This paper presents a novel strategy for congestion management in a distribution system which occurs due to the uncoordinated charging of plug-in electric vehicles (PEVs). The large proliferation of PEVs has been significantly creating impact on the transportation and power sector in recent times. However, uncoordinated charging of PEVs leads to congestion in distribution feeders when the penetration of PEVs is reasonably high. A charging coordination strategy of grid-to-vehicle (G2V) and vehicle-to-grid (V2G) is formulated considering the large PEVs penetration in the distribution system. In this paper, popular ensemble method i.e. gradient boosting regression tree method (GBM) is employed to predict the state-of-charge (SOC) for PEVs batteries. The charging coordination of G2V-V2G mode is developed in order to study the maximum PEVs penetration in distribution system with an objective function of minimizing overall cost. The strategy is implemented in workplace car park powered by grid and grid-connected photovoltaic generation located in industrial areas of IEEE 38 bus radial distribution system. An analytical study is presented to evaluate the maximum possible PEV penetration that the distribution network can accommodate without violating the grid constraints. Moreover, the performance of proposed strategy with or without the presence of solar powered car park is analyzed to assess the maximum possible PEVs penetration in the network so that no congestion occurs in the distribution lines.

This review paper critically examines the role of demand response (DR) in energy management, considering the increasing integration of renewable energy sources (RESs) and the rise in electric vehicle (EV) adoption. As the energy landscape shifts toward sustainability, recognizing the synergies and challenges offered by RESs and EVs becomes critical. The study begins by explaining the notion of demand response, emphasizing its importance in optimizing energy usage and grid stability. It then investigates the specific characteristics and possible benefits of incorporating RESs and EVs into DR schemes. This assessment evaluates the effectiveness of DR techniques in leveraging the variability of renewable energy generation and managing the charging patterns of electric vehicles. Furthermore, it outlines important technological, regulatory, and behavioral impediments to DR’s mainstream adoption alongside RESs and EVs. By synthesizing current research findings, this paper provides insights into opportunities for enhancing energy efficiency, lowering greenhouse gas emissions, and advancing sustainable energy systems through the coordinated implementation of demand response, renewable energy sources, and electric vehicles.

The increasing integration of renewable energies into electrical grids necessitates accurate forecasting of meteorological variables, particularly solar irradiance. This study presents a novel long-term solar irradiance forecasting approach, utilizing meteorological data from the National Renewable Energy Laboratory spanning 1988–2022. Focusing on five input variables—solar irradiance, dew point, temperature, relative humidity, and wind speed—this study evaluates the predictive performance of 13 data-driven models, comprising ten machine learning (ML) and three deep learning (DL) algorithms. Among them, gradient boosting regressor (GBR) and recurrent neural network (RNN) emerged as top performers in ML and deep learning, respectively. In order to choose the most suitable model for the long and short term, four forecast time-horizons (1, 8, 16, and 24 h) were also taken into consideration for the accurate models. A feature selection process using Pearson’s coefficient identified the most relevant inputs, while quantile regression was employed for uncertainty assessment, mean prediction interval, and prediction interval coverage probability models. This study demonstrates that RNN excels in short-term predictions, while GBR is more effective for long-term forecasts. A new hybrid approach GBR-RNN model was developed, achieving superior performance in terms of RMSE, MAE, and R2 metrics. This multi-model approach, integrating both ML and DL techniques, enhances solar irradiance forecasting by addressing input uncertainty and considering various forecast horizons. The findings contribute to the ongoing advancement of renewable energy forecasting by providing robust, accurate, and uncertainty-aware predictive models. Moreover, this approach helps identify the best-performing model, enabling more reliable and precise solar irradiance forecasts for energy management. This highlights both the improvement in forecasting methods and the importance of selecting the best model for accuracy.

This article proposes a novel strategy for congestion mitigation in a distribution system by charging coordination strategy of plug-in electric vehicle (PEV) considering grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes. The G2V mode of a large figure of PEVs creates a congestion scenario in the distribution system. Therefore, a coordinated charging strategy has been considered in this work to mitigate distribution system congestion. Moreover, a precise estimation and prediction of PEVs state-of-charge (SOC) is necessary while formulating PEVs coordinated strategy. The study of the work is two folded. First, for the first time, a combination of machine learning approach like gradient boosting method-Bayesian optimization (GBM-BO) is considered in prediction of PEVs SOC at the finishing of trip. Second, a coordinated charging scheme is established based on particle swarm optimization (PSO) and firefly algorithm (FA) using the PEVs SOC at the finishing of trip. The charging coordination strategy is analyzed on the 38-bus radial distribution system integrated with solar powered charging-cum parking lot (SPCPL). The machine learning based prediction results reveal the significant reduction in errors between predicted and calculated values. The results further reveal the reduction in PEVs charging cost and congestion scenario while considering SPCPL in the distribution system.

Ultra-fast charging stations (UFCS) present a significant challenge due to their high power demand and reliance on grid electricity. This paper proposes an optimization framework that integrates deep learning-based solar forecasting with a Genetic Algorithm (GA) for optimal sizing of photovoltaic (PV) and battery energy storage systems (BESS). A Gated Recurrent Unit (GRU) model is employed to forecast PV output, while the GA maximizes the Net Present Value (NPV) by selecting optimal PV and BESS sizes tailored to weekday and weekend demand profiles. The results show that PV integration alone improves the NPV by €6.19 million, while combining PV and BESS increases it to €33.97 million. With projected cost reductions, the system achieves a peak NPV of €34.05 million. Reliability analysis using Energy Sufficiency Ratio (ESR) and Autonomy Ratio (AR) confirms enhanced self-sufficiency and reduced grid dependency. This study demonstrates the techno-economic potential of hybrid renewable-powered UFCS using intelligent forecasting and evolutionary optimization.

Currently, electric vehicles (EVs) are the most liked mode for green transportation. However, the vehicle-to-grid (V2G) technology can reduce the peak demand on the power grid, which is an efficient way to encourage the integration of EVs. This paper proposes a multi-objective-based economic dispatch management including EVs to minimize the generator cost and active power loss. The entire system is retained for keeping in mind the economic operation of the whole system. Then, EVs are introduced to the system, taking into account vehicle requirements and load demands and considering EV constraints. The target of the proposed work is to demonstrate how effectively large-scale EVs can participate in valley filling and peak load shaving along with multi-objective-based cost and loss reduction. The proposed optimization problem is employed in an IEEE 30-bus system. The multi-objective grasshopper optimization algorithm and the ant-lion optimization are compared to observe the minimum cost and total loss of the system. The results show that the total generation cost and power loss of the system decrease due to the V2G mode of operation. In addition, EVs provide an alternative method for dealing with peak load, while filling the off-peak hours effectively. The total generation cost and power loss for 24 h using MOGOA without implementation of EVs are 8,757.128 $/hr and 65.28509 MW, respectively, and with EVs, the total generation cost and power loss for 24 h are 8,617.077 $/hr and 55.65349 MW, respectively. Thus, with the implementation of EVs, the total generation cost reduced by 1.59% and the total power loss reduced by 14.75%, and with MOALO, the total generation cost and power loss for 24 h without EVs are 8,977.077 $/hr and 44.20877 MW, respectively, and with EVs, the total generation cost and power loss for 24 h are 8,923.529 $/hr and 41.69524 MW, respectively. Thus, with the implementation of EVs, the total generation cost reduced by 0.59% and the total power loss reduced by 5.68%. The analysis of the results demonstrates how effectively EVs in the V2G mode can reduce the dependency over the grid power during the time of peak load demand.