Progress in the Development and Implementation of Residential EV Charging Stations Based on Renewable Energy Sources

Abstract

Charging electric vehicles (EVs) is of great concern both for future vehicle owners and grid operators, with charging at home being the preferred solution by 90% of owners. In addition, the supplied electricity needs to be clean in order to reduce emissions. This paper presents solutions for charging EVs at home using renewable electricity that fulfils such needs. It discusses: (1) the current landscape; (2) the latest hardware developments in the fields of renewable sources and storage; (3) software optimization for home energy management; (4) residential charging station standards and incentives offered by governments; (5) the evolution towards designing smart homes with low energy consumption from the grid; (6) case studies of particular interest.

1. Introduction

1.1. Current Needs

Transportation is the second source of GHG emissions worldwide, and it could grow to 65% by 2050 [1] without counter measures to limit it. A number of simulation scenarios indicate that with efficient counter measures, transport sector emissions could be reduced by 68% [1]. Surveys show that citizens are concerned about this, with 97% of Chinese, 70% of Germans, and 52% of US inhabitants intending to change their travel habits [2].

Meanwhile, as the price of Li-ion batteries has decreased, the price of electric vehicles (EVs) has decreased as well. As a result, the increase in EV manufacturing is spectacular, Figure 1, as it has been stimulated, in addition, by government incentives. Further useful evolutions are foreseen and considered important in the years to come.

According to the EV30@30 scenario, the EV share is expected to be 70% of all vehicle sales by 2030 (where 28% are two/three-wheelers) in China. Almost half of all vehicles sold in 2030 in Europe will be EVs, 37% in Japan, over 30% in Canada and the United States, 29% in India, and 22% on average in other countries [3].

Infrastructure for EV energy supply is going to be the next bottleneck [2]. The electric energy required for charging EVs was computed in [3] to reach almost 640 terawatt-hours (TWh) by 2030 in the New Policies Scenario, Figure 2, and 110 TWh in the EV30@30 scenario.

Such an increase in electricity usage will require governments and energy suppliers to invest in infrastructure.

One important component of this infrastructure is, in the view of potential customers, the charging infrastructure. This aspect is considered important by the political actors. As mentioned in [4], “The US Bipartisan Infrastructure Law provides nearly $5 billion to support establishing a 500,000-station EV charging infrastructure” and “The European Union’s Green Deal, announced in December 2019, targets one million charging points by 2025 to reduce greenhouse gas emissions from transport by 90% compared to 1990 levels”.

Private companies are also trying to take advantage of this business opportunity. As mentioned in [5,6], Shell intends to operate over 500,000 EV charging stations by 2025 and 2,500,000 by 2030.

Figure 2. The electric energy required for charging EVs in the New Policies Scenario [7]. STEPS Stated Policies Scenario; APS Announced Pledges Scenario.

Figure 2. The electric energy required for charging EVs in the New Policies Scenario [7]. STEPS Stated Policies Scenario; APS Announced Pledges Scenario.

Figure 3 shows how important charging station availability is in the view of EV customers from some large (from the perspective of an EV volume criteria) countries.

With regard to the placement of charging stations, studies show that charging at home is mostly preferred. According to [7], 90% of chargers will be at home, accounting for about 65% of the energy demand required. Related figures are offered in other studies, such as [8].

1.2. Technological Developments

Further studies [1] showcase decreases of (85%) in prices for solar energy, (55%) for wind energy, and (85%) for lithium-ion batteries between 2010 and 2019. These decreasing trends are continuous.

Due to these results, applications using the above elements have increased, for example more than ten times for solar and more than 100 times for electric vehicles, when referring to the domains presented in this paper. A case in point is made in the title in reference [9]: “Residential Energy Storage Deployments Skyrocket”. This increase will be followed by “new ways of enabling electric charging systems to fit into electricity grids creating synergistic benefits to grids, improving the value of electric transit, and reducing range anxiety for EV users”, according to [3].

Considering the above developments and the fact that charging at home is mostly preferred, it is easy to understand why the result in [10] is that in 2030 the relative majority of chargers are expected to be within households in the US, with 44% of electricity being consumed by them. To ease the stress on the grid, measures, such as incentives to install charging stations (CSs) and for using renewable energy on site and creating residential microgrids (MGs) with EV charging capabilities, are necessary.

The development of residential charging stations, based on MGs with self-renewable energy sources, will also be stimulated by the significant jump in electricity prices that occurred in 2022.

1.3. Residential MGs with CSs

It is important, of course, to give a definition of residential EV charging stations based on renewable energy sources and their role. In fact, this represents a residential smart MG (SMG) supplied by renewable sources, with EV charging station capabilities and with or without public access.

The description of the residential smart microgrids with EV charging capabilities can be found in [10], with an illustration of the main components made in Figure 4.

The renewable energy components, specifically, PV modules and small wind turbines, together with energy storage based on stationary Li-ion batteries and power electronics components, are sourced directly from the market. Such a solution increases the reliability of the MG and reduces the implementation time of the solution.

The SMG has communication capabilities and is able to communicate with the SMG components and with a smart city digital network. The MGG (MG general controller) with energy management system (EMS) or home energy management system (HEMS) software is able to optimise the energy usage according to the weather forecast, the electricity price of the grid, and the EV charging requirements. For the EV charging station with public access, a charging schedule and financial management can be implemented on-line.

This type of AC MG with EV charging is the solution implemented in practice nowadays. It uses PV modules and small wind turbines, energy storage based on stationary Li-ion batteries, mainly after 2017, and power electronics components from the PV market. These components are reliable and proved by experience solutions. The AC MG with EV charging solution is presented and can be found in many papers referenced in this review, [11].

There are several papers presenting and discussing DC MG solutions [12,13,14,15,16,17], but the DC MG remains a niche as the market does not offer equipment under reasonable conditions (price, availability). The situation is rather counterintuitive, as PVs and EVs and storage batteries are DC components, and the small wind turbine’s output is in DC, too. However, until the main grid remains AC, Edison loses again.

The rest of the paper is organised as follows: Section 2 deals with the evolution of the hardware components and the contributions to the residential MGs with EV charging capabilities; Section 3 discusses the software evolution of AC residential MGs with EV charging stations; Section 4 investigates the evolutions in charging standards and the regulatory environment; Section 5 presents design related papers of residential MGs with EV charging capabilities; and Section 6 reveals several case studies of interest. Some concluding remarks are made in the last section.

2. Hardware Developments

Seven years have passed since Bhatti A.R. and his team wrote and published their reviews [18,19]. This is a long time for science and technology given the pace of research in the field. Many developments have taken place in the area covered by those papers. What changed is the topic of the present paper.

This work aims to cover the area of residential MGs supplied by renewable sources with EV charging station capabilities exclusively, considering that such solutions will cover the main area in the field of CSs, at least for the next 7–10 years.

2.1. PVs

Developments have led to the use of two classes of photovoltaic converters of interest for the domain discussed: the classic PVs and BIPVs, meaning building-integrated PVs.

A useful example of the changes taking place with the advent of the new generations can be found in

Table 1. Types and efficiencies of PV cells, data used from [20].

Type of Cell	Module Efficiency (%)	Application Field
Monocrystalline Si	14–20	Conventional/roof
Heterojunction Monocrystalline Si	26
Si (amorphous cell)	10.2
Polycrystalline Si	12–16	Conventional/roof
III-V cells
GaAs (thin film cell)	29.1 ± 0.6	BIPV
GaAs (multicrystalline)	18.4
InP (crystalline cell)	24.2
Thin film		BIPV
CIGSSe (submodule)	19.8
CdTe (cell)	21.0
Others
Dye (submodule)	8.8
Perovskite (minimodule)	21.4	Conventional/roof
Organic (cell)	15.2	BIPV

.

