Generation scheduling and micro-grid energy management using differential evolution algorithm

Thiswork proposes a Differential Evolution (DE) algorithm based energy management system (EMS) to optimize the performance of a microgrid in the presence of renewable energy sources. Various types of distributed generators (DGs), namely Micro Turbine (MT), Phosphoric Acid Fuel Cell (PAFC), Wind Turbine (WT), Photo-voltaic (PV) and battery (batt) are present in the micro-grid. Out of these DGs, WT and PV have a randomized natural behavior. To optimize the micro-grid operation and cover the uncertainty in power generation from the renewable resources, the incentive based demand response programs are incorporated in the EMS. The performance of EMS is optimized by minimizing the operating cost of the micro-grid and by minimizing its pollutant emissions using DE algorithm. The results obtained using DE algorithm are compared with the Particle Swarm Optimization (PSO) based reported results. On comparison the proposed DE algorithm is found to be superior in terms of solution quality and convergence speed.