AC-DC Smart Hybrid Microgrids: Modeling, Control and Applications

### **Chapter 1**

## A Review on the Driving Forces, Challenges, and Applications of AC/DC Hybrid Smart Microgrids

*Maria Fotopoulou, Dimitrios Rakopoulos, Fotis Stergiopoulos and Spyros Voutetakis*

### **Abstract**

The majority of Medium Voltage (MV) and Low Voltage (LV) power systems are based on and operate using Alternating Current (AC) infrastructures. Yet, modern energy market needs, which promote more decentralized concepts with a high Renewable Energy Sources (RES) penetration rate and storage integration, bring Direct Current (DC) to the forefront. In this sense, AC/DC hybrid smart microgrids constitute a newly-introduced research field with a variety of potential applications that combine the benefits of both AC and DC systems. The purpose of this chapter is to review the advantages and disadvantages of AC/DC hybrid grids and analyze potential applications that would benefit from such infrastructures. Also, the most significant efforts and requirements for the constitution of a solid regulatory framework for AC/DC hybrid grids are presented, to pave the way towards their wider adoption by the market.

**Keywords:** AC/DC hybrid microgrid, microgrid applications, medium voltage, low voltage, framework

### **1. Introduction**

Electrical grids, from the early stages of their implementation up until the past few decades, have been traditionally based on the energy generated from fossil fuels, such as coal, oil and natural gas [1]. As a result, and taking into account the technological expertise and the available technology at the time they were developed, most architectures were designed to be centralized. Moreover, the overall energy system architecture had a unidirectional approach, from centralized units of production to dispersed customers based on Alternating Current (AC) transmission and distribution networks, a process that has not been friendly to the environment [2].

Yet, the modern socioeconomic requirements and the need to take effective actions for the protection of the environment are challenging the traditional approach of electrical grid architectures [3]. The need for sustainability in the energy sector,

underpinned by the recent technological developments, has led to the development of distributed, environmental-friendly and predominantly DC-based power supply system's [4]. Such systems typically include photovoltaic (PV) panels, Battery Energy Storage Systems (BESS), fuel cells, etc. [5]. This type of Distributed Energy Resources (DER) is most efficiently incorporated in smart microgrids [6]. Smart microgrids constitute advanced architectures, the key elements of which are smart sensors, advanced metering infrastructures, information technologies, Internet of Things (IoT), Cloud of Things and real-time communication systems. In this way, they enable the digitalization and decentralization of the grid, thus allowing for the efficient and seamless Renewable Energy Sources (RES) integration, the management of multiple distributed power supply units, the bidirectional power flow and a variety of grid-flexible services, such as black-start, island-mode operation and congestion management [7]. Furthermore, since many of the DER and some loads utilize DC power, research is oriented towards the design and development of AC/DC hybrid smart microgrids [8]. The structural differences between the traditional AC electrical grid and the AC/DC hybrid smart microgrid are presented in **Figure 1**.

This chapter aims to review the motives and applications of AC/DC hybrid smart microgrids. For this purpose, it is structured as follows: the driving forces for the development of AC/DC hybrid smart microgrids are analyzed in Section 2, their possible applications are analyzed in Section 3, the challenges regarding the regulatory framework for their wider adoption by the market are presented in Section 4, and finally conclusions are summarized in Section 5.

*A Review on the Driving Forces, Challenges, and Applications of AC/DC Hybrid Smart… DOI: http://dx.doi.org/10.5772/intechopen.101973*

### **2. Driving forces and challenges for the development of AC/DC hybrid smart microgrids**

The integration of DC systems in AC-based infrastructures provides a new framework of grid capabilities [9]. As it is foreseen, DC solutions need to "harmoniously co-exist" with the already available AC infrastructures, developing hybrid architectures in which the best result of both approaches can be achieved [10].

In this manner, AC/DC hybrid smart microgrids bring a new perspective into a variety of applications [11]. As opposed to the traditional AC infrastructures, some of their main advantages include:


**Figure 2.**

*Efficient integration of DC power supply and DC loads in AC/DC hybrid smart microgrids.*

long distances, compared to their AC counterparts, owing to less severe stability issues. This feature makes DC integration into already existing AC grids extremely valuable for the future expansion of the grid and the connection of remote loads (such as in islands or isolated locations) or remote RES installations [20]. Major technological developments have enabled the increase of DC operating voltage levels in the order of kV, allowing efficient and reliable power transfer even for distances in the order of several thousands of kilometers.

• **Power quality enhancement** [21]: DC connections can enhance the power quality of weak grids, as they provide a type of isolation "firewall" that prevents the propagation of disturbances, as depicted in **Figure 3**. In particular, the conversion from AC to DC power decouples the AC part of the grid, so that the remaining infrastructure can cope with undesirable resonances which would otherwise impose a threat in its stability and robustness. A typical example of such cases is AC harmonic oscillations, frequency instability and low inertia, which AC grids may face, due to the existence of inductive and capacitive elements, etc. In this sense, AC/DC hybrid smart microgrids present a clear advantage, especially when it comes to the connection between two AC grids with the use of DC subsystems. Furthermore, the DC connection enables the effective integration of AC systems operating with different voltage/frequency levels.

**Figure 3.** *DC connections prohibit the propagation of disturbances.*

*A Review on the Driving Forces, Challenges, and Applications of AC/DC Hybrid Smart… DOI: http://dx.doi.org/10.5772/intechopen.101973*

**• Reduction of visual impact** [22]**:** Since DC lines carry only active power and have no skin effect, less current capacity is required and the necessary distance between the conductors can be reduced. This, in combination with the fact that fewer lines are required than in the respective AC systems, results in a smaller size of DC towers. The smaller size of DC towers, compared to the size of AC equivalent structures, is considered to be an advantage of DC grids. This attribute, however minor it may seem, is quite beneficial considering overpopulated areas such as cities or places where the visual impact of the grids should be minimized, such as in tourist attractions, monument areas, preserved ecosystems, etc.

On the other hand, AC/DC hybrid smart microgrids have certain drawbacks. DC technologies and the connection between AC and DC technologies have not been thoroughly studied as the common AC grids. This is attributed to the fact that the entire concept of electrical energy production, transmission and distribution has been built on AC technology, which has provided the means to progress and develop simple and cost-effective AC equipment over the years.

In this context, the implementation of DC solutions has certain disadvantages such as the lack of specific standards [23]. For a newly-introduced system, such as the AC/DC hybrid grid to establish its case against the traditional AC "status quo", the definition of certain parameters needs to be specified. Since AC/DC applications are not as well-known and commonly used as AC applications, there is a general lack of standardized practices regarding their design and operation. This issue needs to be addressed, for the AC/DC hybrid grids to effectively enter the worldwide market.

