerant is an HCFC and therefore carries chlorine that affects the ozone layer, it is not on the list of fluorinated greenhouse gases that will be eliminated, because the life in the atmosphere is very short (26 days). The properties of this agent are close to the characteristics of the ideal refrigerant: adequate operating pressures, zero GWP potential, zero ODP potential, non-flammable, non-toxic, and adequate volumetric capacity. R1233ze(E) is classified as safety class A1 [15].

Based on the energy performance and physical properties of the selected agents, it was decided that the most suitable agent, to be used in the studied heat pump, is R1233ze(E) refrigerant.

For the refrigerant considered the most suitable, the way in which the energy efficiency (COP) and the heat flow produced at the condenser depend on the subcooling temperature and on the superheating of the vapors at the compressor suction, respectively, was studied. The results are shown in the **Figure 9**.

It is found that the greatest influence upon the heat pump performances has the subcooling degree of the condensate, while the influence of the degree of overheating at the compressor suction is practically insignificant. For this reason, the minimum value of 10 degrees was considered for the degree of overheating, which ensures that the state corresponding to the compressor discharge does not enter in the wet area and was investigated the way in which only the degree of subcooling influences the performance of the heat pump. The results are shown in **Figure 10**. It is found that the

#### **Figure 9.**

*Modification of the coefficient of performance and of the thermal flux produced at the condenser, depending on the subcooling temperature and the degree of overheating (source: simulation in EES software by authors).*

#### **Figure 10.**

*Modification of the thermal flux produced at the condenser, the cooling power, the compressor driving power and the flow of the hot agent, depending on the subcooling temperature, for vapors superheating of 10 degrees (source: simulation in EES software by authors).*

lower the cooling temperature, the higher the specific cooling power and the lower the required refrigerant flow. As a result, the power required to drive the compressor is reduced. Although the specific thermal load of the condenser increases, the decrease of the refrigerant flow has a stronger influence and as a result the heat flow yielded at the condenser decreases, causing a slight decrease of the heat flow provided in the district heating network. The decrease in the drive power of the compressor has a greater influence on the energy balance, so that overall, the coefficient of performance increases.

#### **4. The coverage of the HD system**

The city of Călimănești is part of the climate zone III of Romania, for which the conventional outdoor temperature for the cold season is *tec* ¼ �18°*C* [16]. For residential and tertiary buildings, the conventional interior design temperature may be considered *tic* ¼ þ20°*C*, as [17]. In these conditions, considering the usual thermal

*Heat Pump to Increase the Efficiency of a Geothermal Heating System in the City… DOI: http://dx.doi.org/10.5772/intechopen.107252*

characteristics of the buildings and the need of DHW, the maximum thermal load of the DH system of the Călimănești city was evaluated as follow:

maximum thermal load for heating: *<sup>Q</sup>*\_ *max heat* ¼ 3500 kW;maximum thermal load for DHW preparation: *<sup>Q</sup>*\_ *max dhw* ¼ 500 kW.

Assuming that the heat transfer characteristics are unchanged in relation with outdoor temperature, the thermal load of the DH system, which must be performed by the geothermal station, varies linearly with the temperature of the external environment *te* [°C]*,* according to the relation:

$$
\dot{Q}\_{GTS} = \dot{Q}\_{heat} \frac{t\_{ic} - t\_{\varepsilon}}{t\_{ic} - t\_{\text{cc}}} + \dot{Q}\_{dhw}^{max} \left[ kW \right] \text{for} t\_{\varepsilon} \le 10^{\circ}C \tag{13}
$$

The variation of the thermal load in relation to the outdoor temperature is shown in **Figure 11**. It is considered that the district heating system is put into operation when the mean daily temperature of the external environment is lower than 10°C, producing thermal agent both for the preparation of domestic hot water and for heating. For mean daily outdoor temperatures above 10°C, the system prepares agent for domestic hot water only.

In the cold season 2020-2021, the variation of the mean daily outdoor temperature and necessary thermal load for heating system in the Călimănești zone, is shown in the **Figure 12** [18]. In recent years, due to global warming, mean temperatures during the cold season have been higher than usual. It can be noted that mean outdoor temperatures below 0°C was recorded in the area for a few days, only in the second half of January and in February, the thermal load during this period being about 70% of the maximum load calculated, based on the conventional temperatures. The system was started on Octomber 01, 2020 and was stopped on April 30, 2021.

The operation diagram of the geothermal station coupled with the heat pump is shown in the **Figure 13**. By cooling the geothermal water with Δ*t*<sup>1</sup> ¼ 45°*C* (from 95–50°C) the heat flow introduced in the system is:

$$
\dot{Q}\_{he} = \dot{m}\_{gw} \varepsilon\_w \Delta t\_1 = \dot{m}\_{he} \varepsilon\_w \Delta t \text{ [kW]} \tag{14}
$$

**Figure 11.** *The thermal load of district heating system in relation with outdoor temperature according to Eq. (13).*

**Figure 12.** *Variation of mean daily outdoor temperature in area and thermal load, in the cold season of 2020-2021 [18].*

**Figure 13.** *Operation diagram of the geothermal station (source: the drawing of authors).*

and by recovering the residual heat from the geothermal water, the heat flow introduced by the heat pump into the system is:

$$
\dot{Q}\_{hp} = \dot{m}\_{gw} c\_w \Delta t\_2 \frac{COP}{COP - 1} = \dot{m}\_{hp} c\_w \Delta t \text{ [kW]} \tag{15}
$$

where, Δ*t*<sup>2</sup> ¼ 20°*C* (from 50–30°C) is the degree of geothermal water cooling in the heat pump evaporator, COP is energy efficiency, and Δ*t* ¼ 40°*C* (from 85–45°C) the temperature difference between the outlet and the inlet of the thermal agent sent into the district heating system.

The two expressions determine the total heat flow that the geothermal station coupled with the heat pump, introduces into the district heating system:

*Heat Pump to Increase the Efficiency of a Geothermal Heating System in the City… DOI: http://dx.doi.org/10.5772/intechopen.107252*

$$\dot{Q}\_{GTS} = \dot{m}\_{gw} c\_w \left(\Delta t\_1 + \Delta t\_2 \frac{COP}{COP - 1}\right) = \left(\dot{m}\_{h\epsilon} + \dot{m}\_{hp}\right) c\_w \Delta t \quad [kW] \tag{16}$$

For the maximum available flow of geothermal water of 10 l/s, the maximum heat flow delivered to the DH system, according to relation (16), is *Q*\_ *max GTS* ¼ 2889 kW of which 1820 kW directly from geothermal water and 1069 kW from the heat recovered by means of the heat pump. For the operating conditions of the heating system, the energy efficiency of the heat pump was considered *COP* = 4.5, according to cycle analyze. The external limit temperature up to which the system can operate only with the thermal energy produced in the geothermal water heat exchanger is:

$$\mathbf{t}\_{\epsilon}^{\mathbf{g}w} = \mathbf{t}\_{i\epsilon} - \frac{\dot{\mathbf{Q}}\_{h\epsilon}^{\max} - \dot{\mathbf{Q}}\_{dhw}^{\max}}{\dot{\mathbf{Q}}\_{heat}^{\max}} (\mathbf{t}\_{ic} - \mathbf{t}\_{cc}) [^{\bullet}\mathbf{C}] \tag{17}$$

and the external limit temperature up to which the system can operate coupled with heat pump is:

$$\mathbf{s}\_{\epsilon}^{w+hp} = \mathbf{t}\_{ic} - \frac{\dot{\mathbf{Q}}\_{h\epsilon}^{\max} + \dot{\mathbf{Q}}\_{hp}^{\max} - \dot{\mathbf{Q}}\_{dhw}^{\max}}{\dot{\mathbf{Q}}\_{heat}^{\max}} (\mathbf{t}\_{ic} - \mathbf{t}\_{ec}) [\text{"C}] \tag{18}$$

According to the operating conditions of the heating system, for the limit temperatures specified by relations (16) and (17) the following values resulted for the limit temperatures: *t gw <sup>e</sup>* ¼ 5*:*7°*C* and *t gw*þ*hp <sup>e</sup>* ¼ �5*:*5°*<sup>C</sup> . For outdoor temperature lower than t gw*þ*hp <sup>e</sup>* , gas hot water boilers must also be started. The **Figure 14** shows how the geothermal station can cover the thermal load of the district heating system, depending on the level of the outside temperature.

The adjustment of the geothermal station functioning, so that the heat flow produced can cover the thermal load determined by the outdoor temperature, can be done by changing the flow of the thermal agent sent in the network, with the constant maintenance of its temperature (quantitative regulation) or with the constant maintenance of

the flow of the agent sent in the network and the change of its temperature (qualitative regulation). Only quantitative adjustment has been considered in this discussion.

