Joint Scheduling Optimization of Virtual Power Plants and Equitable Profit Distribution Using Shapely Value Theory

The installation capacity of wind and solar photovoltaic power is continually increasing, which makes renewable energy grid connection and power generation an important link of China’s power structure optimization. A virtual power plant (VPP) is an important way to help distributed energy resource grid connection and promote renewable energy industry development. To study the economic scheduling problem of various distributed energy resources and the profit distribution problem of VPP alliance, this study builds a separate operation scheduling model for individual VPP and a joint operation scheduling model for VPP alliance, as well as the profit distribution model. The case study verifies the feasibility and effectiveness of the proposed model. The sensitivity analysis provides information about VPP decision-making in accordance with the policy environment development trend.

[1]  Anastasios G. Bakirtzis,et al.  Optimal Offering Strategy of a Virtual Power Plant: A Stochastic Bi-Level Approach , 2016, IEEE Transactions on Smart Grid.

[2]  Taher Niknam,et al.  An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation , 2012 .

[3]  Jamshid Aghaei,et al.  Multi-objective self-scheduling of CHP (combined heat and power)-based microgrids considering demand response programs and ESSs (energy storage systems) , 2013 .

[4]  I. G. Moghaddam,et al.  Risk-averse profit-based optimal operation strategy of a combined wind farm–cascade hydro system in an electricity market , 2013 .

[5]  Miadreza Shafie-khah,et al.  Development of a virtual power market model to investigate strategic and collusive behavior of market players , 2013 .

[6]  S. M. Moghaddas-Tafreshi,et al.  Bidding Strategy of Virtual Power Plant for Participating in Energy and Spinning Reserve Markets—Part I: Problem Formulation , 2011, IEEE Transactions on Power Systems.

[7]  P. Asmus.  Microgrids, Virtual Power Plants and Our Distributed Energy Future , 2010 .

[8]  Mohammad Kazem Sheikh-El-Eslami,et al.  Decision making of a virtual power plant under uncertainties for bidding in a day-ahead market using point estimate method , 2013 .

[9]  Enrico Zio,et al.  A risk-based simulation and multi-objective optimization framework for the integration of distributed renewable generation and storage , 2014 .

[10]  Rahmat-Allah Hooshmand,et al.  A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems , 2017 .

[11]  M. Sheikh-El-Eslami,et al.  A medium-term coalition-forming model of heterogeneous DERs for a commercial virtual power plant , 2016 .

[12]  Xu Wang,et al.  Risk assessment and bi-level optimization dispatch of virtual power plants considering renewable energy uncertainty , 2017 .

[13]  Henrik W. Bindner,et al.  Utilization of Flexible Demand in a Virtual Power Plant Set-Up , 2015, IEEE Transactions on Smart Grid.

[14]  Ana Paula Barbosa-Póvoa,et al.  Optimal investment and scheduling of distributed energy resources with uncertainty in electric vehicle driving schedules , 2014 .

[15]  M. P. Moghaddam,et al.  Modeling of interactions between market regulations and behavior of plug-in electric vehicle aggregators in a virtual power market environment , 2012 .

[16]  Alireza Zakariazadeh,et al.  Day-ahead resource scheduling of a renewable energy based virtual power plant , 2016 .

[17]  Nikos D. Hatziargyriou,et al.  Decision trees aided scheduling for firm power capacity provision by virtual power plants , 2014 .

[18]  Ramachandra Kota,et al.  An Agent-Based Approach to Virtual Power Plants of Wind Power Generators and Electric Vehicles , 2013, IEEE Transactions on Smart Grid.

[19]  J Liu.  Prospect of Technology for Large-Scale Wind Farm Participating Into Power Grid Frequency Regulation , 2014 .

[20]  M. Tajeddini,et al.  Risk averse optimal operation of a virtual power plant using two stage stochastic programming , 2014 .

[21]  S. M. Moghaddas-Tafreshi,et al.  Bidding Strategy of Virtual Power Plant for Participating in Energy and Spinning Reserve Markets—Part II: Numerical Analysis , 2011, IEEE Transactions on Power Systems.

[22]  Chresten Træholt,et al.  Generic Virtual Power Plants: Management of distributed energy resources under liberalized electricity market , 2009 .

[23]  J. O. Petinrin,et al.  Impact of renewable generation on voltage control in distribution systems , 2016 .

[24]  Yongqian Liu,et al.  Reviews on uncertainty analysis of wind power forecasting , 2015 .

[25]  Duarte Valério,et al.  Multi‐agent management system for electric vehicle charging , 2015 .

[26]  Kit Po Wong,et al.  Short-term operational planning framework for virtual power plants with high renewable penetrations , 2016 .

[27]  Rahmat-Allah Hooshmand,et al.  A new simultaneous placement of distributed generation and demand response resources to determine virtual power plant , 2016 .

[28]  Scott W. Kennedy,et al.  A Novel Demand Response Model with an Application for a Virtual Power Plant , 2015, IEEE Transactions on Smart Grid.

[29]  Christof Wittwer,et al.  Decentralised optimisation of cogeneration in virtual power plants , 2010 .

[30]  T. Muneer,et al.  Energy supply, its demand and security issues for developed and emerging economies , 2007 .

[31]  Saeed Rahmani Dabbagh,et al.  Risk-based profit allocation to DERs integrated with a virtual power plant using cooperative Game theory , 2015 .

[32]  Li Geng-yin.  Survey on reserve decision of power systems with large scale wind power integration , 2013 .

[33]  Mohammad Sadeghi,et al.  Optimal operation of microgrids through simultaneous scheduling of electrical vehicles and responsive loads considering wind and PV units uncertainties , 2016 .

[34]  Nikos D. Hatziargyriou,et al.  Decision Trees-Aided Active Power Reduction of a Virtual Power Plant for Power System Over-Frequency Mitigation , 2015, IEEE Transactions on Industrial Informatics.

[35]  Kameshwar Poolla,et al.  Cooperation of Wind Power and Battery Storage to Provide Frequency Regulation in Power Markets , 2017, IEEE Transactions on Power Systems.

[36]  Zhongfu Tan,et al.  Multi-objective stochastic scheduling optimization model for connecting a virtual power plant to wind-photovoltaic-electric vehicles considering uncertainties and demand response , 2016 .

[37]  S. Surender Reddy,et al.  Day-Ahead and Real Time Optimal Power Flow considering Renewable Energy Resources , 2016 .

[38]  Zhongfu Tan,et al.  A bi-level stochastic scheduling optimization model for a virtual power plant connected to a wind–photovoltaic–energy storage system considering the uncertainty and demand response , 2016 .

[39]  Marco Giuntoli,et al.  Optimized Thermal and Electrical Scheduling of a Large Scale Virtual Power Plant in the Presence of Energy Storages , 2013, IEEE Transactions on Smart Grid.

[40]  Gang He,et al.  China’s clean power transition: Current status and future prospect , 2017 .

[41]  Shi You.  Developing Virtual Power Plant for Optimized Distributed Energy Resources Operation and Integration , 2010 .

[42]  Shahram Jadid,et al.  Stochastic multi-objective operational planning of smart distribution systems considering demand response programs , 2014 .

[43]  Dong Yue,et al.  MAS-Based Hierarchical Distributed Coordinate Control Strategy of Virtual Power Source Voltage in Low-Voltage Microgrid , 2017, IEEE Access.

[44]  Wang Dan,et al.  Concept and Development of Virtual Power Plant , 2013 .