The effort to increase the global efficiency of the module is high. Two important directions in this effort, already used on the market product, are to be mentioned: (a) new PV cell converting technics such as the heterojunction technology, HJT, with a thin crystalline silicon wafer covered by ultra-thin amorphous silicon layers, like a sandwich; (b) constructive alterations such as double-glass design (bifaciality). The use of both measures mentioned yields modules with 21.68% efficiency [21]. Moreover, HJT produces 20% more energy during the life of the system due to the amorphous layers able to convert diffuse solar energy. The solution increases the resilience of the module, too, protecting the electrodes deposited on the crystalline layer. The manufacturer guarantees 91.25% of the initial yield after 30 years.

2.1.1. Classic PVs

PVs come in the form of PV modules of different materials and construction. Efforts were made to increase the conversion efficiency and the resilience parameters of the modules. These increases were accompanied by measures to reduce costs by improving the manufacturing procedures as well as replacing some costly materials. As a result, PV prices decreased continuously. The price evolution in the last 12 years is reflected in Figure 5 [22]. The prices are in a range depending on different manufacturers and applications at utility scale. It can be seen that the prices decreased until 2020, when the crises of supply driven by COVID-19 created a small increase.

For residential applications, the prices of PV systems are higher than the utility scale ones. The reasons are: relatively more cumbersome mounting work, differences according to residence particularities, transport, a variety of mounting structures, and the PV modules used. The PV modules used for houses are generally smaller than those used for utilities due to the necessity of manoeuvring in limited spaces. Practitioners recommend not exceeding 160 × 100 cm in PV module size to avoid damages [23].

The differences in prices and their evolution in the last years can be observed in Figure 6.

In 2021, prices ranged from about 2 USD/W to 6 USD/W for off-grid systems [22]. Prices in the case of some types of applications can be higher.

It must be mentioned that the diagrams in Figure 5 and Figure 6 also consider BIPVs. The prices for BIPVs are generally higher than the prices for normal PVs.

PV technology, and RESs in general, is a solution for the electricity required by the spread of EVs (encouraged by governments and the self-consciousness of many people about the dire consequences of GGC). The fact that the energy crisis has raised the price of electricity offered by the utilities has resulted in the fact that grid parity has been attained (grid parity refers to the moment when PV can produce electricity at a price below the price of electricity consumed from the grid) in many countries.

The above facts are encouraging more and more people to build their residential energy system and add a residential charging station to it.

Because such a system has many problems to be solved, in the future perspective of the smart home part of a smart city, a lot of studies in the field were developed followed by many papers reflecting the results. In what follows, the results of some papers of interest are discussed.

Paper [20] presents the latest solutions to improve the efficiency of PV panels by recovering or eliminating the residual thermal energy issued by the PV conversion in the PV modules. There are two beneficial consequences. One is to keep the PV module efficiency close to that mentioned at STC, by cooling it. The second is the possibility to use the thermal energy in the building or to convert it into electricity by the Seebeck effect. However, all these come with a higher price. Another advantage is that higher efficiency means more energy picked from the same area, a matter which counts when considering the restricted build area offered in many situations. The topic is of interest, as the more than 150 reviewed papers by [20] show.

In their published review papers [18,19,24], Bhatti A.R. and his team described various aspects related to EV charging using solar photovoltaic energy. They covered almost all topics such as PV systems, EV chargers electronics, charging modes, PV–grid charging systems, PV-standalone charging, storage, modelling, optimization, and control, prospects of V2G and V2V. People interested in this can find useful and interesting information.

As seven years have passed, many of the aspects are already consecrated, but many have developed much like PVs, charger electronics, storage, modelling, optimization, and control. Economic software for PV charging, a much developed topic over the years, is not properly considered. Residential charging stations are only mentioned in four lines [18], and the system characterization is vague, if not inadequate. Seven years is a long interval for science and technology today.

Taking into account the evolutions of PV system components, many studies are concentrated on the economic evaluation of the residential electricity supplying the PV-based system. Some of them are specific for the site considered due to the connection between the place considered and the solar energy available. In [25], it is shown that, due to the local variation, the self-consumption ratio of the grid-connected residential PV system varies significantly across months. The battery size increases the self-consumption ratio and the load charging affects the self-consumption. The economic feasibility of the battery system highly depends on subsidies and policies. The conclusion of the authors of [25] is that “Increasing electricity pricing, decreasing PV feed-in tariff and falling cost in batteries can provide the home PV-battery system more attractiveness”. Paper [26] evaluates the profitability of residential PV systems without subsidies in Italy. It establishes at which value of self-consumption a residential PV battery system becomes economically viable. Two PV sizes were considered, 3 kW and 6 kW, and several steps of battery capacity. As a result, 3456 scenarios were obtained. Of them, few were positive in 2016: six scenarios (0.3%) for the 3 kW PV-storage system and 25 scenarios (1.4%) for the 6 kW PV-storage system without subsidies. In paper [27], a review of PV residential US systems’ status in 2022 is performed. Among the conclusions of interest are: (a) the fact that, due to energy costs and the incentives for residential PV systems, the profitability varies greatly from state to state; (b) the median cost for PV systems (i.e., PVs, inverter, and controls) has decreased from about 12.00 USD/Watt in 2000 to 2.75 USD/Watt in 2020, and the median size of the PV arrays has increased from 2.5 kW in 2000 to 6.5 kW in 2019; (c) bigger PV systems are installed where more electricity is used monthly. The last conclusion validates the fact that the profitability threshold for PV systems’ electricity was passed. In [28], a review of the recent progress in residential NZEBs (net zero energy buildings) is made. Renewable energy is considered the main factor in this progress. All the conversion types are involved for solar energy, as it can be seen in Figure 7.

The best positions to place the PV modules on the roofs of an already built environment in cities can be found using URSUS-PV, a free web software tool [29]. It implements an image processing model based on LiDAR images. The software uses data on latitude and meteorological data and extracts the characteristics of the roofs (the mean inclination, orientation, sizes). Involving photovoltaic energy calculation models, it offers short-term (hourly for one-day-ahead) and long-term (daily average for the year) prediction and estimation of the available solar energy.

2.1.2. BIPVs

BIPVs is a field of application for PV modules, which has developed as a specialised branch nowadays. This development is justified by the existence of huge available surfaces in cities, on the one hand, and the advantage of bringing the electricity produced to nearby customers with reduced costs, on the other. The evolution of the solutions is expressed by two terms: building-integrated photovoltaics (BIPVs), which means to mount PV modules on existing buildings, and building applied photovoltaics (BAPVs), which means applying PV structures according to the design and construction of the building. Here we will use only BIPVs as a generic term.

The places where BIPVs can be installed are illustrated in Figure 7. In addition to those, other types of BIPVs must be mentioned: windows, overhead glazing, and PV walkable floor [30]. Paper [30] is a comprehensive review. It contains the description of BIPV systems. BIPV and BIPV/T systems (see Figure 7 for principia) found in the literature are reviewed from the energetic, economic, and environmental aspects, and types and performance indicators in building applications are described. A lot of studies are reviewed: simulation and numerical studies, cell module design studies, grid integration experimental studies, and policy and strategies studies.

