Optimising Energy Systems in Smart Urban Areas

Bohumír Garlík

#### Abstract

In this chapter, the urban structure will be defined with zero or almost zero energy consumption, followed by pollution parameters. Energy systems are designed as networks of energy-intensive local hubs with multiple sources of hybrid energies, where different energy flows are collected on the same busbar and can be accumulated, delivered, or transformed as needed into the intelligent urban area. For analysis of the purpose function of our energy system, a micro-network of renewable energy sources (RES) is defined by penalization and limitations. By using fuzzy logic, a set of permissible solutions of this purpose function is accepted, and the type of daily electricity consumption diagrams is defined when applying cluster analysis. A self-organising neural network and then a Kohonen network were used. The experiment is to justify the application of new procedures of mathematical and informatics-oriented methods and optimisation procedures, with an outlined methodology for the design of smart areas and buildings with near zero to zero energy power consumption.

Keywords: unit commitment, microgrid, fuzzy logic, cluster analysis, intelligent building

### 1. Introduction

Efforts to increase energy savings (electricity, heat, water, gas and fuels), reductions of greenhouse gases and the most environmentally friendly approaches lead to the need not only to deal with buildings and elements within the territory as separate entities in the future, but also to try with maximum effectiveness to design the whole area or city region in which these sub-elements, which will interact and communicate with each other. With this approach, it is possible to considerably improve the behaviour of the whole territory, which is also able to react flexibly to situations in the area, for example, current traffic or air conditions.

The way to apply this approach is the concept of smart cities [25], which combines various principles of efficient object design, operations management, especially with significant energy savings and sharing of information into one functional unit. Given the current absence of any method of approach to the creation of smart cities on a global scale, our work aims to outline the basic approaches in individual parts of intelligent city design, that is, urban areas and descriptions of the possible variants of the solution of sub-elements of the area. The aim of the contents of this chapter will be to illustrate the methodology of this issue.

The principles of a smart city can be divided into several areas of human activity:

Intelligent/smart devices and wireless metres transmit information through broadband networks and provide intelligence that citizens and city organisations

For example, in our intelligent Rohan Island area, 250 users can test their energy management system and gain insight into the energy consumption of their appliances, allowing for energy consumption monitoring and remote switching on and switching off appliances. In the intelligent area of the Rohan Island, 500 houses will be equipped with smart energy metres displaying energy consumption. Other energy savings have been or are recommended to be discussed in brainstorming sessions. In our Rohan Island project, 500 households will be equipped with smart metres with displays, and personal energy-saving targets will be determined for each household. The goal is to save at least 14% of energy and reduce CO2 emissions by the same amount. The tallest building in our fictitious smart office area is testing which smart building technology will be best suited to make office buildings more sustainable and more environmentally friendly. Information obtained through smart connections and understanding based on data analysis will be used to provide more effective solutions. In our Rohan Island area—a shopping precinct with many cafes and restaurants and 40 small businesses—solutions for a more sustainable environment will be tested, such as electric vehicle use logistics, energy-saving light bulbs for night light, garbage containers with solar power, smart metres and displays for energy consumption and incentives and benefits from energy savings. A Prague future smart city has recently experimented with crowdsourcing (mass idea exchange of members), that is, it is practically a collaboration since the very beginning of the project, with open innovations, in order to involve its citizens in finding better solutions for public spaces and mobility. Ambitious targets have been set: to reduce CO2 emissions by 40% and energy consumption by 20% with the imple-

can put into practice, thus ensuring its optimisation [29].

Optimising Energy Systems in Smart Urban Areas DOI: http://dx.doi.org/10.5772/intechopen.85342

mentation of smart zero energy or near zero energy areas by 2025.

