Optimized Power Dispatch for Smart Building and Electric Vehicles with V2V, V2B and V2G Operations

Abstract

The operation of smart buildings (with solar, storage and suitable power routing infrastructure) can be optimized with the addition of parking stations for electric vehicles (EVs) with vehicle-to-everything (V2X) operations including vehicle-to-vehicle (V2V), vehicle-to-building (V2B) and vehicle-to-grid (V2G) operations. In this paper, a multi-objective optimization framework is proposed for the smart charging and discharging of EVs along with the maximization of revenue and savings of smart building (prosumers with solar power, a battery storage system and a parking station) and non-primary/ordinary buildings (consumers of electricity without solar power, a battery storage system and parking station). A mixed-integer linear program is developed to maximize the profits of smart buildings that have bilateral contracts with non-primary buildings. The optimized charging and discharging (V2X) of EVs at affordable rates utilizing solar power and a battery storage system in the smart building helps to manage the EV load during on-peak hours and prevent utility congestion. The results indicate that in addition to the 4–9% daily electricity cost reductions for non-primary buildings, a smart building can achieve up to 60% of the daily profits. Further, EVs can save 50–69% in charging costs while performing V2X operations.

1. Introduction

Enhancing the dependability of the power supply and the flexibility of building energy through integrating electric vehicles (EVs) into smart buildings offers enormous potential [1,2]. The decreasing costs of solar PV modules (0.3 USD/kWp [3,4]) and lithium-ion battery storage system storage (156 USD/kWh [5]) have encouraged consumers with bulk consumption (buildings) to install the local resource of electricity to reduce the dependence on utility (grid). With an increase in EV adoption, prosumers (smart buildings) may contribute to bettering the running of the power systems. Smart buildings with solar PV and batteries can assist in charging the EVs during on-peak hours [6].

Several studies have been reported in the literature to maximize the profits associated with installing EV charging stations in smart buildings with vehicle-to-everything (V2X) capabilities including vehicle-to-vehicle (V2V), vehicle-to-building (V2B) and vehicle-to-grid (V2G) operations. However, the focus is still on the local optimization of the building load or parking station. For instance, the authors in [7] have proposed an optimal battery storage system sizing for maximizing profit in a charging station. The promotion of solar PV for the charging of EVs to maximize the system operator profit is proposed in [8]. The authors in [9], enabled V2V power transfer from a shared electric vehicle fleet to reduce the impact on distribution grids. For many mobile EVs, authors proposed an intelligent V2V charging navigation technique in [10]. Electric vehicles (EVs) can be charged at a single charging station using the offline and online scheduling methods developed in [11]. In [12], the authors suggested a flexible energy management protocol that might assist EVs in achieving more adaptable and intelligent charging and discharging behaviors.

The studies mentioned earlier examined various business strategies and integrated energy management systems, but there is a lack of existing literature on the profit models for EVs with V2X capability. Previous research works have not taken a comprehensive approach to modeling vehicle-to-everything (V2X) operations including V2V, V2B and V2G along with the optimization of the financial benefits of both prosumers (smart building and EVs) and consumers (non-primary buildings) through bilateral contracts between the smart and non-primary buildings. Therefore, this paper introduces a novel techno-economic framework that enables lucrative power sharing among the following entities: (a) an EV fleet with V2V, V2B and V2G capabilities, (b) a smart building with a provision of rooftop PV, dedicated storage, and charging infrastructure, and (c) non-primary buildings that are only connected to the grid (without solar PV and storage).

The proposed work utilizes a mathematical model to maximize the potential profits of the smart building while providing monetary incentives to the non-primary buildings and the EVs’ fleet. The non-primary buildings have bilateral contracts with the smart building, which stipulate a minimum contracted load at a predetermined tariff lower than the time of use (TOU) cost. The parking station in the smart building facilitates the optimized charging of the EVs’ fleet using power from the grid, solar power, a battery storage system, and a power trade withing the EVs as well as optimized discharging to the grid, smart building, non-primary buildings, and battery storage system and power trading among electric vehicles. The smart building trades electricity to both the EVs’ fleet and the non-primary buildings at rates lower than the TOU tariff, fostering lucrative interaction and active engagement among the participating entities. During on-peak hours, the EVs’ fleet benefits from attractive charging rates provided by the smart building, utilizing solar energy and stored power, thereby supporting the utility by shaping the load curve during on-peak grid hours. This model, to the best of the authors’ knowledge, represents an optimized operation for the grid, local building loads, rooftop solar PV, battery storage system, and the EVs’ fleet, yielding benefits for multiple parties.

