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To determine the optimal size of an energy storage system (ESS) in a fast electric vehicle (EV) charging station, minimization of ESS cost, enhancement of EVs’ resilience, and reduction of peak load have been considered in this article. Especially, the resilience aspect of the EVs is focused due to its significance for EVs during power outages. First, the stochastic load of the fast-charging station (FCS) and the resilience load of the EVs are estimated using probability distribution functions. This information is utilized to maintain the energy level in the ESS to ensure the resilience of EVs during power outages. Then, the annualized cost of the ESS is determined using the annual interest rate and lifetime of ESS components. Finally, the optimal ESS size is determined using the annualized ESS cost, penalty cost for buying power during peak hours, and penalty cost for resilience violations. Simulations along with sensitivity analysis of uncertainties (market price, arrival time of EVs, and the residual energy level of EVs), number of EVs in the FCS, and converter ratings are conducted. Simulation results have shown that increasing the penalty cost for peak intervals is a viable solution to decrease the peak load while controlling the cost of the FCS.

In the conventional operation of a wind farm (WF) system, the operation point of each wind turbine generator (WTG) is determined to capture maximum energy individually using maximum power point tracking (MPPT) algorithm. However, this operation strategy might not ensure the maximum output power of WF due to wake effect among WTGs. Therefore, this paper develops a multi-agent-based cooperative learning strategy among WTGs using deep reinforcement learning to enhance the overall efficiency of WF by minimizing the wake effect. WTG agents are learnable units and they interact with others as an extensive-form game based on a cooperative model to achieve a common goals (i.e. maximum output power of the WF). In this game, WTG agents carry out their actions sequentially and measure a common reward which is used to update the knowledge of all agents. During the training process, WTG agents use different deep neural networks (DNNs) to improve their actions for achieving the higher reward in the long run by optimizing the weights of DNNs in each learning step. After the training process, WTG agents are able to determine optimal set-points with different input information to minimize the wake effect and to maximize the output power of the WF. Moreover, an operation strategy for the entire WF system is proposed to ensure that the WF always complies with grid-code constraints from transmission system operators, including the requirement of limited power and reserve power. In order to show the effectiveness of the proposed method, a comparison between the results using the proposed method and the conventional MPPT method is also presented in different cases, and the results show that the proposed method can increase the output power of the WF in the range of 1.99% to 4.11% with different layouts.

The existing distributed operation schemes for microgrids lack the ability to determine the power selling to the grid during normal operation mode and are unable to provide service reliability to critical loads, during islanded operation mode. In order to overcome these issues, in this study, we have proposed a distributed operation method for both grid-connected and islanded modes of microgrids. Unlike the existing studies, where the utility grid is considered as a dispatchable generator, the bi-directional flow of power with the grid is considered in this study. Similarly, different load agents are considered for different priority loads to assure the service reliability to the critical loads during islanding. A two-step operation method is proposed for both grid-connected and islanded mode operations. During the first step, each agent in the network shares information with its neighboring agents to determine the total load and available renewable power in the network. Whereas, in the second step, each agent in the network determines the optimal operation points based on the local information received from the neighboring agents. Moreover, a modified cost function for the battery is also proposed in this study, which utilizes the information of market price and load to enhance the battery operation. A comparison is made between the centralized method, conventional distributed method, and the proposed distributed operation method. Simulation results have proved the effectiveness of the proposed method for realizing distributed operation for microgrids in both grid-connected and islanded modes.

$Q$ -learning-based operation strategies are being recently applied for optimal operation of energy storage systems, where, a $Q$ -table is used to store $Q$ -values for all possible state-action pairs. However, $Q$ -learning faces challenges when it comes to large state space problems, i.e., continuous state space problems or problems with environment uncertainties. In order to address the limitations of $Q$ -learning, this paper proposes a distributed operation strategy using double deep $Q$ -learning method. It is applied to managing the operation of a community battery energy storage system (CBESS) in a microgrid system. In contrast to $Q$ -learning, the proposed operation strategy is capable of dealing with uncertainties in the system in both grid-connected and islanded modes. This is due to the utilization of a deep neural network as a function approximator to estimate the $Q$ -values. Moreover, the proposed method can mitigate the overestimation that is the major drawback of the standard deep $Q$ -learning. The proposed method trains the model faster by decoupling the selection and evaluation processes. Finally, the performance of the proposed double deep $Q$ -learning-based operation method is evaluated by comparing its results with a centralized approach-based operation.

