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The optimal scheduling of networked microgrids considering the coupled trading of energy and carbon emission allowance (CEA) has been extensively studied. Notably, the scheduling is performed on a daily basis, whereas the CEA is usually checked and determined once a year. The temporal mismatch between the daily scheduling and the yearly CEA should be addressed to realize the dynamic valuation of CEA. In this paper, the optimal scheduling of networked microgrids considering the temporal equilibrium allocation of annual CEA is investigated. Firstly, a CEA decomposition model is developed, which allocates allowance to individual microgrids and further decomposes them temporally using the entropy method. Secondly, a Lyapunov optimization-based low-carbon scheduling model is introduced to manage carbon emissions within each dispatch interval, ensuring annual CEA compliance and daily economic efficiency. Thirdly, a Stackelberg game-based energy–carbon coupling trading model is presented, which considers the uncertainties caused by fluctuations in external electricity and carbon prices to optimize trading prices and strategies of the microgrids. Finally, a test system is used to demonstrate the significant effects of emission reduction and the economic benefits of the proposed methods. [ABSTRACT FROM AUTHOR]

Networked microgrids (NMGs) enhance the resilience of power systems by enabling mutual support among microgrids via dynamic boundaries. While previous research has optimized the locations of mobile energy storage (MES) devices, the critical aspect of MES capacity sizing has been largely neglected, despite its direct impact on costs. This paper introduces a two-stage optimization framework for MES sizing, pre-positioning, and re-allocation within NMGs. In the first stage, the capacity sizing and pre-positioning of MES devices are optimized before a natural disaster. In the second stage, the re-allocation and active power output of MES devices are adjusted post-disaster, with boundary switches operated based on the damage scenarios. The framework restores unserved loads by either forming isolated microgrids using MES or re-establishing connections between microgrids via smart switches. The proposed framework is modeled mathematically and solved using a customized progressive hedging algorithm. Extensive experiments on modified IEEE 33-node and 69-node systems demonstrate the model's effectiveness and applicability in improving system resilience. [ABSTRACT FROM AUTHOR]

Due to the increase in electric energy consumption and the significant growth in the number of electric vehicles (EV) at the level of the distribution network, new networks have started using new fuels such as hydrogen to improve environmental indicators and at the same time better efficiency from the excess capacity of renewable resources. In this article, the services that can be provided by hydrogen refueling stations and charging electric vehicles in the optimal performance of microgrids have been investigated. The model proposed in this paper includes a two-stage stochastic framework for scheduling resources in microgrids, especially hydrogen refueling stations and electric vehicle charging. In this model, two main goals of cost minimization and greenhouse gas emissions are considered. In the proposed framework and in the first stage, the service range of microgrids is determined precisely according to the electrical limitations of distribution systems in emergency situations. Then, in the second stage, the problem of energy management in each microgrid will be solved centrally. In this situation, various indicators including the output energy of renewable sources, smart charging of hydrogen and electric vehicle charging stations (EV/FCV) and flexible loads (FL) are evaluated. The final mathematical model is implemented as a multivariate integer multiple linear problem (MILP) using the GUROBI solver in GAMS software. The simulation results on the modified IEEE 118-Bus network show the positive effect of the presence of flexible loads and smart charging strategies by charging stations. Also, the numerical derivation shows that the operating costs of the entire system can be reduced by 4.77% and the use of smart charging strategies can reduce greenhouse gas emissions by 49.13%. • Embedding network security constraints to ensure that each microgrid operates within safe voltage levels and avoids line capacity. • Examining the impacts of flexible loads and the charging patterns of EVs and FCVs on operational costs. • Incorporating hydrogen storage systems and refueling stations in the model to mitigate the adverse effects of renewable energy fluctuations. • Establishing a multi-objective model to consider both economic and environmental aspects of energy management in the proposed model. [ABSTRACT FROM AUTHOR]

A networked microgrid is composed of multiple nearby microgrids linked together to gain additional flexibility for resilient operations. Networked microgrids collaborate to prevent power shortages in microgrid clusters by sharing critical renewable and energy storage resources. However, controlling the local resources of each microgrid, including the energy storage systems' charging and discharging, maintaining the DC bus voltage, and even overseeing the power shared by multiple microgrids, is challenging. Therefore, a microgrid control technique and distributed energy management are used cooperatively in this study to handle the shared power between a system of networked microgrids incorporating photovoltaics and battery energy storage systems. Numerical simulation results from a networked microgrid system verify the accuracy and soundness of the suggested distributed energy management under several operating conditions, including renewable uncertainties and sequential load variations in different zones. The applicability of the suggested technique is confirmed by hardware implementation, and several operational scenarios further evaluate the proposed system on a practical two-microgrid system located in the Florida International University (FIU) testbed. [ABSTRACT FROM AUTHOR]

