Robust optimization for optimal day-ahead economic dispatch of CCHP considering wind-load uncertainty

Abstract

Distributed wind power generators (WPGs) have been increasingly deployed into power system through combined cooling, heat, and power (CCHP) system. However, uncertain load and intermittent wind power generation adversely affect the economy of the system operation. In order to improve the economy of the CCHP operation, this paper presents a two-stage robust optimization model for the CCHP day-ahead (DA) economic dispatch considering the uncertainties of wind power generation and electric load demand. The model aims at minimizing the total operation cost. The day-ahead first stage is to decide the state and base generation of controllable generators that can withstand the worst-case scenario of the uncertainty while the real-time (RT) second stage is to adjust the generation of the generators according to the actual scenario of the uncertainty. To solve the model, duality theory, Big-M method, and column-and-constraint generation (C&CG) decomposition approach are employed to convert the model into tractable master problem and sub problem, so that a column-and-constraint generation iteration algorithm can be applied to figure out the optimal dispatch solution. Finally, experimental results reveal the effectiveness of the presented model and the employed method.