Microgrids are considered as a potential solution to deal with major power disruption events due to their ability of islanding and potential to sustain the penetration of renewables. In order to elaborate the role of microgrids in enhancing the resilience of power systems, a three-step analysis is carried out in this paper. In the first step, the general backgrounds of resilience in power systems are presented, which comprise of disaster modeling, resilience analysis methods, and resilience enhancement efforts by different regions. In the second step, the use of microgrids as a resilience resource is analyzed, where formation of microgrids, networked microgrids, and dynamic microgrids along with resiliency of multi-energy networks are explored. In the third step, the strategies utilized by microgrids for enhancing their resilience during major outage events are analyzed. These strategies include proactive scheduling, outage management, feasible islanding, and advanced operation strategies for reducing the impact of major disruptions. The classification of these operation strategies is based on the event occurrence and clearance times. In addition, the resilience strategies used by different types of microgrids, types of energy management systems, communication resilience, and resilience of individual components in microgrids are also analyzed. Finally, research gaps in the existing literature and future directions for improving the available resilience-oriented operation methods for enhancing the resilience of microgrids are presented.

This paper proposes a three-step internal trading strategy for optimal energy sharing among building microgrids (MGs) to minimize the operation cost of the entire system. In the first step, barter trading is performed to exchange energies among building MGs of the network. Building MGs having shortage of electricity and surplus of heat can pair with buildings having surplus of electricity and shortage of heat to exchange their energies. Then, the surplus/shortage energy is updated after carrying out the barter trading. In the second step, building MGs sell their remaining surplus energy to other building MGs that have shortage energy. In the third step, building MGs, which have lower generation cost than the external system and ability to increase their power generation, increase their power generation and trade with other building MGs. These three steps can increase the power sharing among building MGs and reduce the trading with the external systems, which results in reduced operation cost of the network. An additional internal market along with price signals is also presented to carry out the proposed internal trading strategy. The internal market provides a mechanism for trading energies among the building MGs of a combined cooling, heat and power (CCHP) system. A hierarchical energy management system (EMS), including building EMSs (BEMSs) and a community EMS (C-EMS) is developed to minimize the operation cost of the CCHP system. Moreover, a heat pipeline (HPL) system is applied for storing the surplus heat energy for fulfilling the shortage heat energy in the building MGs. By using the proposed strategy, total trading cost of CCHP system is reduced by 7.43% compared with the conventional operation strategy.

In wind farm (WF) systems, the set-points of wind turbine generators (WTGs) is determined by the wind farm operator. A change in the value of set-point in two consecutive intervals leads to power fluctuations in the WF system, i.e. power deviation. A large amount of power deviation may adversely affect the operation of the WF system. Therefore, this paper proposes a strategy for operation of a WF system to minimize the power deviation of WTGs during operation time. By using the proposed strategy, the change in set-points of WTGs is minimized and the output power of WTGs is therefore smoother and avoids unnecessary fluctuations. Several grid-code constraints are also considered for operation of the WF system. This ensures the WF system to operate in compliance with the requirements from transmission system operator. Besides, an additional strategy is also proposed to monitor all events in the WF system. Whenever an event occurs in the system, the set-points of WTGs are rescheduled considering the event. Therefore, the proposed operation strategy is also capable of handling events in the WF system. Finally, the simulation results with and without the proposed method are compared to show the effectiveness of the proposed method.

A resilience-oriented optimization strategy is proposed in this paper by considering feasible islanding in normal operation and survivability of critical loads during emergency period. A resilience index is proposed for assessing the capability of each microgrid to feed local critical loads during sudden power disruptions. Based on the values of the proposed resilience index, local optimization results are revised by changing the commitment status of dispatchable generators and energy storage elements. The uncertainties associated with renewable generations and loads are realized via robust optimization method. The adjustable power bounds, suggested by each microgrid for minimizing the operation cost of the network, are also revised through the proposed resilience index. In emergency mode, decision between feeding of lesser critical loads and battery charging along with a strategy for minimization of load curtailment during switching of scheduling windows is considered. These two considerations assure the survivability of critical loads during emergency period. Finally, an incremental cost consensus algorithm is used for optimal allocation of surplus power among the connected microgrids having unserved loads. Three different network topologies are considered in emergency mode for assessing the performance of the proposed approach.

