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Integration of nodal hourly pricing in day-ahead SDC (smart distribution company) optimization framework to effectively activate demand response

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  • Ghasemi, Ahmad
  • Mortazavi, Seyed Saeidollah
  • Mashhour, Elaheh

Abstract

This paper focuses on using a new nodal hourly electricity pricing to maximize the profit of a LDC (local distribution company). The proposed pricing mechanism determines DA (day-ahead) hourly retail prices based on load specifications. These specifications include location, price elasticity and demand profile. Nodal hourly prices are determined in an optimization framework which schedules the LDC to bid optimally in the DA wholesale market. The LDC considered in this study is equipped with smart grid technology and known as SDC (smart distribution company). This SDC contains DERs (distributed energy resources) including dispatchable and non-dispatchable DGs (distributed generators), BES (battery energy storage) and price sensitive consumers. It can also exchange power with upstream network. The optimization framework considers constraints of DGs and BES as well as AC constraints of the distribution network. Moreover, welfare constraints of load are implemented to maintain customers' satisfaction. This framework determines the optimal bidding strategy of the SDC and nodal hourly prices for end consumers simultaneously in an iterative procedure. The BDT (Benders decomposition technique) with strong cuts is applied to simplify the optimization procedure. Finally, the effectiveness of the proposed strategy is evaluated on several case studies using real data from Ontario power market.

Suggested Citation

  • Ghasemi, Ahmad & Mortazavi, Seyed Saeidollah & Mashhour, Elaheh, 2015. "Integration of nodal hourly pricing in day-ahead SDC (smart distribution company) optimization framework to effectively activate demand response," Energy, Elsevier, vol. 86(C), pages 649-660.
    • Handle: RePEc:eee:energy:v:86:y:2015:i:c:p:649-660
    DOI: 10.1016/j.energy.2015.04.091
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    References listed on IDEAS

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    3. Ahmadi, Abdollah & Charwand, Mansour & Siano, Pierluigi & Nezhad, Ali Esmaeel & Sarno, Debora & Gitizadeh, Mohsen & Raeisi, Fatima, 2016. "A novel two-stage stochastic programming model for uncertainty characterization in short-term optimal strategy for a distribution company," Energy, Elsevier, vol. 117(P1), pages 1-9.
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    5. Davatgaran, Vahid & Saniei, Mohsen & Mortazavi, Seyed Saeidollah, 2019. "Smart distribution system management considering electrical and thermal demand response of energy hubs," Energy, Elsevier, vol. 169(C), pages 38-49.
    6. Sergio Montoya-Bueno & Jose Ignacio Muñoz-Hernandez & Javier Contreras & Luis Baringo, 2020. "A Benders’ Decomposition Approach for Renewable Generation Investment in Distribution Systems," Energies, MDPI, vol. 13(5), pages 1-19, March.
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    11. 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).
    12. Mazidi, Mohammadreza & Monsef, Hassan & Siano, Pierluigi, 2016. "Design of a risk-averse decision making tool for smart distribution network operators under severe uncertainties: An IGDT-inspired augment ε-constraint based multi-objective approach," Energy, Elsevier, vol. 116(P1), pages 214-235.
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