*1.5. Previous V2G Simulations*

The V2G method has been proposed for use in domestic applications, but unfortunately, without the incentives for the EV owners [16,17]. These simulations showed the volatility of the system and the difficulty in predicting the future supply and demand, meaning the simulations can be inaccurate. The supplier of the energy, in this case, has great advantages, through having a less volatile demand, as the V2G method provides peak shaving. Attention should be given to the V2G method for predicting energy demand rather than financial benefits for both parties. Various topologies of V2G with dependence existed where the EVs are connected to the grid. However, 'Vehicle to building' (V2B), 'Vehicle to home' (V2H) and 'Vehicle to load' (V2L) are the same methods, which covers all variations in the process [18].

The depth of discharge is a contributing factor to the results within this method, as the battery that is discharged less will have a longer lifespan [19]. To improve the lifespan of the battery, if the battery does not need to be discharged, then it shouldn't be. Fuel cells can be combined with the V2G technology to provide a more efficient method. With the application of fuel cell vehicles (FCV), there can be a 51% increased income as the required supply is spread out over FCV and V2G instead of using only one method [20].

As the total cost of ownership (TCO) of EVs can vary, changing consumers' opinion on if they will use one or not. This is being amended by the tariff schemes given by the government [21]. Xcel energy's off-peak EV rate is 4.3 cents per kWh compared to on-peak rates of 17 cents, with a four-fold increase, the time of purchase creates an incentive. A suitable EV infrastructure, and smart EV chargers, which enable the demands of the consumer to be met, will allow the expansion of EVs. Carbon neutral building will integrate the use of V2G systems and EVs, whether it's V2G or just the use of EVs rather than standard vehicles. It is undeniable that the use of EVs reduces the carbon footprint of the building on a university campus [22]. EVs and hybrid electric vehicles (HEVs) are becoming more efficient and useable. As the V2G method requires the use of EVs, further research into better performance and attractiveness to consumers' increases the value of the V2G method [23].

V2G methods are not unfamiliar, as is shown in previous research papers. The application of V2G methods into large public and commercial buildings, e.g., university campus, validated by data collected from operational environments and critically analysed by employing advanced algorithms, have not been investigated yet. The surrounding aspects, such as the vehicle range, using ML have been previously analysed without focusing on the buildings and EV users' financial and environmental benefits.

A V2G system is currently being tested in the UK, with no mention of how the system is going to be set up or analysed. There is not enough information available in the literature for predicting energy management of public buildings, particularly with employing machine learning (ML) algorithms [24]. A machine learning algorithm can enhance the quality of projects, such as these, by giving the company an accurate outcome, regarding the financial and energy characteristics of the project. The 'Parkers Vehicle-Grid Integration Summit' showed the implementation of the method, and then the obtaining of the results [25]. The method proposed in Section 2 can predict the outcome before the implementation.

The problem surrounding the growth of EVs and V2G systems is uncertain for both the driver and the building. It is not only difficult to predict the existing V2G system, but also the future of the V2G system needs to be predicted considering the location, scale of the building(s), future improved efficiency in EV batteries, etc.

In this paper, an effective V2G scheme has been developed and implemented into an operational university building, enabling it to be used on a larger scale. Parameters and system requirements, considering the initial cost, and net profit for both the installers and users of the EV chargers for the campus, are shown in Section 2. The on-site energy storage's price is calculated and equated to EV chargers, cost of EV charging stations, their lifespan and the campus profit have been presented in Section 3. In addition, the net profit of both parties, considering the long-term effects of using V2G, application of machine learning (ML) for predicting energy consumption and cost have also been conducted in this section. Critical analysis of achieved results with comparisons has also been presented in this section. Finally, key conclusions are drawn, and future works are suggested in Section 4.