The heat flow required for the DH system needs a water flow to be taken from geothermal borehole:

if *te* ≥10°*C* (production of DHW only)

$$
\dot{m}\_{\text{gw}} = \frac{\dot{\mathcal{Q}}\_{dhw}^{\text{max}}}{c\_w \Delta t\_1} \left[ \text{kg/s} \right] \tag{19}
$$

if 10°*C*>*te* ≥5*:*7°*C* (production of DHW and heating with geothermal water only)

$$
\dot{m}\_{\text{gw}} = \frac{\dot{\mathcal{Q}}\_{heat}^{\text{max}} \frac{t\_{\text{ic}} - t\_{\text{c}}}{t\_{\text{ic}} - t\_{\text{cc}}} + \dot{\mathcal{Q}}\_{dhw}^{\text{max}}}{c\_w \Delta t\_1} \left[ \text{kg/s} \right] \tag{20}
$$

if 5*:*7°*C*>*te* ≥ � 5*:*5°*C* (production of DHW and heating with geothermal water and heat pump)

$$\dot{m}\_{\text{gw}} = \frac{\dot{Q}\_{\text{heat}}^{\text{max}} \frac{t\_{\text{ic}} - t\_{\text{r}}}{t\_{\text{ic}} - t\_{\text{cr}}} + \dot{Q}\_{\text{dhw}}^{\text{max}}}{c\_{\text{av}} \left(\Delta t\_1 + \Delta t\_2 \frac{COP}{COP - 1}\right)} \text{[kg/s]} \tag{21}$$

The HD system can cover the required thermal load using only geothermal water, up the outdoor temperature of +5.7°C, when the maximum geothermal water flow of 10 l/s (9.62 kg/s) is extracted from the borehole. Below this temperature, the DH system coupled to the heat pump can covers the required thermal load up to temperature of �5.5°C, when the flow of extracted geothermal water is also maximum. For lower outdoor temperatures, the thermal load is supplemented by the commissioning of hot water boilers with gaseous fuel.

If the temperature of the thermal agent sent to the district heating system is kept constant, its flow rate depending on the temperature of the external environment is expressed:

$$
\dot{m}\_{\text{tot}} = \dot{m}\_{\text{he}} + \dot{m}\_{hp} = \frac{\dot{Q}\_{heat}^{\text{max}} \frac{t\_{\text{ic}} - t\_{\text{c}}}{t\_{\text{ic}} - t\_{\text{c}}} + \dot{Q}\_{dhw}^{\text{max}}}{c\_w \Delta t} \,\left[\text{kg/s}\right] \tag{22}
$$

The variation of these flows in relation to the outdoor temperature is shown in the **Figure 15**.

Coupling the geothermal station with a heat pump to recover the thermal energy of the wastewater discharged from the geothermal heat exchangers, allows to increase the flow of thermal agent introduced into the district heating system and cover about 70% of its maximum thermal load. Under these conditions, the district heating system can ensure the thermal comfort of consumers only up to an outside temperature of around �6°C (**Figure 16**).

Examining the way in which the climatic conditions in the area have manifested in recent years, by coupling the geothermal station with a heat pump, the maximum available flow of geothermal water can ensure the coverage of the entire thermal load of the heating system, without the use of gas-fired hot water boilers. Due to the global warming phenomenon, the mean daily temperatures during the cold season were higher than the usual temperatures for this period.

As can be seen in the **Figure 16**, according to the weather archive in the area, during the cold period of the 2020-2021 season, in just a few days the average outdoor *Heat Pump to Increase the Efficiency of a Geothermal Heating System in the City… DOI: http://dx.doi.org/10.5772/intechopen.107252*

#### **Figure 15.**

*The geothermal water flow and the thermal agent flow in relation with outdoor temperature, according to Eq. (19)-(22).*

#### **Figure 16.**

*The thermal load coverage in relation with outdoor temperature (source: the drawing of authors).*

temperature dropped below 5°C, the temperatures in the rest of this period being in the range 0 ... + 5°C. Under these conditions, the thermal load of the system can be covered throughout the heating season without the need to come into operation the hot water boilers with gaseous fuel.

#### **5. Conclusions**

• The solution of using medium enthalpy geothermal water, from the deposits located in the lower basin of the Olt River, as a heat source for the district heating system of Călimănești is part of the current concerns to reduce the consumption of conventional fuels, generated both by depletion of reserves and the need to reduce greenhouse gas emissions. At the same time, the geothermal energy being provided free of charge by nature, continuously and renewable, allows the creation of heating systems that provide the population with heat at very low prices.


*Heat Pump to Increase the Efficiency of a Geothermal Heating System in the City… DOI: http://dx.doi.org/10.5772/intechopen.107252*

### **Author details**

Laurentiu Constantin Lipan<sup>1</sup> \* and Sorin Dimitriu<sup>2</sup>

1 Faculty of Power Engineering, Department of Electric Power Systems Engineering, University Politehnica of Bucharest, Romania

2 Department of Engineering Thermodynamics, Internal Combustion Engines, Thermal and Refrigerating Equipment, University Politehnica of Bucharest, Romania

\*Address all correspondence to: laurentiu.lipan@upb.ro

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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[7] Bianchi AM, Marinescu M, Hera D, Dimitriu S, Ivan G, Băltărețu F. Thermal power supply systems in Romania; development directions. In: Proceedings of the XVII-th International Conference of Engineering Thermodynamics (CNT-17); 21-22 May 2009. Brașov. Romania: Transylvania University; 2009. pp. 1-20

[8] Lund WJ, Lienau JP. Geothermal District Heating. In: Proceeding of International Geothermal Days– Conference & Summer School. 26-29 May 2009. Časta-Papiernička; Slovakia: Slovgeotherm; Session 2; Paper II-1; 2009

[9] Werner S. International review of district heating and cooling. In: Energy. Vol. 137. Amsterdam: Elsevier; 2017. pp. 617-631

[10] Rosca M, Antics M. Current status of geothermal energy utilization in Romania. In: Proceeding of International Geothermal Days – Conference & Summer School; 13-17 September 2004. Zakopane; Poland; Paper 4; 2004. pp. 295-304

[11] Bendea C, Bendea G, Rosca M, Cucuteanu D. Current status of geothermal energy utilization in Romania. In: University of Oradea, International Journal of Sustainable Energy. 2013;**4**(4):182-190. ISSN 2067-5534

[12] Dimitriu S, Bianchi AM, Baltărețu F. The up-to-date heat pump – Combined heat and power solution for the complete utilization of the low enthalpy geothermal water potential. International Journal of Energy and Environmental Engineering (IJEE). 2017;**8**(3):189-196

[13] ANRE (National Energy Regulatory Authority). Report on the state of the public service of thermal energy supply in centralized system for 2021 [Internet]. Annexes 2,3. Available from: https:// www.anre.ro/ro/energie-electrica/legisla tie/serviciul-public-de-alimentare-cuenergie-termica

[14] ASHRE. ANSI/ASHRE Standard 34-2019. Designation and Safety

*Heat Pump to Increase the Efficiency of a Geothermal Heating System in the City… DOI: http://dx.doi.org/10.5772/intechopen.107252*

Classification of Refrigerants. Available from: https://www.ashre.org/technicalresources/standards-and-guidelines

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[17] MTCT (The Ministry of Transport, Construction and Tourism) C-107 Normative Regarding the Thermotechnical Calculation in Constructions. 1st ed. Romania: Best Publishing; 2008. ISBN 978-973-738-331-0

[18] Weather archive in Râmnicu Vâlcea [Internet]. Available from: https://rp5. ru/Vremea\_în\_Râmnicu\_Vâlcea

#### **Chapter 12**

## Design of Earth Quake Responses Decentralized Controller in Smart Building Systems

*Doghmane Mohamed Zinelabidine and Eladj Said*

#### **Abstract**

Many building systems are known to have complex structure with large dimension variables that characterize its mathematical models. In such case, it is basically desirable to use avoid the use of centralized controller due to the possibility of dimension increase during its implementation. The design of decentralized controller has faced tremendous success especially for large scale systems. The main objective of this book chapter is to design decentralized controller for building system in order to avoid the damages that will be caused by the earth quake responses. This controller is designed to increase the robustness and improve the smart building system responses toward different earth quakes. The optimized behavior of the control system has been analyzed and tested in the framework of the inclusion-contraction of the overlapping decomposition theories. Moreover, the application of this control strategy to smart building system has led to significantly minimize the damages that can be generally caused by the severe earth quakes. Thence, the obtained results have demonstrated the usefulness of the proposed controller for constructing smart cities.

**Keywords:** decentralized controller, earth quake responses, overlapping decomposition, smart building systems, smart cities

#### **1. Introduction**

Describing the behavior of many mechanical and engineering systems may let us end up with high dimensional mathematical models. The analysis and design problems of such systems become very complex; since the solution may not be found easily due to the huge amount of computation efforts required to simulate and analyze the dynamic process of the system, which may lead to large scale decentralized controller [1]. Therefore, new techniques and strategies should be designed in order to optimize the controller through decomposing it into simpler subsystems, so that, the control of such systems can be combined together in order to control the original global system. The objective of this paper is to design a decentralized optimal controller of wellknown example of overlapping system using extension principle; our work is development of (*L. Bakule* and *J. Rodellar 1995*) paper by improving the performance of

responses using optimization technique. We will see in this book chapter the application of decentralized optimal overlapping decomposition for six floor building system, for which we have given brief mathematical description of the system and the process being applied. It has been found that designing an algorithm for such type of systems is possible and very useful because it satisfies condition for this algorithm (condition of expansion/contraction, condition of contractibility of controllers). The chapter is organized as follows, in the second section, a six-floor building system mathematical model has been carefully described in order to permit the readers understand the dynamic behavior of the system. In section three, the theories of expansion/contraction have been introduced so that it will allows us to design an overlapping decentralized controller for the system under study. In section four, the contractibility of the designed controller has been discussed for application to the original system after decomposition. In the fifth section, we have proposed a controller based on the introduced theories in order to minimize the six floor building system under sever earthquake input signals. The controller robustness and performance have been demonstrated through simulation results in section six. The chapter has been ended up by conclusion and recommendation for implementation in smart building system which will be an important step toward smart cities.

