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ABSTRACT The intermittent and fluctuating nature of wind turbine output power is increasingly being recognized as a significant issue adversely impacting the power quality and stability of electrical grids. With the increasing integration of wind power, this challenge cannot be underestimated. However, a potential solution for mitigating the adverse effects of such fluctuations lies in the Hybrid Energy Storage System (HESS), which encompasses battery energy storage systems (BESS) and supercapacitors (SC). The HESS is equipped with flexible operational modes for charging and discharging, thus enabling grid‐connected microgrids to possess the ability to counteract these oscillations. In this article, a control strategy based on the combination of Q‐learning and fuzzy logic control approaches is presented for tuning the parameters of a utilized two‐stage variable time constant low‐pass filter (LPF) in a grid‐connected microgrid. The proposed strategy adaptively tunes the time constants of LPFs to mitigate wind power fluctuations. Furthermore, practical constraints for the energy storage systems and their interfaced converters, such as preventing overcharge/discharge, ramp rate requirements, and certain maximum power conversion ranges, have been taken into account. Numerical simulation results verify the effectiveness of the proposed two‐stage variable time constant LPF for output wind power fluctuation reduction considering practical constraints of HESS. [ABSTRACT FROM AUTHOR]

On account of slowly reducing of fossil-fuel, profitable utilization of available fossil-fuel to produce electric power is a major concern for electric power producers. Here, short-period fuel constrained power, heat, natural gas and clean water augmentation planning (FCPHGWAP) of isolated microgrid considering carbon capture is presented. FCPHGWAP finds out the most economical and consistent augmentation plan to meet the prophesied power, heat, natural gas and clean water demand over a short-term time horizon whilst satisfying several technical as well as societal constraints. An archetypical system having extant diesel generators (DGs), PVT panel, wind turbine generator (WG), micro-cogeneration unit (MCU), battery energy storage system (BESS), plug-in electric vehicles (PEVs), thermal energy storage system, natural gas storage unit, carbon capture unit (CCU), electrolyzer, hydrogen storage unit and aspirant PVT panel, WGs, MCUs and PEVs is taken into consideration. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients and grey wolf optimization are used to solve this FCPHGWAP problem. It is observed that net present value having fuel constraints is less than that of without fuel constraints. • Water, gas, power and heat generation expansion planning of isolated MG is studied. • Fuel constraints of DGs and MCUs are taken into consideration. • Clean water, natural gas, heat and power reserve margin are taken into account. • Carbon captured from MCUs and DGs is used to produce methane. • Hydrogen is generated from clean water produced by desalinating sea water. [ABSTRACT FROM AUTHOR]

The increase in wind power‐based microgrids emphasizes the importance of addressing stability challenges during low‐voltage ride‐through (LVRT) events in weak AC grid‐connected doubly fed induction generator systems. Compliance with grid standards, notably LVRT capabilities, is critical as wind power plants integrate increasingly into power systems, raising concerns about generation loss and post‐fault oscillations in microgrids. Previously, researchers have utilized techniques like fuzzy logic, ant colony, and genetic algorithms for static synchronous compensator (STATCOM) tuning to enhance microgrid stability during fault scenarios. This study uses the grasshopper optimization algorithm (GOA), particle swarm optimization (PSO), and a novel hybrid GOA‐PSO. On the main grid, the power system is subject to both symmetrical and asymmetrical faults. The proposed novel technique aims to improve LVRT, minimize generation loss during faults, and reduce after‐fault oscillations by optimizing reactive power flow between the point of common coupling and the microgrid while adhering to the LVRT grid code. MATLAB/Simulink is utilized to evaluate the LVRT performance of a 16 MW DFIG‐based microgrid operating in grid‐connected mode. The performance of the GOA‐PSO‐tuned STATCOM is evaluated by comparing it with conventional, PSO, and GOA‐tuned STATCOM in three fault scenarios. The comparison shows that GOA‐PSO‐tuned STATCOM improves grid stability and reliability. [ABSTRACT FROM AUTHOR]

To enhance the dynamic performance and robustness of the voltage control system of islanded microgrid inverters, a new control strategy combining integral sliding mode (ISM) control and composite nonlinear feedback (CNF) control is proposed. In ISM control, firstly, a new reaching law is designed to improve movement quality in the reaching phase by improving the power term and introducing the inverse tangent function. Then, to improve disturbance observation accuracy, a variable gain extended state observer is designed by improving the regulation mechanism of the state variables according to deviation control. Using the idea of variable damping, linear and nonlinear feedback are combined into CNF control. Specifically, linear feedback provides a small damping ratio for faster system response, while nonlinear feedback increases the damping ratio to improve steady‐state performance. Simulation results show that the integral sliding mode‐composite nonlinear feedback (ISM‐CNF) control strategy cam balances convergence speed and chattering better and achieves higher steady‐state accuracy than conventional strategies. Moreover, ISM‐CNF control has better robustness to reference voltage variations and disturbances caused by sudden load changes. Therefore, the ISM‐CNF control strategy can accomplish voltage control of islanded microgrid inverters quickly and steadily, effectively suppressing system disturbances and enhancing stability and power quality. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In the context of the continuous development of new energy vehicles, an increasing number of electric vehicles (EVs) are being integrated into microgrids, which impacts the operation of microgrids. It is necessary to analyze the emission scheduling of microgrids connected with EVs to ensure the smooth and reliable operation of microgrids with EV integration. This paper aims to realize optimal microgrid scheduling. This article took the case of EV connection with microgrids through orderly charging and discharging. Firstly, mathematical models for each output unit in the microgrid were established. Then, aiming to minimize both the operational cost and pollution emission treatment cost of the microgrid, an emission dispatch optimization model was developed. The whale optimization algorithm (WOA) was chosen as the solving algorithm, and an improved WOA (IWOA) was obtained by optimizing the original WOA in terms of population initialization and position updating parameters. An example analysis revealed that the IWOA demonstrated superior optimization performance compared to other optimization algorithms. In the scenario of orderly charging and discharging, the EVs discharged during peak hours and charged during off-peak hours to balance the microgrid load. The operating cost obtained through the IWOA was 967.25 yuan, and the pollution emission treatment cost was 241.52 yuan, resulting in a total cost of 1208.77 yuan. These results confirm the reliability of the proposed IWOA in solving the model and its applicability in optimizing microgrids with EV integration. [ABSTRACT FROM AUTHOR]

