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An interval-based nested optimization framework for deriving flexibility from smart buildings and electric vehicle fleets in the TSO-DSO coordination

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  • Mansouri, Seyed Amir
  • Nematbakhsh, Emad
  • Jordehi, Ahmad Rezaee
  • Marzband, Mousa
  • Tostado-Véliz, Marcos
  • Jurado, Francisco

Abstract

Emerging renewable-based transmission and distribution systems, despite many environmental and economic benefits, due to the intermittent nature of their production resources, compared to traditional systems, need more flexibility capacities, which necessitates the need for more suppliers of flexibility. To deal with these challenges, a nested framework is presented to derive the required flexibility of the transmission system operator (TSO) from distributed energy resources (DERs) and active end-users such as smart buildings (SBs) and electric vehicle (EV) fleets at the distribution level. To this end, a novel mechanism to design the demand response program (DRP) is introduced in which tariffs with time-varying rewards are built based on flexibility requirements. The coordination between TSO and distribution system operator (DSO) is initially modeled as a bi-level non-linear programming (NLP) problem, in which the upper-level is day-ahead (DA) operational planning of DS considering the schedules received from SBs, while the lower-level is DA operational planning of the TS. The bi-level NIL problem is transformed into a single-level linear programming (LP) problem by Krush Kuhn Tucker (KKT) conditions, Big-M method and Strong Duality Theory (SDT), which makes it computationally tractable. Finally, a two-stage interval-based algorithm solves the obtained single-level problem to secure the planning against uncertainties where battery energy storage systems (BESSs) are responsible for dealing with extreme conditions. The simulation results testify that the proposed interval-based nested framework has improved the economic, technical and security aspects of the TSO-DSO coordination since it has reduced the daily costs of the energy and flexibility markets, relieved lines congestion and improved voltage characteristics.

Suggested Citation

  • Mansouri, Seyed Amir & Nematbakhsh, Emad & Jordehi, Ahmad Rezaee & Marzband, Mousa & Tostado-Véliz, Marcos & Jurado, Francisco, 2023. "An interval-based nested optimization framework for deriving flexibility from smart buildings and electric vehicle fleets in the TSO-DSO coordination," Applied Energy, Elsevier, vol. 341(C).
    • Handle: RePEc:eee:appene:v:341:y:2023:i:c:s0306261923004269
    DOI: 10.1016/j.apenergy.2023.121062
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    References listed on IDEAS

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    3. Wu, Binrong & Yu, Sihao & Peng, Lu & Wang, Lin, 2024. "Interpretable wind speed forecasting with meteorological feature exploring and two-stage decomposition," Energy, Elsevier, vol. 294(C).
    4. Dai, Shuangfeng & Mansouri, Seyed Amir & Huang, Shoujun & Alharthi, Yahya Z. & Wu, Yongfei & Bagherzadeh, Leila, 2024. "A multi-stage techno-economic model for harnessing flexibility from IoT-enabled appliances and smart charging systems: Developing a competitive local flexibility market using Stackelberg game theory," Applied Energy, Elsevier, vol. 373(C).
    5. Qing, Ke & Du, Yuefang & Huang, Qi & Duan, Chao & Hu, Weihao, 2024. "Energy scheduling for microgrids with renewable energy sources considering an adjustable convex hull based uncertainty set," Renewable Energy, Elsevier, vol. 220(C).
    6. He, Ruofan & Wan, Panbing, 2024. "Electricity market integration in China: The role of government officials’ hometown ties," Energy, Elsevier, vol. 303(C).
    7. Moon-Jong Jang & Taehoon Kim & Eunsung Oh, 2023. "Data-Driven Modeling of Vehicle-to-Grid Flexibility in Korea," Sustainability, MDPI, vol. 15(10), pages 1-16, May.
    8. An, Yimeng & Dang, Yaoguo & Wang, Junjie & Zhou, Huimin & Mai, Son T., 2024. "Mixed-frequency data Sampling Grey system Model: Forecasting annual CO2 emissions in China with quarterly and monthly economic-energy indicators," Applied Energy, Elsevier, vol. 370(C).
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    10. Zhou, Yuekuan & Liu, Xiaohua & Zhao, Qianchuan, 2024. "A stochastic vehicle schedule model for demand response and grid flexibility in a renewable-building-e-transportation-microgrid," Renewable Energy, Elsevier, vol. 221(C).

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