A Review on the Allocation of Sustainable Distributed Generators with Electric Vehicle Charging Stations

Abstract

Environmental concerns and the Paris agreements have prompted intensive efforts towards greener and more sustainable transportation. Persistent expansion of electric vehicles (EV) in the transportation sector requires electric vehicle charging stations (EVCSs) to accommodate the increased demand. Offsetting demand and alleviating the resultant electrical grid stress necessitates establishing grid-integrated renewable energy sources (RESs) where these sustainable strategies are accompanied by variable-weather-related obstacles, such as voltage fluctuations, grid instability, and increased energy losses. Strategic positioning of EVCSs and RES as distributed generation (DG) units is crucial for addressing technical issues. While technical constraints have received considerable attention, there is still a gap in the literature with respect to incorporating the additional complex optimization problems and decision-making processes associated with economic viability, social acceptance, and environmental impact. A possible solution is the incorporation of an appropriate multi-criteria decision analysis (MCDA) approach for feasible trade-off solutions. Such methods offer promising possibilities that can ease decision-making and facilitate sustainable solutions. In this context, this paper presents a review of published approaches for optimizing the allocation of renewable energy DG units and EVCSs in active distribution networks (ADNs). Promising published optimization approaches for the strategic allocation of multiple DG units and EVCSs in ADNs have been analyzed and compared.

1. Introduction

The last two decades have witnessed an increased recognition of environmental concerns, including air pollution and the climate-related effects of carbon dioxide (CO₂) and other greenhouse gas (GHG) emissions [1]. The development of successful strategies for sustaining life on the planet is now of the utmost importance. One consequence of today’s high level of awareness of sustainability issues among consumers and producers is the rising popularity and prevalence of electric vehicles (EVs), both locally and internationally.

In addition to the desire for a more sustainable future, drivers consider several factors when deciding to use EVs, such as cost, range, government rebates, and driving experience. Although the use of charging stations is generally low overall, another difficulty for drivers is the restricted availability of EV charging stations during busier peak hours, which poses a significant inconvenience that prompts owners to take advantage of incentives provided by electricity companies, which hope to alleviate strain on the networks by encouraging avoidance of these periods. If widespread EV adoption is to become an effective and sustainable strategy for tackling climate change, the expansion of EV charging station accessibility remains a challenge. This is because adding additional stations may not prove cost-efficiency and the key is the ability to cover the demand during peak periods [2,3].

An additional factor is that a possible ultimate transition to a more sustainable, entirely electric transportation system will create significant strain on power systems above and beyond anticipated levels. The original designers of existing grid infrastructure did not foresee the necessity of handling such intense, overwhelming, and widespread demand.

Many alternative solutions to the deployment of a greater number of EV charging stations for high-capacity needs are available, such as plug-in hybrid EVs (PHEVs). One significant possibility might be electrified roads (eRoads) and electric road systems (ERSs), which can provide a continuous power supply that enables EVs to maintain mobility for long-distance trips. ERS infrastructure is considered suitable for both trucks and cars because its utilization can overcome the challenges associated with the number of fast chargers required for accommodating all types of vehicles [2,3]. ERS infrastructure can reduce the need for large battery capacities in vehicles, thus resolving weight constraints and lowering GHG emissions, including those associated with battery materials. For heavy-duty vehicles, ERS and depot charging, which is illustrated in Figure 1, are the most cost-effective solutions early in the transition process, while public fast charging stations (FCSs) become increasingly feasible from a cost perspective as a larger share of truck traffic is electrified. A combination of ERS and fast charging infrastructure might be the most effective and fastest option for decarbonizing heavy-duty transport and rendering it more sustainable.

International agreements and commitments, such as the Paris Agreement, Conference of the Parties (COP) 27–28, and similar initiatives, have prompted many countries to adopt more environmentally friendly energy generation and transportation options as a means of reducing GHGs and promoting sustainability [4]. Several stakeholders, including governmental bodies, businesses, and private organizations, have been allocating resources with respect to the expansion of charging infrastructure, aiming to enhance the convenience and availability of EVCSs for EV users [2].

Governments around the world are putting in place attractive incentives, legislation, and regulations that will significantly promote the utilization of EVs: tax credits, rebates, decreased registration fees, bus lane privileges, and low- or zero-emission zones. International best practices include projects such as the UK’s Electric Nation and Electric Avenue, considered to be a major milestone in the UK’s journey toward a more sustainable cleaner energy future. The program has involved 700 domestic EV owners participating in 18-month trials, producing 2 million hours of car-charging data across a number of regions [3]. More than 300 chargers have also been supplied and installed as part of the Sciurus project aimed at corroborating the technical and commercial potential of domestic vehicle-to-grid (V2G) charging [3]. Smart charging and V2G projects in the Netherlands have demonstrated efficient EV operation with sustainable, renewable energy generation (REG) in distribution networks (DNs) [5].

