A Comprehensive Evaluation Model on Optimal Operational Schedules for Battery Energy Storage System by Maximizing Self-Consumption Strategy and Genetic Algorithm

Abstract

Building an energy storage system is beneficial when solar panels are not producing sufficient energy. However, there is a major issue in terms of feasibility and efficiency. These limitations could be overcome by the deployment of optimal operational strategies. In previous studies, researchers typically focused on finding problem-solving strategies in such situations with only one or two evaluation indicators, lacking a comprehensive evaluation of the integrated objective. Moreover, few studies propose a general model of battery systems suitable for forecast-based operation scenarios with different energy demand features. Therefore, this study developed a comprehensive evaluation model for the operational schedule optimization of a battery energy storage system with a detailed and holistic analysis as well as practicality in implementation. In order to consume the maximum allowable rate of PV generation as promptly and completely as possible, this model was based on a maximizing self-consumption strategy (MSC). A genetic algorithm was applied to time match PV generation and load demand with full consideration of comprehensive techno-economic indicators and total operation cost as well. The model was validated within a typical American house to select the best battery system according to techno-economic indicators for the three types of batteries analyzed. It was discovered that the three types of batteries including Discover AES, Electriq PowerPod2 and Tesla Powerwall+ could all be considered as options for energy storage, and there exist subtle differences in their technical performance during the short charging and discharging phases. Discover AES has the advantage of using PV generation in a timely manner to suit load demand during the long-term operation of a battery energy storage system. With the proper prediction of building energy demand by means of a machine learning approach, the model’s robustness and predictive performance could be further extended. The machine learning approach proved feasible for adapting our optimization model to various battery storage scenarios with different energy demand features. This study is novel in two ways. Firstly, hierarchical optimization was conducted with a genetic algorithm using the MSC strategy. Secondly, the machine learning approach was applied in conjunction with the genetic algorithm to perform online optimization for the predictive schedule. Additionally, three main advantages of the methodology proposed in this paper for producing an optimal operational schedule were identified, which are as follows: generic applicability, convenient implementation and good scalability. However, the charging and discharging performance of the battery energy storage system was simulated under short-term operation with regular solar radiation. Long-term operation considering solar fluctuation should be investigated in the future.