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Risk-based planning of the distribution network structure considering uncertainties in demand and cost of energy

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  • Esmaeeli, Mostafa
  • Kazemi, Ahad
  • Shayanfar, Heidarali
  • Chicco, Gianfranco
  • Siano, Pierluigi

Abstract

Technical and financial uncertainties may put distribution system planning at risk. In this paper, a new risk-based planning method is proposed which pays more attention to low-probability and high consequences events in energy supplying systems. The proposed approach is adopted for determining the optimal structure of a Medium Voltage network where risk-based determination of the radial network structures is implemented through an uncertainty model of the system's variables based on discrete states, called scenarios. The cost of distribution system planning consists of investment cost, maintenance cost, power losses cost, reliability cost, and technical risk cost. In this paper, appropriate models are proposed to consider the monetary effects of technical risks. The proposed approach is applied to a test system consisting of 52 electric load points and two substations. It is observed that the proposed risk-based method for planning the optimal network structure can properly reduce the cost of extreme events, therefore reducing the concerns of distribution system operators about these possible situations.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:119:y:2017:i:c:p:578-587
    DOI: 10.1016/j.energy.2016.11.021
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    References listed on IDEAS

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    1. Abbasi, Ali Reza & Seifi, Ali Reza, 2015. "Considering cost and reliability in electrical and thermal distribution networks reinforcement planning," Energy, Elsevier, vol. 84(C), pages 25-35.
    2. Esmaeeli, M. & Kazemi, A. & Shayanfar, H.A. & Haghifam, M.-R., 2015. "Multistage distribution substations planning considering reliability and growth of energy demand," Energy, Elsevier, vol. 84(C), pages 357-364.
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    5. Wouters, Carmen & Fraga, Eric S. & James, Adrian M., 2015. "An energy integrated, multi-microgrid, MILP (mixed-integer linear programming) approach for residential distributed energy system planning – A South Australian case-study," Energy, Elsevier, vol. 85(C), pages 30-44.
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    Cited by:

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    2. Yu, L. & Li, Y.P. & Huang, G.H. & Fan, Y.R. & Nie, S., 2018. "A copula-based flexible-stochastic programming method for planning regional energy system under multiple uncertainties: A case study of the urban agglomeration of Beijing and Tianjin," Applied Energy, Elsevier, vol. 210(C), pages 60-74.
    3. Barelli, L. & Bidini, G. & Pelosi, D. & Ciupageanu, D.A. & Cardelli, E. & Castellini, S. & Lăzăroiu, G., 2020. "Comparative analysis of AC and DC bus configurations for flywheel-battery HESS integration in residential micro-grids," Energy, Elsevier, vol. 204(C).
    4. 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.
    5. 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).
    6. S. Muhammad Bagher Sadati & Jamal Moshtagh & Miadreza Shafie-khah & João P. S. Catalão, 2017. "Risk-Based Bi-Level Model for Simultaneous Profit Maximization of a Smart Distribution Company and Electric Vehicle Parking Lot Owner," Energies, MDPI, vol. 10(11), pages 1-16, October.

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