The current tendency toward increases in energy prices makes it necessary to discover new ways in which to provide electricity to end consumers. Cooperation among the various self-consumption facilities that form energy communities based on networked microgrids could be a more efficient means of managing the renewable resources that are available. However, the complexity of the associated control problem is leading to unresolved challenges from the point of view of its formulation. The optimization of energy exchanges among microgrids in the day-ahead electricity market requires the generation of an optimal profile for the purchase of energy from and sale of energy to the main grid, in addition to enabling the community to be charged for any deviation from the schedule proposed in the regulation service market. Microgrids based on renewable generation are systems that are subject to inherited uncertainties in their energy forecast whose interconnection generates a distributed control problem of stochastic systems. Microgrids are systems of subsystems that can integrate various components, such as hybrid energy storage systems (ESS), generating multiple terms to be included in the associated cost function for their optimization. In this work, the problem of solving complex distributed stochastic systems in the Mixed Logic Dynamic (MLD) framework is addressed, as is the generate of a tractable formulation with which to generate deterministic values for both exchange and output variables in interconnected systems subject to uncertainties using hybrid, stochastic and distributed Model Predictive Control (MPC) techniques.

Networked microgrids (NMGs) enhance the resilience of power systems by enabling mutual support among microgrids via dynamic boundaries. While previous research has optimized the locations of mobile energy storage (MES) devices, the critical aspect of MES capacity sizing has been largely neglected, despite its direct impact on costs. This paper introduces a two-stage optimization framework for MES sizing, pre-positioning, and re-allocation within NMGs. In the first stage, the capacity sizing and pre-positioning of MES devices are optimized before a natural disaster. In the second stage, the re-allocation and active power output of MES devices are adjusted post-disaster, with boundary switches operated based on the damage scenarios. The framework restores unserved loads by either forming isolated microgrids using MES or re-establishing connections between microgrids via smart switches. The proposed framework is modeled mathematically and solved using a customized progressive hedging algorithm. Extensive experiments on modified IEEE 33-node and 69-node systems demonstrate the model’s effectiveness and applicability in improving system resilience.

Electric power networks face vulnerabilities from various hazards, including extreme weather and natural disasters, resulting in prolonged outages and service disruptions. This paper proposes prosumer-centric networked electrical microgrids as a solution. EMGs integrate DERs, like SPV panels, WTs, BESSs, and BEVs, to form autonomous microgrids capable of operating independently during grid disruptions. The SMA was used to identify the appropriate allocation of DERs and BEVs to improve the resilience of the system. Prosumers, acting as both producers and consumers, play a crucial role by generating and sharing electricity within the microgrid. BEVs act as mobile energy storage units during emergencies. Load management and demand response strategies prioritize the energy needs for essential facilities, ensuring uninterrupted operation during adverse weather. Robust communication and control systems improve the emergency coordination and response. The resilience analysis focused on two case studies: moderate and severe damage, both under varying weather conditions. Simulations and experiments assessed the microgrid performance with different levels of DERs and demand. By testing on the IEEE 69-bus RDS, evaluated the EMGs' strengths and limitations, demonstrating their potential to enhance distribution grid resilience against natural disasters. [ABSTRACT FROM AUTHOR]

The increasing impact of climate change and rising occurrences of natural disasters pose substantial threats to power systems. Strengthening resilience against these low-probability, high-impact events is crucial. The proposition of reconfiguring traditional power systems into advanced networked microgrids (NMGs) emerges as a promising solution. Consequently, a growing body of research has focused on NMG-based techniques to achieve a more resilient power system. This paper provides an updated, comprehensive review of the literature, particularly emphasizing two main categories: networked microgrids' configuration and networked microgrids' control. The study explores key facets of NMG configurations, covering formation, power distribution, and operational considerations. Additionally, it delves into NMG control features, examining their architecture, modes, and schemes. Each aspect is reviewed based on problem modeling/formulation, constraints, and objectives. The review examines findings and highlights the research gaps, focusing on key elements such as frequency and voltage stability, reliability, costs associated with remote switches and communication technologies, and the overall resilience of the network. On that basis, a unified problem-solving approach addressing both the configuration and control aspects of stable and reliable NMGs is proposed. The article concludes by outlining potential future trends, offering valuable insights for researchers in the field. [ABSTRACT FROM AUTHOR]

Recent increases in the frequency of natural disasters and cyberattacks underscore the significance of enhancing outage management and service restoration in distribution networks. Traditionally, the distribution system operator (DSO) is responsible for service restoration, including locating damaged components, reconfiguring the network, and scheduling distributed generators (DG). However, the traditional single entity in the distribution network is being replaced by several individual entities with the growth of self-governed microgrids (MG), including the DSO and microgrid operators (MGO). Due to privacy concerns, these entities will schedule their resources in pursuit of their individual objectives, while still being physically connected. In this paper, a decentralized method is developed to model the independent service restoration of the DSO and MGOs while describing the interaction between them. Decision authority is given to each entity to autonomously schedule and operate the generation and load demand. Each entity solves its own optimization problem to maximize its own served loads based on the resources it possesses and the information it shares. The proposed algorithm uses an iteration procedure to negotiate among different entities and achieve consensus. A test case of an IEEE 33-node test system with multiple networked MGs is studied to investigate the feasibility and effectiveness of the proposed decentralized restoration scheme.