In order to minimize the peak load of electric vehicles (EVs) and enhance the resilience of fast EV charging stations, several sizing methods for deployment of the stationary energy storage system (ESS) have been proposed. However, methods for assessing the optimality of the obtained results and performance of the determined sizes under different conditions are missing. In order to address these issues, a two-step approach is proposed in this study, which comprises of optimality analysis and performance evaluation steps. In the case of optimality analysis, random sizes of battery and converter (scenarios) are generated using Monte Carlo simulations and their results are compared with the results of sizes obtained from sizing methods. In order to carry out this analysis, two performance analysis indices are proposed in this study, which are named the cost index and the power index. These indices respectively determine the performance of the determined sizes in terms of total network cost and performance ratio of power bought during peak intervals and investment cost of the ESS. During performance evaluation, the performance of the determined sizes (battery and converter) are analyzed for different seasons of the year and typical public holidays. Typical working days and holidays have been analyzed for each season of the year and suitability of the determined sizes is analyzed. Simulation results have proved that the proposed method is suitable for determining the optimality of results obtained by different sizing methods.

Supply and demand balance is required to assure normal operation in microgrids. Especially, in stand-alone microgrid operation, load shedding is inevitable to meet the balance. In order to enhance the reliability of stand-alone microgrids, deployment of suitable sized controllable distributed generators is required, which results in an increase of investment cost. Therefore, a tradeoff between economical investment and reliable operation is required. In stand-alone multimicrogrid (MMG) system, the reliability can be improved by cooperative operation. However, solving the tradeoff is still a challenging problem. In this paper, an operation strategy is proposed for MMG systems where each microgrid has different frequency requirement according to load configurations to improve reliability from the economical viewpoint. A hierarchical energy management system is developed to implement the proposed strategy for MMGs operation. The validity of the proposed method is evaluated by using hardware-in-the-loop simulation. Test results showed the effectiveness for reducing load shedding and operation cost of MMG systems.

In this paper, a hybrid energy management system is developed to optimize the operation of a wind farm (WF) by combining centralized and decentralized approaches. A two-stage optimization strategy, including distributed information sharing (stage 1), and centralized optimization (stage 2) is proposed to find out the optimal set-points of wind turbine generators (WTGs) considering grid-code constraints. In stage 1, cluster energy management systems (CEMSs) and transmission system operator (TSO) interact with their neighboring agents to share information using diffusion strategy and then determine the mismatch power amount between the current output power of WF and the required power from TSO. This amount of mismatch power is optimally allocated to all clusters through the CEMSs. In stage 2, a mixed-integer linear programming (MILP)-based optimization model is developed for each CEMS to find out the optimal set-points of WTGs in the corresponding cluster. The CEMSs are responsible for ensuring the operation of WF in accordance with the requirements of TSO (i.e., grid-code constraints) and also minimizing the power deviation for the set-points of WTGs in each cluster. The minimization of power deviation helps to reduce the internal power fluctuations inside each cluster. Finally, to evaluate the effectiveness of the proposed method, several case studies are analyzed in the simulations section for operation of a WF with 20 WTGs in four different clusters.

Optimal sizing of stationary energy storage systems (ESS) is required to reduce the peak load and increase the profit of fast charging stations. Sequential sizing of battery and converter or fixed-size converters are considered in most of the existing studies. However, sequential sizing or fixed-converter sizes may result in under or oversizing of ESS and thus fail to achieve the set targets, such as peak shaving and cost reduction. In order to address these issues, simultaneous sizing of battery and converter is proposed in this study. The proposed method has the ability to avoid the under or oversizing of ESS by considering the converter capacity and battery size as two independence decision variables. A mathematical problem is formulated by considering the stochastic return time of electrical vehicles (EVs), worst-case state of charge at return time, number of registered EVs, charging level of EVs, and other related parameters. The annualized cost of ESS is computed by considering the lifetime of ESS equipment and annual interest rates. The performance of the proposed method is compared with the existing sizing methods for ESS in fast-charging stations. In addition, sensitivity analysis is carried out to analyze the impact of different parameters on the size of the battery and the converter. Simulation results have proved that the proposed method is outperforming the existing sizing methods in terms of the total annual cost of the charging station and the amount of power buying during peak load intervals.

