A Novel SW Tool for the Evaluation of Expected Benefits of V2H Charging Devices Utilization in V2B Building Contexts

Abstract

Energy systems need a complete decarbonization within the next 20–30 years, calling for the introduction of CO₂-free renewable energy sources (RES). All final uses must face this challenge, now finally including the transportation sector which should mostly be electrified. This option could constitute both a challenge and an opportunity for the electric grid. In fact, connection to the grid of all electric vehicles (EVs) together with their electricity storage systems (ESSs) could reduce issues due to the nonprogrammable use of RES in electricity production; to this aim, sufficiently smart bi-directional vehicle-to-grid technologies (V2G) have to be designed and widely installed. Parallelly, electric grid capabilities must become fully bidirectional in all nodes, both physically and in terms of ICT capabilities (so-called smart grid paradigm). In the meanwhile, some of those V2G technologies may already be locally implemented in individual home contexts. Following previous research activity about the identification of potential users of the most promising V2H technologies and on the evaluation of their expected benefits in terms of local renewable energy auto-consumption and/or local consumption auto-feeding performance, the author aims his attention to the numerical evaluation of the further benefits obtainable through the combined utilization of a number of V2H technologies all acting on the same “building” energy node; this approach is normally referred to in the literature as a vehicle-to-building (V2B) application. The SW tool which was developed to this aim is fully physically consistent, scalable, modular, open-source, and user-friendly, and it can be distributed under request to other research groups. In the simulations performed, V2H devices all used the same controlling approach, but offered their services to a “building” energy community, defined by the instantaneous sum of the energy behaviors of all the individual users. The simulation results show that building environments make it possible to intersect energy fluxes far beyond single user expectation, leading to very energy grid performances. In particular, renewable energy auto-consumption ratios become higher than 50%, and almost all local electric final uses may be fed through grid-connected vehicular ESSs (100% home auto-feeding ratio). This limits building–grid interactions to much more predictable residual ESS charging phases, as well as the sale of PV panel overproduction. The performance obtainable through the simulated V2B approach proved to be much higher than that obtainable through the same V2H technologies acting on single individual grids (which were estimated in a previous study by the same research group), ranging from 25% to 69% in terms of PV auto-consumption ratios (with higher values only obtainable for “nocturnal workers”, living in their home mostly during the daytime); moreover, a poor performance was recorded in terms of local consumption auto-feeding, ranging from 27% to 81% (with higher values only obtainable for those users mostly inhabiting their home during the night-time).

1. Introduction

This paper reports the completion of extended research activity on V2H systems, which were previously described and reported in a previous study [1]. Therefore, for brevity purposes, the introductory description of the research context, as well as that of the mathematical model used to gain the research results, is very limited in this paper. Readers are invited to download the previously edited open-access paper by the same authors for a much more detailed focus.

According to the International Energy Agency (IEA), the share of electric vehicles (EVs) in the light-duty (LD) transport sector, making reference to the number of actually running vehicles, actually remains low, approximately 1%, but will increase very rapidly in the near future, reaching about 10% by 2030 (see Figure 1) [2].

This electrification of the transportation sector will, therefore, be very fast and massive, producing huge problems, especially in terms of transient power requests that need to be locally and globally fulfilled at almost all electricity grid nodes. Many BEV ESSs will in fact be connected to the grid in very restricted areas and for limited time periods. Moreover, since (locally and globally) the electric grid must always be instantaneously in balance, huge problems will also rapidly arise due to the contemporaneous penetration of unpredictable RESs in electricity production (which is obviously welcome and no longer delayable). This will certainly limit the possibility of fast-charging vehicle facilities and lower electricity power quality) [3,4].

