Predictive Energy Management and Optimization of Battery Storage in Hybrid Power Systems

Integrating battery energy storage systems (BESS) into hybrid distributed generation (DG) systems with both renewable energy (RE) and non-renewable energy (non-RE) sources is crucial for mitigating RE intermittency and reducing dependence on non-RE generation. However, BESS entails high capital costs and suffers degradation from frequent charge-discharge cycles, shortening its lifespan and increasing costs. While limiting BESS usage slows degradation, it may lead to RE curtailment and reduced profitability. This trade-off underscores the need to jointly optimize the sizes of BESS, renewable-energy-based DG (REDG), and non-renewable-energy-based DG (non-REDG), along with the energy management system (EMS), to balance system flexibility and storage stress. DG sizing and energy management systems (EMS) are interdependent, where a well-designed EMS that accounts for degradation factors can guide the optimal sizing of DG and BESS, thereby ensuring long-term economic performance and system reliability. Therefore, this paper proposes a predictive rule-based EMS embedded within an optimization framework, aiming to determine the optimal sizes and locations of REDGs, non-REDGs, and BESS units. The EMS regulates the state of charge (SoC) based on peak and off-peak periods to optimize BESS usage, thereby reducing the depth of discharge (DoD) and minimizing degradation. Additionally, a BESS resting strategy, guided by Artificial Neural Networks (ANN), schedules resting periods to alleviate battery stress, mitigating both calendric and cyclic aging. To optimize DG and BESS placement while minimizing degradation, power loss, and excessive costs, an Adaptive Hybrid Differential Evolution and Particle Swarm Optimization (AHDEPSO) is proposed. This method enhances search efficiency by integrating Differential Evolution (DE) with Particle Swarm Optimization (PSO) and utilising an adaptive scaling factor to achieve dynamic balance. When tested on the IEEE 33-bus and IEEE 69-bus systems, the framework achieved average cost savings of 0.72% and 5.42% compared to other optimization algorithms, while reducing BESS degradation by 0.0066% and 0.0055%, and lowering power loss costs by 20.17% and 11.70%, respectively. AHDEPSO also cut computational time by 38.3% compared to traditional methods. These results demonstrate the effectiveness of the approach in optimizing DG and BESS placement, reducing costs, and enhancing system performance.