An additional consideration is the ongoing move toward increasing the penetration of sustainable renewable energy sources (RESs) to improve grid reliability and resilience. However, these energy sources also create numerous system challenges stemming from their intermittent nature [6,7]. With respect to REG and EV, intermittency has been effectively mitigated by significant advancements in battery technology, which have led to improvements in energy density, cost reduction, and expanded driving ranges [8]. However, core issues at the distribution level still include voltage drops and line losses due to EVCS-related sharp growth in power demand, and increasing numbers of EVs are further affecting DNs in terms of energy management, giving rise to concerns regarding EVs and power quality. Figure 2 illustrates the basic architecture of an active distribution network (ADN) characterized by REG and EVCSs.

Since EVCSs consume significant power for EV charging, they impose serious stress on the power grid, further increasing system current losses, which are already a major concern for utilities. For this reason, an improper REG allocation can create problems with respect to both supply and demand sides [9,10]. Other factors, such as grid overloads; power quality issues; economics; and environmental issues, including land usage, sustainability, and climate change, also underline the necessity for DNs to engage in efforts directed at the effective planning of efficient resource allocation across short and long planning horizons [11]. Further, the enormous amount of power drawn by EVCSs for large-scale EV fleets will have a considerable impact on the distribution grid side [12].

A possible solution explored in this review is an integrated planning and scheduling approach whereby a framework could be presented for achieving multi-objective goals across techno-economical and socio-environmental aspects of REG-fed EV fleets. The review highlights limitations of previous work in this regard and points out opportunities for system improvement based on novel optimization techniques for the simultaneous achievement of multiple objectives. It also suggests the utilization of a multi-criteria decision analysis (MCDA) approach to solve the conflicting objectives and ease the decision-making process. The aim of identifying related research gaps is to pave the way for contributions that will address the research issues, questions, objectives, assumptions, and possibilities related to energy management with EVCSs and REG in an ADN. Such contributions can help accelerate the development of a truly sustainable future.

This paper focuses on the integration of EVCS infrastructure and REG in distribution networks (DNs). It discusses the challenges of EV charging, grid stress, and the need for efficient resource allocation to alleviate this stress. Solutions proposed include integrated planning, optimization algorithms, and multi-objective approaches to address technical, economic, and environmental objectives. Following this introduction, Section 2 presents the challenges associated with the integration of EV charging infrastructure with REG, while Section 3 considers technical aspects of EVCSs in electrical grids. In Section 4, an overview of renewable energy sources’ history and their modelling is discussed, and Section 5 investigates various optimization techniques and objectives of REG and EVCS allocation in distribution networks. Section 6 presents the conclusions of this review.

2. Challenges in Integrating EV Charging Infrastructure with REG

When both distributed generation (DG) units and EVCSs are integrated into the grid simultaneously, it can lead to increased technical stress on the grid due to various factors such as increased load demand, voltage fluctuations, and power quality issues, especially with the presence of unscheduled or uncontrolled EV charging [13]. The increased loads and requirements of grid reinforcement further exacerbate this issue. Other difficulties are related to the complexity of the problem and the trade-offs between large numbers of conflicting objectives that must be optimized. Technical challenges include voltage profile drops (duck curves), stressed grid conditions, system overloads, fluctuations due to intermittent REG penetrations, and active and reactive DN power losses [14]. In addition to their technical counterparts, economic challenges include the costs associated with EVCS power usage, active and reactive power losses, and operations and maintenance costs (O&M) [15]. Socio-environmental challenges include GHG emissions, social acceptability issues related to user comfort, and the utilization of sustainable energy sources as stipulated in sustainable development goals (SDGs) [16].

2.1. EVCS Grid Services Provided

Potential EVCS impact and benefits identified in the literature are related to the service that EVCSs provide to the power system, which can be classified as follows:

• Active/reactive power support, including enhanced frequency regulation, reduced load fluctuations, valley filling, peak shaving, voltage regulation, and minimization of system and energy loss [9,12];
• Integration of renewables such as solar and wind along with fuel cells, micro-turbines, energy storage systems, battery energy management systems, and other embedded resources [14,17].

2.2. Technical Aspects of EVCSs in Electrical Grids

Technical EVCS aspects are affected primarily by incentives that encourage a reduction in power consumption during peak hours and measures for ensuring reliable power system operation. The main motivation is the achievement of the following goals, which can be conflicting and challenging, requiring decisions that would enable them to be addressed simultaneously [18,19]:

• Improved voltage stability;
• Reduced transmission/distribution grid congestion;
• Deferred preventive/outage maintenance;
• Increased integration of renewables;
• Enhanced power system flexibility, stability, and reliability.

