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Open AccessArticle

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

1
College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
2
Yancheng Power Supply Company of State Grid Jiangsu Electric Power Company, Yancheng 224002, Jiangsu Province, China
*
Authors to whom correspondence should be addressed.
Energies 2019, 12(10), 1918; https://doi.org/10.3390/en12101918
Received: 22 April 2019 / Revised: 12 May 2019 / Accepted: 15 May 2019 / Published: 20 May 2019
(This article belongs to the Special Issue Smart Management of Distributed Energy Resources)
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. View Full-Text
Keywords: virtual power plant (VPP); stochastic adaptive robust model; binding scenario identification approach; central air-conditioning system (CACS); multiple markets virtual power plant (VPP); stochastic adaptive robust model; binding scenario identification approach; central air-conditioning system (CACS); multiple markets
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MDPI and ACS Style

Sun, G.; Qian, W.; Huang, W.; Xu, Z.; Fu, Z.; Wei, Z.; Chen, S. Stochastic Adaptive Robust Dispatch for Virtual Power Plants Using the Binding Scenario Identification Approach. Energies 2019, 12, 1918.

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