A multi-objective stochastic optimization model for electricity retailers with energy storage system considering uncertainty and demand response

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 supp...

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Vydané v:	Journal of cleaner production Ročník 277; s. 124017
Hlavní autori:	Yang, Shenbo, Tan, Zhongfu, Liu, ZhiXiong, Lin, Hongyu, Ju, Liwei, Zhou, Fengao, Li, Jiayu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 20.12.2020
Predmet:	business enterprises clean energy electricity Electricity retailers energy Energy storage system financial economics Flexible power purchase markets Multi-objective stochastic optimization model power generation purchasing risk supply balance sustainable development Three-stage solution algorithm uncertainty Uncertainty analysis Three-stage solution algorithm Multi-objective stochastic optimization model Energy storage system Uncertainty analysis Flexible power purchase Electricity retailers
ISSN:	0959-6526, 1879-1786
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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. •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.
AbstractList	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. •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. 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.
ArticleNumber	124017
Author	Ju, Liwei Li, Jiayu Lin, Hongyu Zhou, Fengao Tan, Zhongfu Liu, ZhiXiong Yang, Shenbo
Author_xml	– sequence: 1 givenname: Shenbo surname: Yang fullname: Yang, Shenbo email: ysbo@ncepu.edu.cn organization: School of Economics and Management, North China Electric Power University, Beijing, 102206, China – sequence: 2 givenname: Zhongfu surname: Tan fullname: Tan, Zhongfu email: tzhf@ncepu.edu.cn organization: School of Economics and Management, North China Electric Power University, Beijing, 102206, China – sequence: 3 givenname: ZhiXiong surname: Liu fullname: Liu, ZhiXiong email: liuzhixiong_tju@126.com organization: State Grid Jibei Electronic Economic Research Institute, Beijing, 100038, China – sequence: 4 givenname: Hongyu surname: Lin fullname: Lin, Hongyu email: hone@ncepu.edu.cn organization: School of Economics and Management, North China Electric Power University, Beijing, 102206, China – sequence: 5 givenname: Liwei surname: Ju fullname: Ju, Liwei email: hdlw_ju@ncepu.edu.cn organization: School of Economics and Management, North China Electric Power University, Beijing, 102206, China – sequence: 6 givenname: Fengao surname: Zhou fullname: Zhou, Fengao email: zhoufengao@ncepu.cn organization: School of Economics and Management, North China Electric Power University, Beijing, 102206, China – sequence: 7 givenname: Jiayu surname: Li fullname: Li, Jiayu email: 18401689197@163.com organization: School of Economics and Management, North China Electric Power University, Beijing, 102206, China
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Keywords	Three-stage solution algorithm Multi-objective stochastic optimization model Energy storage system Uncertainty analysis Flexible power purchase Electricity retailers
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Snippet	The development of electricity retailers with energy storage systems expands the energy use ways of users, promotes the consumption of clean energy power...
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SubjectTerms	business enterprises clean energy electricity Electricity retailers energy Energy storage system financial economics Flexible power purchase markets Multi-objective stochastic optimization model power generation purchasing risk supply balance sustainable development Three-stage solution algorithm uncertainty Uncertainty analysis
Title	A multi-objective stochastic optimization model for electricity retailers with energy storage system considering uncertainty and demand response
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