61

In this chapter, we will try to combine the structure of our city—the imaginary intelligent area of Rohan Island—with energy consumption and consequently the pollution parameters. For our experiment, the energy set was chosen as a network composed of energy centers (22/0.4 kV transformer station) with multiple hybrid energy sources where different energy flows are collected on the same busbar and can be accumulated, delivered or transformed as needed. Individual energy centres interact with each other. It is complicated to describe and define it in a comprehensible manner at the municipal level (since it would go beyond the scope of the problem that is dealt with in this chapter). Similarly, it also concerns a challenging generation of new operational models based on existing critical urban infrastructures. Critical infrastructure consists of elements or systems of elements (buildings, equipment, resources or public infrastructure) and their operators. Disruption of this function would have a serious impact on the state's security, ensuring the basic living needs of the population, the health of the people or the economy of the state. This is the reason why this issue will be discussed here in terms of assessing the impact on unexpected situations associated with the safety and quality of energy. The transmission and distribution systems of electricity, natural gas, potable water supply, road and rail transport, communication and information systems and others play an important role in crisis management at the level of cities, urban areas, municipalities and municipalities with extended powers. Therefore, our solution is also focused on specific specifications of the technical and operational values of intelligent information transmission and intelligent networks at the level of the extent of the impact of disruption of their functions. The activities of thermal, electrical and portable infrastructures are considered as qualification characteristics of the energy centre, but they are not taken into account. The experimental part in our case shows that the analysis and optimised layout of the energy system serves one urban district—the


Due to the comprehensiveness of the concept of smart cities, this chapter will focus on technological areas such as buildings, energy and media, individual and public transport, public space technologies and information, and from these areas, we will be more interested in saving electricity.

The exact and unambiguous definition of the smart area or smart cities has not yet been established on a global scale, but in general it can be said that in order to be considered as smart, all elements and objects contained in that territory must be designed as smart.

So, it does not concern just the building itself but also energy and media supply systems, water supply, waste management, management of all kinds of transport, public lighting and IOT. Instrumentation of the urban system means that the operation of this system can produce data based on key performance indicators, basically making the system a measurable tool and an intelligent metre.

Instrumentation appears to be appropriate to provide urban networks with efficient use of resources, transport and energy services and other public services. Intelligence refers to the ability to use the information gathered to model behavioural patterns and thus to develop predictive models of probable outcomes, allowing for better decision-making and erudite functions. Pilot testing on our experimental intelligent urban area "Rohansky ostrov" (Rohan Island, Prague 8 district) provides information on how to consume electricity more efficiently (we can also focus on water consumption, consumption of heat, natural gas and oil). In our imaginary intelligent area, Figure 1 shows intelligent instrumentation is widely observed.

Figure 1. The architectural design of the locality with newly designed objects in highlighted colour.

#### Optimising Energy Systems in Smart Urban Areas DOI: http://dx.doi.org/10.5772/intechopen.85342

The principles of a smart city can be divided into several areas of human activity:

Due to the comprehensiveness of the concept of smart cities, this chapter will focus on technological areas such as buildings, energy and media, individual and public transport, public space technologies and information, and from these areas,

The exact and unambiguous definition of the smart area or smart cities has not yet been established on a global scale, but in general it can be said that in order to be considered as smart, all elements and objects contained in that territory must be

So, it does not concern just the building itself but also energy and media supply systems, water supply, waste management, management of all kinds of transport, public lighting and IOT. Instrumentation of the urban system means that the operation of this system can produce data based on key performance indicators, basi-

Instrumentation appears to be appropriate to provide urban networks with efficient use of resources, transport and energy services and other public services.

behavioural patterns and thus to develop predictive models of probable outcomes, allowing for better decision-making and erudite functions. Pilot testing on our experimental intelligent urban area "Rohansky ostrov" (Rohan Island, Prague 8 district) provides information on how to consume electricity more efficiently (we can also focus on water consumption, consumption of heat, natural gas and oil). In our imaginary intelligent area, Figure 1 shows intelligent instrumentation is widely

cally making the system a measurable tool and an intelligent metre.

The architectural design of the locality with newly designed objects in highlighted colour.

Intelligence refers to the ability to use the information gathered to model

• Political (city management level)

• Social (city population level)

Zero and Net Zero Energy

• Technological (business level)

designed as smart.

observed.

Figure 1.

60

we will be more interested in saving electricity.

Intelligent/smart devices and wireless metres transmit information through broadband networks and provide intelligence that citizens and city organisations can put into practice, thus ensuring its optimisation [29].

