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Designing a decision model to assess the reward and penalty scheme of electric distribution companies

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  • Ghasemi, Mostafa
  • Dashti, Reza

Abstract

In this paper, a decision-making model for the appropriate implementation of the reward and penalty scheme (RPS) for electricity distribution companies (EDCs) is presented to be used to update the parameters of the RPS in each regulatory period and also to improve the efficiency of this scheme. In this regard, an objective function is considered in order to optimize investment costs, imposed costs, and the amounts of reward and penalty for the distribution company. This optimization is based on the failure rate of equipment and is formulated as a non-linear programming (NLP) Problem. In the optimized objective function, all existing equipment in the distribution network is divided into three categories medium-voltage, low-voltage, and substation equipment and the imposed cost on the distribution company is modeled using the Markov process. The proposed model has been applied to an electricity distribution company in Iran. Finally, the optimized values of costs incurred by the distribution company from outage, cost of investment aimed at outage reduction, and failure rates in the three named categories are obtained and can be used to determine the new parameters of the RPS in the subsequent regulatory period.

Suggested Citation

  • Ghasemi, Mostafa & Dashti, Reza, 2018. "Designing a decision model to assess the reward and penalty scheme of electric distribution companies," Energy, Elsevier, vol. 147(C), pages 329-336.
    • Handle: RePEc:eee:energy:v:147:y:2018:i:c:p:329-336
    DOI: 10.1016/j.energy.2018.01.021
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    References listed on IDEAS

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    1. Carnero, María Carmen & Gómez, Andrés, 2017. "Maintenance strategy selection in electric power distribution systems," Energy, Elsevier, vol. 129(C), pages 255-272.
    2. Adefarati, T. & Bansal, R.C., 2017. "Reliability assessment of distribution system with the integration of renewable distributed generation," Applied Energy, Elsevier, vol. 185(P1), pages 158-171.
    3. Esmaeeli, Mostafa & Kazemi, Ahad & Shayanfar, Heidarali & Chicco, Gianfranco & Siano, Pierluigi, 2017. "Risk-based planning of the distribution network structure considering uncertainties in demand and cost of energy," Energy, Elsevier, vol. 119(C), pages 578-587.
    4. Ghasemi, Mostafa & Dashti, Reza, 2017. "A risk-based model for performance-based regulation of electric distribution companies," Utilities Policy, Elsevier, vol. 45(C), pages 36-44.
    5. Huang, Yalin & Söder, Lennart, 2017. "Evaluation of economic regulation in distribution systems with distributed generation," Energy, Elsevier, vol. 126(C), pages 192-201.
    6. Abeysinghe, Sathsara & Wu, Jianzhong & Sooriyabandara, Mahesh & Abeysekera, Muditha & Xu, Tao & Wang, Chengshan, 2018. "Topological properties of medium voltage electricity distribution networks," Applied Energy, Elsevier, vol. 210(C), pages 1101-1112.
    7. Iversen, Emil B. & Morales, Juan M. & Madsen, Henrik, 2014. "Optimal charging of an electric vehicle using a Markov decision process," Applied Energy, Elsevier, vol. 123(C), pages 1-12.
    8. Davidov, Sreten & Pantoš, Miloš, 2017. "Stochastic assessment of investment efficiency in a power system," Energy, Elsevier, vol. 119(C), pages 1047-1056.
    9. Dashti, Reza & Afsharnia, Saeed & Ghasemi, Hassan, 2010. "A new long term load management model for asset governance of electrical distribution systems," Applied Energy, Elsevier, vol. 87(12), pages 3661-3667, December.
    10. Božič, Dušan & Pantoš, Miloš, 2013. "Assessment of investment efficiency in a power system under performance-based regulation," Energy, Elsevier, vol. 51(C), pages 330-338.
    11. Hong, Ying-Yi & Chang, Wen-Chun & Chang, Yung-Ruei & Lee, Yih-Der & Ouyang, Der-Chuan, 2017. "Optimal sizing of renewable energy generations in a community microgrid using Markov model," Energy, Elsevier, vol. 135(C), pages 68-74.
    12. Dashti, Reza & Afsharnia, Saeed & Ghaderi, Farid, 2010. "AGA (Asset Governance Assessment) for analyzing affect of subsidy on MC (Marginal Cost) in electricity distribution sector," Energy, Elsevier, vol. 35(12), pages 4996-5007.
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    2. Janjic, Aleksandar & Velimirovic, Lazar Z. & Vranic, Petar, 2021. "Designing an electricity distribution reward-penalty scheme based on spatial reliability statistics," Utilities Policy, Elsevier, vol. 70(C).

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