A rapidly increasingly number of ESSs connected to the grid will, therefore, be required for grid balances purposes. A possible, maybe partial, solution to this lack of a huge grid could be found if vehicle ESSs are smartly and fully bidirectionally used when grid-connected through so-called vehicle-to-grid (V2G) charging devices [5]. Some of these V2G charging devices may be rapidly introduced in the market, not requiring additional contractual schemes and/or complex measuring devices to be implemented at consumer–supplier grid exchanging nodes; this is the case for all charging devices connected in single homes (vehicle-to-home (V2H) devices) and/or in small buildings/condominiums (vehicle-to-building (V2B) devices) [6]. In fact, the utilization of V2H/V2B devices in place of a simple EV charger automatically makes it possible to transform any single node of the energy grid into a fully potentially bidirectional smart node, making it possible to generate and control all the power fluxes sketched in Figure 2.

However, V2H charging devices will only be appealing for some users, depending on their BEV usage style (daily mileage, timespan, and frequency), as well as on their characteristics as a final electricity user at their domestic nodes; V2H technologies, in fact, are expected to be more appealing in new-concept houses with photovoltaic plants (PV) installed, with a high local share of electric final uses, e.g., due to installed electric HVAC devices, both for winter heating (HP) and for summer cooling (AC) purposes [7,8]. The applicability of V2H technologies in all contexts is, therefore, not at all foreseeable; hence, relevant attention has been given to the development of V2H optimization tools in recent years, which could increase their energy and economic performance. These optimization tools could use a number of different approaches, mostly aimed at their energy optimization, limiting their impact on the National grid [9,10,11,12,13,14], while also considering the impact of charging method on the state of health and the whole lifecycle state of charge predictions for Li-ion ESSs [15,16].

While the main focus of the authors in this research activity was the derivation of an open-source freely distributable simulation tool for an evaluation of the expected performance of V2H technologies when connected to a local grid, but not the synthesis or derivation of novel and more sophisticated V2H device control algorithms, a very brief overview is given herein of the main recent literature advances in this field; for a more detailed review on the different approaches in this field, readers are referred to [9].

Slama [10] proposed a V2H control algorithm which takes into account both expected BEV utilization and local meteorological conditions. Mohammad et al. [11] used a heuristic gray wolf optimization algorithm solving a nonlinear multi-objective optimization algorithm, considering both the total daily energy demand and its expected time distribution. Ouramdane et al. [12] presented an optimization algorithm based on the interior-point algorithm, comparing two case studies (with and without a V2H device), while also obtaining some V2H interesting design considerations. Wang et al. [13] instead used a Jaya algorithm to derive their control algorithm, under several constraints related to electricity cost, demand profile type, expected PV production, and BEV mileage. Lastly, Irtija et al. [14] used an economical approach based on labor economics laws, considering different types of household users and solving a contract-theory optimization problem. All different approaches proved to be valid and applicable in different contexts with detailed objectives, while considering some key issues related to algorithm functionality and applicability in real operating devices due to their computational complexity and/or to the exchange of the required ICT interaction involving the grid, V2H device, and vehicle ESS (through the vehicle battery management system (BMS)).

2. Expected Performance of Single V2H Devices

Here, the results of a previous study [1] by the same research group are very briefly summarized. The performance of single V2H devices has already been evaluated in many different application contexts and may be here used as a reference for results obtainable through a combined utilization of a certain number of V2H technologies, all acting on the same building energy node. In particular, the authors proposed an SW tool aimed at the energetic optimization of intelligent V2H technologies for BEVs. The proposed SW was developed in Matlab-Simulink and is freely distributable under request to the authors. It models the dynamic behavior of the vehicular ESS when connected to the grid, as well as the control strategy chosen for the V2H charging technologies. It permits easily defining local user electricity production and consumption habits on an hourly basis. Currently, the data refer to a typical Italian context, as better detailed in [1]. The tool models in detail the energy flows at the main relevant nodes, permitting the calculation and optimization of local grid performance indicators, as briefly discussed below and better detailed in [1]. The tool was used to identify the most promising categories among expected users for V2H devices. To this aim, the typical user electricity demand for an Italian user was defined, according to some previous experimental campaigns and literature data [17,18,19].

