Optimal scheduling of DG and EV parking lots simultaneously with demand response based on self-adjusted PSO and K-means clustering

Recently, the proliferation of distributed generation (DG) has been intensively increased in distribution systems worldwide. In distributed systems, DGs and utility-owned electric vehicle (EV) to grid aggregators have to be efficiently scaled for cost-effective network operation. Accordingly, with the penetration of power systems, demand response (DR) is considered an advanced step towards a smart grid. To cope with these advancements, this study aims to develop an innovative solution for the day-ahead sizing approach of energy storage systems of EVs parking lots and DGs in smart distribution systems complying with DR and minimizing the pertinent costs. The unique feature of the proposed approach is to allow interactive customers to participate effectively in power systems. To accurately solve this optimization model, two probabilistic self-adjusted modified particle swarm optimization (SAPSO) algorithms are developed and compared for minimizing the total operational costs addressing all constraints of the distribution system, DG units, and energy storage systems of EV parking lots. The K-means clustering and the Naive Bayes approach are utilized to determine the EVs that are ready to participate efficiently in the DR program. The obtained results on the IEEE-24 reliability test system are compared to the genetic algorithm and the conventional PSO to verify the effectiveness of the developed algorithms. The results show that the first SAPSO algorithm outperforms the algorithms in terms of minimizing the total running costs. The finding demonstrates that the proposed near-optimal day-ahead scheduling approach of DG units and EV energy storage systems in a simultaneous manner can effectively minimize the total operational costs subjected to generation constraints complying with DR.