1. Introduction
The concept of smart, environmentally friendly, and sustainable cities is crucial to assessing how well nations have advanced their civilizations and development [
1,
2,
3]. The goal of developed nations’ research and development efforts is to create greener cities and communities that enhance the state of the environment worldwide and reduce pollution from human activity [
4]. To accomplish a comprehensive energy solution, it is crucial to control the demand for and distribution of produced energy [
3,
4]. Furthermore, it is also necessary to implement various forms of renewable energy technology in cities and societies [
5]. To enable sustainable energy for cities, a mix of several renewable energy sources, such as solar, wind, geothermal, tidal, etc., is necessary [
6]. Intelligent energy management strategies can be implemented at all levels, starting at home and extending to every nook and cranny of the city, including transportation, schools, hospitals, factories, streets, etc. [
7]. The increasing penetration of renewables has driven power systems to operate closer to their stability boundaries, increasing the risk of instability [
8]. With the upcoming dynamic power generation in many countries, the installed capacity of power generation can gradually and effectively use energy to promote energy conservation, which plays an important role in achieving sustainable energy development [
8,
9]. The authors discussed the rapid development of power grid technology in the mix with the electric vehicles (EVs) industry (V2G) [
10,
11]. Using large-scale EV charging piles in the area to realize vehicle network interaction allows large-scale EVs and EVs to take part in economic optimization management, while the use of an energy storage system allows users to create energy arbitrage by discharging during price peaks and charging during off-peak periods if a variable energy price is considered [
9].
The control methods of the microgrid (MG) are more diversified, and the development of safety emergency response capabilities has become a current research hotspot. In terms of reducing the valley gap, the literature uses the temporal and spatial characteristics of EVs to construct an orderly charging and discharging load for EVs and a real-time electricity price response model [
12]. EVs and other power generation equipment can take part in the economic dispatch of the MG. To study the different strategies between EV power stations and MG, an economic dispatch optimization model was constructed [
13]. To solve the increase in the popularity of the complex EV access point network, it has been proposed large-scale EVs be connected to the network, and there is a good deal of optimization scheduling methods. EVs are effectively used to optimize charging and reduce system load peaks and valleys [
14]. However, the economic dispatch model of the literature mentioned above considered three factors of an MG, while the user benefits and safety of MG operation do not cogitate the performance of MG management and the participation of EV users [
8,
15].
These days, smart parking lots are becoming more and more popular since they offer a workable solution to power outages [
16]. Systems for managing energy can benefit from heuristic algorithms since they speed up decision-making and develop a novel heuristic algorithm for MG energy management [
17]. The principle behind this algorithm is to avoid wasting the available renewable potential at each period. Model predictive control is used to reduce the output power loss caused by converter failure, panel shading, and dirt buildup on wind and PV panels [
18].
Authors discussed the dimensional optimization algorithm for optimizing scheduling problems, such as the endless combination of particle swarm optimization (PSO) algorithm and differential evolution algorithm (DE), and the random particle swarm algorithm (RDPSO) [
19,
20]. Authors proposed that WOANN predicts the required control gain parameters of the hybrid renewable energy systems to maintain the power flow, based on the active and reactive power variation on the load side [
19]. The imperialist competition algorithm (ICA) combined mutation, destruction, and selection of a variety of different operators with PSO and other methods studied [
21,
22]. However, these methods have some shortcomings in finding the optimal solution and the best ability to overcome them [
23]. Based on the above analysis, starting from the management side of the MG operator, we comprehensively considered the three factors of MG operation safety, environmental governance, and user participation. A preliminary EV participation in the MG operation management optimization model was established to realize the operating and management costs, environmental pollution control costs, and the lowest mid-term cost [
24]. The multi-agent chaotic particle swarm optimization (MACPSO) algorithm combined with the chaotic particle swarm optimization algorithm and chaotic particle swarm optimization (CPSO) algorithm was used to solve the problem [
25]. The demand and response characteristics of each power generation unit, large-scale EVs, and electric load in the region were different, and the constraint conditions of each power generation unit presented nonlinear characteristics [