3. EV Charging Strategies and Their Impact on Distribution Level

The application of EVs across energy management systems encompasses numerous elements including optimization techniques and energy management. The optimization techniques are needed as a means of addressing the associated challenges, focusing on achieving sustainable and efficient EV charging while considering grid stability and renewable energy utilization. Energy management plays an important role in considering the anticipated surge in EV adoption in the future, lack of inclusive planning horizon, and multi-objective and conflicting criteria for practical trade-off solutions [19,20].

3.1. Charging Strategies

Managing grid congestion requires a thorough analysis of algorithms for the dynamic scheduling of EV charging. Time-of-use tariffs and demand response (DR) systems are employed for optimizing consumer charging behavior. Smart charging strategies include evaluating the environmental benefits and challenges associated with EV penetration in DNs, conducting life-cycle EV assessments, and integrating EVs as distributed energy resources to enable sustainable grid planning and a future seamless operation [21]. Different types of charging methods are discussed and illustrated in this section; however, in practical applications, conductive charging is the technique most often utilized for charging EV batteries.

3.1.1. Battery Swapping

Battery swapping (BS) is a rapid process where the EV’s depleted battery is substituted at a designated battery swapping station with a fully charged one, as shown in Figure 3. This method effectively manages the charging, discharging, and battery swapping processes, thereby benefiting the battery swapping station and substantially reducing the charging time for the EV owner. However, challenges such as battery ownership, finding compatible batteries, and the complexity of infrastructure interfere with the widespread adoption of battery swapping. Additionally, the high initial capital costs and the need for standardized battery technologies pose obstacles to the advancement of battery swapping stations, despite their benefits of high efficiency, quick changeovers, and reduced grid stress [13,22].

3.1.2. Static Conductive Charging

Static conductive charging (SCC), whereby an actual connection is established between the EV and the power grid via cables while the vehicle is in stationary mode, is a prevalent means of charging EVs [13]. This method is commonly used with commercial EVs, and charger types are categorized as either onboard or offboard. Conductive charging is preferred over other methods due to its practicality and widespread application in real-world scenarios [22]. Figure 3 illustrates the basic concept of SCC, which represents the classical method of EV charging.

3.1.3. Dynamic Conductive Charging

Dynamic conductive charging (DCC) is a type of charging that requires a physical connection between a vehicle and the power source while the vehicle is in motion. It enables continuous power supply and charging as a vehicle moves along a road. DCC involves two main charging methods. One is illustrated in Figure 4 and entails providing power to EVs through conductive elements embedded in roads or rails. To provide power, the second method requires the use of overhead catenary wires like those employed with electric trains and trams. DCC could potentially reduce the need for large battery capacities, and with proper charging control, it could decrease power demands on the grid during peak hours, when large numbers of heavy-duty vehicles are using the same infrastructure at the same time. However, a DCC charging strategy is used primarily for heavy-duty transport vehicles such as buses and trucks and is acknowledged to be less suitable for regular EVs.

3.1.4. Static Inductive Charging (SIC)

In SIC, electric power is transmitted from the power grid to the EV using an electromagnetic field without the need for a physical connection. This technology provides several benefits, including enhanced safety through the mitigation of electric shock hazards; however, it may encounter charging inefficiencies due to limitations such as non-compliant windings and a comparatively wide air gap. Static inductive charging is possible when the EV remains stationary throughout the charging process [23].

3.1.5. Dynamic Inductive Charging (DIC)

DIC allows the EV to be charged while in motion, as illustrated in Figure 5. This novel strategy is implemented by constructing specialized lanes on highways for inductive charging from the road surface. It has the potential to reduce the size of the EV’s battery and increase mileage by allowing it to be charged while in motion.

Moreover, because the size of the battery is reduced, DIC has the potential to mitigate several EV drawbacks, including limited driving ranges, lengthy charging times, and higher initial costs in comparison to vehicles with conventional internal combustion engines. DIC also permits the operation of V2G, which enables integration between EVs and a smart grid, a feature that could potentially reduce peak power demands on the distribution network through the concept of demand-side management (DSM) [24]. However, the substantial initial investment required for wireless power transfer (WPT) charging infrastructure remains a challenge [25].

An overview of the planning and scheduling of EV charging methods is provided in

Table 1. Comparison of EV charging methods versus criteria ¹ [23].