Also, there is difficulty regarding the integration of DC systems into existing AC grids, to form AC/DC hybrid grids. More specifically, AC technologies have a simple design that has been studied and developed for many decades and is well known to grid developers and system operators. On the other hand, fewer specialists have studied DC technologies to that extent [24]. As a result, the incorporation of DC solutions to the existing grid bears difficulties in comparison with the application of AC solutions. In simple words, AC and DC systems have different starting points: the AC technology is proven and mature, whereas DC technology is in a developing process to be established, considering that power electronics converters started being utilized in the last quarter of the past century.

This is also reflected in protection and safety apparatus [25]. Once a new system is proposed, protection issues including switches, grounding and fault management systems need to be studied and established. In the case of AC/DC hybrid grids, there are protection issues that are not only related to the lack of standards but also the very nature of DC current. To be more specific, breaking a DC circuit is considered to be more difficult than the respective AC circuit because there is no natural zero crossing of the current to minimize the arc effect. For this purpose, major research efforts are carried out for the development of switchgear that can accommodate the secure disruption of DC voltages in the order of kVs, to enable the safe and reliable development of AC/DC grid infrastructures.

Furthermore, the point of common coupling between the AC and DC parts of the AC/ DC hybrid smart microgrid introduces complexity to the overall architecture. Since a common AC distribution transformer is not capable of providing DC links, the AC/DC hybrid grid requires a different type of interface. Following the latest technological developments, this interface is the Solid State Transformer (SST) [26] which is an advanced power converter that can provide multiple ports, regardless of the voltage level or type. In this sense, it can be connected to the MV side of the AC distribution line and provide AC and DC connections at both MV and LV levels. This active power converter is modular, scalable and capable of providing grid-flexible services. Yet, to function properly, it requires advanced control systems [27], which take into account both AC and DC components, have high maintenance requirements and their advantages are reflected in their cost.

Nevertheless, all of these disadvantages can be overcome if particular attention is given to aspects where DC technology may have significant potential, to be firmly established, starting to build from that point forward. The increasing use and development of modern power electronic converters can significantly help for the diffusion of DC technology providing the necessary framework, backed up by the wider application of RES technologies.

Overall, the development of AC/DC hybrid smart microgrids appears to have many advantages, rendering them a key driver in paving the way towards energy efficiency, sustainability and mitigation of anthropogenic climate change. For them to be established in the wider market, the main applications that would highlight their potential need to be taken into consideration.

### **3. Main applications of AC/DC hybrid smart microgrids**

This section aims to showcase modern examples of applications of AC/DC hybrid smart microgrids, which mostly concern buildings, public installations, remote installations, DC-based applications and transportation:

• **Buildings:** One of the most promising applications that would benefit from the AC/DC hybrid smart microgrid architecture is the building sector. Due to environmental as well as economic concerns, PV panels are commonly installed in buildings [28]. Surplus PV generation is usually stored in BESS, which can smooth the mismatch between the PV generation profile and the load profile. Since the PVs and the storage are both installed within the building, they allow the minimization of transmission losses (and thus, the minimization of primary energy consumption and associated CO2 emissions). Both of these power supply units originally produce DC power. Also, a proportion of the overall load of the building, such as electronic appliances, DC motors, power electronics, batteries, etc., originally consumes DC power [29]. Therefore, it is evident that it would be more beneficial if at least part of the building's power supply was based on DC power distribution, as presented in **Figure 4** [30].

Yet, it should be noted that while this approach could be easily implemented on the side of the DC power supply, it would be more difficult to implement on the DC consumption side, as most DC devices are designed to include (internally) an AC/DC converter to operate with AC grids [31]. This is a barrier that needs to be overcome by the manufacturers of these devices through the proper and widely publicized dissemination of DC capabilities. However, it should be noted that even without the proposed modification of DC loads, it would be beneficial to have a DC sub-grid (in the overall AC grid of the building) for the connections between the PVs, BESS and other DC-based power supply units.

• **District/Distribution level:** The suitability of AC/DC hybrid smart microgrids can be expanded from a single building application to a district-level application, as presented in **Figure 5** [32–34]. As in buildings, so in distribution grids, the penetration of DC-based RES and storage renders the AC/DC hybrid configuration

*A Review on the Driving Forces, Challenges, and Applications of AC/DC Hybrid Smart… DOI: http://dx.doi.org/10.5772/intechopen.101973*

more effective than the conventional AC one. This topic has gained much attention over the past few years at both Medium Voltage (MV) and Low Voltage (LV) levels.

• **Public installations:** A beneficiary of AC/DC hybrid grids could be various public installations. More specifically, as part of public works and services, older lighting equipment is often replaced by LED technology in most public spaces, roads and highways. Such initiatives help reduce the effect on the environment, as LEDs are more efficient than conventional lighting equipment. Since LED lights constitute DC-based technologies, they would naturally be more efficiently powered by DC lines (incorporated in the overall AC design, thus forming AC/DC hybrid grids) producing a significant economic impact, as public lighting costs are a major part of public expenditure [35, 36].

**Figure 4.** *Building with AC/DC hybrid smart microgrid architecture.*

**Figure 5.** *District with AC/DC hybrid smart microgrid architecture.*


Data centers are extremely significant facilities, whose importance is gradually increased over time, leading to the increase of required capacity for information storage. Future data centers could entail power levels up to a few MWs in order to properly function. The majority of loads in data centers are of digital nature and operate on DC power. This means that AC connections would not facilitate their development as there would be significant losses and reliability issues due to the required conversion stages. The aforementioned facts favor the adoption of AC/ DC hybrid architectures in data centers [39].

Also, as mentioned above, there is a recent, increasing need for efficient EV charging stations. In particular, when it comes to vehicles, the need to a) protect the environment from the emissions of fossil fuels, b) reduce the noise level in the urban field and c) reduce the cost of transportation, has led the car manufacturers to focus on developing and producing EVs. The sales of EVs are gradually increasing around the world and it is estimated that in the near future they shall completely replace fossil-fuel-powered vehicles [40]. EVs need charging at regular intervals and their batteries are inherently DC-power sources [41]. Therefore, charging EVs in DC-based charging stations is more effective than the respective AC alternative, which encourages the research and development towards AC/DC hybrid smart microgrids including EV charging stations [42].

In addition to the arguments described above, it is noted that research is also oriented towards data centers and EV charging stations with PV and/or BESS installations for the purpose of reduction of a) cost, b) energy footprint and c) dependence on the main grid. These amendments furtherly favor the deployment of AC/DC hybrid grids for these applications [43].

• **Transportation:** There is a variety of applications in transport at both MV and LV levels that either already uses DC power or are prompted to do so. Typical examples include ships, urban transport and railways.

Ships constitute a special, isolated from the main grid, application that needs large amounts of power to operate properly. Also, their design has certain limitations, due to constraints imposed by the ship's needs, including constant power availability, space and weight concerns and the presence of pulsed electric loads. DC systems, which have less volume and weight and are more appropriate for handling electronic loads (compared to their AC counterparts) are proposed to be a viable solution for ships, thus forming AC/DC architectures [44].

