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A new long term load management model for asset governance of electrical distribution systems

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  • Dashti, Reza
  • Afsharnia, Saeed
  • Ghasemi, Hassan

Abstract

Long term load management (LTLM) is one of the key factors in making decisions regarding new investments in distribution systems. However, none of the previous studies have investigated the effect of external factors such as governance, urban planning and social behavior factors on LTLM. In this paper, a new LTLM model is proposed to determine the influence of external factors on LTLM. Distribution system development indices have been used to obtain asset governance targets; these indices can help Distribution Companies (DISCOs) compromise between reliability and running the system economically. Capacity utilization and the number of maneuver points are used here to do LTLM and improve asset governance. Numerical studies on a real distribution system (city with population about 200,000) have been conducted and sensitivity analysis of maneuver points and capacity utilization level with respect to external factors is studied and analyzed. The results show the feasibility of the proposed LTLM to obtain higher efficiency from the viewpoint of cost and quality service compared to conventional LTLM.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:87:y:2010:i:12:p:3661-3667
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    References listed on IDEAS

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    2. Andersen, F.M. & Larsen, H.V. & Juul, N. & Gaardestrup, R.B., 2014. "Differentiated long term projections of the hourly electricity consumption in local areas. The case of Denmark West," Applied Energy, Elsevier, vol. 135(C), pages 523-538.
    3. Shao, Zhen & Gao, Fei & Yang, Shan-Lin & Yu, Ben-gong, 2015. "A new semiparametric and EEMD based framework for mid-term electricity demand forecasting in China: Hidden characteristic extraction and probability density prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 876-889.
    4. Dashti, Reza & Yousefi, Shaghayegh & Parsa Moghaddam, Mohsen, 2013. "Comprehensive efficiency evaluation model for electrical distribution system considering social and urban factors," Energy, Elsevier, vol. 60(C), pages 53-61.
    5. F. M. Andersen & H. V. Larsen & L. Kitzing & P. E. Morthorst, 2014. "Who gains from hourly time‐of‐use retail prices on electricity? An analysis of consumption profiles for categories of Danish electricity customers," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 3(6), pages 582-593, November.
    6. Abdullah, M.A. & Agalgaonkar, A.P. & Muttaqi, K.M., 2014. "Assessment of energy supply and continuity of service in distribution network with renewable distributed generation," Applied Energy, Elsevier, vol. 113(C), pages 1015-1026.
    7. 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.
    8. Zhou, Kaile & Yang, Shanlin & Shao, Zhen, 2016. "Energy Internet: The business perspective," Applied Energy, Elsevier, vol. 178(C), pages 212-222.
    9. Andersen, F.M. & Larsen, H.V. & Gaardestrup, R.B., 2013. "Long term forecasting of hourly electricity consumption in local areas in Denmark," Applied Energy, Elsevier, vol. 110(C), pages 147-162.

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