The use of renewable and sustainable energy sources (RSESs) has become urgent to counter the growing electricity demand and reduce carbon dioxide emissions. However, the current studies are still lacking to introduce a planning model that measures to what extent the networks can host RSESs in the planning phase. In this paper, a stochastic power system planning model is proposed to increase the hosting capacity (HC) of networks and satisfy future load demands. In this regard, the model is formulated to consider a larger number and size of generation and transmission expansion projects installed than the investment costs, without violating operating and reliability constraints. A load forecasting technique, built on an adaptive neural fuzzy system, was employed and incorporated with the planning model to predict the annual load growth. The problem was revealed as a non-linear large-scale optimization problem, and a hybrid of two meta-heuristic algorithms, namely, the weighted mean of vectors optimization technique and sine cosine algorithm, was investigated to solve it. A benchmark system and a realistic network were used to verify the proposed strategy. The results demonstrated the effectiveness of the proposed model to enhance the HC. Besides this, the results proved the efficiency of the hybrid optimizer for solving the problem.

This paper proposes a multi-objective planning framework for electric vehicle (EV) charging stations in emerging power networks that move towards green transportation electrification. Four cases are investigated to study the impacts of EV integration on environmental and economic requirements. In order to facilitate the installation of EV charging stations, the proposed model is formulated to combine the planning models of renewable energy systems, energy storage systems (ESSs), thyristor-controlled series compensators, and transmission lines into the EV-based planning problem. The first objective function aims to maximize EVs’ penetration by increasing the networks’ capacity to supply charging stations throughout the day, whereas the second objective, on the other hand, emphasizes lowering the carbon dioxide emissions from fossil fuel-based generation units in order to benefit the environment. The third objective is to meet the financial requirements by lowering the initial investment and operating costs of the installed devices. The proposed model is written as a multi-objective optimization problem that is solved using the multi-objective version of the Gazelle optimization algorithm (MGOA). The efficiency of the MGOA was tested by solving a set of four benchmark test functions and the proposed problem. The obtained results demonstrated the MGOA’s superiority in solving multi-objective optimization problems when compared to some well-known optimization algorithms in terms of robustness and solution quality. The MGOA’s robustness was between 20% and 30% and outperformed other algorithms by 5%. The MGO was successful in outperforming the other algorithms in providing a better solution. The Egyptian West Delta Network simulations revealed a 250 MWh increase in the energy supplied to EVs when energy storage was not used. However, storage systems were necessary for shifting EV charging periods away from high solar radiation scenarios. The use of ESS increased greenhouse gas emissions. When ESS was installed with a capacity of 1116.4 MWh, the carbon emissions increased by approximately 208.29 million metric tons. ESS’s role in improving the EV’s hosting capacity grows as more renewables are added to the network. ESS’s role in improving the EV’s hosting capacity rises as more renewables are added to the network.

Today, generation and transmission expansion planning (G&TEP) to meet potential load growth is restricted by reliability constraints and the presence of uncertainties. This study proposes the reliability constrained planning method for integrated renewable energy sources and transmission expansion considering fault current limiter (FCL) placement and sizing and N-1 security. Moreover, an approach for dealing with uncertain events is adopted. The proposed planning model translates into a mixed-integer non-linear programming model, which is complex and not easy to solve. The problem was formulated as a tri-level problem, and a hybridization framework between meta-heuristic and mathematical optimization algorithms was introduced to avoid linearization errors and simplify the solution. For this reason, three meta-heuristic techniques were tested. The proposed methodology was conducted on the Egyptian West Delta system. The numerical results demonstrated the efficiency of integrating G&TEP and FCL allocation issues in improving power system reliability. Furthermore, the effectiveness of the hybridization algorithm in solving the suggested problem was validated by comparison with other optimization algorithms.