#	Charging Method		Duration	Efficiency	Size	Range	Ownership
1	Conductive Charging (CC)		High	High	High	Depends on (SoC)	Owner
2	Battery Swapping (BS)		Lowest	High	High	Depends on (SoC)	EVCS
3	Inductive Charging	Static (ICS)	High	Lowest	High	Depends on (SoC)	Owner
4		Dynamic (ICD)	No effect	Lowest	Lowest	Lowest	Owner

¹ Duration: EV charging duration and power usage; efficiency: increased efficiency with reduced system losses; deferral: infrastructure delays for new asset installation; size: battery size and energy required with tariff; range: EV range per consumer satisfaction; ownership: battery ownership (owner or EVCS rental).

, which displays charging methods (CMs) versus EVCS criteria with REG. The selection of the charging method may impact the distribution of EVCSs or DG units by influencing the infrastructure needs, charging rate, user preferences, and integration with the power grid infrastructure [8,23].

3.2. Impact of Charging Strategies on Distribution Level

The effects of different charging strategies on load profiles, voltage regulation, and other vital factors in the DN will be presented in the following subsections.

3.2.1. Impact on DN Stress Conditions

EV charging in parking lots without proper coordination adds stress to the DN, which affects the voltage profile, increases power losses, and causes rebound peaks. These issues can be addressed with the application of an intelligent grouping method that includes consideration of EV travel data and battery status as a means of enhancing charging schedules. A charging/discharging priority model that takes into account factors such as the remaining energy demand and contribution level can be employed for setting the EV charging priority [26]. However, grouping related to REG sizing and EVCS placement needs further exploration, especially with respect to ways in which energy management can maximize EV system integration without costly infrastructure upgrades [2]. In addition, to address power loss, load fluctuations, and unstable power system operation, smart cities use machine learning techniques, such as deep neural networks and decision trees, in order to improve EV energy management and charging procedures [27,28]. However, these techniques are data-intensive and require high-quality data for training and testing.

3.2.2. Impact of Charging Schedule

For EV charging schedules, the distribution network operator (DNO) can utilize EV aggregators to reduce losses and avoid voltage fluctuations. As depicted in Figure 6, each aggregator must coordinate charging procedures whereby each user can satisfy the lowest possible charging costs and shortest wait times [13,28]. However, this approach is data-intensive and becomes complex if multi-objectivity and multi-dimensionality trade-off solutions are considered.

3.2.3. Impact of Increased Load Demand

Strategic planning and effective deployment of charging infrastructure are essential for meeting rapidly increasing demand and avoiding power grid failures [29]. Accurate demand forecasting and load management are crucial in estimating and planning energy demand as well as charging infrastructure requirements [30]. However, the impact of load growth must be considered, and active DN planning must be properly addressed. In addition, the impact of alternative charging options, such as a BS infrastructure, must be explored from a techno-economic perspective [31].

3.2.4. Optimization Strategies for EV Penetration

Optimization of EV penetration encompasses planning, scheduling, multi-objective optimization techniques, V2G/G2V, smart distribution networks (SDNs), EV management, and other SDN concepts. All optimization techniques have consistent EV-based decision variables, including charging station placement, charging rates, charging protocols, charging infrastructure technology selection, grid expansion planning, and energy storage utilization [32]. EV-based objectives/criteria include load demand, power quality, grid stability, environmental impact, cost-effectiveness, social acceptance, minimization of peak demand, reduction in charging costs, minimization of greenhouse gas emissions, and optimization of energy utilization. From the viewpoint of smart grid technologies for EV integration, DR and V2G systems include consideration of decision variables such as DR strategies, while aiming at objectives such as grid stability, load balancing, energy efficiency, and renewable energy integration. Controlled charging through DR programs reduces the peak load and its associated technical challenges, resulting in a flatter voltage profile [26].

3.2.5. Distribution Transformer Overload

Power pricing, non-EV load utilization, and EV user behavior must all be coordinated with the charging of numerous EVs. The goal is to prevent transformer overload naturally while also relieving EV consumers’ concerns regarding charging rates and driving range. Figure 7 illustrates the energy management components of a smart grid system, including distribution transformers (DTs) and EV home charging with REG integration. Reinforcing the grid with renewables must be addressed from an energy management viewpoint. The authors of [33] have proposed an energy management decision-making tool to mitigate the impact of high EV penetration on distribution transformer aging. The proposed approach demonstrated a significant reduction in the transformer probability of failure from 74% to 13% by using the proposed approach. A fuzzy logic-based tool was presented in [34] to alleviate the impact of the stochastic EV charging load on transformer aging. As reported in [34], a grid reinforcement strategy can reduce the loss of transformer life by about three to five times.

