Abstract
With the increasing number of population and the rising demand for electricity, providing safe and secure energy to consumers is getting more and more important. Adding dispersed products to the distribution network is one of the key factors in achieving this goal. However, factors such as the amount of investment and the return on the investment on one side, and the power grid conditions, such as loss rates, voltage profiles, reliability, and maintenance costs, on the other hand, make it more vital to provide optimal annual planning methods concerning network development. Accordingly, in this paper, a multilevel method is presented for optimal network power expansion planning based on the binary dragonfly optimization algorithm, taking into account the distributed generation. The proposed objective function involves the minimization of the cost of investment, operation, repair, and the cost of reliability for the development of the network. The effectiveness of the proposed model to solve the multiyear network expansion planning problem is illustrated by applying them on the 33-bus distribution network and comparing the acquired results with the results of other solution methods such as GA, PSO, and TS.
Highlights
In traditional networks, the transmission and distribution unit was utilized only to transfer the energy from the production unit to consumers, but with an increase in the use of a distributed generation, a part of the production was assigned to the distribution system
The proposed optimization method is tested on a 33-bus IEEE-standard network to solve the multiyear network expansion planning diagram of thisnetwork networktoissolve shown in
A multilevel method is presented for optimal network power development planning based on the binary dragonfly optimization algorithm considering the distributed generations (DGs)
Summary
The transmission and distribution unit was utilized only to transfer the energy from the production unit to consumers, but with an increase in the use of a distributed generation, a part of the production was assigned to the distribution system. Solving the multiyear network expansion planning (MNEP) problem is difficult due to the existence of large variables and complex mathematical model [10]. The objective function of the problem could include different factors such as investment cost, operating and maintenance cost, and reliability cost These factors could be integrated with the coefficients weight or be solved through multi-objective models such as the dynamic ant colony search algorithm [11,12]. Ahmadigorji and Amjady used the binary whale optimization algorithm to solve the expansion planning of distribution networks based on the multiyear DG-incorporated framework [26]. Vita provided a new model based on the decision-making algorithm in order to optimize the size and placement of distributed generation [28].
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