#### **2. System description**

Construction engineering is very important term that gathers many disciplines that varies from physics, mechanics, electronics and control [1]. It is applied science, for which engineers build different structures within the scope of civil engineering, smart building systems; thus, it is scientific discipline to the design of building that defines smart cities [2]. People combined a practical knowledge of materials and construction with the mathematics and science that were then available [3].

Consider the mechanical second order building system shown in **Figures 1** and **2**. The system is composed of six floor build in concrete cement and it is under continuous vibrations created by the continuous movement of the earth and earthquakes. The system's dynamic is described by first order differential equation written in the matrix form, the size of the matrix is proportional to the number of floors and earthquake sensors installed for each as well as the actuators at the level of each floor or set of floors. The actuators are designed to create a counter force synchronously to the building dynamic.

The system shown in **Figure 1** can be represented by the following mathematical model

$$\begin{cases} M\ddot{q} + D\dot{q} + \text{S}q = Bu \\ \\ \nu = \text{C}q \\ \nu = V\dot{q} \end{cases} \tag{1}$$

Where:

*M* : 6 � 6Z is the mass matrix, symmetric, positive definite matrix.

*D* : 6 � 6Z is the damping matrix.

*S* : 6 � 6Z is the stiffness matrix.

*Design of Earth Quake Responses Decentralized Controller in Smart Building Systems DOI: http://dx.doi.org/10.5772/intechopen.106217*

#### **Figure 1.**

*Overlapping structure of building system [1].*

*q* : 6 � 1Z is the displacement vector, represents the degree of freedom of the system.

*B* : 6 � 3Z is the input matrix, represents locations of actuators.

*u* : 3 � 1Z is the input signal, sinusoidal signal in this example.

Eq. (1) indicate the response of building system to earthquake; **Figure 3** shows a failure response to real earthquake system [1, 4].

Eq. (1) can be written as.

or

$$\begin{cases} q = T^l q\_\epsilon \\ u = U u\_\epsilon \\ \nu = G^l \nu\_\epsilon \\ v = H^l v\_\epsilon \end{cases} \tag{2}$$

Where *T<sup>I</sup> <sup>T</sup>* <sup>¼</sup> *In* <sup>¼</sup> *<sup>I</sup>*6, *UU<sup>I</sup>* <sup>¼</sup> *Im* <sup>¼</sup> *<sup>I</sup>*<sup>3</sup> *<sup>G</sup><sup>I</sup> <sup>G</sup>* <sup>¼</sup> *Ip* <sup>¼</sup> *<sup>I</sup>*6, *<sup>H</sup>TH* <sup>¼</sup> *Ir* <sup>¼</sup> *<sup>I</sup>*6. And *T<sup>I</sup>* , *U<sup>I</sup>* , *G<sup>I</sup>* , *H<sup>I</sup>* indicates the pseudo-inverse of *T*, *U*, *G*, *H* respectively [2].

**Figure 2**. *Overlapping structure of real building system [1].*

**Figure 3.** *Failure response of a building system to earthquake disturbances [1].*

*Design of Earth Quake Responses Decentralized Controller in Smart Building Systems DOI: http://dx.doi.org/10.5772/intechopen.106217*

$$
\begin{aligned}
\begin{Bmatrix}
\begin{bmatrix}M\_{11}&M\_{12}&M\_{13}\\M\_{21}&M\_{22}&M\_{23}\\M\_{31}&M\_{32}&M\_{33}\end{bmatrix}\end{bmatrix}\begin{bmatrix}\ddot{q}\_{1}\\\ddot{q}\_{2}\\\ddot{q}\_{3}\\\end{bmatrix}+\begin{bmatrix}D\_{11}&D\_{12}&D\_{13}\\D\_{21}&D\_{22}&D\_{23}\\D\_{31}&D\_{32}&D\_{33}\end{bmatrix}\begin{bmatrix}\dot{q}\_{1}\\\dot{q}\_{2}\\\dot{q}\_{3}\\\end{bmatrix}+\begin{bmatrix}S\_{11}&S\_{12}&S\_{13}\\\dot{q}\_{2}&S\_{22}&S\_{23}\\\theta\_{3}&S\_{32}&S\_{33}\end{bmatrix}\begin{bmatrix}q\_{1}\\q\_{2}\\q\_{3}\\\end{bmatrix}\\&=\begin{bmatrix}0&B\_{21}&0&0\\0&B\_{22}&0\\0&0&B\_{33}\\\end{bmatrix}\begin{bmatrix}u\_{1}\\u\_{2}\\u\_{3}\\\end{bmatrix}\\&=\begin{bmatrix}V\_{11}&0&0\\0&C\_{22}&0\\0&0&C\_{33}\\\end{bmatrix}\begin{bmatrix}q\_{1}\\q\_{2}\\q\_{3}\\\end{bmatrix}\\&=\begin{bmatrix}V\_{11}&0&0\\0&C\_{22}&0\\0&0&C\_{33}\\\end{bmatrix}\begin{bmatrix}q\_{1}\\q\_{2}\\q\_{3}\\\end{bmatrix}\\&=\begin{bmatrix}V\_{11}&0&0\\0&V\_{22}&0\\0&0&V\_{33}\\\end{bmatrix}\begin{bmatrix}\dot{q}\_{1}\\\dot{q}\_{2}\\\end{bmatrix}\tag{3}\end{aligned}\tag{3}\end{aligned}\tag{3}$$

Where dashed lines indicate the subsystems, in this example we have only two subsystems *S*<sup>1</sup> and *S*<sup>2</sup> that shares common informations (*M*22, *D*22, *S*22, *B*22,*C*22,*V*22) [1].

#### **3. Expansion and contraction**

We have the overlapping system (1) and we want to transform it into nonoverlapping system described by

*se* : *Me*€*qe* þ *Deq*\_*<sup>e</sup>* þ *Seqe* ¼ *Beue ye* ¼ *Ceqe ve* ¼ *Veq*\_*<sup>e</sup>* 8 >< >: (4)

To do that we consider the transformation matrices between systems (1) and (3)

$$\begin{cases} q\_\epsilon = Tq \\ u\_\epsilon = U^I u \\ \mathcal{Y}\_\epsilon = G\mathcal{Y} \\ v\_\epsilon = Hv \end{cases} \tag{5}$$

The system described by Eq. (3) is said to be an expansion of the system described by (1) (or the system described by (1) is considered to be a contraction of the system (3)) if there exist transformation *T*, *U*, *G* and *H* that satisfied Eq. (4) so that for any initial states *qe*ð Þ <sup>0</sup> , *<sup>q</sup>*\_*e*ð Þ <sup>0</sup> � � and for any input *ue*ð Þ*<sup>t</sup>* <sup>∈</sup>*R<sup>m</sup>* for all *<sup>t</sup>*≥0 [5], we have

$$\begin{cases} q\_\epsilon(\mathbf{0}) = Tq(\mathbf{0}) \\ \dot{q}\_\epsilon(\mathbf{0}) = T\dot{q}(\mathbf{0}) \Rightarrow \begin{cases} q\_\epsilon(t) = Tq(t) \\ \dot{q}\_\epsilon(t) = T\dot{q}(t) \\ \nu\_\epsilon(t) = H\nu(t) \end{cases} \end{cases} \tag{6}$$

Essentially there exist two (02) methods to derive condition of extension:

• Method one

Requires working directly with the matrix second order equation in both original and extended system which means we need to use the matrices *M* and *Me* [1].

• Method two

Starts by transforming the second order system into an equivalent first order system ) requires working with *<sup>M</sup>*�<sup>1</sup> and *Me* �1 .

