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This paper addresses the reconfiguration of two link power distribution networks (PDNs) under two scenarios: horizontal and vertical interconnections. Recent metaheuristic algorithms such as the Slime Mould Algorithm (SMA) and Artificial Hummingbird Algorithm (AHA) are used for identifying the optimal tie switches (TSs) to insert within each PDN upon interconnection. Both PDNs, of 33 bus each, are horizontally or vertically interconnected. Simulation results, using MATLABTM, demonstrate significant improvements in both power loss reduction and voltage profile improvement. Specifically, after the insertion of the fifth TS, a reduction of over 28% and 39% in power losses is achieved in the horizontal and vertical scenarios, respectively. Additionally, the voltage magnitude (V m v m) reaches a value of 0.9423 pu and 0.9481 pu, respectively. Furthermore, the results show that AHA outperforms other techniques to efficiently reconfigure interconnected PDNs. • Implementing robust PDN reconfiguration for optimal interconnection of two microgrids. • Providing detailed assumptions for TS parameter calculations in real-world simulations. • Studying two scenarios: interconnected MGs with horizontal and vertical connections. • Using SMA and AHA to optimize TS positions in each MG and between MGs. • Improving the voltage profile and minimizing power losses in interconnected MGs. [ABSTRACT FROM AUTHOR]

This paper presents triple-objective stochastic energy planning and management of a deltoid structure in which a microgrid, nano-grid, and main grid connect and exchange power simultaneously. In addition, the impact of hydrogen stations due to the growth of hydrogen vehicles and their crucial role in the power system's future to reduce pollution is also discussed. Moreover, the effect of time-based demand response programs (TOU) according to the elasticity matrix (different operators and price-sensitive flexible loads) for the proposed multilateral grid is investigated under diverse scenarios. Stochastic planning is performed to make results more realistic and authentic. The uncertain parameters for stochastic planning include wind pace, solar radiation, fuel rate, and various demands. The assumed triple objective functions for the proposed planning are the microgrid's profit, the nano-grid's cost, and the total multilateral grid's pollution. The problem is modeled as mixed-integer linear programming (MILP) and solved using the GAMS and LP metric approach. The final results show that by implementing the supposed planning, the microgrid's profit increases by about 22.53 $/day (10.8%), and the nano-grid's cost decreases by about 1.31 $/day (9.8%). On the other hand, the total environmental pollution is reduced significantly and reaches 1.06 kg/day. [Display omitted] • A deltoid scheme of a multilateral grid with diverse operators has been assumed. • Solving and validating triple-objective problems using the LP metric method. • Implementation of DRP on multi-grid by considering stochastic-deterministic planning. • Peak reduction was obtained, and the load curve was flattering more using DRPs. • The hydrogen station was positively affected by the power system's contamination. [ABSTRACT FROM AUTHOR]

This paper focuses on operating a microgrid when uncertain islanding events and net load are stochastic, from the perspective of a microgrid operator. To derive effective and reliable decisions that can adapt when these uncertain factors sequentially reveal in a given planning horizon, multistage stochastic optimization models that address the dynamics and probabilistic nature of uncertainty can be used. However, since they suffer from the curse of dimensionality in that the number of scenarios needed grows exponentially with regard to the number of time periods, it is not practical to use the models directly. To overcome this drawback, this paper proposes scalable optimization approaches that efficiently derive good-quality solutions that are adaptable to sequential realization. In particular, integrated optimization models are presented to deal with both uncertain factors based on the combination of the proposed models for each of the two, which is scalable compared to the standard multistage model used in the literature. Numerical experiments demonstrate that practical-sized instances can be efficiently solved using the proposed models, whereas the standard multistage model in the literature, despite well-known decomposition methods being applied, cannot provide solutions within a reasonable time. The results also demonstrate the effectiveness of the solutions from the proposed models compared with the standard multistage model. • Operating microgrids under stochastic islanding and net load is studied. • Scalable optimization models are proposed for each uncertain factor and integrated. • The solutions from the proposed models are adaptable to sequential realization. • The proposed models show computational benefits over the standard multistage model. • The effectiveness of the solutions from the proposed models is also demonstrated. [ABSTRACT FROM AUTHOR]

This paper addresses the distributed state estimation-based centralized control design problem for Internet-of-Things (IoT)-enabled microgrid systems in the presence of deception attacks. Firstly, a microgrid with multiple synchronous generators is described in a state-space model. Furthermore, the states of the microgrid power system are monitored by using a set of sensors linked through IoT-enabled networks, where attacks are presumed to occur during measurements. In particular, the deception attacks are capable of compromising grid security through the malicious manipulation of measurement data. To tolerate this effect in this paper a resilient distributed state estimation design based control algorithm is developed. The microgrid system is then controlled by a centralized scheme that uses only estimates generated by a certain group of IoT-enabled networked nodes. Subsequently, an augmented system is developed that incorporates both the microgrid system and the estimator error dynamics. Using Lyapunov stability theory and matrix inequality approach, the proposed local and neighboring estimator based control gains are designed to ensure the accurate estimation of load and energy source output and continuous power supply. Specifically, the particle swarm optimization algorithm is used for optimizing the distributed state estimation-based controller to achieve the desired performance. Finally, simulation results demonstrate that the developed algorithm is capable of estimating the state of the microgrid and controlling its operations. The result reveals that microgrids can provide a constant flow of electricity while being resistant to deception attacks and disruptions caused by the networks. • Resilient control algorithm is developed for IoT-enabled microgrid system model. • The model considers noise, inaccurate measurement in sensors and deception attacks. • Estimated state is embedded in controller to obtain desired microgrid performance. • Algorithm illustrates the ability to resist deception attacks, noises and gains. [ABSTRACT FROM AUTHOR]

DC microgrids (MGs) have gained significant attention due to their ability to complement renewable energy generation. In order to improve the operational stability of DC MGs, a predefined-time-based secondary controller is proposed to restore voltage drops caused by droop control and achieve current allocation among agents according to expected ratios. To conserve communication resources, the communication topology is established as a directed network. Each distributed generator (DG) receives voltage and current information from neighboring devices and uses its own current and voltage regulators to achieve control objectives. Compared to existing controllers, the convergence time upper bound of the proposed approach is independent of the initial value and can be directly adjusted as a parameter. The stability of the proposed control strategy is then verified using Lyapunov theory. Finally, hardware-in-loop test results are conducted to validate the performance of the control strategy, and its superiority is demonstrated through comparison with existing control methods. • The upper limit of the convergence time can be predefined as an input parameter. • A proportional consensus is achieved for the current with a directed topology. • The effectiveness is validated by using the RT-LAB experimental platform. [ABSTRACT FROM AUTHOR]

In this paper, a new flexible load-reliant cost-driven framework is proposed for peer-to-peer decentralized energy trading of an industrial town in which demand response mechanism is modeled for all active energy consumers (hereinafter referred to as prosumers). In this industrial town, the prosumers employ multiple photovoltaic panels accompanied by battery and hydrogen energy storage systems (B&HESSs) to deliver the required energy to their own loads and sell the surplus energy to other neighboring prosumers. The objective function to be minimized includes: (i) cost of energy trading within the industrial town; (ii) cost of energy trading between the industrial town and neighboring microgrids; and, (iii) cost of energy trading with the upstream grid. The framework also considers power balance constraint for prosumers and microgrids, feeder capacity limits, allowable maximum capacity of energy trading among prosumers, admissible maximum capacity of energy trading among prosumers and microgrids, voltage bounds for prosumers and microgrids, charging and discharging restrictions of the B&HESSs, flexible load constraint, as well as logical constraints. The resulting non-convex mixed-binary linear optimization problem is solved using a linear programming solver. Case study using the IEEE 33-node distribution test system is presented here in order to illustrate the effectiveness of the newly developed framework. According to simulation results, several conclusions can be briefly drawn: (i) applying the B&HESSs brought about a decrease of 12.33% in the total cost of energy trading between the industrial town-owned prosumers and upstream grid; (ii) exerting the B&HESSs is accompanied by a decrease of 44.33% in the total cost of energy trading among the industrial town-owned prosumers; (iii) the utilization of the B&HESSs is associated with a decrement of 77.15% in the total cost of energy trading between the industrial town and neighboring microgrids; (iv) considering voltage security limits increased the willingness of the industrial town-owned prosumers to participate in demand response mechanism up to 27.53%. • To design a large-scale industrial town incorporating the PV panels , BESSs and HESSs. • To improve both power sharing among prosumers and peak load of the upstream PDG. • To propose the multilateral interdependence among prosumers, MGs and upstream PDG. • To formulate a flexible load-reliant cost-driven framework for the P2P energy trading. [ABSTRACT FROM AUTHOR]