For example, in our intelligent Rohan Island area, 250 users can test their energy management system and gain insight into the energy consumption of their appliances, allowing for energy consumption monitoring and remote switching on and switching off appliances. In the intelligent area of the Rohan Island, 500 houses will be equipped with smart energy metres displaying energy consumption. Other energy savings have been or are recommended to be discussed in brainstorming sessions. In our Rohan Island project, 500 households will be equipped with smart metres with displays, and personal energy-saving targets will be determined for each household. The goal is to save at least 14% of energy and reduce CO2 emissions by the same amount. The tallest building in our fictitious smart office area is testing which smart building technology will be best suited to make office buildings more sustainable and more environmentally friendly. Information obtained through smart connections and understanding based on data analysis will be used to provide more effective solutions. In our Rohan Island area—a shopping precinct with many cafes and restaurants and 40 small businesses—solutions for a more sustainable environment will be tested, such as electric vehicle use logistics, energy-saving light bulbs for night light, garbage containers with solar power, smart metres and displays for energy consumption and incentives and benefits from energy savings. A Prague future smart city has recently experimented with crowdsourcing (mass idea exchange of members), that is, it is practically a collaboration since the very beginning of the project, with open innovations, in order to involve its citizens in finding better solutions for public spaces and mobility. Ambitious targets have been set: to reduce CO2 emissions by 40% and energy consumption by 20% with the implementation of smart zero energy or near zero energy areas by 2025.

In this chapter, we will try to combine the structure of our city—the imaginary intelligent area of Rohan Island—with energy consumption and consequently the pollution parameters. For our experiment, the energy set was chosen as a network composed of energy centers (22/0.4 kV transformer station) with multiple hybrid energy sources where different energy flows are collected on the same busbar and can be accumulated, delivered or transformed as needed. Individual energy centres interact with each other. It is complicated to describe and define it in a comprehensible manner at the municipal level (since it would go beyond the scope of the problem that is dealt with in this chapter). Similarly, it also concerns a challenging generation of new operational models based on existing critical urban infrastructures. Critical infrastructure consists of elements or systems of elements (buildings, equipment, resources or public infrastructure) and their operators. Disruption of this function would have a serious impact on the state's security, ensuring the basic living needs of the population, the health of the people or the economy of the state. This is the reason why this issue will be discussed here in terms of assessing the impact on unexpected situations associated with the safety and quality of energy.

The transmission and distribution systems of electricity, natural gas, potable water supply, road and rail transport, communication and information systems and others play an important role in crisis management at the level of cities, urban areas, municipalities and municipalities with extended powers. Therefore, our solution is also focused on specific specifications of the technical and operational values of intelligent information transmission and intelligent networks at the level of the extent of the impact of disruption of their functions. The activities of thermal, electrical and portable infrastructures are considered as qualification characteristics of the energy centre, but they are not taken into account. The experimental part in our case shows that the analysis and optimised layout of the energy system serves one urban district—the urban area (Rohan Island). The associated optimised parametric layout of energy generation infrastructures is a feature of the property of the urban area. An extra vulnerability is due to domino and cascading effects, excessive system complexity and lack of backup. The aim is to protect the information systems (IS) for critical infrastructure (CI), including emergency communication preparedness and protection of materials and equipment which support the IS. For this purpose, a European Programme for Critical Infrastructure Protection (EPCIP) has been set up, and a Critical Infrastructure Warning Information Network (CIWIN) has been built. The European Union is currently planning to increase the protection of Critical Information Infrastructure (CII) in order to ensure the proper functioning of critical infrastructure. The term CII refers to telecommunications, computer systems (including software), the Internet, transmission networks and so on. Nowadays, an especially important component is the Internet, due to its considerable expansion. In our case, the optimisation of energy will be to find solutions for the technical equipment of buildings, such as the internal distribution of engineering and telecommunication networks, starting with the connection to the public distribution of these networks at the level of RES micro-networks. The basic types of energy used in the Czech Republic to produce electricity include thermal, nuclear, solar (sunlight), water and wind.

as a set of energy centres [5], defined as "entities", which uptake energy at entrance ports connected to RES micro-network locations and electricity distribution, and natural gas infrastructures provide certain required energy services, such as electricity, heating, cooling, etc. on the output ports. Inside the centre, energy is transformed and conditioned using, for example, combined energy and heat technology (CHP/FC) transformers, information and communication technology (ICT), compressors, heat exchangers and other equipment. Realistic facilities that can be considered as energy centres include industrial enterprises, larger buildings (hospitals and shopping centres), urban areas and isolated energy systems (trains, trams, etc.). In many cases, other forms of energy to urban areas and vice versa are converted using electricity; thus other forms of energy are generated by electrical energy. From this point of view, the energy system will soon host most energy sources and can be considered a centre of interest for further consideration and