*A Review on the Driving Forces, Challenges, and Applications of AC/DC Hybrid Smart… DOI: http://dx.doi.org/10.5772/intechopen.101973*

Urban transport vehicles and railways are one of the early adopters of DC architectures. In many cases, motors and auxiliary circuits inside urban transport vehicles use DC power. As a result, they form DC power systems, drawing power from the main AC grid of the city, through the appropriate AC/DC converters [45].

Overall, there is a variety of applications that could benefit from AC/DC hybrid smart microgrids. **Table 1** summarizes the aforementioned categories of applications, along with some of their main features, i.e., a justification for which type of architecture is suitable for each category, their voltage level and comments. It is noted that the main factors for each application are related either to the increase of RES and DC loads or to the reliability and robustness of DC connections.


### **Table 1.**

*Applications of AC/DC hybrid smart microgrids [21, 46, 47].*

### **4. Challenges regarding AC/DC hybrid smart microgrids**

Several developments regarding AC/DC hybrid smart microgrids have taken place over the past few years, with fruitful results presented in the worldwide literature. Although, their effectiveness in certain applications is evident and generally accepted by the research community, there is several factors that inhibit their wide deployment, as presented in **Figure 6** [47].

First of all, these developments have been conducted separately, taking into account and focusing on the specific needs of each application, hence lacking a more general and common framework of the application. To establish their place in the market and challenge the dominance of conventional AC grids, a common legislative background for AC/DC hybrid smart microgrid solutions is considered to be a necessity [48]: it is important to establish standards upon which the architecture of AC/DC hybrid smart microgrids can be designed and implemented.

In this sense, DC compatible equipment needs to be developed by the manufacturers [21]. More specifically, one of the main reasons why such advanced grids are researched is the ascending amalgamation of DC devices in the overall load of the system. As mentioned previously, such devices include EVs, computers and other electronic devices, power electronics, DC motors, LED lights, etc. Nevertheless, currently, most of these devices are designed to be powered by AC sources. To be efficiently incorporated in AC/DC hybrid smart microgrids, they need to be designed to be powered by DC sources by incorporating a DC/DC converter [49]. For this purpose, it is imperative to establish mechanisms that promote and provide financial support to the cooperation between public and private entities, researchers and industry, allowing the development of DC-compatible equipment that is not yet available as well as suitable disconnecting and protection devices [50].

Furthermore, there is a generalized requirement for the standardization of the voltage level on hybrid grid applications, new safety regulations and suitable protection mechanisms [51–54]. More specifically, a major challenge for voltage standardization on the DC part of hybrid grids is the use of different voltage levels in distributed generation, residential, commercial and industrial demand sides. So far,

**Figure 6.** *Challenges for AC/DC hybrid smart microgrids.*

### *A Review on the Driving Forces, Challenges, and Applications of AC/DC Hybrid Smart… DOI: http://dx.doi.org/10.5772/intechopen.101973*

the research community has not agreed to use a specific DC voltage level or even set clear limits between what is considered to be "low", "medium" and "high" voltage, in terms of standardization. Without voltage levels standardization it is impossible to develop appliances, equipment and devices that are directly connected to DC busses. It is inconvenient for manufacturers to design DC products capable of operating with different voltage levels. To speed up the incorporation of DC technologies in the distribution grid, voltage standardization is by far the highest priority. In this way, stakeholders, equipment manufacturers, consumers and users can be attracted to hybrid grids, increasing their readiness level.

### **5. Conclusions**

This chapter reviewed the motives and applications of AC/DC hybrid smart microgrids. This type of grid constitutes a milestone in the evolution of electrical transmission and distribution systems, as it facilitates the efficient incorporation of the majority of RES, storage as well as DC-based loads, while also having AC connections for the service of AC generation and consumption. Indicative applications that would benefit from such architectures include buildings, data centers, EV charging stations, etc. However, the wider adoption of AC/DC hybrid smart microgrids requires a more coordinated effort in terms of the regulatory framework, so that their DC part can be as highly standardized as the AC one.

### **Funding**

This research has received funding from the European Union's Horizon 2020 research and innovation programme, TIGON (Towards Intelligent DC-based hybrid Grids Optimizing the Network performance), under grant agreement No 957769, https://cordis.europa.eu/project/id/957769.

### **Conflict of interest**

The authors declare no conflict of interest.

### **Author details**

Maria Fotopoulou, Dimitrios Rakopoulos\*, Fotis Stergiopoulos and Spyros Voutetakis Centre for Research and Technology Hellas, Athens, Greece

\*Address all correspondence to: rakopoulos@certh.gr

© 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 2**

## Hierarchical Control of an Islanded AC Micro Grid Using FS-MPC and an EMS

*Andreas Pedersen, Ibrahim Ahmed and Lucian Mihet-Popa*

### **Abstract**

Microgrids and distributed energy resources (DERs) are gaining popularity owing to their efficient operation, autonomy, and dependability. Microgrids provide several new opportunities, one of which is the ability to deliver electricity continuously, even in the event of a grid failure. This chapter will first describe the modeling of DER components in a microgrid, with each component using Finite Set-Model Predictive Control (FS-MPC) for controlling the inverters to be robust, to have a fast response, to account for multiple objectives, and to eliminate manual tuning. In addition, droop control will be used to provide a voltage reference for the FS-MPC. The PV-inverter will operate as a grid- forming inverter, while the other inverters will serve as gridfeeding inverters. The proposed inverter models are validated using simulations. The microgrid has been modeled using MATLAB-Simulink software package. A supervisory controller for energy management system of the microgrid to operate in different power flows through the proposed control algorithm has also been designed. The simulation results show the effectiveness and robustness of the proposed controller during dynamic performance and transients, and the developed energy management system algorithm successfully controlled the power flow to ensure continuous power delivery to the load under all circumstances.

**Keywords:** AC microgrid (MG), droop control, finite-set model predictive control (FS-MPC), hierarchical control, islanded operation, energy management system (EMS), energy storage systems (ESSs)

### **1. Introduction**

Today's society needs a dependable supply of electricity to consumers and prosumers, with high power quality. As a result of the continual adoption of novel technologies, the structure of the power grid in many countries is continuously developing, posing difficulties with energy flow changes, capacity limits, and high investment expenditures to update the power grid. For many years, power grids have been digitalized to allow centralized monitoring and administration of the power network, which is a result of the emergence of new technologies [1–6]. This "smart" digitalized grid has been a reality for the high-voltage section of the power system for a

considerable amount of time, and the modernization of the low-voltage distribution sector is also in progress [7–9]. In the next years, utility grids will depend more and more on renewable energy, and users will reap the benefits of smart technologies such as electric car chargers and smart meters [10–13].

This gives an opportunity to further digitize the distribution (low-voltage) sector of the grid. One method to do this is by establishing a microgrid. In the event of maintenance or grid failure, microgrids should be able to function independently of the utility grid [14].

Hierarchical control structures consist of a primary control layer that has a quick response in milliseconds, a secondary control layer that is used to reduce steady-state errors and acts in a couple of seconds, and finally, a tertiary control layer that controls the active and reactive energy flow within the microgrid by sending power references either manually by the grid operator or automatically by an Energy Management System (EMS) that balances the net power within the microgrid [1–3].

