Stochastic AC Network-constrained Scheduling of CAES and Wind Power Generation in Joint Energy and reserve market: Toward More Realistic Results

TL;DR

This paper introduces a two-stage stochastic day-ahead scheduling model that integrates wind power and CAES, using a novel linearized AC optimal power flow approach to improve market decision-making accuracy and computational efficiency.

Contribution

It proposes a two-level LAC-OPF method and incorporates thermodynamic CAES characteristics for more realistic energy and reserve market scheduling.

Findings

The proposed model outperforms traditional AC-OPF, DC-OPF, and LAC-OPF approaches.

The two-level LAC-OPF reduces computational burden and improves accuracy.

Simulation results validate the effectiveness of the proposed methodology.

Abstract

In this paper, a two-stage stochastic day-ahead (DA) scheduling model is proposed incorporating wind power units and compressed air energy storage (CAES) to clear a co-optimized energy and reserve market. The two-stage stochastic programming method is employed to deal with the wind power generation uncertain nature. A linearized AC optimal power flow (LAC-OPF) approach with consideration of network losses, reactive power, and voltage magnitude constraints is utilized in the proposed two-stage stochastic DA scheduling model. Using an engineering insight, a two-level LAC-OPF (TL-LAC-OPF) approach is proposed to (i) reduce the number of binary variables of the LAC-OPF approach which decreases the computational burden, and (ii) obtain LAC-OPF pre-defined parameters adaptively so that the accuracy of LAC-OPF approach is increased as a result of reducing artificial losses. Furthermore, as the CAES efficiency depends on its thermodynamic and operational conditions, the proposed two-stage stochastic DA scheduling model is developed by considering its thermodynamic characteristics to obtain a more realistic market decision at the first place. The proposed model is applied to IEEE 30-bus and 57-bus test systems using GAMS software, and is compared with three traditional approaches, i.e., AC-OPF, DC-OPF, and LAC-OPF. Simulation results demonstrate effectiveness of the proposed methodology.