This paper presents a resilience-oriented energy management approach (R-OEMA) designed to bolster the resilience of networked microgrids (NMGs) in the face of extreme events. The R-OEMA method strategically incorporates preventive scheduling techniques for hydrogen (H2) systems, renewable units, controllable distributed generators (DGs), and demand response programs (DRPs). It seeks to optimize the delicate balance between maximizing operating revenues and minimizing costs, catering to both normal and critical operational modes. The evaluation of the R-OEMA framework is conducted through numerical simulations on a test system comprising three microgrids (MGs). The simulations consider various disaster scenarios entailing the diverse durations of power outages. The results underscore the efficacy of the R-OEMA approach in augmenting NMG resilience and refining operational efficiency during extreme events. Specifically, the approach integrates hydrogen systems, demand response, and controllable DGs, orchestrating their collaborative operation with predictive insights. This ensures their preparedness for emergency operations in the event of disruptions, enabling the supply of critical loads to reach 82% in extreme disaster scenarios and 100% in milder scenarios. The proposed model is formulated as a mixed-integer linear programming (MILP) framework, seamlessly integrating predictive insights and pre-scheduling strategies. This novel approach contributes to advancing NMG resilience, as revealed by the outcomes of these simulations. [ABSTRACT FROM AUTHOR]

In this paper, an optimal economic dispatch model is proposed for networked microgrids in normal and contingency operations using particle swarm optimization. To solve the optimal economic dispatch problem, a summation of two objective functions is formulated, which is to minimize the amount of load to be shed and operation cost of the networked microgrids. The performance of the proposed optimal economic dispatch model was evaluated for several scenarios in independent and networked modes based on two IEEE 9-bus test systems. An analytical model based on optimal power flow was developed to identify the optimal buses of the microgrids that should be linked to form the networked microgrids. The results indicate that the proposed model minimizes the operation cost by up to 2.8% and minimizes the amount of load to be shed by up to 5.6% of the networked microgrids. Moreover, the findings show that the operation of the microgrids is more favorable when the microgrids operate in networked mode compared with that when the microgrids work independently, and the cooperation strategy for power sharing significantly reduces the amount of load to be shed for the microgrids. [ABSTRACT FROM AUTHOR]

Networked microgrids emerged from the growing deployment of microgrids in the distribution network. The coordinated operation of networked microgrids offers technical and economical benefits to microgrid owners, customers, grid/utility, and other stakeholders. The optimal capacity sizing of renewable energy sources and battery energy storage systems allows microgrids to minimize costs and maximize reliability. A multi‐objective optimization problem is developed for optimal sizing in a networked microgrid consisting of four different microgrids. The annual energy costs and loss of power supply probability index are taken as objectives. Peer‐to‐peer and peer‐to‐grid energy trading approaches are employed. The peer‐to‐peer energy trading among microgrids employs the proposed "proportional trading method" via a networked microgrid manager or aggregator. The multi‐objective optimization problem formulated is solved using Multi‐Objective Particle Swarm Optimization. The individual objective optimization results for annual energy cost and loss of power supply probability are also analyzed. The proposed method decreases the interaction between the grid and the MGs, and the usage of renewable energy sources is enhanced. The capacity of battery energy storage systems is lowered by 96%, 53.2%, 48.86%, 21% for respective microgrids in networked microgrids. The results of proportional peer‐to‐peer energy trading‐based multi‐objective optimization show that trading energy among microgrids minimizes annual energy cost by 0.75% while maintaining system reliability. [ABSTRACT FROM AUTHOR]

This paper proposed an distributed energy management for modern distribution systems with various actively interfaced participants, such as distributed energy resources (DERs), flexible loads, and different kinds of microgrids. Given different ownerships, objectives, requirements on autonomy and data privacy, a multi-objective optimization model considering various network operational objectives and constraints was formulated to coordinated the operation of all active participants for better economics and/or system operator preferred performances. The formulated optimization problem was iteratively solved in a distributed way using the alternating direction method of multipliers (ADMM) algorithm. For each iteration, all participants could adjust their schedules based on their own objectives and the price and nodal unbalance signals received. Then, the distribution management system (DMS) updates the nodal price signals according to the generation-load unbalance at the corresponding bus calculated using the previously updated schedules of all participants. The updated price and nodal unbalance signals are distributed to participants on corresponding buses for another iteration until the calculated generation-load unbalance of all buses are small enough, i.e., power balance is achieved at each bus under updated price signals. In addition, the analytical hierarchy process (AHP) was employed to determine the corresponding weighting coefficients of different objectives. The validity of proposed method is verified by simulations on a modern distribution systems with several actively interfaced microgrids, DERs and flexible loads. The effects of weighting coefficients on the obtained solution as well as convergence of ADMM are also investigated.

A networked microgrid is composed of multiple nearby microgrids linked together to gain additional flexibility for resilient operations. Networked microgrids collaborate to prevent power shortages in microgrid clusters by sharing critical renewable and energy storage resources. However, controlling the local resources of each microgrid, including the energy storage systems’ charging and discharging, maintaining the DC bus voltage, and even overseeing the power shared by multiple microgrids, is challenging. Therefore, a microgrid control technique and distributed energy management are used cooperatively in this study to handle the shared power between a system of networked microgrids incorporating photovoltaics and battery energy storage systems. Numerical simulation results from a networked microgrid system verify the accuracy and soundness of the suggested distributed energy management under several operating conditions, including renewable uncertainties and sequential load variations in different zones. The applicability of the suggested technique is confirmed by hardware implementation, and several operational scenarios further evaluate the proposed system on a practical two-microgrid system located in the Florida International University (FIU) testbed.

