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Minimizing pricing policies based on user load profiles and residential demand responses in smart grids

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  • Rasheed, Muhammad Babar
  • R-Moreno, María D.

Abstract

This paper considers the time of use (TOU) pricing scheme to propose a consumer aware pricing policy (CAPP), where each customer receives a separate electricity pricing signal. These pricing signals are obtained based on individualized load demand patterns to optimally manage the flexible load demand. The main objective of CAPP is to reduce the peaks in overall system demand in such a way that the pricing signals remain non-discriminatory. To achieve this goal, firstly the mathematical model of CAPP comprising TOU electricity price, and its variation based on consumption patterns is formulated. Secondly, the proposed CAPP model is further extended by integrating renewable energy and storage sources to overcome the possible creation of rebound peaks due to scheduling. This objective is achieved by implementing a control variable modeling the upper bound of the low tariff area. Thirdly, the cost minimization optimization problem is solved by using a Genetic Algorithm (GA) with the objective of the fair price distribution. Numerical and simulation results are obtained to validate the proposed model in terms of convergence, optimality, and cost reduction objective function. Results reveal that each customer receives a separate electricity price signal based on his demand pattern without affecting the utility/retailer revenue. Furthermore, the total cost results are also compared with and without TOU & CAPP schemes to further validate the nondiscrimination in electricity price.

Suggested Citation

  • Rasheed, Muhammad Babar & R-Moreno, María D., 2022. "Minimizing pricing policies based on user load profiles and residential demand responses in smart grids," Applied Energy, Elsevier, vol. 310(C).
    • Handle: RePEc:eee:appene:v:310:y:2022:i:c:s0306261921017104
    DOI: 10.1016/j.apenergy.2021.118492
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    References listed on IDEAS

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    1. Lu, Renzhi & Hong, Seung Ho, 2019. "Incentive-based demand response for smart grid with reinforcement learning and deep neural network," Applied Energy, Elsevier, vol. 236(C), pages 937-949.
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    Cited by:

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    2. Ma, Siyu & Liu, Hui & Wang, Ni & Huang, Lidong & Goh, Hui Hwang, 2023. "Incentive-based demand response under incomplete information based on the deep deterministic policy gradient," Applied Energy, Elsevier, vol. 351(C).
    3. Razzak, Abdur & Islam, Md. Tariqul & Roy, Palash & Razzaque, Md. Abdur & Hassan, Md. Rafiul & Hassan, Mohammad Mehedi, 2024. "Leveraging Deep Q-Learning to maximize consumer quality of experience in smart grid," Energy, Elsevier, vol. 290(C).
    4. Zafar Mahmood & Benmao Cheng & Naveed Anwer Butt & Ghani Ur Rehman & Muhammad Zubair & Afzal Badshah & Muhammad Aslam, 2023. "Efficient Scheduling of Home Energy Management Controller (HEMC) Using Heuristic Optimization Techniques," Sustainability, MDPI, vol. 15(2), pages 1-22, January.

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