Multiple Microgrids System Research Articles

Robust Collaborative Scheduling Strategy for Multi-Microgrids of Renewable Energy Based on a Non-Cooperative Game and Profit Allocation Mechanism

The Multiple Microgrid System (MMG) facilitates synergistic complementarity among various energy sources, reduces carbon emissions, and promotes the integration of renewable energy generation. In this context, we propose a two-stage robust cooperative scheduling model for MMGs based on non-cooperative game theory and a benefit allocation mechanism. In the first stage, considering electricity price fluctuations and uncertainties in wind and solar power outputs, a robust optimization approach is applied to establish an electric energy management model for MMGs. This model enables point-to-point energy sharing among microgrids. In the second stage, addressing the benefit allocation problem for shared electric energy, we introduce a Cost Reduction Ratio Distribution (CRRD) model based on non-cooperative game theory. The generalized Nash equilibrium is utilized to determine the benefit distribution for shared electric energy. Finally, through case studies, the proposed model is validated, ensuring fair returns for each microgrid. The results indicate that the proposed model optimizes the operational states of individual microgrids, reduces operational costs for each microgrid, and lowers the overall total operational costs of the MMG system. Additionally, an investigation is conducted into the impact of electricity price uncertainty coefficients and confidence levels of wind and solar uncertainties on the operational costs of microgrids.

Optimization Strategy for Shared Energy Storage Operators-Multiple Microgrids with Hybrid Game-Theoretic Energy Trading

To address the issue of low utilization rates, constrained operational modes, and the underutilization of flexible energy storage resources at the end-user level, this research paper introduces a collaborative operational approach for shared energy storage operators in a multiple microgrids (ESO-MGs) system. This approach takes into account the relation of electricity generated by MGs and the integration of diverse energy storage resources managed by ESO. A hybrid game-theoretic energy trading strategy is employed to address the challenges associated with energy trading and revenue distribution in this joint operational mode. Firstly, a multi-objective master–slave game optimization model is developed with the objective of maximizing the revenue earned by shared energy storage operators while simultaneously minimizing the operational costs of multiple microgrids. Secondly, acknowledging the peer-to-peer (P2P) energy sharing dynamics inherent in the multiple microgrid system, a non-co-operative game model is formulated. This model seeks to establish a multi-microgrid Nash equilibrium and equitable income allocation. Finally, leveraging the Karush–Kuhn–Tucker (KKT) conditions and drawing upon the principles of strong duality theory, precise dimensionality reduction is executed on the master–slave game model. The non-co-operative income is iteratively determined using the alternating direction multiplier algorithm. The empirical findings of this study indicate that the integration of electric vehicle clusters contributes to flexible storage resources for shared energy storage operators. Moreover, the proposed hybrid game optimization strategy enhances the overall benefits for shared energy storage operators and multiple microgrids, thereby affirming the economic viability and reliability of this innovative strategy.

Distributed Online Incentive Scheme for Energy Trading in Multi-Microgrid Systems

Microgrids (MGs) are essential components of a smart grid, and they play an important role in assisting smart grid operation. Furthermore, multi-MG systems, which can further improve smart grid operation, raise significant challenges in terms of management and interoperation because of their increased complexity. To address this, we propose a multi-bid online incentive scheme for energy trading in multi-MG systems, in which MGs act as buyers and sellers to trade surplus energy. In the proposed system, buyer MGs can submit multiple bids to different sellers, and the distributed MG system operator matches buyer and seller MGs in a way that maximizes the utility of buyers. Theoretical analysis demonstrates that the proposed method meets the properties of incentive compatibility and individual rationality. Moreover, through detailed performance evaluation, the proposed scheme was capable of achieving better performance in comparison with existing schemes regarding utility, energy purchasing cost, and buyer MG’s satisfaction ratio. Our experimental results also demonstrated that the proposed scheme could shift the system peak-load, improving the robustness of the multi-MG system. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In this article, we addressed the energy trading issues in multi-microgrid (MG) system, and proposed a distributed incentive mechanism to achieve more flexible and efficient energy allocation. Most existing works on energy trading use centralized methods, which means all electrical users will submit only one bid to the platform, and finally the platform decides to match the trading parties. This will lead to users unable to actively choose trading partners, thus reducing the volume of the whole market. To this end, in this paper, we proposed a distributed incentive mechanism for the multiple microgrid system, in which the buyer microgrids are able to submit multiple bids to different sellers. Moreover, each seller microgrids act as a distributed energy market, and the distributed MG system operator matches the buyers and sellers which aims to maximize the utility of buyers. The proposed scheme is helpful in managing the energy trading among the multi-MG system and can be readily implemented in the real-world large-scale electricity trading market.