For illustration motives to assist in mimicking the model’s applicability, we used three real buildings in Auckland, New Zealand, and a fleet of fifteen EVs. The power sector of New Zealand is used as an example to assesses the optimized power trade to maximize profits for each building (smart and non-primary) as well as the EVs’ fleet, resulting in tangible benefits.

The remaining sections are arranged as follows: The system overview, which describes the framework and numerous system component parameters, is presented in Section 2. In Section 3, a mathematical formulation of the proposed framework is presented. To show the practical applicability of the model, Section 4 discusses the numerical cases and simulation results, followed by conclusions in Section 5.

2. System Overview

Figure 1 depicts the several entities engaged in the power trading network including EVs, the utility with on-peak and off-peak price plan, and three different buildings.

• Smart Building: This building has a rooftop solar PV (400 kWp in this case) with the provision of 400 kWh of storage. Additionally, 15 EVs can use the available parking station for charging and discharging.
• Non-primary Building(s): These include buildings close to the smart building, that lack rooftop solar power, a battery storage system, and an EV parking station. These buildings have bilateral load agreements with the smart building. This study considers two non-primary buildings with power trading agreements with the smart building.
• Electric Vehicles: The parking station within the smart building can charge and discharge 15 EVs’ fleet. EVs can transfer power between vehicles, smart buildings (load and battery storage system), non-primary buildings, and the utility.

Table 1. Size and costs of solar power, battery storage system and charger.

System Entity	Solar	Battery	Charger
Size	400 kWp	400 kWh	10 kW × 15
Price of a single unit	0.2 USD/W [3]	145 USD/kWh [5]	570 USD/Charger
Life (months)	300	120	300
Total Cost (USD)	92 k	58 k	8.5 k [13,14]
Average price for a day (USD)	18	29	1.7

provides an overview of the system component sizes and related costs.

3. Problem Formulation

An optimization problem using a mixed-integer linear programming (MILP) approach is formulated in ILOG Optimization Studio to achieve optimized power dispatch and cost savings for smart and non-primary buildings, as well as a fleet of electric vehicles. The power flow diagram in Figure 1 illustrates how power is distributed between the smart building, non-primary buildings, the grid, and the EVs. The diagram also highlights that the smart building and EVs can provide power back to the grid at significantly lower rates compared to the time of use (TOU) tariff rates. Additionally, the smart and non-primary buildings can acquire electricity from the EVs at a slightly reduced tariff compared to the TOU pricing. Further, this study primarily focuses on the techno-economic evaluation of the system, and it is worth noting that power quality constraints can be incorporated into the proposed model to enhance the practicality of the results [15,16]. By considering power quality factors, the model can provide more comprehensive and accurate assessments of the system’s performance and feasibility.

The goal of the optimization is to determine the values of decision variables that maximize the overall profit for the smart and non-primary buildings, as well as the EVs’ fleet. The optimization problem includes 359 decision variables that are non-negative and 32 decision variables that are binary. A multi-objective optimization problem is solved using a profit model, which ensures the highest profitability for the smart building, non-primary buildings, and the EVs’ fleet. One hour is chosen as the time step for the optimization model. The non-primary buildings are interconnected with the smart building, allowing power sharing between them. It is guaranteed that the minimum contracted load demand of the non-primary buildings, when purchasing electricity from the smart building, will always be supplied by the smart building, using power from sources such as solar PV, storage, or power imported from the grid. The smart building features a parking station that enables EVs to be charged either directly from the grid or through solar PV or battery storage system sources. The primary building provides power to the EVs at reduced rates rather than charging directly from the grid. Additionally, the EVs’ fleets can bilaterally trade with each other depending upon the state of power (SOP) of the participating vehicles. The objective function and set of 469 inequality constraints and 45 equality constraints provide optimal power dispatch in accordance with the suggested profit model.

