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Ensuring the secure and cost-effective operation of smart power microgrids has become a significant concern for managers and operators due to the escalating damage caused by natural phenomena and cyber-attacks. This paper presents a novel framework focused on the dynamic reconfiguration of multi-microgrids to enhance system's security index, including stability, reliability, and operation costs. The framework incorporates distributed generation (DG) to address cyber-attacks that can lead to line outages or generation failures within the network. Additionally, this work considers the uncertainties and accessibility factors of power networks through a modified point prediction method, which was previously overlooked. To achieve the secure and cost-effective operation of smart power multi-microgrids, an optimization framework is developed as a multi-objective problem, where the states of switches and DG serve as independent parameters, while the dependent parameters consist of the operation cost and techno-security indexes. The multi-objective problem employs deep learning (DL) techniques, specifically based on long short-term memory (LSTM) and prediction intervals, to effectively detect false data injection attacks (FDIAs) on advanced metering infrastructures (AMIs). By incorporating a modified point prediction method, LSTM-based deep learning, and consideration of technical indexes and FDIA cyber-attacks, this framework aims to advance the security and reliability of smart power multi-microgrids. The effectiveness of this method was validated on a network of 118 buses. The results of the proposed approach demonstrate remarkable improvements over PSO, MOGA, ICA, and HHO algorithms in both technical and economic indicators. [ABSTRACT FROM AUTHOR]

The rapid expansion of energy storage integration has not provided sufficient time to strengthen and expand the transmission and distribution network. This issue can lead to PCC congestion in green multi-microgrid (MMG) systems. In these systems, microgrids operate independently and connect to the grid at a point of common coupling (PCC) without sharing operational data with neighboring microgrids. To address this issue, this paper proposes a bi-level optimization model designed to reschedule hydrogen storage systems. The first level allows each microgrid to optimize its energy transactions with the grid and communicates any unbalanced energy to the second level, where a hydrogen management system (HMS) is introduced. The HMS optimizes virtual hydrogen prices to address the PCC congestion and maximize the MMG's profit. These virtual prices are then sent to the first level, allowing the microgrids to reschedule the hydrogen storage systems based on these virtual prices. Finally, the MMG's profit is fairly allocated among the microgrids using the Shapley value method. The proposed method's effectiveness is demonstrated using simulations, which show a six percent increase in MMG profit compared to scenarios that only share PCC capacity while maintaining the data privacy of all the involved microgrids. [ABSTRACT FROM AUTHOR]

With the increasing integrations of renewable energy resources into distribution networks (DNs) and microgrids (MGs), the imperative for an effective market scheduling mechanism becomes paramount to enhance the operational safety, reliability, and economic efficiency of distribution grids. Taking advantage of bi-level programming theory, this study meticulously formulates a comprehensive optimization scheduling model for the multi-MGs distribution network. The upper-level optimization objective is to minimize both the operational losses and total costs of the DN. Concurrently, the lower-level optimization pursues the maximization of daily operational revenue for MGs. Recognizing the pervasive impact of the inherent uncertainty associated with renewable energy sources on system safety and reliability, a cutting-edge scenario-based stochastic planning framework is introduced. The methodology integrates a heuristic matrix matching approach to effectively handle the intricate challenges posed by uncertainties from wind and photovoltaic generations. Moreover, in addressing the proposed nonlinear models, a sophisticated method is employed, utilizing the second-order cone relaxation and linearization methods. These methods meticulously transform the upper and lower-level models into second-order cone planning and mixed-integer linear programming issues, respectively. Finally, the proposed methodologies are rigorously scrutinized and validated with intricate case studies, providing a nuanced understanding of their efficacy. The empirical results underscore the theoretical feasibility and superiority of the proposed scheduling scheme. Notably, the operational performance of the DN as well as the economic viability of multiple MGs can also be significantly improved. [ABSTRACT FROM AUTHOR]

In the context of "dual carbon", restrictions on carbon emissions have attracted widespread attention from researchers. In order to solve the issue of the insufficient exploration of the synergistic emission reduction effects of various low-carbon policies and technologies applied to multiple microgrids, we propose a multi-microgrid electricity cooperation optimization scheduling strategy based on stepped carbon trading, a hydrogen-doped natural gas system and P2G–CCS coupled operation. Firstly, a multi-energy microgrid model is developed, coupled with hydrogen-doped natural gas system and P2G–CCS, and then carbon trading and a carbon emission restriction mechanism are introduced. Based on this, a model for multi-microgrid electricity cooperation is established. Secondly, design optimization strategies for solving the model are divided into the day-ahead stage and the intraday stage. In the day-ahead stage, an improved alternating direction multiplier method is used to distribute the model to minimize the cooperative costs of multiple microgrids. In the intraday stage, based on the day-ahead scheduling results, an intraday scheduling model is established and a rolling optimization strategy to adjust the output of microgrid equipment and energy purchases is adopted, which reduces the impact of uncertainties in new energy output and load forecasting and improves the economic and low-carbon operation of multiple microgrids. Setting up different scenarios for experimental validation demonstrates the effectiveness of the introduced low-carbon policies and technologies as well as the effectiveness of their synergistic interaction. [ABSTRACT FROM AUTHOR]

An increase in the deployment of Distributed Energy Resources (DERs) and Renewable Energy (RE) resources is a promising paradigm in the decentralized energy era. It has motivated multi-Microgrids (MGs) to trade energy directly with others in the Local Energy Market (LEM), as well as with the main grid. The LEM has become a popular platform that covers several shortcomings of surplus/deficient energy, which can also manage the increasing connection of multi-microgrids, meet internal balance, and maximize the social welfare of the community Microgrid (MG). Moreover, in the LEM, the MGs would like to provide some payoff to encourage each other to exchange their energy locally. However, designing an appropriate market framework, privacy protection, and the community’s unbalanced energy supply and demand is challenging. To cope with these challenges, in this study, an LEM for a multi-microgrid system is designed to maximize the social welfare of the community, and a decentralized clearing algorithm based on the Alternating Direction Method of Multipliers (ADMM) is proposed for local market clearing and privacy protection. The Community Manager (CM) is used as an intermediate coordinator between the interconnected MGs. This way, the computation process will be completely distributed, and the privacy of each MG will be protected. Moreover, considering the utility function for the consumers and energy providers, an equivalent cost model based on internal pricing is proposed to state the willingness of the utility and motivate the participants to join LEM. Finally, an illustrative example and a case study are used to demonstrate the efficiency and effectiveness of the proposed design of LEM and algorithm in terms of social welfare and power balance. In our study, we found that by using dynamic pricing in conjunction with our proposed model, the social welfare of the energy community can be increased by 14.25%. This demonstrates the significant economic benefits and effectiveness of our approach in the Local Energy Market (LEM).

