Abstract
The current study combines solar photovoltaic (PV) power, energy storage batteries and wind power to propose a novel and effective method for energy management and optimisation of hybrid renewable energy systems. Optimising the management and coordination of many energy sources is becoming essential, as the world moves towards sustainable energy choices. This study seeks to improve system performance, reliability and operating costs, using a unique hybrid control and power management paradigm, the Improved Human Evolutionary Optimisation (IHEO) algorithm, presented as a novel optimisation method that balances energy flow, generation, storage and consumption. This study shows that the proposed model greatly improves the efficiency of the operation of hybrid systems. The optimisation’s main objective is to reduce the overall cost of energy production while maintaining a smart energy management system. In this context, the cost function accounts for energy losses during electricity distribution as well as the generation costs of solar, wind, and battery storage. By minimising energy losses and optimising power flow between energy sources (wind, solar), storage (battery) and load demand, this can be used to assess system performance. The applied approach ensures system stability, optimises the use of renewable energy sources and reduces the power imbalance. The improved effectiveness of the IHEO algorithm in this study in minimising energy losses, lowering operating costs and enhancing overall system efficiency is demonstrated by thorough comparison with conventional particle swarm optimisation (PSO). Further to this, the integration of MPPT with PV systems and the IHEO algorithm enhances energy extraction efficiency by dynamically optimising power flow, ensuring maximum output from renewable sources under varying environmental conditions. Additionally, the BESS charging current ripple is also reduced from ±15 A to ±3 A using the model applied in this study, confirming smoother and safer battery charging operation. The key novelty lies in using IHEO for global exploration to find the best solution and PSO for local refinement to improve battery coordination with renewables and smooth DC-link regulation, which is then compared with conventional WOA.