For the cooperative operation of networked microgrids, a distributed energy management considering network operational objectives and constraints is proposed in this work. Considering various ownership and privacy requirements of microgrids, utility directly interfaced distributed energy resources (DERs) and demand response, a distributed optimization is proposed for obtaining optimal network operational objectives with constraints satisfied through iteratively updated price signals. The alternating direction method of multipliers (ADMM) algorithm is utilized to solve the formulated distributed optimization. The proposed distributed energy management provides microgrids, utility-directly interfaced DERs and responsive demands the opportunity of contributing to better network operational objectives while preserving their privacy and autonomy. Results of numerical simulation using a networked microgrids system consisting of several microgrids, utility directly interfaced DERs and responsive demands validate the soundness and accuracy of the proposed distributed energy management. The proposed method is further tested on a practical two-microgrid system located in Adjuntas, Puerto Rico, and the applicability of the proposed strategy is validated through hardware-in-the-loop (HIL) testing. [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]

The locations of marine ranch and its surrounding islands are far away from the mainland, so the cost is too high to keep power supply by laying submarine cables from the mainland. Therefore, it is necessary to build a land-sea relay networked microgrids with high proportion of renewable energy, which is able to provide multi-energy complementary and reliable power for marine ranch and its surrounding islands. It is noteworthy that the networked microgrids is a typical cyber-physical system. In order to deeply reveal the cyber physical fusion characteristics of fishery networked microgrids, the morphology and key techniques of fishery networked microgrids are summarized from two aspects: physical system and cyber system. And the interactions of cyber and physical system are described, including the support from cyber system for the physical system as state perception and control, and the power support from physical system for cyber system. Based on the above interactions, the related research on coupling analysis of cyber-physical system is summarized. Finally, some urgently studied problems have been looked forward to in fishery networked microgrids cyber-physical system.

Networked microgrids (MGs) with inverter-based and droop-controlled distributed energy resources (DERs) require operational optimization with guaranteed stability performance to ensure the stable energy supply with minimum cost, yet it remains an open challenge. Additionally, the discrete nature of MGs leads to convergence issues to existing optimization methods thereby leading to difficulties obtaining feasible solutions for large-scale networks. This article develops a paradigm for discrete droop control to improve microgrids’ controllability in managing voltage and frequency fluctuations. With the emergence of Internet of Things, the computational tasks are distributed among local resources. The utilized Distributed and Asynchronous Surrogate Lagrangian Relaxation (DA-SLR) method distributes the optimization tasks among the MGs and efficiently coordinates the distributed subsystems. A small-signal model of the operational optimization is then developed to verify the system’s stability. Numerous case studies have proven the DA-SLR’s efficacy in comparison to various variations of the alternating direction of multipliers method (ADMM).

Networked microgrids have many advantages for consumers and small energy producers, including higher reliability than non-networked microgrids. However, energy transaction, network interconnection, the intermittent nature of renewable sources, and other problems lead to challenges in the practical implementation of networked microgrids. Despite its favorable use of space, a floating PV system presents challenges that differ from those associated with its land-based counterparts because it is prone to the motion of the surface of the water, resulting in an unpredictable power output. This work presents a hierarchical energy management system (EMS) to address these issues. In level 1 of the EMS, which is for the overall management thereof, a blockchain model is used to manage transactions among microgrids. A grid synchronization algorithm is implemented in level 2 of the EMS, which manages the interconnection of microgrids. Level 2 is activated when an energy transaction between microgrids is needed. An on-line recurrent neural network (RNN)-based controller for an energy storage system (ESS), which is designed specifically to mitigate the problem caused by a floating PV platform, is deployed in level 3 as a local controller. The results of a hardware-in-the-loop (HIL) simulation demonstrate that the EMS can properly coordinate the levels in the hierarchical scheme to interconnect and provide power support between the microgrids. Real-time simulation results show that the ESS controller responds well, proving the viability of the hardware controller. According to these findings, the hierarchical EMS that is proposed in this work can solve the considered problems.