Due to the intermittent nature of RES, it is required to incorporate a backup power source such as a battery storage system, and perhaps an additional fuel-based power source, so that the microgrid may continue to run even if the battery is depleted or the maximum discharge current is reached [15–17].

The authors in [18] proposed a MPC strategy developed in Python to optimize energy production and load management for interactive buildings integrated PV & BESS (battery energy storage system). The forecasting method used in the study involves Weighted Moving Average (WMA) combined with Trigg's tracking signal and adjustment formulas. This method includes sensitivity parameters and thresholds that allow a stricter or looser approach to be taken in forecasting time series. The proposed method adjusts the forecasted values for the rest of the planning horizon based on the deviation detected between forecasted and real-time series. The adjustment formula of a building's PV production is different from the adjustment formula of its load since the production of PV is more predictable than the load of a building, especially when it comes to residential loads.

In these research papers [19–22] the performance of the MPC design procedure for DC-DC and DC-AC converters applied to a PV system was analyzed. The authors in [19] presented a continuous control set MPC designed for a DC-DC buck converter used in a MPPT of a PV module, while in [20] an adaptive MPC for current sensorless MPPT in PV systems was evaluated. The papers [21, 22] address the optimal control problems of a grid-connected PV inverter system MPC-based MPPT method. The steady-state and dynamic performance of the MPC-based system are verified and compared with traditional controllers.

Furthermore, authors in [23] presented an examination of a predictive control method designed to prevent imbalances between the load demand and the generation capacity in an islanded microgrid. The Nonlinear Model Predictive Control (NMPC) is utilized to calculate load shedding and manage energy from batteries within an optimization framework. This results in the establishment of an optimal control problem that integrates all the microgrid's operating conditions, including load priorities for disconnection, and charging and discharging cycles of batteries. Simulation results of the microgrid's performance with and without the Microgrid Central Controller (MGCC) were compared. The results demonstrate that the control strategy can improve the reliability of the microgrid when operating in islanded mode, as the control strategy can maintain the voltage and frequency of the microgrid within safe limits and achieve a correct balance between generated power and load demand.

### *Hierarchical Control of an Islanded AC Micro Grid Using FS-MPC and an EMS DOI: http://dx.doi.org/10.5772/intechopen.110815*

The motivation to carry out this study is the growing interest of RES based DG units & ESSs. This chapter is focused on modeling and simulation of an AC microgrid, developed in MATLAB-Simulink environment, which consists of a hydrogen fuel cell, a solar farm, a wind turbine, and a utility grid [24–32]. Verification by simulations with a hierarchical control structure to operate the AC microgrid in islanded mode has been performed. The primary control mainly consists of FS-MPC, where the solar farm inverter is modeled as a grid-forming inverter [29, 30, 33], and FS-MPC, which was modeled as described in [34–38], is shown to be highly robust in a variety of different scenarios. The EMS is created to protect the battery' SOC, and the maximum charging/discharging current from being reached while keeping the power balance stable in the microgrid. The constraints of operating the AC microgrid in islanded operation are the maximum discharging off the battery and fuel cell, and the stochastic RES. The novelty of this research chapter is the use of FS-MPC in the primary control with a new EMS algorithm that is highly robust during islanded operation. The main contributions of this chapter can be summarized as follows:


The chapter is organized as follows. Section 2 describes the modeling of DER components in the AC microgrid, including the design of the LCL grid inverter filter, while in Section 3 the proposed EMS strategy is described and validated by simulations, using many different scenarios. The conclusion section summarizes the main outcomes of the paper.

### **2. Modeling of DER components**

This section describes how we created and developed detailed models for many microgrid components that make up the proposed microgrid. In the following sections, each component, including equations, parameters, and other design factors, will be thoroughly examined. The MATLAB-Simulink software program is used to implement all simulation models. The various component models are well-known and available in the literature, however the parameters, filter designs, and converter designs have been adjusted and chosen to match the requirements of the proposed microgrid model.

The general structure of the proposed microgrid is displayed in **Figure 1**. A solar farm, a wind turbine, a lithium-ion battery, a hydrogen fuel cell, and a utility grid are all part of the proposed AC microgrid. The AC microgrid (MG) architecture has been chosen instead of a DC architecture owing to its compatibility with existing

### **Figure 1.**

*General structure of the proposed microgrid.*

infrastructure and greater flexibility in power distribution network. AC MGs are emerging and becoming more attractive structures with integration of RES based DG units and ESSs in order to manage our future energy demands based flexibility, digitalization and energy transition, but also as a viable and reliable solution to the population without access to energy or with poor energy supply to effectively reduce the greenhouse gas emissions. The microgrid requires a battery to handle electrical loads during periods of low renewable energy generation because renewable energy supply is highly variable and depends on the environmental conditions. The microgrid may be confronted with extended periods of low irradiance and low wind speed, potentially resulting in a fully discharged battery. In this instance, if the utility grid is unavailable, a hydrogen-fueled fuel cell can be employed to meet the load demand.

### **2.1 PV system**

Multiple photovoltaic arrays with a combined capacity of 60 kW make up the solar farm. A boost converter is used to boost the DC output of the photovoltaic arrays and keep the PV modules' generation at its highest level. A three-phase full-bridge inverter is employed because the microgrid's PCC is a three-phase AC system, and the threephase square waves from the inverter are subsequently filtered using an LCL filter. **Figure 2** illustrates the model [39].

### *2.1.1 Photovoltaic array*

The MATLAB-Simulink special power system library provides the photovoltaic array model/block/subsystem, which we have utilized in the microgrid simulation. *Hierarchical Control of an Islanded AC Micro Grid Using FS-MPC and an EMS DOI: http://dx.doi.org/10.5772/intechopen.110815*

**Figure 2.** *Model of the PV system [39].*

It is a five- parameter single-diode model that uses a light-generated current source (*Iph*) and a diode current (*Id*) to simulate an ideal PV cell with a series resistance (*Rs*) and a parallel-coupled shunt resistance (*Rsh*) to simulate a more practical solar cell and more accurately describes the solar cells' power losses. The one-diode model is one of the most popular models because of the good compromise between simplicity and precision [7, 24, 33, 37–39].

The I-V characteristic of the solar cell can then be derived by using the singleexponential Shockley equation for the diode, and the resistances to get Eq. (1) [33].

$$I = I\_{ph} - I\_0 \left( e^{\frac{V + R\_s I}{nV\_t}} - 1 \right) - \left( \frac{V + R\_s I}{R\_{sh}} \right) \tag{1}$$

where *I*<sup>0</sup> is the reverse saturation current, *n* is the ideality factor of the diode, and *Vt* is the thermal voltage. Eq. (2) describes the light-generated current *Iph* which is based on the value of irradiance (*G*), the cell temperature (*Tc*), the STC (Standard Test Condition) of the irradiance (*Gref*), and cell temperature (*Tref*), the temperature coefficient *ki* (*A*/°*C*), and the short-circuit current at STC (*Isc*) [29].

