Estimating the Robust P-Q Capability of a Technical Virtual Power Plant Under Uncertainties

• Published in: IEEE transactions on power systems 2020-11, Vol.35 (6), p.4285-4296
• Main Authors: Tan, Zhenfei, Zhong, Haiwang, Xia, Qing, Kang, Chongqing, Wang, Xuanyuan Sharon, Tang, Honghai
• Format: Article
• Language: English
• Subjects: adjustable robust optimization; aggregation model; capability curve; Computational geometry; Convexity; Distributed generation; Distributed power generation; Electrical loads; Energy sources; Feasibility; Load modeling; Mathematical model; Optimization; Power flow; Power plants; Reactive power; Robustness; Robustness (mathematics); Scheduling; Uncertainty; uncertainty set; Virtual power plant; Virtual power plants; Volatility
• ISSN: 0885-8950; 1558-0679
• DOI: 10.1109/TPWRS.2020.2988069

The technical virtual power plant (TVPP) is a promising paradigm to facilitate the integration of distributed energy resources (DERs) while incorporating operational constraints of both DERs and networks. Due to the volatility and limited predictability of DER generation and electric loads, the output capability of the TVPP is uncertain. In this regard, this paper proposes the robust capability curve (RCC) of the TVPP, which explicitly characterizes the allowable range of the scheduled power output that is executable for the TVPP under uncertainties. Implementing the RCC can secure the scheduling of the TVPP against unexpected fluctuations of operating conditions when the TVPP participates in the transmission-level dispatch. Mathematically, the RCC is the first-stage feasible set of an adjustable robust optimization problem. An uncertainty set model incorporating the variable correlation and uncertainty budget is employed, which makes the robustness and conservatism of the RCC adjustable. A novel methodology is proposed to estimate the RCC by the convex hull of several points on its perimeter. These perimeter points are obtained by solving a series of multi scenario-optimal power flow problems with worst-case uncertainty realizations identified based on a linearized network configuration. Case studies based on the IEEE-13 test feeder validate the effectiveness of the RCC to ensure the scheduling feasibility while hedging against uncertainties. The computational efficiency of the proposed RCC estimation method is also verified based on larger-scale test systems.