Extreme climate-driven events such as hurricanes, floods, and wildfires are becoming more intense in areas exposed to these threats, requiring approaches to improve the resilience of the electrical infrastructure serving these communities. Long-duration outages caused by such high impact events propagate to economic, health, and social consequences for communities. As essential service providers, electric utilities are mandated to provide safe, economical and reliable electricity to their customers. The public is becoming less tolerant to these more frequent disruptions, especially in view of technological advances that are intended to improve power quality, reliability and resilience. One promising solution is state-of-the-art microgrids and the advanced controls employed therein. This paper presents and demonstrates an approach to technoeconomic analysis that can be used to value the avoided economic consequences of grid resilience investments, as applied to the islands of Vieques and Culebra in Puerto Rico. This valuation methodology can support policies to incorporate resilience value into any investment decision-making process, especially those which serve the public interest. [ABSTRACT FROM AUTHOR]

An MILP-based distributed energy management for the coordination of networked microgrids is proposed in this paper. Multiple microgrids and the utility grid are coordinated through iteratively adjusted price signals. Based on the price signals received, the microgrid controllers (MCs) and distribution management system (DMS) update their schedules separately. Then, the price signals are updated according to the generation–load mismatch and distributed to MCs and DMS for the next iteration. The iteration continues until the generation–load mismatch is small enough, i.e., the generation and load are balanced under agreed price signals. Through the proposed distributed energy management, various microgrids and the utility grid with different economic, resilient, emission and socio-economic objectives are coordinated with generation–load balance guaranteed and the microgrid customers' privacy preserved. In particular, a piecewise linearization technique is employed to approximate the augmented Lagrange term in the alternating direction method of multipliers (ADMM) algorithm. Thus, the subproblems are transformed into mixed integer linear programming (MILP) problems and efficiently solved by open-source MILP solvers, which would accelerate the adoption and deployment of microgrids and promote clean energy. The proposed MILP-based distributed energy management is demonstrated through various case studies on a networked microgrids test system with three microgrids. [ABSTRACT FROM AUTHOR]

Communication-dependent and software-based distributed energy resources (DERs) are extensively integrated into modern microgrids, providing extensive benefits such as increased distributed controllability, scalability, and observability. However, malicious cyber-attackers can exploit various potential vulnerabilities. In this study, a programmable adaptive security scanning (PASS) approach is presented to protect DER inverters against various power-bot attacks. Specifically, three different types of attacks, namely controller manipulation, replay, and injection attacks, are considered. This approach employs both software-defined networking technique and a novel coordinated detection method capable of enabling programmable and scalable networked microgrids (NMs) in an ultra-resilient, time-saving, and autonomous manner. The coordinated detection method efficiently identifies the location and type of power-bot attacks without disrupting normal NM operations. Extensive simulation results validate the efficacy and practicality of the PASS for securing NMs.

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]

In this study, a novel two-step optimization model is developed for maximizing the amount of internal power trading in a distribution network comprising several networked microgrids. In the first step, a soft actor-critic-based optimization model is developed to help the retailer agent in determining dynamic internal trading prices for its local microgrid network. A better internal price encourages microgrids to increase the amount of internal power trading, and thus the retailer’s profit is also increased. Unlike deep Q learning-based methods, the proposed method is able to handle large state and action spaces. In addition, using entropy-regularized reinforcement learning helps to accelerate and stabilize the learning process and also prevents trapping in local optima. In the second step, an optimization model is developed to facilitate internal trading among various networked microgrids using a cooperative strategy. Since the policy network plays the role of an approximator, the learning model can handle uncertainties in the distribution network. Finally, results of the proposed model show the superiority of the proposed model over the direct power trading schemes. [ABSTRACT FROM AUTHOR]

Microgrids have become increasingly popular in recent years due to technological improvements, growing recognition of their benefits, and diminishing costs. By clustering distributed energy resources, microgrids can effectively integrate renewable energy resources in distribution networks and satisfy end-user demands, thus playing a critical role in transforming the existing power grid to a future smart grid. There are many existing research and review works on microgrids. However, the thermal energy modelling in optimal microgrid management is seldom discussed in the current literature. To address this research gap, this paper presents a detailed review on the thermal energy modelling application on the optimal energy management for microgrids. This review firstly presents microgrid characteristics. Afterwards, the existing thermal energy modeling utilized in microgrids will be discussed, including the application of a combined cooling, heating and power (CCHP) and thermal comfort model to form virtual energy storage systems. Current trial programs of thermal energy modelling for microgrid energy management are analyzed and some challenges and future research directions are discussed at the end. This paper serves as a comprehensive review to the most up-to-date thermal energy modelling applications on microgrid energy management.

Networked microgrids (MGs) have several advantages over individual MGs such as reliability improvement and cost reduction. To promote the mutual connection of individual MGs, a rational and predictable profit-sharing rule is required. This study investigates a rule for the fair distribution of profit in networked MGs according to their contributions that come from connecting between them. Cooperative game theory defines profit-sharing problems such as the Nash bargaining solution (NBS) and Shapley value. However, as the two solution concepts are used assuming that the network is complete, they do not account for the positional contribution of each MG in a given network. We propose a variation of the Shapley value designed for an incomplete network, the Myerson value. We investigate how Myerson value-based profit-sharing rule can account for both the role and positional contributions of each MG. Using Korean data, we compare the profit distribution results for the three sharing rules (the NBS, Shapley value, and Myerson value). The result confirms that the proposed rule fairly distributes the profit according to one's contribution, even when MGs are incompletely connected.

