Optimization of battery storage systems for off-grid solar energy solutions: a comparative study of sequential quadratic programming and genetic algorithm approaches

This paper presents an optimization study for battery storage systems in off-grid residential solar energy applications. The research focuses on balancing energy efficiency, storage capacity, and cost-effectiveness using two optimization models: Sequential Quadratic Programming (SQP) and a Genetic Algorithm (GA) with a penalty function. The study compares these approaches under specific technical constraints, including usable capacity, continuous power rating, and instantaneous power rating. Results indicate that the SQP model outperforms the GA in terms of cost optimization while meeting all operational requirements. Additionally, the paper explores the potential of emerging cement battery technology, which could offer novel solutions for energy storage in future applications. While promising, cement batteries remain under development and require further investigation to assess their viability in real-world scenarios.