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<p>The assessment of customer electricity demand potential is a critical part of further stimulating electricity demand growth and promoting utility development. The main aim of this paper is to investigate how electricity demand response potential can be assessed under uncertainty. The decision variables are set through a two-stage stochastic planning model, focusing on fuzzy decision-making through linear programming in interval planning, combining with consequent opportunity constraint planning and fuzzy opportunity constraint planning to realize artificial regulation of unfavorable factors, and finally completing the treatment of uncertain factors. Combining a Gaussian mixture model to construct a model for measuring electricity demand response potential. It has been found that different electricity pricing policies lead to other potential demand response values for peak electricity load. The optimal electricity pricing and revenue of each user will be greatly affected by the participation of different users in the electricity demand response. The optimal power pricing decreases from 3-4 when the number of users is 4 to 2.5 when the number of users is 20 because of the generation of scale rewards, and the benefits to the users increase substantially when the number of users participating in electricity demand response is 20. Evaluating customer electricity demand in an uncertain environment can help drive electricity system reform to some extent.</p>
</abstract>

The assessment of customer electricity demand potential is a critical part of further stimulating electricity demand growth and promoting utility development. The main aim of this paper is to investigate how electricity demand response potential can be assessed under uncertainty. The decision variables are set through a two-stage stochastic planning model, focusing on fuzzy decision-making through linear programming in interval planning, combining with consequent opportunity constraint planning and fuzzy opportunity constraint planning to realize artificial regulation of unfavorable factors, and finally completing the treatment of uncertain factors. Combining a Gaussian mixture model to construct a model for measuring electricity demand response potential. It has been found that different electricity pricing policies lead to other potential demand response values for peak electricity load. The optimal electricity pricing and revenue of each user will be greatly affected by the participation of different users in the electricity demand response. The optimal power pricing decreases from 3-4 when the number of users is 4 to 2.5 when the number of users is 20 because of the generation of scale rewards, and the benefits to the users increase substantially when the number of users participating in electricity demand response is 20. Evaluating customer electricity demand in an uncertain environment can help drive electricity system reform to some extent.

Assessment of Electricity Demand Response Potential under Consideration of Uncertainties

State Grid Hebei Electric Power Co., Ltd.

State Grid Hebei Xiong’an New Area Electric Power Supply Company Xiong’an New Area

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The assessment of customer electricity demand potential is a critical part of further stimulating electricity demand growth and promoting utility development....

Assessment of Electricity Demand Response Potential under Consideration of Uncertainties

Publié en ligne: 05 juil. 2024

Pages: -

Reçu: 13 mars 2024

Accepté: 09 juin 2024

DOI: https://doi.org/10.2478/amns-2024-1656

Mots clés
Uncertainty, Demand response potential, Gaussian mixture model, Interval linear programming, Electricity pricing

© 2024 Xudong Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Assessment of Electricity Demand Response Potential under Consideration of Uncertainties

Publié en ligne: 05 juil. 2024

Pages: -

Reçu: 13 mars 2024

Accepté: 09 juin 2024

DOI: https://doi.org/10.2478/amns-2024-1656

Mots clésUncertainty, Demand response potential, Gaussian mixture model, Interval linear programming, Electricity pricing

© 2024 Xudong Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Uncertainty, Demand response potential, Gaussian mixture model, Interval linear programming, Electricity pricing