As a promising approach to managing distributed energy, the use of microgrids has attracted significant attention among those managing continuous connections to distribution networks. However, the barriers of the data sharing among different microgrids, the uncertainty of the distributed renewable sources and loads, and the nonlinear optimization of power flow make traditional model-based optimization methods difficult to be applied. In this paper, a data-driven coordinated active and reactive power optimization method is proposed for distribution networks with multi-microgrids. A multi-agent deep reinforcement learning (MADRL) method is used to protect the data privacy of each microgrids. Moreover, attention mechanism, which pays attention to crucial information, is presented to overcome the problem of slow convergence caused by the dimensionality explosion of the optimized variables. Two types of agents, controlling discrete action and continuous action devices, respectively, are formulated in coordinated optimization, which reduces voltage violations and improves the system operation efficiency. In addition, in order to improve the performance of the online agent model under variable operation conditions, the transfer learning is embedded in the training process of the MADRL. The proposed method is verified on a modified IEEE 33-bus distribution network with nine microgrids. • MAAC is proposed to accelerate the training process and alleviate the high-dimensional non-convex problem for microgrids. • Transfer learning is combined with MAAC to further accelerate the multi-agent convergence process. [ABSTRACT FROM AUTHOR]

The increasing integration of renewable energy sources with power systems has led to greater uncertainties, heightening the need for more flexible services. Demand-side resources can provide significant flexibility cost-effectively, making it crucial to design new business models to harness their flexible capacities. Hence, this paper introduces a three-stage techno-economic framework designed to enhance the exploitation of flexible capacities from demand-side resources such as Internet-of-Things (IoT)-Enabled Appliances (IEAs), Battery-Integrated Rooftop Solar Panels (BIRSPs), and Electric Vehicle (EV) smart charging systems in the local flexibility market. The proposed framework operates in three stages. First, smart homes with IEAs, BIRSPs, and smart charging systems assess their flexibility and report it to their Microgrid (MG) operators. Next, MG operators optimize strategies to maximize market returns and report capacities to the Distribution System Operator (DSO). Finally, the DSO manages flexibility transactions based on MG inputs. A Stackelberg game theory model using a gradient-based method updates the coupling variables between market parties, ensuring convergence in the second and third stages within a decentralized space with limited information sharing. Implemented on a modified IEEE 69-node distribution system, the model fully utilizes the potential of smart homes, including IEAs, BIRSPs, and smart charging systems in the local flexibility market. It reduces the Upstream Network (UN) share in the local flexibility market by 56.86% and decreases DSO costs for energy balancing by 22.36%. • Multi-stage model for releasing IoT-enabled smart home capacities in flexibility markets • Gradient-based Stackelberg game boosts market convergence and limits info sharing • Game theory sets market prices based on all parties' scheduling strategies • Reducing costs by releasing all demand-side capacities into the local flexibility market [ABSTRACT FROM AUTHOR]

In recent years, with the increasing proportion of photovoltaic (PV) power generation in grid-connected microgrids, suppressing power fluctuations at the point of common coupling (PCC) has become a challenge. This paper proposes a collaborative power dispatch algorithm for battery energy storage systems (BESSs) based on multi-agent reinforcement learning (MARL), aiming to suppress the PCC power fluctuations caused by the uncertainty of PV power generation. First, a distributed multi-agent communication framework is developed, which defines the neighboring areas of agents based on the physical distances between BESSs to reduce the communication and computational cost of agents. Subsequently, a distributed multi-agent dueling double deep Q-network power dispatch algorithm based on the communication framework is proposed. In the proposed algorithm, a distributed Markov decision process is designed, enabling agents to share actions and rewards with neighboring agents locally to collaboratively learn optimal charging and discharging actions for suppress PCC power fluctuations. Finally, the scalability and effectiveness of the proposed algorithm in suppressing PCC power and voltage fluctuations and reducing operational cost are validated through simulation experiments based on the IEEE-33 bus and IEEE-141 bus systems. The simulation results demonstrate significant advantages of the proposed algorithm compared with other baseline MARL and traditional optimization methods. [Display omitted] • A study on PCC power suppression in AC grid-connected microgrids based on BESSs. • A distributed multi-agent communication framework is developed to reduce cost. • A distributed MAD3QN algorithm is proposed to collaboratively learn optimal actions. • Results show proposed algorithm outperforms baseline MARL and optimization methods. [ABSTRACT FROM AUTHOR]

In this work, proof of Authority (PoA)-based Ethereum blockchain is utilized to carry out the peer-to-peer (P2P) energy transactions with an adaptive controller operating in a distributed manner. A federated average learning of recurrent zeroing neural dynamics designed self-adaptive fractional-order proportional integral derivative (FAL-ZND FOPID) controller is proposed for distributed frequency control of networked microgrid (NMG) system. By employing a blockchain-enabled distributed control system and implementing supplementary control, the proposed method efficiently regulates the frequency of P2P energy trading. The contract participation matrix, which facilitates the transmission of energy demand information from consumers to prosumers, is computed as part of the supplementary control. Thus, it provides the power reference signals to prosumers who participate in ancillary frequency services. Overall, the blockchain implementation ensures that the transfer of signals remains secure from cyber threats. To showcase this concept, the prosumer and consumer nodes are established within the blockchain network using Raspberry Pi devices. These devices are then connected to the NMG setup in OPAL-RT through the socket interface and communicate via TCP/IP protocol. • A PoA based blockchain is employed for distributed control of networked microgrid with P2P energy trading. • The proposed distributed control system is protected against malicious attacks using a PoA-based blockchain. • A contract participation matrix is developed as a supplementary control for frequency regulation. • A Federated Average Learning technique used to enhance the performance of ZND control. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) distributed generators (DGs) are inherently stochastic and have low inertia owing to their weather dependence and connection to an inverter. Frequency regulation presents a significant challenge for the high penetration of PV-DGs to microgrids. Recently, a virtual synchronous generator (VSG) has been proposed to enhance the inertia of microgrids because it mimics the behavior of synchronous generators. However, few studies have utilized VSG-based DGs for secondary frequency regulation owing to their stochastic power output. In this study, a model predictive control scheme integrated with two-layer moving-horizon estimation (TL-MHE) observer is proposed and applied to the secondary frequency control of a PV high-penetration microgrid. The local observer estimates the power disturbances in each PV-DGs, whereas the centralized observer estimates the system states and load disturbances. The two-layer observer design decouples multiple observations and reduce the order of the observation equations. Compared with conventional centralized observer-based controllers, the proposed scheme can obtain better observation results. The model prediction controller optimizes the restorative power from each DG in real time to minimize frequency fluctuations, thus affording better control performance. Simulation results show that the proposed TL-MHE method can accelerate the system frequency recovery, reduce frequency- fluctuations peaks, and reduce frequency deviation by more than 80% compared with the conventional method. • A model predictive control scheme integrated with two-layer moving-horizon estimation observer is proposed. • The two-layer observer design decouples multiple observations and reduce the order of the observation equations. • We use deloading virtual synchronous generator control to make the renewable energy participating in the secondary frequency regulation. • The proposed method overcomes the stochasticity of each generating unit and load. [ABSTRACT FROM AUTHOR]

Flexible microgrids with dynamic boundaries have recently been introduced in the literature. With the ability to reconfigure the topology of the microgrids dynamically through remotely controlled switches, flexible microgrids with dynamic boundaries can further improve the resiliency and energy efficiency of microgrids with distributed energy resources (DERs). This paper focuses on the optimal operation considering one of the predominant characteristics of microgrids and distribution systems – unbalanced networks and loads. In existing literature, balanced modeling of microgrids is more common due to its attractive simplicity. The three-phase power unbalance has not been considered as a constraint on the generation units in a microgrid. Negative sequence constraints have also been neglected. In this article, we propose a set of constraints that is specifically related to the capabilities of inverter interfaced resources to supply unbalanced current/power when the microgrid is islanded from the main distribution grid. We incorporate the new set of constraints into two optimization formulations leveraging two convex relaxations of the three-phase power flow equations: mixed-integer linear programming (MILP) and mixed-integer semidefinite programming (MISDP) that optimize the dispatch of controllable switches and DERs in the microgrid. The algorithms are then extended to networked microgrids with grid-forming sources. We test the algorithms on a realistic community microgrid model in Puerto Rico as well as standardized IEEE distribution test feeders. The testing results demonstrate the performance of the proposed algorithms. The MILP is fast and scalable, and the MISDP enforces the negative sequence voltage constraints. • Optimal operation of dynamic boundary MGs considering network and load unbalance. • Integration of unbalanced power and voltage constraints to ensure stable operation. • Fast and scalable mixed-integer linear programming model. • Mixed-integer semidefinite programming captures unbalanced voltage. • Better renewable utilization and resiliency in dynamic boundary microgrids. [ABSTRACT FROM AUTHOR]

Penetration of Distributed Energy Resources (DER) causes overloading or overvoltage in the grid-edge. Traditional measure of DER curtailment could decrease the economy or operating rate. Centralized optimal control of the total grid-edge is usually too complex to solve and requires ubiquitous information system. So Distributed Energy Resource Management System with Distribution Grid Stabilization (DERMS-DGS) was proposed. It decomposes the optimization into one Distribution Grid Operator (DGO) and multiple Micro Grid Operators (MGOs), with an improved Generalized Benders Decomposition (GBD). DGO realizes the optimal power flow of the grid with demand response, and MGO realizes the optimal operation of adjacent DERs in the demand side. To guarantee the convergence of GBD, the feasible region of MGO was estimated by a dedicated optimization so that the feasibility cut was avoided. Typical seasonal distribution grid models for China in 2030 were formulated according to real data and trend. Simulation result showed that, compared with the baseline technology which is based on DER curtailment, DERMS-DGS resolved more overloading or overvoltage, made 3.4% less profit in urban and 90.8% more profit in suburban with DER penetration speed 100%, and 22.7% more profit in urban and 229.5% more profit in suburban with DER penetration speed 150%, and significantly reduced PV curtailment or EV waiting. [Display omitted] • Distributed Energy Resources (DER) damages the stability of the distribution grid. • Traditional method is complex centralized optimization or isolated device control. • Generalized Benders Decomposition realizes the distributed optimization of grid-edge. • Avoiding the feasibility cut ensures the convergence of the master problem. • Distributed optimal management brings more profit when DER penetration is higher. [ABSTRACT FROM AUTHOR]

Microgrids have become increasingly popular in the United States. Supported by favorable federal and local policies, microgrid projects can provide greater energy stability and resilience within a project site or community. This paper reviews major federal, state, and utility-level policies driving microgrid development in the United States. Representative U.S. demonstration projects are selected and their technical characteristics and non-technical features are introduced. The paper discusses trends in the technology development of microgrid systems as well as microgrid control methods and interactions within the electricity market. Software tools for microgrid design, planning, and performance analysis are illustrated with each tool's core capability. Finally, the paper summarizes the successes and lessons learned during the recent expansion of the U.S. microgrid industry that may serve as a reference for other countries developing their own microgrid industries.

