Risk-involved modeling of electrical and thermal energy storage systems in smart apartment building utilizing downside risk constraints approach. (May 2023)

• Record Type: Journal Article
• Authors: Guo, Qun; Ni, Yanling; Liu, Chen; Wang, Xinchi; Nojavan, Sayyad
• Is Part Of: Journal of energy storage. Volume 61(2023)
• Publication Date: 2023-05
• Subjects: Boiler-based smart apartment building -- Smart home -- Downside risk constraints (DRC) -- Risk-in-cost -- Risk analysis; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2023.106709
• Languages: English
• ISSNs: 2352-152X

Abstract

Abstract: After restructuring in power systems, new players such as home energy management systems face various problems, and optimal scheduling of such intelligent systems under uncertainties can be considered one of the mentioned problems. So, a novel and efficient risk management approach named the downside risk constraints approach is used in this paper to manage the risk raised due to various uncertainties in the boiler-based apartment building. First, a stochastic optimization approach is used to model the uncertain nature of the electricity market price and the heat demands in the provided model. After that, the downside risk constraints approach minimizes the risk cost associated with various uncertainties. By analyzing the obtained results, it can be found that by considering the downside risk constraints approach, the risk cost level can plunge to 0 by paying 7.89 % more money. So, the owner can spend a few extra bills to be entirely resistant to uncertainties. The General Algebraic Modeling System (GAMS) platform is utilized in this paper to solve the proposed mixed-integer-linear-based apartment building model. Highlights: Risk-involved scheduling of smart appliances apartment building is studied. Downside risk constraints approach is proposed to model risk. Aim is to minimize the total operation cost of a residential building. Aim is to manage the risk associated with various uncertainties. All uncertainties are modelled via stochastic programming.