Consider the system (1) and its expansion (3); define the state vectors *x*, *xe* as: *<sup>x</sup>* <sup>¼</sup> *<sup>q</sup><sup>T</sup>*, *<sup>q</sup>*\_ *<sup>T</sup>* � �*<sup>T</sup>* , *xe* ¼ *qe <sup>T</sup>*, *q*\_*<sup>e</sup> <sup>T</sup>* � �*<sup>T</sup>* then Eqs. (1) and (2) can be written as

$$s\_{\mathbf{x}} : \begin{cases} \dot{\mathbf{x}} = A\_{\mathbf{x}} \mathbf{x} + B\_{\mathbf{x}} u \\ \mathbf{y}\_{\mathbf{x}} = \mathbf{C}\_{\mathbf{x}} \mathbf{x} \end{cases} \tag{7}$$

$$\begin{aligned} \varkappa\_{\rm ex} &: \begin{cases} \dot{\varkappa}\_{\varepsilon} = A\_{\rm ex} \varkappa\_{\varepsilon} + B\_{\rm ex} u\_{\varepsilon} \\ \qquad \jmath\_{\rm ex} = C\_{\rm ex} \varkappa\_{\varepsilon} \end{cases} \end{aligned} \tag{8}$$

Where

$$\begin{cases} A\_{\mathbf{x}} = \begin{bmatrix} \mathbf{0}\_{6 \times 6} & I\_6 \\\\ -M^{-1}\mathbf{S} & -M^{-1}D \end{bmatrix} \\\\ B\_{\mathbf{x}} = \begin{bmatrix} \mathbf{0}\_{6 \times 3} \\\\ M^{-1}B \end{bmatrix} \\\\ \mathbf{C}\_{\mathbf{x}} = \operatorname{diag}(\mathbf{C}, \mathbf{V}) \end{cases}$$

And

$$\begin{cases} A\_{\rm ex} = \begin{bmatrix} \mathbf{0}\_{8 \times 8} & I\_8 \\\\ -M\_{\epsilon}^{-1} \mathbf{S}\_{\epsilon} & -M\_{\epsilon}^{-1} D\_{\epsilon} \end{bmatrix}, \\\\ B\_{\rm ex} = \begin{bmatrix} \mathbf{0}\_{8 \times 4} \\\\ M\_{\epsilon}^{-1} B\_{\epsilon} \end{bmatrix} \\\\ \mathbf{C}\_{\rm ex} = \operatorname{diag} \left( \mathbf{C}\_{\epsilon}, V\_{\epsilon} \right) \end{cases}$$

Consider the transformation *T*, *U*, *G* satisfying (6) for the original system; define the transform for the expanded system as.

*Td* ¼ *diag T*ð Þ , *T* ; *Cd* ¼ *diag G*ð Þ , *H* .

This implies that

$$\begin{cases} \varkappa\_{\varepsilon}(\mathbf{0}) = T\_d \mathfrak{x}(\mathbf{0}) \\ u(t) = U u\_{\varepsilon}(t) \end{cases} \Rightarrow \begin{cases} \varkappa\_{\varepsilon}(t) = T\_d \mathfrak{x}(t) \\ \mathcal{y}\_{\varepsilon \varepsilon}(t) = \mathcal{C}\_d \mathfrak{x}(t) \end{cases} \tag{9}$$

*Design of Earth Quake Responses Decentralized Controller in Smart Building Systems DOI: http://dx.doi.org/10.5772/intechopen.106217*

#### A. Theorem one

The system *se* is an extension of the system *s* or equivalently *s* is dis-extension of *se* if and only if there exists full rank transformation matrices *T*, *U*, *G* and *H* such that

$$\begin{cases} M\_{\epsilon}^{-1} \mathbf{S}\_{\epsilon} T = T \mathbf{M}^{-1} \mathbf{S} \\ M\_{\epsilon}^{-1} D\_{\epsilon} T = T \mathbf{M}^{-1} D \\ M\_{\epsilon}^{-1} B\_{\epsilon} = T \mathbf{M}^{-1} B U \\ \mathbf{G} \mathbf{C} = \mathbf{C}\_{\epsilon} T \\ H V = V\_{\epsilon} T \end{cases} \tag{10}$$

These equations are found by transforming the system (1) and (3) into state space model [6].

Eq. (10) can be written as

$$\begin{cases} \mathcal{M}\_{\epsilon}{}^{-1} = T\mathcal{M}^{-1}T^{l} + \mathcal{M}\_{cq} \\\\ \mathcal{S}\_{\epsilon} = T\mathcal{S}T^{l} + \mathcal{S}\_{cq} \\\\ D\_{\epsilon} = TDT^{l} + D\_{cq} \\\ B\_{\epsilon} = TBU + B\_{cq} \\\\ \mathcal{C}\_{\epsilon} = GCT^{l} + \mathcal{C}\_{\epsilon} \\\ V\_{\epsilon} = HVT^{l} + V\_{\epsilon} \end{cases} \tag{11}$$

With *Me*, *Se*, *De*, *Be*,*Ce* and *Ve* are given by

$$\begin{aligned} &M\_{\epsilon}=\begin{bmatrix}M\_{11}&M\_{12}&0&M\_{13}\\M\_{21}&M\_{22}&0&M\_{23}\\M\_{21}&0&M\_{22}&M\_{23}\\M\_{31}&0&M\_{32}&M\_{33}\end{bmatrix},\quad D\_{\epsilon}=\begin{bmatrix}D\_{11}&D\_{12}&0&D\_{13}\\D\_{21}&D\_{22}&0&D\_{23}\\D\_{21}&0&D\_{22}&D\_{23}\\D\_{31}&0&D\_{32}&D\_{33}\end{bmatrix},\\ &S\_{\epsilon}=\begin{bmatrix}S\_{11}&S\_{12}&0&S\_{13}\\S\_{21}&S\_{22}&0&S\_{23}\\S\_{21}&0&S\_{22}&S\_{23}\\S\_{31}&0&S\_{32}&S\_{33}\end{bmatrix},\quad B\_{\epsilon}=\begin{bmatrix}B\_{11}&0&0&0\\0&B\_{22}&B\_{22}&0\\0&B\_{22}&B\_{22}&0\\0&0&0&B\_{33}\end{bmatrix},\\ &C\_{\epsilon}=\begin{bmatrix}C\_{11}&0&0&0\\0&C\_{22}&0&0\\0&0&C\_{22}&0\\0&0&0&C\_{33}\end{bmatrix},\quad V\_{\epsilon}=\begin{bmatrix}V\_{11}&0&0&0\\0&V\_{22}&0&0\\0&0&V\_{23}&0\\0&0&V\_{33}&0\end{bmatrix}\end{aligned}$$

Where *Mqc*, *Sqc*, *Dqc*, *Bqc*,*Cqc* and *Vqc* are complementary matrices defined in such way to satisfy the condition of extension [6, 7].

#### A. Theorem two

The system (2) is an expansion of the system (1) if

$$\begin{cases} M\_{qc}T = \mathbf{0} \\ K\_{qc}T = \mathbf{0} \\ D\_{qc}T = \mathbf{0} \\ B\_{qc} = \mathbf{0} \\ C\_{qc}T = \mathbf{0} \\ V\_{qc}T = \mathbf{0} \end{cases} \tag{12}$$

Eq. (12) is satisfied by choosing the complementary matrices as

$$[.]\_{q\epsilon} = \begin{bmatrix} \mathbf{0} & \mathbf{0}.\mathbf{5}[.]\_{12} & -\mathbf{0}.\mathbf{5}[.]\_{12} & \mathbf{0} \\ \mathbf{0} & \mathbf{0}.\mathbf{5}[.]\_{22} & -\mathbf{0}.\mathbf{5}[.]\_{22} & \mathbf{0} \\ \mathbf{0} & -\mathbf{0}.\mathbf{5}[.]\_{22} & \mathbf{0}.\mathbf{5}[.]\_{22} & \mathbf{0} \\ \mathbf{0} & -\mathbf{0}.\mathbf{5}[.]\_{32} & \mathbf{0}.\mathbf{5}[.]\_{32} & \mathbf{0} \end{bmatrix} \tag{13}$$

#### **4. Contractibility of controllers**

Let us consider the controller given by Eq. (14), for the overlapping building system

$$
\omega = F\mathbf{y} + L\mathbf{z} + w \tag{14}
$$

And Let us consider the controller given by Eq. (15) for the expanded system of the building system:

$$
\mu\_{\varepsilon} = F\_{\varepsilon} \underline{v}\_{\varepsilon} + L\_{\varepsilon} \underline{v}\_{\varepsilon} + w\_{\varepsilon} \tag{15}
$$

Where *w* and *we* are the external inputs of the smart building system [1, 8].

#### A. Theorem three

The controller described by Eq. (15) is contractible to the controller given by Eq. (14) if and only if

$$\begin{cases} FC = UF\_t GC\\ LV = UL\_t HV \end{cases} \tag{16}$$

#### B. Theorem four

If Eq. (2) is an extension of Eq. (1) and if Eq. (2) is stable (respectively asymptotically stable) then Eq. (1) is stable (respectively asymptotically stable) [6, 9]. *Design of Earth Quake Responses Decentralized Controller in Smart Building Systems DOI: http://dx.doi.org/10.5772/intechopen.106217*

#### **5. Decentralized optimal output feedback control**

#### A. Problem's frame

Consider the system (7); our goal is to find control law *u* ¼ *Kyx* to minimize the cost function

$$J = \int\_{-\infty}^{+\infty} (\mathbf{x}^T Q \mathbf{x} + \mathbf{u}^T R \mathbf{u}) dt \tag{17}$$

Such that the closed loop system

$$\mathcal{S}\_{\mathfrak{c}} = \begin{cases} \dot{\mathfrak{x}} = (\mathcal{A} + B\_{\mathfrak{x}} K \mathcal{C}\_{\mathfrak{x}}) \mathfrak{x} \\ \mathcal{Y}\_{\mathfrak{x}} = \mathcal{C}\_{\mathfrak{x}} \mathfrak{x} \end{cases} \tag{18}$$

is asymptotically stable

B. Problem Solution

First we have the following two subsystems that have been extracted from the expanded system

$$\begin{cases} M\_1 \ddot{q}\_{\epsilon1} + D\_1 \dot{q}\_{\epsilon1} + S\_1 q\_{\epsilon1} = B\_1 u\_{\epsilon1} \\ y\_{\epsilon1} = C\_1 q\_{\epsilon1} \\ v\_{\epsilon1} = V\_1 \dot{q}\_{\epsilon1} \end{cases} \tag{19}$$

$$\begin{cases} M\_2 \ddot{q}\_{\epsilon2} + D\_2 \dot{q}\_{\epsilon2} + S\_2 q\_{\epsilon2} = B\_2 u\_{\epsilon2} \\ y\_{\epsilon2} = C\_2 q\_{\epsilon2} \\ v\_{\epsilon2} = V\_2 \dot{q}\_{\epsilon2} \end{cases} \tag{20}$$