A microgrid cluster (MGC) is formed by interconnected geographically adjacent microgrids (MGs), which can effectively improve power supply reliability. To fulfill the requirements of coordination between MGs while exerting the autonomy ability of each MG, this paper proposes a hierarchical distributed control method for DC MGCs with MG autonomous-cooperative mode switching. The proposed method can not only realize the proportional current sharing between the MGs and the voltage regulation of the common bus but also allow MGs to operate in autonomous or cooperative mode by establishing and disconnecting the inter-MG communication links. In addition, considering that the delay of inter-MG communication links affects multiple control links of the proposed control method, a delay-dependent stability analysis method based on Padé approximation and eigenvalue spectrum comparison is proposed. By stability analysis, the time delay margin (TDM) is determined, and the key link that determines the TDM is identified as the observer based on the proportional-integral (PI) consensus algorithm. On this basis, the scattering transformation (ST) is introduced to improve the stability of the observer under delay and thus enhance the TDM of DC MGCs, which is confirmed by stability analysis based on a new system model integrating node variables and edge variables. Finally, the performance of the proposed control method and stability analysis results are verified by hardware-in-loop (HIL) tests and MATLAB/Simulink simulations • A novel hierarchical distributed control method for DC MGCs is proposed. • The proposed control allows MGs to join in cooperation or operate autonomously. • How to identify the key control link determining the time delay margin is studied. • The scattering transformation approach is introduced to improve system stability. • A new system model integrating node and edge variables is established for stability analysis. [ABSTRACT FROM AUTHOR]

The integration of a large number of distributed resources into an active distribution network presents significant challenges, including high control dimensionality, strong output uncertainty, and low utilization of renewable energy. This paper introduces a distributed optimization strategy for networked microgrids based on network partitioning to alleviate the computational burden, reduce operating costs, and enhance the utilization of renewable energy. The active distribution network is partitioned into networked microgrids, and a two-layer distributed optimization model is developed for their management. The first layer focuses on intra-day distributed optimal dispatch, balancing power and load by managing various flexible resources and the exchange power between virtual microgrids. The second layer, real-time distributed power tracking optimization, coordinates flexible resources within virtual microgrids to mitigate photovoltaic power fluctuations and track intra-day dispatch instructions. Simulation results demonstrate that the proposed network partitioning method reduces dispatch costs by 5.3 % and increases the utilization of distributed PV by 3 %, compared to the NP method that only considering modularity. Moreover, calculation times for intra-day dispatch and real-time power tracking are reduced by approximately 26 % and 50 %, respectively, compared to centralized control. • A network partitioning method is proposed to partition the active distribution network into networked microgrids. • A distributed model is proposed to dispatches flexible resources and coordinates interactions between virtual microgrids. • A Synchronous Alternating Direction Method of Multipliers algorithm is developed to solve the distributed model in parallel. [ABSTRACT FROM AUTHOR]

Microgrid is an important way to optimize the distributed power generation and its optimal scheduling to ensure reliable and economical operation. This study constructs a multi-objective optimization model for a microgrid energy management system involving degradation cost and carbon trading mechanism. A carbon trading mechanism is to reduce greenhouse gas emissions; meanwhile, a demand response strategy is employed to optimize energy load demand. The energy storage system mathematical model is considered and degradation cost is introduced to change the corresponding control strategy. A hybrid energy storage is used in this model to smooth out the solar power and wind power fluctuations. Hence, a multi-objective artificial hummingbird optimization algorithm is proposed and uses to solve the optimal operation strategy of the microgrid. The final optimal operation strategy is obtained from the Pareto solution set using TOPSIS. The results show that the proposed microgrid system has 20.2 % lower total operating costs, 4.5 % lower carbon emissions, and 32.6 % longer battery life than the conventional microgrid system, which is critical for improving the operation stability, economy, low carbon of the system, and extending the service life of the battery. [Display omitted] • This study constructs a multi-objective artificial hummingbird algorithm. • A microgrid energy management system is involving degradation cost and carbon trading mechanism. • A hybrid energy storage is used in this model to smooth out the solar power and wind power fluctuations. • The final optimal operation strategy is obtained from the Pareto solution set using TOPSIS. • The system has 20.2 % lower total operating costs, 4.5 % lower carbon emissions, and 32.6 % longer battery life. [ABSTRACT FROM AUTHOR]

Microgrid clusters, characterized by their dynamically changing topologies and flexible operational modes ranging from grid-tied to autonomous functioning, present significant challenges for conventional protective measures. To address these complexities, this paper proposes a novel topology-aware fault diagnosis approach that integrates Message Passing Neural Networks (MPNNs) with a Graph-Lasso-based topology identification method. Our framework is designed to enhance the precision and adaptability of fault diagnosis in microgrid clusters, which are crucial for maintaining system stability. The proposed model demonstrates over 99% accuracy in fault localization and over 97% in fault type recognition, even under varying topological conditions and data loss scenarios. Through the application of digital twin technology, the framework not only improves the diagnostic capabilities but also supports the intelligent operation and maintenance of microgrid clusters, thereby contributing to the overall reliability and quality of their electrical systems. • MPNN model for swift, precise microgrid cluster fault diagnosis. • Transient simulations and stochastic disturbances for boost learning. • Topology analysis to enhance adaptability of operational changes. [ABSTRACT FROM AUTHOR]

The shift towards renewable energy sources, which is especially significant in the residential sector, relies on distributed energy resources (DER) like PV systems, heat pumps, battery storage systems, and electric vehicles. Integrating DERs into low-voltage distribution networks presents challenges, including potential grid instabilities. Energy sharing is a consumer-centric market approach, allowing consumers and prosumers to establish renewable energy communities (RECs) and share energy generated via DERs. Existing literature concerning energy sharing often prioritizes highlighting the benefits it offers to participants, rather than examining its direct impacts on established system boundaries such as distribution grid infrastructure. To this end, we employ a sequential modeling approach to study the integration of energy sharing schemes facilitated by RECs and their impacts on grid performance metrics, such as component loading, voltage magnitudes, and grid reinforcement costs. We examine twelve scenarios reflecting different REC configurations, DER adoption levels, and pricing strategies for both current (2023) and future (2037) contexts in Germany. Our findings indicate that implementing energy sharing not only results in considerable cost savings at the community level (with potential savings of up to 80% compared to scenarios without energy sharing) but also brings about significant reductions in grid asset loading (with decreases in transformer loading of up to 68% and line loading of up to 62%, compared to baseline scenarios). Conversely, we show that energy sharing can significantly influence voltage magnitudes at various nodes within the grid, potentially leading to substantial increases in grid reinforcement costs in future scenarios (i.e., 2037). Our research provides valuable insights for REC participants, regulators, and DSOs to understand the impacts of energy sharing on European low-voltage distribution networks and explore mitigation options, such as grid reinforcement measures. • Modeling of energy sharing in low-voltage distribution networks. • Analysis of Energy systems' configurations, pricing, and future projections. • Assessment of Grid performance on loading, voltage, and reinforcement costs. • Implementing real-world data from Munich, Germany, with upcoming regulations. • Provision of insights into renewable energy communities for, e.g., regulators, utilities, and DSOs. [ABSTRACT FROM AUTHOR]

