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. (1st June 2016)

• Record Type: Journal Article
• Authors: Ju, Liwei; Tan, Zhongfu; Yuan, Jinyun; Tan, Qingkun; Li, Huanhuan; Dong, Fugui
• Is Part Of: Applied energy. Volume 171(2016)
• Page Start: 184
• Page End: 199
• Publication Date: 2016-06-01
• Subjects: Virtual power plant -- Energy storage system -- Uncertain -- Demand response -- Bi-level model; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2016.03.020
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Our research focuses on Virtual Power Plant (VPP). Virtual Power Plant consists of WPP, PV, CGT, ESSs and DRPs. Robust optimization theory is introduced to analyze uncertainties. A bi-level stochastic scheduling optimization model is proposed for VPP. Models are built to measure the impacts of ESSs and DERPs on VPP operation. Abstract: To reduce the uncertain influence of wind power and solar photovoltaic power on virtual power plant (VPP) operation, robust optimization theory (ROT) is introduced to build a stochastic scheduling model for VPP considering the uncertainty, price-based demand response (PBDR) and incentive-based demand response (IBDR). First, the VPP components are described including the wind power plant (WPP), photovoltaic generators (PV), convention gas turbine (CGT), energy storage systems (ESSs) and demand resource providers (DRPs). Then, a scenario generation and reduction frame is proposed for analyzing and simulating output stochastics based on the interval method and the Kantorovich distance. Second, a bi-level robust scheduling model is proposed with a double robust coefficient for WPP and PV. In the upper layer model, the maximum VPP operation income is taken as the optimization objective for building the scheduling model with the day-ahead prediction output of WPP and PV. In the lower layer model, the day-ahead scheduling scheme is revised with the actual output of the WPP and PV under the objectives of the minimum system net load and the minimum system operation cost. Finally, the independent micro-grid in a coastal island in eastern China is used for the simulation analysis. The results illustrate that the model can overcome the influence of uncertainty on VPP operations and reduce the system power shortage cost by connecting the day-ahead scheduling with the real-time scheduling. ROT could provide a flexible decision tool for decision makers, effectively addressing system uncertainties. ESSs could replace CGT to provide backup service for the WPP and PV, to smooth the VPP output curve and to improve the WPP and PV grid connection by its charging–discharging characteristics. Meanwhile, IBDR and PBDR could smooth the load curve to the maximum extent, link the generation side with the demand side to minimize abandoned power value and reach the optimum benefit of VPP operation.