Procurement of ILM in Deregulated Power Systems Using Computational Intelligence Tools

Interruptible Load Management (ILM) is an effective demand-side resource that allows customers to utilize less electricity in case the system is under contingency. Applying interruptible load management strategy reduces energy consumption by interrupting a portion from customers' loads and compensate them with an acceptable offer. This paper introduces an intelligent framework to design a strategy for interruptible load management based on Fuzzy logic techniques and Artificial Neural Network (ANN). Furthermore, a comparative analysis of the two used techniques is carried out to show which one has the capability to follow the target objective obtained by GAMS software. Simulation results show that the ANN successfully traces the target values of strategy and concurrently minimizes the secondary reserve service cost without violating the system's capacity.