Distributed secondary control is widely used in the hierarchical control structure of islanded microgrids. However, the information exchanged between distributed generators (DGs) may be intercepted by eavesdroppers, leading to the risk of privacy leakage and even data poisoning attacks that affect the stability of microgrids. Existing privacy-preserving distributed secondary control strategies suffer from low accuracy, high communication and computational overhead, and the poor convergence properties. Moreover, the objectives of multi-bus voltage regulation and proportional reactive power sharing cannot be achieved. To overcome these shortcomings, a privacy-preserving distributed average estimator is innovatively designed, where the states containing privacy information are decomposed into two parts based on the state decomposition method. The average estimation relies on the partial information exchange between neighboring DGs, thus avoiding the leakage of sensitive information. Furthermore, to address the difficulty that the state difference generated by the state decomposition leads to a long convergence time, a time-based generator is designed to effectively resolve the conflict between the privacy-preserving level and the convergence time. On this basis, a privacy-preserving distributed secondary voltage control is proposed, which preserves the privacy of the microgrid states while achieving average voltage regulation and proportional reactive power sharing within a predefined time, maintaining the system voltage stability and preventing the DGs from being heavily or lightly loaded. Finally, a hardware-in-the-loop platform for an islanded microgrid is built and the convergence and privacy-preserving performance of the proposed control strategy is verified. [Display omitted] • A privacy-preserving distributed average estimator is proposed. • A time-based generator is designed to achieve predefined-time convergence. • A privacy-preserving distributed secondary voltage control is proposed. • The proposed control can also be extended to frequency and active power regulation. • The hardware-in-the-loop platform for an islanded microgrid is built. [ABSTRACT FROM AUTHOR]

Due to the increasing microgrid group and shared energy storage integration into active distribution network (ADN), it is necessary to effectively coordinate these complexity energy elements. Therefore, a master-slave game schedule strategy is constructed for ADN based on microgrid group and shared energy storage. The time-of-use electricity price is decided by the ADN as the main body, so the microgrid group and shared energy storage should respond to the electricity price as the subordinate body, which may consider the safe operation and the peak shaving schedule. Moreover, the two-stage power interaction strategy between the microgrid group and shared energy storage is developed by the time-of-use electricity price. In the first stage, the energy storage leasing demand of microgrid group can be calculated through multi-objective optimization algorithms. Then, the charging and discharging strategy is formulated for the shared energy storage which can meet the power demand of the microgrid group and respond to distribution network schedule by the remaining capacity. In the second stage, a schedule strategy is formulated for the cooperative alliance considering power interaction among microgrids and a mechanism of benefit allocation. Furthermore, the equilibrium solution of the master-slave game may be solved through the Quantum Particle Swarm Optimization (QPSO) algorithm nested with the cplex solver. At last, the effectiveness and rationality of the proposed strategy can be verified by the improved IEEE33 bus system. • A master-slave game schedule strategy is proposed for ADN based on microgrid group and SES to solve the problem of pricing and optimization in multi-entity game. • The innovative multi-objective energy storage leasing model for microgrids is constructed by introducing the net load mean square deviation and the surplus/shortage power into the optimization objective function. • A multi-stage distributed iterative algorithm is proposed to effectively solve the leasing model and game equilibrium solution, which can avoid nested iterations in the solving process. • The benefit allocation mechanisms for MGCO is developed based on the power interaction ratio among microgrids to improve the efficiency and rationality of distribution. [ABSTRACT FROM AUTHOR]

Detecting and characterizing malicious operations in electricity markets presents substantial challenges due to the innate complexity of power networks. This paper explores adversarial attacks in electricity markets, i.e., we consider the scenario in which malicious market participants make undetectable changes to the input data of other participants, resulting in significant negative impacts on their operations. We propose a novel methodology to generate adversarial attacks, which leverages linear optimization theory and statistical learning to generate suboptimal, feasible solutions or super-optimal, infeasible solutions that convincingly resemble optimal solutions. The methodology is general and applicable for generating adversarial attacks on optimization-based decision systems. We use it to analyze the potential economic losses, policy making, and social consequences resulting from compromised demand-side operations. In particular, we focus on scenarios where certain demand side-players, i.e., microgrids, or prosumers in a general sense, engage in malicious activities by targeting other microgrids and the independent system operator. We model the electricity market dynamics using an equilibrium problem with equilibrium constraints (EPEC). To make the model easier to solve, the EPEC is reformulated using a combination of the Fisher–Burmeister function and strong duality theorem. Results show that microgrids can artificially decrease its consumption by up to 12% without triggering alarms. Additionally, this amount can be increased by an additional 14% to 22%, contingent on the strategy employed to monitor the observable variables. We also study the implications of the increasing utilization of solar generation, showing its potential to mitigate the impact of adversarial attacks while also introducing new risks to the system. • Methodology generates adversarial attacks on optimization-based decision systems. • Electricity market dynamics are modeled as an EPEC. • Strong duality theory for non-convex bi-level problems with quadratic lower level. • Renewable electricity mitigates the impact of adversarial attacks on the market. [ABSTRACT FROM AUTHOR]

Embedding incremental cost (IC) function into the primary control layer of dispatchable virtual synchronous machines (VSMs) enables decentralized economic dispatch (ED) within autonomous microgrids (μ Grids). This integration minimizes generation costs and provides virtual inertia, enhancing frequency stability. However, it introduces persistent frequency deviations due to the decentralized nature of the model, necessitating secondary frequency control methods to address the deviation. Existing decentralized secondary frequency control strategies, while capable of addressing these deviations, fail to maintain the ED objective. In response to this challenge, this paper first proposes a practical control framework based on integral controllers for grid-forming VSMs. The goal is to restore the frequency of an autonomous μ Grid in a decentralized manner while achieving ED. Although the proposed controllers effectively maintain ED, a small, controllable frequency error must be preserved to sustain decentralized ED. Secondly, the paper proposes a dynamic reactive power control loop to enhance μ Grid stability while retaining the dynamic characteristics of VSMs. Comprehensive small-signal stability analyses are conducted under various conditions to verify improvements in the stability margin. The efficacy of the proposed scheme is validated through extensive time-domain simulations and control-in-loop real-time OPAL-RT simulator tested on the IEEE 38-node μ Grid. • Embedding the incremental cost function into the virtual synchronous machines model. • The combination enables online economic dispatch (ED) and virtual inertia provision. • Frequency restoration, online ED, and inertia emulation are achieved simultaneously. • A dynamic outer voltage controller to improve microgrid stability limit is proposed. • Verified via real-time experiment, small signal stability, and sensitivity analysis. [ABSTRACT FROM AUTHOR]

This paper presents a two-stage stochastic mixed-integer nonlinear programming model, which is linearized to a mixed-integer linear programming (MILP) form, for optimizing the energy–water–carbon (EWC) nexus in remote AC/DC microgrids aimed at sustainable community development. The model optimizes the selection, location, and operation of diesel generators, voltage source converters, photovoltaic systems, wind turbines, and battery energy storage systems while managing CO2 emissions. It accurately models the AC/DC microgrid's operation, determining optimal voltages, currents, and converter states (inverter or rectifier). Uncertainties in power demand, water consumption, solar irradiation, and wind speed are modeled using stochastic scenarios generated via k-means clustering. The MILP model is implemented in GAMS and solved with the commercial solver CPLEX, ensuring global optimization. Results show that integrating distributed energy resources, compared to a case without these elements, reduces CO2 emissions of the diesel generators and the operational costs of the EWC nexus, highlighting the proposed approach's environmental and economic benefits. These findings underline the importance of incorporating distributed energy resources in the sustainable development of microgrids. • New MILP model for EWC nexus in remote AC/DC microgrids optimizes planning. • Accurate steady-state modeling of hybrid AC/DC microgrids. • Optimal integration of distributed energy resources is proposed. • Accurate VSCs modeling for optimal operation. [ABSTRACT FROM AUTHOR]

This paper studies the long-term energy management of a microgrid coordinating hybrid hydrogen-battery energy storage. We develop an approximate semi-empirical hydrogen storage model to accurately capture the power-dependent efficiency of hydrogen storage. We introduce a prediction-free two-stage coordinated optimization framework, which generates the annual state-of-charge (SoC) reference for hydrogen storage offline. During online operation, it updates the SoC reference online using kernel regression and makes operation decisions based on the proposed adaptive virtual-queue-based online convex optimization (OCO) algorithm. We innovatively incorporate penalty terms for long-term pattern tracking and expert-tracking for step size updates. We provide theoretical proof to show that the proposed OCO algorithm achieves a sublinear bound of dynamic regret without using prediction information. Numerical studies based on the Elia and North China datasets show that the proposed framework significantly outperforms existing online optimization approaches, reducing operational costs and loss of load by approximately 60% and 90%, respectively, compared to the model predictive control method. Additionally, the introduction of long-term reference tracking contributes to over 50% of this reduction. These benefits can be further enhanced with optimized settings for the penalty coefficient and step size of OCO, as well as more historical references. • Long-term energy management of microgrid considering seasonal uncertainties and seasonal storage. • A prediction-free two-stage coordinated optimization framework. • SoC reference of hydrogen storage generated using kernel regression with historical and AI-generated scenarios. • A virtual-queue-based online convex optimization algorithm with expert-tracking. • Numerical studies on Elia and North China with ground-truth datasets spanning 10 years. [Display omitted] [ABSTRACT FROM AUTHOR]

