An evolutionary simulation optimization framework for interruptible load management in the smart grid. (August 2018) - Bastani, Mehrad; Thanos, Aristotelis E.; Damgacioglu, Haluk; Celik, Nurcin; Chen, Chun-Hung
Highlights: We present a simulation optimization framework for interruptible load scheduling. Our framework utilizes new generation of smart meters for device level control. Optimization model minimizes the total interruption cost considering desired load. Simulation and design ranking are utilized for robust interruption load program. Our framework outperforms the current algorithms in terms of feasibility and cost. Abstract: Demand response (DR) is one of the most promising ways to control peak energy demand in power networks that allows customers to make informed decisions regarding their energy consumption, and helps the energy providers reduce the peak load demand and reshape the load profile. Most of the existing DR strategies consider constant energy loads for devices in the system; however, energy load variation poses a major challenge to the feasibility of the solutions acquired by existing techniques. In this paper, we propose an evolutionary simulation optimization framework to implement an interruptive DR strategy on a smart grid with uncertain device loads. The proposed framework aims at adjusting the peak demand to the desired demand curve while ensuring the network reliability. This framework includes three components that interact and cooperate with each other: (1) a genetic algorithm that progressively improves the existing scenarios or discovers new scenarios for the interruption, (2) a simulation model that simulates the performance of selected scenarios, and (3) a design ranking algorithm that optimizes the allocation of simulation replications and identifies the top m best scenarios. The effectiveness of the proposed framework is demonstrated on a simulated smart grid that includes 29 different types of devices. The results of the proposed framework are quite promising in terms of feasibility where it acquires at least 4 times as many feasible solutions as existing approaches do.