The penetration of renewable energy sources has been intensified during the last decade to tackle the climate crisis by providing clean energy. Among various renewable energy technologies, wind turbines and photovoltaic systems have received increasing attention from investors. Generally, electronic power converters are used to control renewable generations. The present study discusses the power management of smart distribution networks enriched with wind and photovoltaic units. The model aims to minimize the expected network operating cost of the system formulated as an objective function regarding AC optimal power flow constraints. In addition, stochastic programming based on unscented transformation is adopted to model the probable behavior of loads, renewable generations, and energy market prices. The model employs a linear approximation model to burden the complexity of the problem and achieve the optimum solution. The problem is tested to a 33-bus system using the General Algebraic Modeling System (GAMS). The obtained results confirm the proposed model’s potential in reducing energy costs, power losses, and voltage deviations compared to conventional power flow studies. In the proposed scheme compared to network load distribution studies, the active and reactive power losses, network energy costs, and voltage deviations are improved by about 40.7%, 33%, 36%, and 74.7%, respectively.

This paper presents the generation and transmission expansion planning (GTEP) considering the switched capacitive banks (SCBs) allocation in the power system, including the demand response program (DRP). This scheme is based on the system flexibility. The objective function of the scheme minimizes the expected planning cost that is equaled to the summation of the total construction costs of the SCBs, the generation units (GUs) and the transmission lines (TLs), and the operating cost of the GUs. It is concerned with the AC power flow constraints, the planning-operation model of the mentioned elements, the DRP operation formulation, and the operating and flexibility limits of the network. In the following, the scenario-based stochastic programming is used to model the uncertainty parameters, such as the load and renewable power of wind farms. Then, the hybrid evolutionary algorithm, based on the combination of the crow search algorithm and the grey wolf optimizer, is used to determine the optimal point with the approximate unique solution. Finally, the scheme is applied on the transmission networks, the numerical results confirm the capabilities of the proposed scheme in simultaneously improving the flexibility, operation, and economic situation of the transmission network, so that the hybrid algorithm achieves the optimal solution in a shorter computation time, compared with the non-hybrid algorithms. This algorithm has a low standard deviation of about 92% in the final response. The proposed scheme with the optimal planning of the lines, sources, and capacitor banks, together with the optimal operation of the DRP succeeded in improving the energy loss and the voltage deviation by about 30–36% and 25–30%, compared with those of the power flow studies.