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Regime-switching based vehicle-to-building operation against electricity price spikes

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  • Zhang, Lei
  • Li, Yaoyu

Abstract

Electricity price may present very large spikes due to imbalance between generation and demand, especially during heavily loaded periods. Such peak price may incur significant cost to building operation. With the vehicle-to-building (V2B) technology, electric vehicle battery can be used as temporal energy source for the building load for a short period, which leads to a possible solution for reducing the energy cost during peak-price periods. In this paper, the problem of reducing the energy cost due to the peak price is approached from the prospective of risk management. A regime-switching based risk management scheme is proposed for the V2B operation based on the availability of electric vehicles (EV) plugged in the parking lots attached to the building. In the low risk regime, the objective is to minimize the EV charging cost. While in the high risk regime, the objective is to reduce the potentially high energy cost due the peak price via the power stored in EV batteries. Based on Markov regime-switching model, the operation minimizes the conditional value at risk involved. Simulation results show that the proposed framework can greatly reduce the energy cost against the electricity peak prices.

Suggested Citation

  • Zhang, Lei & Li, Yaoyu, 2017. "Regime-switching based vehicle-to-building operation against electricity price spikes," Energy Economics, Elsevier, vol. 66(C), pages 1-8.
    • Handle: RePEc:eee:eneeco:v:66:y:2017:i:c:p:1-8
    DOI: 10.1016/j.eneco.2017.05.019
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    References listed on IDEAS

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    4. Rafał Weron, 2009. "Heavy-tails and regime-switching in electricity prices," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 69(3), pages 457-473, July.
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    Cited by:

    1. Xingping Zhang & Yanni Liang & Yakun Zhang & Yinhe Bu & Hongyang Zhang, 2017. "Charge Pricing Optimization Model for Private Charging Piles in Beijing," Sustainability, MDPI, vol. 9(11), pages 1-15, November.
    2. Wooyoung Jeon & Sangmin Cho & Seungmoon Lee, 2020. "Estimating the Impact of Electric Vehicle Demand Response Programs in a Grid with Varying Levels of Renewable Energy Sources: Time-of-Use Tariff versus Smart Charging," Energies, MDPI, vol. 13(17), pages 1-22, August.
    3. Ma, Shao-Chao & Yi, Bo-Wen & Fan, Ying, 2022. "Research on the valley-filling pricing for EV charging considering renewable power generation," Energy Economics, Elsevier, vol. 106(C).
    4. Cao, Sunliang, 2019. "The impact of electric vehicles and mobile boundary expansions on the realization of zero-emission office buildings," Applied Energy, Elsevier, vol. 251(C), pages 1-1.

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    More about this item

    Keywords

    Vehicle-to-building; Electricity price modeling; Spikes; Regime switching models; Risk management; Smart grid; Demand response; Electric vehicles;
    All these keywords.

    JEL classification:

    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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