ABSTRACT To enhance the power quality of microgrid inverters and reduce the influence of changes in inductance parameters and external disturbances on the direct power control of the inverter system, a terminal sliding mode control strategy with a variable exponential power reaching law has been proposed. The designed new reaching law comprises a variable exponential term and an enhanced power term. The variable exponential term contains an arctangent function, and the power term coefficient is replaced by a function about the sliding mode. Therefore, the convergence rate can be adaptively adjusted based on different stages of the system, ensuring a faster rate of convergence throughout the process of approaching the sliding mode surface. To weaken the effects of changes in inductance parameters, a power disturbance observer is designed to estimate the internal disturbances induced by the filtered inductance in the system. Subsequently, a sliding mode control law containing disturbance observations is derived. Moreover, a variable exponential terminal sliding surface is designed to adjust the convergence rate of system errors on the sliding surface in stages, thereby enhancing the control performance of the system. The simulation results show that the new reaching law has faster convergence rate and better dynamic performance. The convergence speed of the system error can be accelerated by the designed variable exponential terminal sliding surface. The sliding mode control strategy with the variable exponential power reaching law is applicable to the power control system of three‐phase inverters in microgrids, thereby significantly enhancing the dynamic performance and robustness of the system. [ABSTRACT FROM AUTHOR]

Electric vehicles, known for their eco-friendliness and rechargeable–dischargeable capabilities, can serve as energy storage batteries to support the operation of the microgrid in certain scenarios. Therefore, photovoltaic-storage electric vehicle charging stations have emerged as an important solution to address the challenges posed by energy interconnection networks. However, electric vehicle charging loads exhibit notable randomness, potentially altering load characteristics during certain periods and posing challenges to the stable operation of microgrids. To address this challenge, this paper proposes a hierarchical optimal dispatching strategy based on photovoltaic-storage charging stations. The strategy utilizes a dynamic electricity pricing model and the adaptive particle swarm optimization algorithm to effectively manage electric vehicle charging loads. By decomposing the dispatching task into multiple layers, the strategy effectively solves the problems of the "curse of dimensionality" and slow convergence associated with large numbers of electric vehicles. Simulation results demonstrate that the strategy can effectively achieve peak shaving and valley filling, reducing the load variance of the microgrid by 24.93%, and significantly reduce electric vehicle charging costs and distribution network losses, with a reduction of 92.29% in electric vehicle charging costs and 32.28% in microgrid losses compared to unorganized charging. Additionally, this strategy can meet the travel demands of electric vehicle owners while providing convenient charging services. [ABSTRACT FROM AUTHOR]

Accelerated environmental impacts are a growing concern in the modern world. Electric mobility and the transition to a cleaner energy matrix have become increasingly discussed topics. In this context, this work presents a framework for controlling an electric vehicle (EV)-charging station integrated into a microgrid application as a basis for creating the infrastructure integrated into a smart grid concept. Considering the electrification of the transportation sector future perspectives, a brief review is conducted on the impacts of EV fleet growth in different countries and how smart-charging technologies are identified as solutions for mitigating the negative effects of energy and power consumption associated with EV-charging stations. An analysis of the technical characteristics and the tools that enable the deployment of a fleet-charging operator are examined, specifically focusing on the communication protocol for EVs, such as the OCPP (Open Charge Point Protocol) parameterization/configuration. A new EV-charging station control method is proposed to manage the impacts of distributed solar photovoltaic generation and mitigate the effects of the duck curve. Finally, an integration architecture via IEC 61850 for these elements is proposed, in a practical implementation for variable power control, considering different strategies to deal with distributed generation impact using EV-fleet-charging power demand dynamic management. [ABSTRACT FROM AUTHOR]

Decarbonization is driving power systems toward more decentralized, self-governing models. While these technologies improve efficiency, planning, operations, and reduce the carbon footprint, they also introduce new challenges. In modern grids, particularly with the integration of power electronic devices and high penetration of Renewable Energy Sources (RES) and Inverter-Based Resources (IBRs), traditional reliability concepts may no longer ensure adequate performance due to systemic restructuring. This shift necessitates new or significantly modified reliability indices to capture the characteristics of the evolving power system. Ensuring converter reliability is essential for effective planning, which requires precise, component-to-system-level modeling, as different converters impact system performance indicators. However, the existing literature in this field faces a significant limitation, as most studies focus on a singular perspective. Some examine reliability at the device-level, others at the component-level, while broader reviews in power systems often emphasize system-level analysis. In this paper, we aim to bridge these gaps by comprehensively reviewing the interconnections between these levels and analyzing the mutual influence of power converter and system reliability. A key point to highlight is that, with the rapid evolution of modern power grids, decision-makers must adopt a multi-level approach that incorporates insights from all levels to enable more accurate and realistic planning and operational strategies. Our ultimate goal is to provide an in-depth investigation of studies addressing the unique challenges posed by modern power grids. Finally, we will highlight the gaps in the literature and suggest directions for future research. [ABSTRACT FROM AUTHOR]

Global challenges related to sustainable development are increasingly focusing on the use of digital twin technology as a universal tool for optimizing and monitoring renewable energy installations. This article discusses digital twin technology as a support for sustainable development based on the analysis of microgrid structures. Digital twins allow the creation of virtual models of physical systems. This capability facilitated the accurate replication of the microgrid model at Kielce University of Technology using ETAP (Electrical Transient Analyzer Program) software (version 22.5). The operational parameters of the microgrid structure were analyzed for the examined power range of the photovoltaic installation to determine the possibilities of expanding the existing installation. The impact of the photovoltaic installation's power on the operational parameters of the microgrid structure was visualized, and final conclusions were formulated. Moreover, the integration of digital twin technology into renewable energy systems not only enhances operational efficiency but also plays a pivotal role in advancing sustainability objectives. Through real-time monitoring and predictive maintenance, digital twin technology facilitates the optimization of energy production and distribution, thereby reducing waste and contributing to the overall sustainability of energy systems. This technology enables the simulation of various scenarios, such as fluctuations in energy demand or the integration of new renewable sources, which can inform more sustainable decision-making processes. In the context of microgrids, digital twin technology ensures that energy production is closely aligned with consumption patterns, minimizing energy losses and enhancing grid resilience. Furthermore, digital twin technology supports the sustainable expansion of renewable installations by providing detailed insights into potential environmental impacts and the long-term sustainability of various energy configurations. As the demand for clean energy continues to grow, digital twin technology will be indispensable in achieving a balance between energy needs and environmental stewardship, ensuring that the expansion of renewable energy sources contributes positively to global sustainability objectives. [ABSTRACT FROM AUTHOR]

