Aggregation model of various demand-side energy resources in the day-ahead electricity market and imbalance pricing system. (May 2023)

• Record Type: Journal Article
• Authors: Iwafune, Yumiko; Kazuhiko, Ogimoto; Kobayashi, Yuki; Suzuki, Kenta; Shimoda, Yoshiyuki
• Is Part Of: International journal of electrical power & energy systems. Volume 147(2023)
• Publication Date: 2023-05
• Subjects: Aggregator -- Demand-side energy resources -- Day-ahead market pricing -- Imbalance pricing system -- Electric vehicles -- Heat pump water heaters; 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.2022.108875
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: Aggregator model of demand-side energy resources are proposed. Procurement cost is minimized for day-ahead market and an imbalance pricing system using DSRs. The impact of PV forecast error on procurement cost is found to be largest. Control saturates as the proportion of controllable demand-side energy resources increases. Abstract: We propose a tool for aggregating a large number of buildings' electricity demand and photovoltaic (PV) generation, controllable heat pump water heaters (HPWH) and electric vehicles (EV) to minimize the procurement cost in a market transaction consisting of a day-ahead market and an imbalance pricing system. The novelty of this research is that the aggregator's strategy can be modeled including various uncertainties, and the value of multiple demand-side resources (DSRs) can be quantified within realistic market rules. The model shows that the ability to control the imbalance through real-time control of DSRs has significant economic benefits. In the control of HPWH hot water storage operation, the procurement cost can be reduced by 13% compared to nighttime operation when the same number of units are installed as the aggregate target rooftop PV and residential demand. The electric vehicle storage battery charge/discharge control can reduce the total cost by 38% compared to nighttime charging under the same conditions. The impact of the PV-prediction error on the procurement cost was found to be generally larger than those of other parameters, and the market-price-prediction error was found to be considerably large during EV charging and discharging optimization. Furthermore, the simulation confirms that the value of control saturates as the proportion of controllable DSRs increases compared to uncontrollable demand and generation.