Analysis of Demand Response Scenarios by Industrial Consumers Using Artificial Electric Power Market Simulations

Abstract

This study analyzed the effect of demand response (DR) on the electric power market using a multi-agent simulation. Firstly, we built a multi-agent simulation for the electric power market based on the data of the Japan electric power exchange market (JEPX) and a factory. Using the multi-agent simulation, we tested possible DR scenarios. We then compared these scenarios in terms of the two metrics, which are newly defined in this study: cost and CO2 emission reduction efficiencies. The findings of this study are as follows: working time shift in the summer showed the best performance in the reduction in cost and CO2. However, the best performance in the winter was achieved by peak-shift of the factory demand based on indices. Through this study, we considered the electric power features of both the seasons and time of the day in the simulation and investigated the effects of complex DR scenarios using multi-agent simulation.