Global warming, pollution, and the depletion of fossil fuels have compelled human beings to explore alternate sources of energy and cleaner modes of transport. In recent years, renewable energy sources (RES) have been massively introduced to the grid. Furthermore, Electric Vehicles (EVs) are becoming popular as a cleaner mode of transport. However, the introduction of RESs and EVs to the grid has imposed additional challenges on the grid operators because of their random nature. This review aims to focus on the integration of RES and EVs to the grid, thereby presenting the global status of RESs and EVs, the impact of integrating RESs and EVs to the grid, the challenges of integrating RES and EV to the grid, optimization techniques for EV and RES integration to the grid, and mitigation techniques. A total of 153 research papers are meticulously reviewed, and the findings are put forward in this review. Thus, this review will put forward the latest developments in the area of EV and RES integration into the grid and will enlighten the researchers with the unsolved questions in the area that need investigation.

This research manuscript proposes an Improved Manta Ray Foraging Optimization (IMRFO) algorithm for the power system congestion cost problem. The goal of the proposed Congestion Management (CM) strategy is twofold: firstly, the Generator Sensitivity Factors (GSF) is determined to select and involve the most influential power system generators that will reschedule their real power to alleviate the excess power flow in congested transmission lines. Secondly, the IMRFO has been developed and applied to attain the minimum possible congestion cost. The IMRFO has been formulated with the inclusion of correction factors in the exploration and exploitation phases to improve the coordination between these phases. The effectiveness of IMRFO has been measured considering its effective performance on the 23 conventional benchmark functions. 39 bus New England and IEEE-118 bus test system has been utilized to authenticate the effectiveness of the CM approach with the application of IMRFO. The outcomes highlight that the congestion cost achieved with IMRFO has been reduced by, of 16.08%, 13.73%, 11.78%, and 4.48 % for the 39-bus system and 14.84%, 12.97%, 9.63%, and 6.85% for 118 bus system when compared to the Bacteria Forge Optimization (BFO), Grey Wolf Optimization (GWO), Sine-Cosine Algorithm (SCA), and Original MRFO. The results gained with the implementation of IMRFO on the CM problem portrays appreciable minimization in the congestion cost, enhancement in the system voltage and losses, generates better convergence profile and computational time when contrasted with the recent optimization methods.

The paper introduces a novel method for identifying the location of harmonic-generating sources in smartgrids. The method utilizes a Dual-Tree Complex Wavelet Transform (DTCWT) of voltage and current signals measured at a specific point in the network. By applying DTCWT Transform, the signals are decomposed, and three non-active power quantities are extracted to represent the harmonic components within the system exclusively. These chosen non-active power quantities serve as indicators of the presence of harmonics in the system. Through analysis and comparison of these quantities, the method enables determining the precise location of the dominant harmonic generating source. This information is valuable for effectively addressing and mitigating harmonic issues in the network. Leveraging DTCWT and focusing on non-active power quantities provides a valuable tool for power system engineers and operators to diagnose and mitigate harmonic issues, ultimately improving power quality and system performance. This study presents a new feature extraction method to compute Non-active power quantities based on DTCWT due to its shift-invariant property.

In the restructured power system, where uncertainties are common, managing congestion becomes a crucial aspect of power system operation and control. Congestion management aims to alleviate the power system transmission line congestion while meeting the system constraints at minimal cost. This research introduces a generation rescheduling method for congestion management in the electricity market, leveraging an innovative nutcracker optimizer algorithm. The nutcracker optimizer algorithm, inspired by nutcrackers’ food accumulation mechanisms, is a recently developed nature-inspired algorithm. The efficacy of this proposed approach is assessed across modified IEEE 30-bus, and IEEE 118-bus test systems, considering the system parameters. The effectiveness of the proposed congestion management with the nutcracker optimizer algorithm is analyzed by comparing its results with those generated by other recent optimization techniques. Results demonstrated that the nutcracker optimizer algorithm surpasses other comparative methods, requiring fewer fitness function evaluations, avoiding local optima, and displaying encouraging convergence traits. Implementing this approach can assist the system operators in swiftly addressing contingencies, ensuring secure and reliable power system operation within a deregulated environment.

This research work proposes a Hybrid Modified Grey Wolf Optimization–Sine Cosine Algorithm for the multi-objective optimal scheduling of hybrid power system taking into consideration the risk factor arising due to the intermittent/uncertain nature of the renewable power generation sources. The hybrid power system is modelled considering the thermal generation units, wind energy system, solar photo voltaic system, electric vehicle and battery energy storage system. The multi-objective optimization problem is proposed based on the simultaneous minimization of the total operating cost and system risk. The conditional value at risk is introduced as the risk index to analyse the system risk due to uncertainties in power deliveries by the renewable energy resources, electric vehicle and battery energy storage system during the scheduling process. The integral contribution of this research work focuses on the establishment an optimal generation schedule based on the combined optimization of the total operating cost and system risk. The simultaneous minimization of the operating cost and the risk index is performed with the multi-objective Hybrid Modified Grey Wolf Optimization–Sine Cosine Algorithm and has been used to develop a Pareto-optimal front. The implementation of the fuzzy min–max technique is opted to fetch the best compromised solution. The standard test systems of IEEE-30 bus and Indian-75 bus system are used to validate the potency of the proposed approach. Comparative analysis has been established to highlight the results obtained with the proposed approach is appreciable than other optimization techniques.