Park microgrids, valued for their efficiency and flexibility, require privacy-conscious energy management to ensure a trusted scheduling and trading environment. This paper, focusing on park microgrids with shared energy storage, designs an energy management strategy that comprehensively considers shared energy storage, scheduling transparency, and privacy security. First, a blockchain-based energy management platform is established, forming an energy dispatch consensus committee to execute decentralized scheduling management and decision-making. Next, an optimized energy scheduling smart contract for park microgrids is designed, considering Time-of-Use (ToU) pricing and storage arbitrage to formulate the day-ahead electricity purchase and sales plans as well as the shared energy storage operation plans. Then, a privacy protection strategy based on the Shamir secret sharing scheme is proposed, effectively preventing data leakage during blockchain interactions. Finally, through case analysis, the superiority of the proposed method in microgrid optimized scheduling, data tamper-resistance, and privacy protection is demonstrated. [ABSTRACT FROM AUTHOR]

The transformation of power system networks is slowly taking shape, the advent of interruptive technological platforms dealing with digitalization and real‐time trading of power has gained attention based on incorporation of more renewables into the grid. The stochastic nature of renewables pauses security of supply challenges and other related stability concerns, and for this reason efficient methods are investigated in this review to build an understanding of microgrid energy management system (MG‐EMS) and distribution‐based energy management strategies aimed at transforming the conventional grid network into smart grid network. In essence, propagating a technological shift to microgrids which have proven to be ideal distribution networks for residential and commercial loads, have become indispensable in handling distributed energy resources (DER), such as solar, PV, wind, battery energy storage systems (BESS) and small‐scale microgrids, for example in case of excess supply, energy storage system (ESS) has been formulated as a solution to curb excess supply and can offer ancillary services to the grid network. Within the perspective of electricity generation and distribution, microgrid control methodologies, distribution network (DN) management approaches and incumbent optimization strategies used to coordinate and manage grid‐level uncertainties are investigated. In addition, this study proposes distributionally robust optimization (DRO), to manage and mitigate risks associated to shortage or oversupply of power from RESs. [ABSTRACT FROM AUTHOR]

Unlike the inverters in the traditional alternating‐current (AC) microgrid, those in a microgrid with series microsource inverters (SMSI‐MG) are connected to the power grid after being cascaded. The authors of this study first divide the control sections according to the degree of grid voltage dips and formulate a coordinated scheme to suppress fluctuations in the output powers of the SMSI‐MG. For the section in which the degree of unbalanced grid voltage dips is relatively low, a current‐limiting strategy that reduces the output power of the SMSI‐MG through the coordinated control of the generator subunits (CCGU) is proposed. More active power can be provided by the SMSI‐MG when the proposed strategy is used, than in the strategy that is based on changing the reference power, and the output reactive power of the SMSI‐MG can be automatically changed with the degrees of dip and unbalance of the grid voltage. The Light Gradient‐Boosting Machine (LightGBM) is used to establish a mapping relationship between the parameters characterizing overcurrent and the reduction quantity in output active power of the SMSI‐MG to implement the CCGU‐based current‐limiting strategy. The complex collaborative control is simplified to improve the low‐voltage ride through (LVRT) capability of the SMSI‐MG. [ABSTRACT FROM AUTHOR]

Renewable energy sources have been widely installed and operated in power systems, particularly in microgrids in the form of distributed generation units. This issue requires efficient energy management tools which take into account the inherent uncertainties of such energy resources. Thus, this paper presents a stochastic framework aimed at scheduling the renewable energy-based and thermal units in a coordinated way. The generation units comprise fuel cell units with proton exchange membrane known as PEMFC-CHP producing heat and power, concurrently. Moreover, the uncertainties arising from wind and solar power as well as market prices are characterized by deploying scenario-based optimization. The mentioned framework considers storing hydrogen and the model is presented within a stochastic mixed-integer nonlinear programming (MINLP) framework. The resulting problem is simulated on a modified 33-bus distribution network and tackled using the modified marine predators algorithm (MMPA)algorithm. The obtained results indicate that the revenue increases by more than 5% compared to other optimization algorithms. Furthermore, taking into account CHP will increase the total profit of the system by more than 15%. • Proposing optimal management of smart parking considering upscale electricity price with Hydrogen Storage Systems (HSS). • Proposing fuel cell units with proton exchange membrane which generate heat and power simultaneously (PEMFC-CHP). • Optimal coordinated scheduling of renewable energy resources in micro-grids improve objective function. • Suggesting the strategy of storing hydrogen is also considered for PEMFC-CHP units. [ABSTRACT FROM AUTHOR]

Amidst the increasing complexity of microgrid optimization, characterized by numerous decision variables and intricate non-linear relationships, there is a pressing need for highly efficient algorithms. This study introduces a tailored Mixed Integer Nonlinear Programming (MINLP) model that optimizes the charging and discharging schedules of electric vehicles (EVs) and energy storage systems (ESS) while incorporating Distributed Flexible AC Transmission System (D-FACTS) devices. To address these challenges, a novel approach based on the Large-Scale Two-Population Algorithm (LSTPA) is proposed. The model's effectiveness was evaluated using a 33-node microgrid, where the proposed method achieved a total purchased energy of 1.2 MWh, a voltage deviation of 0.0357 p.u, and a CPU time of 551 s, outperforming traditional methods like NSGA-II, PSO, and JAYA. Additionally, in a 69-node microgrid, the approach resulted in a total purchased energy of 0.3 MWh and a voltage deviation of 0.0078 p.u. These results demonstrate the superior performance of the proposed method in terms of energy efficiency, voltage stability, and computational time, advancing the efficiency of microgrid management. [ABSTRACT FROM AUTHOR]

Extreme weather causes an increase in power system failure. Previous studies on system resilience have often overlooked the user-side of a microgrid. This study proposes composite resilience indices (RI) based on the power supply of a large-scale manufacturing campus microgrid to quantify its ability to withstand extreme events. The proposed RI consider load priority, minimum supply load, total energy supplied, and the performance recovery-to-degradation slope ratio in an islanding microgrid. Accordingly, this study presents a three-stage resilience optimal dispatch and reconfiguration strategy, including energy-level scheduling, grid-level reconfiguration, and dynamic-level verification. A multi-objective optimization approach is used for energy scheduling, followed by system reconfiguration via DIgSILENT system modeling to meet the grid code and maximize load supply. Value at Risk methods are used to verify microgrid stability and load shedding requirements, supported by the virtual synchronous generator control of the energy storage system. The test results from a practical large-scale manufacturing campus microgrid validate the proposed approaches for enhancing system resilience considering load values. [ABSTRACT FROM AUTHOR]

