**2. Literature Review**

IoE represents a global interconnected network that is comprised of various household and industrial appliances, electric devices, large and small, as well as smart grids that interconnect them all together. Another important element of IoE is, of course, the presence of smart meters, or sensors that constantly monitor all processes within this network and send signals across the grid, helping the IoE to understand the appliances schedule and the consumers to adjust their energy consumption patterns and usage. [17–19]. All in all, it becomes quite apparent that IoE is capable of helping in achieving and further increasing the sustainability for the smart grids through making the production, transmission, and consumption of electric energy more e fficient and economically feasible. With all that benefits smart grids and IoE are o ffering, it is quite surprising that many agents at the contemporary energy market (e.g., car producers, energy suppliers, or utility companies), still have certain troubles in adjusting to the new technological advancements [20,21] (even though it is apparent that their deployment requires substantial capital investments). However, it quite clear that new types of energy demand and supply that would include more renewable energy sources and prosumers would inevitably lead to the profound changes in world's electrical networks. With regard to that, smart grids o ffer a whole scale of opportunities how to tackle these changes depending on the concrete situation, business models, regulation, and power infrastructure [22,23].

Figure 1 that follows, shows how the flexibility and innovations trends in IoE framework and its applications to high renewables penetrations of electricity market are driving energy transition. There are three main aspects to be considered that mark the transformation of the sector (digitalisation, decentralisation, and electrification).

**Figure 1.** Flexibility and innovations in the Internet of Energy (IoE) and high renewables framework. Source: Own results.

With regard to the above, one can see how the aspects outlined above impact the transition to the high renewables penetrations of electricity market: digitalisation includes ICT solutions to exchanging data and energy in the framework of IoE, decentralisation embeds the distributed energy resources (DERs) that would help to further decentralise the future power system, while electrification means the increasing share of electric transport, industry, and buildings.

Furthermore, high penetration of renewables also makes the IoE an indispensable tool for the dynamic demand response, when the utilities need to reschedule or delay the operation of the appliances located in households and businesses during the periods when demand levels spike. This is particularly useful due to the intermittent nature of RES.

In the future, high-renewables electricity system market design would encompass the new concepts of energy consumers' and producers' relationships, market operations, as well as electric energy trading [24–27]. IoE and the smart grid would allow for the two-way flow of information and energy in the real time as we know it from the world wide web (WWW) and the Internet [28]. There would be many interesting and useful applications that might be helpful in mitigating power system operation hurdles and natural challenges. For example, Pina et al. [29] analyze the impact of demand side managemen<sup>t</sup> strategies in the evolution of the electricity mix of Flores Island in the Azores and conclude that IT solutions might improve the operation of the existing installed capacity. Moreover, Strielkowski et al. [30] explain in greater detail how photovoltaic system owners can control their use of electricity using the "power manager" gateways and battery storage for achieving the economically e ffective outcome. Another interesting case study involving the IoE and its implications for the smart grids is the autonomic power system (APS), that presents a novel concept of "self\*" (self-configuring, self-healing, self-optimizing, and self-protecting) system [31]. APS constituted a system-wide approach with the decentralized intelligence making autonomous decisions required for meeting the priorities of the system's stakeholders, employing the integration of a vast number of flexible, diverse, and independently controlled technologies in system operation and planning [4,32,33].

When it comes to the technical details and characteristics for the IoE applied to the electricity market in terms of high variable renewable resources penetration, various layers of IoE in smart grids covering managemen<sup>t</sup> services (security control, data monitoring, customer or market data) and applications (smart homes, electric vehicles, demand response, and demand side management) can be considered: e.g., transport layer, physical layer, network layer, or application layer [34,35].

IoE and smart grids would allow system operators to promptly react to peaks or failures in electric energy demand and also to forecast these issues well in advance and to adjust to these situations by optimizing energy generation from, say, RES, accordingly. All these would increase energy market efficiency and profitability [36–39]. In a way, it is quite similar to the so-called "cashless economy", when the Internet is used to optimize payments and money transfers, and banks, financial institutions, as well as governmen<sup>t</sup> regulators have an instant snapshot of all of those activities and transactions.

Energy companies operating on the future high-renewables and smart solutions-driven electricity market would be involved into the generation of energy from renewable sources and natural gas, energy trading and tailor-made energy services and developments for companies [40,41]. They would offer their customers a reliable and environmentally friendly energy supply based on the sustainable use of renewable energies.

