Methodology for real impact assessment of the best location of distributed electric energy storage. (October 2016)

• Record Type: Journal Article
• Authors: Gonçalves, José; Martins, António; Neves, Luís
• Is Part Of: Sustainable cities and society. Volume 26(2016)
• Page Start: 531
• Page End: 542
• Publication Date: 2016-10
• Subjects: Distributed electric energy storage -- Genetic algorithms -- Electric networks -- Energy profiles -- Energy service -- Methodology; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2016.05.010
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: An improved genetic algorithm for electrical energy storage allocation was proposed. Implications of different charge/discharge profiles in the distribution network. Proposed methodology for distributed electric energy storage system (DEESS) assessment. Regulatory framework for energy storage integration within energy markets. Pricing options for balancing intermittent renewable production and energy storage. Abstract: This paper presents a methodology to provide a decision maker, e.g. the distribution system operator, information on the associated impacts of the operation of distributed electric energy storage systems (ESS 1 ) in an urban environment, in order to support the choice of the best locations of storage units. The developed methodology uses three types of profile prototypes based on actual data, obtained through clustering techniques. These profiles, which include electricity demand, electricity prices and renewable electricity production, are used to optimize the placement of electric energy storage units. The paper considers expected attitudes of the main stakeholders towards distributed electric ESS implementation, and discusses possible regulatory framework options to define the distributed electric ESS business model. The model was applied to a case study using the nanophosphate lithium-ion battery technology as an example. Results show a significant influence of the charge/discharge profile of batteries on the choice of their best locations.