Managing the current high penetration of renewable energy sources globally poses a significant challenge due to the distributed and diverse nature of generation components. To maximize real-time power generation, these resources need to perform optimally. Digital twin technology offers a comprehensive framework for managerial support by replicating grid features in a digital environment. This research creates a digital twin of the microgrid to optimize power generation, focusing on computational efficiency and self-healing control. The framework is tested in a laboratory microgrid, with modeling performed using a polynomial regression algorithm. Optimization is achieved through a gradient descent algorithm, and the self-healing model is implemented using a logistic regression algorithm. Real-time data extracted from the microgrid drives this process. The results can be utilized for predictive analysis before deploying a microgrid or to optimize generation in existing systems using the digital twin model. Even though the research focuses on a single microgrid unit, it introduces a framework proposal for extensively distributed microgrids integrating multiple renewable energy sources. • Data-driven modeling approach for microgrid digital twin establishment. • Accurate renewable energy forecasting models for efficient grid operation. • Enhanced grid resilience through self-healing capabilities of digital twin. • Optimized renewable generations with MG digital twin models. • Proposed a scalable framework for large-scale grid networks with microgrids. [ABSTRACT FROM AUTHOR]

Hydrogen, as a high specific energy green carrier, is promising for power generation and transportation. This paper proposes a transactive energy cooperation framework for an end-user-oriented hydrogen-based community microgrid considering refueling preferences of hydrogen vehicles (HVs) and industrial hydrogen demand to minimize social cost. In this framework, an aggregator obtains stacking profits in both the electricity and hydrogen markets through flexible interconversion, storage, and interaction of electricity and hydrogen. For resident users, the refueling preferences of HVs are analyzed to reduce cost through demand response (DR). A novel energy density-weighted asymmetric Nash bargaining (DANB) method is designed to fairly assess the combined contribution, considering both electricity and hydrogen bargaining metrics for end-users. An alternating optimization procedure with the adaptive alternating direction method of multipliers (ADMM) algorithm is designed to solve the mixed-integer linear programming (MILP) problem in the social operation cost minimization process. Comparison results show the algorithm convergence advantage, the cost-effectiveness and scalability of the cooperation framework, as well as the rationality of the DANB and the flexibility of HVs in DR. Besides, the optimal configuration of the battery, the hydrogen tank, and the electrolyzer in the framework is explored, alongside the economic feasibility of electricity–hydrogen–electricity technology. • A hydrogen microgrid framework with hydrogen vehicle fueling preferences is proposed. • A novel energy density-weighted asymmetric Nash bargaining (DANB) method is devised. • An alternating optimization procedure with the adaptive ADMM algorithm is designed. [ABSTRACT FROM AUTHOR]

Microgrids (MGs) can effectively integrate the high penetration distributed energy resources (DERs) for the energy and environmental crisis. Still, fluctuations in intermittent DERs may lead to severe frequency and voltage deviations or even the instability of islanded MGs. Secondary control has successfully compensated for the frequency and voltage deviations. Research on secondary control mainly focused on steady-state operating objectives, i.e., frequency and voltage recovery and power sharing. Still, improving the dynamic responses of secondary control is crucial for system-stable operation, especially in the presence of synchronous DERs. This is because these units can cause undesired oscillatory modes, leading to system instability. In addition, because of the line impedance effect, accurate reactive power sharing is usually ignored when voltage recovery is considered. This will lead to an overload of MGs. To improve the dynamics of secondary control while trading off voltage regulation and reactive power sharing, we propose a Lyapunov-Function (LF)–based distributed secondary control strategy. In the proposed LF-based control strategy, the frequency, voltage, and power information are introduced into feedback control based on the Lyapunov stability theorem. Therefore, the improved frequency and voltage deviations and accurate power sharing can be guaranteed when the Lyapunov function asymptotically attenuates to zero. The inherent conflict between voltage regulation and reactive power sharing is addressed by converging the average voltage to the rated reference. Besides, the improved dynamic performance of secondary control is achieved by considering global power variations, which are obtained by distribution-level phasor measurement units. Global power variations can establish control actions to dampen system oscillations and accelerate system restoration. Furthermore, the controller design method based on the Lyapunov stability theorem can naturally promise the stability of the proposed secondary frequency and voltage controllers. Numerical simulations on the IEEE 34-bus system validate that the proposed control strategy can ensure the significantly improved dynamic performance of secondary control while achieving steady-state operation objectives. • The improved dynamic performance of secondary control of islanded microgrids is achieved. • Global power information measured by D-PMUs is used in secondary controller design. • The dynamic goal of secondary frequency control is ensured by distributed control. • Voltage regulation and reactive power sharing are achieved by distributed control. • Rigorous proof for the stability of the proposed strategy by the Lyapunov theorem. [ABSTRACT FROM AUTHOR]

The utilization of artificial intelligence in the design and operation of a microgrid (MG) can contribute to improve its energy efficiency, resiliency, and cost of energy supply. This research proposes a new approach to conduct a comprehensive analysis for transforming existing low-voltage networks into MGs to achieve the net-zero goal by 2050. A data-driven machine learning-based clustering and profiling approach is designed and implemented to extract the data, constraints, and dependencies from the historical data. Furthermore, the constraints and dependencies are utilized for determining the renewable energy sources' capacity. A Bi-level optimization technique is developed to ensure appropriate coordination of cost and renewable energy source (RES) capacity. A comprehensive analysis is carried out utilizing real historical demand and generation data of an energy community in Australia. Based on the clustered analysis, the consecutive day's data are considered for the analysis. The findings reveal that the proposed microgrids achieve higher renewable RES utilization and lower electricity costs compared to grid-connected systems, with the potential to reduce carbon emissions by up to 98.23% when transitioning from coal-based grid systems to the proposed microgrid system. Additionally, a transformation from a grid time-of-use tariff-based system to the proposed microgrid setup can lead to a cost reduction of 65.45%. These case studies will also assist the researcher in identifying new, potential ideas and industries to accelerate the implementation of remote community microgrids. [Display omitted] • New approach combining AI-based clustering and profiling techniques and a PSO-MILP optimization algorithm. • Detailed modeling of energy resources and demand profiles through mathematical formulation including H 2 demand. • A bi-level optimization integrating two advanced algorithms for efficient capacity and operation solutions. • An AI-based algorithm that clusters and profiles, representing a year of meteorological and energy data. • Economic benefits and net zero feasibility of a microgrid using real data of an energy community. [ABSTRACT FROM AUTHOR]

Distributed energy sources (DERs) and microgrids (MGs) will play an important role in improving the resilience, reliability and sustainability of the grid through dedicated generation, load management and additional capacity struggling to cope with challenges. This study addresses the challenges faced by MG systems, especially in monitoring voltage-frequency operation (V/F) using the proposed two-layer operation scheme that aims to improve MG performance. A pioneering approach is to determine controller coefficients with information from the system components using hierarchical deep-learning-based recurrent convolutional neural network (HDL-RCNN)-excluded attributes have enabled these distributions themselves to determine the optimal conditions for optimal V/F control. Further, the fractional order proportional integral derivative (FOPID) approach, along with the root of the proposed technique, will serve as comparative methods to assess the performance of the HDL-CNN approach. The effectiveness of the proposed method is demonstrated through implementation and validation using the MATLAB/SIMULINK platform. • The study focuses on optimal management of voltage and frequency operations in microgrid systems. • A dual-layered strategy aims to efficiently optimize microgrid operations. • A pioneering approach utilizes a hierarchical deep-learning-based RCNN for optimizing microgrid operations management. [ABSTRACT FROM AUTHOR]

Privacy preservation and coordination of networked microgrids (NMGs) are conventionally contradictory objectives. To address this, this paper proposes a regional-privacy-preserving operation method for NMGs that collaboratively learns differentiated policy (DP) of each microgrid (MG) at the edge by using a designed federated deep reinforcement learning (FDRL) algorithm. In the proposed method, a scalable edge-cloud cooperative framework is designed to integrate multiple independently controlled regional MGs into the existing distribution network (DN) without affecting its operation model. With the proposed framework, MGs can collaboratively optimize the local operation costs and global DN voltage by the respective regional control agent which controls local distributed energy resources power based on the decentralized partially observable Markov decision process. The proposed framework models differentiated private neural network (NN) models for each MG agent at the edge to efficiently address diverse regional tasks, and models a global NN at the cloud server to achieve collaborative training. The differentiated local policy of each MG control agent is learned via edge computing with the proposed DP-FDRL algorithm, which solves different regional tasks, achieves global coordination, and avoids exchanging the raw energy data among different agents simultaneously. By only transiting the global model parameters during the coordinated training process, the private NN models of each agent at the edge are also preserved to the MGs locally. Numerical studies validate that the proposed framework can effectively handle the complex privacy-preserving NMGs coordinated operation problem with collaborative learning through the DP-FDRL algorithm. • Edge-cloud architecture for coordinated operation of networked microgrids (NMGs). • Scalable separate operation of MGs in NMGs with non-private information exchange. • Differentiated-policy federated deep reinforcement learning (DP-FDRL) algorithm. • Edge-cloud privacy preserving cooperative learning thorugh the DP-FDRL algorithm. [ABSTRACT FROM AUTHOR]

