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Sustainable Development Strategies in Power Systems: Day-Ahead Stochastic Scheduling with Multi-Sources and Customer Directrix Load Demand Response

Author

Listed:
  • Jiacheng Liu

    (School of Electrical Engineering, Sichuan University, Chengdu 610065, China)

  • Shan Huang

    (School of Electrical Engineering, Sichuan University, Chengdu 610065, China)

  • Qiang Shuai

    (School of Electrical Engineering, Sichuan University, Chengdu 610065, China)

  • Tingyun Gu

    (Electric Power Research Institute of Guizhou Power Grid, Guiyang 550007, China)

  • Houyi Zhang

    (Electric Power Research Institute of Guizhou Power Grid, Guiyang 550007, China)

Abstract

Increasing the installed capacity of renewable energy sources (RESs) in the power system is significant for advancing sustainable development. As the proportion of RESs rapidly increases in power systems, the inherent stochasticity and variability of renewable energies significantly reduce the regulatory capacity of generation resources. To compensate for the lack of power system flexibility, it is necessary to coordinate the participation of load-side resources in demand response (DR). Therefore, this paper proposes a solution to the diminished flexibility of power systems. It introduces a day-ahead stochastic scheduling model for an integrated thermal-hydro-wind-solar system. This model relies on customer directrix load (CDL) to efficiently absorb RES output. CDL represents an ideal load profile shape. Firstly, the stochastic scenario sets of RES output were modeled using Monte Carlo simulations, and the complementary characteristics between wind and solar output are considered using Copula theory. Then, CDL is introduced into day-ahead scheduling model, which considers relevant demand-side responsive load constraints. Secondly, customer-side DR effectiveness model is proposed to obtain the shaping load profile after DR, based on quantitative customer response effectiveness evaluation metrics. Lastly, system-side stochastic scheduling model of high-proportion RES power system is proposed based on the shaping load profile. Case studies were conducted on a modified IEEE-6 bus system. These studies show that the model effectively addresses the uncertainty of RES. It improves the power system’s regulation capability. Additionally, it promotes the absorption of RES.

Suggested Citation

  • Jiacheng Liu & Shan Huang & Qiang Shuai & Tingyun Gu & Houyi Zhang, 2024. "Sustainable Development Strategies in Power Systems: Day-Ahead Stochastic Scheduling with Multi-Sources and Customer Directrix Load Demand Response," Sustainability, MDPI, vol. 16(6), pages 1-21, March.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:6:p:2589-:d:1361348
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    References listed on IDEAS

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    1. Yin, Rongxin & Kara, Emre C. & Li, Yaping & DeForest, Nicholas & Wang, Ke & Yong, Taiyou & Stadler, Michael, 2016. "Quantifying flexibility of commercial and residential loads for demand response using setpoint changes," Applied Energy, Elsevier, vol. 177(C), pages 149-164.
    2. Schmidt, Johannes & Cancella, Rafael & Pereira, Amaro O., 2016. "The role of wind power and solar PV in reducing risks in the Brazilian hydro-thermal power system," Energy, Elsevier, vol. 115(P3), pages 1748-1757.
    3. McPherson, Madeleine & Stoll, Brady, 2020. "Demand response for variable renewable energy integration: A proposed approach and its impacts," Energy, Elsevier, vol. 197(C).
    4. Kirkerud, J.G. & Nagel, N.O. & Bolkesjø, T.F., 2021. "The role of demand response in the future renewable northern European energy system," Energy, Elsevier, vol. 235(C).
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