Let us transform these two subsystems into state space form to get

$$\begin{aligned} \varkappa\_1 &: \begin{cases} \dot{\varkappa}\_{\epsilon 1} = A\_{\epsilon \epsilon 1} \varkappa\_{\epsilon 1} + B\_{\epsilon \epsilon 1} u\_{\epsilon 1} \\ \varkappa\_1 = C\_{\epsilon \epsilon 1} \varkappa\_{\epsilon 1} \end{cases} \end{aligned} \tag{21}$$

$$\begin{aligned} \varkappa\_2 &: \begin{cases} \dot{\varkappa}\_{\epsilon2} = A\_{\epsilon\varepsilon2} \varkappa\_{\epsilon2} + B\_{\epsilon\varepsilon2} \mu\_{\epsilon2} \\ \varkappa\_2 = C\_{\epsilon\varepsilon2} \varkappa\_{\epsilon2} \end{cases} \end{aligned} \tag{22}$$

Where:

$$\begin{cases} A\_{\text{ex1}} = \begin{bmatrix} \mathbf{0}\_{(2+2)\times(2+2)} & I\_{(2+2)\times(2+2)} \\ \mathbf{ } -\mathbf{M}\_1^{-1}\mathbf{S}\_1 & -\mathbf{M}\_1^{-1}D\_1 \end{bmatrix} \\\\ B\_{\text{ex1}} = \begin{bmatrix} \mathbf{0}\_{(2+2)\times(2+2)} \\ -\mathbf{M}\_1^{-1}B\_1 \end{bmatrix} \\\\ \mathbf{C}\_{\text{ex1}} = \text{diag}(\mathbf{C}\_1, \mathbf{V}\_1) \end{cases}$$

**213**

$$\begin{cases} A\_{\mathrm{ex2}} = \begin{bmatrix} \mathbf{0}\_{(2+2)\times(2+2)} & I\_{(2+2)\times(2+2)} \\ -M\_2^{-1}\mathbf{S}\_2 & -M\_2^{-1}D\_2 \end{bmatrix}, \\\ B\_{\mathrm{ex2}} = \begin{bmatrix} \mathbf{0}\_{(2+2)\times(2+2)} \\ -M\_2^{-1}B\_2 \end{bmatrix} \\\ \mathbf{C}\_{\mathrm{ex2}} = \operatorname{diag}(\mathbf{C}\_2, V\_2) \end{cases}$$

We will try to generate the optimal output feedback for each subsystem as *ui* ¼ *Kiyi* ; *i* ¼ 1, 2, to each subsystem we associate the performance index:

$$J\_i = \int\_{-\infty}^{+\infty} (\mathbf{x}\_i^T \mathbf{Q}\_i \mathbf{x}\_i + \mathbf{u}\_i^T \mathbf{R}\_i \mathbf{u}\_i) dt \, i = 1, 2 \tag{23}$$

The necessary and sufficient conditions of optimality for each subsystem are:

$$\begin{cases} \phi\_i^T P\_i + P\_i \phi\_i + Q\_i + \mathbf{C}\_{xi}^T K\_i^T R\_i K\_i \mathbf{C}\_{xi} = \mathbf{0} \\\\ K\_i = -R\_i^{-1} B\_{xi}^T P\_i L\_i \mathbf{C}\_{xi}^T \left(\mathbf{C}\_{xi} L\_i \mathbf{C}\_{xi}^T\right)^{-1} \\\\ \phi\_i L\_i + L\_i \phi\_i^T + X\_{0i} = \mathbf{0} \end{cases} \tag{24}$$

Where *<sup>ϕ</sup><sup>i</sup>* <sup>¼</sup> *Ai* <sup>þ</sup> *BxiKiCxi*, *<sup>X</sup>*0*<sup>i</sup>* <sup>¼</sup> *<sup>x</sup>*0*ix<sup>T</sup>* 0*i* ; generally we take *x*0*<sup>i</sup>* ¼ *I*. The optimal cost can be found as:

$$J\_i = .5 \,\text{trace}(P\_i X\_{0i}) \tag{25}$$

And the optimal control law as *ui* ¼ *Kiyi* where

$$K\_i = \begin{bmatrix} K\_{11}^i & K\_{12}^i & K\_{13}^i & K\_{14}^i \\ K\_{21}^i & K\_{22}^i & K\_{23}^i & K\_{24}^i \end{bmatrix} \tag{26}$$

The control law for expanded system is

$$K\_{i} = \begin{bmatrix} K\_{11}^{1} & K\_{12}^{1} & \mathbf{0} & \mathbf{0} & K\_{13}^{1} & K\_{14}^{1} & \mathbf{0} & \mathbf{0} \\ K\_{21}^{1} & K\_{22}^{1} & \mathbf{0} & \mathbf{0} & K\_{23}^{1} & K\_{24}^{1} & \mathbf{0} & \mathbf{0} \\ \mathbf{0} & \mathbf{0} & K\_{11}^{2} & K\_{12}^{2} & \mathbf{0} & \mathbf{0} & K\_{13}^{2} & K\_{14}^{2} \\ \mathbf{0} & \mathbf{0} & K\_{21}^{2} & K\_{22}^{2} & \mathbf{0} & \mathbf{0} & K\_{23}^{2} & K\_{24}^{2} \end{bmatrix} \tag{27}$$

and the contracted control law for the original system is:

$$K = \begin{bmatrix} K\_{11}^1 & K\_{12}^1 & 0 & K\_{13}^1 & K\_{14}^1 & 0\\ K\_{21}^1 & K\_{22}^1 + K\_{11}^2 & K\_{12}^2 & K\_{21}^1 & K\_{24}^1 + K\_{13}^2 & K\_{14}^2\\ \mathbf{0} & K\_{21}^2 & K\_{22}^2 & \mathbf{0} & K\_{23}^2 & K\_{24}^2 \end{bmatrix} \tag{28}$$

To apply this control law for the original mechanical system, we must write it in the form:

$$
\omega = F\mathbf{y} + L\mathbf{z} + w \tag{29}
$$

*Design of Earth Quake Responses Decentralized Controller in Smart Building Systems DOI: http://dx.doi.org/10.5772/intechopen.106217*

Where: *w* is the external input to the mechanical system [6, 10]. We have

$$\begin{cases} \ K = [F, L] \\ K\_{\epsilon} = [F\_{\epsilon}, L\_{\epsilon}] \end{cases} \tag{30}$$

With

$$\begin{cases} F = \begin{bmatrix} K\_{11}^1 & K\_{12}^1 & 0\\ K\_{21}^1 & K\_{22}^1 + K\_{11}^2 & K\_{12}^2\\ 0 & K\_{21}^2 & K\_{22}^2 \end{bmatrix} \\\ L = \begin{bmatrix} K\_{13}^1 & K\_{14}^1 & 0\\ K\_{23}^1 & K\_{24}^1 + K\_{13}^2 & K\_{14}^2\\ 0 & K\_{23}^2 & K\_{24}^2 \end{bmatrix} \end{cases} \tag{31}$$

Now the projection of this control law onto the original mechanical system gives [11]:

$$\begin{cases} M\ddot{q} + (D + BLV)\dot{q} + (K + BFC)q = Bw \\ y = Cq \\ v = V\dot{q} \end{cases} \tag{32}$$

#### **6. Simulation results and discussion**

Results founded in this paper using Matlab environment, where first, we started with centralized non-optimal controller for each floor as shown in figures below (**Figures 4**–**6**).

Then we apply decentralized non-optimal controller for the same floors (**Figures 7**–**10**).

We notice that in common floor (**Figures 8** and **10**) the response of system in closed loop form still effective; this is according to the interconnection between subsystems **Figure 11**.

**Figure 4.** *Centralized non-optimal control system (flour 2).*

**Figure 5.** *Centralized non-optimal control system (flour 4).*

#### **Figure 6.**

*Centralized non-optimal control system (flour 6).*

**Figure 7.** *Decentralized non-0ptimal control sub-system 1(flour 2).*

**Figure 8.** *Decentralized non-optimal control sub-system 1(flour 4).*

*Design of Earth Quake Responses Decentralized Controller in Smart Building Systems DOI: http://dx.doi.org/10.5772/intechopen.106217*

**Figure 9.**

*Decentralized non-optimal control sub-system 2(flour 2).*

**Figure 10.** *Decentralized non-optimal control sub-system 2(flour 4).*

**Figure 11.** *Centralized optimal control system (flour 2).*

**Figure 12.** *Centralized optimal control system (flour 6).*

An optimization technique was applied for centralized and decentralized controller as show in figure below:

Even when we applied optimization responses in **Figure 12** still valuable which may make damages to the building. The cost function of centralized controller is *Jt* <sup>¼</sup> <sup>7</sup>*:*<sup>31</sup> <sup>∗</sup> <sup>10</sup><sup>3</sup> while decentralized controller gives (**Figures 13**–**16**).

