Providing a Novel Approach Based on Decomposition for Congestion Management considering EVCSs, Wind Farms, and DLC

Abstract

The acceptance of renewable energy sources and electric vehicle charging stations (EVCSs) around the world is growing, which changes the network’s congestion. From the operator’s perspective, congestion management is essential for network reliability despite increased demand. Additionally, it is among the most crucial elements in preventing network market power. Therefore, the items affecting the congestion changes of network lines, in particular, congested lines, are important for the operator. In this paper, using an analytical method and structural decomposition, the impact of wind farms, EVCSs, and demand response to changes in network line congestion in various cases has been investigated. In addition, some indices have been proposed to evaluate the ability of each item to change the congestion at low and peak loads. The proposed method has been implemented on the IEEE standard 24-bus network, and the results have been analyzed. This method is an effective tool for the network operator to predict the state of the network due to changes in items.

Integrated Demand Response Optimization for Consumer With Herd Mentality: A Genetic Simulated Annealing Approach

“…Ref. [17] adopts a linear programming method, considers power storage, charging ,and CHP, establishes a cost function model, and considers uncertainty, which makes the problem closer to reality. The EH system is optimized from the perspective of cost, four demand response schemes are defined and evaluated, and the most economical and feasible scheme is obtained.…”