As BIPV/T systems are costlier, it is important to evaluate the potential and the economic results. By modelling at the global level, the authors of paper [31] found that the efficiency of the PV-T collectors vary from 21.6% to 63.3% by region, with energy produced being around 29.5 PWh (62%) electricity and 18.1 PWh (38%) thermal energy. Single and multi-apartment buildings were estimated with the highest rooftop BIPV/T potential. The authors are working now on developing economic aspects, as the domain has not been incentivized until now. The necessity of such an evaluation is also mentioned in [22], in Section 6. Even if some application domains are not incentivized, regulations are trying to impel the BIPV progress. Paper [32] describes and comments on EU policy in this respect.

The economic significance of the sector is estimated by the specialists. In [33], it is estimated that “the residential DER market will grow at a compound annual growth rate (CAGR) of 6.1% from $40.5 billion in 2022 to $68.8 billion by 2031. The forecast growth for solar panels is the fastest growing technology, showing a 7.0% CAGR”.

Such an economic opportunity has attracted many new achievements in the manufacturing field. New solutions and products are reported frequently: ref. [34] presents a series of coloured BIPVs with a high 21.5% efficiency, see Figure 8; ref. [35] discusses “a highly automated factory for the production of façade and roof panels”; ref. [36] reviews photovoltaic-thermal solar tiles (BIPV/T); ref. [37] reports on “solar tile with 19.3% efficiency”; and ref. [38] on windows with 15.5% efficiency. Many are experimental studies such as [39], dealing with BIPV system performance and efficiency, and ref. [40], wherein an off-grid electrical vehicle charging station using light concentrated BIPV/T is studied. Use cases are reported: in [41] a historical building enhanced with BIPVs is presented (see Figure 9); paper [42] shows innovative design solutions for a prefabricated BIPV wall in megacities where land resources are scarce and buildings are high, Singapore in this case; and in [43], a project for the multifamily housing complex is announced.

2.2. Storage

Nowadays, storage of electricity means batteries. As a result of a synthesis made in 2020 in [1], lithium-ion batteries (LIBs) available in 2020 are superior to other batteries in terms of battery “life, energy density, specific energy, and cost” [1].

Our review will try to present the evolution in the last years, especially oriented toward the economic aspects, but considering other batteries too, namely flow ones.

As these days the prices of Li-ion batteries for residential applications are, for reliable suppliers, around 100–200 USD/kWh for BYD [44] (depending on the size and type of residential unit) and 117.4 USD/kWh at [45] (a 13.5kWh residential unit) for USA and 200 USD/kWh on the Alibaba site for Tesla Powerwall 2, it seems that a sensible threshold was surpassed.

Economic analysts in source [9] consider that residential energy storage associated with renewable energy generation will become a standard for residential refurbishment and new construction, defining the grid’s future.

Two important parameters for a renewable energised residential system are improved by storage: reliability of supply and self-consumption (the amount of produced energy used directly). While the reliability of supply is difficult to measure, self-consumption can be measured easily.

A good starting point in assessing the technical and economic progress of battery storage for residential systems with RESs is paper [46], which gives an interesting review of the 2013 state of the art. The block diagram of the system is similar to those of today’s residential systems. The differences are: (a) from the 25 systems considered with storage batteries, only one involved Li-ion technology; (b) in the load structure, no EVs are mentioned. A techno–economic model was created using technological parameters and economic parameters (eight electricity price scenarios) as inputs. Among the results, the profitability index of the storage investment evolution forecast (2013–2022) and the optimal storage size evolution forecast (2013–2022) are of interest. More than 1400 combinations of PV systems and storage sizes were used to obtain the profitable ones. It is concluded that the storage increases the self-consumption and, of course, premiums for self-consumption will raise the profitability of storage. An updated calculation with today’s parameters would be of interest.

It is important to evaluate self-consumption considering the prices in establishing a good system choice [47]. As researchers at HTW Berlin have established, a good AC-coupled storage system (bidirectional inverter and Li-ion battery) obtains a self-consumption of 80% to 85% for an AC system in a residential building charging an EV and other loads. They considered the best obtained self-consumption results for three classes of bidirectional inverter and Li-ion battery, one below 5 kWh (price 1400 EURO/kWh), the second up to 10 kWh (for a price of 1000 EURO/kWh), and the third over 10 kWh with a median price of 870 EURO/kWh in the middle of 2022.

A comparative study [48], published in 2022, confirms the statement from [1], made in 2020, showing a LCOS (levelized cost of storage) of 25.85 cents/kWh for Li-ion, 28.18 cents/kWh for reversible solid oxide cell, and 41.73 cents/kWh for proton exchange membrane reversible fuel cell.

Flow batteries are considered to be cost-competitive for GWh scale energy storage applications. Paper [49] studied the flow battery in the case of a residential load in a solar PV-flow battery system. The results show that a round-trip efficiency of 80% for the flow battery can be obtained for the natural fluctuations that arise from the PV system and from the load.

In [50], many storage technologies are studied using 114 papers. The results of the synthesis show that Li-ion is the most efficient with 95% from all storage technologies and the most reliable with the longest lifetime and number of charging–discharging cycles (for LFP) within the battery category.

In [51], a 2014 German research model revealed the results for a “typical German home owner 5000 kWh annual electricity demand, 7 kW_p roof-top PVs, residential battery systems (RBS) with 4 kWh effective capacity, current feed-in tariffs and expected price trajectory for electricity supply” “while increasing the residential self-supply with electricity from 42% (stand-alone PV) to 64% (PV plus RBS)”. In an interesting associated survey, customers expressed, to a great extent, strong interest in RBSs. The reasons are: “saving money through higher PV self-supply” (80% of all customers), “avoiding future electricity price rises” (70%), “independent electricity supply for my home (self-sufficiency)” (69%), “and environmental concerns (66%)”. All the reasons remain extremely current. A new survey would probably increase all the above percentages.

In [52], a simulation model for typical single-family German houses in Germany with a rooftop PV-battery system was used. The average household of four-to-six people and the most common PV-system size of 5 kWp were considered in Germany in 2015. The novelty was the use of the first two models of Tesla’s Powerwall as residential battery systems, BESSs. Even if the prices for the Powerwall were four times smaller than those for usual Li-ion on the market, the main conclusions were: (a) The economic value of the BESSs is disputable: “a large increase of self-consumption, by usage of storage, results in higher savings”; (b) “The question of the optimal sizing of PV-systems and BESS remains open”.

In paper [53], published in 2018, the modelling study results of the profitability of PV-battery systems for Italy are presented. It established that the results depend heavily on the self-consumption rate, which must be equal to or greater than 39%, 43%, 48%, and 52% for the ranges of 0.5, 1.0, 1.5, and 2.0 kWh BESS per installed kW of PVs for a positive NPV (net present value). It must be stressed that the considered BESS are lead–acid batteries. As a subsidy, the fiscal detraction of 50% was considered; the Italian Council of Ministers approved the ESS.

Paper [54] is a case study presenting a methodology for establishing self-sufficiency with solar PVs and physical batteries for countries situated above 60 degrees latitude, with cold and dark winters, Finland in this case. Two methods for increasing self-consumption in the case of a prosumer residence are studied: the use of physical energy storages or the grid only. For the two houses under study, a physical battery energy storage increased self-consumption by 20 to 30%. The authors found that obtaining a positive economic result will require battery prices to be reduced by half compared to 2020, or the electricity prices to increase 2–3 times.

