Optimizing energy management of hybrid wind generation-battery energy storage units with long-term memory artificial hummingbird algorithm under daily load-source uncertainties in electrical networks

Abstract

Due to the unpredictable nature of renewable energy sources, it is necessary to have a storage system to store energy during off-peak hours and supply it during peak hours. However, if the storage units in a network are not properly connected, the benefits of the storage system cannot be realized. To improve the performance of radial distribution networks, this research proposes an optimal locating and sizing problem of battery energy storage (BES) and a renewable source of wind turbine distributed generation (WTDG). To solve this allocation problem, a multi-objective function (MOF) based on techno-economic parameters of total active power losses (TAPL), total voltage deviation (TVD), and investment cost of integrated devices is optimized using a modified version of the Artificial Hummingbird Algorithm (AHA) that incorporates a long-term memory component. With this enhancement, the algorithm can make decisions based on multiple past experiences, allowing it to consider a wider range of promising locations during the optimization process. This integration of long-term memory reduces the risk of premature convergence or stagnation and allows the algorithm to gain a broader perspective, potentially leading to improved performance and exploration of the solution space. The results demonstrate the effectiveness of the Long-term Memory Artificial Hummingbird Algorithm (LMAHA) in optimizing the MOF and enhancing the performance of the electrical distribution networks of IEEE 33 and 69 bus.