Optimal Dispatch of a Virtual Power Plant Considering Demand Response and Carbon Trading

Zuoyu Liu, Weimin Zheng, Feng Qi, Lei Wang, Bo Zou, Fushuan Wen and You Xue
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Zuoyu Liu: School of Electrical Engineering, Zhejiang University, No. 38 Zheda Rd., Hangzhou 310027, China
Weimin Zheng: State Grid Zhejiang Electric Power Co., Ltd., No. 8 Huanglong Rd., Hangzhou 310007, China
Feng Qi: School of Electrical Engineering, Zhejiang University, No. 38 Zheda Rd., Hangzhou 310027, China
Lei Wang: State Grid Zhejiang Economic Research Institute, No.1 Nanfu Road, Hangzhou 310008, China
Bo Zou: State Grid Zhejiang Economic Research Institute, No.1 Nanfu Road, Hangzhou 310008, China
Fushuan Wen: Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
You Xue: School of Electrical Engineering, Zhejiang University, No. 38 Zheda Rd., Hangzhou 310027, China

Energies, 2018, vol. 11, issue 6, 1-19

Abstract: The implementation of demand response (DR) could contribute to significant economic benefits meanwhile simultaneously enhancing the security of the concerned power system. A well-designed carbon emission trading mechanism provides an efficient way to achieve emission reduction targets. Given this background, a virtual power plant (VPP) including demand response resources, gas turbines, wind power and photovoltaics with participation in carbon emission trading is examined in this work, and an optimal dispatching model of the VPP presented. First, the carbon emission trading mechanism is briefly described, and the framework of optimal dispatching in the VPP discussed. Then, probabilistic models are utilized to address the uncertainties in the predicted generation outputs of wind power and photovoltaics. Demand side management (DSM) is next implemented by modeling flexible loads such as the chilled water thermal storage air conditioning systems (CSACSs) and electric vehicles (EVs). On this basis, a mixed integer linear programming (MILP) model for the optimal dispatching problem in the VPP is established, with an objective of maximizing the total profit of the VPP considering the costs of power generation and carbon emission trading as well as charging/discharging of EVs. Finally, the developed dispatching model is solved by the commercial CPLEX solver based on the YALMIP/MATLAB (version 8.4) toolbox, and sample examples are served for demonstrating the essential features of the proposed method.

Keywords: virtual power plant (VPP); demand response (DR); carbon trading mechanism; uncertainty; electric vehicle (EV) (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: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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