A relationship (1) for the calculation of risk is defined. This relationship reflects the basic reference variables for the risk calculation, which are the likelihood and severity of the impact of an extraordinary event. In addition, a member taking into account the existing security measures is also included against the classic risk statement. These variables are a function of partial relationships for the calculation

f Zð Þ <sup>D</sup> <sup>∙</sup> ND

<sup>B</sup> (1)

<sup>B</sup> <sup>¼</sup> f Zp <sup>∙</sup> Np

where R is the level of risk, P is the probability of occurrence of an extraordinary event, D is the severity of the impact of an emergency, Zp is the level of vulnerability of the rated equipment that affects the likelihood of an extraordinary event, Np represents the level of threat assessment affecting the probability of occurrence of an emergency, ZD is the level of vulnerability of the rated equipment that affects the severity of the impact of an emergency, ND is the level of threat assessment affecting the severity of the impact of an emergency and B is the level of security measures. In the second step, partial relationships are established to calculate the vulnerability, hazards and workability. The resulting variables of these relationships are a function of the criteria defined in the previous paragraph. These are the following

• Rating of the vulnerability level of the equipment affecting the likelihood of an

• Rating of the vulnerability of the equipment affecting the severity of the

• Rating the level hazard of threat affecting the likelihood of an emergency

Zp ¼ f KZ ð Þ <sup>P</sup>;KZZ (2)

ZD ¼ f KZ ð Þ <sup>K</sup>;KZO;KZR (3)

NP ¼ f KN ð Þ PP (4)

1.1 Assessment of risks on equipment is expressed in two steps

of vulnerability, hazards and implemented measures [7]:

Optimising Energy Systems in Smart Urban Areas DOI: http://dx.doi.org/10.5772/intechopen.85342

<sup>R</sup> <sup>¼</sup> <sup>P</sup> <sup>∙</sup> <sup>D</sup>

in-depth studies.

five relationships:

occurrence:

63

emergency occurrence:

impact of an emergency:

Many European countries are aiming for a significant reduction in CO2 emissions by 2050 as well as a reduction in the demand for energy per capita. The European Commission is looking for cost-effective ways to direct Europe's economy towards more climate-friendly and cost-efficient methods. This low-carbon emission economy strategy gives the European Union an incentive to reduce emissions by up to 80% by 2050 compared to levels in 1990. To achieve this, 40% of emissions should be reduced by 2030 and 60% by 2040. All sectors must contribute, and the transition must be appropriate and acceptable; particularly generation and distribution of energy, as well as transport and buildings, are among the main sectors for implementing CO2 reduction. There are also three main pillars on which the structure of urban energy systems is based [1, 2, 30]. The energy sector has the greatest potential to limit emissions. It can almost completely eliminate CO2 emissions by 2050. Electricity could actually partially replace fossil fuels in transport and heating. In addition, electricity can be produced with zero emissions using wind, solar, water and biomass energy or other low-emission sources, such as nuclear power plants or fossil-fuelled power plants equipped with carbon capture and storage technologies. This will, however, require high investment in smart grids and micro-network technology [3, 4]. In the short term, the greatest progress can be found for petrol and diesel engines that could be produced with highly improved fuel utilisation and thus more and more efficient. In the longer term, the engagement of hybrid and electric cars will result in a sharp reduction in emissions.

Regarding the European Union strategy planning, emissions from residential and commercial buildings can almost entirely be reduced by approximately 90% by 2050. Energy efficiency will be drastically increased by:


Electricity begins to play a key role in the smart urban energy system. In all existing top examples, the concept of a smart city [6] (or urban area) is based on a recurring cyclical economy and shared resources. Urban energy systems can be seen Optimising Energy Systems in Smart Urban Areas DOI: http://dx.doi.org/10.5772/intechopen.85342

urban area (Rohan Island). The associated optimised parametric layout of energy generation infrastructures is a feature of the property of the urban area. An extra vulnerability is due to domino and cascading effects, excessive system complexity and lack of backup. The aim is to protect the information systems (IS) for critical infrastructure (CI), including emergency communication preparedness and protection of

