Modeling the risk-based decisions of the microgrid in day-ahead energy and reserve markets considering stochastic dispatching of electrical and thermal energy storages. (May 2022)

• Record Type: Journal Article
• Authors: Bahramara, Salah; Shahrokhi, Saman; Sheikhahmadi, Pouria; Khezri, Rahmat; Muyeen, S.M
• Is Part Of: Energy conversion and management. X. Volume 14(2022)
• Publication Date: 2022-05
• Subjects: Electrical and thermal energy storages -- Energy and reserve markets -- Micro-grids -- Renewable energy sources -- Robust two-stage stochastic model
• DOI: 10.1016/j.ecmx.2022.100201
• Languages: English
• ISSNs: 2590-1745

Abstract

Graphical abstract: Highlights: The MGO's bids in the markets are optimized with dispatching of local resources. Uncertainties of electrical demand and power generation of PV are modeled. A robust two-stage stochastic optimization model is developed. The most capacity of electrical energy storage is used to provide reserve capacity. With increasing the robust parameter, the MGO's cost increases. Abstract: In this paper, the electrical and thermal energy management problem of a micro-grid operator (MGO) is addressed under uncertainties aiming at participating in the day-ahead energy and reserve markets. For this purpose, a robust two-stage stochastic model is developed to protect the first stage MGO's decisions, i.e., its bids in the energy and reserve markets, against the uncertainty of the real-time energy market price. This is done through stochastic dispatching of the MG resources which includes the electrical and thermal energy storages and the combined heat and power unit as the second-stage decisions. The results showed that the MGO's expected total cost decreases when it participates in both the energy market and the reserve market in comparison with the case it only participates in the energy market. Also, the risk-based behavior of the MGO showed that increasing the robust parameter decreases the reserve provided for the market and the net power trading with the market. However, the proposed robust two-stage stochastic model leads to a smaller reduction of the MGO's first-stage decisions in the worst case in comparison with the conventional methods, i.e. deterministic and probabilistic ones. This issue proves the effectiveness of the proposed approach to protect the MGO's decisions against the uncertainties.