Another important aspect is smart meters. Their numbers are increasing, and their usage is becoming notorious in all aspects of energy generation and saving. However, in some cases, as for example, Rausser et al. [42] demonstrate using a case study from the Republic of Ireland, their deployment has little e ffect of the energy consumption behaviour of the households and individuals. Nevertheless, smart meters would also be a very important pillar of the high-renewables sustainable energy system [43,44]. In the future market design, the peak load shave would be achieved by shifting the usage time of the energy without changing the total energy consumption [45]. It works on the application of smart meters to collect data and optimize energy production [46]. Although this strategy improves the conventional grid, the IoE framework is not considered a mathematical model

and the inclusion of renewable energy sources is lacking. The introduction of smart meters and IoE connected power supplies has allowed consumers to track and monitor their energy consumption and save energy costs. This has many practical applications and technical solutions, in particular in Smart Cities that represent a communication infrastructure o ffering a concise, unified and a ffordable access to municipal services including energy supply [47].

Similar technology is expected to make aggressive progress in the areas of energy production and transmission [48,49]. This massive and rapid growth is aimed at e fficient use of resources in power generation and higher operational e fficiency to meet growing energy needs [50–52].

With limited participation of demand resources, the wholesale market functioned mainly with network operators selling large central station equipment to meet the steady demand. Renewable energy today is cheaper than coal and nuclear power in most parts of such advanced economies as, for example, the United States and more cost-competitive with natural gas [53,54].

Furthermore, it becomes obvious that the targeted rapid increase in power supply from intermittent renewable sources in many countries is a fundamental challenge to the smooth functioning of many power systems. Wind and solar power are the fastest growing forms of renewable energy [55,56]. The supply of wind and solar energy is largely determined by wind speed and solar radiation which can be correlated only slightly with the times of electricity demand [57–59]. It is this feature of renewable energy intermittent power supply that adds cost to the entire generation system that is implicitly paid for by either other producers, consumers or taxpayers [60,61]. With the constantly increasing size and quantity, today's generation and energy costs are often competitive with coal and nuclear without taking into account the reserve capacity and complexity of grid connectivity that a ffect their value in a system [62]. If it burns out and displaces electricity from other sources, it can reduce the profitability of these sources and increase supply prices. It has been proposed to use all electricity from wind and solar power which greatly simplifies the managemen<sup>t</sup> of the electricity grid [63,64].

Effective wholesale electricity markets are crucial for rapid and a ffordable decarbonization, as they demonstrably invest e fficiently and rapidly in new technologies. However, the electricity markets will only support massive investments in clean energy if they are able to send e fficient price signals as decarbonization increases [65,66]. The scale and pace of investment required to halt the climate crisis means that wind and solar energy will almost certainly play an important role in future power systems because of their low cost and speed of deployment. However, variable power and the marginal cost of wind, solar and other forms of variable renewable energy (VRE) jeopardize the ability of current market designs to send the required price signals [67,68]. New variable resources such as sun and wind are one of the biggest drivers for more flexibility. There are many ways to unlock flexibility: new and more flexible gas systems, storage of all sizes, power electronics to regulate wind and solar power, and a constellation of connected devices ready to consume electricity smarter. Restructuring wholesale electricity markets, which work best by avoiding specific technology revenues, must find new and improved ways to assess flexibility and allow current and future market participants to provide them at minimal cost. The uneven geographic distribution of wind and solar potential is likely to burden the grid at some sites, resulting in transmission and distribution restrictions. Some electricity markets, such as the California Independent System Operator (CAISO) in Germany and the United Kingdom, have begun to recognize variable and resilient electrical resources to varying degrees [69,70]. In addition, the Federal Energy Regulatory Commission (FERC) and Pennsylvania-New Jersey-Maryland (PJM) Interconnection policy makers in the United States are also shifting their focus to the role that battery energy storage and flexible resources such as distributed resource aggregators (DRAs) play in the development of electricity markets [71]. However, regulatory, economic and technological barriers have largely prevented the participation of demand resources such as batteries and smart thermostats in the wholesale electricity market. With limited participation of demand resources, the wholesale energy market functioned mainly with network operators selling large central station equipment to meet the steady demand.

