A Blockchain-Based Game Approach to Multi-Microgrid Energy Dispatch

As the current global environment is deteriorating,distributed renewable energy is gradually becoming an important member of the energy internet.Blockchain,as a decentralized distributed ledger with decentralization,traceability and tamper-proof features,is an importantway to achieve efficient consumption andmulti-party supply of new energy.In this article,we establish a blockchain-based mathematical model of multiple microgrids and microgrid aggregators’revenue,consider the degree of microgrid users’preference for electricity thus increasing users’reliance on the blockchainmarket,and apply the one-master-multiple-slave Stackelberg game theory to solve the energy dispatching strategy when each market entity pursues the maximum revenue.The simulation results show that the blockchain-based dynamic game of the multi-microgrid market can effectively increase the revenue of both microgrids and aggregators and improve the utilization of renewable energy.

Research on Operation Optimization of Energy Storage Power Station and Integrated Energy Microgrid Alliance Based on Stackelberg Game

With the development of renewable energy technologies such as photovoltaics and wind power,it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through the deployment of energy storage.To solve the problem of the interests of different subjects in the operation of the energy storage power stations(ESS)and the integrated energy multi-microgrid alliance(IEMA),this paper proposes the optimization operation method of the energy storage power station and the IEMA based on the Stackelberg game.In the upper layer,ESS optimizes charging and discharging decisions through a dynamic pricing mechanism.In the lower layer,IEMA optimizes the output of various energy conversion coupled devices within the IEMA,as well as energy interaction and demand response(DR),based on the energy interaction prices provided by ESS.The results demonstrate that the optimization strategy proposed in this paper not only effectively balances the benefits of the IEMA and ESS but also enhances energy consumption rates and reduces IEMA energy costs.

Demand-side Response Strategy of Multi-microgrids Based on an Improved Co-evolution Algorithm

An effective modeling and optimization method,which takes into account source-load-storage coordination,and full-time collaborative optimization within and outside micro-grids,is introduced.Considering the operational conditions of various resources and their interactions,an energy management model for microgrids is proposed aiming at maximization of renewable energy utilization and minimization of overall system costs.The model is suitable for both real-time pricing and time-of-use mechanisms.In microgrids,demand response and economic energy storage dispatch are introduced to enhance selfcoordination and self-balancing ability among different resources.Depending on whether there is still an imbalance between supply and demand after coordination within a niicrogrid,trade between it and external microgrids are optimized in a orderly manner by considering different transaction prices and usage rights.Finally,three different schemes are designed,where the Lagrangian multiplier method as well as a co-evolution algorithm are used to solve and analyze different examples,verifying the reliability and validity of the method proposed in this paper.

Implementing an Optimal Energy Management System for a Set of Microgrids Using the Harmony Search Algorithm

A microgrid(MG)refers to a set of loads,generation resources and energy storage systems acting as a controllable load or a generator to supply power and heating to a local area.The MG-generated power management is a central topic for MG design and operation.The existence of dispersed generation(DG)resources has faced MG management with new issues.Depending on the level of exchanges between an MG and the main grid,the MG operation states can be divided into independent or grid-connected ones.Energy management in MGs aims to supply power at the lowest cost for optimal load response.This study examines MG energy management in two operational modes of islanded and grid-connected,and proposes a structure with two control layers(primary and secondary)for energy management.At the principal level of control,the energy management system is determined individually for all MG by taking into consideration the probability constraints and RES uncertainty by the Weibull the probability density function(PDF),generation resources’power as well as the generation surplus and deficit of each MG.Then,the information of the power surplus and deficit of each MG must be sent to the central energy management system.To confirm the proposed structure,a case system with two MGs and a condensive load is simulated by using a multi-time harmony search algorithm.Several scenarios are applied to evaluate the performance of this algorithm.The findings clearly show the effectiveness of the proposed system in the energy management of several MGs,leading to the optimal performance of the resources per MG.Moreover,the proposed control scheme properly controls the MG and grid’s performance in their interactions and offers a high level of robustness,stable behavior under different conditions and high quality of power supply.

