In this work, a DC microgrid consists of a solar photovoltaic, wind power system and fuel cells as sources interlinked with the utility grid. The appropriate sizing and positioning of electric vehicle charging stations (EVCSs) and renewable energy sources (RESs) are concurrently determined to curtail the negative impact of their placement on the distribution network’s operational parameters. The charging station location problem is presented in a multi-objective context comprising voltage stability, reliability, the power loss (VRP) index and cost as objective functions. RES and EVCS location and capacity are chosen as the objective variables. The objective functions are tested on modified IEEE 33 and 123-bus radial distribution systems. The minimum value of cost obtained is USD 2.0250 × 10⁶ for the proposed case. The minimum value of the VRP index is obtained by innovative scheme 6, i.e., 9.6985 and 17.34 on 33-bus and 123-bus test systems, respectively. The EVCSs on medium- and large-scale networks are optimally placed at bus numbers 2, 19, 20; 16, 43, and 107. There is a substantial rise in the voltage profile and a decline in the VRP index with RESs’ optimal placement at bus numbers 2, 18, 30; 60, 72, and 102. The location and size of an EVCS and RESs are optimized by the modified teaching-learning-based optimization (TLBO) technique, and the results show the effectiveness of RESs in reducing the VRP index using the proposed algorithm.

The set-up for a new electrical system for variable renewable energy resources (RERs) will bring additional capital costs and therefore upgradation of the existing systems is by far the best option to ensure little to no greenhouse gases (GHGs) or harmful pollutants. This paper reports advanced power electronic converter-based devices (PECs) which play a crucial role in conversion and control activities in connecting RERs to power grids they cannot only bypass the associated challenges but can also be an efficient and potential equipment in power and energy sector. The interfacing of PECs with RERs is explored in this work is powered by an advanced framework in which smart inverters (SIs) are also utilized to maintain the regulated output voltage in line with the grid requirement and the effectiveness of the developed system is verified on MATLAB environment. The capabilities of SIs are emphasized which ranges from enhanced coordination between the devices, better efficiency and seamless integration of renewables with the utility grid to ensure holistic operation. A detailed analysis is performed and the outcomes reveal the suitability and applicability of the proposed work and in particular, interfacing PECs with the utility grid not only improves the performance of the system but also increases efficiency and limits the challenges associated with the integration of renewables in the mainstream grid to a considerable extent. The peak voltage of 300 V is produced by employing SIs from solar PV system which corresponds to an RMS voltage of 212.13 V within the permissible frequency range.

This paper reports the optimum operation of a virtual power plant (VPP) to reduce the reliance on coal and gas-based energy generation. This practice not only promotes the incorporation of renewable-based energy sources but also reduces the reliance on fossil-based resources which are limited in the reserves. The selected VPP system comprises small hydropower, wind turbine, solar photovoltaics and fuel cells accompanied by a co-generation unit. The proposed VPP is also equipped with a storage facility applicable as a flexible storage option i.e., an electric vehicle followed by an energy storage system. In addition, the primary objective of this work is to lessen the overall cost associated with the system along with a reduction in the generated emissions. Moreover, a Golden Jackal-based Optimization inspired by nature is utilized in a single and multi-objective framework to attain the desired target objectives. Furthermore, the scheduling of the VPP system is carried out on a day-ahead basis and the outcome in terms of cost and emission is compared with the available work in the literature and the result reveals the effectiveness of the system in terms of quality solutions sets for the objectives, minimum computational time and better convergence behavior. In particular, there is a reduction in cost by 2.1% and 0.62% when compared with the salp swarm algorithm and beluga whale algorithm followed by 2.12% and 1.18% in emission. Furthermore, the cost/emission of the VPP system is also abridged by employing a Pareto-based approach which shows the suitability and effectiveness of the developed system.

The accelerated shift to green transportation demands new ideas to perform effective management of charging systems for electric vehicles (EVs). This paper explores the approaches of digital twins (DTs) to manage smart charging powered by renewable energy along with grid supply integration to EV fleets. DTs create a virtual environment that enables energy flow, charging times, and grid connections by modelling various parameters of charging stations. The DT also supports forecasting techniques, enhanced usage of renewable energy, and battery storage handling to improve performance and reduce operational costs to promote environmental friendliness. The work also gives important insights into the necessity of DTs in smart charging, related operational constraints, and the relevance of DTs in community-based sustainable development. This paper similarly emphasizes the crucial role of DTs in improving the management and control of EV fleets, resulting in greater independence and shorter payback periods while establishing the foundation for future and scalable applications. DTs offer ample opportunities in this domain, and this research aims to open new avenues to enable future developments that aim to expand the DTs’ functionality for wider energy applications by refining optimization tools with greater energy efficiency. This work comprehensively highlights the potential of DTs and their crucial role in promoting future transportation along with a few limitations like data latency and cybersecurity-related issues to enable seamless integration of EVs with the virtual counterparts.

Global warming and the energy crisis are two significant challenges in the world. The prime sources of greenhouse gas emissions are the transportation and power generation sectors because they rely on fossil fuels. To overcome these problems, the world needs to adopt electric vehicles (EVs) and renewable energy sources (RESs) as sustainable solutions. The rapid evolution of electric mobility is largely driven by the development of EV charging infrastructures (EVCIs), which provide the essential support for large-scale EV adoption. As the number of CIs grows, the utility grid faces more challenges, such as power quality issues, power demand, voltage instability, etc. These issues affect the grid performance, along with the battery lifecycle of the EVs and the charging system. A charging infrastructure integrated with the RES-based microgrid (MG) is an effective way to moderate the problem. Also, these methods are about reframing how smart sustainable cities generate, distribute, and consume energy. MG-based CI operates on-grid and off-grid based on the charging demand and trades electricity with the utility grid when required. This paper presents state-of-the-art transportation electrification, MG classification, and various energy sources in the DC MG. The grid-integrated DC MG, international standards for EV integration with the grid, impacts of CI on the electrical network, and potential methods to curtail the negative impact of EVs on the utility grid are explored comprehensively. The negative impact of EV load on the voltage profile and power loss of the IEEE 33 bus system is analysed in three diverse cases. This paper also provides directions for further research on grid-integrated DC MG-based charging infrastructure.

Solar energy is a pollutant-free renewable energy source (RES) that is abundantly available. Solar photovoltaic (PV) panel’s output power is linearly related to the amount of irradiance reaching the arrays at any one time. To mitigate the power fluctuations, a storage system can be used to amend the unbalance between the PV output power and the demand power of the grid. This paper aims to integrate solar PV with a utility grid to supplement the demand of a load. Energy management system (EMS) estimates and monitors the actual energy generation and use. The optimum solar PV EMS with electric vehicle (EV) as a storage unit is developed based on the load data. The linear program-based optimization method is used to decrease the extent of energy import from the utility grid and the total cost of variable-priced electricity. The proposed system operational performance is analyzed using MATLAB with EV as a storage unit.