A multi-objective stochastic optimization model for electricity retailers with energy storage system considering uncertainty and demand response. (20th December 2020)

• Record Type: Journal Article
• Authors: Yang, Shenbo; Tan, Zhongfu; Liu, ZhiXiong; Lin, Hongyu; Ju, Liwei; Zhou, Fengao; Li, Jiayu
• Is Part Of: Journal of cleaner production. Volume 277(2020)
• Publication Date: 2020-12-20
• Subjects: Electricity retailers -- Energy storage system -- Flexible power purchase -- Multi-objective stochastic optimization model -- Uncertainty analysis -- Three-stage solution algorithm; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2020.124017
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: The development of electricity retailers with energy storage systems expands the energy use ways of users, promotes the consumption of clean energy power generation, and facilitates the development of electricity market. However, due to the imperfect trading mechanism and uncertainties of power supply and demand, the business risk continues to be large, which leads to limited sustainable development of electricity retailers. Therefore, based on the design of a trading mechanism of electricity retailers with energy storage systems in the electricity market, this paper constructs a multi-objective stochastic optimization model of the retailers' flexible purchase of electricity, aiming at minimizing the cost of electricity retailers and maximizing the consumption of clean energy power generation. In the model, the uncertainty of supply and demand and the demand response are taken into consideration. The results show that: (1) Compared with the conventional optimization model, considering the uncertainty in the medium-and-long-term electricity trading, although the demand for clean energy is reduced, the retail cost of temporary electricity in the day-ahead trading is also reduced. (2) The retailers make full use of flexible power charging and discharging, so the extra cost of purchasing electricity in the day-ahead trading caused by the uncertainty of clean energy is reduced via the energy storage demand response. (3) A price-based demand response can change the electricity purchase demand of retailers, improve the transaction volume between retailers and clean power enterprises in the medium-and-long-term electricity trading, and reduce the energy purchase cost while improving the consumption of clean energy power generation. (4) Due to the impact of initial investment cost and other factors, there is no obvious linear relationship between the capacity of energy storage systems and the reduction of retail energy purchase cost; therefore, it is necessary to apply energy storage systems reasonably to achieve the optimal economic benefits. Highlights: The trading strategy for ER-ESS participating in electricity market is formulated. The energy storage and price-based demand response models are constructed. The respective solving algorithms for the regular and uncertainty model are built.