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Demand-side flexibility and demand-side bidding for flexible loads in air-conditioned buildings

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  • Utama, Christian
  • Troitzsch, Sebastian
  • Thakur, Jagruti

Abstract

Demand-side flexibility (DSF) has been touted as a possible solution to the challenges in power system operation arising from increasing intermittent renewables penetration and the emergence of electric vehicles. In Singapore, where around 24 to 60% of electricity demand in buildings could be attributed to heating, ventilation, and air conditioning (HVAC) purposes, air-conditioned buildings represent a significant flexibility resource which could be used to provide DSF and help tackle these challenges. This study aims to investigate the DSF potential of Singapore’s building stock and to explore how this potential could be realized through demand-side bidding. To this end, a building energy modeling tool with explicit modeling of the relationship between occupant comfort and HVAC load with model predictive control, CoBMo, is used. CoBMo allows optimal load scheduling to be expressed as a linear programming problem: minimizing overall electricity cost while maintaining occupant comfort. A price-based market clearing model is developed to evaluate demand-side bidding implementation, for which a case study on Singapore’s Downtown Core district is developed. Three scenarios with possible future utility-scale photovoltaic (PV) penetration in Singapore’s electricity system are explored, alongside a sensitivity analysis and a comparison between centralized dispatch and demand-side bidding with price-quantity pairs and linear curves. Results of the analysis show that DSF potential varies between building types, depending on cooling load and occupancy schedule. When extreme price fluctuations happen in future Singapore electricity market with 10 GWp PV penetration, demand-side bidding could aid consumers to utilize their DSF potential by encouraging more effective energy use and in turn, reducing their total electricity cost.

Suggested Citation

  • Utama, Christian & Troitzsch, Sebastian & Thakur, Jagruti, 2021. "Demand-side flexibility and demand-side bidding for flexible loads in air-conditioned buildings," Applied Energy, Elsevier, vol. 285(C).
    • Handle: RePEc:eee:appene:v:285:y:2021:i:c:s0306261920317815
    DOI: 10.1016/j.apenergy.2020.116418
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    References listed on IDEAS

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    Cited by:

    1. Dongsu Kim & Yeobeom Yoon & Jongman Lee & Pedro J. Mago & Kwangho Lee & Heejin Cho, 2022. "Design and Implementation of Smart Buildings: A Review of Current Research Trend," Energies, MDPI, vol. 15(12), pages 1-17, June.
    2. Hessam Golmohamadi, 2022. "Demand-Side Flexibility in Power Systems: A Survey of Residential, Industrial, Commercial, and Agricultural Sectors," Sustainability, MDPI, vol. 14(13), pages 1-16, June.
    3. Wanlei Xue & Xin Zhao & Yan Li & Ying Mu & Haisheng Tan & Yixin Jia & Xuejie Wang & Huiru Zhao & Yihang Zhao, 2023. "Research on the Optimal Design of Seasonal Time-of-Use Tariff Based on the Price Elasticity of Electricity Demand," Energies, MDPI, vol. 16(4), pages 1-17, February.
    4. Cao, Yujie & Cheng, Ming & Zhang, Sufang & Mao, Hongju & Wang, Peng & Li, Chao & Feng, Yihui & Ding, Zhaohao, 2022. "Data-driven flexibility assessment for internet data center towards periodic batch workloads," Applied Energy, Elsevier, vol. 324(C).
    5. Alexander Brem & Ken Bruton & Paul D. O’Sullivan, 2021. "Assessing the Risk to Indoor Thermal Environments on Industrial Sites Offering AHU Capacity for Demand Response," Energies, MDPI, vol. 14(19), pages 1-28, October.
    6. Dong, Lianxin & Wu, Qing & Hong, Juhua & Wang, Zhihua & Fan, Shuai & He, Guangyu, 2023. "An adaptive decentralized regulation strategy for the cluster with massive inverter air conditionings," Applied Energy, Elsevier, vol. 330(PA).
    7. Zhou, Yuekuan, 2022. "Demand response flexibility with synergies on passive PCM walls, BIPVs, and active air-conditioning system in a subtropical climate," Renewable Energy, Elsevier, vol. 199(C), pages 204-225.
    8. Lee, Kuan-Cheng & Yang, Hong-Tzer & Tang, Wenjun, 2022. "Data-driven online interactive bidding strategy for demand response," Applied Energy, Elsevier, vol. 319(C).

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