The reverse saturation current is given by Eq. (2), where the *Isc* is the short-circuit current, *Voc* is the open-circuit voltage, *Vto* is the STC thermal voltage, *Eg* is the energy bandgap of the semiconductor, and the energy bandgap at T = 0 K (*Ego*) [29].

$$I\_0 = \frac{I\_{SC}e^{\left(\frac{E\_{pr}}{V\_{br}} - \frac{E\_r}{V\_t}\right)}}{e^{\left(\frac{V\_{OC}}{nN\_SC\_{tp}}\right)} - 1} \cdot \left(\frac{T\_C}{T\_{ref}}\right)^3 \tag{2}$$

Next, the semiconductors energy bandgap value at any cell temperature (*Tc*) is described by Eq. (3), where the *αgap*, and *βgap* are the characteristic parameters of the semiconductor [33, 35].

$$E\_{\rm g} = E\_{\rm go} - \frac{a\_{\rm gap}}{b\_{\rm gap} + T\_{\rm C}} \tag{3}$$

However, several solar cells are connected in series in a photovoltaic module, and some modules may have multiple parallel branches of the series connections. The solar cell equations can be scaled up by representing the number of solar cells connected in series as (*Ns*) and the number of parallel branches (*Np*). The scaling is performed on

the module current (*Im* = *Np*I), module voltage (*Vm*= *Ns*V), module series resistance *Rsm* <sup>¼</sup> *Ns Np Rs* and module shunt resistance *Rshm* <sup>¼</sup> *Ns Np Rsh* [29, 32].

Furthermore, by denoting the number of PV modules connected in series by (*Nsm*) and the number of series strings connected in parallel by (*Npm*), the PV modules can be scaled up to create a PV array.

The MPP (Maximum Power Point) of the PV array current (*Img*) and PV array voltage (*Vmg*) can therefore be characterized using the Eqs. (4) and (5), respectively [33].

$$I\_{mg} = N\_{pm} \left(\frac{I\_{mmR}}{1000}G + \left(\frac{dI\_{scm}}{dT}\right)(T\_c - T\_{ref})\right) \tag{4}$$

$$V\_{mg} = N\_{sm} \left( N\_t V\_t \ln \left( 1 + \frac{\text{Iscm} - I\_{mg}}{I\_{scm}} \left( e^{\frac{V\_{am}}{N\_t V\_t}} - 1 \right) \right) - I\_{mg} R\_{sm} \right) \tag{5}$$

Where **I***mmg* is the rated MPP current of the module at STC, **I***scm* is the short-circuit current of the module at STC, *Vocm* is the open-circuit voltage of the module at STC. The PV- module operating temperature (*Tc*), can then be found for any irradiance condition, and ambient air temperature (*Tair*), as shown in Eq. (6). NOCT is the normal operating cell temperature at an irradiance of 800 *W*/*m*<sup>2</sup> , and an ambient air temperature of 20°C [33].

$$T\_c = T\_{air} + \frac{NOCT - 20}{800}G \tag{6}$$

It is worth noting that the single-diode model has poor accuracy for extremely low irradiances, but the two-diode model can be utilized to improve accuracy in these cases. The two-diode model, on the other hand, is substantially slower to simulate because it has seven parameters and two exponential components, and the accuracy at low irradiances has no effect on the overall output power.

### *2.1.2 Boost converter and MPPT*

The boost converter is a DC/DC converter that increases the output voltage through active switching. The boost converter contains an input capacitor, an inductor, an IGBT, a diode, and an output capacitor and is built using blocks from the MATLAB-Simulink special power system library. **Figure 3** shows the MATLAB-Simulink model.

When the gate of the IGBT receives a square wave of sufficient magnitude, it conducts (ON state), creating a short circuit between the inductor and the negative input. The inductor on the input side stores energy in the magnetic field, and the current will only pass through the IGBT because the diode, capacitor, and load all have much greater impedances.

There is no path through the IGBT when it is turned off, and the abrupt drop in current causes the inductor to generate a back EMF with the polarity of the voltage across it during the ON period. As a result, two voltages are generated, one from the supply and the other from the inductor. The current going through the diode is now charging the capacitor and powering the load at the same time. Even if no current passes from the input to the output during the ensuing on-period, the output capacitor will retain charge and continue to power the load [7].

Because the output voltage remains constant in a steady state, the integral of the inductor voltage over one period is zero. Eq. (7) can therefore be used to explain the *Hierarchical Control of an Islanded AC Micro Grid Using FS-MPC and an EMS DOI: http://dx.doi.org/10.5772/intechopen.110815*

### **Figure 3.**

*MATLAB-Simulink model of the boost converter modeled with blocks from Simscape/SPS library and the MPPPT controller based on a MATLAB function.*

dynamics in CCM (Continuous Conduction Mode). After that, divide both sides of Eq. (7) with.

*Ts* and rearrange to get Eq. (8) [7].

$$(V\_i t\_{on} + (V\_i - V\_o)t\_{q\tilde{f}} = 0) \tag{7}$$

$$\frac{V\_o}{V\_i} = \frac{T\_s}{t\_{off}} = \frac{1}{1 - D} \tag{8}$$

Where *Vi* is the input voltage, *Vo* is the average output voltage, *ton*, and *toff* are the time the IGBT is switched on, and off during one period respectively,*Ts* is the switching period, and *D* is the duty cycle.

We can define the minimal amount of inductance required to function at CCM when choosing the inductor. Eq. (10) may be used to compute the critical inductance value, whereas Eq. (9) can be used to calculate the duty cycle [7].

$$D = \mathbf{1} - \frac{V\_{mpp}}{V\_{o-nom}} \tag{9}$$

$$L\_c = \frac{V\_{mpp}D}{\Delta I\_l f\_{sw}}\tag{10}$$

Where *Vmpp* is the PV-maximum array's rated voltage at maximum irradiance and lowest ambient temperature. The nominal output voltage is *VoNom*, the inductor ripple current is Δ*IL*, and the switching frequency is *fsw*. Eqs. (11) and (12) may then be used to compute the capacitance required at the input and output [7].

$$\mathbf{C}\_{in} = \frac{\Delta I\_L}{8 \Delta V\_{pv} f\_{sw}} \tag{11}$$

$$C\_{out} \geq \frac{V\_o D}{f\_{sw} \Delta V\_o R} \tag{12}$$

### *2.1.3 MPPT algorithm*

To always generate the maximum possible power with the PV array, an MPPT (Maximum Power Point Tracking) algorithm is used [37]. The algorithm that is used in this simulation is called P&O (Perturb & Observe) and its flowchart is visualized in **Figure 4**. The MPPT generates a voltage reference in the model. The duty cycle for the PWM generation is then produced by feeding the difference between the observed voltage and the voltage reference into a PI controller.