This paper presents an energy management strategy for day-ahead scheduling of networked microgrids and is named as nested energy management system . In the proposed nested energy management strategy, the surplus power existing in the inner level microgrids is reflected as a resource and deficit as a load to the outer level microgrids. The operation cost of networked microgrids with nested energy management systems is lesser as compared to conventional decentralized or hybrid energy management strategies. Similarly, due to the inclusion of data (surplus/deficit) from inner level microgrids, customer privacy also increases. In islanded mode, the resilient performance of disconnected microgrids is enhanced due to their ability to form sub-groups . The probability of subgrouping for L-layers of microgrids is given by (L-2)/L. In grid-connected mode, various cases with diverse generation costs at each microgrid level along with the time-of-use market price signals have been evaluated. In islanded mode, the effect of battery energy storage system and disconnection of microgrids from the network have been evaluated. Hybrid ac/dc microgrids have also been considered to evaluate the effectiveness of the proposed approach. Simulation results have proved the viability of the proposed approach.

Conventionally, community energy management system (CEMS) is provided with the information of surplus and shortage amounts only at each time interval. This limited information may lead to an increase in the operational cost of the multimicrogrid (MMG) systems. This paper suggests informing the CEMS about the adjustable power also, in addition to the surplus and shortage information. This additional information will result in a variety of options for the CEMS to fulfill the load demands of its network. CEMS will choose among various available options, which include trading with the power grid, buying from a controllable distributed generation plant, buying from a community battery energy storage system (CBESS), or controlling the adjustable power: increasing or decreasing the generation of controllable units. CBESS can either be controlled by CEMS or can act as an autonomous entity. The effects of both the operational options have been analyzed and economically efficient mode is suggested for MMG systems. Demand response (DR) is also considered in the proposed model. The incorporation of DR will ensure the supply reliability of the MMG system in addition to the reduction in operational cost. In contrast to the conventional single or two-step multimicrogrid optimization algorithms, a multistep hierarchical optimization algorithm based on a multiagent system is proposed in this paper. Easy to implement and computationally inexpensive mixed integer linear programming models are developed for each step.

A resilience-constrained operation strategy is proposed in this paper using a battery energy storage system (BESS) as a resilience resource. A proactive operation scheme is proposed for the pre-disturbance phase by formulating resiliency cuts for the state of charge (SoC) of BESS units. These resiliency cuts can assure the survivability of critical loads for n intervals after the occurrence of an event. A resilience constraint index is formulated to access the feasibility of the operation model for each selected value of n. In the emergency mode, different possible scenarios are analyzed and a unified survivability enhancement strategy is proposed, which comprises of dynamic penalty costs and target SoC. Dynamic penalty costs can potentially avoid the unnecessary load shedding by shifting it toward the possible end of the scheduling horizon. Similarly, the inclusion of target SoC for the last interval of the scheduling horizon can mitigate the load shedding of critical loads on the following day. A two-step adaptive robust optimization scheme is adopted to incorporate the prevailing uncertainties in loads and renewables in the operation model. Finally, the impact of various decision parameters, involved in the problem formulation, is analyzed with reference to the proposed resilience-constrained operation strategy for hybrid microgrids.

Energy management system (EMS) is responsible for the optimal operation of microgrids. EMS adjusts its operational schedule for near future by using the available information. Market price signals are generally used for the operation of microgrids, which are obtained by using estimation/ forecasting methods. However, it is difficult to precisely predict the market prices due to the involvement of various complex factors like weather, policy, demand, errors in forecasting methods, and fuel cost. Therefore, in this paper, the uncertainties associated with the real-time market price signals (buying and selling) are realized via a robust optimization method. In addition to market price signals, uncertainties associated with renewable power sources and forecasted load values are also considered. Initially, a deterministic model is formulated for an ac/dc hybrid microgrid. Then a min–max robust counterpart is formulated by considering the worst-case uncertainties. Finally, an equivalent mixed integer problem is formulated by using linear duality and other optimality conditions. The developed model can provide feasible solutions for all the scenarios if the uncertainties fluctuate within the specified bounds. The effect of market price uncertainties on internal power transfer and external power trading, operation cost, the state-of-charge of energy storage elements, and unit commitment of dispatchable generators is analyzed. Taguchi’s orthogonal array (OA) method is used to find the worst-case scenario within the specified uncertainty bounds. Then, Monte Carlo method is used to generate various scenarios within the uncertainty bounds to evaluate the robustness of the selected scenario via Taguchi’s OA method. Finally, a violation index is formulated to evaluate the robustness of the proposed approach against the deterministic model. Simulations results have validated the robustness of the proposed optimization strategy.

