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An interval optimization strategy of household multi-energy system considering tolerance degree and integrated demand response

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  • Su, Yongxin
  • Zhou, Yao
  • Tan, Mao

Abstract

Under the background of the popularity of household gas-electric equipment, an interval optimization method considering the Integrated Demand Response (IDR) and tolerance degree is proposed to deal with the household load scheduling. The proposed method mainly tackles the dynamic switching of energy forms (natural gas and electricity) and uncertain variables in the system caused by household load and photovoltaic (PV). The thermostatically controllable loads contain gas-electric heater and air conditioning, and the interruptible loads contain gas-electric kitchen stove and clothes washer. An interval optimization model is formulated to optimize the operation cost and emissions of a household multi-energy system. The uncertain parameters are modeled as interval numbers, and the constraints are relaxed by tolerance degree. To solve the equation, the interval optimization model is transformed into a deterministic optimization model based on the interval order relation and interval possibility degree. Then, Genetic Algorithm is applied to solve the deterministic optimization model. Simulation results indicate that interval optimization is robust to the uncertainties and flexible to meet users’ demands. Both IDR and tolerance degree are able to optimize users’ energy cost, and the optimization results are accumulative. For a gas-electric equipment installed home, the proposed method can reduce expenditures by up to 23.4% compared with traditional method that do not consider IDR, tolerance degree and interval optimization.

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  • Su, Yongxin & Zhou, Yao & Tan, Mao, 2020. "An interval optimization strategy of household multi-energy system considering tolerance degree and integrated demand response," Applied Energy, Elsevier, vol. 260(C).
    • Handle: RePEc:eee:appene:v:260:y:2020:i:c:s0306261919318318
    DOI: 10.1016/j.apenergy.2019.114144
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    10. Lu, Na & Wang, Guangyan & Su, Chengguo & Ren, Zaimin & Peng, Xiaoyue & Sui, Quan, 2024. "Medium- and long-term interval optimal scheduling of cascade hydropower-photovoltaic complementary systems considering multiple uncertainties," Applied Energy, Elsevier, vol. 353(PA).
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    14. Wagner, Lukas Peter & Reinpold, Lasse Matthias & Kilthau, Maximilian & Fay, Alexander, 2023. "A systematic review of modeling approaches for flexible energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
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    17. Liu, Jizhen & Ma, Lifei & Wang, Qinghua, 2023. "Energy management method of integrated energy system based on collaborative optimization of distributed flexible resources," Energy, Elsevier, vol. 264(C).
    18. Zheng, Shunlin & Qi, Qi & Sun, Yi & Ai, Xin, 2023. "Integrated demand response considering substitute effect and time-varying response characteristics under incomplete information," Applied Energy, Elsevier, vol. 333(C).
    19. Mota, Bruno & Faria, Pedro & Vale, Zita, 2024. "Energy cost optimization through load shifting in a photovoltaic energy-sharing household community," Renewable Energy, Elsevier, vol. 221(C).
    20. Mahtab Kaffash & Glenn Ceusters & Geert Deconinck, 2021. "Interval Optimization to Schedule a Multi-Energy System with Data-Driven PV Uncertainty Representation," Energies, MDPI, vol. 14(10), pages 1-20, May.
    21. Zhao, Liyuan & Yang, Ting & Li, Wei & Zomaya, Albert Y., 2022. "Deep reinforcement learning-based joint load scheduling for household multi-energy system," Applied Energy, Elsevier, vol. 324(C).

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