The cost functions of subsystems 1 and 2 are respectively *<sup>J</sup>*<sup>1</sup> <sup>¼</sup> <sup>2</sup>*:*<sup>47</sup> <sup>∗</sup> <sup>10</sup><sup>3</sup> and *<sup>J</sup>*<sup>2</sup> <sup>¼</sup> <sup>1</sup>*:*<sup>41</sup> <sup>∗</sup> <sup>10</sup>3.

**Figure 13.** *Decentralized optimal control sub-system 1 (flour 2).*

**Figure 14.** *Decentralized optimal control sub-system 1 (flour 4).*

**Figure 15.** *Decentralized optimal control sub-system 2 (flour 2).*

*Design of Earth Quake Responses Decentralized Controller in Smart Building Systems DOI: http://dx.doi.org/10.5772/intechopen.106217*

**Figure 16.** *Decentralized optimal control sub-system 2 (flour 4).*

#### **7. Conclusion**

The overlapping decomposition method has been presented in this book chapter, and then the inclusion principle has been introduced to provide a mathematical framework for decentralized control. The inclusion of the cost function has also been discussed to incorporate the optimal control problem for large scale smart building systems where the concept of contractibility of controllers has been discussed. Optimal decentralized dynamic output feedback controllers design has been proposed for six-floor building systems with overlapping structure. Non-optimal overlapping centralized and decentralized controllers are designed for which we found that decentralized controller give better results. Furthermore to improve these results we developed an optimization technique that allow us not just design optimal controller but also minimize the cost function of the whole system for decomposed decentralized (*J*<sup>1</sup> <sup>¼</sup> <sup>2</sup>*:*<sup>47</sup> <sup>∗</sup> <sup>10</sup><sup>3</sup> , *<sup>J</sup>*<sup>2</sup> <sup>¼</sup> <sup>1</sup>*:*<sup>41</sup> <sup>∗</sup> <sup>10</sup><sup>3</sup> ) in comparing to centralized (*Jt* <sup>¼</sup> <sup>7</sup>*:*<sup>31</sup> <sup>∗</sup> <sup>10</sup><sup>3</sup> ) controller; where we it is clear that *J*<sup>1</sup> þ *J*2≺*Jt*. The obtained results have demonstrated the superiority of the proposed controller to design smart building system toward smart cities.

#### **Acknowledgements**

This paper is a summary of final year project' part for getting research master of M. Z. Doghmane, at the department of electrification (Ex-INH), university of Boumerdes, Algeria.

#### **Author details**

Doghmane Mohamed Zinelabidine<sup>1</sup> \* and Eladj Said<sup>2</sup>

1 Faculty of Hydrocarbons and Chemistry, University M'hamed Bouguerra, Algeria

2 Department of Automation and Electrification of Industrial Procedures, Algeria

\*Address all correspondence to: doghmane\_m@yahoo.com

© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Design of Earth Quake Responses Decentralized Controller in Smart Building Systems DOI: http://dx.doi.org/10.5772/intechopen.106217*

#### **References**

[1] Doghmane MZ, Kidouche M, Eladj S, Belahcene B. Design of Optimal Decentralized Controller Using Overlapping Decomposition for smart building system. In: Hatti M, editor. Artificial Intelligence and Renewables Towards an Energy Transition. ICAIRES 2020. Lecture Notes in Networks and Systems. Vol. 174. Tipasa, Algeria: Springer, Cham; 2021. DOI: 10.1007/ 978-3-030-63846-7\_16

[2] Mendil C, Kidouche M, Doghmane MZ. Modeling of hydrocarbons rotary drilling systems under torsional vibrations: A survey. In: Hatti M, editor. Artificial Intelligence and Renewables Towards an Energy Transition. ICAIRES 2020. Lecture Notes in Networks and Systems. Vol. 174. Tipasa, Algeria: Springer, Cham; 2021. DOI: 10.1007/978-3-030- 63846-7\_24

[3] Reitherman R, Anagnos T, Meluch W. Building Bridges between Civil Engineers and Science Museums. USA: Consortium of Universities for Research in Earthquake Engineering; 2008

[4] Schweier C, Markus M, Steinle E. Simulation of earthquake caused building damages for the development of fast reconnaissance techniques. Natural Hazards and Earth System Sciences. 2004;**2004**(4):285-293

[5] Iftar L. Decentralized estimation and control with overlapping input-state output decomposition. Automatica. 1993;**29**:511-516

[6] Doghmane MZ, Kidouche M, Ahriche A. Decentralized overlapping control design with application to rotary drilling system. IETE Journal of Research. 2021. DOI: 10.1080/ 03772063.2021.1886602

[7] Bakule L, Lunze J. Decentralized Design of Feedback Control for Large-Scale Systems. Priloha casopisu Kybernetika, Supplement to the journal Kyberneitika: ACADEMIA, praha; 1988

[8] Mendil C, Kidouche M, Doghmane MZ. A study of the parametric variations influences on stick-slip vibrations in smart rotary drilling systems. In: Hatti M, editor. Artificial Intelligence and Renewables Towards an Energy Transition. ICAIRES 2020. Lecture Notes in Networks and Systems. Vol. 174. Tipasa, Algeria: Springer, Cham; 2021. DOI: 10.1007/ 978-3-030-63846-7\_67

[9] Ikeda M, Siljack DD. Overlapping decentralized control with input, state, and output inclusion. Control Theory and Advanced Technology. 1986;**2**(2): 155-172

[10] Palacios-Quinonero F, Rodellar J, Rosell JM. Sequential design of multioverlapping controllers for longitudinal multi-overlapping systems. Applied Mathematics and Computation. 2010; **217**(3):1170-1183

[11] Mendil C, Kidouche M, Doghmane MZ. Automatic control of a heat exchanger in a nuclear power station: The classical and the fuzzy methods. International Conference on Advanced Electrical Engineering (ICAEE). 2019;**2019**:1-6. DOI: 10.1109/ ICAEE47123.2019.9014661

#### **Chapter 13**

## The State of Renewable Energy in China and Way Forward in New Scenario Policies

*Daniel Adu, Ransford O. Darko, Boamah Kofi Baah and Agnes Abbey*

#### **Abstract**

China has a huge undeveloped potential in the field of renewable energy and represents the highest nation in hydropower both in installation and generation worldwide with which contributes greatly in their energy supply and helps in elimination energy demand situation in the country to direct the energy system toward the area of sustainably, clean, and quality energy supply for its safety in the country. The manuscript discusses the current renewable energy systems in China, the potentials for various renewable energy sources, and way forward. It also discusses the expected state of renewable energy in the next four decades in China and the various strategic development techniques. The current renewable energy situations, both demand and supply situations, have been discussed. Summary of the approaches for new development by the various energy sectors in the country as well as support of the government in renewable energies has been analyzed.

**Keywords:** renewable energy, potential, energy consumption, hydropower, wind power, solar power

#### **1. Introduction**

With the advent of renewable energy and high investment risk, the main financial support areas of R & D activities for commercial applications are REPG [1], among the significant financial support for the Chinese government's special funds for renewable energy, for the development of renewable energy-related activities, including resource exploration, and the development of standards, as well as the development of the project [2]. A small number of specific regulations were allotted for the special fund. For example, the Operation Views on Promoting the Development of Wind Power Generation Industry in 2006 was released by NDRC and MOF. It specified that the government would select several competent wind turbine and component manufacturers in which the R&D and testing of new products could be supported by the Renewable Energy Development Special Fund [3]. In the same way, appropriate laws and regulations also specified that the R&D of new products, as well as site industrialization of key technologies in the solar power industry, will be

supported by a portion of the special fund [4, 5]. In addition to the special funds for the development of renewable energy, the government of China has as well provided R&D funds for renewable energy through the following plans and programs, for example, The National Basic Research Program of China ("973" Program), The "Five-year Plans," and The National Natural Science Fund and The National High Technology Research and Development Program of China ("863" Program), [6]. Similarly, Liu et al., on the other hand, have divided China's current renewable energy policy into several categories, including development planning, industry guidance and technical support, cost sharing, price incentives, legal responsibility, and promotion [7].

### **2. China's energy structure (supply and demand)**

China's energy demand is currently led by industrial sector, but there will be a drastic change in future. While total energy demand will remain as present in 2050, the structure will change. There will be a rise in the transport and building sectors energy consumption, while the industrial sector consumption will be reduced. Based on the policy report, the final energy demand is expected to reach 3397 Mtce by the year 2050 [8]. This rising demand can be resolved through renewable sources. This is true for both scenarios; nevertheless, the electrification and share of renewable energy are great in the Below 2°C Scenario. Here, 54% of energy demand will be electrical energy in 2050 in the scenario compared to 37% in the Stated Policies Scenario. Industrial area fossil energy use is mainly substituted by electricity. China is on the path to a greener and more diversified energy supply. The higher dependence on coal is removed and substituted with nonfossil energy sources. In both scenarios, the energy demand will peak around 2030, and by 2050 the Below 2°C Scenario will have an energy demand of 3202 Mtce [8]. **Figure 1** shows the main energy installed capacity and peak demand in China by regions. **Figure 2**(**a**, **b**) shows the final energy demand (Mtce) in 2050 in the two scenarios compared with today, by sector and fuel type. There was an increase in total energy production from 627.7 million tce to 3600 between 1978 and 2014, with the consumption also rising by 5.69 times during the same period, attainment of 4260 million tce in 2014. **Figure 3** shows China's Primary Energy Consumption between 2010 and 2016. **Figure 4** compares the energy consumption by source in China between 2010 and 2016 [8].