Ensuring the secure and cost-effective operation of smart power microgrids has become a significant concern for managers and operators due to the escalating damage caused by natural phenomena and cyber-attacks. This paper presents a novel framework focused on the dynamic reconfiguration of multi-microgrids to enhance system’s security index, including stability, reliability, and operation costs. The framework incorporates distributed generation (DG) to address cyber-attacks that can lead to line outages or generation failures within the network. Additionally, this work considers the uncertainties and accessibility factors of power networks through a modified point prediction method, which was previously overlooked. To achieve the secure and cost-effective operation of smart power multi-microgrids, an optimization framework is developed as a multi-objective problem, where the states of switches and DG serve as independent parameters, while the dependent parameters consist of the operation cost and techno-security indexes. The multi-objective problem employs deep learning (DL) techniques, specifically based on long short-term memory (LSTM) and prediction intervals, to effectively detect false data injection attacks (FDIAs) on advanced metering infrastructures (AMIs). By incorporating a modified point prediction method, LSTM-based deep learning, and consideration of technical indexes and FDIA cyber-attacks, this framework aims to advance the security and reliability of smart power multi-microgrids. The effectiveness of this method was validated on a network of 118 buses. The results of the proposed approach demonstrate remarkable improvements over PSO, MOGA, ICA, and HHO algorithms in both technical and economic indicators.

With the increasing integrations of renewable energy resources into distribution networks (DNs) and microgrids (MGs), the imperative for an effective market scheduling mechanism becomes paramount to enhance the operational safety, reliability, and economic efficiency of distribution grids. Taking advantage of bi-level programming theory, this study meticulously formulates a comprehensive optimization scheduling model for the multi-MGs distribution network. The upper-level optimization objective is to minimize both the operational losses and total costs of the DN. Concurrently, the lower-level optimization pursues the maximization of daily operational revenue for MGs. Recognizing the pervasive impact of the inherent uncertainty associated with renewable energy sources on system safety and reliability, a cutting-edge scenario-based stochastic planning framework is introduced. The methodology integrates a heuristic matrix matching approach to effectively handle the intricate challenges posed by uncertainties from wind and photovoltaic generations. Moreover, in addressing the proposed nonlinear models, a sophisticated method is employed, utilizing the second-order cone relaxation and linearization methods. These methods meticulously transform the upper and lower-level models into second-order cone planning and mixed-integer linear programming issues, respectively. Finally, the proposed methodologies are rigorously scrutinized and validated with intricate case studies, providing a nuanced understanding of their efficacy. The empirical results underscore the theoretical feasibility and superiority of the proposed scheduling scheme. Notably, the operational performance of the DN as well as the economic viability of multiple MGs can also be significantly improved.

The rapid expansion of energy storage integration has not provided sufficient time to strengthen and expand the transmission and distribution network. This issue can lead to PCC congestion in green multi-microgrid (MMG) systems. In these systems, microgrids operate independently and connect to the grid at a point of common coupling (PCC) without sharing operational data with neighboring microgrids. To address this issue, this paper proposes a bi-level optimization model designed to reschedule hydrogen storage systems. The first level allows each microgrid to optimize its energy transactions with the grid and communicates any unbalanced energy to the second level, where a hydrogen management system (HMS) is introduced. The HMS optimizes virtual hydrogen prices to address the PCC congestion and maximize the MMG’s profit. These virtual prices are then sent to the first level, allowing the microgrids to reschedule the hydrogen storage systems based on these virtual prices. Finally, the MMG’s profit is fairly allocated among the microgrids using the Shapley value method. The proposed method’s effectiveness is demonstrated using simulations, which show a six percent increase in MMG profit compared to scenarios that only share PCC capacity while maintaining the data privacy of all the involved microgrids.

The development of renewable-energy-based microgrids is being considered as a potential solution to lessen the unrelenting burden on the centralized utility grid. Furthermore, recent studies reveal that integrated multi-microgrid cluster systems developed in urban communities maximize the effectiveness of microgrids and greatly decrease the utility grid dependence. However, due to the uncertain nature of renewable energy sources and frequent load variations, these systems face issues with unintentional islanding operations. This can create severe damage to the microgrid's performance in its stable operating condition and lead to undesired transient responses. Hence, islanding must be identified rapidly to take preventive measures to address the issue. This requires the development of a suitable anti-islanding technique that is faster in terms of accuracy and timely detection. With this intention, this paper proposes a decision-tree-based fuzzy logic (DT-FL) controller for the rapid identification of islands in an urban community multi-microgrid cluster. The DT-FL controller's operation includes two steps. First, the decision tree extracts the electrical parameters at the point of common coupling of the multi-microgrid system. Second, these extracted parameters are utilized for the online tuning of the fuzzy logic controller, for the fast detection of islanding. The multi-microgrid cluster under study, along with the proposed islanding technique, is implemented in the MATLAB-2021a software. The efficacy of the proposed DT-FL controller is validated by comparing its performance with that of the conventional fuzzy logic controller under different test scenarios. From the results, it is observed that the proposed DT-FL controller shows superior performance in terms of the islanding detection time as well as the transient response of the system when compared with the conventional controller. [ABSTRACT FROM AUTHOR]

In the context of “dual carbon”, restrictions on carbon emissions have attracted widespread attention from researchers. In order to solve the issue of the insufficient exploration of the synergistic emission reduction effects of various low-carbon policies and technologies applied to multiple microgrids, we propose a multi-microgrid electricity cooperation optimization scheduling strategy based on stepped carbon trading, a hydrogen-doped natural gas system and P2G–CCS coupled operation. Firstly, a multi-energy microgrid model is developed, coupled with hydrogen-doped natural gas system and P2G–CCS, and then carbon trading and a carbon emission restriction mechanism are introduced. Based on this, a model for multi-microgrid electricity cooperation is established. Secondly, design optimization strategies for solving the model are divided into the day-ahead stage and the intraday stage. In the day-ahead stage, an improved alternating direction multiplier method is used to distribute the model to minimize the cooperative costs of multiple microgrids. In the intraday stage, based on the day-ahead scheduling results, an intraday scheduling model is established and a rolling optimization strategy to adjust the output of microgrid equipment and energy purchases is adopted, which reduces the impact of uncertainties in new energy output and load forecasting and improves the economic and low-carbon operation of multiple microgrids. Setting up different scenarios for experimental validation demonstrates the effectiveness of the introduced low-carbon policies and technologies as well as the effectiveness of their synergistic interaction.