The development of 100% renewable energy (RE) systems provides a viable solution for achieving the global target of carbon neutrality. To support the reliable and economical operation of RE-based local energy networks, this paper presents a joint scheduling model for grid-scale RE generation and hydrogen-based flexible loads. The direct load control (DLC) through hydrogen-electrical microgrids is analytically modeled for leveraging the intrinsic flexibility of demand-side multi-energy synergy. To handle the uncertainty and volatility of RE generation, a risk-averse stochastic programming method with the receding-horizon mechanism is developed. Also, the power balancing cost in scheduling objectives is represented as a conditional value-at-risk (CVaR) measure to control the risks of fully RE supply. Case studies on an exemplary RE system confirm the effectiveness and economic benefits of the proposed method. The hydrogen-enabled DLC can largely mitigate the supply–demand mismatches, which shows a great potential to facilitate 100% RE scenarios. • A joint scheduling model of grid-scale renewable energy generation and hydrogen-based flexible loads is proposed to achieve carbon neutrality. • Direct load control through hydrogen-electrical microgrids is analytically modeled and implemented. • A risk-averse stochastic receding-horizon scheduling method is developed to handle the uncertainty of renewable energy generation. • Power balancing cost is represented as conditional value-at-risk (CVaR) to control the operational risks. • Results of case studies indicate the feasibility and cost-effectiveness of methods application for a 100% renewable energy scenario. [ABSTRACT FROM AUTHOR]

In this paper, a microgrid groups with shared hybrid energy storage (MGs-SHESS) operation optimization and cost allocation strategy considering flexible ramping capacity (FRC) is proposed. Firstly, a joint system containing MGs with SHESS is constructed and its operation modes are analyzed. Secondly, Gaussian mixture model (GMM) and Latin Hypercubic Sampling (LHS) are used to obtain the confidence intervals of prediction errors for wind power, PV, and load. And then the FRC demand and supply of MGs-SHESS are quantified to construct the FRC sufficiency index. Furthermore, a multi-objective optimization model considering FRC sufficiency and operating costs is constructed, and the range of uncertain variables is described by confidence intervals, which transform the model into confidence gap decision theory (CGDT) model form. Further, the basic Shapley method is improved by considering the electricity interaction factor, carbon reduction factor, and FRC supply factor. Then use improved Shapley method to construct the MGs-SHESS two- layer cost allocation model. Finally, the case study results show that: (1) The total costs of SHESS are reduced by 5.89% and the FRC sufficiency is increased by 8.43% compared with decentralized energy storage system (DESS), which indicates that SHESS is able to achieve the co-growth of economy and flexibility. (2) Multi-objective dispatch plan considering FRC sufficiency is able to significantly improve FRC sufficiency by 12.56% at an additional cost of only 1.66% compared to conventional economic dispatch. It is worthwhile because it reduces the risk of power curtailment and load shedding during actual operation. (3) The costs of the CGDT model are reduced by 1.26% and the FRC sufficiency is increased by 1.20% compared with the IGDT method. Moreover, the total costs of the CGDT model are only increased by 1.84% compared with the day-ahead stage after substituting multiple random scenarios. It indicates that the CGDT is more robust in dealing with uncertainty. (4) The costs allocation method considering multi-factors improvement can satisfy the interests of multi-subjects. Even if the decision maker has a slight or moderate tendency to a certain factor, it can maintain the satisfaction of each subject to the allocation results, which improve the stability of the joint operation. [Display omitted] • Constructed a joint operation system of MGs and SHESS. • Incorporating FRC sufficiency into the optimization objective of the MGs-SHESS operation plan. • A CGDT method is used to deal with uncertainty. • Constructed an improved Shapley value cost allocation strategy. [ABSTRACT FROM AUTHOR]

The recent international agreements signed by the majority of developed countries, such as those reached at the Climate Change Conferences in Paris'15 and Dubai'23, propose an increasingly rapid transition toward an energy ecosystem with a clear predominance of renewable energy in electrical power systems. The development of such ambitious energy programs should deal with the inherited stochasticity that renewable energy systems entail, which, when coupled with uncertainty about the capacity of the grid to maintain a power supply owing to increasing demands, decarbonization processes and the widespread closure of nuclear power plants, pose a serious threat to the normal functioning of energy systems. When combined with the escalation of armed conflicts that imply the loss of supply as a result of attacks or cyberattacks on power plants and distribution networks, it becomes clear that the current energy paradigm in which there is a centralized grid supplying numerous consumers, many of whom do not have their own generation capacity, must shift toward increasing the deployment of renewable-energy-based self-consumption facilities. The continuous advances toward a decentralized energy system of this nature will also lead to more cooperation, increasing the presence of energy communities with a great need to strengthen internal resilience as a sustaining factor. Considering the challenging framework of smart grids and energy transition, Microgrids would appear to be the key technology for the aggregation of generation, load and energy storage systems, and a cornerstone with which to provide the resilience and flexibility required for this new renewable-energy-based scenario. In addition to the complexity of the microgrid control problem, the issue of resilience energy management also has to be considered, which refers to the ability to adapt and supply loads during a specified period after a disruptive event with a loss of grid supply. This paper introduces an innovative method based on Model Predictive Control (MPC) techniques with the aim of enhancing resilience in microgrids by maximizing the energy surplus and reducing the aforementioned critical instants at which the capacity to feed loads is minimum in the case of power grid outages. The results obtained show that the proposed algorithm enhances the energy resilience in microgrids while the overall operational cost is optimized. A method with which to enhance the resilience of interconnected microgrids through cooperative optimization methods is also developed and validated. [Display omitted] • New techniques for resilience enhancement in microgrids/ networks of microgrids. • Optimizing energy management by enhancing surplus during critical resilience points. • Cooperative resilience management in networked microgrids with cost efficiency. [ABSTRACT FROM AUTHOR]

Microgrids (MGs) powered by renewable energy sources (RES) like solar and wind face integration and management challenges due to their variability and fluctuating energy prices. Moreover, traditional scheduling strategies often overlook the decay in prediction accuracy over time and lack a mechanism for establishing optimal forecasting horizons. This research addresses this gap by merging machine learning (ML) probabilistic forecasting with robust optimization to create optimal dispatch schedules for RES MGs. It reveals that longer scheduling horizons can reduce dispatch costs but at the expense of forecast accuracy due to increased prediction accuracy decay (PAD). To address this, we propose a novel method that determines how to split the time horizon into timeblocks to minimize dispatch costs and maximize forecast accuracy. This forms the basis of an optimal rolling horizon strategy (ORoHS) which schedules distributed energy resources over varying prediction/execution horizons. The model was evaluated on a simulated renewable microgrid with energy storage. Probabilistic forecasts were generated for wind, solar, and energy prices at different confidence levels. This data fed into a robust optimization model to schedule energy transactions. Results offer Pareto-optimal fronts, showing the trade-offs between cost and accuracy at varying confidence levels. Solar power proved more cost-effective than wind power due to lower variability, despite wind's higher energy output. The ORoHS strategy outperformed common scheduling methods. In the case study, it achieved a cost of $4.68 compared to $9.89 (greedy policy) and $9.37 (two-hour RoHS). • Novel rolling horizon strategy optimally splits time horizon for cost and accuracy • Probabilistic forecasts enable robust RES MG scheduling under uncertainty • RES variability leads to trade-off between prediction accuracy and dispatch costs. • Solar forecasts allow longer, cheaper schedules despite lower power output. [ABSTRACT FROM AUTHOR]

Recently, the intent to use microgrid (MG) technology for urban, residential, and industrial applications has significantly increased. Thanks to the integration of shared storage technologies, these power systems allow for higher penetration of distributed renewable generation (DGs) and better mitigation of imbalances between demand and generation. This responds to social and environmental requirements in terms of decarbonizing energy and also contributes to strengthening smart cities. DC (Direct Current) microgrids offer several advantages compared to AC (Alternating Current) type microgrids, like superior efficiency, better control, stability, compatibility with the DC nature of renewables and storage sources, and the absence of reactive and synchronization problems. However, before fully exploiting the potential of microgrids in renewable-powered smart grids, it is necessary to conduct further research and discussion on critical technical and socio-economic challenges. This paper presents a review of the existing state-of-the-art research in DC microgrid development, relevant challenges related to security, communication, power quality, and operation, as well as the appropriate control and energy management strategies to handle them. As control and energy management strategies considerably impact other performance indicators such as operating cost, emissions, and power system safety, this paper offers a perspective on the potential improvement of such management solutions. • An overview of microgrids and their fundamentals is offered. • A comprehensive analysis of the classifications and applications of microgrids. • An in-depth analysis is conducted on the challenges related to the installation of microgrids. • An overview of potential future study topics that are worth investigating in the field of microgrids. [ABSTRACT FROM AUTHOR]

Online energy management utilizing the real-time information of a residential microgrid (RM) can make full use of renewable energy and demand-side resources at the residential level. However, existing online energy management methods for RMs have poor robustness against environmental changes, which limits their applicability in highly uncertain scenarios. To address this, a novel online energy management method based on the prioritized sum-tree experience replay strategy with a double delayed deep deterministic policy gradient (PSTER-TD3) is proposed in this paper. First, we formulate the sequential scheduling decision problem as a Markov decision process (MDP) problem with the objective of minimizing residential energy costs while simultaneously ensuring household thermal comfort and minimizing range anxiety for electric vehicle usage. Then, using the proposed method, we determine the optimal online scheduling strategy under this objective. By integrating the prioritized experience replay strategy of the summation tree structure into TD3, the agent is able to learn the optimal scheduling strategy in complex environments, and its optimization performance and policy learning efficiency are significantly improved. In addition, its ability to handle multidimensional continuous action spaces helps achieve finer-grained optimization for RMs. The case study results demonstrate that the proposed method can effectively reduce the energy costs of residential microgrids while satisfying household thermal comfort requirements and reducing range anxiety for electric vehicle usage. Moreover, the optimization performance of the proposed method is robust when the uncertainty factors fluctuate violently in the environment. • A MDP with unknown state transition probabilities is established. • the thermal comfort and range anxiety are considered in the HEMS joint scheduling. • A RM online energy management method based on a novel DRL method is proposed. • Results show the superior performance of the proposed energy management method. [ABSTRACT FROM AUTHOR]