Zero and Net Zero Energy

materials and equipment which support the IS. For this purpose, a European Programme for Critical Infrastructure Protection (EPCIP) has been set up, and a Critical Infrastructure Warning Information Network (CIWIN) has been built. The European Union is currently planning to increase the protection of Critical Information Infrastructure (CII) in order to ensure the proper functioning of critical infrastructure. The term CII refers to telecommunications, computer systems (including software), the Internet, transmission networks and so on. Nowadays, an especially important component is the Internet, due to its considerable expansion. In our case, the optimisation of energy will be to find solutions for the technical equipment of buildings, such as the internal distribution of engineering and telecommunication networks, starting with the connection to the public distribution of these networks at the level of RES micro-networks. The basic types of energy used in the Czech Republic to produce electricity include thermal, nuclear, solar (sunlight), water and wind. Many European countries are aiming for a significant reduction in CO2 emissions by 2050 as well as a reduction in the demand for energy per capita. The European Commission is looking for cost-effective ways to direct Europe's economy towards more climate-friendly and cost-efficient methods. This low-carbon emission economy strategy gives the European Union an incentive to reduce emissions by up to 80% by 2050 compared to levels in 1990. To achieve this, 40% of emissions should be reduced by 2030 and 60% by 2040. All sectors must contribute, and the transition must be appropriate and acceptable; particularly generation and distribution of energy, as well as transport and buildings, are among the main sectors for implementing CO2 reduction. There are also three main pillars on which the structure of urban energy systems is based [1, 2, 30]. The energy sector has the greatest potential to limit emissions. It can almost completely eliminate CO2 emissions by 2050. Electricity could actually partially replace fossil fuels in transport and heating. In addition, electricity can be produced with zero emissions using wind, solar, water and biomass energy or other low-emission sources, such as nuclear power plants or fossil-fuelled power plants equipped with carbon capture and storage technologies. This will, however, require high investment in smart grids and micro-network technology [3, 4]. In the short term, the greatest progress can be found for petrol and diesel engines that could be produced with highly improved fuel utilisation and thus more and more efficient. In the longer term, the engagement of hybrid and

electric cars will result in a sharp reduction in emissions.

2050. Energy efficiency will be drastically increased by:

• Modernisation of old buildings to improve energy efficiency

electricity and renewable sources of energy (RES)

• Passive technologies in new buildings

62

Regarding the European Union strategy planning, emissions from residential and commercial buildings can almost entirely be reduced by approximately 90% by

• Fossil fuel substitutes in the areas of heating, cooling and cooking using

Electricity begins to play a key role in the smart urban energy system. In all existing top examples, the concept of a smart city [6] (or urban area) is based on a recurring cyclical economy and shared resources. Urban energy systems can be seen as a set of energy centres [5], defined as "entities", which uptake energy at entrance ports connected to RES micro-network locations and electricity distribution, and natural gas infrastructures provide certain required energy services, such as electricity, heating, cooling, etc. on the output ports. Inside the centre, energy is transformed and conditioned using, for example, combined energy and heat technology (CHP/FC) transformers, information and communication technology (ICT), compressors, heat exchangers and other equipment. Realistic facilities that can be considered as energy centres include industrial enterprises, larger buildings (hospitals and shopping centres), urban areas and isolated energy systems (trains, trams, etc.). In many cases, other forms of energy to urban areas and vice versa are converted using electricity; thus other forms of energy are generated by electrical energy. From this point of view, the energy system will soon host most energy sources and can be considered a centre of interest for further consideration and in-depth studies.

#### 1.1 Assessment of risks on equipment is expressed in two steps

A relationship (1) for the calculation of risk is defined. This relationship reflects the basic reference variables for the risk calculation, which are the likelihood and severity of the impact of an extraordinary event. In addition, a member taking into account the existing security measures is also included against the classic risk statement. These variables are a function of partial relationships for the calculation of vulnerability, hazards and implemented measures [7]:

$$R = \frac{P \bullet D}{B} = \frac{f\left(Z\_p \bullet N\_p\right) f\left(Z\_D \bullet N\_D\right)}{B} \tag{1}$$

where R is the level of risk, P is the probability of occurrence of an extraordinary event, D is the severity of the impact of an emergency, Zp is the level of vulnerability of the rated equipment that affects the likelihood of an extraordinary event, Np represents the level of threat assessment affecting the probability of occurrence of an emergency, ZD is the level of vulnerability of the rated equipment that affects the severity of the impact of an emergency, ND is the level of threat assessment affecting the severity of the impact of an emergency and B is the level of security measures.

