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Optimal eco-emission scheduling of distribution network operator and distributed generator owner under employing demand response program

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  • Hosseinnia, Hamed
  • Modarresi, Javad
  • Nazarpour, Daryoush

Abstract

This paper proposes an optimal operational scheduling framework to integrate the distributed generators (DGs) in the distribution network. This framework is used to maximize the benefit of DG owner and distribution network operator (DNO). In this paper, two optimization model has been proposed to optimum emission and economic operation performance of distribution network in the presence of demand response program (DR). DR under time of use (TOU) pricing is utilized to promote both DG owner and DNO benefits from economic operation issue. The mixed-integer programming (MIP) is used to model a multi-objective problem in General Algebraic Modeling System (GAMS). Then, the problem is solved by employing weighted sum and fuzzy decision making methods. The obtained results reveal that due to positive implementation of DR program, dependency of the distribution network to the upstream network is decreased and load curve become smoother. The under study systems are IEEE 33-bus test system and 101-bus Khoy-Iran actual distribution system which compose electric vehicle parking lot (PL), battery storage, hydrogen storage system (HSS), and local dispatchable generators (LDG).

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  • Hosseinnia, Hamed & Modarresi, Javad & Nazarpour, Daryoush, 2020. "Optimal eco-emission scheduling of distribution network operator and distributed generator owner under employing demand response program," Energy, Elsevier, vol. 191(C).
    • Handle: RePEc:eee:energy:v:191:y:2020:i:c:s0360544219322480
    DOI: 10.1016/j.energy.2019.116553
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    Cited by:

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    2. Firouzi, Mehdi & Setayesh Nazar, Mehrdad & Shafie-khah, Miadreza & Catalão, João P.S., 2023. "Integrated framework for modeling the interactions of plug-in hybrid electric vehicles aggregators, parking lots and distributed generation facilities in electricity markets," Applied Energy, Elsevier, vol. 334(C).
    3. Zhang, Jingrui & Li, Zhuoyun & Wang, Beibei, 2021. "Within-day rolling optimal scheduling problem for active distribution networks by multi-objective evolutionary algorithm based on decomposition integrating with thought of simulated annealing," Energy, Elsevier, vol. 223(C).
    4. Suryakiran, B.V. & Nizami, Sohrab & Verma, Ashu & Saha, Tapan Kumar & Mishra, Sukumar, 2023. "A DSO-based day-ahead market mechanism for optimal operational planning of active distribution network," Energy, Elsevier, vol. 282(C).
    5. Zhang, Jingrui & Zhou, Yulu & Li, Zhuoyun & Cai, Junfeng, 2021. "Three-level day-ahead optimal scheduling framework considering multi-stakeholders in active distribution networks: Up-to-down approach," Energy, Elsevier, vol. 219(C).
    6. Rahimi Sadegh, Ainollah & Setayesh Nazar, Mehrdad & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "Optimal resilient allocation of mobile energy storages considering coordinated microgrids biddings," Applied Energy, Elsevier, vol. 328(C).
    7. Morales-España, Germán & Martínez-Gordón, Rafael & Sijm, Jos, 2022. "Classifying and modelling demand response in power systems," Energy, Elsevier, vol. 242(C).

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