Integrating microgrids within distribution systems can significantly improve the power system's reliability while reducing operating costs. However, due to the unintentional disaster conditions, sometimes distribution systems and microgrids cannot support each other, and the microgrids are forced to work in the islanded mode. Accordingly, we developed an optimal resilient scheduling scheme that guarantees networked microgrids (NMGs) reliable operation in the normal and islanding modes. To achieve this aim, the problem is decomposed into day-ahead normal operation (grid-connected) and real-time islanded examination by benders decomposition algorithm perspective. The specified scheme of NMGs in the normal operation will be scrutinized in the real-time islanded mode. According to the benders decomposition theory, the scheduling of NMGs would be revised in the next iteration if the current schedule is not feasible for possible real-time islanding conditions. The status of thermal units, charging, and discharging of energy storage systems respecting their other constraints are changed depending on the type and severity of mismatches between generation and demand. Three different interconnection topologies are tested for assessing the performance of the proposed method and the impact of transaction energy between NMGs on that. Numerical simulations illustrate the advantages of the proposed scheme and explain its merits.

This paper presents a scheduling framework based algorithm for reducing/shaving the peak loads in a team of cooperating microgrids (TCM) powered smart buildings taking advantages of vehicle-to-building (V2B) concept and operational flexibilities of electric vehicles (EVs). Each microgrid includes a roof-top solar PV, energy storage system, EVs, loads, and advanced metering and communication infrastructure. The main objective is to formulate a constrained optimization problem embedded in a model predictive control (MPC) scheme to optimally control the operation of each microgrid to reduce/shave the peak load in case of occurrence, optimizing the power flows exchanges and energy storages, while ensuring a high quality of service to the EVs owners in each microgrid. The developed predictive model is implemented as a smart energy management based high-level control of the TCM to reduce/shave the peak loads and satisfy the EVs power demands through a coordination of the power exchanges between the microgrids. The algorithm has been tested through a case study to demonstrate its performance and effectiveness.

Conventional power management methods of networked microgrids (NMGs) are limited to the failure of pinned communication terminals and heavy communication burdens. This paper proposes a multilevel dynamic master-slave control strategy via two-level dynamic leaders to realize the resilience enhanced power management of NMGs. The first level dynamic leader with considerations of distributed energy resources (DERs) feature is selected to guide the output of DERs and achieve the power management within individual microgrid (MG). Subsequently, the secondary level leader considering each MG feature is selected among the bidirectional interlinking converters (BICs), whose signals would be shared with other BICs by communication to achieve power management among MGs. Moreover, the local weight selecting method (LWSM) is proposed to automatically select the two-level dynamic leaders according to the real-time system operation state. Compared with conventional methods, the communication among MGs is essentially realized through the dynamic DER leaders instead of pinned ones. Therefore, unreliability issues in the event of pinned terminal outage and converters' communication failure can be fully addressed and the communication bus within each only needs to transmit one DER's signals. The proposed strategy can be also extended to NMGs with various topologies and provide the "plug and play" capabilities of DERs or MGs. Finally, the effectiveness and feasibility of the proposed strategy are verified through the PSCAD/EMTDC platform. [ABSTRACT FROM AUTHOR]

Hybrid ac/dc microgrid clusters are key building blocks of smart grid to support sustainable and resilient urban power systems. Practically, in networked microgrid clusters, the subgrid load-priorities and power quality requirements for different areas vary significantly. In order to realize optimal power exchanges among microgrid clusters, this article proposes a decentralized self-optimizing power control scheme for interlinking converters (ILCs). First, a priority-driven optimal power exchange model of ILCs is built that fully considers the priorities and capacities in subgrids. The whole optimization objective is to minimize the total dc-voltage/ac-frequency state deviations of subgrids. Second, to realize the decentralized power flow control, an optimal-oriented quasi-droop control strategy of ILCs is introduced. Consequently, as each of ILCs only monitors the local ac-side frequency and dc-side voltage signals, the whole optimal power control of the wide-area microgrid clusters is achieved in a decentralized manner without any communication link. Thus, the proposed control algorithm has the features of decreased cost, increased scalability, reduced geographic restrictions and high resilience and robustness in terms of communication faults. Finally, the proposed method is validated by three cases in hardware-in-loop environment. [ABSTRACT FROM AUTHOR]

In three-phase AC/DC hybrid networked microgrids (NMGs), the operational limits of AC/DC interconnected converters and distributed generator (DG) interface inverters increase the non-convexity of the power flow model, and conventional distributed power flow (DPF) algorithms based on heuristic rule may encounter convergence problems when processing limit. This paper proposed a fully DPF calculation method that can robustly handle the non-smooth reactive power limits of converters and non-smooth voltage regulation of step voltage regulators, also reducing the model dependence on the initial values. In this algorithm, the non-smooth constraints were converted into smooth functions, and based on a bi-level augmented Lagrangian alternating direction inexact Newton (ALADIN) method with the second-order convergence rate the original DPF problem was transformed into the problem of distributed step increment optimization. Accurate power flow results can be obtained by exchanging boundary information between microgrids, and the proposed algorithm can converge rapidly with step increment optimization at the second level. Numerical experiments demonstrated the accuracy, convergence, and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Microgrids are gaining popularity by facilitating distributed energy resources (DERs) and forming essential consumer/prosumer centric integrated energy systems. Integration, coordination and control of multiple DERs and managing the energy transition in this environment is a strenuous task. Classical control techniques are not enough to support dynamic microgrid environments. Implementation of Artificial Intelligence (AI) techniques seems to be a promising solution to enhance the control and operation of microgrids in future smart grid networks. Therefore, this paper briefly reviews the control architectures, existing conventional controlling techniques, their drawbacks, the need for intelligent controllers and then extensively reviews the possibility of AI implementation in different control structures with a special focus on the hierarchical control layers. This paper also investigates the AI-based control strategies in networked/interconnected/multi-microgrids environments. It concludes with the summary and future scopes of AI implementation in hierarchical control layers and structures including single and networked microgrids environments.