### *2.1.4 Three-phase square-wave inverter*

The three-phase full-bridge inverter is a switching transistor-based DC/AC converter. A large-value capacitor is utilized in the DC-link to smooth out the input voltage to the inverter since VSIs (Voltage Source Inverters) depend on a consistent DC source. The square-formed sine wave that the inverter outputs as AC voltage must first be filtered before reaching the PCC [38, 40, 41].

The six IGBTs that make up the three-phase, two-level inverter are split into three at the top that are connected to one of the phase outputs from the positive DC input and three more that are connected to the same output from the negative DC input. To

prevent a short circuit, it is crucial that never both of an IGBT's top and bottom levels conduct at once. **Figures 5** and **6** show the three-phase square wave inverter's basic setup and the voltage for each phase, respectively.

### *2.1.5 LCL filter*

For applications that employ a VSI, a filter is necessary to improve the performance of the feedback control and reduce harmonics. There are many different filters that can be utilized, but in this instance an LCL filter is used [27, 28, 37, 38]. The LCL filter offers greater attenuation than using a single high-value inductor. Even at power levels of hundreds of kW, the capacitor and inductor values might be minimal. The current ripple, filter size, and switching ripple attenuation must all be considered while creating an LCL filter for a VSI. Additionally, both the inductor and the capacitor may contribute if the controller is used to regulate reactive power, necessitating

**Figure 5.** *Basic configuration of the three-phase square wave inverter.*

**Figure 6.**

*Three-phase voltages of the three-phase square wave inverter.*

damping to prevent resonance The maximum ripple current **I***m*ax can be calculated with Eq. (15). In this equation, it is assumed that the maximum peak-to-peak current happens at the inverter modulation factor (m = 0.5). Using Eq. (16), the maximum ripple is set to be 10% of the maximum current [40, 41].

$$I\_{\text{max}} = \frac{P\_n \sqrt{2}}{3V\_{ph}}\tag{13}$$

$$
\Delta I\_{L\max} = 0.1 I\_{\max} \tag{14}
$$

With this information, the inverter side inductance *L*1, the grid side inductance *L*2, and the capacitance *Cf* can be calculated by using Eqs. (17), (18), and (19). The capacitors can be connected either in Δ or *Y* configuration. The equations below are for *Y* connection, while for Δ connection the resulting value from Eq. (19) is divided by 3 and the same goes for the damping resistor *gf* [37].

$$L\_1 = \frac{V\_{dc}}{\mathfrak{G}f\_{sw}\Delta I\_{L\max}}\tag{15}$$

$$L\_2 = \frac{\sqrt{\frac{1}{k\_s^2}} + \mathbf{1}}{\mathbf{C}\_f \mathbf{f}\_{sw}^2} \tag{16}$$

$$\mathbf{C}\_{f} = \boldsymbol{\mathfrak{x}} \cdot \mathbf{C}\_{b} \tag{17}$$

where *ka* is the attenuation factor, and *x* is the maximum power factor variation as seen by the grid. Next, the resonant frequency and damping resistor can be calculated by using Eqs. (20), (21) [40, 41].

$$
\rho\_{\rm res} = \sqrt{\frac{L\_1 + L\_2}{L\_1 L\_2 C\_f}} \tag{18}
$$

$$R\_f = \frac{1}{3\alpha\_{res}C\_f} \tag{19}$$

It is important that the resonant frequency is kept between the limits in Eq. (22) [40, 41].

$$10f\_{\mathcal{g}} < f\_{res} < 0.5f\_{sw} \tag{20}$$

All the parameters that have been used for the model can be seen in **Table 1**.

### **2.2 Wind farm**

The wind farm consists of a synchronous machine, which is driven by a wind turbine coupled with a diode rectifier and a boost converter that is used to increase the DC-link voltage. A full-bridge inverter is then used to convert the DC power back to three-phase AC. After that, an LCL filter is employed to remove harmonics and smooth out the square waves coming from the inverter. The model is drawn from the MATLAB-Simulink Simscape/special power systems package, where the parameters are designed to satisfy the microgrid requirements [42].

*Hierarchical Control of an Islanded AC Micro Grid Using FS-MPC and an EMS DOI: http://dx.doi.org/10.5772/intechopen.110815*


**Table 1.** *Parameter for the LCL filter.*

### *2.2.1 Wind turbine*

The wind turbine is modeled using the wind speed *Vw*, the pitch angle *β*, and the rotor speed *ω<sup>t</sup>* as input parameters. The equations used for modeling the wind turbine are shown below in Eqs. (23), and the Simulink block model is shown in **Figure 7**. The mechanical system is based on the equation of motion that is displayed in Eq. (24) [43].

$$P\_m = \frac{1}{2} \rho A v\_w^3 C\_p(\lambda, \beta) \tag{21}$$

$$T\_{mech} - T\_{dec} = I \frac{d\nu}{dt} \tag{22}$$

### *2.2.2 Synchronous machine*

The synchronous machine model has been taken from the specialized power system library and represents the dynamics of the stator, field, and damper windings. It is modeled in the *dq*-reference frame and is based on Eqs. (25)–(33) [6, 44].

$$dV\_d = -i\_d R\_s - \alpha \nu\_q + \frac{d\nu\_d}{dt} \tag{23}$$

$$V\_q = -i\_q R\_s - \alpha \nu\_d + \frac{d\nu\_q}{dt} \tag{24}$$

**Figure 7.** *The MATLAB-Simulink block model of a wind turbine.*

$$V\_0 = -i\_0 R\_0 + \frac{d\mu\_0}{dt} \tag{25}$$

$$V\_{fd} = V\_{fd} = \frac{d\nu\_{fd}}{dt} + r\_{fd}i\_{fd} \tag{26}$$

$$\frac{d\boldsymbol{\mu}\_{kd}}{dt} + \boldsymbol{R}\_{kd}\boldsymbol{i}\_{kd} = \mathbf{0} \tag{27}$$

$$\frac{d\mu\_{kq1}}{dt} + R\_{kq1}i\_{kq1} = 0\tag{28}$$

$$\frac{d\boldsymbol{\mu}\_{kq2}}{dt} + \boldsymbol{R}\_{kq2}\boldsymbol{i}\_{kq2} = \mathbf{0} \tag{29}$$

$$
\begin{bmatrix}
\boldsymbol{\Psi}\_{d} \\
\boldsymbol{\Psi}\_{kd} \\
\boldsymbol{\Psi}\_{fd}
\end{bmatrix} = \begin{bmatrix}
\boldsymbol{L}\_{md} + \boldsymbol{L}\_{f} & \boldsymbol{L}\_{md} & \boldsymbol{L}\_{md} \\
\boldsymbol{L}\_{md} & \boldsymbol{L}\_{lkd} + \boldsymbol{L}\_{f1d} + \boldsymbol{L}\_{md} & \boldsymbol{L}\_{f1d} + \boldsymbol{L}\_{md} \\
\boldsymbol{L}\_{md} & \boldsymbol{L}\_{f1d} + \boldsymbol{L}\_{md} & \boldsymbol{L}\_{fd} + \boldsymbol{L}\_{f1d} + \boldsymbol{L}\_{md}
\end{bmatrix} \begin{bmatrix}
\boldsymbol{i}\_{kq1} \\
\boldsymbol{i}\_{kq2}
\end{bmatrix} \tag{30}
$$

