Optimal Operation of Electric Vehicles and Distributed Generation Resources in Smart Grid Considering Load Management

Abstract

Technology advancement and the global tendency to use renewable energy in distributed generation units in the distribution network have been proposed as sources of energy supply. Despite the complexity of their protection, as well as the operation of distributed generation resources in the distribution network, factors such as improving reliability, increasing production capacity of the distribution network, stabilizing the voltage of the distribution network, reducing peak clipping losses, as well as economic and environmental considerations, have expanded the influence of distributed generation (DG) resources in the distribution network. The location of DG sources and their capacity are the key factors in the effectiveness of distributed generation in the voltage stability of distribution systems. Nowadays, along with the scattered production sources of electric vehicles with the ability to connect to the network, due to having an energy storage system, they are known as valuable resources that can provide various services to the power system. These vehicles can empower the grid or be used as a storage supply source when parked and connected to the grid. This paper introduces and studies a two-stage planning framework for the concurrent management of many electric vehicles and distributed generation resources with private ownership. In the first stage, the aim is to increase the profit of electric vehicles and distributed generation sources; finally, the purpose is to reduce operating costs. The proposed scheduling framework is tested on a distribution network connected to bus 5 of the RBTS sample network. Besides distributed generation sources and electric vehicles, we integrate timeconsistent load management into the system. Due to distributed generation sources such as photovoltaic systems and wind turbines and the studied design in the modeling, we use the Taguchi TOAT algorithm to generate and reduce the scenario to ensure the uncertainty in renewable energy. MATLAB software is used to solve the problem and select the optimal answer.