4. Renewable Energy Distributed Generation

The term DG refers to a small-scale electrical power production source that is connected directly to the DN on the consumer side of the meter near load centers. DG units are classified according to connection and voltage level as either low voltage (LV) or medium voltage (MV) on the DN side, while only LV is typically located on the consumer side. DG can be categorized as either renewable (primarily solar and wind) or non-renewable. High DG penetration levels have changed DNs from passive to active via two-way power transfer. However, the effects of REG intermittency in conjunction with expanded nonlinear EV fleets must be explored with respect to voltage support, loss minimization, expenses resulting from reinforcement deferrals, and GHG emission reduction [35,36]. Techniques for modelling photovoltaic (PV) sources are detailed in [37], with the associated parameters given as follows:

$${\mathrm{P}}_{\mathrm{P}\mathrm{V}}=\left\{\begin{array}{ll}{\mathrm{P}}_{\mathrm{r}}\left({\displaystyle \frac{{\mathrm{G}}_{\mathrm{s}}^2}{{\mathrm{G}}_{\mathrm{s}\mathrm{t}\mathrm{d}}\times {\mathrm{X}}_{\mathrm{c}}}}\right)& \mathrm{f}\mathrm{o}\mathrm{r} 0<{\mathrm{G}}_{\mathrm{s}}\le {\mathrm{X}}_{\mathrm{c}}\\ {\mathrm{P}}_{\mathrm{r}}\left({\displaystyle \frac{{\mathrm{G}}_{\mathrm{s}}}{{\mathrm{G}}_{\mathrm{s}\mathrm{t}\mathrm{d}}}}\right)& \mathrm{f}\mathrm{o}\mathrm{r} {\mathrm{X}}_{\mathrm{c}}\le {\mathrm{G}}_{\mathrm{s}}\le {\mathrm{G}}_{\mathrm{s}\mathrm{t}\mathrm{d}}\\ {\mathrm{P}}_{\mathrm{r}}& {\mathrm{G}}_{\mathrm{s}\mathrm{t}\mathrm{d}}\le {\mathrm{G}}_{\mathrm{s}}\end{array}\right.$$

(1)

where $P_{PV}$ is output power in kW; $P_r$ is rated power in kW; $G_s$ is solar insolation in W/m²; $X_c$ is the irradiance reference; and $G_{std}$ is the standard solar irradiance density, 1000 W/m² (air mass = 1.5, temp = 25 °C).

Similarly, the modelling of wind turbine generators (WTGs) is expressed as follows [38]:

$${\mathrm{P}}_{\mathrm{w}\mathrm{g}}\left(\mathrm{v}\right)=\left\{\begin{array}{ll}0& \mathrm{f}\mathrm{o}\mathrm{r} \left(\mathrm{v}{<\mathrm{v}}_{\mathrm{w}\mathrm{i}}\right) \mathrm{a}\mathrm{n}\mathrm{d} (\mathrm{v}>{\mathrm{v}}_{\mathrm{w}\mathrm{o}})\\ {\mathrm{P}}_{\mathrm{w}\mathrm{r}}\left({\displaystyle \frac{\mathrm{v}-{\mathrm{v}}_{\mathrm{w}\mathrm{i}}}{{\mathrm{v}}_{\mathrm{w}\mathrm{r}}-{\mathrm{v}}_{\mathrm{w}\mathrm{i}}}}\right)& \mathrm{f}\mathrm{o}\mathrm{r} \left({\mathrm{v}}_{\mathrm{w}\mathrm{i}}\le \mathrm{v}\le {\mathrm{v}}_{\mathrm{w}\mathrm{r}}\right)\\ {\mathrm{P}}_{\mathrm{w}\mathrm{r}}& \mathrm{f}\mathrm{o}\mathrm{r} \left({\mathrm{v}}_{\mathrm{w}\mathrm{r}}<\mathrm{v}\le {\mathrm{v}}_{\mathrm{w}\mathrm{o}}\right)\end{array}\right.$$

(2)

where $P_{wg}\left(v\right)$ is output power; $P_{wr}$ is rated power; $v_{wi}$ is cut-in speed; $v_{wo}$ is cut-out speed; $v_{wr}$ is nominal speed.

DGs have numerous benefits, such as providing voltage support, reducing power system loss, improving power quality, enhancing the capacity, balancing the different loads among the DN sections, providing grid reinforcement, and improving the overall system stability [39]. In the optimal planning and sizing of ADNs, EV loads and energy storage systems are essential factors that must be considered, as they play an important role in demand fluctuation and load shaving, and thus grid stability. With time-varying EV loads, determining decision variables from the viewpoint of siting and sizing remains challenging and complex, involving multiple conflicting objectives. The evaluation of objectives/criteria across planning horizons must be estimated with regard to modern distribution mechanisms, based on future requirements [40].