This paper introduces a multi-stage dynamic transmission network expansion planning (MSDTNEP) model considering the N-1 reliability constraint. The integrated planning problem of N-1 security and transmission expansion planning is essential because a single line outage could be a triggering event to rolling blackouts. Two suggested scenarios were developed to obtain the optimal configuration of the Egyptian West Delta Network’s realistic transmission (WDN) to meet the demand of the potential load growth and ensure the system reliability up to the year 2040. The size of a blackout, based on the amount of expected energy not supplied, was calculated to evaluate both scenarios. The load forecasting (up to 2040) was obtained based on an adaptive neuro-fuzzy inference system because it gives excellent results compared to conventional methods. The linear population size reduction—Success-History-based Differential Evolution with semi-parameter adaptation (LSHADE-SPA) hybrid—covariance matrix adaptation evolution strategy (CMA-ES) algorithm (LSHADE-SPACMA)—is applied to solve the problem. The semi-adaptive nature of LSHADE-SPACMA and the hybridization between LSHADE and CMA-ES are able to solve complex optimization problems. The performance of LSHADE-SPACMA in solving the problem is compared to other well-established methods using three testing systems to validate its superiority. Then, the MSDTNEP of the Egyptian West Delta Network is presented, and the numerical results of the two scenarios are compared to obtain an economic plan and avoid a partial or total blackout.

At present, the integration of microgrids into power systems presents significant power quality challenges in terms of the rising adoption of nonlinear loads and electric vehicles. Ensuring the stability and efficiency of the electrical network in this evolving landscape is crucial. This paper explores the implementation of cascading Proportional–Integral (PI-PI) and cascading Fractional-Order PI (FOPI-FOPI) controllers for a Distribution Static Compensator (DSTATCOM) in hybrid microgrids that include photovoltaic (PV) systems and fuel cells. A novel hybrid optimization algorithm, WSO-WOA, is introduced to enhance power quality. This algorithm leverages the strengths of the White Shark Optimization (WSO) algorithm and the Whale Optimization Algorithm (WOA), with WSO generating new candidate solutions and WOA exploring alternative search areas when WSO does not converge on optimal results. The proposed approach was rigorously tested through multiple case studies and compared with established metaheuristic algorithms. The findings demonstrate that the WSO-WOA hybrid algorithm significantly outperforms others in optimizing the PI-PI and FOPI-FOPI controllers. The WSO-WOA algorithm showed an improvement in accuracy, surpassing the other algorithms by approximately 7.29% to 14.1% in the tuning of the PI-PI controller and about 8.5% to 21.2% in the tuning of the FOPI-FOPI controller. Additionally, the results confirm the superior performance of the FOPI-FOPI controller over the PI-PI controller in enhancing the effectiveness of the DSTATCOM across various scenarios. The FOPI-FOPI provided controller a reduced settling time by at least 30.5–56.1%, resulting in marked improvements in voltage regulation and overall power quality within the microgrid.

Planning for the intensive use of renewable energy sources (RESs) has attracted wide attention to limit global warming and meet future load growth. Existing studies have shown that installing projects such as transmission lines, energy storage systems (ESSs), fault current limiters, and FACTs facilitate the integration of RESs into power systems. Different generation and transmission network expansion planning models have been developed in the literature; however, a planning model that manages multiple types of projects while maximizing the hosting capacity (HC) is not widely presented. In this paper, a novel planning framework is proposed to enhance and control the HC level of RESs by comparing various kinds of renewables, ESSs, fault current limiters, and FACTs to choose the right one, economically and technically. The proposed problem is formulated as a challenging mixed-integer non-linear optimization problem. To solve it, a solution methodology based on a developed decision-making approach and an improved meta-heuristic algorithm is developed. The decision-making approach aims to keep the number of decision variables as fixed as possible, regardless of the number of projects planned. While an improved war strategy optimizer that relies on the Runge-Kutta learning strategy is applied to strengthen the global search ability. The proposed decision-making approach depends primarily on grouping candidate projects that directly impact the same system state into four separate planning schemes. The first scheme relies on the impedance of devices installed in any path to optimally identify the location and size of the new circuits and the series-type FACTs. The second scheme is based on optimally determining the suitable types of ESSs. On the other hand, the third scheme optimizes the reactive power dispatched from the ESSs and shunt-type FACTs simultaneously. The fourth scheme is concerned with regulating the power dispatched from different types of RESs. All of the simulations, which were carried out on the Garver network and the 118-bus system, demonstrated the ability of the investigated model to select the appropriate projects precisely. Further, the results proved the robustness and effectiveness of the proposed method in obtaining high-quality solutions in fewer runs compared to the conventional method.