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Natural gas demand response strategy considering user satisfaction and load volatility under dynamic pricing

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  • Zeng, Huibin
  • Shao, Bilin
  • Dai, Hongbin
  • Yan, Yichuan
  • Tian, Ning

Abstract

Under the background that natural gas is continuously consumed as a non-renewable energy source and the growing demand for natural gas in human society, the contradiction of supply and demand between natural gas utilization and social development has become increasingly prominent. Demand response (DR) in the system helps to address this issue. Currently, there is a lack of diverse DR for natural gas systems. However, DR not only maintains the balance between supply and demand in the gas system, but also relieve peak shaving pressure in natural gas systems and instruct users to save and reduce energy. Therefore, based on the price elasticity theory and user satisfaction, this study focuses on the purpose of natural gas users and gas suppliers to participate in DR, and constructs a natural gas DR model from the perspective of dynamic pricing. An effective natural gas DR strategy is proposed by classifying peak, plain and valley of natural gas load by K-means algorithm, and solving the DR model by multi-objective particle swarm optimization (MOPSO). The DR strategy proposed in this study is evaluated in terms of both non-heating and heating periods through actual examples. The results show that the DR strategy proposed in this study reduces the daily load demand of natural gas, reduces the maximum peak-to-valley difference of daily load, slows down the daily load volatility, improves user satisfaction, maximizes the interests of users and gas suppliers, and effectively balances the system demand.

Suggested Citation

  • Zeng, Huibin & Shao, Bilin & Dai, Hongbin & Yan, Yichuan & Tian, Ning, 2023. "Natural gas demand response strategy considering user satisfaction and load volatility under dynamic pricing," Energy, Elsevier, vol. 277(C).
    • Handle: RePEc:eee:energy:v:277:y:2023:i:c:s0360544223011192
    DOI: 10.1016/j.energy.2023.127725
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    References listed on IDEAS

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    2. Lin, Zijie & Xie, Linbo & Zhang, Siyuan, 2024. "A compound framework for short-term gas load forecasting combining time-enhanced perception transformer and two-stage feature extraction," Energy, Elsevier, vol. 298(C).

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