Optimizing Hybrid Microgrid Power Systems for Local Power Distribution: A Study on Combined Photovoltaic and Fuel Cell Systems in the Philippines

In pursuit of energy self-sufficiency and meeting the growing energy demand, the Philippine government has formulated its Energy Road Map for the year 2040, aiming to strengthen, continue, and accelerate the adoption of renewable energy (RE) across the archipelago. This paper presents a proposed multiple microgrid system integrated into an existing distribution system, utilizing renewable energy sources. The proposed model involves the conversion of a section of the distribution system into a microgrid setup, comprising photovoltaic (PV) energy and fuel cell (FC) technologies connected to a 13.2 kV distribution grid. A modified three-phase three-level voltage-sourced converter (VSC) is employed to control the inverter. The proposed modifications result in improved operational efficiency compared to conventional approaches. Various operating cases are considered, each with a designated power source operating according to a predefined schedule. A unified controller is employed across all operating cases, ensuring system stability. Simulation and experimental results conducted through MATLAB/Simulink demonstrate the impact of VSC in terms of voltage regulation, frequency stability, and accumulated power losses. They revealed that voltage regulation for understudy cases ranged from 0.1 to 4.5%, microgrid frequencies were between 59.1 and 60.08 Hz, and power distribution losses were at 1.2–3.3% of the generated power.

Physics-model-free heat-electricity energy management of multiple microgrids based on surrogate model-enabled multi-agent deep reinforcement learning

Reliability and cost-effectiveness in the operation of the multiple microgrid (MMG) system depend on the skillful management of its energy resources. Traditional energy management approaches are physics-model-based, which rely on the precise system parameters (e.g., line parameters) of the electricity and heat network. It is difficult to establish the precise system parameters in practice because they depend on a variety of factors. In this context, this paper proposes a physics-model-free control framework for the energy management of MMGs with heat-electricity energy, consisting of proposed surrogate model and multi-agent deep reinforcement learning (MADRL) approach. An important step is to use historical data to train a surrogate model in supervised manner that can imitate the realistic power and thermal flow calculations. Meanwhile, the energy management problem is reformulated as a Markov game. It is solved by the proposed MADRL-based approach by modeling each MG controller as an agent with a specific objective. The historical trajectories representation, parameter space technology, and deep dense architecture in reinforcement learning are introduced in MADRL to overcome the negative impact brought by the time series data from the input state on the decision-making process and construct an efficient exploration mechanism to overcome inefficient optimization of the MMG system in a multi-agent setting. During training period of MADRL, the trained surrogate models integrate into the environment of the MADRL, which can develop optimal energy management strategy based on the continuous interaction with the surrogate models. The proposed surrogate model enabled MADRL approach can reduce the reliance on precise physical systems and prevent having an impact on the real system while being trained involving trial and error process. Simulation results demonstrate the effectiveness of the proposed control framework.

Multi-stage hybrid energy management strategy for reducing energy abandonment and load losses among multiple microgrids

This paper proposes a multi-stage hybrid energy management strategy for multiple microgrids (MMGs) to reduce energy abandonment and load losses. The proposed energy management model consists of three stages including a decentralized autonomy stage, a coordinated operation stage, and a reserve reallocation stage, respectively. First, in the decentralized autonomy stage, a day-ahead energy management model is established with the objective of minimizing the comprehensive management cost of individual microgrids. Especially, the uncertainty from renewable energy and load demands are quantified by interval forecast algorithm, phase space reconstruction technique, machine learning, and kernel density estimation, which simplifies the forecasting processes while capturing multivariate data more comprehensively. Second, the coordinated operation stage aims to encourage the power interaction among MMGs to achieve maximize the benefit of MMGs while suppressing the power fluctuations of the tie line between the MMGs and the main grid. Then, in the reserve reallocation stage, taking the actual values of energy abandonment and load losses of a week before the management day as the dataset, machine learning algorithms are applied to predict energy abandonment and load losses. Meanwhile, the actual energy abandonment and load losses are reduced by dispatching reserve resources. Finally, simulations on an MMGs system containing four microgrids are conducted to testify the rationality and validity of the proposed energy management strategy.