Paper [55] is another case study. For a generic load, energy circulation, consumption, and prices were computed in the case of a grid-connected PV source, a wind turbine (vertical axis), and a battery storage system on a daily cycle, during the four seasons. The system reduces electricity costs by 2.9 times in winter and completely eliminates costs in summer in two different locations.

Paper [56] mentions that simulations were made in the US Department of Energy’s Lawrence Berkeley National Laboratory on residential systems with a 7 kW PV and 10 kWh of storage operated for backup power purposes. Such a system could sustain 60% to 80% self-consumption in the USA over a year, depending on the region.

Even if the economics are still giving weak results, economic analysts such as those in sources [8,9] and global studies analysts, such as those who published [1,2], are more optimistic. Their statements and the necessity resulting from environmental concerns probably convinced the manufacturers that there is a promising future in the storage domain. As a consequence, one can find bigger storage systems than before with Li-ion batteries of 13.5 kWh, scalable [57], or 15 kWh [58], obviously designed for charging EVs too, or new smaller systems such as in [59], which can be used to charge small micro-mobility dedicated EVs but scalable up to 358.8 kWh of storage capacity. Additionally, manufacturers of power electronics converters are following the same tendency, as shown in the next section.

An interesting evolution can lead to a cheaper system and a higher self-consumption rate. The key words are urban energy communities. Urban energy communities are suggested in [1] by citing papers [60,61] and means integration at the local community level of the renewable sources and the storage systems. An obvious application field is a multi-apartment high building, especially in megacities such as Singapore. In the European Union, the beginning, in this respect, can be found at [62], dealing with energy communities initiatives. The program mentions that “By supporting citizen participation, energy communities can help providing flexibility to the electricity system through demand-response and storage” and “contribute to increasing public acceptance of renewable energy”. But integrated local community renewable sources and storage systems are outside the scope of this review because they require a different local MG design, implying different technical solutions for the components.

A deep insight in battery charging methods and their characteristic parameters can be found in paper [63]. Different types of storage technologies are compared. One important conclusion is made: “Li-ion batteries are likely to dominate … over the next decade.” The impact of the charging level on the life cycle of Li-ion batteries is compared. Results are showing that charging at low rates, around 1C, ensures a longer life cycle for the LiFePO₄ type batteries. LiFePO₄ are considered the optimum type for stationary storage batteries.

2.3. Power Electronics Converters

In this domain, the novelty is the introduction of wide-bandgap transistors and diodes (especially SiC and GaN) in power electronics converters. This has raised the efficiency of the converters to around 99% (fabulous) levels (depending on the power range), at the same time reducing the size by half. Some previous renewable systems benefited most, such as AC PV modules which use module-level power electronics (MLPE). AC PV modules grew by 33% between 2019 and 2021. One-third of the new residential solar installations were using them in this interval due to improved safety, the possibility to install them individually in the most suitable places, the energy yield, the diagnosis of faults at a module level, and digitalization which includes the increased use of AI, software, and communication to increase the efficiency of installation, operation, and maintenance (O&M) [64].

There are a lot of achievements announced for other systems, too. In [65], the authors present a DC-coupled hybrid inverter for rooftops available in four power classes: 5 kW, 6 kW, 8 kW, and 10 kW. It is able to convert up to a 1000 V supply from PV arrays with power ranging from 7.5 kW to 15 kW. The new hybrid inverter reaches a maximum efficiency of 98.2% and a maximum European efficiency of 97.5%, and it is compatible with direct coupled high-voltage Li-ion batteries. The paper [66] shows a single-phase hybrid inverter series from 3 kW to 9 kW, an efficiency rating of 97.6%, and a European efficiency of 97.0%. A big charge/discharge current is used for the battery, so it works in a shorter time at a higher power. The study [67] deals with single-phase inverters for residential PVs of 3.6 kW, 4.2 kW, 5.0 kW, and 6.0 kW. The series has an excellent efficiency for this power level, of 98.1%, and a European efficiency of 97.5%.

In [68], the authors present an interesting outdoor inverter solution for MGs that can integrate multiple energy sources, such as wind, hydro and solar, battery storage, and biomass diesel generators. The MG can be connected to other MGs through AC coupling and other types of external storage such as lead–acid, gel, lithium, and flow battery technologies. In fact, it is very suitable to be used for energy communities, too.

The paper [69] reveals two new wall-mountable single-phase inverters for residential use. They are grid-interactive inverters with rated power outputs of 3 kW/97.7% efficiency and 5 kW/98% efficiency.

In [70], a new residential hybrid storage system is reported. The inverter can operate with a LiFePO4 battery ranging from 9.9 to 19.9 kWh. The used LiFePO4 battery is reported to have a round-trip efficiency of 93.93%. The figures confirm that the LiFePO4 battery is the best stationary Li-ion storage solution.

A comprehensive review on power electronics charger topologies can be found in [71], where the topologies are illustrated, and different solutions are discussed. A detailed classification of the EV charging technologies is made, and the safety standards in EV chargers are summarized. Furthermore, a classification of the charging methods is presented, and a qualitative comparison between the methods is performed. Discussing the future of EV charging systems, the authors are confident that wide-bandgap (WBG) devices, such as SiC and GaN, will be used in EV chargers due to their technical performances.

2.4. Small Wind Turbines

The development of small wind turbines is rather slow in the residential field, especially due to higher investment costs and higher maintenance requirements in comparison with PV generators. Due to these reasons, the market is weak. Analyses made by reputable institutions show that the new installed capacity figures remain rather constant [72], or they have been steadily declining in the USA since 2012, according to [73]. However, small wind turbines, SWTs, are important for the field of hybrid RESs-based residential solutions, especially in zones above 50 degrees latitude and below 30 degrees latitude and in temperate climate areas where wind is abundant. Small wind turbine energy compensates for weak solar during the winter season and so decreases the request for larger PV areas and larger storage systems. This is a reason for a more optimistic forecast for SWTs made in 2021 according to [73].

Small wind turbines offer some interesting solutions for build environments in cities, where the available area for PVs is restricted, as it can be remarked in what follows.

Paper [74] presents a review of SWT technology. After a categorization of SWTs, a description of the construction, design, control, and manufacturing of small HAWTs (horizontal axis wind turbines) follows. Then VAWTs (vertical axis wind turbines) are presented, where a detailed description of the different types (Darrieus and Savonius) is given, followed by descriptions of the construction and design of each type and of the performances based on experimental and numerical studies conducted by different authors. Details about positioning on buildings are presented and commented on. Very important in the build environment, acoustic aspects are discussed using the results from 10 research papers.

Wind turbines, even small ones, have cumbersome sizes for buildings. VAWTs are easier to implement from this point of view. Their O&M activities are easier to operate. Review paper [75] presents the case of vertical axis wind turbines, VAWTs, focusing on the integration with urban infrastructure and some of the major developments of VAWTs, for urban applications. The qualities of VAWTs are presented: relatively low environmental impact and adaptable characteristics for unsteady wind in cities. VAWTs produce electricity from any direction with low cut-in wind speed. Savonius and Darrieus VAWTs and the combination of the two are described, and their performances are discussed. At the end, it was concluded that further research is necessary to make VAWTs a viable technology for the urban environment.

The next logical step in our evaluation is paper [76]. It reviews the wind and photovoltaic energy hybrid system. An important statement is made in the introduction: ”For any hybrid system sizing of various systems is very much necessary to ensure the reliability of the supply while keeping the cost of the system low”. From this statement derives a review of 27 optimal sizing studies and the methods used in the optimization process. The performance assessment tools are presented and discussed. Software tools for hybrid systems are introduced, and a summary of a few software tools for HRES, giving their key features, is provided.