**Figure 1.** *The main energy installed capacity and peak demand in China by regions.*

*The State of Renewable Energy in China and Way Forward in New Scenario Policies DOI: http://dx.doi.org/10.5772/intechopen.106492*

#### **Figure 2.**

*Final energy demand (Mtce) in 2050 in the two scenarios compared with today, by sector and fuel type.*

#### **Figure 3.**

*China's Primary Energy Consumption between 2010 and 2016. \* Primary energy comprises commercially traded fuels, including modern renewables used to generate electricity. Notes: Oil consumption is measured in million tonnes; other fuels are measured in million tonnes of oil equivalent.*

As stated by Li Fulong, consumption of coal during the first half of 2017 was about 1.83 billion metric tons making 59.8% of the total energy. There was a rise of 3% in natural gas and nonfossil fuels mix to 29%. Based on the energy sector's 5-year plan for 2016–2020, China targets to bring down the country's share of coal energy mix down to less than 58%. China's total energy consumption is expected to reach 5 billion tons by 2020, bringing about an annual increase of approximately 2.5% between 2016 and 2020. As stated by the plan, there will be an increase in the share of nonfossil fuels to over 15% with the share of natural gas also reaching 10% [9].

#### **Figure 4.**

*China's Primary Energy Consumption by fuel type between 2010 and 2016. \* Primary energy comprises commercially traded fuels, including modern renewables used to generate electricity. Notes: Oil consumption is measured in million tonnes; other fuels are measured in million tonnes of oil equivalent.*

#### **3. Current situation of renewable energy resources in China**

As stated by the 13th Five-Year Plan of China (between 2015 and 2020), energy from nonfossil was expected to constitute 15% of the entire main energy consumption during 2020. Based on the statistics of China's energy council, China's national total electricity consumption as on October 2017 was 513.0 billion kWh, an increase of 5.0% over the same period in 2016. The primary industry consumption was 8.4 billion kWh of electricity, an increase of 3.6%; the secondary industry consumption also was 365 billion kWh of electricity, an increase of 3.0%; tertiary industry consumption was 70.8 billion kWh of electricity, an increase of 12.4%; and urban and rural resident's consumption was 68.8 billion kWh of electricity, an increase of 8.7%. All increased during the same time in 2016. The national installed capacity also reached 1.67 billion kW, and thus, 6000 kW and over power plants, an increase of 7.3% compared to 2016. Thus, hydropower was 300 million kW, thermal power was 1.08 billion kW, nuclear power was 35.82 million kW, and wind power was 160 million kW [10]. On the side of generation, the hydropower generation capacity reached 923.4 billion kWh within the first 10 months of 2017, an increase of 2.2% compared to the same time in 2016. Others like thermal power generation capacity reached 3799.3 billion kWh, an increase of 5.4%, nuclear power generation reached 203.6 billion kWh, an increase of 18.4, and wind power generating capacity reached 239.7 billion kilowatts, an increase of 25.3%. **Figures 3** and **4** show the power production and installed capacity in the country as at the end of October 2017 with the year-on-year change in % [11] (**Figures 5** and **6**). **Figure 7** shows Electricity Generation from Renewables in China by source, 2008–2015.

According to China's electricity council, power system in China is still subjugated by coal; nevertheless, the 2016 additional installed capacity of solar power of 77 GW and wind power and solar power total of up to 149 GW brought about the higher portion of renewable installed capacity. Wind turbines generated 241 TWh of electricity in 2016, thus, an increase of 30% on that of 2015. Solar power generation also adds up by 72% with an increase in generation of 66TWh in 2016. Nuclear power stations also saw growth in 2016 compared to 2015 with a generation of additional 24% electricity reaching 213 TWh [13]. **Figure 8** shows the installed power generating capacity in China together with the additions in 2016, while **Figure 9** shows year-onyear growth in power production as **Figure 10** shows the energy generation mix in the country 2016.

*The State of Renewable Energy in China and Way Forward in New Scenario Policies DOI: http://dx.doi.org/10.5772/intechopen.106492*

#### **Figure 5.**

*Power production from renewables in China 2017. \*Thermal includes coal, gas, oil, and biomass.*

#### **Figure 6.**

*Renewable energy installed capacity in China 2017. \*Thermal includes coal, gas, oil, and biomass.*

#### **4. Renewable energy potential**

China has a huge undeveloped potential in the field of renewable energy, and the government has recently been aware of these resources and begun to take steps to take advantage of that potential. A study has shown that the country will be able to meet the emerging new demand if all the targets set by the government are achieved as it will yield new renewable energy generation capacity of 362 GW [15].

#### **4.1 Hydropower**

China leads globally in the field of hydropower installment capacity and generation. The country in 2016 saw growth in its overall installed hydro capacity of about 11.74 GW making a total of 330 GW [16]. China is the leading country of hydropower development in the world with the world's leader in hydropower scale and project capacity development, with the largest hydropower station in the world's situated along the Yangtze River. China had 117 GW of installed capacity in 2005 with the aforementioned of 300 GW by 2030. According to World Watch Institute, China has hydropower potential capacity of 500 GW [17]. The strong water resources in China are mainly in the western underdeveloped and along the east coast where electricity demand increases. Most of China's water resources are in Sichuan, Tibet, and Yunnan, where the Yangtze River begin in Tibet and flows through Sichuan; the Pearl began in Yunnan [18]. China has greatly made a perfect use of the various most important channels in the country for hydroelectric development. **Figure 3** shows The Three

#### **Figure 7.**

*Electricity Generation from Renewables in China by source, 2008–2015 (GWh) [12].*

#### **Figure 8.**

*The installed power generating capacity in China together with the additions in 2016 in GW [13].*

**Figure 9.** *Year-on-year growth in power production (TWh) [13].*

Gorges Dam, while **Figure 4** shows the largest hydroelectric plants in China with their generation and installed capacity (**Figure 11**) [19].

#### **4.2 Wind**

With hydropower, wind power has a great opportunity to become a major source of renewable energy in China. The Chinese government has developed a potential power generation capacity for the development of China's wind power market, with its ability to increase its planned capacity by 2020 to generate 30 GW. China experienced a significant increase in energy from the wind farm throughout the country. [20]. The wind power industry in China has succeeded in rapid growth due to government intervention. China is a world leader in wind power generation, with the largest installed capacity of any nation [21] and continued rapid growth in new wind *The State of Renewable Energy in China and Way Forward in New Scenario Policies DOI: http://dx.doi.org/10.5772/intechopen.106492*

**Figure 10.** *China's electricity mix in 2016 in TWh [14].*

**Figure 11.** *The Three Gorges Dam.*

facilities [22]. In 2016, the country added 19.3 GW of wind power generation capacity [23] reaching a total capacity of 149 GW, [12] with an electricity generation of 241 TWh representing 4% of national total electricity consumption. China's projection is to attain 250 GW of wind capacity by 2020 as part of government's promise to produce 15% of all electricity from renewable resources [24]. The status of the base has been included in the policy and market with the development of tax incentives. In 2004, the Commission carried out a "wind power concession" for a 20-year work cycle to lessen the in-grid wind power tariff through the establishment of wind farms with high capacity [25]. Prior to 2006, the Commission agreed to construction of five large wind farms, all of having no less than 100 (MW) capacity [25]. As shown **Figure 12**. When the law of renewable energy was put into effect in 2006, the grid company was obliged to sign a grid connection agreement with the wind power generating company and purchase the full amount of the wind power generated by it [25]. **Table 1** shows wind power installed capacity (MW) and generation (GWh) in China 2007–2016.

#### **4.3 Biomass power**

Biomass is a multistep process of producing synthetic hydrocarbon fuels made from biomass that can be converted to both solid and liquid and gaseous fuels through

#### **Figure 12.**

*Small wind turbines and solar power panels as well as wind farm in China.*

chemical and biological processes [26]. Biomass is anticipated to contribute roughly between 15% and 50% of the world's primary energy consumption by the year 2050 [27]. Biomass in China is a larger energy source than most would think because of the huge rural population. Eighty percent of biomass energy is located in rural China with the principal source being crop residue [28]. Approximately 4 billion tons of crop residues and wood fuels are burnt using stoves in the western rural areas [29] The Chinese government is making great strides to develop rural renewable energy, because biomass is a good already established one, and they are considering further development and efficiency gains in the use of traditional biomass. The government has begun to promote the development of biomass energy to achieve multiple projects at the national level. They participated in the planting and reforestation program in Wuhan, Guangdong Province. Over the past 14 years, the region has grown to 170.600 hectares with an increase in coverage from 31.5% to 49.4%, almost 20% of the increase in [29] and 62.8% increase in annual production capacity, and the increase can be repeated in other parts of the country. Due to the abundant domestic biomass resources, the biomass energy industry in China is rapidly developing [30].