#### **3. Promoting High-Renewables Smart Grids**

High-renewables smart grids would bring in many useful solutions to the existing energy market problems. Flexible demand, micro-generation and energy storage technologies can reduce the emerging demand peaks, while smart network technologies increase the utilization of existing network assets. The concept represents a shift from asset redundancy to more intelligent operation through real-time coordination of new flexible technologies. Figure 2 that follows offers the comparison of the costs of electricity obtained from the renewable energy sources.

**Figure 2.** Comparison of the costs of electricity obtained from the renewable energy sources. Source: [72].

Overall, it becomes obvious that in spite of the popularity of RES-based technologies, the costs of electricity are the lowest when it comes from the large hydropower plants, which is followed by the geothermal energy and biomass. Photovoltaics and wind turbines are just starting to catch up in terms of economic efficiency.

In general, it would be quite straightforward to simultaneously and holistically assess the impact of high-renewables smart grid technologies across all timescales and system levels via capturing the overall economic value of these technologies for three milestone dates (2020, 2030, and 2050). In the current situation (2020) the value of flexibility is not that high, the deployment of energy storage is not justified for a cost higher than €1100/kW with the optimal storage capacity of 2 GW, and its overall economic value for the power system for the European country a size of the United Kingdom or Germany being around €0.5 billion per year.

There have been recent developments—in the UK with electricity market reform, in Germany with the Energiewende, and in the State of New York with its Reforming the Energy Vision—for ideas from the new round of natural experiments in electricity market organization, currently underway in jurisdictions with 80% or more low carbon electricity targets.

A fundamental feature of the high-renewables electricity system market design's vision is the integration of a vast number of flexible, diverse, and independently controlled technologies in system operation and planning. Figure 3 that follows depicts the investments into smart grids by areas. It is apparent that over the past few years the share of investments into the rest of the network is declining, while the investments into the power equipment as well as to the smart grid infrastructure and smart meters are slowly but steadily increasing.

Table 1 that follows shows the contrasting description of two market designs—namely the Internet of Energy and high-renewables market design versus the current (state-of-the-art) design that employs current technological advancements.

**Figure 3.** Investments into smart grids by areas. Source: [73].



Source**:** Own results based on [74].

With regard to the above, it appears important to consider how one can better balance supply and demand, aiming towards an electricity market where prices are reflective of costs to the overall system. For example, in the United Kingdom alone, smart power could save consumers up to £8 billion a year by 2030 [75]. Moreover, it can also ensure that the UK meets its 2050 carbon targets and secures its energy supply for generations to come. As a result, three major innovations can be identified as the key drivers of success: interconnection, storage, and demand flexibility [76].

Another issue that has to be mentioned in connection with promoting and sustaining high-renewables smart grids is the Big Data which can be used for many activities and innovations within the IoE framework design. In the energy markets, Big Data represents a valuable asset and a source of all possible information about the consumption and behaviour of users, households, and companies [77]. Thanks to the smart meters, Internet and fast mobile technologies, all that information can be collected and transferred quickly to be analysed. However, one has to realize that due to the extensive sizes of such datafiles, traditional methods (e.g., econometric or statistical analysis) are no longer applicable. Instead, computer algorithms and artificial intelligence (AI) are employed which is similar to the recent developments in the "regular" Internet nowadays.

The applications of the Big Data might range from building more accurate predictions and extrapolations that would forecast demand and output more precisely to minimizing asset failures of the system. There is a need for system operators (SO) to become data led organizations (as distinct from asset availability managers) which constitutes an important argumen<sup>t</sup> in the debate of the value of independent system operators.

IoE and Big Data are also crucial for energy prosumers that are more entangled into peer-to-peer (P2P) energy market arrangements through buying, selling, and producing their own energy from the renewables. The sharing economy-type of high-renewables energy market that is being created and would be likely to dominate in many countries in the nearest future calls for optimized and fast collection and processing of all available data.

## **4. Smart Network Technologies**

With all of the above explained, one also has to point out some noteworthy di fferences between the smart grids and the Internet, to which they are often compared to. Similar to the Internet (although with a wider diversity of resources), smart grids constitute an interconnected system of devices and controls, nevertheless, all these systems are dynamically evolving and changing as the demand of electricity of the di fferent types of consumers is shifting due to the changing market conditions. The blockchain technology that is used in Bitcoin, the world's most popular cryptocurrency, might provide help in dealing with this highly sophisticated environment for achieving cost-e ffective energy solutions.