Microgrids have the capability to enhance the resiliency of power systems by supplying local loads during emergency situations. However, the disturbance incident and clearance times cannot be predicted precisely. Therefore, this study is focused on enhancing the resiliency of hybrid microgrids considering feasible islanding and survivability of critical loads. The optimisation problem is decomposed into normal and emergency operation problems. In normal operation, unit commitment status of dispatchable generators and schedules of batteries are revised to ensure a feasible islanding following a disturbance event. In emergency operation, the decision between charging of batteries for future dispatch and feeding of lesser critical loads is considered. In addition, a strategy for minimisation of load curtailment during switching of scheduling windows is also considered. These two considerations can mitigate the curtailment of critical loads during the emergency period. Finally, a resiliency index is formulated to evaluate the performance of the proposed strategy during emergency operation. Numerical simulations have demonstrated the effectiveness of the proposed strategy for enhancing the resiliency of hybrid microgrids.

Energy management systems (EMSs) of microgrids (MGs) can be broadly categorized as centralized or decentralized EMSs. The centralized approach may not be suitable for a system having several entities that have their own operation objectives. On the other hand, the use of the decentralized approach leads to an increase in the operation cost due to local optimization. In this paper, both centralized and decentralized approaches are combined for managing the operation of a distributed system, which is comprised of an MG and a community battery storage system (CBESS). The MG is formed by grouping all entities having the same operation objective and is operated under a centralized controller, i.e., a microgrid EMS (MG-EMS). The CBESS is operated by using its local controller with different operation objectives. A Q-learning-based operation strategy is proposed for optimal operation of CBESS in both grid-connected and islanded modes. The objective of CBESS in the grid-connected mode is to maximize its profit while the objective of CBESS in islanded mode is to minimize the load shedding amount in the entire system by cooperating with the MG. A comparison between the Q-learning-based strategy and a conventional centralized-based strategy is presented to show the effectiveness of the proposed strategy. In addition, an adjusted epsilon is also introduced for epsilon-greedy policy to reduce the learning time and improve the operation results.

Due to the uncertainty in output power of wind farm (WF) systems, a certain reserve capacity is often required in the power system to ensure service reliability and thereby increasing the operation and investment costs for the entire system. In order to reduce this uncertainty and reserve capacity, this study proposes a multi-objective stochastic optimization model to determine the set-points of the WF system. The first objective is to maximize the set-point of the WF system, while the second objective is to maximize the probability of fulfilling that set-point in the real-time operation. An increase in the probability of satisfying the set-point can reduce the uncertainty in the output power of the WF system. However, if the required probability increases, the set-point of the WF system decreases, which reduces the profitability of the WF system. Using the proposed method helps the WF operator in determining the optimal set-point for the WF system by making a trade-off between maximizing the set-point of WF and increasing the probability of fulfilling this set-point in real-time operation. This ensures that the WF system can offer an optimal set-point with a high probability of satisfying this set-point to the power system and thereby avoids a high penalty for mismatch power. In order to show the effectiveness of the proposed method, several case studies are carried out, and the effects of various parameters on the optimal set-point for the WF system are also analyzed. According to the parameters from the transmission system operator (TSO) and wind speed profile, the WF operator can easily determine the optimal set-point using the proposed strategy. A comparison of the profits that the WF system achieved with and without the proposed method is analyzed in detail, and the set-point of the WF system in different seasons is also presented.