5. Optimization of REG and EVCS Allocation in Distribution Grids

The growing body of literature published in recent years addresses the technical challenges of DG and ECVS allocation, highlighting its increasing importance. Various methodologies and algorithms have been employed to optimize the placement and size of RES and/or EVCSs in distribution systems to enhance system performance and efficiency [41]. Much research, focused on technical optimization, has prioritized improvements in voltage stability indices (VSIs) and minimization of total voltage deviation as objective functions to reduce the impact associated with voltage instability and overall system losses within the distribution network [42]. One approach aims to improve VSI by using evolutionary algorithms, such as genetic algorithm [43]. The work introduced a new technique called holomorphic embedding load flow technique (HELM) to determine voltage stability indices for different nodes in the distribution system. In [44], voltage deviation improvements and power loss minimization were achieved by implementing a novel technique called chaotic student psychology-based optimization (CSPBO) algorithm. The work proposed in [45] utilized particle swarm optimization (PSO) to achieve optimal allocation of DG units and EVCSs, enhancing voltage stability and minimizing voltage deviations across the distribution network. The work in [46] emphasized the importance of optimal planning for the EV charging stations due to the increasing demand for EVs leading to significant impacts on the power system. The integration of an EVCS can increase peak loads, affecting power losses and voltage deviations, and potentially cause thermal limit violations. Coordinated charging schemes and optimal placement of charging stations are proposed solutions to manage peak loads effectively, maintain voltage stability, and reduce power losses. Furthermore, in [47], the authors present a two-stage optimization model for deciding the location of solar power plants in an Algerian municipality. The study highlights the use of distributed generation optimization models for planning of distribution systems, demonstrating the focus on technical indices such as power loss reduction and voltage profile improvement. Some important optimization strategies will be presented in the following subsections.

5.1. Multi-Objective Optimization

The evaluation of the current literature acknowledged the need for multi-objective optimization that balances social, economic, and environmental considerations for sustainable and efficient power distribution networks. With the integration of EVs that have different load behaviors, the dynamics of the distribution network have changed, and now possibly require adjustments in the size or placement of DG units to maximize grid benefits [48]. It is crucial to determine the optimal placement and size of DG and EVCSs initially to prevent expensive modifications or future relocation costs. Several studies have aimed to reduce the expenses associated with investment, planning, maintenance, and carbon tax costs. One approach used to minimize carbon tax costs while considering the allocation of various components like substations, distribution transformers, RES, and energy storage sources (ESSs) is by leveraging an algorithm called the cooperative parallel VNS (variable neighborhood search) for solving large-scale problems. The work in [28] addressed various economic factors, including the cost of investment in RESs, transportation, electricity, and the cost of distribution network development. The authors employed a hybrid of metaheuristics-based algorithms which combined a genetic algorithm with PSO (GA-PSO), alongside mixed-integer nonlinear programming (MINLP), to achieve techno-economic optimization objectives. An effective sequential quadratic programming (SQP) solution mechanism was adopted in [49] to ensure customer satisfaction, maintaining and guaranteeing a low EV charging price and achieving expected revenue by the private investor. Such an approach is efficient for dealing with nonlinear problems.

From a socio-environment perspective, the work in [50] considered optimization for reducing carbon emissions and the associated risks, including the planning costs influenced by carbon taxes. The optimization model used a two-stage stochastic mixed-integer linear programming (MILP) model to optimally resolve the distribution system expansion planning problem. This study evaluated risks through a dual lens: operational costs and carbon taxes, aiming to find ways to help achieve a balance between environmental goals while also considering the economic aspects. The authors in [51] investigated environmental factors, including reducing carbon emissions and mitigating air pollution through promoting the use of clean and sustainable energy sources for EV charging. Additionally, they aimed to improve public satisfaction with green mobility options, by increasing the reliability and efficiency of the power supply, which led to a large penetration rate of EVs.

The presence of multi-objectivity introduces challenges associated with problem complexity and the need to make trade-offs between potentially numerous and conflicting goals. The MCDA technique is anticipated to facilitate the process of trade-off for the conflicting objectives. Researchers in [52] implemented an MCDA-based technique called a fundamental hierarchical analysis process (AHP) that utilizes geographic information systems (GIS) for selecting the optimal placement for EVCS, by considering various criteria and integrating them to meet the demand for EV charging.

5.2. Novel Optimization Strategies

The state-of-the-art optimization techniques for integrating DGs and EVCSs into distribution networks are making significant progress in terms of innovative methodology and approaches. A review of the latest literature of optimization techniques for allocating DG units and EVCS is summarized in

Table 2. A review of recent novel research in optimization strategies for DG and EVCS integration ¹.