3.1. Objective Function

The objective function, denoted by Equation (1), seeks to maximize the smart building’s net profit. It can sell electricity to a variety of parties participating in the power trading industry, including utility (net metering), non-primary buildings, and electric vehicles (parking station). The smart building’s net profit in Equation (1) is calculated by deducting the daily costs of the battery storage system, solar power, and charger from the total income received. The best way to maximize the net income of the smart building is given by Equation (2).

$$P^{Max}=R^{Max}-(BatteryCost+SolarPVCost+ChargerCost)$$

(1)

$$R^{Max}=Max(R_g+R_{B_i}+R_{E_n}+R_{B_1}-O_B-C_{E_n}-C_B-T_{B_i}-P_{E_n});\forall \left(n\right);i=\mathrm{2,3}$$

(2)

Power exports to the utility ($R_g$), B₂ and B₃ ($R_{B_i}$), and Evs ($R_{E_n}$) make up the smart building’s revenue. The savings achieved by satisfying the smart building’s load demand are also included in its income ($R_{B_1}$). The costs incurred by the smart building include a flow of cash to the utility to satisfy the contracted loads of non-primary buildings ($T_{B_i}$), charging the battery storage system ($C_B$), charging electric vehicles ($C_{E_n}$), importing electricity from the utility to satisfy the outstanding load of B₁ ($O_B$), and paying the electric vehicles for buying electricity ($P_{E_n}$). The terms in (2) are further defined as follows:

$$R_g=\sum _{t=1}^{24}\left(T_{SG}\left(t\right)\ast E_s\left(t\right)+T_{BG}\left(t\right)/{\delta }_{db}\ast E_s\left(t\right)\right)$$

(3)

$$R_{B_i}=\sum _{t=1}^{24}(T_{{SB}_i}\left(t\right)\ast E_{{CB}_i}(t)+T_{{BB}_i}\left(t\right)/{\delta }_{db}\ast E_{{CB}_i}(t));\hspace{1em}i=2,3$$

(4)

$$R_{E_n}=\sum _{n=1}^{15}\sum _{t=1}^{24}\left({(T}_{{BE}_n}\left(t\right)\ast E_{E_{B_1}}(t))+{(T}_{{SE}_n}\left(t\right)\ast E_{E_{B_1}}(t))+(T_{{GE}_n}(t)\ast E_{EG}(t))\right){\ast \delta }_{ce}\ast \Delta t$$

(5)

$$R_{B_1}=\sum _{t=1}^{24}{T}_{{SB}_1}\left(t\right)\ast \left(E_p\left(t\right)-E_s\left(t\right)\right)+T_{{BB}_1}\left(t\right)/{\delta }_{db}\ast \left(E_p\left(t\right)-E_s\left(t\right)\right)\ast \Delta t$$

(6)

$$O_B=\sum _{t=1}^{24}\left({\begin{array}{c}\\ T\end{array}}_{{GB}_1}\left(t\right)\ast E_p\left(t\right)\right)\ast \Delta t$$

(7)

$$C_{E_n}=\sum _{t=1}^{24}\left(T_{{GE}_n}\left(t\right)\ast E_p\left(t\right)\right)\ast \Delta t$$

(8)

$$C_B=\sum _{t=1}^{24}\left(T_{GB}\left(t\right)\ast {\delta }_{cb}\ast E_p\left(t\right)\right)\ast \Delta t$$

(9)

$$T_{B_i}={\sum }_{t=1}^{24}\left(T_{{GB}_i}\left(t\right)\ast \left(E_p\left(t\right)-E_{{CB}_i}\left(t\right)\right)\right)\ast \Delta t$$

(10)

$$P_{E_n}=\sum _{n=1}^{15}\sum _{t=1}^{24}\left((T_{E_n{B}_1}\left(t\right)+T_{E_{n}B}\left(t\right))\ast E_{EX}\left(t\right)\right)\ast \Delta t$$

(11)