In the second step, partial relationships are established to calculate the vulnerability, hazards and workability. The resulting variables of these relationships are a function of the criteria defined in the previous paragraph. These are the following five relationships:

• Rating of the vulnerability level of the equipment affecting the likelihood of an emergency occurrence:

$$Z\_p = f(\mathbf{K}Z\_P, \mathbf{K}Z\_Z) \tag{2}$$

• Rating of the vulnerability of the equipment affecting the severity of the impact of an emergency:

$$\mathbf{Z}\_{\rm D} = f(\mathbf{K}\mathbf{Z}\_K, \mathbf{K}\mathbf{Z}\_{\rm O}, \mathbf{K}\mathbf{Z}\_{\rm R}) \tag{3}$$

• Rating the level hazard of threat affecting the likelihood of an emergency occurrence:

$$N\_P = f(\text{KN}\_{PP}) \tag{4}$$

• Rating the hazard level of the threat affecting the severity of the impact of an emergency:

$$N\_D = f(\text{KN}\_A, \text{KN}\_E, \text{KN}\_P) \tag{5}$$

Potential (KNP)—the magnitude of the threat's effect (strength, robustness and yield) is considered by the potential range of impact on the asset. Criteria index

Efficiency (KBU)—the ability of security measures to minimise the impact of

Feasibility (KBR)—the availability and usability of technological measures to

Financial difficulty (KBF)—the availability of financial resources to implement

Duration (KBC)—the time required to implement security measures. Criteria

Within this range, the urban energy centre microcosm is one of the most important infrastructures, which is defined as "A group of interconnected loads and distributed energy sources within clearly defined power limits that act as one controllable and manageable entity over the network. The microprocessor can be connected to and disconnected from the network to allow it to operate in both the network connection mode and the Isolated/Autonomous mode". In the CIGRE definition (French: Conseil International des Grands Réseaux Électriques), energy resources are a means of generating and storing resources (heat, etc.). The CIGRE is a leading worldwide community dedicated to the world's knowledge development

In our research, we focused on the goal of understanding the differences between micro-networks and intelligent networks. A microsystem is basically a local island network that can function as a stand-alone or network-connected system. It is powered by gas turbines or renewable energy sources and includes dedicated converters and interconnections to connect to an existing network. Specialpurpose filters overcome harmonic problems while increasing the quality and efficiency of electrical power. In short, we are thinking of building a micro-network as a local power provider with limited advanced management tools where the smart grid is a broadband provider with sophisticated capabilities to support automated decision-making. When implementing buildings with zero or almost zero energy consumption, the co-operation of the micro-network of RES with the intelligent network within the 22 kV distribution system takes place. An example of our microsystem that is subjected to our experiment is shown in Figure 2.

Micro-networks are the superior physical infrastructure unit that the city's energy centre operates on. If this serves the municipal energy centre [3, 4], then the

• Independence of urban infrastructures (mobile electricity infrastructure, gas infrastructure, water systems, waste recycling, wastewater treatment)

• Restricted RES penetration, which cannot be considered significant in cities

It can be said that the first issues are that infrastructure and urban systems are viewed as individual [7], that is, transport, sewage and water supply, which are

following issues need to be considered for micro-network activity:

where micro-networks exist (as they are known and defined)

usually highly interactive and interdependent (Figure 3).

65

1.5 Criteria relating to the assessment of the level of security measures

the threat and its impact on the asset. Criteria index value: 1–5.

programme for creating and sharing expertise in energy systems.

minimise the threat. Criteria index value: 1–5.

Optimising Energy Systems in Smart Urban Areas DOI: http://dx.doi.org/10.5772/intechopen.85342

security measures. Criteria index value: 1–5.

1.6 Municipal energy centres and micro-networks

value: 1–5.

index value: 1–5.