Consensus-based Optimal Control Strategy for Multi-microgrid Systems with Battery Degradation Consideration

Consensus has been widely used in distributed control,where distributed individuals need to share their states with their neighbors through communication links to achieve a common goal.However,the objectives of existing consensus-based control strategies for energy systems seldom address battery degradation cost,which is an important performance indicator to assess the performance and sustainability of battery energy storage(BES)systems.In this paper,we propose a consensusbased optimal control strategy for multi-microgrid systems,aiming at multiple control objectives including minimizing battery degradation cost.Distributed consensus is used to synchronize the ratio of BES output power to BES state-of-charge(SoC)among all microgrids while each microgrid is trying to reach its individual optimality.In order to reduce the pressure of communication links and prevent excessive exposure of local information,this ratio is the only state variable shared between microgrids.Since our complex nonlinear problem might be difficult to solve by traditional methods,we design a compressive sensing-based gradient descent(CSGD)method to solve the control problem.Numerical simulation results show that our control strategy results in a 74.24%reduction in battery degradation cost on average compared to the control method without considering battery degradation.In addition,the compressive sensing method causes an 89.12%reduction in computation time cost compared to the traditional Monte Carlo(MC)method in solving the control problem.

New protection scheme for internal fault of multi-microgrid

Multi-microgrids have many new characteristics, such as bi-directional power flow, flexible operation and variable fault current consisting of the different control strategy of inverter interfaced distributed generations (IIDGs), which all present challenges in multi-microgrid protection. In this paper, the current and voltage characteristics of different feeders are analyzed considering faults at different locations of the multi-microgrid. Based on the voltage and current distribution characteristics of the line parameters, a new protection scheme for the internal faults of multi-microgrids is proposed, which takes the change of phase difference and amplitude of measured bus admittances as the criterion. This proposed scheme has high sensitivity and reliability, is based on a simple principle, and can be easily adjusted. Simulation results using PSCAD/EMTDC verify the correctness and effectiveness of the protection scheme.

Multi-energy Management of Interconnected Multi-microgrid System Using Multi-agent Deep Reinforcement Learning

The multi-directional flow of energy in a multi-microgrid(MMG) system and different dispatching needs of multiple energy sources in time and location hinder the optimal operation coordination between microgrids. We propose an approach to centrally train all the agents to achieve coordinated control through an individual attention mechanism with a deep dense neural network for reinforcement learning. The attention mechanism and novel deep dense neural network allow each agent to attend to the specific information that is most relevant to its reward. When training is complete, the proposed approach can construct decisions to manage multiple energy sources within the MMG system in a fully decentralized manner. Using only local information, the proposed approach can coordinate multiple internal energy allocations within individual microgrids and external multilateral multi-energy interactions among interconnected microgrids to enhance the operational economy and voltage stability. Comparative results demonstrate that the cost achieved by the proposed approach is at most 71.1% lower than that obtained by other multi-agent deep reinforcement learning approaches.

Decision-making Method of Sharing Mode for Multi-microgrid System Considering Risk and Coordination Cost

Power sharing can improve the benefit of the multi-microgrid(MMG)system.However,the information disclosure may appear during the sharing process,which would bring privacy risk to a local microgrid.Actually,the risk and coordination cost are different in different sharing modes.Therefore,this paper develops a decision-making method to decide the most suitable one of three mostly used sharing modes(i.e.,cooperative game with complete information,cooperative game with incomplete information,and noncooperative game).Firstly,power sharing paradigms and coordination mechanisms in the three modes are formulated in detail.Particularly,different economic operation models of MMG system are included to analyze the economic benefit from different sharing modes.Based on the different disclosed information,the risk cost is evaluated by using the simplified fuzzy analytic hierarchy process(FAHP).And the coordination cost for different sharing modes is expressed in different functions.In addition,a hierarchical evaluation system including three decision-making factors(e.g.,economics,risk,and coordination)is set up.Meanwhile,a combination weighting method(e.g.,the simplified FAHP combined with the anti-entropy weight method)is applied to obtain the weight of each factor for comprehensive evaluation.Finally,the optimal sharing solution of MMG system is decided by comparing and analyzing the difference among the three sharing modes.Numerical results validate that the proposed method can provide a reference to deciding a suitable sharing mode.

An overview of the operation architectures and energy management system for multiple microgrid clusters

The emerging novel energy infrastructures,such as energy communities,smart building-based microgrids,electric vehicles enabled mobile energy storage units raise the requirements for a more interconnective and interoperable energy system.It leads to a transition from simple and isolated microgrids to relatively large-scale and complex interconnected microgrid systems named multi-microgrid clusters.In order to efficiently,optimally,and flexibly control multi-microgrid clusters,cross-disciplinary technologies such as power electronics,control theory,optimization algorithms,information and communication technologies,cyber-physical,and big-data analysis are needed.This paper introduces an overview of the relevant aspects for multi-microgrids,including the out-standing features,architectures,typical applications,existing control mechanisms,as well as the challenges.