ABSTRACT Microgrids are a development trend and have attracted a lot of attention worldwide. The control system plays a crucial role in implementing these systems and, due to their complexity, artificial intelligence techniques represent some enabling technologies for their future development and success. In this paper, we propose a novel formulation of an economic model predictive control (economic MPC) applied to a microgrid designed for a faculty building with the inclusion of a predictive model to deal with the energy demand disturbance using a recurrent neural network of the long short‐term memory (RNN‐LSTM). First, we develop a framework to identify an RNN‐LSTM using historical data registered by a smart three‐phase power quality analyzer to provide feedforward power demand predictions. Next, we present an economic MPC formulation that includes the prediction model for the disturbance within the optimization problem to be solved by the MPC strategy. We carried out simulations with different scenarios of energy consumption, available resources, and simulation times to highlight the results obtained and analyze the performance of the energy management system. In all cases, we observed the correct operation of the proposed control scheme, complying at all times with the objectives and operational restrictions imposed on the system. [ABSTRACT FROM AUTHOR]

A new trend in the transportation sector globally can be observed in a shift away from gasoline-powered vehicles to Hydrogen-based fuel-cell electric vehicles (FCEVs), aimed at reducing harmful emissions. However, there are challenges in producing hydrogen for vehicle stations related to microgrid design and energy management under uncertain conditions. This research sought to identify the optimum design of an electric microgrid to provide the required energy for electric loads, together with a hydrogen refueling station. The microgrid under study consists of various renewable energy resources (RERs), such as photovoltaic (PV) devices, wind power systems, and hydrogen storage systems. The energy management strategy (EMS) aims to reduce the total costs (investment, operation, replacement, procurement energy costs) considering four uncertain parameters associated with PV panels, FCEVs, wind turbines, and power demand. A three-level EMS is proposed based on testing various solutions: without RERs or a hydrogen energy storage system (Level 1); with RERs and a hydrogen energy storage system (Level 2), with RERs and hydrogen energy storage that includes demand side response (DSR) (Level 3). The results indicate annual cost savings of 1.946 E+06 $ for Level 2 and 2.001 E+06 $ for Level 3, compared to Level 1. • Optimal design of interconnected microgrid for refuel stations of FCEV and electric load. • A three-level stochastic energy management strategy is presented with renewable energy sources. • The energy management is solved with RERs, hydrogen storage and demand side response. • Considering uncertainties of load, FCEV and the generated power of RERs. [ABSTRACT FROM AUTHOR]

Microgrids have emerged as a crucial focus in power engineering and sustainable energy research, with utility-scale microgrids playing a significant role in both developed and developing countries like the Philippines. This study presents a comprehensive framework for utility-scale microgrid planning, emphasizing the sustainable integration of renewable energy resources to the distribution grid. The framework addresses the operational modes of grid-connected and islanded microgrids, emphasizing the seamless transition between these modes to ensure a continuous power supply. By leveraging local distributed energy resources, the microgrid aims to reduce dependence on the main transmission grid while enhancing resilience and reliability. The proposed planning framework not only eases the economic burden of constructing renewable energy sources but also aids distribution utilities in maximizing local resources to achieve sustainable energy goals. Through a detailed network analysis and modeling, the framework provides a robust foundation for optimizing the energy mix and enhancing the overall system performance. This research contributes to advancing microgrid technology as a key driver towards achieving UN Sustainable Development Goals, particularly in promoting clean and affordable energy access. [ABSTRACT FROM AUTHOR]

With the widespread use of fossil fuels, the Earth's environment is facing a severe threat of degradation. Traditional large-scale power grids have struggled to meet the ever-growing demands of modern society. The implementation and functioning of microgrids not only enhance the use of renewable energy sources but also considerably diminish the environmental damage resulting from fossil fuel consumption. However, the inherent instability of renewable energy presents a major challenge to the reliability of microgrids. To address the uncertainties of wind and photovoltaic power generation, it is urgent to adopt effective operational control methods to adjust power distribution, thereby achieving an economically efficient system operation and ensuring a reliable power supply. This paper utilizes a microgrid system consisting of wind power, photovoltaic power generation, thermal power units, and energy storage devices as the research object, establishing an economic dispatch model aimed at minimizing the total operating cost of the system. To solve this problem, the paper introduces second-order oscillatory particles and improves the Particle Swarm Optimization algorithm, proposing a second-order oscillatory chaotic mapping particle swarm optimization (SCMPSO). The simulation results show that this method can effectively optimize system operating costs while ensuring the stable operation of the microgrid. [ABSTRACT FROM AUTHOR]

Advancing decarbonization critically depends on the integration of PV systems into microgrids. However, this integration faces challenges, including the variability of photovoltaic solar energy production, the demands of energy management, and the complexities of grid synchronization and communication. To address these challenges, a PV emulator platform is an essential tool. This paper presents a novel four-layer PV emulator platform that seamlessly integrates power systems, control systems, measurement instrumentation, and communication processes. The proposed platform enables the emulation of I-V curves and the dynamic adjustment of operating points—including both the maximum power point (MPP) and power reserve point (PRP)—as well as temperature and irradiance while providing sufficient power capacity for microgrid integration. To validate its effectiveness, the platform was assessed for its capability to adjust operating points, such as MPPs or PRPs, under varying irradiance and temperature conditions. The results show that the platform effectively adjusts operating points with a deviation of less than 5% from theoretical values and successfully tracks a sequence of operating points. This performance underscores the platform's potential in integrating and managing PV systems within microgrid environments, thereby advancing the path to decarbonization. [ABSTRACT FROM AUTHOR]

In the complex environment of microgrid deployments targeted at geographic regions, the seamless integration of renewable energy sources meets a variety of essential challenges. These include the unpredictable nature of renewable energy, characterized by intermittent energy generation, as well as ongoing fluctuations in load demand, the vulnerabilities present in distribution network failures, and the unpredictability that results from unfavorable weather conditions. These unexpected events work together to disturb the delicate balance between energy supply and demand, raising the alarming threat of system instability and, in the worst cases, the sudden advent of damaging blackouts. To address this issue, a fuzzy logic-based energy management system has been developed to monitor, manage, and optimize energy consumption in microgrids. This study focuses on the control of diesel generators and utility grids in a grid-connected microgrid which manages and evaluates numerous energy consumption and distribution features within a specified system, e.g., building or a microgrid. An energy management system is suggested based on fuzzy logic as a swift fix for complications with effective and competent resource management, and its presentation is compared with both the grid-connected and off-grid modes of the microgrid. In the end, the results exhibit that the proposed controller outclasses the predictable controllers in dropping sudden variations that arise during the addition of sources of renewable energy, supporting the refurbishment of the constant system. [ABSTRACT FROM AUTHOR]