Previously mentioned paper [28] has a subsection dedicated to SWTs reviews, but it does not bring new information, apart from some case studies of interest for their experimental results.

Following the old path does not bring much progress, as research [77] reports. The researchers used several types of blades for HAWTs. At the end of arduous work, a prototype called ATT micro wind turbine showed the highest performance.

However, the expressed idea, ”The concept of replacing commercial wind turbines available in the market with an equivalent array of ATT micro wind turbines in terms of swept area” backed with some comparisons made by calculations is confirmed by other researchers. The idea is that an area of small VAWTs or HAVTs produces more energy in low wind conditions than a bigger WT. This fact is due to the lower inertia of the WT, enabling the area of the smaller WTs to convert more energy by exploiting the low winds better.

More technical details about the MG can be found in [78].

For many researchers, in analysing the present WT technical stage, it was obvious that the limits were attained.

New approaches must be found. One new approach is presented in [79]. In this new patented technology, which was validated through joint research with Sandia National Laboratories and Texas Tech University, Aeromine has created a bladeless wind energy system. It works based on the theories of Daniel Bernoulli, made in the 18th century. So, from now on, the term wind turbine is not adequate. Without rotating rotor blades and other moving parts, this new wind energy converter generates more energy in less space. It is motionless and virtually silent. In the announcement made in [79], the company says, “it can generate up to 50% more electricity than a comparable solar power array, yet costs no more than solar and uses only 10% of the available roof space”. From this moment, it remains to be seen to what extent experimental studies confirm the above statements. An illustration of the new wind converter can be seen in Figure 10.

3. Dedicated Software

Aside from the technological evolutions, many studies are dedicated to modelling and simulations of different aspects related to AC residential MGs with EV charging stations. The proposed software can be divided in two main categories: optimization of the solutions and economics and management. There are cases when the categories are merged, as the boundaries are not very restrictive.

3.1. Optimization Software

3.1.1. Infrastructure Optimization

A valuable paper for this topic is review paper [80], conducted in 2017. Important and useful ideas resulting from the review are presented below.

(a)

an EV as a load almost doubles the consumption of electricity of a residence. Self-RES-based electricity is then useful.

(b)

Adding a BESS makes sense by providing additional flexibility for the EV charging process.

(c)

A presented PV + BESS + EV system modelling algorithm shows a maximisation of self-consumption and decreased overall grid electricity exchange by a factor of two.

(d)

An optimal charging station with a BESS achieves a two-fold better ROI (return of investment) than a PV station only. This result was obtained considering 2016 prices.

Paper [81] presents the optimization research software for photovoltaic power system status in 2018. The synthesis of the simulation tools based on the field of application can be seen in Figure 11. Then, the optimization criteria are discussed. Here, the authors introduce the conclusion that the ideal solution for any PV (and by extension RES+ BESS) system is obtained by the best compromise between power reliability and system cost. For the reliability analysis, the relationships of loss of load probability (LOLP) and loss of power supply probability (LPSP) are introduced. The formulae of net present cost (NPC), levelized cost of energy (LCOE), and life cycle cost (LCC) are introduced for the cost issues. System optimization techniques are presented, and the following methods are summarised: numerical methods and computer-aided methods such as genetic algorithm (GA), particle swarm optimization (PSO), and evolutionary programming (EP). The papers in which these techniques are used are presented in tabular format.

Hereinafter, the results of the optimisation techniques with interesting outcomes are shown. In [82], an optimization model studying infrastructure-dependent technology adoption is presented. A result is how a policymaker should choose between allocating subsidies for charging infrastructure investment or consumer BEV purchases. The results show that a reallocation of funds toward charging infrastructure subsidies is more effective for stimulating BEV diffusion.

Paper [83] shows how different software methods are used to optimally find the right place for charging stations based on optimally assessed RESs in a big city. Figure 12 illustrates the methodology. TRNSYS genetic algorithm and integer partition algorithm software are used. The coverage ratio for PV usable roof area was 43.2%, in comparison with the results from other methods, i.e., 37.2–40.7%, and the life cycle cost (LCC) obtained was 584 kUSD in comparison with 779–1101 kUSD for the renewable powered charging stations.

A standalone hybrid WT/PV with a BESS system is modelled and simulated using TRNSYS software in [84]. PV source and WT powers are simulated using the natural resources local data, the manufacturer data, and empirical formulae. The natural resources local data are implemented for 26 different regions in China. The installed capacity ratio between the PVs and the WTs is taken as the index of the distribution strategy to optimise the hybrid system. The optimal capacity ratio of the PVs and the WTs is found for each of the 26 regions. Figure 13 illustrates the complementarity between PV and WT resources.

Paper [85] uses a bio-based optimization algorithm, salp swarm algorithm (SSA), belonging to the family of genetic algorithm (GA) and particle swarm optimization (PSO). The SSA is used to obtain the best combination of the control parameters of the grid-tie inverter of a PV source with an EV charging station through the grid-connected AC-bus. During sunny and cloudy days, the PV source voltage and current vary. The proposed controller is compared with an analytical-based controller by simulations. The analytical controller shows a total harmonic distortion (THD) for the voltage of 0.82% and for the current of 4.5%, while the SSA-based controller reduces the THD for the voltage to 0.49% and of current to 1.63%.

3.1.2. Charging Optimization

Even if paper [85] does not include software tools, it is a useful introduction into the field of charging technologies and charging strategies. The paper is an updated overview in the field of charging different types of EVs, using papers published between 2019 and 2022.

In paper [86], the optimization of charging techniques and algorithms for smart charging are reviewed. A synthesis of them is offered in Figure 14.

In the domain of residential charging optimization, 14 papers are on residential buildings charging, but another 14 are in non-residential buildings following the remark of complementarity between them, as EV charging with on-site PV generation is limited by the low fraction of EVs which is at “residential buildings during the day when the solar power production peaks” (so many of them are going to be charged in non-residential buildings). The potential of residential buildings to fill the night load valley is closer to 100% with EV smart charging.

Paper [52] reviews the optimization methods used in studying the charging/discharging strategies. It presents the optimization problem with different aspects such as the most common optimization algorithms, the most used optimised elements, and their equations and optimization constraints. The residential case is not particularly presented. Paper [87] uses a multi-criteria optimization methodology for scheduling of hybrid PV/EV/BES system charging/discharging scheduling for the next 24 h. Paper [88] is a case study of an MG with photovoltaic power and vehicle-to-grid technology establishing the smart charging of electric vehicles. Linear programming was used. Paper [89] deals with non-coordinated EV fleet smart charging, aiming to avoid grid overcharging and its consequences. The genetic algorithm is involved for optimization, as it can be seen in Figure 15.

Paper [90] deals with the residential homes EV charging process. Different charging scenarios and methods are computed using linear programming. The results show the cost reductions of the charging process from 46.9% to 75.2%. In order to combine the renewable generation uncertainty and the pricing stimulated EV demand response, a bi-level programming-based MG scheduling is developed in paper [91]. The algorithm is called JAYA-IPM, following the theory of the powerful algorithm JAYA for dealing with complex optimization issues and the optimization method IPM (interior point method) for linear programming methods. The simulation results show that the algorithm is able to guide EV drivers for a suitable demand response. Paper [92] describes how to predict the EV charging load in the urban residential case. A data-driven and parameterized model is proposed for the EV charging process. Paper [93] largely describes six optimal strategies for minimising the charging costs in the housing sector. Three are smart unidirectional (G/H2V) and three are smart bidirectional (V2G). Then, a new V2G algorithm named optimal logical control (V2G-OLC) is proposed based on a logical command series. The results show good efficiency in certain conditions.