Individual V2H technologies may in fact be not particularly interesting for some users, depending on their specific characteristics in terms of home consumption and vehicle usage and connection to the grid (e.g., BEV daily milage and frequency of use, home electric appliances, and home-connected RES power plants). The software was tested in both uni- and bidirectional V2H function modes and for different user types, divided into five groups, differentiated in terms of electric appliances, presence for heating and cooling purposes, PV power plants, home consumption habits, and vehicle connection timespans to the local electric grid:

• “Office workers” (OW): out of home between 8 a.m. and 5 p.m. on workdays.
• “Smart workers” (SW): home may be inhabited almost continuously.
• “Morning shift workers” (MW): out of home between 6 a.m. and 2 p.m. on workdays.
• “Afternoon shift workers” (AW): out of home between 2 p.m. and 11 p.m. on workdays.
• “Nocturnal shift workers” (NW): out of home between 11 p.m. and 8 a.m. on workdays.

For a complete definition and description of the user categories, please see [1]. As already underlined, the proposed SW tool was found to be easily adaptable to simulate a variety of possible different contexts. Tool development was funded by the Italian Ministry of Economic Development (through the RSE funding scheme); by means of this funding scheme, it is open-source, and its code is available under request.

A simple algorithm was modeled in the tool, aimed at the energy optimization of local and grid performance. The logic of the algorithm is briefly sketched in Figure 3.

The proposed control scheme was established to be easily implemented on real-time machines to control V2H systems. In fact, it does not require any particular communication with BEV BMSs. The proposed control strategy was also implemented in an experimental collaboration (with ENEA and Cassino University) to realize a wireless V2H prototype. This activity should be completed during the present year.

The control algorithm’s logic is based on the continuous comparison of two timespans, defining whether or not to habilitate the V2H functionality of the device:

• T2L (time to leave): remaining time to planned BEV disconnection from the grid.
• T2R (time to recharge): time for full charge of the ESS at nominal V2H power.

V2H functionality is enabled if T2L > T2R, provided the following:

• ESS charge by PV energy, up to nominal V2H power (PV2V State), up to ESS full SOC;
• ESS discharge, to supply residual local demand, up to the V2H nominal discharge power (V2H State), until ESS minimum SOC (eventually defined by the user).

When T2L ≤ T2R, the system instead enters standard charging mode at V2H nominal power (G2V State) up to its full SOC.

To evaluate the operational efficiency of the V2H system, two energy performance indicators were defined:

• PV auto-consumption ratio: fraction of PV production used by local instantaneous uses. Simple V2H devices which implement unidirectional functionalities (only able to modulate charging power during charging operations, sometimes referred to as V1H or V1G devices in the literature) may also be effective in substantially increasing this indicator.
• Home auto-feeding ratio: fraction of total domestic consumption (excluding ESS charging) fed locally, not taking electricity from the grid. This indicator has a max value of 1, which would state that the only electricity coming from the grid is that needed for BEV charging. This energy, in turn, may be predictable in time and stabilized in power, strongly limiting grid problems and, therefore, strongly reducing electricity price.

Both uni- and bidirectional (1D and 2D) smart charging functionalities were modeled and tested, with 1D devices being those EV chargers which may only modulate the ESS charge (e.g., giving priority to electric energy coming locally from a PV array) but cannot feed local energy consumption through the local ESSs if needed.

The results of those simulation activities for individual use contexts (V2H devices) were extensively reported and discussed in [1]. Significant overall benefits were obtainable in all simulated cases. However, as clearly shown by the data in

Table 1. Single V2H devices—performance table for all the simulated cases.