This study addresses the inherent challenges associated with the limited flexibility of power systems, specifically emphasizing uncertainties in solar power due to dynamic regional and seasonal fluctuations in photovoltaic (PV) potential. The research introduces a novel supervised machine learning model that focuses on regression methods specifically tailored for advanced microgrid energy management within a 100% PV microgrid, i.e. a microgrid system that is powered entirely by solar energy, with no reliance on other energy sources such as fossil fuels or grid electricity. In this context, "PV" specifically denotes photovoltaic solar panels that convert sunlight into electricity. A distinctive feature of the model is its exclusive reliance on current solar radiation as an input parameter to minimize prediction errors, justified by the unique advantages of supervised learning. The performance of four well-established supervised machine learning models—Neural Networks (NN), Gaussian Process Regression (GPR), Support Vector Machines (SVM), and Linear Regression (LR)—known for effectively addressing short-term uncertainty in solar radiation, is thoroughly evaluated. Results underscore the superiority of the NN approach in accurately predicting solar irradiance across diverse geographical sites, including Cairo, Egypt; Riyadh, Saudi Arabia; Yuseong-gu, Daejeon, South Korea; and Berlin, Germany. The comprehensive analysis covers both Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI), demonstrating the model's efficacy in various solar environments. Additionally, the study emphasizes the practical implementation of the model within an Energy Management System (EMS) using Hybrid Optimization of Multiple Electric Renewables (HOMER) software, showcasing high accuracy in microgrid energy management. This validation attests to the economic efficiency and reliability of the proposed model. The calculated range of error, as the median error for cost analysis, varies from 2 to 6%, affirming the high accuracy of the proposed model. Highlights: Introduction of a novel approach: This study presents a pioneering methodology focusing exclusively on supervised machine learning techniques for short-term uncertainty management within 100% PV microgrids, aimed at optimizing energy management efficiency. Comprehensive comparative analysis: Through meticulous comparative investigation, various regression techniques for predicting solar radiation within PV microgrids are scrutinized, providing valuable insights into their efficacy across diverse environmental conditions. Validation across diverse locations: The validation of findings across four distinct geographical locations, encompassing both PV and concentrated PV (CPV) systems, substantially enhances the generalizability of the results, advancing the understanding of solar radiation prediction dynamics for renewable energy integration strategies. [ABSTRACT FROM AUTHOR]

To enhance the operational economy and energy utilization efficiency of the microgrid, this paper takes the minimization of the comprehensive cost of microgrid operation and environmental protection as the objective function and constructs the microgrid power dispatching model including wind and solar, gas, diesel power generation and energy storage units. By using an improved Sparrow Search Algorithm (ISSA) to optimize the particle filter algorithm, an improved particle filter (IPF) algorithm is developed for microgrid optimization scheduling strategies. Simulation examples demonstrate that, compared to traditional SSA and ISSA algorithms, the proposed algorithm has the advantages of shorter computation time and higher solution accuracy, which also proves its strong practicability in the microgrid optimal dispatching. [ABSTRACT FROM AUTHOR]

To meet the greenhouse gas reduction targets and address the uncertainty introduced by the surging penetration of stochastic renewable energy sources, energy storage systems are being deployed in microgrids. Relying solely on short-term uncertainty forecasts can result in substantial costs when making dispatch decisions for a storage system over an entire day. To mitigate this challenge, an adaptive robust optimization approach tailored for a hybrid hydrogen battery energy storage system (HBESS) operating within a microgrid is proposed, with a focus on efficient state-of-charge (SoC) planning to minimize microgrid expenses. The SoC ranges of the battery energy storage (BES) are determined in the dayahead stage. Concurrently, the power generated by fuel cells and consumed by electrolysis device are optimized. This is followed by the intraday stage, where BES dispatch decisions are made within a predetermined SoC range to accommodate the uncertainties realized. To address this uncertainty and solve the adaptive optimization problem with integer recourse variables in the intraday stage, we proposed an outer-inner column-and-constraint generation algorithm (outer-inner-CCG). Numerical analyses underscored the high effectiveness and efficiency of the proposed adaptive robust operation model in making decisions for HBESS dispatch. [ABSTRACT FROM AUTHOR]

The application of virtual synchronous generator (VSG) control in flywheel energy storage systems (FESS) is an effective solution for addressing the challenges related to reduced inertia and inadequate power supply in microgrids. Considering the significant variations among individual units within a flywheel array and the poor frequency regulation performance under conventional control approaches, this paper proposes an adaptive VSG control strategy for a flywheel energy storage array (FESA). First, by leveraging the FESA model, a variable acceleration factor is integrated into the speed-balance control strategy to effectively achieve better state of charge (SOC) equalization across units. Furthermore, energy control with a dead zone is introduced to prevent SOC of the FESA from exceeding the limit. The dead zone parameter is designed based on the SOC warning intervals of the flywheel array to mitigate its impact on regular operation. In addition, VSG technology is applied for the grid-connected control of the FESA, and the damping characteristic of the VSG is decoupled from the primary frequency regulation through power differential feedback. This ensures optimal dynamic performance while reducing the need for frequent involvement in frequency regulation. Subsequently, a parameter design method is developed through a small-signal stability analysis. Consequently, considering the SOC of the FESA, an adaptive control strategy for the inertia damping and the P/ω droop coefficient of the VSG control is proposed to optimize the grid support services of the FESA. Finally, the effectiveness of the proposed control methods is demonstrated through electromagnetic transient simulations using MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

The rapid fault isolation is the necessity for the proficient operation of the microgrid because it can increase resiliency of the system by restoration of the power distribution system after isolation of the faulty area. In modern power system, many renewable and nonrenewable sources are integrated through different types of converters; therefore, it is too much tedious to develop a protection scheme which can easily detect and isolate faults under such unpredictable faulty conditions. Further, variation in weather conditions and the fault current level during such conditions is not predictable for traditional protection schemes and needs modification. In addition to above difficulties in the microgrid, distinct category of the AC faults makes protection task more difficult when fault resistance is varying due to change in grounding conditions. Motivated by the above challenges in the proposed microgrid, an ensemble of kNN-based protection scheme has been devised in this work to provide robustness to the microgrid. The tasks of the mode detection, fault detection/classification and faulty section identification under varying weather conditions have been considered in grid-connected and islanded modes as a class of the problem. Discrete wavelet transform has been used to pre-process the measured voltage and current signals retrieved from the appropriate bus. To validate the protection scheme, numerous cases of dissimilar operating scenarios have been considered under both of the operating modes. The results in Sect. 6 indicate that protection scheme is efficient and reliable to increase the robustness of the microgrid. [ABSTRACT FROM AUTHOR]