Hierarchical energy management systems (EMSs) are becoming popular for operation of a multi-microgrid (MMG) systems. In hierarchical EMS, a community EMS is required for cooperative operation of all the microgrids (MGs), which requires an extensive communication infrastructure. It results in several challenges in operation of MMG system, such as higher cost, lower reliability, and higher communication burden. In order to overcome these problems, this paper proposes a diffusion strategy-based distributed optimization for operation of an MMG system. The proposed strategy is divided into two main steps. In the first step, the total amount of shortage power is determined in the entire MMG system. In the second step, the amount of sharing power among MGs is determined using the information of total shortage power in the MMG system. By applying the proposed strategy, the direct trading with the utility grid can be reduced due to the sharing of surplus power among the MGs of the network. The numerical results show that the MMG system can optimally operate by using the proposed method without a community EMS.

Demand response (DR) programs are generally utilized for reducing peak loads in grid-connected microgrids. However, DR programs can also be applied to islanded microgrids for enhancing reliability. Therefore, in this paper, a reliability-oriented DR program is proposed for enhancing the survivability of loads during islanded periods. The objective of the proposed DR scheme is to enhance the utilization of renewables by shifting loads from off-peak renewable generation intervals to peak renewable generation intervals. This will increase the reliability of the service by supplying more loads on one hand. On the other hand, it will increase the profit of renewable owners by reducing the curtailment of renewables. Three cases with 18 scenarios are simulated to determine the appropriate ratio of shiftable loads, the degree of renewable penetration, and size of energy storage elements.

In this paper, an optimal energy management scheme for islanded building microgrid networks is proposed. The proposed building microgrid network comprises of several inter-connected building microgrids (BMGs) and an external energy supplier. Each BMG has a local combined heat and power (CHP) unit, energy storage, renewables and loads (electric and thermal). The external energy system comprises of an external CHP unit, chillers, electric heat pumps and heat pile line, for thermal energy storage. The BMGs can trade energy with other BMGs of the network and can also trade energy with the external energy supplier. In order to efficiently utilize the components of the BMGs and the network, the concept of adjustable power is adopted in this study. Adjustable power can reduce the operation cost of the network by increasing/decreasing the power of dispatchable units. In addition, the failure/recovery of components in the BMGs and the external system are also considered to analyze the performance of the proposed operation method. In order to optimally utilize the available resources during events, precedence among loads of BMGs and the external energy supplier is considered. Simulation results have proved the applicability of the proposed method for both normal islanded mode and with outage/recovery of equipment during the operation horizon. Finally, sensitivity analysis is carried out to analyze the impact of change in components&rsquo, parameters values on the saved cost of the network.

In this paper, a multi-objective optimization method is proposed to determine trade-off between conflicting operation objectives of wind farm (WF) systems, i.e., maximizing the output power and minimizing the output power fluctuation of the WF system. A detailed analysis of the effects of different objective&rsquo, s weight values and battery size on the operation of the WF system is also carried out. This helps the WF operator to decide on an optimal operation point for the whole system to increase its profit and improve output power quality. In order to find out the optimal solution, a two-stage optimization is also developed to determine the optimal output power of the entire system as well as the optimal set-points of wind turbine generators (WTGs). In stage 1, the WF operator performs multi-objective optimization to determine the optimal output power of the WF system based on the relevant information from WTGs&rsquo, and battery&rsquo, s controllers. In stage 2, the WF operator performs optimization to determine the optimal set-points of WTGs for minimizing the power deviation and fulfilling the required output power from the previous stage. The minimization of the power deviation for the set-points of WTGs helps the output power of WTGs much smoother and therefore avoids unnecessary internal power fluctuations. Finally, different case studies are also analyzed to show the effectiveness of the proposed method.

The increased penetration of renewables is beneficial for power systems but it poses several challenges, i.e., uncertainty in power supply, power quality issues, and other technical problems. Backup generators or storage system have been proposed to solve this problem but there are limitations remaining due to high installation and maintenance cost. Furthermore, peak load is also an issue in the power distribution system. Due to the adjustable characteristics of loads, strategies on demand side such as demand response (DR) are more appropriate in order to deal with these challenges. Therefore, this paper studies how DR programs influence the operation of the multi-microgrid (MMG). The implementation is executed based on a hierarchical energy management system (HiEMS) including microgrid EMSs (MG-EMSs) responsible for local optimization in each MG and community EMS (C-EMS) responsible for community optimization in the MMG. Mixed integer linear programming (MILP)-based mathematical models are built for MMG optimal operation. Five scenarios consisting of single DR programs and DR groups are tested in an MMG test system to evaluate their impact on MMG operation. Among the five scenarios, some DR programs apply curtailing strategies, resulting in a study about the influence of base load value and curtailable load percentage on the amount of curtailed load and shifted load as well as the operation cost of the MMG. Furthermore, the impact of DR programs on the amount of external and internal trading power in the MMG is also examined. In summary, each individual DR program or group could be handy in certain situations depending on the interest of the MMG such as external trading, self-sufficiency or operation cost minimization.