3.1.3. Smart Grids Optimization

Smart grids include smart loads in their structure, in our case smart homes as fundamental bricks.

According to [94], a document find on the EU site, the definition of a smart home is: “A “smart house” is actually a dwelling where an organised home automation system connects all the electrical devices to manage lighting, heating, air conditioning, ventilation, security (burglar) alarm system, audio and video system, call devices, energy control equipment, presence, automation (door, windows, blinds, gates), technical alarms (for example in case of unwanted water spillage) etcetera.” Under “etcetera” we must include EVs, a RES system (sometimes with storage), and Wi-Fi communication means, including Internet. The home automation system must include a smart home energy management system, HEMS, with the job to optimise the house energy consumption. The optimization includes the adaptation of the consumption according to information/requirements from the SG controller.

Paper [95] studies a HEMS optimization method. The block diagram of the HEMS is presented in Figure 16. A multi-objective energy management model is built taking into account household electrical comfortable consumption in the variable economic cost conditions. The model involves an improved genetic algorithm able to obtain better electricity consumption optimization results. The method involves consumers who control the electricity comfort cost and the potential consumption bill.

Paper [96] studies a HEMS algorithm for household appliances having different demand response (DR) values, in dynamic pricing conditions. The house has renewable energy generation and storage systems and a PHEV as bidirectional load. The proposed HEMS algorithm uses linear programming (LP). As in a PhD thesis, the modelling issues are largely debated. The consumer comfort level model introduces the possibility for the consumer to adjust the comfort level for each controllable load. The results show that controlling and scheduling the appliances individually yields a significant reduction of 18.6% on the consumer’s electricity bill.

Paper [97] is a step toward energy communities, as it deals with a multi-apartment smart building, SB. A mixed binary linear programming (MBLP) method is developed to find the optimal size of battery energy storage system capacity for smart buildings, SB, which have photovoltaic (PV) panels and electrical vehicles (EVs). The MBLP model provides a plan for the SB EMS to control the power among the components of the SB and find the optimal power contract of the building. Simulations show a reduction of 34% on the electricity bill.

In paper [98], a community of smart houses, each having EVs, controllable appliances, energy storage, and distributed generation sources connected to the same distribution transformer, is analysed for minimising the community energy cost. The optimization algorithm uses mixed-integer linear programming (MILP). Different scenarios are studied. Paper [99] introduces the concept of: integrated community energy systems, ICESs, with smart houses as basic units. ICESs exchange the electricity with a grid. A mixed integer linear programming optimization model is used in this PhD study. An SG (more exactly an SMG, but that was the author’s choice) consisting of PV generation plants, ESSs, and 692 houses as loads is studied in [100]. A multi-objective optimization algorithm implying a genetic algorithm and a fuzzy logic controller is built. The algorithm is used for the optimal sizing of the combined PV plants and ESSs, resulting in gains of approximately 4% for the total cost of ownership and 17% reductions for the voltage deviations.

A conclusion of Section 3.1.1 is that the above research studies are useful with the condition that the residential dwellings are smart ones. This means that they are able to participate in co-ordinated actions in the grid and, depending on availability, to offer charging services for smart cities.

3.2. Economics and Management

After reviewing many papers on HEMSs, the authors of paper [101] conclude that “HEMSs enable consumers to make energy-efficient choices without compromising comfort, through optimal management of appliance usage and EV charging in Home Area Networks”. Modern HEMSs incorporate many intelligent devices controlled by microcontrollers connected to the home area network. The devices work together through distributed protocols, which increase the resilience of the HEMSs.

The paper above highlights the importance of energy management, controlled by software means.

Paper [102] develops a day-ahead energy management modelling in order to evaluate the operating costs in variable conditions (RESs and EV charging). The net present value (NPV) economical concept is used to evaluate the plans. An optimization model for determining the capacity of RESs, wind turbines, and (PV) systems is proposed. BESSs and EVs are considered, together with the grid, to soften the variability of RESs. A mixed-integer linear programming (MILP) method was used for the model.

Paper [103] studies economical optimization strategies, considering prosumers’ and aggregators’ objectives, to evaluate the price of electricity for charging EVs. EV chargers and BESSs were considered flexible loads. An open-source cross-platform integrated development environment Spyder, using Python language, is used for simulations. The solutions of the optimization problem are obtained using the Gurobi solver. Statistical data are used for benchmarking the models. The solutions show how profitable it is for prosumers (with different equipment) to be part of aggregation strategies.

In publication [96], a HEMS algorithm is reported. By the algorithm, the HEMS automatically controls the household dispatchable appliances which have different demand response (DR) values. Several mathematical optimization methods such as linear programming, genetic algorithms, and heuristic methods are used. RESs and energy storage systems including batteries and electric vehicles were considered. Load scheduling simulations, considering real-time and day-ahead evolution of the electricity prices, were performed over 24 h periods. The results showed that the renewable energy system with HEMS algorithm could significantly reduce household electricity bills. Extending the simulations to the local community, HEMSs could contribute to reductions in peak energy consumption and to further household energy cost reductions.

In paper [104], a model predictive control (MPC) method is proposed. The model considers a PV–WT–battery system connected to the main grid. As the strategy developed, the variability of the time-of-use (TOU) electricity tariff is used in favour of the system economics, earning from selling the renewable energy and stored energy to the grid. Paper [105] studies the energy management of a household with controllable electric loads and an EV. The house is supplied by a PV–Wind–Battery hybrid system and a distribution grid. Different scenarios are studied: V2H (vehicle to home), H2V (home to vehicle), and G2V (grid to vehicle). The management system algorithm is built using a linear programming model with non-linear constraints.

Paper [106] studies the energy consumption of an educational building with laboratories, a parking lot with EV chargers, EVs, PV source, and a BESS, connected to the distribution grid. It presents an optimal energy management algorithm (OEMA) for peak load minimization. Linear programming algorithms are used. Scheduling EV charging with OEMA minimised the EVs charging cost and the on-peak load of the building.

Paper [107] deals with a residential area local grid connected to the same distribution transformer. All buildings have an energy management system (EMS). Some buildings have a PV system, and some have charged EVs at home. A 100 kW wind turbine is connected to the local grid. The proposed method is a novel privacy-preserving approach for decentralised optimization, meaning that it does not require a central controller. The proposed method named SEPACO-IDA (a multi-objective optimization problem type) exploits the load flexibility in residential areas to answer to the variable nature of RESs and reduce the peak load. The results show the superiority of SEPACO-IDA over other decentralised optimization approaches.

Other papers dealing with the economics and management topic are [96,97], already discussed above.

Paper [108] is a special case. Entitled “Optimal Design and Model Predictive Control of Standalone HRES: A Real Case Study for Residential Demand Side Management”, with its 46 pages, it looks more like a preliminary research report. All the components of a standalone residential electricity system are largely presented. For example, for the power electronics components, converters, nine pages are filled, including grid connected converter control (it is a standalone grid!). A summarising table analyses 55 papers from a control point of view mentioning demerits and giving marks for the work quality. There are 98 papers in the references. A very impressive “The applied to control and management strategy for an optimised model” software is presented. A lot of simulations based on the model are made. The optimal result is mentioned to consist “of 21.1 kW PV, 5 kW WT, 5.96 kW power converter and 38 BSS units with 2.37 kWh each capacity”. The local RES potential is presented as being very good! An amount of 5 WT of 1 kW each are proposed, and a unique 150 V DC bus directly connects the above-mentioned components. How to connect the RESs and the storage to the DC bus without converters remains a mystery. The unique common power converter then rises and converts the voltage from DC to AC.