Adapting the power and energy systems by integrating renewable sources is necessary to address climate change. On the other hand, microgrids are gaining prominence in meeting power and energy requirements, including in remote locations. Consequently, the power system's penetration of renewable energy-based microgrids is increasing. Planning an isolated microgrid necessitates cost-effective capacity sizing of energy sources and storage systems for maintaining continuity in power supply. Considering the variability and uncertainty of photovoltaic (PV), wind energies, and load variations, deciding the optimal size of renewable-rich, isolated microgrids is challenging. Batteries and fuel cells are potential storage solutions for managing variability. However, a more trustworthy sizing approach is necessary to maximize power availability at the lowest possible cost, even in the face of uncertainty. Moreover, providing the microgrid owner with the opportunity to choose from a range of optimal solutions is also essential. Therefore, incorporating the uncertainty handling feature with the help of robust assessment under worst-case scenarios in the multi-objective optimization method can provide a more trustworthy solution. In this connection, a novel algorithm is proposed that instills robustness in the solutions provided by traditional non-dominated sorting genetic algorithm-II (NSGA-II), which can offer multiple break-even solutions. The isolated microgrids with PV, wind as sources, and storage technologies such as Lithium-ion (Li-ion) batteries, fuel cells, and a combination of both, a less explored scenario, have been compared to determine the effective storage option over the long run while considering uncertainties in renewable energy and load variations. The NSGA-II benchmark solutions developed under these uncertainties and variations are used to validate the robustness of the solutions obtained from the proposed robust algorithm. With a good trade-off between the cost and availability aspects, the proposed algorithm is found to be superior in getting maximum availability with minimum cost under uncertainties. [ABSTRACT FROM AUTHOR]

At present, the integration of microgrids into power systems presents significant power quality challenges in terms of the rising adoption of nonlinear loads and electric vehicles. Ensuring the stability and efficiency of the electrical network in this evolving landscape is crucial. This paper explores the implementation of cascading Proportional–Integral (PI-PI) and cascading Fractional-Order PI (FOPI-FOPI) controllers for a Distribution Static Compensator (DSTATCOM) in hybrid microgrids that include photovoltaic (PV) systems and fuel cells. A novel hybrid optimization algorithm, WSO-WOA, is introduced to enhance power quality. This algorithm leverages the strengths of the White Shark Optimization (WSO) algorithm and the Whale Optimization Algorithm (WOA), with WSO generating new candidate solutions and WOA exploring alternative search areas when WSO does not converge on optimal results. The proposed approach was rigorously tested through multiple case studies and compared with established metaheuristic algorithms. The findings demonstrate that the WSO-WOA hybrid algorithm significantly outperforms others in optimizing the PI-PI and FOPI-FOPI controllers. The WSO-WOA algorithm showed an improvement in accuracy, surpassing the other algorithms by approximately 7.29% to 14.1% in the tuning of the PI-PI controller and about 8.5% to 21.2% in the tuning of the FOPI-FOPI controller. Additionally, the results confirm the superior performance of the FOPI-FOPI controller over the PI-PI controller in enhancing the effectiveness of the DSTATCOM across various scenarios. The FOPI-FOPI provided controller a reduced settling time by at least 30.5–56.1%, resulting in marked improvements in voltage regulation and overall power quality within the microgrid. [ABSTRACT FROM AUTHOR]

In the forthcoming era of power grids emphasizing clean energy and green transportation, stringent safety and reliability standards are imperative. This study addresses the limitations of traditional reinforcement learning in managing the control performance degradation due to the extensive integration of new energy sources in microgrid by proposing a multi-step self-correcting Q-learning algorithm. This algorithm features a self-correcting estimator for accurate system state estimation and an eligibility trace mechanism to expedite convergence, facilitating rapid controller responses to system fluctuations and minimizing the impact of frequency regulation delays. The simulation section of this paper presents an enhanced two-area load frequency control model, integrating wind power and electric vehicle modules, and subjected to various disturbances to mimic real-world power system load changes. The results demonstrate that the proposed algorithm excels in control performance metrics when com- pared to existing methods. [ABSTRACT FROM AUTHOR]

In recent decades, with the expansion of distributed energy generation technologies and the increasing need for more flexibility and efficiency in energy distribution systems, microgrids have been considered a promising innovative solution for local energy supply and enhancing resilience against network fluctuations. One of the basic challenges in the operation of microgrids is the optimal management of voltage and frequency in the network, which has been the subject of extensive research in the field of microgrid operational optimization. The energy demand is considered a crucial element for energy management due to its fluctuating nature over the day. The use of demand response strategies for energy management is one of the most important factors in dealing with renewables. These strategies enable better energy management in microgrids, thereby improving system efficiency and stability. Given the complexity of optimization problems related to microgrid management, evolutionary optimization algorithms such as the Imperialist Competitive Algorithm (ICA) and Genetic Algorithm (GA) have gained great attention. These algorithms enable solving high-complexity optimization problems by considering various constraints and multiple objectives. In this paper, both ICA and GA, as well as their hybrid application, are used to significantly enhance the voltage regulation in microgrids. The integration of optimization techniques with demand response strategies improves the overall system efficiency and stability. The results proved that the hybrid method provides valuable insights for optimizing energy management systems. [ABSTRACT FROM AUTHOR]

The life cycle of a microgrid covers all the stages from idea to implementation, through exploitation until the end of its life, with a lifespan of around 25 years. Covering them usually requires several software tools, which can make the integration of results from different stages difficult and may imply costs being hard to estimate from the beginning of a project. This paper proposes a unified platform composed of four modules developed in MATLAB 2022b, designed to assist all the processes a microgrid passes through during its lifetime. This entire platform can be used by a user with low IT knowledge, because it is completed with fill-in-the-blank alone, as a major advantage. The authors detail the architecture, functions and development of the platform, either by highlighting the novel integration of existing MATLAB tools or by developing new ones and designing new user interfaces linked with scripts based on its complex mathematical libraries. By consolidating processes into a single platform, the proposed solution enhances integration, reduces complexity and provides better cost predictability throughout the project's duration. A proof-of-concept for this platform was presented by applying the life-cycle assessment process on a real-case study, a microgrid consisting of a photovoltaic plant, and an office building as the consumer and energy storage units. This platform has also been developed by involving students within summer internships, as a process strengthening the cooperation between industry and academia. Being an open-source application, the platform will be used within the educational process, where the students will have the possibility to add functionalities, improve the graphical representation, create new reports, etc. [ABSTRACT FROM AUTHOR]