#### **4.4 Solar**

China is leading globally in terms of solar PVs installation since 2013 and leading the world's largest market for both photovoltaics and solar thermal energy. It increased its total PV capacity to 77.4 GW [31] and was the first country to pass 100 GW of cumulative installed PV capacity in 2017 [32]. China currently has six factories that produce no less than 2 GW/year each of monocrystalline, polycrystalline, and noncrystalline photovoltaic cells. They comprise LDK Solar Co, Wuxi Suntech Solar Energy Co., Ltd. 50 MW/year of solar, Yunnan Semi-Conductor Parts Plant 2 MW/year of mono-crystalline cells, the Baoding Yingli Solar Energy Modules Plant, 6 MW/year of polycrystalline cells and modules, the Shanghai Jiaoda Guofei Solar Energy Battery Factory 1 MW/year of modules, and the Shanghai PV Science and Technology Co., Ltd. 5 MW/year of modules [32]. **Figure 13** shows SolarGIS-Solar-map-China-Mainlands.

Some of the technologies used as solar collectors and photovoltaic modules include the following: Molten Salt Storage Technology, the process uses inorganic *The State of Renewable Energy in China and Way Forward in New Scenario Policies DOI: http://dx.doi.org/10.5772/intechopen.106492*


### **Table 1.**

*Wind power installed capacity (MW) and generation (GWh) in China 2007–2016 [24].*

### **Figure 13.**

*SolarGIS-Solar-map-China-Mainlands.*

**Figure 14.** *Target for renewable energy generation by 2020 (GW).*

salts to transfer energy generated by solar PV systems into solar thermal using heat transfer fluid rather than oils as some storage system have; and Solar Panel with Built-In Battery: with this application, the rechargeable battery is built into the solar panel itself, rather than operating as two stand-alone systems. According to scientists, conjoining the two into one system could lower costs by 25% compared to existing products.

### **5. Perspectives of renewable energy development in China**

Studies are currently being conducted on other renewable energy sources, such as energy marine current energy, wave energy, ocean thermal energy, and salinity

*The State of Renewable Energy in China and Way Forward in New Scenario Policies DOI: http://dx.doi.org/10.5772/intechopen.106492*


**Table 2.**

*China's new policies scenarioi capacity by technology (GW).*

gradient energy, not forgetting tidal energy. Nevertheless, they are hardly ever being used for commercial power generation due to high cost, poor dependability, low efficacy, poor stability, and small size [33, 34]. The total accessible reserve of ocean energy resources in China is anticipated to be about 1000 GW with great potential for exploitation [35]. **Figure 14** shows the Target for Renewable Energy Generation by 2020 and **Table 2** also shows China's New Policies Scenario Installed Capacity by Technology [36].

#### **5.1 Switching to a cleaner source of power**

A good distribution and policy support helps to make renewables cheaper, and solar PVs turn out to be the cheapest form of energy generation in China.

Installed low-carbon capacity, led by hydropower, wind, and solar PV, raises quickly and constitutes 60% of total capacity by 2040. Average solar PV projects in China become cheaper than both new and existing gas-fired power plants around 2020 and cheaper than new coal-fired capacity and onshore wind by 2030 [37].

There was a rise of 1.3% in China's energy consumption in 2016. Increase during these 2 years (2015 and 2016) has been the lowest aside 1997–1998. In spite of this, China continues to be the world's largest growth market for energy within 16th successive years. Renewable power (without hydro) increased by 14.1% in 2016, beneath the 10-year average, but the largest rise on record (53 Mtce). The wind gave over half of renewables growth, while solar energy contributed almost a third despite accounting for only 18% of the total [2]. According to China's energy agency, the country will cultivate \$361 billion into renewable power generation by 2020 which will create over 13 million jobs in the sector. This is containing in the National Energy Administration (NEA) blueprint document which outlines their agenda for national energy sector development throughout their 5-year 2016 to 2020 period. The NEA also mentions that renewable power installed capacity including wind, hydro, solar, and nuclear power will constitute about half of new electricity generation by 2020 [38]. **Figure 15** shows China's Primary Energy Consumption by a source in the new portfolio (**Figure 16**).

Based on IRENA the global roadmap, renewable energy portion in China's energy mix is anticipated to reach 17% by 2030 comparable to 13% in 2010. This analysis, however, displays that the country can convincingly achieve modern renewables to

#### **Figure 15.** *Installed power generation capacity in China in the New Policies Scenario 2040 of a total of 3188GW.*

**Figure 16.** *China's primary energy consumption.*

26%. China can save over USD 200 billion a year by 2030, helping in the improvement of health and reducing CO2 emissions [39]. Under the high renewable energy penetration scenario, total power generation in China will be 15.2 trillion kWh in 2050. This consist of 1038 kW coal, 466 kW natural gas, 649 kW nuclear, 2187 kW hydro, 5350 kW wind, and 4130 kW of solar. All these energy resources were in billions. This, therefore, constitutes total of 85.8% of renewable energy power generation and 91% of nonfossil energy [40].


**Table 3.**

*China's renewable energy development goals by 2050a.*

*The State of Renewable Energy in China and Way Forward in New Scenario Policies DOI: http://dx.doi.org/10.5772/intechopen.106492*

#### **6. China's renewable energy goals by 2050**

China has turned out to be the world leader in renewable energy and made a great investment. According to [41] report, China promised US\$286 billion for the development of renewable energy as well as US\$376 billion for energy conservation projects in 2011–2015. According to [42] report, the total investment of the world in 2015 was US\$286 billion with China contributing 102.9 billion in 2015 as the world's largest investor in the renewable energy development. China hosted over 25% of the world's non-hydro renewable capacity as at the end of 2015, being 63.1 and 117.0% higher than the United States and Germany, correspondingly [43]. In 2006, China enacted the Renewable Energy Law and Pricing Law that implemented tax reduction, financial support, subsidy policies, and measures for renewable energy applications [37].

Summary of the renewable energy targets by the Research Committee of China has been tabulated in **Table 3** (G1–G3) considering three cases comprising 2020, 2030, and 2050. The high uptake scenario would be a challenge to achieve. Nevertheless, there is a possibility of achieving it in realistic for China to achieve 45% of their energy from renewables to increase the county's total energy consumption of 5.8 billion tce by 2050 (G4 in **Table 3**) [44].

#### **7. Conclusions**

This manuscript has discussed the various renewable resource potentials in China. It was revealed that hydroelectric power has the highest installed and development potential in the country with its development in most of the cities and provinces in the country. Other resources such as wind, solar, and biomass were also observed to have a great potential in the country which are yet to be developed. The various techniques to eliminate coal and fossil fuel use to overcome pollution and emission control by the government have also been discussed. This collaboration will continue to lead and guide the world toward further green development and make massive green supports for the welfare of people. The various Development Strategic Action Plan for and Instantaneous of renewable energy targets by the various Research Committees in the country have also been analyzed and suggested appropriate solutions for achievement.

#### **Acknowledgements**

The authors would like to express their gratitude to Mrs. Joyce Adu, Mrs. Joyce Oppong, Dr. Asare Bediako, Sam Adu-Kumi Jnr., and Majeed Usman Koranteg for their proof reading and correction. This work was supported by the Special Funds of the National Social Science fund of China, grant number 18VSJ038.

#### **Conflict of interest**

The authors have complied with ethical requirements: submission implies that, if accepted, it will not be published elsewhere in the same form, in English or in any other language, without the written consent of the publisher. The authors have no conflict of interest to declare.

Thank you for your consideration of the work.

*The State of Renewable Energy in China and Way Forward in New Scenario Policies DOI: http://dx.doi.org/10.5772/intechopen.106492*

### **Author details**

Daniel Adu1 \*, Ransford O. Darko2 \*, Boamah Kofi Baah3 and Agnes Abbey4

1 School of Management Science and Engineering, Jiangsu University, Zhenjiang, China

2 Department of Agricultural University, University of Cape Coast, Ghana

3 University of Professional Studies, Accra, Ghana

4 Department of Renewable Energy, Cape Coast Technical University, Cape Coast, Ghana

\*Address all correspondence to: adudaniel39@yahoo.com and chiefrodark@yahoo. com

© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

### **References**

[1] Ouyang XL, Lin BQ. Levelized cost of electricity (LCOE) of renewable energies and required subsidies in China. Energy Policy. 2014;**70**:64e73

[2] Ministry of Finance. National Development and Reform Commission, National Energy Administration (MOF/ NDRC/NEA), Tentative procedures for the management of collecting and using renewable energy development special fund. January 2012

[3] National Development and Reform Commission. Ministry of Finance (NDRC/MOF), implementation opinions on promoting the development of wind power generation industry. 2006.

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## *Edited by Amjad Almusaed and Asaad Almssad*

Rapid urbanization has led to many problems in cities, including climate change, deteriorating infrastructure, disorganized labor forces, and diminishing resources. This book presents a well-grounded vision for the kind of future city we need to live in by encapsulating the most salient and practical implementations of the many responsibilities and functions that characterize the modern metropolis. Furthermore, this book uses the idea of sustainability to show and analyze many theories and approaches to handling the topic of modern sustainable smart cities, as well as the effects they have on human life and the natural environment through sustainable development objectives and aims supported by the United Nations.

Sustainable Smart Cities - A Vision for Tomorrow

Sustainable Smart Cities

A Vision for Tomorrow

*Edited by Amjad Almusaed and Asaad Almssad*