The takeaway from the above example is that software is very useful when the results are confronted with the technical reality and feasibility.

4. Charging Strategies, Standards and Incentives

According to [7] “Private chargers account for 90% of all chargers in 2030”. It is necessary to “maximize the use of renewables for charging, incentivise and streamline charging infrastructure integration”. [8] We estimate the industry needs to install more than 15,000 chargers per week within the European Union by 2030. The contribution of the research in what regards CSs is presented in what follows.

4.1. Charging Strategies

Paper [52] classifies charging and discharging strategies found in the literature. The pros and cons of each strategy are presented, as well as the impact on the grid. Recommendations are made for their specific applications. A total of 14 different charging strategies are presented, and two of them are considered the best. In order to facilitate a comparison, the strategies are presented in tables.

A comprehensive review of electric vehicle charging problems is given in paper [108]. The allocation of the charging stations, CSs, is considered an optimization problem and a section is dedicated to this. The summary of studies related to CS placement optimization is published with the following columns: Objective/Optimization/Technique Used/Implementation/Simulation Setup/Tool Used/Results. Residential CSs were considered indirectly as single point CSs. Paper [109], another review paper, has a subsection dedicated to the “Optimal placement and sizing of EVCS”. A total of 40 papers optimising CS placement and sizing are briefly presented.

Paper [110] is an overview of smart charging with PV sources. In regard to residential buildings, the flexibility of EV charging at home is underlined. With the V2G function enabled, as connected storage units, EVs can provide the grid many services before starting for the first trip fully charged. This defines the smart charging function of EVs, which can fill the valley of the typical household load. The useful results are reduced voltage sags and grid losses.

A detailed feasibility study [111] was made for the nocturnal EV charging case for a residential user. The residential user was a prosumer with a PV and storage system. Three factors were considered in the analysis: energy, economic, and environmental. Three different scenarios were analysed: grid charging; the energy coming partly from the grid and partly from the PV; and the storage system. Considering different daily average travelled distances by EVs, the influence of the size of the PV and storage system on the three factors was determined. The results showed that the NPV, net present value, was negative for reduced distances travelled and positive when the distance travelled was over a threshold value. In fact, this is another nice argument in favour of e-micro-mobility in cities. Another conclusion from the result is that an incentive plan is recommended to consider a combined incentivized purchase of EVs and solar technologies.

Paper [112] studies residential complexes (RCs) equipped with PV systems and EV cases. It presents an optimization algorithm based on MILP for optimal energy management, as grid connected RC involving real-time pricing (RTP) is considered. The benefit comes from demand response programs (DRPs) applied under real-time pricing for charging EVs at the local RC CSs. As a result, the total expected cost of the RC is reduced by up to 37.31%. The results involve the V2G solution, and the RTP system is not mentioned, considering the demand price elasticity coefficient (−0.5, 0) only.

Papers [113,114] study CSs with multiple charging points and different combinations of RESs. The CSs are grid connected and no storage is used, but the V2G technology is considered. The results of the experiments are useful for the residential case due to the power levels of the equipment involved.

4.2. Charging Standards

The CS structure and operation modes were developed over time in different regions of the world with a diversity of solutions. This situation created difficulties in the spread of EVs across the world. The situation was sensed, and, in [13], the following recommendation is made: “Harmonised charging standards are a key prerequisite for the deployment of electric mobility”.

In review paper [115], the electric vehicle charging standards have a dedicated section. The charging infrastructure standards are presented by regions. Europe uses IEC standards, USA uses SAE and IEEE standards, Japan uses CHAdeMO, and China uses Guobiao (GB/T). The standards are compared using tables with parameters. The results show that IEC61851 and SAE J1772 have almost the same requirements, only some terms are different. Additionally, IEC standards and Guobiao AC charging standards are similar. Charging connectors are presented, and, as they are different, interconnection solutions are presented. Control and communication infrastructure in EV charging have a dedicated subsection. Figure 17 suggests the standards field of application in the residential charging case.

As a communication system has to connect EVs, CSs, and a grid for smart charging control and management, a subsection reviews the dedicated literature. A synthetic image is offered in Figure 18. Different communication protocols are presented, and some comments are made.

Paper [116], published in October 2022, deals with charger technology. For obvious reasons, it reviews the standards in the field. Some updates can be found in comparison with [115]. The Japanese standard JEVS is introduced.

Considering the state of the art in the field of EV charging infrastructure, many associations and organisations are issuing recommendations. In position paper [117], one can find the following recommendations for smart infrastructure in buildings:

-

“Pre-cabling should be installed for every parking space in residential buildings;

-

charging points should be capable of smart charging and, where appropriate, bidirectional charging;

-

non-proprietary and non-discriminatory approach towards communication protocols and standards.”

In publication [118] IEA warns: ”charging standards, standardisation of communication protocols (modes) that enable smart charging and bidirectional charging will become increasingly important.”. Publication [119] considers that, for charging scaling infrastructure, there are the following “key factors: open standards, interoperability, and energy management” to fulfil.

4.3. Policies and Incentives

There is a consensus on the aspect of the necessity for actions to support and promote the development of CSs. Document [7] recommends to “maximize the use of renewables for charging, incentivise and streamline charging infrastructure integration” and remarks that “Adoption of digital technologies and smart charging can alleviate the need for grid upgrades”. Paper [120], analysing the rapid evolution of the Asian market, finds that “The rapid development of mature EV markets is supported by strong policy actions on four fronts: official EV targets, restrictions on ICE production and sales, consumer incentives, and support for EV charging infrastructure”.

The policies and incentives adopted until now to encourage EVs and their environment are presented in [109],

Table 2. Policy mechanisms and categories.

Financial Incentives	Non-Financial Incentives	Supporting Charging Infrastructure	Raising Consumers’ Awareness
Point of sale grant Sale tax and VAT exemptions Post purchase rebates Income tax credits	Other different initiatives

. Paper [110] presents the incentives by state and region. In Asia, the support consists of “Fixed capital investment for installation of EV charging stations”.

AUD151 million to be infused in the charging station is programmed in Australia. In the USA, there is a 30% subsidy in the installation of home charging stations (max $1000). In Europe, ref. [121] reports the following situation by country: “France €300 tax credit (crédit d’impôt transition énergétique; CITE) on the purchase and installation of an EV charger at your main residence; Germany KfW-Bank: 10–30% incentive for the purchase and installation of a wallbox charger; Spain Private individuals and companies can get Moves II subsidies of up to 30–40% of the purchase and installation costs; Sweden’s ’Charge at Home’ program is a support scheme for individuals wishing to purchase home chargers. Individuals can receive up to 50% or SEK 10,000 (€960) for hardware and installation costs of home chargers; UK The Electric Vehicle Home Charge Scheme (OZEV) enables individual buyers of eligible EVs to receive a grant for up to 75% (capped at £350, inc. VAT) of the total purchase and installation costs of one EV charger for their home”. The rest of the countries support the public CSs. Residential RESs are treated separately.