As global energy demand continues to rise, integrating renewable energy sources (RES) into power systems has become increasingly important. However, the intermittent nature of RES, such as solar and wind, presents challenges for maintaining a stable energy supply. To address this issue, energy storage systems are essential. One promising technology is micro-compressed air energy storage (micro-CAES), which stores excess energy as compressed air and releases it when needed to balance supply and demand. This study investigates the integration of micro-CAES with RES in a 19-home microgrid in northern Portugal. The research aims to evaluate the effectiveness of a microgrid configuration that includes 100 kW of solar PV, 70 kW of wind power, and a 50 kWh micro-CAES system. Using real-world data on electricity consumption and local renewable potential, a simulation is conducted to assess the performance of this system. The findings reveal that this configuration can supply up to 68.8% of the annual energy demand, significantly reducing reliance on the external grid and enhancing the system's resilience. These results highlight the potential of micro-CAES to improve the efficiency and sustainability of small-scale renewable energy systems, demonstrating its value as a key component in future energy solutions. [ABSTRACT FROM AUTHOR]

In the context of modern power systems, the reliance on a single-time-of-use electricity pricing model presents challenges in managing electric vehicle (EV) charging in a way that can effectively accommodate the variable supply and demand patterns, particularly in the presence of wind power generation. This often results in undesirable peak–valley differences in microgrid load profiles. To address this challenge, this paper introduces an innovative approach that combines time-of-use electricity pricing with the flexible energy storage capabilities of electric vehicles. By dynamically adjusting the time-of-use electricity prices and implementing a tiered carbon pricing system, this paper presents a comprehensive strategy for formulating optimized charging and discharging plans that leverage the inherent flexibility of electric vehicles. This approach aims to mitigate the fluctuations in the microgrid load and enhance the overall grid stability. The proposed strategy was simulated and compared with the no-incentive and single-incentive strategies. The results indicate that the load peak-to-trough difference was reduced by 30.1% and 18.6%, respectively, verifying its effectiveness and superiority. Additionally, the increase in user income and the reduction in carbon emissions verify the need for the development of EVs in tandem with clean energy for environmental benefits. [ABSTRACT FROM AUTHOR]

This paper proposes goal-function-based decentralized control of microgrids. In addition to being an instrument for maintaining the grid voltage and frequency stability, each grid-tie inverter generates a current component with the aim of compensating for voltage distortion in the node where it is connected. The designed goal-function does not need to rely on the assumption that a microgrid is dominantly inductive or resistive to derive its control law, as is mostly the case with the droop-based approach. The priorities of the proposed scheme can be adjusted according to user preferences. In addition, the control algorithm is independent of network topology, can be applied in both islanded and non-islanded microgrids, and secure system scalability. The proposed method is verified by detailed simulations. [ABSTRACT FROM AUTHOR]

The current generation of renewable energy remains insufficient to meet the demands of users within the network, leading to the necessity of curtailing flexible loads and underscoring the urgent need for optimized microgrid energy management. In this study, the deep learning-based Adaptive Dynamic Programming Algorithm (ADPA) was introduced to integrate real-time pricing into the optimization of demand-side energy management for microgrids. This approach not only achieved a dynamic balance between supply and demand, along with peak shaving and valley filling, but it also enhanced the rationality of energy management strategies, thereby ensuring stable microgrid operation. Simulations of the Real-Time Electricity Price (REP) management model under demand-side response conditions validated the effectiveness and feasibility of this approach in microgrid energy management. Based on the deep neural network model, optimization of the objective function was achieved with merely 54 epochs, suggesting a highly efficient computational process. Furthermore, the integration of microgrid energy management with the REP conformed to the distributed multi-source power supply microgrid energy management and scheduling and improved the efficiency of clean energy utilization significantly, supporting the implementation of national policies aimed at the development of a sustainable power grid. [ABSTRACT FROM AUTHOR]

Non-wires alternatives and microgrid technologies are maturing and present great opportunities for electric utilities to increase the benefits they offer to their customers. They have the potential to decrease the cost of resolving traditional electrical system loading issues, contribute to carbon emissions reductions, and improve the electrical distribution system's resilience to extreme weather events. The authors of this manuscript present a review of the research on microgrids and their practical applications. This is leveraged with the past work of the authors of this manuscript and other authors to develop specific objectives for microgrids, practical criteria for engineers to consider when deploying microgrids, stochastic methods to optimize microgrid designs, and black start requirements. This guidance is then used for the design of actual networked microgrids being deployed with adaptive boundaries. [ABSTRACT FROM AUTHOR]

Microgrids are a growing segment of the energy industry, representing a paradigm shift from remote central station power plants toward more localized, distributed generation. An efficient energy management structure is essential for a DC Microgrid with a PV system combined with a Hybrid Energy Storage System (HESS) of Battery and Supercapacitor. The combined supercapacitor and battery storage system provides a quick control for the DC-link voltage. It stabilizes the system and helps achieve smooth PV power delivery to loads. The boost converter used for the PV system is controlled by a Maximum Power Point Tracking (MPPT) method to extract maximum power and maintain a constant voltage at the DC bus despite the daily change in solar power. The boost-buck converter is used to control the charging and discharging processes of the BESS to maintain a constant voltage at the DC bus. To achieve the seamless operation of DC Microgrid with HESS during the power fluctuations, this work proposes power coordination and control scheme has been proposed. To analyse the performance and efficacy of the proposed DC Microgrid with HESS are implemented in MATLAB/SIMULINK. [ABSTRACT FROM AUTHOR]

The finest microgrid design has been proposed in this research to ensure a smooth energy supply for the University of Management and Technology (UMT) Lahore, Sialkot Iqbal campus, Pakistan at the cheapest price. The design methodology considered resource availability, environmental feasibility, economic feasibility, and renewable integration. The university can utilize the energy from the Sun, fossil fuel, and national grid. Integrating the available energy resources, data collection, and forecasting multiple parameters were simulated to design the optimal microgrid. The optimal design is selected based on the net present cost (NPC), renewable fraction (RF), operating cost (OC), repair and maintenance cost (O and MC), etc. Out of the 48 viable simulated designs, the optimal design has a PV system, generators, and the grid to fulfill energy demand at a net present cost of $116,987, with a renewable portion of 95%. In contrast, the worst simulated design depends on generators and battery backup, resulting in a $2.93 million NPC with a renewable segment of 24.3%. [ABSTRACT FROM AUTHOR]

