A Two-Step Framework for Energy Local Area Network Scheduling Problem with Electric Vehicles Based on Global–Local Optimization Method

To reduce the fluctuation of renewable energy (RE) supply and improve the economic efficiency of the power grid, the energy local area network (ELAN), which is a subnetwork of the energy internet (EI), plays an important role in specific regions. Electric vehicles (EVs), as virtual energy storage (VES) in ELANs, are helpful to decrease the fluctuations of RE supply. However, how to use EVs in ELANs is a complex issue, considering the uncertainties of EVs’ charging demand, the forecast data errors of RE sources, etc. In this paper, a typical ELAN structure is established, taking into account RE sources, load response system, and a distributed energy storage (DES) system including EVs. A two-step optimization framework for ELAN scheduling problem is proposed. A global optimization model based on forecast data is built to maximize the income of ELAN, and an online local optimization model is introduced to minimize the correction cost utilizing prior knowledge. Finally, the proposed two-step optimization framework is applied to a series of real-world ELAN scheduling problems. The results show that DES system with EVs can reduce the volatility of RE supply evidently, and the proposed method is able to maximize the income of the ELAN efficiently.

[1]  Sun Yunlian,et al.  The method of charging piles planning in parking lot , 2016, 2016 IEEE International Conference on Power System Technology (POWERCON).

[2]  Haitao Liu,et al.  Multi-Objective Dynamic Economic Dispatch of Microgrid Systems Including Vehicle-to-Grid , 2015 .

[3]  Xinghuo Yu,et al.  Distributed Multi-DER Cooperative Control for Master-Slave-Organized Microgrid Networks With Limited Communication Bandwidth , 2019, IEEE Transactions on Industrial Informatics.

[4]  A. Aslan,et al.  Renewable and non-renewable energy consumption and economic growth in emerging economies: Evidence from bootstrap panel causality , 2017 .

[5]  Bo Jiang,et al.  Deploying Energy Routers in an Energy Internet Based on Electric Vehicles , 2016, IEEE Transactions on Vehicular Technology.

[6]  Saifur Rahman,et al.  Demand Response as a Load Shaping Tool in an Intelligent Grid With Electric Vehicles , 2011, IEEE Transactions on Smart Grid.

[7]  Jianxiao Zou,et al.  An optimal dispatching strategy for V2G aggregator participating in supplementary frequency regulation considering EV driving demand and aggregator’s benefits , 2017 .

[8]  Ozan Erdinc,et al.  Economic impacts of small-scale own generating and storage units, and electric vehicles under different demand response strategies for smart households , 2014 .

[9]  Peter J. Fleming,et al.  Multi-objective energy storage power dispatching using plug-in vehicles in a smart-microgrid , 2016 .

[10]  Tao Jiang,et al.  Dynamic economic dispatch of a hybrid energy microgrid considering building based virtual energy storage system , 2017 .

[11]  Seppo Sierla,et al.  Internet of Energy Approach for Sustainable Use of Electric Vehicles as Energy Storage of Prosumer Buildings , 2018, Energies.

[12]  Hosam K. Fathy,et al.  Transport-Based Load Modeling and Sliding Mode Control of Plug-In Electric Vehicles for Robust Renewable Power Tracking , 2012, IEEE Transactions on Smart Grid.

[13]  Xiangning He,et al.  A Graph Theory Based Energy Routing Algorithm in Energy Local Area Network , 2017, IEEE Transactions on Industrial Informatics.

[14]  Shihong Miao,et al.  Reliability Evaluation of a Distribution Network with Microgrid Based on a Combined Power Generation System , 2015 .

[15]  Mohsen Kalantar,et al.  Operational scheduling of electric vehicles parking lot integrated with renewable generation based on bilevel programming approach , 2017 .

[16]  Zhou Wu,et al.  Optimal operation of photovoltaic/battery/diesel/cold-ironing hybrid energy system for maritime application , 2018, Energy.

[17]  Mark Jennings,et al.  A review of urban energy system models: Approaches, challenges and opportunities , 2012 .

[18]  Josep M. Guerrero,et al.  Distributed Coordination of Islanded Microgrid Clusters Using a Two-Layer Intermittent Communication Network , 2018, IEEE Transactions on Industrial Informatics.

[19]  Nuh ERDOGAN,et al.  A fast and efficient coordinated vehicle-to-grid discharging control scheme for peak shaving in power distribution system , 2018 .

[20]  Evgueniy Entchev,et al.  Hybrid battery/supercapacitor energy storage system for the electric vehicles , 2018 .

[21]  Mohammad Hassan Moradi,et al.  Optimal management of microgrids including renewable energy scources using GPSO-GM algorithm , 2016 .

[22]  Jeremy J. Michalek,et al.  Estimating the potential of controlled plug-in hybrid electric vehicle charging to reduce operational and capacity expansion costs for electric power systems with high wind penetration , 2014 .

[23]  Tetsuo Tezuka,et al.  The Synergies of Shared Autonomous Electric Vehicles with Renewable Energy in a Virtual Power Plant and Microgrid , 2018, Energies.

[24]  Ruoli Tang,et al.  A novel optimal energy-management strategy for a maritime hybrid energy system based on large-scale global optimization , 2018, Applied Energy.

[25]  Juan C. Vasquez,et al.  DC Microgrids—Part I: A Review of Control Strategies and Stabilization Techniques , 2016, IEEE Transactions on Power Electronics.

[26]  Brayima Dakyo,et al.  Energy Management in the Decentralized Generation Systems Based on Renewable Energy—Ultracapacitors and Battery to Compensate the Wind/Load Power Fluctuations , 2015, IEEE Transactions on Industry Applications.

[27]  Abdollah Kavousi-Fard,et al.  Impact of plug-in hybrid electric vehicles charging demand on the optimal energy management of renewable micro-grids , 2014 .

[28]  Josep M. Guerrero,et al.  A Multiagent-Based Consensus Algorithm for Distributed Coordinated Control of Distributed Generators in the Energy Internet , 2015, IEEE Transactions on Smart Grid.