Optimal Allocation and Capacity Determination of Energy Storage Systems Within Active Distribution Networks Utilizing an Enhanced Sparrow Search Approach

Abstract

Energy storage systems are pivotal in enhancing the low-carbon footprint and operational stability of active distribution networks. The paper introduces a novel approach for the intentional placement and capacity determination of energy storage devices within active distribution networks, highlighting the integration of carbon trading mechanisms. A robust optimization framework is developed to minimize active power losses, voltage deviations, and overall network costs while incorporating a tiered carbon trading model to assess its economic implications on the active distribution network's financial landscape.

Enhancements to the sparrow search algorithm are implemented in this study by incorporating chaotic initialization, sine-cosine mapping, and Levy flights, which augment the algorithm's diversity and capacity for comprehensive exploration. This refinement aims to strengthen the optimization process. This refinement leads to the development of a multi-objective optimization algorithm that leverages an advanced version of the sparrow search methodology. Which employs a Pareto front to conduct a more detailed and sophisticated investigation of the potential solution landscape.

The energy storage system's optimal layout within the active distribution network is determined by applying the Evidence Weighted Medi (EWM) approach in combination with the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). Experimental outcomes indicate that the refined algorithm significantly boosts the precision and effectiveness of the solution outcomes set. Moreover, the energy storage configuration, optimized with consideration of carbon trading, is crucial for promoting renewable energy integration and substantially reducing active power losses and voltage fluctuations in the network.