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Analysis of Carbon Neutrality Strategies of Industrial Consumers Using Electric Power Market Simulations [in Japanese]

Long CHENG, Masanori HIRANO, Kiyoshi IZUMI

Journal of Information Processing, vol.65, no.1, pp. 46-60, 2024


Abstract

With electricity liberalization and decarbonization becoming increasingly popular, electricity procurement for industrial consumers becomes more and more complicated. This study analyzed power procurement strategies for a factory to achieve carbon neutrality. For the analysis, a multi-agent simulation for an electric power market was constructed. The simulation consisted of a spot market imitating Japan Electric Power Exchange (JEPX), electricity consumption and generation agents, and a factory agent reflecting actual power consumption patterns. Then, a new procurement strategy for the factory utilizing all available methods, including PV, FC, storage batteries, and DR, was created. Using the simulation, the effects of each procurement method were analyzed in terms of the total cost of achieving carbon neutralization, and the effectiveness of the DR scenario was improved and verified. Results showed that PV had a remarkable cost reduction effect, and the effect of FC increased significantly with its generation unit cost decreased. The effect of SB and DR was not as significant as PV but still effective at least. Finally, we created a new DR scenario incorporating the operation of PV, because the consideration of PV operation in the DR scenario is necessary based on the results. Through experiments, the effectiveness of the proposed scenario was confirmed in terms of cost-effectiveness for decarbonization.

Keywords

electric power market; multi-agent simulation; carbon neutrality; power procurement; factory;

doi

10.20729/00231730


bibtex

@journal{Cheng2024-ipsj,
  title={{Analysis of Carbon Neutrality Strategies of Industrial Consumers Using Electric Power Market Simulations [in Japanese]}},
  author={Long CHENG and Masanori HIRANO and Kiyoshi IZUMI},
  journal={Journal of Information Processing},
  volume={65},
  number={1},
  pages={46-60},
  doi={10.20729/00231730},
  year={2024}
}