With the increasing penetration of distributed renewable generations (DRGs), microgrids will play an important role in the future power system. This paper studies the coordinated scheduling strategy of networked microgrids with private data exchange limitations and local management independence. Based on an adaptive robust optimization method, a coordinated scheduling model of networked systems considering the uncertainty of renewable generations is established. Then distributed algorithms are developed to meet the needs of data privacy protection of individual microgrids. The Augmented Lagrangian (AL) decomposition method decomposes the model into several sub-problems, and an alternate optimization method is developed to speed up the solution. Case studies demonstrate the effectiveness of the proposed model and the solution methods.

The benefits of networked microgrids in terms of economics and resilience are investigated and validated in this work. Considering the stochastic unintentional islanding conditions and conventional forecast errors of both renewable generation and loads, a two-stage adaptive robust optimization is proposed to minimize the total operating cost of networked microgrids in the worst scenario of the modeled uncertainties. By coordinating the dispatch of distributed energy resources (DERs) and responsive demand among networked microgrids, the total operating cost is minimized, which includes the start-up and shut-down cost of distributed generators (DGs), the operation and maintenance (O&M) cost of DGs, the cost of buying/selling power from/to the utility grid, the degradation cost of energy storage systems (ESSs), and the cost associated with load shedding. The proposed optimization is solved with the column and constraint generation (C&CG) algorithm. The results of case studies demonstrate the advantages of networked microgrids over independent microgrids in terms of reducing total operating cost and improving the resilience of power supply. [ABSTRACT FROM AUTHOR]

A neural ordinary differential equations network (ODE-Net)-enabled reachability method (Neuro-Reachability) is devised for the dynamic verification of networked microgrids (NMs) with unidentified subsystems and heterogeneous uncertainties. Three new contributions are presented: 1) An ODE-Net-enabled dynamic model discovery approach is devised to construct the data-driven state-space model which preserves the nonlinear and differential structure of the NMs system; 2) A physics-data-integrated (PDI) NMs model is established, which empowers various NM analytics; and 3) A conformance-empowered reachability analysis is developed to enhance the reliability of the PDI-driven dynamic verification. Extensive case studies demonstrate the efficacy of the ODE-Net-enabled method in microgrid dynamic model discovery, and the effectiveness of the Neuro-Reachability approach in verifying the NMs dynamics under multiple uncertainties and various operational scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes a novel Neural Lyapunov method-based transient stability assessment framework for power electronics-interfaced networked microgrids. The assessment framework aims to determine the large-signal stability of the networked microgrids and to characterize the disturbances that can be tolerated by the networked microgrids. The challenge of such assessment is how to construct a behavior-summary function for the nonlinear networked microgrids. By leveraging strong representation power of neural network, the behavior-summary function, i.e., a Neural Lyapunov function, is learned in the state space. A stability region is estimated based on the learned Neural Lyapunov function, and it is used for characterizing disturbances that the networked microgrids can tolerate. The proposed method is tested and validated in a grid-connected microgrid, three networked microgrids with mixed interface dynamics, and the IEEE 123-node feeder. Case studies suggest that the proposed method can address networked microgrids with heterogeneous interface dynamics, and in comparison with conventional methods that are based on quadratic Lyapunov functions, it can characterize the stability regions with much less conservativeness. [ABSTRACT FROM AUTHOR]

Extreme events can cause severe power system damage. Resilience-driven operation of networked microgrids (MGs) has been heavily studied in literature. There is, though, little research considering the influence of resilience on decision making for planning. In this paper, a three-level model is suggested to solve the optimal sizing problem of networked MGs considering both resilience and cost. In the first level, a meta-heuristic technique based on an adaptive genetic algorithm (AGA) is utilized to tackle the normal sizing problem, while a time-coupled AC OPF is utilized to capture stability properties for accurate decision-making. The second and third levels are combined as a defender-attacker-defender model. In the former, the suggested AGA is utilized to generate attacking plans capturing load profile uncertainty and contingencies for load shedding maximization, while a multi-objective optimization problem is suggested for the latter to obtain a trade-off between cost and resilience. Simulations considering meshed networks and load distinction into critical and non-critical are developed to demonstrate algorithm effectiveness on capturing resilience at the planning stage and optimally sizing multiple parameters. The results indicate that higher resilience levels lead to higher investment cost, while sizing networked MGs leads to decreased investment in comparison with standalone MGs sizing. [ABSTRACT FROM AUTHOR]