Case ID	Annual Energy Balances [kWh/Day]											Performance Indicators
	PV System				Home Node				Grid Node			PV Auto-Cons.	Home Auto-Feeding
	PV2V	PV2H	PV2G	PVTot	H2V	H2G	H2PV	HTot	G2H	G2V	GTot
Base Case	0	4.018	11.87	15.89	0	10.54	4.018	14.56	10.54	12.78	23.32	25.29%	27.60%
Case1A—Office Worker 1D	1.23	4.018	10.64		0	10.54	4.018	14.56	10.54	11.59	22.12	33.02%	27.61%
Case1B—Office Worker 2D	0.794	4.016	11.08		7.286	3.253	4.016	14.56	3.253	20.03	23.29	30.28%	77.65%
Case2A—Smart Worker 1D	4.333	4.097	7.454		0	10.57	4.097	14.67	10.57	8.475	19.05	53.07%	27.93%
Case2B—Smart Worker 2D	4.085	4.1	7.704		7.79	2.778	4.1	14.67	2.778	17.25	20.03	51.51%	81.06%
Case3—Morning Worker 2D	2.491	4.036	9.363		7.788	2.742	4.036	14.57	2.742	18.9	21.64	41.08%	81.17%
Case4—Afternoon Worker 2D	5.055	5.281	5.556		3.091	6.181	5.281	14.55	6.181	11.15	17.33	65.04%	57.53%
Case5—Nocturne Worker 2D	6.551	4.497	4.848		4.261	5.79	4.497	14.55	5.79	10.95	16.74	69.50%	60.2%

, the best results were obtained when implementing fully bidirectional V2H functionalities for those users whose BEV may remain grid-connected throughout the daytime (especially smart and morning shift workers). In this case, the highest performance was reached in terms of both home auto-feeding and PV auto-consumption ratios. Table 1 reports the results of the main simulation performed, as a reference for the subsequent research activities which were the main object of this paper.

Summarizing the presented results, it may be stated that the performance of V2H technologies, while acting on single individual grids, fell into the following ranges:

• PV auto-consumption ratio from 25% to 69%, with higher values only obtainable for “nocturnal workers”, living in their home mostly during the daytime;
• Home auto-feeding ratio from 27% to 81%, with higher values only obtainable for those users mostly inhabiting their home during the night-time.

These values may be used as a reference for results obtained through the combined usage of the same V2H technologies in an overall building context (a full V2B approach).

3. Expected Performance of V2B Devices

Considering the previously presented results about the utilization of individual V2H technologies, the author’s aim was to evaluate the overall performance obtainable by the combined utilization of a set of V2H technologies, all using the same controlling approach but acting on a “building”, thus offering their services to a “building” energy community, which can be considered the instantaneous sum of the energy behaviors of all individual users. Some examples of this approach and a preliminary evaluation of the performance of those devices can be found in the literature [6,20,21,22].

Again, the reader should consider that one of the main aims of the research group in this research activity was to develop and make available for the scientific community an easy-to-use, open-access, and freely distributable simulation tool to be further used by other research groups simulating control algorithms for different grid contexts and/or V2H technologies, both for individual applications and for more complex V2B contexts. For this reason, the objective of this paper was also to explicitly show the interface of the developed SW, its graphic user interface, and the typology and amplitude of simulated variables, both on a single V2H device level and on a V2B level. To this aim, a number of graphs are reported in the Appendix A for readability. In the text of the paper, only the main figures resulting from these simulations are reported and discussed in detail.

In the V2B context, single V2H devices intermittently connected to the grid were preliminarily categorized into five subgroups corresponding to the five previously described user categories. V2H technologies may be categorized into subgroups because each subgroup has the same habits in terms of vehicle ESS connection to the grid. Simulations were based on the hypothesis that a predefined intervention priority exists between the subgroups, thus establishing with a “cascade” approach. In other words, each V2H subgroup defines behavior on the basis of the control algorithm for individual V2H systems (sketched in Figure 3) to satisfy a local user, as determined by the algebraic sum of instantaneous overall building appliances and PV productions, as well as that of the power fluxes already implemented by V2H subgroups with a higher predefined priority.

