This section presents and discusses the main findings of all of the reviewed works considering RESs’ and EVs’ integration objectives.

The literature contains reviewed works that can be categorized according to EV type, as outlined in Table 1. The findings indicate that the investigation pertaining to the amalgamation of RES and EVs in power systems predominantly concentrates on full electric EVs, with a limited amount of research directed towards other types of EVs.

Based on the data presented in Table 2, it can be inferred that the majority of the works analyzed involve the utilization of hybrid renewable energy systems that rely on both solar photovoltaic and wind turbine energy sources. Hydrogenation [115,132,146,189], fuel cells [26,104,127,174,194], thermal power [95,121], and biogas plant [95] are among the other limited options available.

EVs are gaining popularity at a rapid pace thanks to their comparatively lower emissions of pollutants in comparison with other types of vehicles. This section pertains to research initiatives that seek to mitigate emissions through the utilization of RESs’ and EVs’ integration. The aforementioned initiatives aim to decrease emissions through the mitigation of fossil fuel dependency [62,70,80,81,84,97,121,146,160,165,180,184].

The Italian energy system was subjected to modeling in accordance with a selected regulation strategy in order to optimize its technical and economic operation by minimizing primary energy consumption and CO₂ emissions, as evidenced in [62]. The work of [184], on the other hand, examines various plausible scenarios for Italy, which involve the expansion of renewable and storage capacity to meet the ever-increasing demand for electrification, particularly in the context of electric vehicles.

The work of [70] introduces a framework for possibilistic stochastic programming that combines possibilistic programming, flexible programming, and chance-constrained programming. This framework is designed to facilitate planning for the mitigation of pollutant emissions in the presence of RESs and EVs. In the work of [80], a discourse is held regarding the optimization of EVs’ and RESs’ employment in a power grid with the aim of reducing emissions. The article [81] presents an electric vehicle recharging mechanism that aims to decrease CO₂ emissions and improve fuel economy. Meanwhile, the work of [97] discusses a dynamic wireless electric vehicle charging system that utilizes wind power. In [121], the authors investigate a smart charging–discharging operation for cyber-physical energy systems. Lastly, the work of [180] explores the concept of a slow-charging option. The study presented in [84] investigates a multi-objective approach for economic emission dispatching, while the work of [146] examines a solution to the emission dispatch problem that incorporates risk considerations. In [160], an alternative solution is proposed for ascertaining the quantity of EVs in operation, which offers adaptable full EV functionality and mitigates emissions. The article in [165] presents a schedule for unit commitment pertaining to plug-in hybrid EVs in the context of uncertain conditions, with the aim of achieving reduced greenhouse gas emissions.

An EV charging station is a piece of equipment that facilitates the connection of an electric vehicle to an external source of electricity. This enables the recharging of the batteries in both plug-in hybrid and fully electric vehicles. Certain charging stations possess sophisticated functionalities such as intelligent metering, cellular compatibility, and network connectivity, while others adopt a more streamlined approach and prioritize fundamental features. An EV parking lot is a designated area that is exclusively reserved for the purpose of accommodating electric vehicles while they are connected to an EV charging station. Table 3 presents a taxonomy of research works related to the integration of RESs and EVs in the context of charging stations or parking lots. Based on the information presented in the table, it can be inferred that a significant proportion of the associated research endeavors pertain to electric vehicle charging stations.

Battery energy storage systems refer to a collection of devices that are capable of storing energy generated from RESs, such as wind and solar PV energy. These systems can then release the stored energy in an efficient manner to meet consumer demand, resulting in a stable electricity supply and potential cost savings. Batteries, commonly referred to as rechargeable or storage batteries, are devices designed to store energy and have the capability to be recharged. Several distinct types of batteries can be identified, including the flow cell battery, lithium-ion battery, lithium ferrophosphate battery, lithium sulphur battery, solid-state battery, lead acid battery, and nickel cadmium battery.

The works of [23,71,137,152,155,156,159,173,178] have concentrated on enhancing the effectiveness of battery energy storage systems that are present in the sites of WT and/or PV units. The battery structures associated with graphene [31], lithium-air and lithium-sulfur [32], vanadium redox flow [67], and lithium-ion [73] are of interest. In the work of [107], a proposed scheme for managing EV charging involves battery protection. The work of [144] discusses a battery charger, while the work of [189] presents information on the effect of discharging batteries.

Numerous research works have been published focusing on the most efficient control approaches for the incorporation of EVs along with RESs. The controlled charging of EVs is studied considering high wind penetration in [46] and hybrid wind and photovoltaic penetration for demand-side response in [101]. The load frequency control for EVs integrating wind and solar energy [105,129,157], or solar energy and biomass [195], is also discussed. A hybrid control approach tuned via utilizing the optimal power flow problem is proposed in [60]. A fuzzy logic controller for EVs and wind energy is proposed in [170].

A multitude of scholarly publications have been produced with a focus on identifying optimal control methodologies for integrating EVs with RESs. The study of controlled charging of electric vehicles has been examined in relation to high wind penetration in [46] and in the context of demand-side response for hybrid wind and photovoltaic penetration in [101]. The article delves into the topic of load frequency control for electric vehicles that incorporate renewable energy sources such as wind and solar energy [105,129,157], as well as solar energy and biomass [195]. The article in [60] presents a proposed hybrid control approach that has been fine-tuned through the utilization of the optimal power flow problem. The proposal of a fuzzy logic controller for electric vehicles and wind energy can be found in [170].

The EV aggregator bears the responsibility of ensuring the seamless functioning and provision of services of EVs on the electricity market. The work of [94] proposes optimal bidding strategies for EV aggregators in day-ahead energy and ancillary services markets that account for variable wind energy. The article in [110] presents an aggregator designed to effectively manage EV loads and optimize PV energy utilization through the implementation of intelligent charging protocols that leverage controllable EV demand. The literature discusses stochastic models that consider the integration of aggregated plug-in electric vehicle fleets into power systems, in conjunction with either wind energy or both wind and solar energy. These models are presented in [132,139], respectively. The literature has documented the use of aggregators for managing EVs and PV generation [145], EVs and direct controllable loads while considering various sources of uncertainty [163], and EVs and distributed hybrid RESs [175].

In the work of [26], a proposed energy infrastructure is presented that is both renewable and cost-effective. The article in [46] presents a discussion on the cost reductions that can be achieved through the implementation of controlled charging of EVs. The article in [61] presents a model for dispatching production costs. A study analyzing the effects of the charging profile of electric vehicles on production costs is presented in [63]. The study presented in [65] investigates the reduction in daily electricity expenses through the integration of RESs and EVs. The topic of cost-efficient interactions between EVs and RESs is discussed in [80]. The research conducted in [160,165] explores the operations of the smart grid, which consider the discharging and charging of EVs and RESs. The aim of this approach is to achieve a reduction in both costs and emissions.