Nowadays, renewable energy generation has gained a lot of attention in the global electrical energy production. This has led to an increased integration of distributed generators into the traditional electrical grid, standalone generations as well as the formation of microgrids. The continuous increase in microgrid penetration has resulted in the microgrids becoming neighbours to one another, thus, favouring the interconnection of these microgrids to form the multi-microgrids. The multi-microgrids infrastructure has the potential to improve energy availability, reliability and reduction in the net cost of energy through sharing of resources. The notion of unidirectional power flow that normally applies to the traditional electrical grid is no longer applicable in this context. This paper investigates the advantages of several microgrids’ interconnection on the system reliability within the town of Goma in the Democratic Republic of the Congo (DRC) using the Homer Grid software for optimal sizing of components considering technical and economic aspects. From the potential assessment of this case study, it is realized that the methane gas present in Kivu Lake and solar PV are potential distributed sources that can be used. In addition, it was observed that when connected to the Virunga private grid, the system with PV can be sold to the grid with a payback period of about seven years. The system made of Generator, PV and battery was found to be the most reliable. This led to the conclusion that, the interconnection of several microgrids produces a more stable and reliable system, especially when different sources of energy are used.

The concept of interconnected multi-microgrids (MMGs) is presented as a promising solution for the improvement in the operation, control, and economic performance of the distribution networks. The energy management of the MMGs is a strenuous and challenging task, especially with the integration of renewable energy resources (RERs) and variation in the loading due to the intermittency of these resources and the stochastic nature of the load demand. In this regard, the energy management of the MMGs is optimized with optimal inclusion of a hybrid system consisting of a photovoltaic (PV) and a wind turbine (WT)-based distributed generation (DGs) under uncertainties of the generated powers and the load variation. A modified Capuchin Search Algorithm (MCapSA) is presented and applied for the energy management of the MMGs. The MCapSA is based on enhancing the searching abilities of the standard Capuchin Search Algorithm (CapSA) using three improvement strategies including the quasi-oppositional-based learning (QOBL), the random movement-based Levy flight distribution, and the exploitation mechanism of the prairie dogs in the prairie dog optimization (PDO). The optimized function is a multi-objective function that comprises of the cost and the voltage deviation reduction along with stability enhancement. The effectiveness of the proposed technique is verified on standard benchmark functions and the obtained results. Then, the proposed method is used for energy management of IEEE 33-bus and 69-bus MMGs at uncertainties conation. The results depict that the energy management with inclusion of WTs and PVs using the proposed technique can reduce the cost and summation of the VD by 46.41% and 62.54%, and the VSI is enhanced by 15.1406% for the first MMG. Likewise, for the second MMG, the cost and summation of the VD are reduced by 44.19% and 39.70%, and the VSI is enhanced by 4.49%. [ABSTRACT FROM AUTHOR]

This study proposes a Load Frequency Control (LFC) approach for Multi-Microgrids (MMGs) using the World Cup Optimization (WCO) algorithm. The approach aims to improve the frequency regulation of MMGs while maintaining the power balance between generation and load. The LFC system consists of a Proportional-Integral (PI) controller and a Proportional Integral Derivative (PID) controller, which are tuned using the WCO algorithm to optimize the controller parameters. The effectiveness of the proposed approach was evaluated using a test system comprising of two interconnected MMGs. The simulation results demonstrate that the proposed LFC approach using the WCO outperforms other optimization algorithms and effectively maintains the frequency within acceptable limits during various load variations and disturbances. Additionally, the proposed approach ensures power balance among the MMGs, leading to a stable operation of the entire power system. [ABSTRACT FROM AUTHOR]

In this paper, a two-stage model of an integrated energy demand response is proposed, and the quantitative relationship between the two main concerns of investors, i.e., investment return and investment cycle and demand response, is verified by the experimental data. Energy storage technology is a key means through which to deal with the instability of modern energy sources. One of the key development paths in the electricity market is the development by energy merchants of energy storage power plants in the distribution network to engage in a grid demand response. This research proposes a two-stage energy storage configuration approach for a cold-heat-power multi-energy complementary multi-microgrid system. Considering the future bulk connections of distributed power generation, the two most critical points of energy storage station construction are the power generation equipment and specific scenarios for serving the community, as well as the purchase and sale price of electricity for serving the community microgrid (which directly affects the investment revenue). Therefore, this paper focuses on analyzing the different impacts caused by these two issues; namely, the two most important concerns for the construction of energy storage configurations. First, the basic model enabling wholesale electricity traders to construct energy storage power plants is presented. Second, for a multi-microgrid system with a complementary cold-heat-power multi-energy scenario, a two-stage optimum allocation model is constructed, whereby the upper model calculates the energy storage allocation problem and the lower model calculates the optimal dispatch problem. The lower model's dispatch computation validates the upper configuration model's reasonableness. Finally, the two-layer model is converted to a single-layer model by the KKT condition, and the nonlinear problem is converted to a linear problem with the big-M method. The validity is proved via mathematical examples, and it is demonstrated that the planned energy storage plants by merchants may accomplish resource savings and mutual advantages for both users and wholesale power traders. [ABSTRACT FROM AUTHOR]

Abstract This article proposes a novel comprehensive multi‐layer power management system (PMS) along with its smart distribution network (SDN) constraints as bi‐level optimization to address the participation of multi‐microgrids (MMGs) in day‐ahead energy and ancillary services markets. In the first layer of the proposed model, optimal programming of MMG‐connected SDN is considered, in which Microgrids (MGs) participation in the markets is performed to bidirectionally coordinate sources and active loads along with the operator of MGs. In the second layer, the bidirectional coordination of operators of MGs and SDN, that is PMS, is executed in which energy loss, voltage security, and expected energy not‐supplied (EENS) are minimized as weighted sum functions. The problem of the difference between costs and revenues of MGs in markets is minimized subject to constraints of linearized AC‐power flow, reliability, security, and flexibility of the MGs. To obtain a single‐level model, the Karush–Kuhn–Tucker method is applied, and a hybrid stochastic‐robust programming is implemented to model uncertainties associated with the load, renewable power, energy price, mobile storage energy demand, and network equipment accessibility. The contributions of this paper include the simultaneous modelling of several economic indicators, multi‐layer energy management modelling, and stochastic mixed modelling of uncertainties. The efficiency of this method is validated by simultaneously evaluating the optimum condition of technical and economic indices of several SDNs and MGs. Flexibility of 0.022 MW is obtained for the proposed scheme, which is close to zero (100% flexibility). The voltage security index is increased to 22 by the mentioned scheme, which is close to its normal value, that is, 24. The voltage deviation is below 0.07 p.u. Energy losses are reduced by about 30% compared with that in power flow studies, and the EENS reaches roughly 3 MWh, that is, close to zero (100% reliability).