Instead, in real applications, a further control challenge would be that of synthetizing algorithms defining intervention priority laws among various V2H devices contemporaneously connected to the same “building” energy node. This will be required because the energy services performed for the “building” energy community by the single V2H devices, in most real cases, would probably be measured and remunerated to individual owners of the connected V2H devices. All these aspects, however, may be addressed and solved on a building/condominium basis (as normally occurs for common energy supply), without requiring any formal interaction with the electric energy supplier.

To show the further potential of V2B applications, with respect to independent utilization of a certain number of installed V2H devices, a typical condominium was defined, and a relevant upgrade was made to the previously described SW tool, which was dedicated to the simulation of individual V2H technologies. A typical building was modeled on the hypothesis that all individual users may be categorized into five subgroups, according to the five previously defined user categories.

According to this hypothesis, the behavior of the connected vehicles can also be calculated as the dynamic sum of five “macro” V2H devices, each simulating the overall behavior of all devices belonging to a single user category. It is the author’s belief that the proposed number and typology of user categories are sufficient to model almost all possible contexts. However, if needed, they can be easily increased by modifying the SW through its graphical user interface.

Below, the results of this further research activity devoted to the evaluation of the expected performance of a combined utilization of V2H technologies in V2B contexts are reported. In detail, the performance was evaluated in a V2B context in the following cases:

• Without V2H systems (base case—used as reference);
• With V2H systems with unidirectional (1D) functionality only;
• With V2H systems with full bidirectional (2D) functionality.

In all three cases, the condominium consisted of 20 total users, divided as outlined below.

• Ten users without an electric car:

  o

  3 “smart workers;

  o

  2 “office workers”;

  o

  2 “morning shift workers”;

  o

  2 “afternoon shift workers”;

  o

  1 “nocturnal shift worker”.

• Ten users with an electric car:

  o

  3 “smart workers;

  o

  2 “office workers”;

  o

  2 “morning shift workers”;

  o

  2 “afternoon shift workers”;

  o

  1 “nocturnal shift worker”.

3.1. Base Reference Case—No V2B Functionality Present

A complete typical year of condominium life was simulated using the proposed SW tool. The overall results of the simulation are shown in Figure A1 and Figure A2, in the Appendix A. Energy data are in kWh/day, while performance indicators are dimensionless. The X-axis values in all reported graphs denote the hours from simulation start. Therefore, the timespan is 8760 h for a complete annual simulation and 168 h for weekly simulations. The readers should also note that simulated weeks go from Monday to Sunday.

The annual simulation shows that, in this case, the self-consumption of PV production is limited to 27% and the self-supply of condominium loads (exclusively from PV) is limited to approximately 30%.

Figure A3, Figure A4, Figure A5 and Figure A6 provide a more in-depth description of the entire condominium network behavior in four standard weeks, with reference to winter, spring, summer, and autumn periods, respectively, which are characterized by very different consumption habits during the days, as well as different predictable availability in terms of solar energy entering the PV arrays.

It is important to recall that the figures reported in this paper correspond exactly to the graphical output of the developed SW, which is totally open-source and available under request.

Figure A1 reports the integral energy results during the simulated period (normally an entire year). Figure A2, Figure A3, Figure A4, Figure A5 and Figure A6 report the time-resolved evolutions of power values in the important nodes of the electric grid. With reference to the power fluxes sketched in Figure 2, the following can be established:

• The first row is dedicated to the “PV node”: thicker line relative to overall PV production (PV); thinner lines for single energy contribution to the node—to local demand (PV2H), to vehicle ESS (PV2V), and to the grid (PV2G).
• The second row is dedicated to the “home node”: thicker line relative to overall home demand (Home); thinner lines for single energy contribution to the node—from the Grid (G2H) and from vehicle ESS (V2H).
• The third row is dedicated to the “grid node”: thicker line relative to overall grid flow (G); Thinner lines for single energy contribution to the node—to vehicle ESS charging (G2V), to feed house demand (G2H), and from the PV plant to the grid (<0) (PV2G).
• The fourth row shows the number of ESSs contemporaneously connected to the infrastructure.
• The fifth row shows the SOC evolution for the vehicle ESS vs. time.
• The sixth row is dedicated to the “ESS node”: thicker line relative overall resulting flow (ESS); thinner lines for single energy contribution to the node—due to road consumption (V2Road, <0), for ESS charging from the grid (G2V), for ESS charging from the PV (PV2V), and to household appliances (V2H, <0).

3.2. Case 1—Condominium Network with Unidirectional V2B Functionality

In this section, the results of the simulation of the condominium network itself are presented, where V2H functions are implemented by the 10 charging stations, with simply unidirectional functions, aimed at maximizing the self-consumption of local PV production (referring to a condominium with a total of 20 families, including those without electric vehicles).

The simulation of the entire year of operation of the grid, shown in Figure A7 and Figure A8, shows how self-consumption from PV increases to 52% (strongly increasing the performance obtained in the base case (27%)).

Subsequently, as already presented for the previous case, the detailed trends of the main variables in four simulated weeks (winter, spring, summer, and autumn) are reported in Figure A9, Figure A11, Figure A13 and Figure A15, respectively.

For each of the 4 weeks, however, a detailed scheme is also reported with a graphical representation of the “cascade” management, using the same basic algorithm, of the five groups of storage systems connected to the network, related to the five different categories of possible users. The diagrams relating to the four different weeks are shown in Figure A10, Figure A12, Figure A14 and Figure A16, respectively. In each of these representations, the categories of users are represented in different columns (from left to right): MW, AW, NW, OW, and SW. The same order is followed to define the priority of use of the storage systems defined in the simulation.

Briefly entering a discussion of the behavior of the V2B system (see, e.g., Figure A13 and Figure A14), on a condominium basis, in summer, a good part of the summer production from PV feeds the PC electrical loads for heating. Therefore, only a few partial recharge operations can be carried out at the expense of the PV (in red, third row of the graphs) by increasing self-consumption.

Conversely, Figure A11, Figure A12, Figure A15 and Figure A16 show that, since there are no electrical loads for heating and/or cooling in spring and autumn, many of the recharging operations of connected vehicles can be carried out at the expense of PV production (in red, third row of the graphs) by increasing self-consumption.

An intermediate behavior between those described above can be found in winter (see Figure A9 and Figure A10) when refrigeration loads are high but not fully matched to the large availability of PV energy. Therefore, several opportunities remain for PV energy to recharge storage systems (in red, third row of the graphs).

Lastly, Figure A17 summarizes the management of the accumulation systems during the entire simulated year.

3.3. Case 2—Condominium Network with Complete V2B Functionality

Lastly, the same condominium network was simulated, with fully bidirectional V2H functions implemented by the 10 charging stations.

This configuration is aimed not only at maximizing the self-consumption of local PV production (referring to a total of 20 condominium users, including those without electric vehicles), but also at self-supplying most of the consumption of the building through the 10 connected accumulation systems.

The simulation of the entire year of operation of the grid, shown in Figure A18 and Figure A19, shows how self-consumption from PV increases to 51% (strongly increasing the performance obtained in the base case (27%) but not significantly decreasing the performance compared to Case 1 with V2H unidirectional use). At the same time, the 10 connected storage systems proved to be sufficient to power all condominium users that were not fed directly from the PV. In this way, 100% self-supply of grid consumption and the maximum possible time concentration were achieved, as well as the maximum possible hourly predictability of the grid’s energy withdrawal plans.