Ref./Year	Optimization Objective	Novelty	Limitations/Future Work
[43] 2024	To find the optimum location and size for DG considering several technical and economic factors	In proposing an optimization model that utilizes HELM-based voltage stability index (VSI) for allocating DG units in ADN	The use of HELM and GA can be computationally intensive for LSDN. Also, the model did not account for DG uncertainty
[19] 2023	To propose a multi-objective planning framework for the optimal allocation of EVCSs and RESs, considering advanced control schemes	In planning a framework optimizing EVCS and RES allocation, addressing challenges from stochastic REG and EVs using advanced controls	Omits positive role of flexible load resources like EVs and heaters in multi-MES cluster dispatch
[17] 2023	To solve complexity in managing integrated strategies for active EDNs: NR, DR, voltage control, enhancing computational efficiency	In proposing an efficient modular scheme for optimizing unbalanced ADN with uncertain EV and PV	Lacks scalability proof for complex networks, ignores uncertainties in the proposed scheme
[41] 2023	To minimize overall power loss in RDS while ensuring voltage stability by simultaneously allocating DG, EVCS, and STATCOM	In introducing a novel method called BESA to determine the optimal sizes of DG, DSTATCOM, and EVCS in RDS	The comparison of BESA with other optimization algorithms is limited to the specific test systems used in the research
[53] 2021	To minimize planning costs and to improve system reliability by optimally allocating various components.	In solving the planning of MV/LV LSDN by considering GHG mitigation, RES allocation, and ESS allocation simultaneously	The lack of a detailed analysis of the impact of RES and ESS allocation on the network’s reliability and ignorance of uncertainties in the planning process
[39] 2021	To minimize real losses and voltage deviation while maximizing VSI in the DN through the optimal allocation of DG	In the application of I-DBEA, which includes the integration of DE and fuzzy set theory to select the best PSO	Penalty function approaches, where small coefficients delay possible solutions and high ones cause premature convergence.
[54] 2020	To optimally allocate hybrid solutions based on a combination of FACTS devices and DG units in power systems	In the utilization of Tabu search to achieve multi-objective optimization for the allocation of FACTS and DG units	Not considering social and environmental factors in the optimization process

¹ Table Abbreviations: BESA: bald eagle search algorithm; DSTATCOM: distribution static compensator; ESS: energy storage systems; FACTS: flexible AC transmission systems; GA: genetic algorithm; HELM: holomorphic embedding load flow method; I-DBEA: improved decomposition based evolutionary algorithm; LSDN: large-scale distribution network; MESs: micro-energy systems;NR: network reconfiguration; PSO: particle swarm optimization; RDS: renewable distribution system.

, offering an understanding of the novel aspects of each study and how it has advanced knowledge in the field. The table provides additional insight about each study listed: the main objective, novelty, or contribution to the existing body of knowledge, and identification of limitations or future work required, which indicates existing potential research gaps. The studies listed in Table 2 aim for optimum allocation of DG and/or EVCSs to achieve either single- or multi-objective optimization. The novelty aspects of these research studies lie in proposing new optimization models, whether creating a new optimizer or by using a hybrid approach that combines existing optimization techniques from the literature. With respect to limitations or future research directions for these novel studies, the information presented in Table 2 highlights only the primary limitations. It is worth noting that there may be additional, less significant limitations that are not explicitly discussed here.

To offer a further understanding of the novel aspects of each study and how it has advanced knowledge in the field,

Table 3. Comparative analysis of recent novel research in optimization for DG and EVCS integration ¹. (✓: covered and X: not covered).

Ref./Year	Energy Source	Optimization Technique	Optimization Factors				Case Study
			Technical	Economic	Environmental	Social
[43] 2024	DG	GA + HELM	✓	✓	X	X	IEEE 33-Bus radial distribution system
[19] 2023	PV + WTG	Metaheuristics (MODA) + fuzzy sets to produce optimal solutions	✓	✓	X	✓	IEEE 69-bus system
[17] 2023	PV	MH (MOPSO)	✓	✓	X	X	IEEE 123-node test feeder
[41] 2023	PV + WTG	BESA	✓	X	X	X	IEEE 34-bus and IEEE 118-bus systems
[53] 2021	PV + WTG	CPVNDS	✓	✓	✓	✓	LSDS with 200 nodes in MV and 1672 nodes in LV
[39] 2021	PV + Wind	I-DBEA	✓	X	X	X	IEEE 33-bus, 69-bus, and 119-bus standard RDNs
[54] 2020	DG	Tabu Search	✓	✓	X	X	IEEE 300-bus power system

¹ Table Abbreviations: BESA: bald eagle search algorithm; CPVNDS: cooperative parallel variable neighborhood decomposition search; GA: genetic algorithm; HELM: holomorphic embedding load flow method; I-DBEA: improved decomposition based evolutionary algorithm; LSDS: large-scale distribution system; MODA: multi-objective dragonfly algorithm; MOPSO: multi-objective particle swarm optimization;RDNs: radial distribution networks.

provides more details of the energy source used, optimization technique employed, and the axes on which the optimization for each study is based: technical, economic, environmental, or social. As a means of signifying the practical applicability of the work for future use, the test system employed for each study has been indicated. It can be observed from Table 3 that almost all studies presented aim for either technical or techno-economic optimization objectives. A few of them have considered socio-environmental studies as a part of the optimization process. In terms of optimization techniques, all the novel research works listed in the table utilized metaheuristics-based techniques, which shows the importance of metaheuristics in solving DG and EVCS optimization problems in the electrical distribution network.

