A game-theoretic approach for price-based coordination of flexible devices operating in integrated energy-reserve markets. (15th December 2019)

• Record Type: Journal Article
• Authors: Gong, Xuan; De Paola, Antonio; Angeli, David; Strbac, Goran
• Is Part Of: Energy. Volume 189(2019)
• Publication Date: 2019-12-15
• Subjects: Integrated market -- Demand response -- Electric vehicles -- Storage -- Reserve service -- Game theory; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2019.116153
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: This paper presents a novel distributed control strategy for large scale deployment of demand response. In the considered framework, large populations of storage devices and electric vehicles (EVs) participate to an integrated energy-reserve market. They react to prices and autonomously schedule their operation in order to optimize their own objective functions. The price signals are obtained through the resolution of an optimal power flow problem that explicitly takes into account the impact of demand response on the optimal power dispatch and reserve procurement of committed generation. Differently from previous approaches, the adopted game-theoretic framework provides rigorous theoretical guarantees of convergence and optimality of the proposed control scheme in a multi-price setup that includes ancillary services. The performance of the coordination scheme is also evaluated in simulation on the PJM 5-bus system, demonstrating its capability to flatten demand profiles and reduce the costs of generators and flexible devices. Highlights: Distributed large-scale coordination of storage operation and charging of electric vehicles. Minimization of energy costs and optimal provision of reserve are combined. Multi-agent framework based on sequential price broadcasts and operational updates. Price signals are derived from an integrated energy-reserve market. Rigorous proof of convergence and global optimality of the proposed scheme.