To address the challenges faced by modern power systems—such as efficiency, dynamics, reliability, control, stability, economy and planning—significant efforts have been made to develop advanced techniques, tools and scientific innovations across various disciplines. Among these, the ‘digital twin’ (DT) has emerged as one of the most reliable and rapidly evolving technologies, now widely integrated into diverse applications. The incorporation of DT technology into energy systems marks a paradigm shift in achieving sustainable, efficient and resilient modern power grids. Although considerable research has been conducted on DT applications in the power sector, comprehensive reviews of its role in transforming power grids to accommodate high levels of renewable energy sources (RESs), smart grid technologies, vehicle‐to‐grid (V2G) systems and energy storage solutions remain limited. This paper seeks to bridge that gap by exploring the critical role of DT technology in this transformation. It examines the historical evolution, fundamental components and diverse applications of DT technology across modern grid systems. Detailed analyses focus on DT's application in modernising power grids, particularly in RES integration, energy storage, transmission and distribution, smart grid advancements and V2G systems. Additionally, the paper reviews progress, investments, standards, regulations and the key stakeholders driving DT advancements in power grids. Finally, major challenges, limitations and future perspectives for DT applications in next‐generation power grids are discussed. Key findings reveal that while DT technology delivers significant benefits—such as improved operational efficiency, enhanced grid stability, greater reliability, cost reduction, cybersecurity and resilience through real‐time monitoring, predictive maintenance and optimised energy management—addressing existing limitations is crucial to maximising DT's potential in advancing and modernising sustainable power grids.

Electric vehicles (EVs) are getting increased attention due to their potential as reliable sources of transportation. In addition, EVs can be employed as a temporary energy storage system (ESS) in electrical power systems using the grid-to-vehicle (G2V), charging phase, and vehicle-to-grid (V2G), discharging phase, technologies. Renewable energy resources (RERs) are a clean and sustainable form of energy but are often intermittent in nature. Integrating ESS with RERs can mitigate the unpredictable fluctuations of the electrical power generated from RERs, but the investment cost of installing ESS is often high. In this paper, we investigate the financial benefit obtained from using EVs as a temporary ESS in a Microgrid (MG) integrated with a PV plant. The problem formulation considers the capital cost of the EV charging stations (EVCSs), feed-in tariff (FIT), and incentives given to the EVs’ owners. A planning algorithm is developed to find the required number of EVCSs needed to maximize the profit of the suggested system. The effectiveness of the proposed algorithm is illustrated using the Jordan University of Science and Technology (JUST) MG. Four different final state of charge (SOC) of EV scenarios are considered. Results show that the suggested system can be economically and technically feasible. Employing EVs, in G2V and V2G schemes, decreases the MG dependency on the grid and yields annual profit as well.

Renewable energy systems (RESs) are affordable, clean and sustainable. However, their output power is intermittent. Therefore, RESs are usually combined with an energy storage system or conventional sources to make the overall operation uninterruptable. Optimal sizing of hybrid energy system components is imperative to be financially and technically feasible. In this study, a multi‐objective optimisation based on a hybrid optimisation procedure, which combines the exploitation ability of the biogeography‐based optimisation (BBO) with the exploration ability of the particle swarm optimisation (PSO), is used to handle the system design. This algorithm is known as greedy particle swarm and BBO algorithm (GPSBBO). Weighted sum method is added to the GPSBBO to handle the multi‐objective nature of the design problem. A case study for a hybrid wind‐PV energy system design in the standalone and grid‐connected configurations is presented to illustrate the proposed method. Coverage of two sets, hypervolume and diversity performance indices are used to compare results of the proposed method to non‐dominated sorting genetic algorithm and the multi‐objective PSO. These indices show an improved performance of the suggested method in finding the optimal system design.

Unit commitment (UC) stands out as a significant challenge in electrical power systems. With the rapid growth in power demand and the pressing issues of fossil fuel scarcity and global warming, it has become crucial to enhance the utilization of renewable energy sources. This study focuses on addressing the UC problem by incorporating a wind farm and proposes a modified version of the metaheuristic African vultures optimization algorithm (AVOA) in binary form, utilizing the sigmoid transfer function. The modified AVOA employs multiple phase‐shift tactics to overcome premature local optima. By determining the on/off status of generating units, the modified AVOA improves the algorithm's effectiveness. Additionally, the paper develops an auto‐regressive moving average model (ARMA) to forecast wind speeds, with the AVOA assisting in selecting the optimal orders ( q and p ) of the ARMA model. This is done using historical wind speed data to capture uncertainty in the wind speed. The wind power is then calculated using various models and integrated into the UC problem. The effectiveness of the modified AVOA is examined on the IEEE 30‐bus system. The binary AVOA (BAVOA) outperforms several algorithms presented in the case study, demonstrating its superiority. Furthermore, the results indicate that BAVOA delivers superior outcomes within the discrete search space when compared to the continuous search space.

The impact of different photovoltaic models for a combined solar array and pumped hydro storage system was investigated. Al-Wehda dam located in Harta city in the northern of Jordan was used to validate the approach. The two-diode (TD), single-diode (SD), and ideal single-diode (ISD) solar models were evaluated in terms of the solar array size, reliability, and ecological effects. The impoundment of Al-Wehda dam was taken as the upper reservoir of the pumped hydro facility of the proposed renewable energy system. It was found that the PV power is more accurately modelled by considering the recombination loss in the TD solar model. This leads to a more realistic sizing and precise system evaluation. Results were obtained using the particle swarm optimization (PSO) algorithm and the whale optimization algorithm (WOA) for validation purposes. For instance, the PSO results showed that the realistic TD model is reliable, with an index of reliability of 98.558%. Further, it is the most ecological solution with an annual emissions reduction of 21.5198 Gg. The optimized values are 44,840 solar panels and 65.052 M.m³ of the lower reservoir volume for the TD model. The number of PV panels are reduced by 16.67% and 7.93%, respectively, with the ISD and SD relative to the TD model.

A study combining wind power with pumped hydro energy storage for the Jordanian utility grid is presented. Three solvers of the Matlab optimization toolbox are used to find the optimal solution for the cost of energy in a combined on-grid system. Genetic algorithm, simulated annealing (SA), and pattern search (PS) solvers are used to find the optimal solution. The GA solution of 0.0955388 $/kWh is economically feasible. This is 28.7% lower than the electricity purchased from the conventional utility grid. The discounted payback period to recover the total cost is 10.271 years. The suggested configuration is shown to be feasible by comparing it to real measurements for this case and a previous wind-only case. It is shown that the indicators of the optimal solution are improved. For instance, carbon dioxide emissions (E_CO2) and conventional grid energy purchases are reduced by 24.69% and 24.68%, respectively. Moreover, it is shown that the benefits of adding hydro storage, combined with increasing the number of wind turbine units, reduces the cost of energy of renewables (COE_Renewables). Therefore, combining hydro storage with wind power is economically, environmentally, and technically a more efficient alternative to the conventional power generation.