Abstract
Transmission expansion planning (TEP) is an important part of power system expansion planning. In TEP, optimal number of new transmission lines and their installation time and place are determined in an economic way. Uncertainties in load demand, place of power plants, and fuel price as well as voltage level of substations influence TEP solutions effectively. Therefore, in this paper, a scenario based-model is proposed for evaluating the fuel price impact on TEP considering the expansion of substations from the voltage level point of view. The fuel price is an important factor in power system expansion planning that includes severe uncertainties. This factor indirectly affects the lines loading and subsequent network configuration through the change of optimal generation of power plants. The efficiency of the proposed model is tested on the real transmission network of Azerbaijan regional electric company using a discrete artificial bee colony (DABC) and quadratic programming (QP) based method. Moreover, discrete particle swarm optimization (DPSO) and decimal codification genetic algorithm (DCGA) methods are used to verify the results of the DABC algorithm. The results evaluation reveals that considering uncertainty in fuel price for solving TEP problem affects the network configuration and the total expansion cost of the network. In this way, the total cost is optimized more and therefore the TEP problem is solved more precisely. Also, by comparing the convergence curve of the DABC with that of DPSO and DCGA algorithms, it can be seen that the efficiency of the DABC is more than DPSO and DCGA for solving the desired TEP problem.
Highlights
Transmission expansion planning (TEP) is a sub-problem of power system expansion planning problem that its main goal is optimizing network expansion costs [1]
In order to resolve this problem and including fuel price uncertainty in transmission expansion planning, the static transmission expansion planning (STEP) problem considering network losses and uncertainty in fuel price using a discrete artificial bee colony (DABC) algorithm is solved in the present work
In order to verify the accuracy of the proposed approach, the proposed expansion planning problem was solved by discrete particle swarm optimization (DPSO) [2] and decimal codification genetic algorithm (DCGA) [7] in addition to DABC and convergence process of three methods for all scenarios are shown in Figs 8 to 10
Summary
Transmission expansion planning (TEP) is a sub-problem of power system expansion planning problem that its main goal is optimizing network expansion costs [1]. In [1], wind energy and network contingencies were included in TEP problem based on the quantile value of each plan cost without considering substation expansion and fuel price uncertainty. The fuel price is an important factor in power system expansion planning that includes severe uncertainties This factor indirectly affects the lines loading and subsequent network configuration by changing the optimal power generation of power plants. In order to resolve this problem and including fuel price uncertainty in transmission expansion planning, the STEP problem considering network losses and uncertainty in fuel price using a discrete artificial bee colony (DABC) algorithm is solved in the present work. The main contribution of current study compared to other research works is analyzing impact of fuel price uncertainty on transmission expansion planning problem considering expansion of substations from the voltage level point of view
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