Voltage-Dependent Load Model-Based Short-Term Distribution Network Planning Considering Carbon Tax Surplus
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
IET Generation, Transmission and Distribution, Volume 13, issue 17
AbstractNowadays, 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.
Decision making, Power generation planning, Power distribution planning, Taxation, Investment, Optimisation, Distribution networks
Melgar-Dominguez , O D , Pourakbari-Kasmaei , M , Lehtonen , M & Mantovani , J R S 2019 , ' Voltage-Dependent Load Model-Based Short-Term Distribution Network Planning Considering Carbon Tax Surplus ' , IET Generation, Transmission and Distribution , vol. 13 , no. 17 , pp. 3760–3770 . https://doi.org/10.1049/iet-gtd.2018.6612