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Bi-Level Optimal Capacity Planning of Load-Side Electric Energy Storage Using an Emission-Considered Carbon Incentive Mechanism

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  • Jieran Feng

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Hao Zhou

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

Abstract

The decarbonization of the power system forces the rapid development of electric energy storage (EES). Electricity consumption is the fundamental driving force of carbon emissions in the power system. However, the current EES capacity planning research that considers the load-side carbon emission responsibility is still limited. To fill this research gap, this paper proposes a carbon incentive mechanism while considering load-side carbon emission responsibility. Additionally, a bi-level optimal capacity planning model of the load-side EES based on carbon emission flow (CEF) theory is proposed. The upper level obtained the bus carbon intensities through the optimal economic dispatch and passed them to the lower level. Considering the carbon incentive mechanism, the lower level optimized the EES capacity. Finally, the model was tested by MATLAB/Gurobi in the modified IEEE-39 bus power system. The results show that under the stimulation of the carbon incentive mechanism, the bi-level optimal capacity planning model of the load-side EES could effectively promote peak shaving, valley filling, and carbon reduction. Furthermore, compared with the two existing EES subsidy policies, the proposed carbon incentive mechanism is verified to be more conducive to reducing system carbon emissions.

Suggested Citation

  • Jieran Feng & Hao Zhou, 2022. "Bi-Level Optimal Capacity Planning of Load-Side Electric Energy Storage Using an Emission-Considered Carbon Incentive Mechanism," Energies, MDPI, vol. 15(13), pages 1-18, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4592-:d:846086
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    2. Hongli Liu & Luoqi Wang & Ji Li & Lei Shao & Delong Zhang, 2023. "Research on Smart Power Sales Strategy Considering Load Forecasting and Optimal Allocation of Energy Storage System in China," Energies, MDPI, vol. 16(8), pages 1-18, April.

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