Journal
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
Volume 126, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ijepes.2020.106561
Keywords
Electricity retailer; Shared energy storage; Matching degree; Life cycle cost; Investment evaluation
Categories
Funding
- Young Elite Scientists Sponsorship Program of Chinese Society of Electrical Engineering [CSEE-YESS-2018006]
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This paper proposes an optimal planning approach for shared energy storage based on cost-benefit analysis to minimize the electricity procurement cost of electricity retailers. The method establishes a multi-time scale electricity purchase model and screens and classifies retailers based on a matching degree function, selecting a collective that maximizes profits from planning shared ES. Through life cycle cost modeling and benefit distribution based on contribution degree, the study shows that the proposed approach reduces costs for different groups of electricity retailers and highlights the effectiveness of energy storage in reducing costs.
With the rapid development of energy storage (ES) technology, it has gradually become a vital facility to cope with the intermittent renewable generation and reduce the users' electricity purchase cost. However, the limited application of the ES has suffered from its high capital cost. This paper proposes an approach of optimal planning the shared energy storage based on cost-benefit analysis to minimize the electricity procurement cost of electricity retailers. First, the multi-time scale electricity purchase model is established. Then the retailers are screened and classified based on the proposed matching degree function to select the collective of retailers, which maximizes the profits of planning the shared ES. The life cycle cost model and the equivalent cycle life method are used to evaluate the benefit of investing the shared ES. The benefit distribution among the collective is conducted based on the contribution degree of each retailer. In the case study, the optimization results of the shared ES for high-matching and low-matching groups are compared in detail. The simulation results illustrate that the costs are reduced by 8.83% and 8.03% respectively for the two groups of electricity retailers by the proposed approach. Results also verify that ES can effectively reduce the cost of retailers, and high matching degree can be used as the selection criterion to obtain a greater benefit of the shared ES.
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