Stochastic Adaptive Robust Dispatch for Virtual Power Plants Using the Binding Scenario Identification Approach

Guoqiang Sun, Weihang Qian, Wenjin Huang, Zheng Xu, Zhongxing Fu, Zhinong Wei and Sheng Chen
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Guoqiang Sun: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Weihang Qian: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Wenjin Huang: Yancheng Power Supply Company of State Grid Jiangsu Electric Power Company, Yancheng 224002, Jiangsu Province, China
Zheng Xu: Yancheng Power Supply Company of State Grid Jiangsu Electric Power Company, Yancheng 224002, Jiangsu Province, China
Zhongxing Fu: Yancheng Power Supply Company of State Grid Jiangsu Electric Power Company, Yancheng 224002, Jiangsu Province, China
Zhinong Wei: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Sheng Chen: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China

Energies, 2019, vol. 12, issue 10, 1-23

Abstract: The present study establishes a stochastic adaptive robust dispatch model for virtual power plants (VPPs) to address the risks associated with uncertainties in electricity market prices and photovoltaic (PV) power outputs. The model consists of distributed components, such as the central air-conditioning system (CACS) and PV power plant, aggregated by the VPP. The uncertainty in the electricity market price is addressed using a stochastic programming approach, and the uncertainty in PV output is addressed using an adaptive robust approach. The model is decomposed into a master problem and a sub-problem using the binding scenario identification approach. The binding scenario subset is identified in the sub-problem, which greatly reduces the number of iterations required for solving the model, and thereby increases the computational efficiency. Finally, the validity of the VPP model and the solution algorithm is verified using a simulated case study. The simulation results demonstrate that the operating profit of a VPP with a CACS and other aggregated units can be increased effectively by participating in multiple market transactions. In addition, the results demonstrate that the binding scenario identification algorithm is accurate, and its computation time increases slowly with increasing scenario set size, so the approach is adaptable to large-scale scenarios.

Keywords: virtual power plant (VPP); stochastic adaptive robust model; binding scenario identification approach; central air-conditioning system (CACS); multiple markets (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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