Battery management optimization and lifecycle impact analysis for microgrid operation with V2G implementation / Muhammad Sufyan

The electric power system has been transformed and evolved towards decentralized systems, which interact with each other and within the whole electrical system. In this way, microgrids are essential components to increase the reliability and efficiency of the power system. The critical issue in isolated microgrid is the energy demand balance in the presence of intermittent renewable energy sources. Energy storage systems are the adequate solution to balance the demand/supply issue and support the ancillary services such as voltage regulation and reserve requirement. However, due to high installation cost of storage system, their sizing is essential for optimized operation of microgrid. The first part of this work proposes energy management system to reduce the operating cost of isolated microgrid. The economic scheduling using firefly algorithm is implemented for the optimization of distributed energy sources and ascertain optimal size of energy storage while meeting the load demand. The efficacy of the optimization algorithm is compared with other metaheuristic techniques for economic and reliability indices such as cost of electricity and loss of power supply probability. In the second part, electric vehicles (EV) are incorporated as flexible load in the power system. The EVs charge coordination with vehicle to grid (V2G) technology is performed with respect to economic and technical perspective. The economic objectives encompass cost minimization and profit maximization whereas technical objectives constitute power loss minimization and peak load reduction. The EV users are certainly concerned for cost of battery replacement due to degradation with active participation in V2G energy exchanges. Therefore, battery degradation model is formulated for real time analysis by considering the depth of discharge at each time interval. The operating cost and V2G profit are analyzed with different penetrations of renewable power generation, EV battery capacity and travelling time. The results indicate that proposed energy management approach effectively reduced the operating cost, ensuring the reliability of the microgrid. Since battery sizes influence the operating cost, optimal battery size is calculated to have minimum cost of electricity and prolong the battery lifetime. The proposed method results in 50% cost reduction when compared with the conventional method. The proposed energy management approach is solved using firefly algorithm, artificial bee colony, harmony search algorithm and particle swarm optimization. It was found that firefly algorithm is robust and computationally effective. In addition, EV charge coordination improves the system performance, minimize the power losses and restricts grid overloading. The integration of renewable energy sources (RES) reduces the system cost and maximizes the profit for the EV users. The system losses and cost of electricity is minimum when the RES penetration is increased while different EV capacities yields minimum profit