Subsequently, as already presented for the previous case, a detailed time evolution in main nodal power fluxes is reported, relative to 4 weeks representing normal winter, spring, summer, and autumn conditions (Figure A20, Figure A22, Figure A24 and Figure A26, respectively).

As in the previously simulated Case 1, for each of the 4 weeks, detailed diagrams relating to the graphic representation of the “cascade” management are also shown, using the same basic algorithm, of the five groups of storage systems connected to the network, relating to the five different categories of possible users,. The diagrams relative to the four different weeks are shown in Figure A21, Figure A23, Figure A25 and Figure A27 respectively.

In particular, Figure A25 shows how, on a condominium basis, in summer, a good part of the summer production from PV feeds the PC electrical loads for heating. Therefore, only a few partial recharge operations can be carried out at the expense of the PV (in red, third row of graphs) by increasing self-consumption. Conversely, the entire condominium consumption for heating can be self-powered, in part by production and in part by the accumulation systems connected to the network (in blue, third row of graphs).

On the other hand, Figure A23 and Figure A27 show how, in spring and autumn, since there are no electric loads for heating and/or refrigeration, many of the recharging operations of the connected vehicles can be carried out at the expense of PV production (in red, third row of graphs) by increasing self-consumption.

An intermediate behavior between those described above can be found in winter when refrigeration loads are high but not fully matched to the large availability of PV energy. There are, therefore, several opportunities for the use of energy from PV for recharging storage systems (in red, third row of graphs).

In all seasons, all consumption can be self-powered by the combination of PV production from the 20 users and the 10 storage systems alternatively connected to the grid.

Lastly, Figure A28 summarizes the management of the accumulation systems, with reference to the entire simulated year.

4. Discussion

This paper reports the results of a research activity aimed at evaluating the benefits obtainable through the use of charging systems for electric vehicles that can implement intelligent control logic (uni- or bidirectional). The research activity reported in this paper specifically concerns the combined and coordinated use of a number of V2H devices on a condominium building scale (so-called V2B applications) with the aim of obtaining a further optimization of the behavior of the system, both limiting local energy costs and building/grid interactions and possible criticism. To this aim, a previously developed model of the system, which was already described in detail in a previous paper by the authors [1] was here extended and applied to a much more complex V2B context.

The results relative to individual users [1] showed, as expected, that only some of the possible sketched user categories could obtain substantial benefits through V2H devices, while other kinds of users may be less interested in V2H device installation, with their individual consumption habits more scarcely matching PV energy availability and ESS connection to the grid.

The results here presented, instead, show how the usage of the same V2H devices in a building “condominium” approach makes it possible to intersect energy fluxes far beyond single user expectation, leading to very high expected performance in terms of the introduced indicator values: PV auto-consumption ratio and home (or “building”) auto-feeding ratio.

In particular, the performed simulations showed that renewable energy auto-consumption ratios higher than 50% may be easily obtained on a condominium basis, and that practically all local consumption may be fed through the ESSs (100% home auto-feeding ratio), thus limiting the need for a grid connection to both residual (predictable) ESS charging phases and to grid sales of PV panel overproduction. This performance is much higher than that obtainable by the same V2H technologies acting on single individual grids, which were estimated in a previous study by the same author [1], ranging from 25% to 69% in terms of PV auto-consumption ratios (with higher values only obtainable for “nocturnal workers”, living in their home mostly during the daytime); moreover, a poor performance was recorded in terms of local consumption auto-feeding, ranging from 27% to 81% (with higher values only obtainable for those users mostly inhabiting their home during the night-time).

It is worth underlining that coming to the prospective future of onsite utilization of V2H in V2B contexts, the control algorithm to be used on V2H devices should also define intervention priority rules for individual V2H systems, thus defining and measuring their individual usage, so as to also distribute the economic benefit connected to the services rendered for local users and the entire condominium network. This activity extended beyond the objectives of the present study and will be the subject of subsequent investigations.