Coordinated wind-thermal-energy storage offering strategy in energy and spinning reserve markets using a multi-stage model

• Published in: Applied energy 2020-02, Vol.259, p.114168, Article 114168
• Main Authors: Khaloie, Hooman, Abdollahi, Amir, Shafie-khah, Miadreza, Anvari-Moghaddam, Amjad, Nojavan, Sayyad, Siano, Pierluigi, Catalão, João P.S.
• Format: Article
• Language: English
• Subjects: [formula omitted]-constraint method; Electricity markets; Energy storage system; Environmental-economic; Multi-stage stochastic programming; Offering strategy
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2019.114168

•A bi-objective offering model for a wind-thermal-energy storage system is proposed.•The system’s operation is formulated as a multi-stage stochastic programming model.•The energy storage benefits from the spinning reserve market as a profitable source.•The profitability of the emission trading pattern in the offering problem is studied. Renewable energy resources such as wind, either individually or integrated with other resources, are widely considered in different power system studies, especially self-scheduling and offering strategy problems. In the current paper, a three-stage stochastic multi-objective offering framework based on mixed-integer programming formulation for a wind-thermal-energy storage generation company in the energy and spinning reserve markets is proposed. The commitment decisions of dispatchable energy sources, the offering curves of the generation company in the energy and spinning reserve markets, and dealing with energy deviations in the balancing market are the decisions of the proposed three-stage offering strategy problem, respectively. In the suggested methodology, the participation model of the energy storage system in the spinning reserve market extends to both charging and discharging modes. The proposed framework concurrently maximizes generation company’s expected profit and minimizes the expected emission of thermal units applying lexicographic optimization and hybrid augmented-weighted ∊-constraint method. In this regard, the uncertainties associated with imbalance prices and wind power output as well as day-ahead energy and spinning reserve market prices are modeled via a set of scenarios. Eventually, two different strategies, i.e., a preference-based approach and emission trading pattern, are utilized to select the most favored solution among Pareto optimal solutions. Numerical results reveal that taking advantage of spinning reserve market alongside with energy market will substantially increase the profitability of the generation company. Also, the results disclose that spinning reserve market is more lucrative than the energy market for the energy storage system in the offering strategy structure.