###### 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. p 20202010 July 202013 July 202031 May 20202010 July 20202010 July 20202010 July 20202010 July 2020202010 July 202020202020202020202020202020202020202  
needed. 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]. Several methods are utilized to solve this problem; for instance, integer linear programming [13; 14], nonlinear programming [15; 16], and dynamic programming [17; 18]. Due to the complexity of the problem and the large number of its variables, the use of metaheuristic to obtain the best objective function value has been widely considered. The genetic algorithm (GA), particle swarm optimization algorithm (PSO), simulated annealing algorithm (SA), and other algorithms [19; 20; 21; 22] show a small fraction of the application of metaheuristic to solve the problem. [PERSON] et al. believe that it would be better to place the DGs and improve the feeders simultaneously, rather than separately solving these two problems. In this research, an intelligent method is proposed in which the DGs are employed as the alternatives [11]. [PERSON] et al. proposed a method to develop a distribution network along with the DGs. In this research, the problem is solved taking into account the growth of prices and the uncertainty of DGs [23]. [PERSON] et al. proposed a two-step stochastic programming model for the reliability constrained power system expansion planning. The model has been applied on the IEEE 118-bus power system and IEEE reliability test system [24]. [PERSON] and [PERSON] have provided a model for solving the problem considering the uncertainty of the renewable resources [25]. [PERSON] and [PERSON] used the binary whale optimization algorithm to solve the expansion planning of distribution networks based on the multiyear DG-incorporated framework [26]. [PERSON] et al. presented a new model for the sizing and placement of distributed generation in order to ensure the reduction in total power losses and voltage stability in the power grid [27]. [PERSON] provided a new model based on the decision-making algorithm in order to optimize the size and placement of distributed generation [28]. The main contributions and innovations of this paper are considered as follows:  
1. A multilevel optimization method for optimal network power expansion planning has been proposed. 2. A new version of binary dragonfly's algorithm has been proposed. 3. Considering distributed generation as renewable energies in power expansion planning. 4.