New Generation Computing, 2024
Electricity procurement of industrial consumers is becoming more and more complicated, involving a combination of various procurement methods due to electricity liberalization and decarbonization trends. This study analyzed and improved power procurement strategies for a factory to achieve carbon neutralization through a multi-agent model simulating the electricity market and introduced a factory agent using various procurement methods including PV, FC, storage batteries (SB), and DR. Firstly, we created a new procurement strategy utilizing all methods. Then, by using the simulation model, we assessed the effectiveness of the existing peak shift DR scenarios in terms of cost reduction efficiency. Results revealed that the introduction of PV has led to a counterproductive outcome for DR. Based on the results, we proposed two methods to improve the effectiveness of DR, namely considering the operation of PV in the DR scenario and expanding the range of optional time periods for DR activation. Finally, we made three new DR scenarios based on our proposal. Through experiment, the new scenarios were confirmed to be effective in cost-effectiveness for decarbonization.
Electric power market; Multi-agent simulation; Demand response scenarios; Carbon neutrality; Factory;
@journal{Cheng2024-ngc,
title={{Improvement and Analysis of Peak Shift Demand Response Scenarios of Industrial Consumers Using an Electricity Market Model}},
author={Long Cheng and Kiyoshi Izumi and Masanori Hirano },
journal={New Generation Computing},
doi={10.1007/s00354-024-00282-1},
year={2024}
}