An energy management system (EMS) is proposed in this paper to minimize the amount of load shedding in islanded multi‐microgrids (MMGs) systems in a cost‐effective manner. The suggested EMS adopts a two‐stage hierarchical optimization model. The first stage is called microgrid (MG) optimization, in which the load shedding in each MG is minimized. The day‐ahead schedule of the diesel generator, the charging/discharging power of the battery energy storage system (BESS), the reshaped load profile after applying the demand response programs (DRP), and the shortage/surplus power in each MG are determined in the first stage of optimization. The second stage is MMG optimization, in which the amount of energy shortage in the MMG system is further minimized by energy exchange between individual MGs. The remaining shortage can be reduced with the aid of the community MG resources. The proposed optimization model is formulated as a nonlinear complex optimization problem that can be handled more efficiently by the meta‐heuristic techniques compared with the conventional analytic methods. The political optimizer is used in this paper. To prove the effectiveness of this optimizer in handling the proposed model, we also solved the optimization problem using the particle swarm optimization algorithm, and results are compared. [ABSTRACT FROM AUTHOR]

This article proposes a novel comprehensive multi‐layer power management system (PMS) along with its smart distribution network (SDN) constraints as bi‐level optimization to address the participation of multi‐microgrids (MMGs) in day‐ahead energy and ancillary services markets. In the first layer of the proposed model, optimal programming of MMG‐connected SDN is considered, in which Microgrids (MGs) participation in the markets is performed to bidirectionally coordinate sources and active loads along with the operator of MGs. In the second layer, the bidirectional coordination of operators of MGs and SDN, that is PMS, is executed in which energy loss, voltage security, and expected energy not‐supplied (EENS) are minimized as weighted sum functions. The problem of the difference between costs and revenues of MGs in markets is minimized subject to constraints of linearized AC‐power flow, reliability, security, and flexibility of the MGs. To obtain a single‐level model, the Karush–Kuhn–Tucker method is applied, and a hybrid stochastic‐robust programming is implemented to model uncertainties associated with the load, renewable power, energy price, mobile storage energy demand, and network equipment accessibility. The contributions of this paper include the simultaneous modelling of several economic indicators, multi‐layer energy management modelling, and stochastic mixed modelling of uncertainties. The efficiency of this method is validated by simultaneously evaluating the optimum condition of technical and economic indices of several SDNs and MGs. Flexibility of 0.022 MW is obtained for the proposed scheme, which is close to zero (100% flexibility). The voltage security index is increased to 22 by the mentioned scheme, which is close to its normal value, that is, 24. The voltage deviation is below 0.07 p.u. Energy losses are reduced by about 30% compared with that in power flow studies, and the EENS reaches roughly 3 MWh, that is, close to zero (100% reliability). [ABSTRACT FROM AUTHOR]

• Integration of smart buildings in multi-microgrids is investigated. • Multi-microgrids may transact through distribution system. • Smart buildings include electric water heater and air conditioner. • The results confirm that the proposed model is efficient for 100 % renewable systems. • The possibility of injection to microgrids decreases cost of buildings by 38 %. • Batteries considerably decrease cost of buildings, microgrids and distribution system. Using smart buildings would be a crucial step towards sustainability. Although in the literature, some research effort has focused on integration of smart buildings into power systems; the integration of fully-renewable smart buildings into fully-renewable microgrids and distribution systems has not been addressed before. In this paper, a tri-level hierarchical stochastic framework is developed for the integration of fully renewable smart buildings in fully renewable multi-microgrids (MGs) and distribution systems. The studied smart buildings have electric water heater, air conditioner, PV and battery. In the first level, the model of each smart building is separately solved to find optimal schedule of its resources and the desired electricity exchange with its corresponding MG. In the second level, each MG solves its operational planning model, considering its transactions with the connected smart buildings and finally, in the third level, distribution system operator solves its operational planning model, accepting the electricity exchange requests taken from all MGs. The proposed methodology is applied to a 69-bus 100 % renewable distribution system with 4 100 % renewable microgrids, integrated with 100 % renewable smart buildings. The results confirm the efficacy of the proposed methodology for the integration of 100 % renewable smart buildings in 100 % renewable multi-MGs. The results testify that that building-to-grid (B2G) capability decreases the operation cost of all smart buildings; the average decrease in operation cost of buildings, caused by B2G, is 38 %. The results on the studied case indicate that that the impact of batteries on operation cost of smart buildings is marginal. The results also show that the higher threshold of comfort index in smart buildings, results in their higher operation cost. The results of this research may be helpful for both smart building operators and power system operators. This research is in line with the global energy transition and decarbonisation targets as the considered smart buildings, microgrids and distribution system are fully renewable. [ABSTRACT FROM AUTHOR]

Modern power systems are progressively adopting power-to-gas-based energy storage systems as a standard approach to satisfy their energy requirements. These shared energy storage systems are poised to become a crucial component for future power networks. This paper proposes a novel multi-layer control for microgrid cluster connected to the shared hydrogen energy system. The proposed method involves tertiary, secondary, and primary control layers. A bi-layer optimization configuration method is proposed for tertiary and secondary energy management. The tertiary layer optimizes hydrogen trading among the microgrids and the grid, while the secondary layer ensures cost-effective and low-carbon operation for each microgrid. At the primary level, a modified super-twisting sliding mode controller based on fast-reaching law is used for real-time stability and efficient tracking control. To reduce computational efforts at the primary layer, an intelligent event-triggered framework is introduced. To validate the proposed model, a sophisticated simulation framework has been developed. Hardware-in-the-loop tests have also been conducted to confirm its real-world feasibility. The proposed framework demonstrates promising carbon-aware performance and real-time stability, with an average carbon intensity of 0.0403 kgCO 2 / kWh and a bus voltage tracking accuracy of 99.984 %. • A tri-layer control structure with event-triggering action is introduced. • Electric-hydrogen system is utilized as shared energy storage system. • Bi-layer optimization is performed to minimize cost and carbon intensity. • Real-time stability is achieved using a NN assisted ET-FSTSMC. • Hardware-in-loop experiments are conducted for real-world feasibility. [ABSTRACT FROM AUTHOR]

This article presents a novel energy management approach termed Dynamic Batching EMS (DBEMS) designed specifically for day-ahead scheduling within multi-microgrid (MMG) networks. The prime aim of this strategy is to curtail operational costs within the MMG network, fortify the privacy of individual microgrids, and optimize the utilization of available resources, surpassing existing methodologies like centralized, decentralized, and nested management strategies. The emphasis is chiefly on orchestrating cost-effective operations while bolstering data privacy for each microgrid within the complex MMG framework. The efficacy of the proposed strategy is evaluated across both grid-connected and islanded modes, focusing on a network comprising four microgrids. The model formulation is executed utilizing the Mixed Integer Linear Programming method. In the islanded mode, optimization is conducted for scenarios both inclusive and exclusive of Battery Energy Storage Systems (BESS). Comparative assessments are made against centralized and nested strategies, considering parameters including operational costs, privacy enhancements, load-shedding, computation time, and computational burden. [ABSTRACT FROM AUTHOR]

Microgrids are being evolved as a potential alternative to reduce unrelenting dependency on central utility grids. Moreover, integrated multi-microgrid based clusters are forming in closed vicinities to enhance the benefits of microgrids. However, the power quality problem is one of the key issues to be solved in such systems, which is mainly caused by the rising penetration of nonlinear loads and interfacing of power electronic converters. To address this issue, this paper proposes a new control technique, named “adaptive neuro-fuzzy control strategy”. This controls the inverter of each microgrid in the cluster as well as the voltage source converter-based distribution static compensator located at the point of common coupling between the cluster and the utility grid. This proposed control strategy uses the advantages of both fuzzy logic and artificial neural networks, thereby effectively controlling the system. The proposed technique is modelled in MATLAB/Simulink software 2021a. For the analysis, various power quality indices such as voltage sag/swell, voltage unbalance, frequency deviations, power characteristics, total harmonic distortion, and neutral current compensation are measured. These indices of the proposed controller are compared with conventional PI and fuzzy logic-based controllers in view of various key IEEE/IEC standard tolerances. From these results, the proposed controller has proved its superiority.