One good example of this is the optimization systems that involve o ffice and residential buildings and electric vehicles (EVs). Managers of these buildings face the tasks of providing a logistically balanced system of charging stations for the EVs parked or stationed on their premises. Another technical solution associated with EVs is the possibility of some of them not only to absorb but also to inject power back to the grid (a so-called "vehicle-to-grid" concept, or V2G). Without smart grids and IoE, it would be quite cumbersome to come up with the optimal mix of accurate solutions due to supply and demand uncertainties: e.g., inherent uncertainty of RES or load shifting for reducing peak power consumption [78,79].

Thanks to IoE smart grids can achieve better e fficiency and learn how to optimize the energy needs for both prosumers operating at peer-to-peer (P2P) markets and industrial companies in the production chain. All of that gives a boost for the new sectors such as the solar photovoltaics, intelligent distribution networks, and electric vehicles charging, just to give a few examples. In addition, the rising competition and technological advancements are slowly but gradually changing the current system of distribution networks and old-fashioned grids.

At the planning timescale, flexible demand, micro-generation, and energy storage technologies can reduce the emerging demand peaks, while smart network technologies increase the utilization of existing network assets; both these e ffects are translated in reduced requirements for capital-intensive generation and network capacity investments in the future [80]. Moreover, dynamic demand response shifts the amount of consumer electricity it to when it more optimal from the grid operation point of view [81].

The shift towards carbon-free transport which aims at reducing car emissions and electrify both personal and communal transportation introduced the concept of electric vehicles (EV). Currently, EVs are becoming quite a mundane thing even though their costs and operation might be somewhat higher than in the case of traditional vehicles. Comparing the number of EVs and internal combustion engine (ICE) vehicles, one can find out that in 2019 there were about 5.6 million EVs all around the world (including passenger cars and light commercial vehicles with battery-electric drive, range extender, as well as plug-in hybrids) [82]. This is a steady increase from the 3.2 million EVs in 2018, 1.9 million in 2017, and 1.1 million in 2016 [83]. However, EVs still constitute only a margin of all registered internal ICE vehicles in the world which yielded around 1.4 billion in 2019 [84]. Nevertheless, IoE is becoming one of the key concepts in the electrification of transport, since it promises to coordinate, manage, and match the growing fleet of electric vehicles. For example, one of the pressing issues with EVs today is the occurrence of the charging stations. Nowadays, there are about 160 thousand charging stations worldwide with the expected number to be slightly above 200 thousand in the early 2020 [85,86] Not too frequent in comparison with their fuel counterparts, they often make it di fficult to plan a trip over large distances. One of the solutions might be charging EVs from one another in an interconnected network of drivers who are also energy prosumers (the V2G concept described above). Figure 4 that follows shows a graphic representation of V2G within IoE framework marked by the high renewables penetrations of electricity market. It outlines the key role of the IoE that helps the transition of the power sector through the monitoring the performance of its assets, maintaining more precise operations and control in real time, setting up novel market designs, as well as defining new business models and solutions.

**Figure 4.** Vehicle-to-grid (V2G) within IoE framework and high renewables penetrations of electricity market. Source: Own results.

One of the pioneers in introducing the electric transportation to the daily lives of the people is the European Union that committed to having over 250 million EVs by 2025 all across its Member States, which, although an impressive figure in itself, might not even be enough for the e ffective transition to the climate targets of 2030 [87].

One can see that due to the existing economic and social constraints, electric vehicles might also constitute a problem and distort the power system. They are still too expensive for the average citizen to buy and operate, the charging options are rather limited, and the battery storage is quite modest with the driving range being in a couple of hundreds of kilometres. Thence, technical operation and market optimization need to be used in order for them to become a ubiquitous part of the future smart grids [88].

With the increasing sales of EVs in United States, European Union, China, and India, the vehicle charging issues would accumulate and would need to be solved [89]. For example, in the absence of proper regulations, the majority EV owners would charge their vehicles during the day's peaks, thus placing a burden on the electricity system [90]. IoE might provide solutions for that o ffering demand-side response and optimizing the charging times scheduling and planning. In addition, the rising EV fleet might become a kind of a giant energy storage system that would be able not only accumulate, but also to move large amounts of energy over distance. This might be an interesting solution for the distributed renewable energy generation, even though strict and precise on-time planning is needed, which should involve Big Data, smart meters of the latest generations, as well as faster mobile networks.