Community Market Based Energy Trading Among Interconnected Microgrids With Adjustable Power

Interconnected microgrids (MGs) system is regarded as one of the future power system configurations, where individual MGs can support each other through interchanging energy. This paper presents a community market structure to manage the energy trading among MGs and also with the main grid. In local layer, individual MGs would operate and bid as autonomous entities; in market layer, an independent third party would coordinate the energy transaction among different MGs by mimicking the role of auctioneer. To cost-effectively utilize the controllable units in MGs, the concept of adjustable power is discussed. Apart from the surplus or deficit energy, adjustable power is also incorporated in the individual MGs’ bidding strategies. The local trading prices are determined via the market clearing. We analyze the economic benefits of such an energy trading mechanism and also the value of adjustable power. Numerical results demonstrate the effectiveness of the proposed framework for the energy trading among multiple MGs system.

Co-Operative Optimization Framework for Energy Management Considering CVaR Assessment and Game Theory

In this paper, a bi-level energy management framework based on Conditional Value at Risk (CVaR) and game theory is presented in the context of different ownership of multiple microgrid systems (MMGS) and microgrid aggregators (MAs). The energy interaction between MMGS and MAs can be regarded as a master–slave game, where microgrid aggregators as the leaders set the differentiated tariff for each MG to maximize its benefits, and MMGS as the follower responds to the tariff decision specified by the leader through peer-to-peer (P2P) energy sharing. The P2P energy sharing of MMGS can be regarded as a co-operative game, employing asymmetric Nash bargaining theory to allocate the co-operative surplus. The Conditional Value at Risk model was used to characterize the expected losses by microgrid aggregators due to the uncertainties of renewable energy resources. The Karush–Kuhn–Tucker conditions, Big-M method, and strong duality theory were employed to transform the bi-level nonlinear model of energy management into a single-level mixed integer linear programming model. The simulation results show that when MGs adopt the P2P energy-sharing operation mode, the total operating cost of MMGS can be reduced by 7.82%. The simulation results show that the proposed co-operative optimization framework can make the multiple microgrid systems obtain extra benefits and improve the risk resistance of microgrid aggregators.

An Energy Management System of Campus Microgrids: State-of-the-Art and Future Challenges

The multiple uncertainties in a microgrid, such as limited photovoltaic generations, ups and downs in the market price, and controlling different loads, are challenging points in managing campus energy with multiple microgrid systems and are a hot topic of research in the current era. Microgrids deployed at multiple campuses can be successfully operated with an exemplary energy management system (EMS) to address these challenges, offering several solutions to minimize the greenhouse gas (GHG) emissions, maintenance costs, and peak load demands of the microgrid infrastructure. This literature survey presents a comparative analysis of multiple campus microgrids’ energy management at different universities in different locations, and it also studies different approaches to managing their peak demand and achieving the maximum output power for campus microgrids. In this paper, the analysis is also focused on managing and addressing the uncertain nature of renewable energies, considering the storage technologies implemented on various campuses. A comparative analysis was also considered for the energy management of campus microgrids, which were investigated with multiple optimization techniques, simulation tools, and different types of energy storage technologies. Finally, the challenges for future research are highlighted, considering campus microgrids’ importance globally. Moreover, this paper is expected to open innovative paths in the future for new researchers working in the domain of campus microgrids.

Power sharing through interlinking converters in adaptive droop controlled multiple microgrid system

Multiple microgrids (MMGs) may be interconnected through voltage source converters (VSC) for controlled power sharing among themselves. VSCs in large interconnected micro- grids may use non-communication based droop control to share total power demand in the ratio of their droop coefficients. However, a constant droop ratio restricts the power delivery capability of the VSCs although surplus power may be available in their respective sources. In this paper, along with a unified power management strategy for intra and inter microgrid power sharing in a hybrid AC-DC MMG system, a novel adaptive droop control algorithm is presented that dynamically changes droop coefficients of the VSCs to ensure maximum utilization of available resources in each microgrid under different operating modes. Stability analysis of the proposed control method is performed and comparison of the system performance with fixed and adaptive droop ratio is illustrated by simulating the MMG in MATLAB SIMULINK using real solar irradiance field data. It is validated from simulation results that the adaptive droop control method increases utilization of renewable energy sources within microgrids and reduces energy import from utility grid.