$$
\begin{bmatrix}
\boldsymbol{\Psi}\_{q} \\
\boldsymbol{\Psi}\_{kq1} \\
\boldsymbol{\Psi}\_{kq2}
\end{bmatrix} = \begin{bmatrix}
\boldsymbol{L}\_{mq} + \boldsymbol{L}\_{f} & \boldsymbol{L}\_{mq} & \boldsymbol{L}\_{mq} \\
\boldsymbol{L}\_{mq} & \boldsymbol{L}\_{mq} + \boldsymbol{L}\_{kq1} & \boldsymbol{L}\_{mq} \\
\boldsymbol{L}\_{mq} & \boldsymbol{L}\_{mq} & \boldsymbol{L}\_{mq} + \boldsymbol{L}\_{kq2}
\end{bmatrix} \begin{bmatrix}
\boldsymbol{i}\_{kd} \\
\boldsymbol{i}\_{fd}
\end{bmatrix} \tag{31}
$$

All the nomenclatures of the parameters in the equations can be found in [44].

### *2.2.3 Back-to-Back boost converter*

A library from MathWorks' current collection was also used to select the back-toback boost converter. It is a part of the library's specialized power systems block for wind turbine subsystems. **Figure 8** depicts the model. Three-phase AC from the synchronous machine is fed into the back-to-back boost converters, which are then transformed into DC by a diode bridge (rectifier). The voltage is subsequently increased by the boost converter; for further information on the boost converter, see Section 2.1.1. The three-phase square wave inverter, which is discussed in Section 2.1.3, is then given the stepped-up DC voltage.

**Figure 8.** *The MATLAB-Simulink model of the back-to-back converter with DC-DC boost converter in DC-link.*

### **2.3 Energy storage system**

It is crucial to have the ability to store energy during periods of high-power generation and use it during periods of low generation since the renewable energy sources in the microgrid are very intermittent and dependent on the environment. Additionally, the ESS (Energy Storage System) can be utilized to peak-shave, trade with the grid, and enhance the microgrid's dependability and power quality. It is made up of an L-filter, a three-phase square wave inverter, and a lithium-ion battery bank as illustrated in **Figure 9**. The Simulink model of the ESS is displayed in **Figure 10**.

### **2.4 Lithium-ion battery**

Eq. (34) describes the discharging process of the lithium-ion battery, while Eq. (35) describes the charging process of the battery [28].

**Figure 9.** *Circuit diagram of the bidirectional grid converter.*

**Figure 10.** *The MATLAB-Simulink model of the battery storage system.*

$$f\_1(it, i^\*, i) = E\_0 - K \frac{Q}{Q - it} i^\* - K \frac{Q}{Q - it} it + Ae^{-B.it} \tag{32}$$

$$\,\_{1}f\_{2}(it, i^{\*}, i) = E\_{0} - K \frac{Q}{it + 0.1Q} i^{\*} - K \frac{Q}{Q - it} it + Ae^{-B.it} \tag{33}$$

Where *E*<sup>0</sup> is the constant voltage, *K* is the polarization constant (V/Ah), *i* <sup>∗</sup> is the low- frequency current dynamics, *i* is the battery current, *it* is the extracted capacity in Ah, *Q* is the maximum battery capacity, *A* is the exponential voltage, and *B* is the exponential capacity (**Figure 11**).

### **2.5 Hydrogen fuel cell**

The backup power source is present so that the microgrid can continue to operate in islanded mode even when the energy storage system's state of charge (SOC) is low. The backup power source in this microgrid is a hydrogen fuel cell. The fuel cell is modeled as a dependent voltage source with a series internal resistance and internal diode as displayed in the equivalent electric circuit as depicted in **Figure 12**. The inverter used with the fuel cell is the same as the one used with the battery illustrated in **Figure 9** and it also uses the same control architecture.

**Figure 11.** *Discharge characteristics of the battery storage model at different currents.*

*Hierarchical Control of an Islanded AC Micro Grid Using FS-MPC and an EMS DOI: http://dx.doi.org/10.5772/intechopen.110815*

**Figure 12.** *Equivalent circuit diagram of the fuel cell.*

### **3. Development and testing of an energy management system (EMS) algorithm**

### **3.1 The proposed EMS algorithm**

The suggested approach for controlling the energy flow is depicted in a flowchart in **Figure 13**. The suggested EMS additionally considers the battery limitations, which state that the battery should never be charged below or above the limits (SOC), nor should the maximum charging or discharging current, denoted by *Pbat*,*max*, be exceeded. The algorithm was created using a MATLAB function. Following the validation of the SOC restrictions, the algorithm checks to see if the microgrid is generating more energy than the load is using; if so, the battery is charged in accordance with the restrictions. If the generation is lower than the load, the battery must discharge, or if the SOC is low, the fuel cell must be engaged.

### **3.2 Testing scenarios and results**

A number of scenarios were developed to test the effectiveness and dependability of the suggested EMS in the islanded mode. The results are presented in **Figures 14** and **15**, and a summary of these situations is provided in the **Table 2**. Intially, the load reference were set to 10 kW, and the wind and PV reference were increased to generate the full power. This creates an unbalance as the generation is much larger than the load and since the battery can only absorb 50 kW, the generated power had to be limited to by disabling the MPPT and reducing the PV power reference. That is exactly what the EMS did as it can be seen that the MPPT was disabled shortly after 0 and the PV power was limited while making sure that the battery is charging with a maximum power of 50 kW and the load is kept stable at 10 kW. Next, a step in the load active power reference was applied from 10 to 50 kW and at the same instant, a step in the reactive power was also applied from 0 to 44 kVAR, making the load power factor 0.75 lagging. The controller responds appropiately by increasing the PV power reference to meet the load demand while keeping the battery charging at 50 kW. The reactive power demand is also met by the battery controller. After that, a step in the load reactive power was applied from 44 to 44 kVAR which changes the power factor from lagging to leading and the battery also was able to absorb the reactive

### *Hierarchical Control of an Islanded AC Micro Grid Using FS-MPC and an EMS DOI: http://dx.doi.org/10.5772/intechopen.110815*

### **Figure 14.**

*Active and reactive power of different components in the microgrid under loads steps.*

power without any issues. At 4 seconds, the reactive power was reset to 0 and another step in the active power was applied from 50 to 100 kW to test the system at full load. Since the load is now much higher, the EMS enabled the MPPT to ensure taking the maximum power of 60 kW available from the PV. The remaining power came from the wind and any excess power was used to charge the battery. Finally, at 6 seconds, the generation was reduced evem further to test the scenario where the load is higher than the generation. A step in the irradiance from 1000 to 100 W/m<sup>2</sup> was applied to the PV and a similar step in the wind speed was applied from 15 to 8 m/s. As the wind power generation was gradually reducing, the EMS sent control commands to the battery to supply the remaining power and the battery started discharging up to

### **Figure 15.**

*Three-phase voltage, power, frequency, and RMS value of the load under different scenarios. (a) 10 to 50 kW load step at 2 seconds. (b) 44 to 44 kVAR step at 3 seconds. (c) 50 to 100 kW step at 4 seconds. (d) 1000 to 100 W/ m2 at 6 seconds.*


### **Table 2.**

*Summary of the scenarios tested with energy management system in islanded.*

50 kW, once the battery reached its maximum discharging power, the fuel cell had to be enabled to supply the remaining load power and that is what happened at around 7 seconds. From there on, all the microgrid sources were working in tandem to keep the load power at 100 kW while the EMS adjusted the fuel cell reference based on the power generated by the wind and PV system. These scenarios clearly illustrate that the proposed energy management system is robust and can succesfully control the microgrid under various conditions.