This paper presents a multi-criteria decision-making method to rank loads for load shedding in microgrids. The proposed Fuzzy VIKOR technique is based on the Fuzzy Decision-Making Test and Evaluation Laboratory (DEMATEL) model and the Analytical Network Process (ANP). The load ranking for load shedding in a microgrid is an issue that requires balancing economic and technical criteria, both of which are often in conflict with each other when considering comparative objects. The proposed Fuzzy VIKOR technique aims to solve problems related to conflicting criteria. Fuzzy numbers theory is utilized to handle uncertainty and relativity. Furthermore, the DEMATEL method also establishes Network Relationship Maps (NRM) and normalizes the unweighted supermatrix of ANP for weight values that match the criteria. The proposed method provides a comprehensive approach to evaluate the importance of criteria by determining the correlation and influence between factors, calculating their weights, and then ranking and selecting optimal loads based on the weights of load factors that serve the purpose of load shedding. A microgrid system with 16 buses was deployed to validate the proposed method. [ABSTRACT FROM AUTHOR]

This study aims to ameliorate the contribution capability of doubly-fed induction generator (DFIG) to participate in standalone microgrid operation. The islanded microgrid consists of a solar photovoltaic array for solar energy conversion and battery energy storage in addition to DFIG-based wind energy conversion system. Using a simplified control approach, the study describes multi-mode operation of a DFIG-based AC/DC microgrid using a stator-side solid-state transition switch (SSTS). Using SSTS operation, the DFIG stator can be seamlessly disconnected and reconnected from the point of common coupling without interrupting power to the loads in the microgrid. Additionally, non-ideal AC loads can be handled efficiently without the need for computationally exhaustive approaches to enhance stator voltages and currents. [ABSTRACT FROM AUTHOR]

With the growing concern over climate change and energy security, the Government of India expedited enhancing the share of renewable energy (RE) derived from solar, wind and biomass sources within the energy blend. In this paper, a techno-economic and environmental analysis of a microgrid-integrated electric vehicle charging stations fueled by renewable energy is proposed for a typical area in the State of Karnataka, South India. The power transaction with the grid and the sell-back price to the national grid were investigated. Carbon emissions were also assessed, and 128,406 CO2 kg/Yr can be saved in the grid-connected mode. Also, in this work, different scenarios such as injecting active power, reactive power, and active and reactive power, and injecting active and absorbing reactive power to the grid are comprehensively assessed. Out of four types, type 3 (inject real and reactive power) provides significant reduction in power losses by up to 80.99%. The synchrophasor-technology-based monitoring method is adopted in order to enhance the microgrid system's overall performance. The execution times for different cases with distributed generators (DGs) and electric vehicle charging stations (EVCSs) for conventional systems and micro-phasor measurement units (µPMU) were observed to be 19.07 s and 5.64 s, respectively, which is well accepted in the case of online monitoring. [ABSTRACT FROM AUTHOR]

Countries all over the world are shifting from conventional and fossil fuel-based energy systems to more sustainable energy systems (renewable energy-based systems). To effectively integrate renewable sources of energy, multi-directional power flow and control are required, and to facilitate this multi-directional power flow, peer-to-peer (P2P) trading is employed. For a safe, secure, and reliable P2P trading system, a secure communication gateway and a cryptographically secure data storage mechanism are required. This paper explores the uses of blockchain (BC) in renewable energy (RE) integration into the grid. We shed light on four primary areas: P2P energy trading, the green hydrogen supply chain, demand response (DR) programmes, and the tracking of RE certificates (RECs). In addition, we investigate how BC can address the existing challenges in these domains and overcome these hurdles to realise a decentralised energy ecosystem. The main purpose of this paper is to provide an understanding of how BC technology can act as a catalyst for a multi-directional energy flow, ultimately revolutionising the way energy is generated, managed, and consumed. [ABSTRACT FROM AUTHOR]

Accelerating the green transformation of the power system is the inevitable path of the energy revolution; the increasing installed capacity of new energy and the penetration rate of electricity, uncertainty regarding new energy output, and the rising proportion of distributed power supply access have led to the threat against the safe and stable operation of the current power system. With the increasing uncertainty on both sides of power supply and demand, the microgrid (MG) is needed to effectually aggregate, coordinate, and optimize resources, such as adjustable resources, distributed power supply, and distributed energy storage in a certain area on the demand side. Therefore, in this paper, the uncertainty of wind power and PV is first dealt with by Latin hypercube sampling (LHS). Secondly, differentiated resources in the MG region can be divided into adjustable resources, distributed power supply, and energy storage. Adjustable resources are classified according to demand response characteristics. At the same time, the MG operating cost and carbon trading mechanism (CTM) are comprehensively considered. Finally, a low-carbon economy optimal scheduling strategy with the lowest total cost as the optimization goal is formed. Then, in order to verify the effectiveness of the proposed algorithm, three different scenarios are established for comparison. The total operating cost of the proposed algorithm is reduced by about 30%, and the total amount of carbon trading in 24 h can reach nearly 600 kg, bringing economic and social benefits to the MG. [ABSTRACT FROM AUTHOR]

The shared energy storage device acts as an energy hub between multiple microgrids to better play the complementary characteristics of the microgrid power cycle. In this paper, the cooperative operation process of shared energy storage participating in multiple island microgrid systems is researched, and the two-stage research on multi-microgrid operation mode and shared energy storage optimization service cost is focused on. In the first stage, the output of each subject is determined with the goal of profit optimization and optimal energy storage capacity, and the modified grey wolf algorithm is used to solve the problem. In the second stage, the income distribution problem is transformed into a negotiation bargaining process. The island microgrid and the shared energy storage are the two sides of the game. Combined with the non-cooperative game theory, the alternating direction multiplier method is used to reduce the shared energy storage service cost. The simulation results show that shared energy storage can optimize the allocation of multi-party resources by flexibly adjusting the control mode, improving the efficiency of resource utilization while improving the consumption of renewable energy, meeting the power demand of all parties, and realizing the sharing of energy storage resources. Simulation results show that compared with the traditional PSO algorithm, the iterative times of the GWO algorithm proposed in this paper are reduced by 35.62%, and the calculation time is shortened by 34.34%. Compared with the common GWO algorithm, the number of iterations is reduced by 18.97%, and the calculation time is shortened by 22.31%. [ABSTRACT FROM AUTHOR]