This Article Develops a Novel Multi-Microgrids (MMGs) Participation Framework in the Day-Ahead Energy and Ancillary Services, i.e. Services of Reactive Power and Reserve Regulation, Markets Incorporating the Smart Distribution Network (SDN) Objectives Based on Two-Layer Power Management System (PMS). A Bi-Level Optimization Structure Is Introduced Wherein the Upper Level Models Optimal Scheduling of SDN in the Presence of MMGs While Considering the Bilateral Coordination Between Microgrids (MGs) and SDN’s Operators, i.e. Second Layer’s PMS. This Layer Is Responsible for Minimizing Energy Loss, Expected Energy Not-Supplied, and Voltage Security as the Sum of Weighted Functions. In Addition, the Proposed Problem Is Subject to Linearized AC Optimal Power Flow (LAC-OPF), Reliability and Security Constraints to Make It More Practical. Lower Level Addresses Participation of MGs in the Competitive Market Based on Bilateral Coordination Among Sources, Active Loads and MGs’ Operator (First Layer’s PMS). The Problem Formulation Then Tries to Minimize the Difference Between MGs’ Cost and Revenue in Markets While Satisfying Constraints of LAC-OPF Equations, Reliability, Security, and Flexibility of the MGs. Karush–Kuhn–Tucker Method Is Exploited to Achieve a Single-Level Model. Moreover, a Stochastic Programming Model Is Introduced to Handle the Uncertainties of Load, Renewable Power, Energy Price, the Energy Demand of Mobile Storage, and Availability of Network Equipment. The Simulation Results Confirm the Capabilities of the Suggested Stochastic Two-Layer Scheme in Simultaneous Evaluation of the Optimal Status of Different Technical and Economic Indices of the SDN and MGs.

The increased addition of DERs, their intermittent nature, and the high cost of ESSs are the key hurdles for interconnected multi-microgrids to operate economically. It is important to have a system that is simple to adopt, computationally inexpensive, does not share private data. In this paper, an energy management scheme is proposed for multiple interconnected renewable energy resources within the multi-microgrid paradigm. The core objectives aimed at minimizing the operational cost of each microgrid and utilize RER most economically without any load interruptions. The proposed scheme is performed in two steps. In the first step, the load is shifted in accordance with the daily price curve via resource scheduling i.e., charging and discharging of battery storage, day-ahead forecast of RER generation, and respective loads. In the second stage, the cost of energy is further reduced using opportunistic trading with neighboring microgrids in real-time. The mismatch between forecasted generation and load is also adjusted via either energy trading or with efficient scheduling of available power that is stored during off-peak hours. The effectiveness of the proposed scheme is applied to a test system consisting of four interconnected microgrids. Results indicate that daily average power imported from the utility grid and peak demand is changed from 103 kW to 162 kW, and from 661 kW to 527 kW, respectively. Likewise, as per comparison, significantly favorable results have been achieved in terms of efficient available resources utilization, market trading, and reduction of the respective bills.

In this study, an application for economic load dispatch of multi-microgrid systems has been solved by meta-heuristic methods such as particle swarm optimization and genetic algorithm. The solution to the economic dispatch problem should both provide the optimum cost schedule and satisfy the power system constraints. Multi-microgrid system in this study consists of four fuel-based power generation units and two microgrids with photovoltaic panels as renewable energy sources. Simulations were carried out in two case studies, with and without microgrids. While both proposed methods gave better results than the literature study, the best solution was presented by particle swarm optimization with $106583.7/day and $108395.3/day for the systems with and without microgrids, respectively. The simulation results show that both algorithms achieve optimum and reliable results. Multi-microgrids with renewable energy resources increase system reliability and power quality and decrease emissions, transmission losses, and operating costs. [ABSTRACT FROM AUTHOR]

Hydrogen is regarded as a promising fuel in the transition to clean energy. Nevertheless, as the demand for hydrogen increases, some microgrids equipped with P2H (MGH) will encounter the issue of primary energy deficiency. Meanwhile, some microgrids (MGs) face the difficulty of being unable to consume surplus energy locally. Hence, we interconnect MGs with different energy characteristics and then establish a collaborative scheduling model of multi-microgrids (MMGs). In this model, a federated demand response (FDR) program considering predictive mean voting is designed to coordinate controllable loads of electricity, heat, and hydrogen in different MGs. With the coordination of FDR, the users’ satisfaction and comfort in each MG are kept within an acceptable range. To further adapt to an actual working condition of the microturbine (MT) in MGH, a power interaction method is proposed to maintain the operating power of the MT at the optimum load level and shave peak and shorten the operating periods of MT. In the solution process, the sequence operation theory is utilized to deal with the probability density of renewable energy. A series of case studies on a test system of MMG demonstrate the effectiveness of the proposed method.

In this paper, a two-stage model of an integrated energy demand response is proposed, and the quantitative relationship between the two main concerns of investors, i.e., investment return and investment cycle and demand response, is verified by the experimental data. Energy storage technology is a key means through which to deal with the instability of modern energy sources. One of the key development paths in the electricity market is the development by energy merchants of energy storage power plants in the distribution network to engage in a grid demand response. This research proposes a two-stage energy storage configuration approach for a cold-heat-power multi-energy complementary multi-microgrid system. Considering the future bulk connections of distributed power generation, the two most critical points of energy storage station construction are the power generation equipment and specific scenarios for serving the community, as well as the purchase and sale price of electricity for serving the community microgrid (which directly affects the investment revenue). Therefore, this paper focuses on analyzing the different impacts caused by these two issues; namely, the two most important concerns for the construction of energy storage configurations. First, the basic model enabling wholesale electricity traders to construct energy storage power plants is presented. Second, for a multi-microgrid system with a complementary cold-heat-power multi-energy scenario, a two-stage optimum allocation model is constructed, whereby the upper model calculates the energy storage allocation problem and the lower model calculates the optimal dispatch problem. The lower model’s dispatch computation validates the upper configuration model’s reasonableness. Finally, the two-layer model is converted to a single-layer model by the KKT condition, and the nonlinear problem is converted to a linear problem with the big-M method. The validity is proved via mathematical examples, and it is demonstrated that the planned energy storage plants by merchants may accomplish resource savings and mutual advantages for both users and wholesale power traders.

