Double-layer energy management system based on energy sharing cloud for virtual residential microgrid

Abstract The idea of energy sharing can contribute to achieving the goal of resource optimization by redistributing and sharing idle energy assets. How to design an appropriate energy management strategy in the energy sharing environment has been the focus of intensive research in energy sharing field. In this paper, a new effective double-layer energy management system (EMS) based on the energy sharing cloud (ESC) is developed for a virtual residential microgrid (VRMG). The proposed ESC can be regarded as an open energy sharing environment, where the cloud platform helps cloud users build their VRMGs by providing energy services including renewable energy sources (RESs) generation and energy storage. The mathematical model of the VRMG is formulated, and the energy service prices for RESs generation and energy storage are monthly set separately. Moreover, considering the changes in household load and RESs generation, an energy management strategy of double-layer EMS is designed. The upper-layer EMS helps VRMG obtain the monthly optimal capacity configuration of RESs and energy storage, and the lower-layer EMS realizes the daily electricity scheduling optimization for the VRMG whose objectives are to minimize the total operational cost and maximize the electrical comfort level. Simulation studies demonstrate that the proposed energy sharing mechanism can meet the changing energy needs of the cloud user, and numerical experiments also confirm the performance and effectiveness of the proposed double-layer EMS.

[1]  Ting Wu,et al.  Coordinated Energy Dispatching in Microgrid With Wind Power Generation and Plug-in Electric Vehicles , 2013, IEEE Transactions on Smart Grid.

[2]  Miguel Cruz-Zambrano,et al.  Optimal Energy Management for a Residential Microgrid Including a Vehicle-to-Grid System , 2014, IEEE Transactions on Smart Grid.

[3]  Pablo Sanchis,et al.  Energy management strategy for a renewable-based residential microgrid with generation and demand forecasting , 2015 .

[4]  John E. Fletcher,et al.  Lifetime prediction and sizing of lead-acid batteries for microgeneration storage applications , 2008 .

[5]  Guowei Cai,et al.  Optimal sizing of a wind/solar/battery/diesel hybrid microgrid based on typical scenarios considering meteorological variability , 2019, IET Renewable Power Generation.

[6]  Lei Wang,et al.  Chance Constrained Optimization in a Home Energy Management System , 2018, IEEE Transactions on Smart Grid.

[7]  Amjad Anvari-Moghaddam,et al.  A multi-agent based energy management solution for integrated buildings and microgrid system , 2017 .

[8]  Thomas Kunz,et al.  Peer-to-peer energy trading among smart homes , 2019, Applied Energy.

[9]  Majid Ahmadi,et al.  Optimizing Load Control in a Collaborative Residential Microgrid Environment , 2015, IEEE Transactions on Smart Grid.

[10]  Hao Wang,et al.  Virtual Energy Storage Sharing and Capacity Allocation , 2019, 2020 IEEE Power & Energy Society General Meeting (PESGM).

[11]  Junjie Yang,et al.  A Real-Time Electricity Scheduling for Residential Home Energy Management , 2019, IEEE Internet of Things Journal.

[12]  João P. S. Catalão,et al.  End-User Comfort Oriented Day-Ahead Planning for Responsive Residential HVAC Demand Aggregation Considering Weather Forecasts , 2017, IEEE Transactions on Smart Grid.

[13]  Fang Jicheng,et al.  Electricity scheduling optimisation based on energy cloud for residential microgrids , 2019, IET Renewable Power Generation.

[14]  João P. S. Catalão,et al.  Optimal Household Appliances Scheduling Under Day-Ahead Pricing and Load-Shaping Demand Response Strategies , 2015, IEEE Transactions on Industrial Informatics.

[15]  Hemanshu R. Pota,et al.  Modified PSO algorithm for real-time energy management in grid-connected microgrids , 2018, Renewable Energy.

[16]  Noboru Yamada,et al.  Sizing and Analysis of Renewable Energy and Battery Systems in Residential Microgrids , 2016, IEEE Transactions on Smart Grid.

[17]  David W. Smith,et al.  An Incentive-Compatible Energy Trading Framework for Neighborhood Area Networks With Shared Energy Storage , 2020, IEEE Transactions on Sustainable Energy.

[18]  Chongqing Kang,et al.  Cloud energy storage for residential and small commercial consumers: A business case study , 2017 .

[19]  Joao P. S. Catalao,et al.  A new perspective for sizing of distributed generation and energy storage for smart households under demand response , 2015 .

[20]  Pravin Varaiya,et al.  Sharing Storage in a Smart Grid: A Coalitional Game Approach , 2017, IEEE Transactions on Smart Grid.

[21]  H. Vincent Poor,et al.  A Motivational Game-Theoretic Approach for Peer-to-Peer Energy Trading in the Smart Grid , 2019, Applied Energy.

[22]  Yunfei Mu,et al.  A distributed Peer-to-Peer energy transaction method for diversified prosumers in Urban Community Microgrid System , 2020, Applied Energy.

[23]  Jeremy Rifkin,et al.  The third industrial revolution : how lateral power is transforming energy, the economy, and the world , 2011 .

[24]  Alibakhsh Kasaeian,et al.  Optimizing a hybrid wind-PV-battery system using GA-PSO and MOPSO for reducing cost and increasing reliability , 2017, Energy.

[25]  Fengqi You,et al.  Assumptions and the levelized cost of energy for photovoltaics , 2011 .

[26]  Bo Zhao,et al.  Operation Optimization of Standalone Microgrids Considering Lifetime Characteristics of Battery Energy Storage System , 2013, IEEE Transactions on Sustainable Energy.

[27]  Chongqing Kang,et al.  Decision-Making Models for the Participants in Cloud Energy Storage , 2018, IEEE Transactions on Smart Grid.

[28]  Lalit Goel,et al.  A Two-Layer Energy Management System for Microgrids With Hybrid Energy Storage Considering Degradation Costs , 2018, IEEE Transactions on Smart Grid.

[29]  H. Vincent Poor,et al.  Peer-to-Peer Trading in Electricity Networks: An Overview , 2020, IEEE Transactions on Smart Grid.

[30]  Junjie Yang,et al.  Cost-Effective and Comfort-Aware Electricity Scheduling for Home Energy Management System , 2016, 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom).

[31]  Hassan Ghasemi,et al.  Residential Microgrid Scheduling Based on Smart Meters Data and Temperature Dependent Thermal Load Modeling , 2014, IEEE Transactions on Smart Grid.

[32]  Josep M. Guerrero,et al.  Energy scheduling of community microgrid with battery cost using particle swarm optimisation , 2019, Applied Energy.

[33]  Jianzhong Wu,et al.  Framework design and optimal bidding strategy for ancillary service provision from a peer-to-peer energy trading community , 2020, Applied Energy.

[34]  Salman Kahrobaee,et al.  Optimum Sizing of Distributed Generation and Storage Capacity in Smart Households , 2013, IEEE Transactions on Smart Grid.

[35]  A. Rahimi-Kian,et al.  Cost-effective and comfort-aware residential energy management under different pricing schemes and weather conditions , 2015 .