Multiobjective assessment of distributed energy storage location in electricity networks

Abstract

This paper presents a methodology to provide information to a decision maker on the associated impacts, both of economic and technical nature, of possible management schemes of storage units for choosing the best location of distributed storage devices, with a multiobjective optimisation approach based on genetic algorithms. The methodology was applied to a case study, a known distribution network model in which the installation of distributed storage units was tested, using lithium-ion batteries. The obtained results show a significant influence of the charging/discharging profile of batteries on the choice of their best location, as well as the relevance that these choices may have for the different network management objectives, for example, for reducing network energy losses or minimising voltage deviations. Results also show a difficult cost-effectiveness of an energy-only service, with the tested systems, both due to capital cost and due to the efficiency of conversion.

Keywords

• energy storage
• power distribution networks
• energy profiles
• load levelling energy service
• improved genetic algorithms