In our study, we have focused on a single smart building. However, the proposed model can be extended to encompass multiple ‘M’ smart buildings. These buildings would be equipped with solar panels, battery storage systems, and charging facilities to facilitate various vehicle-to-everything (V2X) operations, including vehicle-to-vehicle (V2V), vehicle-to-building (V2B), and vehicle-to-grid (V2G) operations. In the case of multiple smart buildings, the objective function will be modified and overall revenues of multiple smart buildings will be maximized through Equation (12).

$$R^{Max}=Max\left(R_g^m+R_{B_i}^m+R_{E_n}^m+R_B^m-O_B^m-C_{E_n}^m-C_B^m-T_{B_i}^m-P_{E_n}^m\right);\forall \left(n\right);i=\mathrm{2,3};\forall \left(m\right)$$

(12)

The terms in Equation (12) include revenues earned from the power traded by each smart building with the utility ($R_g^m$), B₂ and B₃ ($R_{B_i}^m$), and EVs ($R_{E_n}^m$). The savings earned by meeting load of each smart building are termed as ($R_B^m$). The costs sustained by smart buildings includes flow of cash to the utility to satisfy the contracted loads of non-primary buildings ($T_{B_i}^m$), charging the battery storage systems of each smart building ($C_B^m$), charging electric vehicles through the parking station in each smart building ($C_{E_n}^m$), importing electricity from the utility to satisfy the outstanding loads of each smart building ($O_B^m$), and payments to the electric vehicles for buying electricity by each smart building ($P_{E_n}^m$).

3.2. Problem Constraints

3.2.1. Storage Constraints

The battery storage system constraints are listed as Equations (13)–(17), including 32 inequality constraints and 15 equality constraints. The two variables in Equation (13) guarantee that the battery storage system cannot be charged and drained at the same time; Equation (14) restricts battery storage system charging; and Equation (15) sets higher and lower limitations on the battery storage system’s charging and discharging, respectively. The battery storage system’s maximum quantity of power that may be drawn is determined by Equation (16). As of the next time slot, the battery storage system power is expressed as Equation (17).

$$\alpha \left(t\right)+\beta \left(t\right)\le 1\hspace{1em}\alpha \left(t\right),\beta \left(t\right)\in \left\{\mathrm{0,1}\right\},\forall \left(t\right)$$

(13)

$$(T_{GB}\left(t\right)+T_{SB}\left(t\right)+T_{E_{n}B}\left(t\right))\ast \alpha \left(t\right)\le {SOP}_{B^{Max}};\forall \left(n\right)$$

(14)

$${SOP}_{B^{Min}}\le SOP\left(t\right)\le {SOP}_{B^{Max}}$$

(15)

$$(T_{{BB}_1}\left(t\right)+T_{{BB}_2}\left(t\right)+T_{{BB}_3}\left(t\right)+T_{BG}\left(t\right)+T_{{BE}_n}\left(t\right))\ast \beta \left(t\right)\le ({SOP}_{B^{Max}}-{SOP}_{B^{Min}});\forall \left(n\right)$$

(16)

$$\begin{array}{ll}{SOP}_B\left(t+1\right)=& {SOP}_B\left(t\right)+\left(({\delta }_{cb}\ast T_{GB}\left(t\right)+{\left(\right({\delta }_{cb}\ast T}_{SB}\left(t\right)\right)+\left(\left({\delta }_{cb}\ast T_{E_{n}B}\left(t\right)\right)\right)\ast \alpha \left(t\right)\\ & -\left({(T}_{BG}\left(t\right)/{\eta }_{disch})+{(T}_{B{B}_1}\left(t\right)/{\delta }_{db})+(T_{B{B}_3}\left(t\right)/{\delta }_{db})+({\delta }_{ce}\ast T_{B{E}_n}\left(t\right)/{\delta }_{db})\right)\ast \beta \left(t\right);\forall \left(n\right)\end{array}$$

(17)

3.2.2. Building Load and Power Trading Constraints

The utility, batteries, or solar can all meet the smart building’s load requirement, which is expressed as 15 inequality constraints as shown in Equation (18). The bilateral contractual power assures that B₂ and B₃ will obtain the minimum contracted loads, modeled as 32 inequality constraints, as shown by Equations (19) and (20), respectively.