Although network availability is crucial in the location’s selection for installing EVCS because it determines whether the necessary power can be consistently supplied to charging stations, other factors are also essential in the selection criteria. Establishing the number of users per time unit enables stations to handle peak demand without long wait times. Battery capacities also play a significant role in selection criteria. For example, a vehicle with a 24 kWh Nissan Leaf battery must be recharged approximately every 121 km, whereas a Tesla model S with a 100 kWh battery requires recharging approximately every 507 km. Incorporating consideration of these factors into the optimization process can ensure the effective placement and frequency of charging stations along travel routes [55].

A further important determinant is the state of charge (SOC) of the batteries at the point of departure. Vehicles that start their journeys with a low SOC (below 30%) will require charging sooner, which eventually affects the placement of charging infrastructure [56]. Energy consumption factors, including temperature, vehicle weight, and driving style, also influence how quickly a vehicle’s battery depletes, thus needing more or fewer charging stops. In [57], the researchers explained how taking into account factors such as driver profiles, driving style, and speed preferences contributed to the optimization of EV charging schedules for the achievement of reduced energy costs, travel time, queue times, and recharging times. The authors of [58] utilized an MILP method in order to minimize costs and improve system efficiency based on consideration of the interactions between EVCS infrastructure and user behaviors.

In [59], researchers presented their examination of a sample of 505 Netherlands EV drivers to study the drivers’ preferences and behaviors regarding routes and charging choices by incorporating key factors such as battery SOC, FCS availability, travel times, and travel costs. Socio-economic characteristics such as gender, age, education, and income were also considered in the utility function for route choices. These characteristics were found to have a significant influence on the route and charging preferences of EV drivers. For example, higher income and education levels were associated with a preference for routes with fast charging. As well, female drivers were more likely to select routes with fast charging due to greater sensitivity to battery power levels, while older drivers exhibited a preference for slow charging, possibly because of concerns about battery health.

Successful cost minimization and efficient deployment of EV charging infrastructure should consider factors such as the geographical distance each vehicle needs to travel and its point of origin, both of which play a crucial role in the determination of the optimal placement and capacity of charging and powering infrastructure. Recent research suggests that analysis of the statistical patterns of vehicle journeys can provide valuable insights with respect to where and how to deploy infrastructure effectively. For example, the authors of [60] highlighted the importance of the geographic clustering of EVs and its impact on charging infrastructure planning in regard to reducing costs and improving operational efficiency. While current efforts devoted to the planning of EV charging infrastructure might not fully address these spatial considerations, acknowledging their significance and integrating them into future planning strategies can lead to more sustainable and cost-effective solutions for supporting widespread EV adoption while minimizing accompanying grid stress.

6. Conclusions

This review has examined research directed at addressing the challenging effects associated with the simultaneous integration of DG and EVCSs into an ADN. Alleviating the negative impact through a determination of the optimal size and location for DG units and EVCSs is an existing problem of considerable interest to researchers, distribution network system operators, and relevant parties. Driven by the crucial imperative of increasing sustainability, persistent advancements have been achieved due to the efforts of scientists and researchers who have attempted to update the latest developments in this field by employing new technologies and introducing new research findings. Optimal decisions regarding DG units and EVCSs must be viewed as multifaceted, extending beyond solely technical perspectives. Any new optimization process should therefore be based not only on technical matters related to the network or other grid parameters, but also on consideration of a broader range of technical, economic, environmental, and social objectives. Any actual planning must be multi-objective in nature and must be compatible with the impact of modern variables across future requirements. MCDA methods offer promising possibilities and can ease the decision-making process. They provide decision makers with a tool for weighting every criterion based on their specific preferences, thus expediting the accommodation of potential exponentially elevated EVCS demand levels. An effective optimization methodology for the comprehensive determination of the optimal placement and size of DG units and EVCSs can help avoid or defer future expensive modifications or relocation costs. Finding efficient and practical solutions will yield substantial benefits that will pave the way for efficient and renewable powering of the anticipated expanding EV presence, reductions in fossil fuel emissions, and enhanced sustainability in the transport sector.