26]. A regional MG under the constraints of nonlinear equations is one difficulty for researchers [
27]. Furthermore, achieving the best states of management cost, environmental pollution, and load fluctuation variance are another topic of discussion [
26]. Authors used the penalty function method to deal with the relevant constraints; this method adds a penalty term to generate a new objective function [
20]. Energy conservation has become a long-term strategic policy for global economic and social development [
27]. The enhancement of energy management can improve energy efficiency and promote energy conservation and emissions reduction [
28]. However, integrating renewable energy and a flexible load makes the integrated energy system a complex dynamic with high uncertainty, bringing great challenges to modern energy management [
29]. With the increasing number of vehicle charging piles installed in recent years, load peak periods are brought to the station area, resulting in an insufficient capacity of the station area, in turn resulting in an overload of the distribution transformer in the station area, increased loss of lines and transformers, and other problems [
30]. A good auxiliary power supply is the key to the coordinated development between vehicle charging and the power grid in a smart MG [
28]. The wind and solar energy generation system can transform the natural resources of the station area into a stable power supply. Authors proposed an energy management method for a grid-connected wind-solar storage MG system with multiple types of energy storage [
31]. Authors have researched optimal energy scheduling of MG considering EV charging load [
32]. According to the two operation modes of the MG, namely grid-connected and isolated islands and the different access modes of EVs, the MG operation control strategy including EVs was customized [
21]. To investigate if solar energy and wind energy are naturally complementary, an energy storage system and an optimized battery energy storage control strategy were combined to put forward a hybrid landscape storage system control strategy considering the charging effect of batteries [
33]. The author discussed the operation energy management strategy of the isolated grid of an MG containing hybrid energy storage [
33]. However, none of these explored strategies were studied with regard to their application in smart stations/MGs and EVs.
This paper proposes large-scale EVs involved with MG operation and its management optimization method. This method first makes full use of the EV natural flexible load property, and the response of the large-scale EV scheduling model is constructed. Then, considering the system’s operation, user participation, and environmental governance, an optimization model is established. The system operation management cost of the MG, environmental pollution control cost, and load fluctuation variance are integrated to achieve an optimal system. Finally, by comparing the optimization results of multiple scenarios, it is verified that the model can realize effective load management in the region and reduce the management cost of MGs and environmental pollution treatment costs to support a healthy society.
The rest of the study is organized as follows,
Section 2 presents the related work,
Section 3 describes the proposed model,
Section 4 explains the results and discussion and the conclusion is presented in
Section 5.
5. Conclusions
The goal of developed nations’ research and development efforts is to create greener cities and communities that enhance the state of the environment worldwide and reduce environmental pollution. EVs will play a critical role in energy systems over the coming years, due to their environmental friendliness and capacity to reduce/absorb superfluous power from renewable energy sources. Meanwhile, a large-scale EV charging pile of regional power grids increases the randomness and uncertainty of the load in the concerned area. The proposed study constructed a large-scale EV response dispatch model that significantly improves the load smoothness of the MG after large-scale EVs are connected, and reduces the impact of the entire MG. The weight coefficients λ1 and λ2 were determined as 0.589 and 0.421, respectively, the controllable power generation output scheme unit was best observed, and the operational management, environmental pollution control, and variance of load fluctuations costs were interestingly observed as lowest at USD 31,983.813, USD 76,695.169, and USD 120.236, respectively. The proposed hybridized optimization method directs EV users to charge from and discharge to the regional MG with the presence of renewable energy resources (wind and PV), which improves the economics of the MG and realizes the operation management and environmental pollution regularized to establish a friendly society.
In the future, studies on different scenarios, including the maximum renewable model, the uncoordinated charging model, the load levelling model, and the charging-discharging model, can be used to further enhance EV demand. Additionally, the effects of various electric vehicle (EV) charging/discharging strategies on the costs associated with operation and the removal of pollutants in remote micro-grid (MG) modes are also relevant areas for future study.