Monitoring the status of existing devices and identification of newly added devices is required in microgrids to adjust the operation schedule followed by any event or integration of a new device. Therefore, in this paper, automatic reconfiguration of microgrids is considered after the addition of a new device or change in the operation status of an existing device by using a multiagent system. This capability of the microgrid is named as auto-configuration function, which is performed by the auto-configurator agent. In case of addition of a new device, the auto-configurator agent is responsible for authorization and registration of the newly added device. In case of change in status of any existing device, the status information is updated. After integration of a new device or change in status of an existing device, re-optimization is carried out by the energy management system (EMS) agent. Agent communication language (ACL) is used to develop a modified contract net protocol (MCNP) for communication among different agents. EMS agent and auto-configurator agent exchange information for economic rescheduling of the microgrid components. Simulation results have shown that the proposed method can be used for optimal operation of microgrids when the configuration changes due to the addition/removal of a device.

In distributed operation, each unit is operated by its local controller instead of using a centralized controller, which allows the action to be based on local information rather than global information. Most of the distributed solutions have implemented the consensus method, however, convergence time of the consensus method is quite long, while diffusion strategy includes a stochastic gradient term and can reach convergence much faster compared with consensus method. Therefore, in this paper, a diffusion strategy-based distributed operation of microgrids (MGs) is proposed using multiagent system for both normal and emergency operation modes. In normal operation, the MG system is operated by a central controller instead of the distributed controller to minimize the operation cost. If any event (fault) occurs in the system, MG system can be divided into two parts to isolate the faulty region. In this case, the MG system is changed to emergency operation mode. The normal part is rescheduled by the central controller while the isolated part schedules its resources in a distributed manner. The isolated part carries out distributed communication using diffusion between neighboring agents for optimal operation of this part. The proposed method enables peer-to-peer communication among the agents without the necessity of a centralized controller, and simultaneously performs resource optimization. Simulation results show that the system can be operated in an economic way in both normal operation and emergency operation modes.

Integration of demand response (DR) programs and battery energy storage system (BESS) in microgrids are beneficial for both microgrid owners and consumers. The intensity of DR programs and BESS size can alter the operation of microgrids. Meanwhile, the optimal size for BESS units is linked with the uncertainties associated with renewable energy sources and load variations. Similarly, the participation of enrolled customers in DR programs is also uncertain and, among various other factors, uncertainty in market prices is a major cause. Therefore, in this paper, the impact of DR program intensity and BESS size on the operation of networked microgrids is analyzed while considering the prevailing uncertainties. The uncertainties associated with forecast load values, output of renewable generators, and market price are realized via the robust optimization method. Robust optimization has the capability to provide immunity against the worst-case scenario, provided the uncertainties lie within the specified bounds. The worst-case scenario of the prevailing uncertainties is considered for evaluating the feasibility of the proposed method. The two representative categories of DR programs, i.e., price-based and incentive-based DR programs are considered. The impact of change in DR intensity and BESS size on operation cost of the microgrid network, external power trading, internal power transfer, load profile of the network, and state-of-charge (SOC) of battery energy storage system (BESS) units is analyzed. Simulation results are analyzed to determine the integration of favorable DR program and/or BESS units for different microgrid networks with diverse objectives.

