Smart Grid Energy Management using Improved Swarm Optimization Algorithm

From an administrative standpoint, a Smart Grid (SG) is emerging as a future power grid that will support a smart energy distribution system. Challenges in this context include the strategic maneuvers used by parties during bidding, the exchange of energy, the appropriate planning and monitoring, and the essential protective systems. The presence of non-dispatchable actors, such as distant producing units and consumers with changing loads, in distribution networks (DNs) results in an increase in trading and management challenges. This research study presented innovative contributions to enhance the performance of smart grid systems, in order to tackle the issues faced by existing smart grid management strategies. This study presented an innovative approach and analyzed its effectiveness based on cost and energy usage factors. We have implemented the optimization technique known as Enhanced Glowworm Swarm Optimization (EGSO). The EGSO algorithm is an enhanced iteration of the existing GSO technique. The GSO technique is a derivative-free algorithm that uses a meta-heuristic approach to mimic the glowing behavior of glow-worms. It can efficiently capture all the highest multimodal functionalities. The EGSO algorithm is employed for the management of load distribution mechanism in order to assign locally accessible energy to consumers in a proportionate manner. The simulation results demonstrate the superior efficiency of the suggested models in comparison to existing methodologies.