Dynamic emission dispatch considering the probabilistic model with multiple smart energy system players based on a developed fuzzy theory-based harmony search algorithm

With the development of electric vehicles, their presence has increased more than ever. Among renewable resources, wind resources are popular due to their ability to generate more energy, more general acceptability and cost-effectiveness. The only challenge facing the use of new energies and electric vehicles is the uncertainty in their generation at different hours of the day and their response to needs based on electric vehicle (EV) battery discharge. Thus, this paper develops the power-pollution dynamic load dispatch, which is a multi-objective optimization problem aiming to reduce fuel costs and pollution emissions simultaneously, by adding EVs. A scenario-based probabilistic method is used to cover the uncertainty in the wind farm. To approach the real situation, the problem is considered dynamically using the output power slope rate, security constraints and increasing/decreasing rate. Since solving the above problem with several conflicting objective functions requires a robust optimization method, a novel multiobjective optimization algorithm based on harmony search is proposed to resolve the problems of classical methods. A new operator is applied to develop its local search. To arrange and select the best solution, the sorting model and fuzzy theory are employed. Application of fuzzy theory has been able to make a good selection between the set of Pareto solutions and improve the search performance in the proposed algorithm. Finally, different 10-unit test systems are proposed to show the efficiency of the proposed model and method. The proposed method and model are discussed and evaluated in different scenarios, which demonstrates the presence of renewable sources significantly reduces pollution and the presence of electric vehicles supplies a part of the load at peak hours. It is observed that the trend of changes in state of charge (SOC) is the same in different cases. The findings demonstrate that varying travel times can charge an electric vehicle's SOC and state of discharge (SOD) but only slightly affect the costs of wind energy that are exaggerated and underestimated.