Abstract
This paper introduces SPLANDID, a novel techno-economic methodology for the optimal sizing, placement, and management of shared Battery Energy Storage Systems (BESSs) in residential communities that minimizes both capital and operational costs, along with energy losses within the community. To address the installation of two types of shared BESSs (i.e., single centralized BESS, multiple distributed BESSs), our methodology offers two distinct approaches: one optimizes the centralized BESS, while the other focuses on optimizing distributed BESSs. We formulate each approach as a constrained optimization problem and solve it using the particle swarm optimization (PSO) algorithm. To validate our methodology, we use real consumption and production patterns collected from households in a residential community in the United Kingdom (UK). We propose and compare three scenarios (i.e., no BESS installation, single BESS installation, and multiple BESSs installation) using various numerical metrics, such as total energy losses and total costs. Simulation results underscore the effectiveness of BESS installation, demonstrating an impressive 82.24% reduction in total costs compared to the benchmark scenario without BESS. Moreover, the installation of multiple distributed BESSs outperforms a single centralized BESS, reducing total costs and energy losses by 17.4% and 49.4%, respectively. Furthermore, the distributed installation proves efficient by decreasing the required storage capacity by 11.82% in contrast to the centralized approach. Compared to the large existing body of literature, this study contributes on several fronts. First, it explores the feasibility of installing multiple distributed shared BESSs in residential communities, departing from the dominant single centralized shared BESS installation in existing studies. The results obtained with this alternative installation strategy are highly promising from several perspectives. Secondly, our methodology is uniquely designed to base planning on actual batteries available on the market, enhancing its practical applicability in real-life scenarios. This approach contrasts with studies that often propose optimal sizes without considering the constraints imposed by the capabilities of existing batteries.
Published Version
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