• Level of security measures:

$$B = f(KB\_U, KB\_R, KB\_F, KB\_C) \tag{6}$$

A weighted arithmetic mean will be used to calculate the individual functions to ensure that all evaluated criteria are adequately represented. At the same time, it should be noted that the criteria related to the assessment of the security level measures include only the newly envisaged security measures. Measures that have already been implemented are reflected in the reduced vulnerability of the facility or reduced likelihood of damage.

#### 1.2 Determining the level of risk

The final step of the critical risk analysis is to define the reference values for determining the resulting level of risk. According to the relationship (1), the level of risk is determined by three variables, namely, the probability of occurrence of an extraordinary event (P), the severity of the impact of an emergency (D) and the level of new security measures (B). Based on this, a 3D model based on the linear shift of the standard risk matrix (P � D) depends on the level of anticipated safety measures (B5 = max., B1 = minimum measures). Using a five-step index scale, all variables reach maximum values of 5 (this ensures the use of arithmetic mean). The resulting risk levels using the five-step index scale are presented in the 3D risk matrix.

#### 1.3 Criteria for assessing the level of vulnerability of the facility

Accessibility (KZP)—the ease with which an asset may be affected, whether natural or anthropogenic. Criteria index value: 1–5.

Security (KZFROM)—represents the level of current asset security. Criteria index value: 1–5.

Criticality (KZTO)—the relevance to the system, subsystem or whole component. The objective is critical if its destruction or damage has a significant impact on the performance of the entire system, subsystem, entity or component. Criteria index value: 1–5.

Renewability (KZO)—estimates the time needed to replace, repair or bridge the damaged or destroyed asset. Criteria index value: 1–5.

Recognisability (KZR)—the time horizon from the origin and identification of the fault after finding its cause. Criteria index value: 1–5.

#### 1.4 Criteria related to threat assessment

Terms of use (KNPP)—a set of external factors (such as daytime, climatic conditions and skills) that create favourable or unfavourable conditions for a natural or anthropogenic threat. Criteria index value: 1–5.

Activability (KNA)—the time horizon of activation of the threat; the longer this horizon is, the less dangerous threat becomes, because there is more time to prepare security measures. Criteria index value: 1–5.

Exposure (KNE)—the time horizon of exposure to an asset; the longer this horizon is, the more threatening the threat becomes. Criteria index value: 1–5.

• Rating the hazard level of the threat affecting the severity of the impact of an

A weighted arithmetic mean will be used to calculate the individual functions to ensure that all evaluated criteria are adequately represented. At the same time, it should be noted that the criteria related to the assessment of the security level measures include only the newly envisaged security measures. Measures that have already been implemented are reflected in the reduced vulnerability of the facility

The final step of the critical risk analysis is to define the reference values for determining the resulting level of risk. According to the relationship (1), the level of risk is determined by three variables, namely, the probability of occurrence of an extraordinary event (P), the severity of the impact of an emergency (D) and the level of new security measures (B). Based on this, a 3D model based on the linear shift of the standard risk matrix (P � D) depends on the level of anticipated safety measures (B5 = max., B1 = minimum measures). Using a five-step index scale, all variables reach maximum values of 5 (this ensures the use of arithmetic mean). The resulting risk levels using the five-step index scale are presented in the 3D risk

Accessibility (KZP)—the ease with which an asset may be affected, whether

Security (KZFROM)—represents the level of current asset security. Criteria

Criticality (KZTO)—the relevance to the system, subsystem or whole component. The objective is critical if its destruction or damage has a significant impact on the performance of the entire system, subsystem, entity or component. Criteria

Renewability (KZO)—estimates the time needed to replace, repair or bridge the

Recognisability (KZR)—the time horizon from the origin and identification of

Terms of use (KNPP)—a set of external factors (such as daytime, climatic conditions and skills) that create favourable or unfavourable conditions for a natu-

Exposure (KNE)—the time horizon of exposure to an asset; the longer this horizon is, the more threatening the threat becomes. Criteria index value: 1–5.

Activability (KNA)—the time horizon of activation of the threat; the longer this horizon is, the less dangerous threat becomes, because there is more time to prepare

1.3 Criteria for assessing the level of vulnerability of the facility

natural or anthropogenic. Criteria index value: 1–5.

damaged or destroyed asset. Criteria index value: 1–5.

ral or anthropogenic threat. Criteria index value: 1–5.