This paper proposes a price-maker bidding and offering model for networked microgrids (NMG) in a pool-based day-ahead electricity market. The objective of this model is to maximize the net revenue of NMG by coordinating the joined individual microgrids to submit aggregated offers/bids to the market operator. A hybrid stochastic-robust optimization framework is developed to offset multiple associated uncertainties. The bidding and offering model is first formulated as a hard-to-solve mixed-integer nonlinear programming (MINLP) problem, which is later converted to its easy-to-solve mixed-integer linear programming (MILP) counterpart. To resolve privacy concerns of each microgrid and improve the scalability of the proposed bidding and offering model, a coordinated scheduling framework for NMG based on the Dantzig-Wolfe decomposition (DWD) method is proposed to obtain the global optimum. Numerical simulations with real-world measured data validate the effectiveness of the proposed price-maker bidding model, which is shown to outperform existing price-taker models. [ABSTRACT FROM AUTHOR]

This paper proposes a blockchain application for transacting energy and carbon allowance in networked microgrids (MGs). MGs submit trading energy and carbon allowance data to the centralized distribution system operator (DSO) operation, which would optimize the provision of energy and carbon allowance trading among MGs for satisfying power distribution network constraints. The hourly demand response along with onsite MG generation and the DSO’s trading exchanges with ISO are considered among market options to maximize MG payoffs and satisfy distribution network constraints. A cooperative game with externalities is applied to model the market behavior of networked MGs. A two-stage payoff allocation problem is devised to allocate the grand coalition payoff to participating MGs. A solution algorithm is proposed which consists of column-and-constraint generation (C&CG) and Karush-Kuhn-Tucker (KKT) conditions to solve the proposed two-stage market optimization problem with acceptable computational performance. Also, blockchain is applied to provide secure and effective transaction settlements and transparent distribution market operations in the proposed transactive energy and carbon allowance trading strategy. The proposed centralized transactive market is tested on a 4-MG system, the IEEE 33-bus system, and the IEEE 123-bus system. The numerical results show the effectiveness of the proposed method in incentivizing MGs to trade energy and carbon allowance while satisfying the distribution network constraints. [ABSTRACT FROM AUTHOR]

This paper proposes a data-driven distributionally robust co-optimization model for the peer-to-peer (P2P) energy trading and network operation of interconnected microgrids (MGs). In particular, three-phase unbalanced MG networks are considered to account for the implementation practices, and the emerging soft open point (SOP) technology is used for the flexible connection of the multi-MGs. First, the energy management in individual MGs is modeled as a distributionally robust optimization (DRO) problem considering the P2P energy trading options and various operational constraints. Later, a novel decentralized and incentive-compatible pricing strategy is developed for P2P energy trading using the alternating direction method of multipliers (ADMM). Furthermore, the uncertainties in load consumption and renewable generation (RG) are taken into account and the Wasserstein metric (WM) is used to construct the ambiguity set of the uncertainty probability distributions (PDs). Consequently, only historical data is needed rather than prior knowledge about the actual PDs. Finally, the equivalent linear programming reformulations are derived for the DRO model to achieve computational tractability. Numerical tests on four interconnected MGs corroborate the advantages of the proposed P2P energy trading scheme and also demonstrate that the proposed DRO model is more effective in handling the uncertainties compared to the robust optimization (RO) and the stochastic programming (SP) models. [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]

Interconnected microgrids are becoming a building block in smart systems. Initiating secure and efficient energy trading mechanisms among networked microgrids for reliability and economic mutual benefits have become a crucial task. Recently, integrating blockchain technologies into the energy sector have gained significant amount of interest, e.g. transactive grid. This paper proposes a two-layer secured smart contract-based energy trading mechanism to allow microgrids to establish coalitions, adjust the electricity-trading price, and achieve transparent and decentralized secure transactions without intervention of a third trusted party. Since reliability benefits are main drivers of microgrids operation in islanded mode, a new decentralized smart contract based-energy trading model for islanded networked microgrids is proposed in the first layer with an objective to achieve demand generation balance. In the second layer, and to achieve a higher security, all executed contracts are verified and saved in a blockchain based on a new developed two-phase consensus method that utilizes practical Byzantine Fault Tolerance (pBFT), and a modified Proof of Stake (PoS). Simulations are conducted in Python environment to validate the proposed energy trading model.

Quantum key distribution (QKD) provides a potent solution to securely distribute keys for two parties. However, QKD itself is vulnerable to denial of service (DoS) attacks. A flexible and resilient QKD-enabled networked microgrids (NMs) architecture is needed but does not yet exist. In this article, we present a programmable quantum NMs (PQNMs) architecture. It is a novel framework that integrates both QKD and software-defined networking (SDN) techniques capable of enabling scalable, programmable, quantum-engineered, and ultra-resilient NMs. Equipped with a software-defined adaptive post-processing approach, a two-level key pool sharing strategy and an SDN-enabled event-triggered communication scheme, these PQNMs mitigate the impact of DoS attacks through programmable post-processing and secure key sharing among QKD links, a capability unattainable using existing technologies. Through comprehensive evaluations, we validate the benefits of PQNMs and demonstrate the efficacy of the presented strategies under various circumstances. Extensive results provide insightful resources for building QKD-enabled NMs in practice.