The uncertainty of renewable energy necessitates reliable demand response (DR) resources for power system auxiliary regulation. Meanwhile, the widespread deployment of energy-consuming 5G base stations (gNBs) drives internet service providers (ISPs) to seek energy expenses reduction. This paper intelligently addresses these two complementary needs by integrating gNBs into the power system dynamic economic dispatch (DED). To achieve this, a combined DED model that incorporates both the power system and 5G communication network is developed, where numerous distributed gNBs and their backup energy storage systems (BESSs) are integrated as a virtual power plant (VPP) to offer power support and obtain economic incentives. Instead of traditional optimization methods, the combined DED model is solved by a cooperative game-based solution method in a benefit-balanced manner, facilitating the power system and 5G network to achieve a win-win DED solution. This method comprises a two-stage game model and a solution algorithm based on improved random drift particle swarm optimization (RDPSO). The improved RDPSO constructs an uncertainty set in each iteration, facilitating its solution of stochastic optimization involving power system uncertainties. Lastly, a VPP management function is developed within the 5G core network (5GC) to efficiently evaluate the VPP's dispatchable capacity and formulate optimal control policies that can minimize control costs. Simulations based on the IEEE 118-bus system and real device data demonstrate that the proposed method can simultaneously reduce the power system's operating costs and increase the ISP's economic revenue. • Addressing complementary needs by integrating gNBs into power system DED. • Game-based solution for DED balancing supply and demand sides benefits. • Improved RDPSO algorithm for power system DED considering uncertainties. • VPP function efficiently evaluating dispatchable capacity and formulating optimal control policy. • Economic and technical win-win results in simulation based on real data. [ABSTRACT FROM AUTHOR]

The massive deployment of microgrids could play a significant role in achieving decarbonization of the electric sector amid the ongoing energy transition. The effective operation of these microgrids requires an Energy Management System (EMS), which establishes control set-points for all dispatchable components. EMSs can be formulated as classical optimization problems or as Partially-Observable Markov Decision Processes (POMDPs). Recently, Deep Reinforcement Learning (DRL) algorithms have been employed to solve the latter, gaining popularity in recent years. Since DRL methods promise to deal effectively with nonlinear dynamics, this paper examines the Twin-Delayed Deep Deterministic Policy Gradient (TD3) performance – a state-of-the-art method in DRL – for the EMS of a microgrid that includes nonlinear battery losses. Furthermore, the classical EMS-microgrid interaction is improved by refining the behavior of the underlying control system to obtain reliable results. The performance of this novel approach has been tested on two distinct microgrids – a residential one and a larger-scale grid – with a satisfactory outcome beyond reducing operational costs. Findings demonstrate the intrinsic potential of DRL-based algorithms for enhancing energy management and driving more efficient power systems. • Application of Deep Reinforcement Learning for microgrid energy management. • A nonlinear battery-loss model is used seeking a realistic approach. • A realistic control system is considered in the model for robust results. • Operational costs are reduced by 2% over previously reported work. • Energy losses in the battery are reduced by 10% in the scenarios simulated. [ABSTRACT FROM AUTHOR]

With the rapid development of shared energy storage (SES) and distributed energy resources, the local energy market (LEM) has become a pivotal platform for the interaction between microgrids and distributed energy. In LEM, the challenge of formulating pricing strategies that effectively align with wholesale market prices, and coordinating SES leasing with energy trading, is crucial for the healthy development of the market. This study introduces a real-time floating pricing (FRTP) strategy based on day-ahead market (DAM) prices and establishes a multi-objective, two-level Stackelberg game model between micro-grid operator (MGO) and the photovoltaic prosumer aggregator (PVPA). The model, constrained by a price cap mechanism, employs distributed algorithms to optimize coordinated pricing strategies for trading and SES leasing. Through simulations of various trading modes, this study thoroughly examines the impact of market signals on MGO's pricing strategies within the LEM. Our findings reveal that: (1) Under the two-level trading model, both MGO and PVPA can maximize their economic benefits. (2) The FRTP strategy increases MGO's revenue by 2.86%, 2.35% and 1.55% compared to fixed pricing, time-of-use (TOU) and hybrid pricing strategies, respectively, while also reducing carbon emissions by 3.68%, 1.35%, and 0.8%. (3) Setting price caps plays a crucial role in managing DAM price volatility. This study provides theoretical insights that enhance the market performance of MGO and PVPA, promoting the market-based integration of distributed energy resources crucial for their effective utilization. • Propose a multi-objective two-layer Stackelberg game model for prosumers and MGO. • The proposed FRTP strategy enables the linkage of LEM prices with DAM prices. • Introduce a "price cap" mechanism as the price discipline for LEM transactions. • A synergistic pricing mechanism for electricity trading and SES leasing is designed. • The proposed trading model improve economic and environmental benefits. [ABSTRACT FROM AUTHOR]

Microgrids are an increasingly popular solution to provide energy resilience in response to increasing grid dependency and the growing impacts of climate change on grid operations. However, existing microgrid models do not currently consider the uncertain and long-term impacts of climate change when determining a set of design and operational decisions to minimize long-term costs or meet a resilience threshold. In this paper, we develop a novel scenario generation method that accounts for the uncertain effects of (i) climate change on variable renewable energy availability, (ii) extreme heat events on site load, and (iii) population and electrification trends on load growth. Additionally, we develop a two-stage stochastic programming extension of an existing microgrid design and dispatch optimization model to obtain uncertainty-informed and climate-resilient energy system decisions that minimizes long-term costs. Use of sample average approximation to validate our two case studies illustrates that the proposed methodology produces high-quality solutions that add resilience to systems with existing backup generation while reducing expected long-term costs. • We generate climate change-informed load and renewable energy production profiles. • We extend a design-and-dispatch problem to a two-stage stochastic program. • Cases with high variation in annual renewable output show the methodology's value. [ABSTRACT FROM AUTHOR]

The transition to renewable energy sources, particularly sources like wind and solar induces a dependency on weather in the supply side of electrical grids. At the same time, the move to electric mobility with uncontrolled charging induces extra peak loads on these grids. These developments can cause grid congestion or an imbalance between the renewable power supply and the demand. Locally balancing the power supply and demand in grid-connected microgrids can alleviate such issues on the main grid. This paper presents a model based control strategy to address the challenge of locally balancing the power supply and demand in a grid-connected microgrid to avoid reaching the threshold rated power output set for large buildings. The microgrid under consideration consists of photovoltaic power sources and a large fleet of electric vehicle chargers (> 150). A model predictive controller is developed that treats the daily vehicle charging as a batch process. Given vehicle charge objectives, the controller utilizes vehicle charger occupancy and photovoltaic power generation forecasting services to distribute power optimally over a fixed period of time. The optimization problem is formulated as a quadratic programming problem and is implemented utilizing open-source Python libraries. The controller was integrated into the control system of a microgrid situated at a corporate office in the Netherlands. The control system oversaw the operation of 174 vehicle chargers. The effectiveness of the model predictive control technology was demonstrated over a three-week period and led to an average daily grid peak power reduction of 59%. • A model-based control strategy is presented in a grid-connected microgrid. • The microgrid under consideration consists of PV and 174 vehicle chargers. • The controller's strategy minimizes power grid peaks and maximizes EV charge energy provided by the PV cells. • The controller was integrated and implemented in the microgrid system. • An average daily grid peak power reduction of 59% was achieved over a three week period. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative approach for achieving optimal energy management (OEM) in renewable-based hybrid microgrids (HMGs). The HMGs exhibit enhanced efficiency, minimizing power loss while maximizing generated power, making them advantageous over traditional MGs. This new approach consists of a modified model predictive control based improved Firefly1to3 algorithm (MMPC-IFA1to3). Here, MMPC adeptly regulates converter switching phenomena, ensuring stability during uncertain conditions in HMG. On the other hand, FA1to3 states that for each member movement of a firefly population, occurs towards three members. Here, the improved nature of FA1to3 is achieved by using the chaotic function to fine-tune the regulation parameter. This unified method generates the best power references for both active and reactive powers, making switching operations in voltage source converters simpler. This paper mainly emphasizes active and reactive power management through objective function minimization. The proposed IFA1to3 approach effectively incorporates constraints to minimize costs, ensure power availability, and mitigate voltage deviations in renewable-based HMGs. Moreover, charging/discharging of energy storage devices and power exchange between the utility grid and DC MG are carried out through the MMPC-IFA1to3 algorithm by monitoring active and reactive loads simultaneously. The corresponding converters are modeled based on the bidirectional power flow to cope with active and reactive power management. Further, the robustness of the proposed approach is verified under different operating conditions, and outcomes are compared with benchmark techniques in a MATLAB/Simulink environment to evidence its superior EM in HMG. Finally, the proposed MMPC-IFA1to3 approach is validated in a real-time environment through the dSPACE DS 1104 embedded processor to evidence its industrial applications through its robustness and OEM during various case studies. • In the hybrid MG, MPC regulates converter of the connected sources, while MMPC controls the VSC converter. • A novel FA, combining FA1to3 with chaotic logistic function, optimizes power references generation in this study. • Proposed MMPC-IFA1to3 approach ensures voltage stability and effectively manages ARP during uncertainties in MG. • The proposed IFA1to3 is more effective for multi-objective functions optimization. • Real-time validation of the proposed approach in dSPACE DS1104 evidences its practical applicability. [ABSTRACT FROM AUTHOR]