Let us show this on a practical example using the data from the United Kingdom (UK). Table 2 that follows shows an example of tari ffs and metered import for the three selected UK regions represented by the Northeast, Central Scotland, and East Midlands (chosen for the best geographical spread and representativeness).


**Table 2.** Tari ffs, distribution charges, and metered import in three selected UK regions.

Source: Own calculations based on [30].

Furthermore, one might model the hypothetical household demand profile (expressed in GW) with various levels of deployment of electric vehicles (EV)—from 0% to 100%. This is a rather simplified model since it neglects some relevant benefits of the restructuring market, since more factors might be needed, as well as the relationship between EV and charging stations. Some of the influential factors in this case might be represented by the location of the charging facility, local subsidiary policies, as well as the battery level of the EV. However, despite this limitation, we can draw the model framework that would constitute an example of reorganizing the energy market.

The model in question is based on our methodology described in our earlier paper (for more details see [87]).

$$(1+\mathbf{x})\,\mathrm{F} + (1+\mathbf{x})\,\mathrm{v} \,\,\mathrm{\*}\,(\mathrm{EC}\,\mathbf{x}\,\,\mathbf{n}) = \mathrm{TR} \tag{1}$$

where:

x—energy tari ffs and charges component;

F—household daily unit power fee (kW);

V—energy unit fee (kWh) for a given household connected to the network;

EC—energy consumption for a given household; N—number of households in the system;

TR—total electricity revenues;

Table 3 that follows shows the differences in tariffs (per household per year) for residential EV and non-EV households in the three UK regions presented above. It becomes apparent that the inclusion of RES (represented here by solar panels) and most importantly EVs shift the balance and make profound changes to the economic situation.

**Table 3.** Differences for residential solar photovoltaic (PV), non-PV, electric vehicle (EV) and non-EV households in UK regions.


Source: Own results.

In addition, let us show the changes in the household demand profile with various levels of EV deployment stemming from the changes. Figure 5 that follows depicts some basic (rather "back-of-the-envelope") results of our calculations. Generally, it becomes apparent that with the rising share of EV in day-to-day transportation decisions, the demand profile can increase by 40%–50% during the peaks. All of these might cause serious issues for the energy systems that are not prepared for such scale of EV penetration.

Overall, there are two cases when EVs do not exhibit smart charging capability and when the EVs exhibit smart charging capability and are scheduled through dynamic pricing. In the former case, EVs are assumed to start charging immediately after returning home; given that most users return home during late afternoon and/or evening hours [17–21] when the non-EVs demand peak occurs, the system peak demand at these hours is significantly increased. In the latter case, the demand response of smart-charging EVs is concentrated and creates a new demand peak at the late-night hours of the day since the latter exhibit the lowest prices due to their low inflexible demand levels.

**Figure 5.** Household demand profile with various levels of EV deployment. Source: Own results.

## **5. Conclusions and Implications**

The commitment to tackle global warming and climate change, with profound decarbonisation of the world's economies, is going to have many economic and social impacts and we all should be ready for them. Thence, political commitments that have an impact on the energy markets should also reflect the existing state of technological development and social acceptance of this development. This social acceptance is a serious issue because not all "traditional" energy providers and customers would be willing to change the old ways. Rapid flow of information and AI-like solutions that allow to optimize the flow of energy from producers to consumers and the flow of information the other way around (and vice versa) might be an issue for some individuals and companies. Some might even bring up an analogy to the "Big Brother" that is constantly monitoring each and every transaction and watching over each and every agen<sup>t</sup> in the grid.

However, the trend might be also quite different as it happened with the ascent of Internet (which also served as an inspiration for the smart grids). Neglected and underestimated at first, the Internet quickly marched into our daily lives and fundamentally changed many principles of the world economy, quickly becoming a mundane issue for the majority of world's population. Its social acceptance is almost absolute, even though some people are starting to comprehend what threats it might cause. Similar things might happen to IoE and smart grids—once their potential is apparent and popularized, there will be no way back in reforming the current energy market design.

One would probably agree with the fact that the transition to the high-renewables electricity markets is going to be accompanied by the increasing share of RES in electricity generation. This will fundamentally alter the power markets within several decades and revert the existing rules that have been obeyed by energy market players for years. Renewable energy sources are gaining in importance with increasing power generation, and could easily cover half of the total energy demand by 2050. The hope of accelerating the energy transition depends on the electrification of a larger part of the total energy consumption. Renewable energy advocates have received much good news over the past few years about falling solar and wind power prices and rapid growth rates in these industries.