The interconnection of multiple microgrids can effectively improve the operating efficiency of the system and reduce the cost of power generation. First, establish an economic environment optimal dispatch model based on the regional interconnected multi‐microgrids system, and formulate a dispatch strategy based on the time‐of‐use electricity price and the supply‐demand relationship between microgrids; Secondly, in order to solve this constrained optimization problem, a whale algorithm improved with five strategies including adaptive inertial weights, dynamic spiral search and generalized opposition‐based learning is proposed to obtain higher solution accuracy and anti‐premature convergence ability; Finally, combined 12 test functions to test the effectiveness of the improved algorithm, and use the improved algorithm to solve the example model containing two microgrids, and analyze it in two states of interconnection operation and non‐interconnection operation. The simulation results show that the regional interconnection operation of the microgrid under the established dispatching strategy can effectively reduce the overall operating cost of the system, and at the same time show the effectiveness of the improved algorithm to solve the optimal dispatching problem. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

With the urgent demand for energy revolution and consumption under China’s “30–60” dual carbon target, a configuration-scheduling dual-layer optimization model considering energy storage and demand response for the multi-microgrid–integrated energy system is proposed to improve new energy consumption and reduce carbon emissions. First, a demand response model of different users and loads in the integrated energy system is established. Second, the upper energy storage configuration model is constructed by introducing shared energy storage in the multi-microgrid–integrated energy system to improve the system’s flexibility, with the optimization goal of the maximum annual profitability of shared energy storage. A carbon trading mechanism considering the dynamic reward coefficient is designed. A low-carbon economic dispatch model of a multi-microgrid–integrated energy system is constructed based on the upper energy storage capacity, charge and discharge power, and user-side demand response with the lowest annual operating cost as the optimization goal. Finally, the effectiveness of the proposed model is verified by case studies in various scenarios. The results illustrate that the proposed model can fully use demand-side controllable resources to improve system energy utilization, effectively reduce carbon emissions, and further improve the operation economy of the multi-microgrid–integrated energy system.

As the proportion of behind-the-meter (BTM) renewable energy resources (RERs) in the distribution network continues to rise, the precise role of RERs in the flexibility market, whether as "Recipients" or "Providers", is becoming less distinct. This observation has led us to explore the integration of flexible load resources and RERs into flexibility markets. Such integration facilitates resource sharing among multiple microgrids (MGs). This paper focuses on the potential emergence of a secondary flexibility (SecFlex) market in the power sector among high proportional BTM-RERs microgrids. Within the concept of SecFlex market, this paper outlines a comprehensive process supporting secondary flexibility trading, which includes "SecFlex market organization–trading volume collation–response schedule optimization". At first, the market mechanism of the SecFlex market is proposed, where players submit an empirical load curve to the market organizer. During the clearing stage, a social welfare equilibrium problem is solved. Subsequently, the equilibrium trading volume is adjusted in the collation stage to account for the distribution network (DN) voltage magnitude balance. Finally, a response schedule strategy is designed to analyze the behaviors of the players. The cvxpy framework is used to solve social welfare and competitive equilibrium in this paper. The results show significant cost reductions for two players, by 77.8% and 24.0% respectively. However, costs for Player 3 have increased substantially, up to 4.6 times the baseline when flexibility is limited. These findings provide theoretical guidance for the development of the SecFlex market. • A secondary flexibility market mechanism with high share of BTM RERS. • A comprehensive process including market, technical, and control aspects. • A trading volume collation method that supports the secondary flexibility market. • Discussion and control analysis of different behavior patterns of market players. [ABSTRACT FROM AUTHOR]

• Techno-economic energy scheduling of distribution system including multi-microgrids. • Multi-objective model of minimizing power loss and maximizing energy trading profit. • Reconfiguration by tie lines and bus selector switches of each microgrid. • Reserve scheduling and storage to cover the generation and load uncertainty. The Microgrid (MG) as an alternative energy system, would reduce power loss, enhance energy consumption efficiency, and improve the system reliability for a sustainable city. Energy scheduling of distribution systems (DSs) including multi-microgrids (MMGs), would result in a multi-objective problem from DS operators' point of view for a techno-economic operation. In this regard, a multi-objective model for operation scheduling of DS and connected MGs, is proposed in this paper, which also considers the system management solutions such as reconfiguration and determination of the energy trading points for MGs connection. The proposed model is applied to a modified IEEE 33-bus distribution system including 4 MGs. Simulation results and analysis based on the fuzzy satisfying method indicate that the simultaneous consideration of distribution system reconfiguration and Bus Selector Switches (BSSs) for MGs can improve the social welfare function by 4.58 % and reduces the energy losses by 15.82 %. [ABSTRACT FROM AUTHOR]

This document is a corrigendum for an article titled "Enhanced bi-level optimal scheduling strategy for distribution network with multi-microgrids considering source-load uncertainties." The correction addresses an error in the authors' affiliation, stating that instead of "State Grid Shandong Electric Power Company, Jinan, Shandong, China," it should be "Zaozhuang Power Supply Company, State Grid Shandong Electric Power Company, Zaozhuang, Shandong, China." The authors apologize for the error and clarify that it does not affect the scientific conclusions of the article. The corrigendum also includes a note from the publisher stating that the claims expressed in the article are solely those of the authors and do not necessarily represent the views of their affiliated organizations or the publisher. [Extracted from the article]

A microgrid is considered to be a smart power system that can integrate local renewable energy effectively. However, the intermittent nature of renewable energy causes operating pressure and additional expense in maintaining the stable operation by the energy management system in a microgrid. The structure of multi-microgrids provides the possibility to construct flexible and various energy trading framework. In this paper, in order to reduce the adverse effects of uncertain renewable energy output, a distributed robust model predictive control (DRMPC)-based energy management strategy is proposed for islanded multi-microgrids. This strategy balances the robustness and economy of single-microgrid system operation by combining the advantages of robust optimization and model predictive control, while coping with the uncertainty of renewable energy sources. Furthermore, a dynamic energy trading market is formed among microgrids, which can enhance the overall economy of the multi-microgrids system. Simulation results verify the feasibility of the proposed DRMPC strategy. [ABSTRACT FROM AUTHOR]

In order to face the challenges of high proportion of wind and photovoltaic in regional multi-microgrids, a coordinated interval optimization scheduling method considering uncertainties of renewable energy is proposed. The interval optimization is used to describe the uncertainties of wind power and photovoltaic through interval numbers, then an interval optimization model for a regional multi-microgrid coordinated operation system including the uncertain objective function and constraints is established, based on the interval order relationship and possibility, the uncertain objective function and constraints can be transformed into deterministic ones. The different micro-source outputs and operating costs are compared under the independent and coordinated operation mode. It is verified that the optimization of the coordinated operation interval of the multi-microgrid can effectively reduce the operating cost of the system and stabilize the load fluctuation. The interval optimization method describes the wind power and photovoltaic as fluctuation intervals, and the corresponding operating cost intervals are obtained with different uncertainties, the scheduling results can more truly reflect the impact of uncertainty on the system operation results. At the same time, this method can provide system dispatchers with information about the influence of wind power and photovoltaic uncertainty on the system optimization results, which is convenient for dispatchers to make decisions.