This paper introduces a novel Multi-Criteria Decision Analysis (MCDA) framework for systematic evaluation and alignment of business models for community microgrids within local energy markets (LEMs). Our approach uniquely integrates social capital as a critical metric for assessing the success of these models. The framework's robustness is demonstrated through a case study application, showcasing its utility as a decision-support tool. This tool effectively bridges the gap between the operational and technical aspects of microgrids with the socio-economic context of the communities they serve. A key contribution is the establishment of a systematic categorisation of business models and LEMs facilitated by the MCDA framework. This approach emphasises the importance of analysing the synergy between business model components (ownership, revenue/pricing, activities, value proposition) and the corresponding market mechanisms, infrastructure, and ICT frameworks. The results demonstrate how the framework guides an informed and context-sensitive selection of business models, leveraging the interconnected socio-technical dynamics inherent to community energy systems. • MCDA framework integrating social capital for community microgrids. • New approach for selecting context-sensitive business models. • Case study demonstrating the framework as an effective decision-support tool. • Analysis of business model components and market mechanisms for optimal selection. [ABSTRACT FROM AUTHOR]

Low-carbon operation of renewable microgrids (MGs) is challenging because of the large share of distributed renewable energy sources (RESs) and increasingly complex loads. Therefore, a favorable energy management system (EMS) should be developed. In this paper, in order to promote the grid-friendly and economic interaction with the main grid, an improved multi-time-scale EMS is proposed for the renewable MGs composed of distributed RESs, battery energy storage (BES) units and flexible loads. Firstly, flexible resources, such as BES units and flexible loads, are integrated to deeply participate in the low-carbon operation of renewable MGs. A dynamic and precise operating reserve scheme based on the probabilistic interval is formulated to ensure that BES units can operate in a coordinated manner between economic scheduling and operating reserve. Then, the demand respond based on the expected kilowatt-hour power curve variance and power fluctuation cost model is constructed for jointly regulating the distribution of the tie-line power. Last, the improved multi-time-scale EMS consists of day-ahead scheduling plan and intraday rolling correction. The intraday rolling scale with a varying rolling window performs optimal rescheduling for remaining periods. The deviation between the day-ahead scheduling plan and actual operation is corrected in real time. Simulations on a park-scale MG revealed the effectiveness of the proposed multi-time-scale EMS. In particular, when the fluctuation coefficient was set to 0.1, compared with the existing day-ahead scheduling plan scenario, the peak-to-valley difference in the proposed method decreased by 62.262%, and this method requires a 2.072% increase in the grid energy cost. Moreover, in terms of actual operation results of two EMS cases, the total operating cost reduction in the proposed multi-time-scale EMS is 2.474% compared with the single-time-scale EMS case. The proposed multi-time-scale EMS case has superior fluctuation metrics, which shows that the interactive operation of in the proposed multi-time-scale EMS is more grid-friendly. [Display omitted] • Flexible resources are deeply participated in the low-carbon operation of microgrids. • A precise operating reserve scheme based on the probabilistic interval is formulated. • The expected power curve variance model is proposed to regulate the tie-line power. • The improved multi-time-scale EMS performs optimal rescheduling for remaining periods. [ABSTRACT FROM AUTHOR]

The operation of existing renewable power grid and trailer-based hydrogen chain integrated system generally ignores the adjustability of energy transfer process, leading to unsatisfactory results. To address the problem, a flexible energy transfer strategy for integrated electric‑hydrogen systems with hydrogen vessels (HVs) is proposed in this paper. First, the continuous adjustability of working time (i.e., transiting and berthing time) of HVs is reasonably modelled without relying on fixed scheduling step. Meanwhile, both the controllability of the transmission line components and the role of overlying ice in enhancing the line thermal stability are analyzed. Additionally, a value assessment model for flexible energy transfer is developed which considers time-related fees (e.g., HV berthing fees) and frequency-related fees (e.g., HV tying/untying fees) of hydrogen transportation, the melting ice value of line power heating, power reserve costs and generation costs. On this basis, an energy scheduling strategy is designed for integrated electric‑hydrogen systems to minimize daily operating costs. This scheduling model is first linearized as a mixed-integer linear programming problem and then solved efficiently using decomposition method. Finally, case studies on a modified IEEE-RTS79 power system and river network in Jiangsu Province verify the effectiveness of the proposed strategy. • A flexible hydrogen transfer model of HVs is developed. • A flexible electricity transfer model is proposed. • A value assessment model for flexible energy transfer is established. • A new algorithm is proposed to solve the dispatching model efficiently. [ABSTRACT FROM AUTHOR]

This study explored methods to operate a sustainable system by improving energy independence and integrating renewable energy into a microgrid. However, there are challenges associated with the intermittency of renewable energy and difficulties in predicting the energy demand of energy systems. Therefore, this study emphasizes the need to analyze solar power generation and predict the cooling and heating demands of energy systems. The target system was a smart farm in which hot and cold water was supplied to the smart farm using a heat pump during seasonal changes. A prediction model for the smart farm was developed to calculate power consumption according to the ambient temperature in summer and winter. The results suggested that additional consideration is needed to optimize system operation and renewable energy use to improve the balance between energy consumption and supply. A comparison of the total power consumed to solar energy generation highlighted the challenge of attaining 100% self-sufficiency rates, reaching 44% in summer and 40% in winter. Analysis of solar power generation and air-source heat pump usage trends provided insights into strategies for achieving energy independence in smart farms. In conclusion, this study provides a model for managing energy demands on smart farms and insights into the optimization of system operation with renewable energy. It also provides a foundational framework for evaluating microgrid systems based on renewable energy and contributes to identifying methods to increase the energy self-sufficiency rates to enable sustainability of smart farms. • Smart farm energy consumption rises with hotter summer and colder winter. • Energy requirements in smart farms fluctuate with seasonal temperature changes. • Energy independence is difficult as solar energy provides only 44% of summer demand. • Optimizing renewables enhances smart farm sustainability and energy independence. [ABSTRACT FROM AUTHOR]

This research presents an interconnected operation model that integrates carbon capture and storage (CCS) with power to gas (P2G), tackles the challenges encountered by integrated electricity-natural gas systems (IEGS) in terms of energy consumption and achieving low-carbon economic operations, and formulates a DRL-based, physically model-free energy optimization management strategy for IEGS, designed to lower operational costs and carbon emissions. Initially, the CCS-P2G interconnected IEGS system undergoes mathematical modeling. Subsequently, the system's uncertainty in optimal scheduling is formulated as a Markov decision process, with the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm facilitating real-time scheduling decisions. Comparative analysis across various scenarios demonstrates that the model offers superior low-carbon economic benefits and enhanced environmental sustainability. Further analysis validates that the optimized scheduling strategy proposed herein advantages in achieving low-carbon financial objectives, convergence speed, and system learning performance, as evidenced by training the model with historical data and the comparative analysis of the DQN and DDPG algorithms. • "Integrates CCS and P2G in IEGS for optimal renewable use." • "Reduces wind and solar curtailment effectively." • "Enhances system's low-carbon economy significantly." • "Utilizes surplus renewable energy for hydrogen production." • "Demonstrates CCS-P2G synergy in minimizing carbon emissions." [ABSTRACT FROM AUTHOR]

This study presents a novel building energy management system scheme addressing the challenges of biased photovoltaic voltage prediction and optimal energy management methods applicable to existing infrastructure. A biased PV prediction model is proposed to obtain predictions aligned with the intended uncertainty tendencies. In addition, an energy management scheme is introduced to increase energy efficiency by reducing the overall cost without requiring additional controllable power sources. Furthermore, a control scheme for optimizing the operational cost while considering the occupants' thermal discomfort is proposed by analyzing the occupancy rate in the building and implementing appropriate temperature settings and elevator operations. This approach has the advantage of minimizing overall electricity cost while considering the users' dissatisfaction, without the installation of additional controllable power resources. Here, the feasibility of the proposed approach is demonstrated through a reduction in the daily electricity cost and peak power by 8.56% and 11.75%, respectively, compared with scenarios without a proper building energy management system. These results demonstrated the effectiveness of the proposed building energy management system for achieving energy efficiency and cost savings in practical building environments. The proposed scheme can be used in buildings with diverse users to reduce energy consumption and overall building cost. • Proposed a building energy management system model robust against data uncertainty. • The proposed scheme reduced energy consumption and overall building cost. • Optimized energy consumption using a biased prediction scheme. • Optimized operational cost while considering the occupants' thermal discomfort. [ABSTRACT FROM AUTHOR]