Allocation of Battery Systems in Transactive Energy Systems Based on Evolutionary Particle Swarm Optimization

Abstract

The dissemination of distributed energy resources (DERs) based on low-carbon technologies has increased as a result of current global efforts to promote the energy transition. This complex scenario requires appropriate solutions for simulating the behavior of increasingly complex networks with a high penetration of renewable energy resources. In this context, this work presents a tool for optimizing the allocation of storage devices in distribution networks based on the evolutionary particle swarm optimization (EPSO) algorithm. This approach is also integrated into the Simulation Model for Transactive Energy Systems (SiMTES), a simulation platform for assessing the behavior of transactive energy systems (TESs). The results demonstrate that the appropriate allocation of battery energy storage systems (BESSs) can mitigate conflicts between the distribution system operator (DSO) and future transactions by prosumers, which could lead to violations of network voltage levels. The study concludes that this process depends on covering key areas of the network rather than any specific node. Key areas are defined as those with a higher likelihood of energy renegotiation due to voltage profile violations. Additionally, since covering multiple areas is important, distributed storage allocations generally perform better than more concentrated allocations with higher capacity at fewer nodes. This holds even in cases where the total storage capacity is lower in the more dispersed allocation.