Voltage-Dependent Load Model-Based Short-Term Distribution Network Planning Considering Carbon Tax Surplus

Ozy D. Melgar-Dominguez, Mahdi Pourakbari-Kasmaei, Matti Lehtonen, José R.S. Mantovani

Research output: Contribution to journalArticleScientificpeer-review

14 Citations (Scopus)
184 Downloads (Pure)


Nowadays, strategies to cope with environmental issues play a crucial role in the development of planning methodologies for electric distribution networks (EDNs). The primary goal of these methodologies is to find the equilibrium point, for which the EDN provides a high-quality service with the most environmentally-committed operation. Based on this fact, an alternative strategic approach for short-term EDN planning is presented. This decision-making scheme is based on classical investment actions to enhance the EDN performance in which the investment and operating costs, as well as the carbon tax surpluses, are minimised simultaneously. Unlike the traditional short-term planning methods and to explore a more accurate approach, the electricity demand is represented by the voltage-dependent exponential load model. By using this representation, substantial benefits related to energy saving can be achieved. To validate the proposed planning scheme, several test cases considering constant power demand and voltage-dependent representation are widely studied on a 135-node distribution network. Additionally, the scalability of the proposed planning scheme is studied by using two medium distribution networks of 42- and 417-node. Numerical results confirm the robustness and applicability of the presented approach as an appropriate way of promoting to an efficient and low carbon tax surplus network.
Original languageEnglish
Pages (from-to)3760–3770
Number of pages10
JournalIET Generation, Transmission and Distribution
Issue number17
Publication statusPublished - 2019
MoE publication typeA1 Journal article-refereed


  • Decision making
  • Power generation planning
  • Power distribution planning
  • Taxation
  • Investment
  • Optimisation
  • Distribution networks


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