Effects of Dynamic Topology Reconfiguration for Optimal Operation in Multi-Microgrid System

Multiple microgrids (MMG) system can reconfigure its topology by adjusting statuses of static switches and tie switches to improve reliability and economics of individual MGs. In this paper, a day-ahead optimal operation approach of MMG system in parallel model with topology reconfiguration is proposed, and a co-evolution algorithm (CEA) based approach is studied to manage multiple MGs more economically. In addition, proper feasible regions are set to ensure feasibility of the solution in CEA. Effectiveness of MMG system reconfiguration as well as cooperative operations of reconfiguration, energy storage systems (ESSs), and dispatchable distributed generations (DGs) are studied via numerical simulations. Specifically, simulation results indicate that MMG system reconfiguration in parallel mode can improve economic operation of MMGs and enhance efficient utilization of dispatchable DGs and ESSs.

Hierarchical Two-Layer Distributed Control Architecture for Voltage Regulation in Multiple Microgrids in the Presence of Time-Varying Delays

The Multiple Microgrids (MMGs) concept has been identified as a promising solution for the management of large-scale power grids in order to maximize the use of widespread renewable energies sources. However, its deployment in realistic operation scenarios is still an open issue due to the presence of non-ideal and unreliable communication systems that allow each component within the power network to share information about its state. Indeed, due to technological constraints, multiple time-varying communication delays consistently appear during data acquisition and the transmission process and their effects must be considered in the control design phase. To this aim, this paper addresses the voltage regulation control problem for MMGs systems in the presence of time-varying communication delays. To solve this problem, we propose a novel hierarchical two-layer distributed control architecture that accounts for the presence of communication latencies in the information exchange. More specifically, the upper control layer aims at guaranteeing a proper and economical reactive power dispatch among MMGs, while the lower control layer aims at ensuring voltage regulation of all electrical buses within each MG to the desired voltage set-point. By leveraging a proper Driver Generator Nodes Selection Algorithm, we first provide the best choice of generator nodes which, considering the upper layer control goal, speeds up the voltage synchronization process of all the buses within each MG to the voltage set-point computed by the upper-control layer. Then, the lower control layer, on the basis of this desired voltage value, drives the reactive power capability of each smart device within each MG and compensates for possible voltage deviations. Simulation analysis is carried out on the realistic case study of an MMGs system consisting of two identical IEEE 14-bus test systems and the numerical results disclose the effectiveness of the proposed control strategy, as well as its robustness with respect to load fluctuations.

A single-leader and multiple-follower stackelberg model for the look-ahead dispatch of plug-in electric buses in multiple microgrids

Plug-in electric buses have great potential to enhance the profits of operators in multiple-microgrid systems via the use of vehicle-to-grid technology. In contrast to stationary energy storage units, a plug-in electric bus can move among different microgrids to provide not only passenger transportation service but also energy transportation service. In this paper, a single-leader and multiple-follower model based on the Stackelberg game method is proposed for the look-ahead dispatch of bus routes and power allocation in microgrids. First, a time-space trip model for a fleet of plug-in electric buses was established, and the total costs of the plug-in electric bus operator were optimized as a leader-level problem. Then a rolling energy management strategy was formulated as a follower-level problem to optimize the total costs of each microgrid operator and to deal with the prediction error of the distributed energy resources and loads. Next, the proposed bi-level optimization problem was transformed into a single-level mixed-integer linear programming problem. Finally, case studies were carried out on three real microgrids in China and on BYD-K9 plug-in electric buses. The simulation results indicated that the costs for a plug-in electric bus operator would decrease by 33.35% in the proposed bi-level model compared with the fixed charging model, and the costs for an isolated MG would decrease by 24.16% compared with the model regardless of the plug-in electric buses.

Economic Optimization of Multiple Microgrids System Based on Alliance Theory

Aiming at the problem of economic operation of multiple microgrids system, a coordinated optimized operation method of multiple microgrids in integrated energy system based on alliance theory was proposed. Firstly, energy storage station was introduced into multiple microgrids optimal scheduling, the energy flow relationship between various links was studied, and an economic optimal dispatch model was established under the alliance mode of multiple microgrids and energy storage station; Secondly, constructed a Shapely value benefits distribution mechanism to carry out the initial benefits distribution of the alliance and the secondary distribution of the multiple microgrids internal benefits. Finally, taking a typical daily scenario as an example, the optimal operation scheduling strategy of multiple microgrids with the participation of energy storage station was obtained. The results showed that the method proposed in this paper can effectively improve the benefits of all participants and ensure the economic operation of multiple microgrids, at the same time, it realized the full consumption of renewable energy.