1.4 Criteria related to threat assessment

security measures. Criteria index value: 1–5.

the fault after finding its cause. Criteria index value: 1–5.

ND ¼ f KN ð Þ <sup>A</sup>;KNE;KNP (5)

B ¼ f KB ð Þ <sup>U</sup>;KBR;KBF;KBC (6)

emergency:

Zero and Net Zero Energy

• Level of security measures:

or reduced likelihood of damage.

1.2 Determining the level of risk

matrix.

index value: 1–5.

index value: 1–5.

64

Potential (KNP)—the magnitude of the threat's effect (strength, robustness and yield) is considered by the potential range of impact on the asset. Criteria index value: 1–5.

### 1.5 Criteria relating to the assessment of the level of security measures

Efficiency (KBU)—the ability of security measures to minimise the impact of the threat and its impact on the asset. Criteria index value: 1–5.

Feasibility (KBR)—the availability and usability of technological measures to minimise the threat. Criteria index value: 1–5.

Financial difficulty (KBF)—the availability of financial resources to implement security measures. Criteria index value: 1–5.

Duration (KBC)—the time required to implement security measures. Criteria index value: 1–5.

#### 1.6 Municipal energy centres and micro-networks

Within this range, the urban energy centre microcosm is one of the most important infrastructures, which is defined as "A group of interconnected loads and distributed energy sources within clearly defined power limits that act as one controllable and manageable entity over the network. The microprocessor can be connected to and disconnected from the network to allow it to operate in both the network connection mode and the Isolated/Autonomous mode". In the CIGRE definition (French: Conseil International des Grands Réseaux Électriques), energy resources are a means of generating and storing resources (heat, etc.). The CIGRE is a leading worldwide community dedicated to the world's knowledge development programme for creating and sharing expertise in energy systems.

In our research, we focused on the goal of understanding the differences between micro-networks and intelligent networks. A microsystem is basically a local island network that can function as a stand-alone or network-connected system. It is powered by gas turbines or renewable energy sources and includes dedicated converters and interconnections to connect to an existing network. Specialpurpose filters overcome harmonic problems while increasing the quality and efficiency of electrical power. In short, we are thinking of building a micro-network as a local power provider with limited advanced management tools where the smart grid is a broadband provider with sophisticated capabilities to support automated decision-making. When implementing buildings with zero or almost zero energy consumption, the co-operation of the micro-network of RES with the intelligent network within the 22 kV distribution system takes place. An example of our microsystem that is subjected to our experiment is shown in Figure 2.

Micro-networks are the superior physical infrastructure unit that the city's energy centre operates on. If this serves the municipal energy centre [3, 4], then the following issues need to be considered for micro-network activity:


It can be said that the first issues are that infrastructure and urban systems are viewed as individual [7], that is, transport, sewage and water supply, which are usually highly interactive and interdependent (Figure 3).

or also known as cogeneration). These resources must be managed and

The load requirement of the RES and electricity grids is cyclical and has a peak daily demand for hours and minutes of the week, that is, weekly peak demand for each month and monthly peak demand for the year. Figures 4 and 5 show the course of electricity demand in our intelligent area on Mondays and Thursdays. The energy resources must be optimised to meet the peak demand of each load cycle, so that the total cost of generating and distributing electricity is minimised. The power system operator must plan the power sources of the grid and equipment

Systemic load has a general mathematical formulation. This load gradually increases during the day and then decreases during the night. The cost of the generated power of individual RES sources is not the same for all sources. Therefore, there is a higher effort to produce more energy at the least cost in units. In addition, several network lines connect one electrical network to another neighbouring power grid. These are called interconnections between networks. When exporting power from one power system to an adjacent power supply system through a connecting line, balanced power is considered a load; and conversely, when such power is imported, it is considered energy production. Flow control through these network distributions is

synchronised to meet the load demand of the microprocessor.

to meet the different load conditions.

Optimising Energy Systems in Smart Urban Areas DOI: http://dx.doi.org/10.5772/intechopen.85342

Figure 4.

Figure 5.

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Electricity demand—Thursday.

Electricity demand—Monday.

#### Figure 2.

Microcosm of fictitious RES intelligent regions of Rohan Island [8].

#### Figure 3.

Example of a municipal power station [9].