With regard to the above, it is important for policymakers and stakeholders to understand the potential and the power of the ongoing "RES revolution" (which, one can argue, still represents an "evolution") and to prepare for the new market reality with all its outcomes and consequences. The profile of renewable energy development is becoming ever more diverse and in some regions of the world more and more challenges will have to be overcome. While global new investments in renewable energies are still relatively high, there has been some decline in recent years.

In addition, it also appears that the deployment of di fferent technologies and policy measures in the high-renewables energy system that would involve IoE would lead to the decentralized approach to system control and operation in the next few decades. Since it may be optimal that large-scale roll-out starts in the early 2020s if one is to reduce the cost of later investments, some governmen<sup>t</sup> incentives may be needed for industry to make early moves.

Moreover, the concept of the "Internet of Energy" is very crucial for the demand flexibility (i.e., the case when demand for the electricity is steered to the grid o ff-peak periods using, for example, price incentives or reduced tari ffs). Flexible energy revolution should be supported and implemented due to the growing demand and wide usage of electric vehicles (EV) and electric heating in the future. These technologies would help us to save carbon emissions, but they would be impossible to sustain without the major changes in the smart grid.

All in all, IoE and high-renewables electricity system market design represent a shift from asset redundancy to more intelligent operation through real-time coordination of new flexible technologies. With flexible demand, micro-generation, and energy storage technologies being able to reduce the emerging demand peaks, smart network technologies will increase the utilization of existing network assets, all representing a shift from asset redundancy to more intelligent operation through real-time coordination of new flexible technologies. Future demand and distributed generation connections are increasingly hard to predict due to uncertainty in timing, volume, and location of new demand, and connections of renewable generation due to their dependence on future technology developments, governmen<sup>t</sup> policies, and market arrangements. Flexible planning frameworks are required to account for uncertainty in future development and identify investment strategies that are cost-e fficient under all possible future realisations. Given all that above, we nevertheless have to stress that the outcomes of this paper mostly emerge from the energy policy aspects of high-renewables smart grids, and therefore does not attribute too much attention to the technical issues of Internet of Energy technique and the related framework applied to high renewables penetrations of electricity market.

Last but not least, high-renewables smart grids-focused electricity markets can increase economic efficiency and profits throughout the whole energy sector. While grid operators today hold natural monopolies, the shift to the high-renewables smart grids populated by millions and billions of prosumers might change the rules of the game and create the new revenue stream that would be beneficial for both energy suppliers (large and small) and energy consumers. Therefore, some forms of governmen<sup>t</sup> funding are appropriate nowadays in order to boost this positive and highly desirable development towards the fundamental change of the existing energy system. This might include, for example, subsidies to the households and businesses for shifting towards electric transport, special preferential tari ffs for renewable energy (feed-in-tari ffs to buy out the "green" electricity), or special programs to increase environmental awareness, and the learning of how to increase energy efficiency by using advanced smart metering that would allow to constantly monitor the energy intake for each individual appliance and to assess it in a concise and comprehensive way (similar to the health data many of us assess using various fitness trackers and smart watches).

Supporting renewable energy demonstration projects to disseminate information in remote areas, training microfinance leaders, and decentralizing the implementation of renewable energy projects can promote the dissemination of renewable energy projects. Most of the support for renewable energy strategies and technologies in developing countries comes from local governments or from international donors, undermining their sustainability as funds vary with changing priorities and crises. Finally, the introduction of innovative strategies and the sustainability of renewable energy markets and technologies can benefit from the adoption of a comprehensive energy governance framework.

All in all, it appears that high-renewables electricity system market design would provide solutions to many issues including the CO2 reduction of mitigating environmental solution and climate change, however it needs more support from relevant stakeholders and policymakers aimed at securing its economic viability and helping to support its social acceptance and awareness.

**Author Contributions:** Conceptualization, W.S., A.F., and E.S.; methodology, W.S. and D.S.; formal analysis, W.S., A.F., D.S., and E.S.; investigation, A.F., D.S., and E.S.; resources, A.F. and E.S.; data curation, W.S., A.F., and E.S.; writing—original draft preparation, W.S., D.S., A.F., and E.S.; writing—review and editing, W.S. and D.S.; visualization, W.S. and A.F.; supervision, W.S.; funding acquisition, A.F. and E.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.