Multi-objective energy management of multiple microgrids under random electric vehicle charging

In view of the increasing development of decentralized power systems and electric vehicles, this paper seeks to improve the energy management performance of multiple microgrid systems under the uncertainty associated with electric vehicle charging. A multi-objective optimization model is established for minimizing the transmission losses, operating costs, and carbon emissions of multiple microgrid systems. Firstly, a novel method is proposed for forecasting electric vehicle charging loads based on a back propagation neural network improved by long short-term memory deep learning. Based on the forecast data, a double layer solution algorithm is proposed, which consists of an adaptive multi-objective evolutionary algorithm based on decomposition and differential evolution at the multiple microgrids layer and a modified consistency algorithm for fast economic scheduling at the single microgrid layer. Finally, a model system composed of four interconnected IEEE microgrids is simulated as a case study, and the performance of the proposed algorithm is compared with that of conventional multi-objective evolutionary algorithms based on decomposition. The simulation results demonstrate the superiority of the global search performance and the rapid convergence performance of the proposed improved algorithm.

Coordinate Planning of Multiple Microgrids and Distribution Network with Mixed AC/DC Interconnection Method

This work explores the coordinate planning methods of multiple microgrids and distribution network with mixed AC/DC interconnection method which consider the coupling influence of interconnection structure and operation mode. The structure and evaluation model of the multiple microgrids system were discussed, and then system planning models were established. In order to solve the complicated combinatorial model, the benders decomposition methods were used to divide the problem into benders master problem, reliability sub-problem and operation sub-problem. Simulation results on the multiple microgrids test system with distribution network show that the proposed methods are effective.

A Study on Optimal Operation Method of Multiple Microgrid System Considering Line Flow Limits

A Study on Optimal Operation Method of Multiple Microgrid System Considering Line Flow Limits

Coordination Strategy for Optimal Scheduling of Multiple Microgrids Based on Hierarchical System

Research on the operation of the multiple microgrid (MMG) has been increasing as the power system is operated through the microgrid. Some of the studies related to MMG have introduced various operation strategies by introducing concepts such as power sharing and power trading for power exchange between microgrids. In this paper, a strategy for obtaining optimal scheduling of MMG systems with power sharing through coordination among microgrids that have no cost function of generation units is proposed. There are microgrid-energy management systems (MG-EMSs) in the lower level that determine individual schedules for each microgrid in a hierarchical system. In the upper level, the microgrid of microgrids center (MoMC) implements the coordination among microgrids. In order to achieve the optimal operation of the entire system, MoMC calculates the amount of power sharing based on a predetermined limit value and allocates the command for coordination to each MG-EMS. MG-EMS changes the individual schedule based on the command. These processes are repeatedly performed, and when the change of the total cost becomes smaller than a specified size, the process is terminated and the schedule is determined. The advantages of the proposed algorithm are as follows. (1) It is a power sharing strategy of multiple microgrids considering multiple feeder structures as well as a single feeder structure for minimizing the operation cost of the entire system; (2) it is a power sharing strategy between microgrids that can be applied in a microgrid where only units that do not have a cost function exist; (3) since it is the optimization of the distributed form, the computation time decreases sharply compared with the one performed at the central center. The verification of the proposed algorithm was performed through MATLAB.

Aggregation of microgrids with DC system

The aspiration of this paper is to scrutinize the feasibility of DC system connecting multiple AC microgrids for improved system reliability. In this paper, first multiple microgrids connected through a back to back connection (with better power flow control as well as frequency isolation) is contemplated. Each of the microgrids can operate in island or grid connected mode. The control and power flow management of the multiple microgrids system are first depicted with a common DC link capacitor. As second step, the DC capacitor is dispersed among the converter terminals as separate converter DC capacitor. As final step, the back to back converters are assumed to be far from each other and connected through DC cables. This result in a multi terminal medium voltage DC system connected to multiple AC microgrids. The active power, reactive power and output current capability of each converter terminal depends on the corresponding microgrid operating mode, DG output, local load demand etc. A DC system connecting multiple AC microgrids (with VSC interfaced DGs and ability to operate in island) bestows a test bench for studying the possibility smaller grids for higher reliability and better power flow management. The control schemes, generalized guidelines for such integrated system are proposed based on currently available standards, equipment, devices, system solutions and market requirements.