Optimal trading strategy for integrated energy company based on integrated demand response considering load classifications. (June 2021)

• Record Type: Journal Article
• Authors: Ma, Zhoujun; Zheng, Yuping; Mu, Chenlu; Ding, Tao; Zang, Haixiang
• Is Part Of: International journal of electrical power & energy systems. Volume 128(2021)
• Publication Date: 2021-06
• Subjects: Integrated energy system -- Integrated demand response -- Integrated energy company -- Mean-variance portfolio; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2020.106673
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: A day-ahead integrated energy market structure is proposed. An optimal trading strategy based on integrated demand response is set up with multi-type loads. A conditional value-at-risk mean-variance portfolio optimization model is designed to address uncertainties. Abstract: Integrated energy systems (IESs) have become a new transition for the traditional electricity industry. In the context of energy mix, end-users participating markets should consider diverse energy mix. Therefore, this paper proposes a day-ahead integrated energy market structure that allows electricity, heat energy and natural gas to trade simultaneously. Aiming at the maximal profit of integrated energy company (IECO) in the market, an optimal trading strategy based on integrated demand response (IDR) is optimized considering complex energy flow constraints of power distribution system, heat system and natural gas system. In this model, the IDR can be carried out based on three steps, i.e. information sharing of the integrated energy market, energy conversion of Energy Hub (EH) and flexible loads scheduling in the retail market. In particular, flexible loads are classified into shiftable loads, reducible loads and convertible loads according to their characteristics. Furthermore, a conditional value-at-risk based mean–variance portfolio model is set up to address the uncertain market prices. Finally, simulation results on a test system with a 33-bus power distribution system, an 11-node natural gas system and a 6-node District Heat System (DHS) demonstrate the effectiveness of the proposed model.