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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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    Cited by:

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    3. Meyabadi, A. Fattahi & Deihimi, M.H., 2017. "A review of demand-side management: Reconsidering theoretical framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 367-379.
    4. Shen, Ziqi & Wei, Wei & Wu, Lei & Shafie-khah, Miadreza & Catalão, João P.S., 2021. "Economic dispatch of power systems with LMP-dependent demands: A non-iterative MILP model," Energy, Elsevier, vol. 233(C).
    5. Monfared, Houman Jamshidi & Ghasemi, Ahmad & Loni, Abdolah & Marzband, Mousa, 2019. "A hybrid price-based demand response program for the residential micro-grid," Energy, Elsevier, vol. 185(C), pages 274-285.
    6. Alasseri, Rajeev & Tripathi, Ashish & Joji Rao, T. & Sreekanth, K.J., 2017. "A review on implementation strategies for demand side management (DSM) in Kuwait through incentive-based demand response programs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 617-635.
    7. 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.
    8. 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.
    9. Ghasemi, Ahmad, 2018. "Coordination of pumped-storage unit and irrigation system with intermittent wind generation for intelligent energy management of an agricultural microgrid," Energy, Elsevier, vol. 142(C), pages 1-13.
    10. Mohseni, Amin & Mortazavi, Seyed Saeidollah & Ghasemi, Ahmad & Nahavandi, Ali & Talaei abdi, Masoud, 2017. "The application of household appliances' flexibility by set of sequential uninterruptible energy phases model in the day-ahead planning of a residential microgrid," Energy, Elsevier, vol. 139(C), pages 315-328.
    11. Liu, Peiyun & Ding, Tao & Zou, Zhixiang & Yang, Yongheng, 2019. "Integrated demand response for a load serving entity in multi-energy market considering network constraints," Applied Energy, Elsevier, vol. 250(C), pages 512-529.
    12. 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).
    13. 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.
    14. Fera, M. & Macchiaroli, R. & Iannone, R. & Miranda, S. & Riemma, S., 2016. "Economic evaluation model for the energy Demand Response," Energy, Elsevier, vol. 112(C), pages 457-468.

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