Interaction between microgrid (MG) and active distribution network (ADN) can effectively improve the operating economics and reliability of power system. A bi-level interactive optimization model for ADN with MGs is studied in this paper. The upper level is a multi-objective problem that minimizes both the operation cost and voltage deviation of each node. The lower level aims to minimize the operation cost of MG. The interaction between adjacent MGs ensures a relatively lower electricity price. To achieve a low-carbon system, carbon trading cost is also considered at both levels. And the model is decoupled into several subproblems and solved using a distributed method. Therefore, the privacy of each entity is ensured. The results prove the effectiveness of the proposed method.

In the case of islanding from the distribution network, problems such as source-load power loss under multi-zone island isolation will challenge the autonomous operation of multi-microgrids with three-phase/single-phase hybrid structure. In order to make full use of the resources in the multi-microgrids to realize the reliable and efficient operation of the system as a whole, this paper proposes a multi-time scale regional autonomous control strategy for multi-microgrids with three-phase/single-phase hybrid structure. The dynamic update islanding combination is controlled by the hourly dynamic island combination and the simultaneous search switching to achieve the overall resource allocation optimization. The source-storage-load power and state redistribution are dynamically realized through minute-level centralized coordination and optimization, so that the constraints of the three-phase unbalance are met and the source-load shedding are minimized. The power fluctuations of the sources and loads within each sub-microgrid are suppressed by using the second-level off-grid real-time power control. Moreover, the tie-line power can be maintained as the original given value and the three-phase unbalance degree index can be achieved. The multi-microgrids model with three-phase/single-phase hybrid structure based on DIgSILENT platform is used to verify the correctness and effectiveness of the proposed strategy.

With the decreasing reserves of fossil energy and the increasing capacity of renewable energy generation, the scale of microgrid based on distributed generations is expanding. However, more operation data and transaction information of microgrids will also bring several problems: the sufficient capacity of the server in the central management needed, the crises of trust among members, the transparency of transaction information and the confidentiality of data storage. In this paper, blockchain technology is used to deal with these problems as distributed data storage technology. A double-layer framework of energy transactions based on blockchain in multi-microgrids is proposed to provide decentralized trading, information transparency and mutual trust system of each node in the trading market. The central node within the microgrid collects the demand information of the trading market in lower layer and sends them to the trading market of multi-microgrids in higher layer to seek the energy transaction. The continuous double auction mechanism is used in the trading market to guarantee free and fair transactions among nodes. The proposed transaction framework effectively reduces the transaction volume with the main grid which improves energy utilization. Comprehensive simulation results are presented to prove the feasibility of the proposed transaction framework.

Summary: Multi‐microgrids (MMGs) interconnect microgirds (MGs) with geographical adjacency and improve the overall efficiency, stability, and reliability of a regional energy system. The uncertainty brought by renewable energy resources (RESs) is one of the challenges facing the schedule of MMGs. To cope with it, a multi‐timescale schedule strategy is proposed. Specifically, in day‐ahead schedule, the on/off states of the units are determined. Then, based on the day‐ahead plan and the more accurate estimation of RESs, the refined schedule is achieved in the intraday. And to enhance the flexibility of the MMGs system, multienergy complementarity is considered in this work. Moreover, how to achieve the coordinated schedule between MGs owned by different parties is another problem facing the MMGs system operation. To solve this, a distributed algorithm, namely predictor corrector proximal multiplier, is introduced. During the solution process, each MG only needs to reveal the exchange power to the virtual controller, which protects the privacy and reduces communication burden. Numerical simulation of an MMGs system with four MGs is carried out. The results show that the proposed distributed method has good convergence performance. Besides, the absolute relative differences of objective function results obtained using the proposed method with the centralized counterpart are less than 2% in the day‐ahead timescale and which can be less than 0.08% in the intraday timescale. Therefore, the economy and flexibility of the system can be achieved. [ABSTRACT FROM AUTHOR]

A new energy management framework for multi-microgrid (MMG) systems composed of high renewable energy sources (RES) is proposed in this paper. In traditional energy management system (EMS), battery energy storage system (BESS) is only considered in a single microgrid (MG) optimization model, which leads to underutilization of storage devices and waste of resources. Considering the advantages of local consumption of RES, this paper develops a hierarchical multi-agent energy management strategy. A two-step model aiming at maximizing the usage of RES is established to help determine the strategy. In the first step, the strategy is developed to maximize the local consumption of RES based on the demand response programs (DRP) and BESS within the independent MG. In the second step, the community BESS and BESSs are used to share the RES of multiple MGs for increasing the utilization ratio of RES. Simultaneously, the output of each controllable distributed generator (CDG) and the transaction with power grid are determined through a distributed adjustable power generation plan query library (DGPS). Moreover, the simulation with a three-MG system proves that the strategy can effectively increase the utilization ratio of RES and reduce the transaction with power grid compared to the traditional strategy.

This paper investigates multi-period optimal energy scheduling and trading for multi-microgrids (MMGs) integrated with an urban transportation network (UTN). Specifically, an optimization based multi-period traffic assignment model is built to characterize the vehicular flows considering rational drivers with time-flexible travel demand. Meanwhile, each microgrid (MG) is modeled to independently schedule its operation by trading energy with other MGs and charging electric vehicles (EVs) with its fast charging stations (FCSs). The EV charging prices could further influence traffic flow distribution of the UTN. To co-optimize the coupled system, a multi-agent based optimal scheduling and trading scheme is proposed, which is facilitated by peer-to-peer communication of transactive energy information among the agents, thus can preserve privacy of the market participants. Within the scheme, the MGs and EVs take their optimal decisions in response to MG energy trading and EV charging prices updated via multi-bilateral negotiation based on the demand-supply relationship. When an equilibrium of the coupled system is reached, each MG can minimize its operation cost, and the traffic condition could also be improved. Case studies of a MMG system coupled with a double-ring UTN are conducted to validate the effectiveness of the scheme. [ABSTRACT FROM AUTHOR]

This paper investigates the possibility of constructing multi-microgrids by interlinking the rural area systems in the Al Wusta governorate of the Sultanate of Oman, which are currently being supplied by diesel generators. It is proposed to enhance the rural system under study by switching off small diesel stations and replacing them with wind turbines. The microgrids formed in this way are then interlinked together to create multi-microgrids. The paper studies the interlinked multi-microgrids under different scenarios; in terms of voltage profiles and power flow using the ETAP software package. This study contributes to the feasibility study of retiring some diesel power plants and using renewable energy resources in rural Oman.

