A Distributed Pricing Mechanism and Optimal Schedulingin Smart Grid using Improved Grey Wolf Optimization

In recent days, energy is most important resource new techniques and methods which introduced to fulfill energy demand for price-based demand response (DR). The preface to this study has shown that load management strategies implemented based on Costlier scheduling for low or consistent energy users results from price-based demand response strategies. Suggested work has taken into account and broadened the same concept to include numerous EV charging regions and corresponding loads from an analytical and implementation point of view. This research provides an innovative approach called Improved Grey Wolf Optimization (IGWO) that uses the distinct energy consumption patterns of EVs in each location to calculate EV charging costs. Dynamic price signal that is non-discriminatory is sent to all EV regions and stations. First, a brand-new mathematical model based on market dynamics and real-time load demand is provided to determine charging prices. The global optimum solution produced by the optimization algorithm successfully converges, confirming the efficacy of the suggested mechanism. Lastly, analysis and simulation outcomes are performed to demonstrate the accomplishments of our suggested task in standings of reasonable cost high user satisfaction distribution. IGWO demonstrated usefulness's revenue is modest by either mechanism or cost saving is 0.69-6.89%.