The wind and solar references were initially maximized to produce the maximum electricity with the load reference set at 10 kW. Due to the imbalance caused by the generation being significantly greater than the load and the battery's ability to store only 50 kW of power, the generated power has to be constrained by turning off the MPPT and lowering the PV power reference. The MPPT was disabled shortly after zero, and the PV power was limited to ensure that the battery was charging with a maximum power of 50 kW and the load was maintained at 10 kW. This is exactly what the EMS performed.

The load's active power reference was then increased from 10 to 50 kW, and at the same time, the load's reactive power was increased from 0 to 44 kVAR, resulting in a load power factor of 0.75 lagging. In order to fulfill the load requirement, the controller increases the PV power reference as necessary, keeping the battery charging at 50 kW. The battery controller also satisfies the demand for reactive power. The power factor was then changed from lagging to leading by applying a step in the reactive power of the load from 44 to 44 kVAR, and the battery was able to absorb the reactive power without any problems.

To test the system at maximum load, the reactive power was reset to 0 at 4 seconds and another step in the active power was applied from 50 to 100 kW. Due to the increased load, the MPPT was enabled to use the full 60 kW of available PV power thanks to the EMS. Wind provided the remaining energy, and any extra was used to recharge the batteries. To test the condition where the demand is more than the generation, the generation was further decreased at 6 seconds. The PV received a step-down in irradiance from 1000 to 100 W/m2 and a comparable step-down in wind speed from 15 to 8 m/s.

The fuel cell had to be enabled in order to supply the remaining load power, which occurred at roughly 7 seconds as the wind power generation rapidly decreased. The EMS had issued control commands to the battery to deliver the remaining power, and the battery began depleting up to 50 kW. The EMS controlled the fuel cell reference depending on the electricity produced by the wind and PV systems, all the microgrid sources continued to cooperate to maintain the load power at 100 kW. These examples unmistakably show how reliable the energy management system is and how successfully it can operate the microgrid under diverse circumstances.

Following that, the battery's SOC constraints were evaluated using the scenarios depicted in **Figure 16**. **Figure 17(a)** shows a zoomed-in plot of the voltage, frequency,

**Figure 16.** *Active and reactive power of different components in the microgrid to test SOC limiting to protect the battery.*

and load consumption at 3 seconds when the controller determined the SOC to be above the limits at 3 s, the battery immediately stopped charging, and the PV could no longer operate at the maximum power point and was given a corresponding reference so the power balance in the microgrid was met. The load is increased to 100 kW at 5 seconds, and the PV irradiance is decreased to 500 W/m<sup>2</sup> . To accommodate the significant increase in load demand, this should cause the battery to begin discharging.

After 8 seconds, the battery starts to deplete until it hits 10%, at which point the EMS sends a command to stop discharging the battery. When this occurs, the EMS responds by igniting the fuel cell to start supplying power right away. In

*Hierarchical Control of an Islanded AC Micro Grid Using FS-MPC and an EMS DOI: http://dx.doi.org/10.5772/intechopen.110815*

**Figure 17.**

*Three-phase voltage, power, frequency, and RMS value of the load under different scenarios. (a) SOC saturation at 90%. (b) 50 to 100 kW step at seconds. (c) SOC saturation at 10%.*

**Figure 17(c)** the voltage, frequency, and load consumption charts are zoomed in. This demonstrates how the EMS can safeguard the battery from overcharging and over-discharging, demonstrating how it accomplishes some of the goals of a BMS (Battery Management System).

### **4. Conclusion**

In summary, this chapter explored the modeling and simulation of microgrid components, including PV system, wind turbine system, battery storage and fuel cell system. The control architecture, developed for the primary control of these components, was based on model predictive control. An energy management system algorithm was successfully designed and developed to control the power flow and to ensure continuous power delivery to the load under all circumstances. The algorithm was tested on a variety of scenarios and proved its robustness and flexibility. When the generation exceeds the load demand and the battery cannot absorb all the excess power, the EMS would disable the MPPT to limit the generation from the solar cell to protect the battery. If the load demand exceeds the generation, the EMS uses the battery to make up for the difference and if the battery power is not sufficient, the fuel cell is activated to provide the rest of the power. Moreover, SOC- based protection scheme was also implemented to ensure that the battery state of charge remains within acceptable limits which increase the lifetime of the battery.

### **5. Future work**

The simulation of the EMS algorithm presented in this paper provides a promising proof- of-concept for its effectiveness in managing electrical energy in a microgrid. However, there are several avenues for further exploration and improvement. One

important step for the validation of the EMS algorithm would be to test it on real hardware, such as a Hardware-in-the-Loop (HIL) platform. This would enable us to evaluate the algorithm's performance in a realistic setting, which includes the various noise and uncertainties that can arise in the physical world. Moreover, we could measure the real-time performance of the algorithm and compare it with the simulation results.

Another potential area for future work is to add new equipment to the microgrid and reconfigure the algorithm accordingly. The EMS algorithm was designed to work with a specific set of components, and its performance may be affected by the addition or removal of equipment. Hence, future expansion of the microgrid may require a readjustment of the algorithm to ensure its optimal operation.

Finally, the implementation of the EMS algorithm presented in this paper was focused on a single microgrid. However, in practice, multiple microgrids can be interconnected to form a larger network, and the EMS algorithm must be adapted to this scenario. Future work could explore the development of a hierarchical control scheme that manages multiple microgrids simultaneously.

### **Funding**

This research was partial funded by the EEA and Norway grant/project DOITSMARTER, contract no 2022/337335.

### **Conflict of interest**

The authors declare no conflict of interest.

### **Author details**

Andreas Pedersen, Ibrahim Ahmed and Lucian Mihet-Popa\* Faculty of Information Technology, Engineering and Economics, Østfold University College, Fredrikstad, Norway

\*Address all correspondence to: lucian.mihet@hiof.no

© 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.

*Hierarchical Control of an Islanded AC Micro Grid Using FS-MPC and an EMS DOI: http://dx.doi.org/10.5772/intechopen.110815*

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## Section 2
