### Literature Review  
It is noted that in the coordinated network expansion planning (CNEP) strategy, the network's technical constraints such as power flow equations should be satisfied [7]. However, the use of AC power flow equations leads to a nonlinear model with a heavy computational burden [8]. In addition, because of the non-convex property of AC power flow equations [9], the different models of CNEP such as transmission expansion planning (TEP) may trap outcomes in local optimums [10]. Several solution methods have been proposed in the literature to solve the CNEP problem. In [11], TEP is modeled as a mixed-integer linear problem considering the system's safety constraints, where the transmission line investment and the operation unit cost are minimized using the DC power flow model. Also, network losses and operation unit costs are linearized by use of the linear piecewise method. In [12], the TEP problem is coordinated with parallel compensator planning using AC power flow equations, in which the particle swarm optimization (PSO) evolutionary method is used to solve the problem. In [13], a differential equation evolutionary algorithm is used to solve the AC-based TEP problem. Generation and transmission expansion planning (GTEP) in a competitive environment possesses uncertain sources, which is thoroughly studied in [14; 15], where for the sake of simplicity, DC power flow equations are used. In other studies, such as [16; 17], a robust model of the problem is introduced to reduce the size of TEP and GTEP problems considering uncertainty, still by using DC power flow along with other constraints. In addition to TEP models, the generation and transmission expansion planning (GTEP) in [18; 19] uses DC power flow equations for the main problem of CNEP. The main reason for this is that DC power flow-based CNEP includes the mixed-integer linear programming (MILP) model; hence, it is solved by a simple method which improves the calculation speed. However, the AC power flow-based CNEP is a mixed-integer nonlinear programming (MINLP) problem that is solved by the iteration-based numerical method and evolutionary algorithms. These models have a huge computational burden with the possibility of trapping outcomes in a local optimal solution, especially in large-scale networks. In addition, the CNEP method has been studied in various conditions in previous research works. For example, the CNEP strategy in [20] is studied as the MILP model in the presence of long-run incremental cost (LRIC)-based pricing and demand response. Also, [21] considers CNEP for planning the generation units, transmission, and wind farms in a demand response-based power system. Moreover, [22] considers the model of [21] at the short-circuit level. Robust and stochastic CNEP is implemented in [23; 24], respectively. In [25], grid-connected electric vehicles and wind sources are used for energy efficiency approaches in generation expansion planning. In [26], tri-level TEP under physical intentional attacks is proposed. In the first level, the network planner looks for an optimal transmission expansion plan to fortify the power network. In the second level, the attacker tries to maximize damage to the network. In the third level, the adverse effects of the attacks on the network operation are minimized by the network operator. Flexibility is a crucial issue in RES-integrated power systems as the intermittency of RESs hurts flexibility. Hence, several research works in the literature have utilized flexible resources in the power system to enhance network flexibility [27; 28; 29]. In [27], the flexibility of a wind power-integrated virtual power plant is improved using demand response (DR) as a flexible resource. In [28], the distributed ESSs amend the flexibility of a solar power-integrated distribution network. Also, in [29], thermal power plants are utilized as flexible resources.