Optimal scheduling of multi-microgrids is one of the important tasks in multi-microgrid operations as it is an effective way to enhance operational and economic performance. However, with the presence of varied distributed dispatchable and non-dispatchable generation and loads in different microgrids make the optimal scheduling very challenging. This paper presents a cross-layer optimal energy scheduling (CLOES) mechanism in a multimicrogrid system, covering different elements of a community in a smart city such as industry, commercial/office, single residence and multi-dwelling unit with their varied nature of distributed generations and loads. The cross-layer sequential coordinated operations are performed between two layer i.e. lower and upper layer. The lower layer consists of different microgrids, whereas the upper layer comprises distribution system operator. The cross-layer sequential interactions between upper and lower layer lead to an optimal energy scheduling for each microgrid which is essential for the reliability of a multi-microgrid system. The importance of internal and external trading prices is described in a unique way for energy trading in a multi-microgrid system. The simulation result and discussions show the effectiveness of the proposed CLOES in terms of cost reduction in a multi-microgrid system compared to the independent external trading of each individual microgrid with the utility grid. [ABSTRACT FROM AUTHOR]

Multi-stakeholders in multi-microgrids (MMGs) always face ubiquitous uncertainties which bring great challenges to the distributed scheduling of the system. To cope with this problem, a stakeholder-parallelizing distributed adaptive robust optimization (SPD-ARO) model is proposed in this paper for the scheduling of hybrid ac/dc MMGs. Stakeholders at the utility-, supply-, and network-levels are treated as lower layer bodies who synchronously conduct scheduling while considering multiple uncertainties. A nested column-and-constraint generation algorithm is applied to address the robustness problems of the lower layer, thus facilitating the rapid solution of the ARO model. A virtual level in the upper layer acts as a coordinating center to realize the global scheduling of tie-lines, and finally determine a robust plan for the MMGs. Focusing on the characteristics of ARO models, an improved analytical target cascading (ATC) method is proposed to develop the SPD framework for MMGs, which improves the optimization effect of the SPD-ARO model. Case studies are used to compare the different frameworks, distributed methods and model parameters, and the optimal results verify the superiority and effectiveness of the SPD-ARO model, the improved ATC method, and the solution method. [ABSTRACT FROM AUTHOR]

This paper proposes a two-stage stochastic bilevel model for energy management of an active distribution network with multi-microgrids. The distribution network operator connected to the upstream high voltage grid and interacting with multi-microgrids has the task of scheduling the whole system to feed loads, providing safe operation and dealing with uncertainty due to changes in the loads and renewable generation. The multi-microgrids connected to the nodes of the distribution system aim to schedule their productions to feed their internal loads and exchange power with the distribution network operator in both day-ahead and real-time frames. With this purpose, a bilevel model is presented for modeling the interaction between the distribution network operator and the multi-microgrids in which the distribution network operator is located at the upper level and all multi-microgrids at the lower level. The proposed bilevel model is formulated as a mathematical program with equilibrium constraints that is transformed into a mixed-integer linear model through Karush–Kuhn–Tucker conditions and linearization rules. A case study illustrates the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

Recent trends in the increasing penetration of renewable energy generation and the increasingly massive integration of electric vehicles (EVs) into multiple-microgrid (MMG) systems pose tremendous challenges to the effective energy management of these systems. However, the solutions of current energy management methods have the disadvantage of excessively limited robustness against uncertain fluctuations in renewable energy generation, and existing hierarchical EV scheduling methods are solved via an iterative process that is prohibitively time-consuming for real-time practical applications. This work addresses the abovementioned issues by constructing an improved data-driven polyhedral uncertainty set based on modified self-organizing feature map neural networks that perform data clustering. In addition, an iteration-free hierarchical framework is proposed for conducting the tri-layer energy management of MMGs with the massive integration of EVs. Specifically, the global positive power factor of EV aggregation is proposed to formulate a set of constraints into the upper-layer model of the tri-layer framework, which can guarantee the feasibility of the middle-and-lower layer models. The value of this factor is determined using a multi-objective optimization algorithm that makes a trade-off between the feasible range of the output of EV aggregators and the degree to which the trip demands of EVs are violated. The superiority and effectiveness of the proposed iteration-free robust hierarchical energy management method for MMGs are verified via simulations involving a practical MMG under high penetrations of renewable energy generation and massive integration of EVs. • Propose the iteration-free hierarchical scheduling method for addressing tri-layer energy management problems for MMGs with massive EVs. • The global positive power factor (GPPF) defined in the upper layer model can ensure the solution of the lower layer model is always feasible without iteration. • The GPPF value enables MG operators to flexibly select a tradeoff for the GPPF based on their operational experiences. • The novel time-series-enabled data-driven polyhedral uncertainty set can produce solutions with better robustness against uncertainties. [ABSTRACT FROM AUTHOR]

This study uses an artificial neural network (ANN) as an intelligent controller for the management and scheduling of a number of microgrids (MGs) in virtual power plants (VPP). Two ANN-based scheduling control approaches are presented: the ANN-based backtracking search algorithm (ANN-BBSA) and ANN-based binary practical swarm optimization (ANN-BPSO) algorithm. Both algorithms provide the optimal schedule for every distribution generation (DG) to limit fuel consumption, reduce CO2 emission, and increase the system efficiency towards smart and economic VPP operation as well as grid decarbonization. Different test scenarios are executed to evaluate the controllers’ robustness and performance under changing system conditions. The test cases are different load curves to evaluate the ANN’s performance on untrained data. The untrained and trained load models used are real-load parameter data recorders in northern parts of Malaysia. The test results are analyzed to investigate the performance of these controllers under varying power system conditions. Additionally, a comparative study is performed to compare their performances with other solutions available in the literature based on several parameters. Results show the superiority of the ANN-based controllers in terms of cost reduction and efficiency.

This paper proposes a bi-layer game-theoretic framework for the interactive energy management (IEM) of multi-microgrids (MMGs) under the presence of uncertainties imposed by both supply and demand sides. The higher-layer of the framework manages the energy trading and the consumption behavior for each microgrid (MG) within a slow timescale, where its operation cost model is designed in terms of economic factors and users’ willingness. The lower-layer runs at higher frequency that relies on a model predictive control approach, and adjusts the operations of MGs to minimize the difference between the planned strategies generated by the higher-layer and the real ones. In doing so, the supply and demand uncertainties as well as the outage events can be handled properly. As the internal prices are adjusted with system net load, the power balance levels in MMG would be enhanced by minimizing operation cost. On this basis, the energy trading among MGs can be achieved directly without an intermediary. We design a decentralized interactive algorithm to implement the bilayer IEM framework by using the Nash equilibrium concept. Furthermore, the emergency situation is covered to enhance the resilience of the system. Numerical simulations on a practical MMG verify the effectiveness of the proposed models. [ABSTRACT FROM AUTHOR]