5. Design of Residential MG with EV Charging Capabilities

The system design is a difficult matter, generally due to many domains implied. The topic of residential microgrid with RESs, storage and EV charging station is a clear example, and the information in the pages above is an illustration. That is one reason why review papers are useful. The design of the system is a rather hard task. Different approaches to a problem give useful solutions for certain aspects.

Review paper [122] is dedicated to sizing methods and optimization techniques of renewable energy and storage systems. It presents and comments on the following aspects: mathematical model of the components; system components equations and modelling; list of meteorological databases, including type of measurements, access, and conditions; a table summarizing the sizing programs. The paper shows that the use of one of the sizing optimization techniques helps to find “the maximum power reliability and the minimum system cost for the future implementation”. It also discusses the optimum combination of the components parameters such as PV array size, the tilt angle of the PV panels, BESS, and WT size. Several optimizing software programs are presented. At the end, the paper concludes that the HOMER software tool is found to be most largely used for its qualities.

Paper [84] performed the optimal sizing design of a hybrid renewable energy system energizing a community stand-alone MG and considered EV charging. The optimization criterion was the economic one. By considering different combinations, the results show that “it is clear that PV/WT/Battery/EV collaboration is the most suitable”.

In paper [10], the design of a smart MG, SMG, supplied by renewable sources, with EV charging station and domestic loads, is presented. The renewable energy components (PV modules and small wind turbines), stationary Li-ion batteries, and power electronics components were chosen from those available on the market, according to estimated values by design. The SMG has a communication capability and is able to communicate with the SMG components and with a smart city digital network. The CS can be public, and the customers can communicate by intelligent phones for scheduling. The charging prices are supposed to be estimated according to the 24 h weather forecast ahead. The SMG has the abilities of a smart home. The SMG has been operating since 2018 and it charges for free only EVs from the institution, until now. The design of each component is presented in the paper with all the technical and scientific considerations and with simulations made.

A reversed problem is presented in paper [122]. The MG system is practically given, and the performances are to be assessed in order to estimate the CS capability. Each component is evaluated considering its parameters, then a model is established for each. An energy management algorithm is built. The experiment was performed for 2 days under different conditions of PVs and battery, with and without EV consumption. The main conclusion is that the maximum of PV energy self-consumption can be obtained when the demand side management optimally integrates and combines activities, including load shifting.

Paper [123] designs a grid-connected photovoltaic battery system for a residential house community. A genetic algorithm is used for sizing the PV and BESS systems. The battery cycles are optimized, taking into account the solar resource variations and the electricity time-of-use tariff. A result of the simulations is that “a group battery offers more value for cost saving, especially for groups with sufficient diversity in their demand”. Paper [101] deals with the same system design for a community of 692 houses. It uses software optimization. One main conclusion is that reducing BESS costs is “the most financially attractive solution to RES integration into the SG”.

There are also several papers such as [40,124,125], dealing with RES-based CS design considerations, but their results are not convincing.

As previously mentioned, there are bold entrepreneurs foreseeing the future for RES-based smart residences with CSs producing equipment for them, already. Such a residential solution is presented in [126] and can be seen in Figure 19.

It was presented in an exhibition by an association of commercial, government, and academic member organizations. It is a non-profit association of commercial, government, and academic member organizations promoting such MGs. The PV magazine published paper [127] provides a guide on how to add a PV array, batteries, electrical panels, net metering devices, and EV chargers to a residence. The guide contains financial and legal advice, tips for optimal use and maintenance, and security advice.

This is good news for those involved in the field of residential EV charging using RESs and storage systems. But the need for optimizing the solution according to the site, the economic, carbon dioxide savings, and customer satisfaction criteria remain for these marketed solutions. There is progress to be made toward the smart home as a building brick of the future smart grid, too.

6. Case Studies

The future of the EV and its CSs is determined by the use of RESs. It is necessary to plan the development of CSs on a scientific basis, with smart grid and smart cities as targets. Residential EV charging using RESs must be considered with respect to this.

The evolution of the residential RES supplied CSs towards smart homes with smart energy management has multiple advantages. The work in [96] proves that, by only implementing smart HEMSs in a grid-connected house with PVs, battery, and PHEV, the consumer saves 18.6% on the electricity bill. This publication, a PhD thesis, has many interesting and useful details related to the residential smart MG case.

Germany is a great case from the point of view of planning e-mobility, including the development of CSs and residential CSs. From a regulations perspective, [128] presents the following measures: in 2015, the federal government adopted the Electric Mobility Act, which assigns privileges to the electric cars on Germany’s roads. The act is in force until 2030. Then, in 2016, an EU directive related to the EV environment was implemented as a law, establishing uniform charging and payment standards. It imposes binding rules in order to harmonize socket standards for publicly accessible CSs. At the end of 2016, the Bundestag adopted an act on tax exemption for the charging of electric vehicles. In 2017, the minimum payment standards unified authentication and payment methods at charging stations. The users of electric vehicles are now able to pay for charging using an app web-based payment system, a credit card, or cash.

The situation is closely followed and supervised by the government and by interested associations. In [129], the author remarks that homeowners want to be “driving on sunshine”, to take advantage as quickly as possible of the high amount of rooftop solar installed by previous governmental programs. Further in the paper, an online database is presented with the available EV chargers for home CSs on the German market. The capabilities of each product series and more features such as interface type, their in-house apps, and built-in protection components are given from 36 key suppliers. Here, control of the charging power is possible with the use of the Open Charge Point Protocol (OCPP) version 1.6.

In [130], the author shows that fewer than 40% of all EV chargers use solar-optimized charging apps. Another way to optimize solar charging is to use a cloud-based charging management system. This management system receives data via the internet from the inverters’ back-end communication systems. The communication of the car with the charger is achieved via ISO15118-20 versions. In the future, based on these facilities, the consumption will be optimized to exploit time-of-use electricity tariffs offered by the electricity provider. Consequently, the charging cost will be minimized by using electricity from solar power or from the electricity provider when the prices are low.

In the report from [131], the German PV rooftop homes situation is presented. The following points are to be noted. From the 15.7 million houses inhabited by one or two families, 10.8 million present prosumer potential; from the latter, only 1.71 million, or 16% have PV systems. Only 255,000, 2% of the potential, are smart houses using HEMSs. The structure of the considered smart house is presented in Figure 20, where components can be identified via colours: PV—blue; heat pump—purple; wall box charger—dark grey; smart meter—light grey; storage—green.

Such reports allow for planning the measures and the investments needed to properly tackle different challenges, such as energy and the global warming crisis, by e-mobility and net zero energy buildings or positive energy buildings solutions.

7. Conclusions

In this review, papers [1,2,3] push for the development of the EV charging station, whose implementation must be encouraged by incentives and legal measures. According to [7,8], 90% of chargers will be at home, accounting for about 65% of the total energy demand. Research is required to obtain optimal CS solutions. From these considerations stem the need for the synthesis provided in this paper.

Residential MGs with RESs, storage, and with EV charging capabilities represent an important solution, with RESs balancing the energy need and solving the emission problem while also addressing other grid problems. New equipment contributes to higher resilience and lower implementation costs. Software optimises the designed solutions. It impacts the charging process by reducing its influence on the grid, while providing economic benefits via energy management solutions. In fact, the grid will benefit from the surplus RES energy supplied by smart residences. E-mobility will be encouraged by such an environment. Dedicated communications will enable the joint optimization of the different processes mentioned above.

Altogether, the above-mentioned solutions will contribute to the implementation of the smart home energy management systems, which are an important step towards true smart grids and smart cities.