In this paper, an optimal energy management strategy for a cooperative multi-microgrid system with combined cooling, heat and power (CCHP) is proposed and has been verified for a test case of building microgrids (BMGs). Three different demand types of buildings are considered and the BMGs are assumed to be equipped with their own combined heat and power (CHP) generators. In addition, the BMGs are also connected to an external energy network (EEN), which contains a large CHP, an adsorption chiller (ADC), a thermal storage tank, and an electric heat pump (EHP). By trading the excess electricity and heat energy with the utility grid and EEN, each BMG can fulfill its energy demands. Seasonal energy demand variations have been evaluated by selecting a representative day for the two extreme seasons (summer and winter) of the year, among the real profiles of year-round data on electricity, heating, and cooling usage of all the three selected buildings. Especially, the thermal energy management aspect is emphasized where, bi-lateral heat trading between the energy supplier and the consumers, so-called energy prosumer concept, has been realized. An optimization model based on mixed integer linear programming has been developed for minimizing the daily operation cost of the EEN while fulfilling the energy demands of the BMGs. Simulation results have demonstrated the effectiveness of the proposed strategy.

Unit commitment of microgrids faces new challenges as the integration of demand response programs and renewable generation increases. In order to address these challenges, robust optimization is utilized for quantifying the impact of demand response uncertainty on the unit commitment of microgrids in this paper. Uncertainties in both shiftable (pricebased demand response) and curtailable (incentive-based demand response) loads are considered in this study. Initially, a cost-minimization deterministic model is formulated. Then, it is transformed to a min-max robust counterpart and finally, a tractable robust counterpart is formulated. The effects of uncertainty on trading power with the utility grid, operation cost, unit commitment of dispatchable generators, and state of charge of battery energy storage system are quantified. Simulations results have proved the robustness of proposed strategy.

The faults in a microgrid (MG) pose many challenges for operation of the system. They may lead to an increase in the operation cost. In this paper, in order to solve the problem, a novel algorithm based on the auto-configuration of MGs is proposed for minimizing the operation cost in grid-connected mode. A centralized energy management system (EMS) is also developed in accordance with the proposed algorithm, which is responsible for communication in the system and performs optimization. The EMS updates the system information and reschedules its resources upon occurrence of the faults or recovery from the faults for assuring economical operation of the MG system.

In this paper, a novel algorithm is proposed for optimal operation of microgrids considering the occurrence of faults or recovery from faults, auto-configuration. The objective of the proposed algorithm is to minimize the operation of the microgrid while ensuring stability. The auto-configuration is defined as the capability of the system to update the status of all components and reschedule healthy components, whenever a fault occurs or the system recovers from a fault. Additionally, an energy management system (EMS) is developed in accordance with the proposed algorithm to maintain the system frequency and operate the system in a cost-effective way.

In this paper, an optimal energy management scheme for building microgrids with rooftop greenhouse is proposed. A building energy management system (BEMS) is utilized for the optimal fulfilment of energy demands in the building and the greenhouse. The exhaust heat generated due to the operation of air conditioners in the building is used for fulfilling the cooling demands of the greenhouse via chillers. In addition to thermal and cooling demands, the four major control parameters (temperature, humidity, light intensity, and CO2 concentration) are also considered for optimal growth of crops in the greenhouse. A multi-agent system (MAS) is adopted to realize the interaction among several households of the building, the greenhouse, and the BEMS. The MAS comprises of several inner-level, intermediate level, and upper-level agents, which are responsible for their respective tasks. The performance of the proposed optimization strategy is evaluated for two seasons of a year, i.e., summer and winter. Numerical simulations have demonstrated the effectiveness of the proposed operation scheme for optimal operation of building microgrids with rooftop greenhouses.

The resiliency of power systems can be enhanced during emergency situations by using microgrids, due to their capability to supply local loads. However, precise prediction of disturbance events is very difficult rather the occurrence probability can be expressed as, high, medium, or low, etc. Therefore, a fuzzy logic-based battery energy storage system (BESS) operation controller is proposed in this study. In addition to BESS state-of-charge and market price signals, event occurrence probability is taken as crisp input for the BESS operation controller. After assessing the membership levels of all the three inputs, BESS operation controller decides the operation mode (subservient or resilient) of BESS units. In subservient mode, BESS is fully controlled by an energy management system (EMS) while in the case of resilient mode, the EMS follows the commands of the BESS operation controller for scheduling BESS units. Therefore, the proposed hybrid microgrid model can operate in normal, resilient, and emergency modes with the respective objective functions and scheduling horizons. Due to the consideration of resilient mode, load curtailment can be reduced during emergency operation periods. Numerical simulations have demonstrated the effectiveness of the proposed strategy for enhancing the resiliency of hybrid microgrids.