$$0\le T_{G{B}_1}\left(t\right)+T_{B{B}_1}\left(t\right)/{\delta }_{db}+T_{S{B}_1}\left(t\right){+T_{E_n{B}_1}\left(t\right)/{\delta }_{de}\le L}_1\left(t\right);\forall \left(n\right)$$

(18)

$$C_{B_i}\left(t\right)\le T_{G{B}_i}\left(t\right)+T_{B{B}_i}\left(t\right)/{\delta }_{db}+T_{S{B}_i}\left(t\right)\le L_i\left(t\right);i=2,3$$

(19)

$$0\le T_{G{B}_i}\left(t\right)+T_{B{B}_i}\left(t\right)/{\delta }_{db}+T_{S{B}_i}\left(t\right)+T_{E_n{B}_i}\left(t\right)/{\delta }_{de}\le L_i\left(t\right)-C_{B_i}\left(t\right);i=2,3;\forall \left(n\right)$$

(20)

3.2.3. Electric Vehicle Constraints

The utility, solar power, or the battery storage system may all be used to charge EVs. A fleet of fifteen electric vehicles (EVs) with a capacity of 24 kWh is taken for simulation purposes. With the increased capacity of electric vehicles (EVs), trading with buildings can occur more frequently during peak hours when the supply from solar and battery sources is limited. EVs can take advantage of off-peak hours to charge their batteries and participate in vehicle-to-everything (V2X) operations based on their state of power (SOP). This allows EVs to actively contribute to energy exchanges and support overall grid stability and management. The parking station in the smart building is further assumed to have 15 Level 2 chargers (SAE-J1772, 208-240 VAC) [6] that are accessible, having an efficiency of 90%. The standard working hours are assumed as 8 a.m. to 6 p.m. Through the t location scale distribution, the EVs’ arrival time is determined [13].

In line with this, Figure 2 displays the various EV-produced arrival timings. Each EV’s stay time is computed using a normal distribution with a mean of 8.5 h. The ${SOP}_{E_n}\left(t\right)$ of each incoming EV is calculated using Monte Carlo simulations. The procedure for the charging and discharging of EVs and trading among the EVs is described in Equations (21)–(32) through 390 inequality constraints and 15 equality constraints. For each Level 2 charger, the term ‘$\epsilon$’ denotes the charger capacity, which is 10 kW.

$${\phi }_{E_n}\left(t\right)+{\varphi }_{E_n}\left(t\right)\le 1 \left({\phi }_{E_n}\right(t),{\varphi }_{E_n}(t\left)\right)\in \left\{\mathrm{0,1}\right\},\forall \left(t\right)\forall \left(n\right)$$

(21)

$$\left(T_{{GE}_n}\left(t\right)+T_{{BE}_n}\left(t\right)+T_{{SE}_n}\left(t\right)\right)\ast {\phi }_{E_n}\left(t\right)\le {SOP}_{{E_n}^{Max}};\forall \left(n\right)$$

(22)

$${SOP}_{{E_n}^{Min}}\le {SOP}_{E_n}\left(t\right)\le {SOP}_{{E_n}^{Max}};\forall \left(n\right)$$

(23)

$$(T_{E_n{B}_1}\left(t\right)+T_{E_n{B}_2}\left(t\right)+T_{E_n{B}_3}\left(t\right)+T_{E_{n}B}\left(t\right)+T_{E_{n}G}(t\left)\right)\ast {\varphi }_{E_n}\left(t\right)\le ({SOP}_{{E_n}^{Max}}-{SOP}_{{E_n}^{Min}});\forall \left(n\right)$$

(24)

$$\begin{gathered} {SOP}_{E_n}(t+1)={SOP}_{E_n}\left(t\right)+\left(\right({\delta }_{ce}\ast T_{{GE}_n}\left(t\right))+({\delta }_{ce}\ast T_{{BE}_n}\left(t\right)/{\eta }_{disch})+({\delta }_{ce}\ast T_{{SE}_n}\left(t\right)\left)\right)\ast {\phi }_{E_n}\left(t\right)-\left(\right({\delta }_{cb}\ast \\ {T}_{E_{n}B}\left(t\right)/{\eta }_{disch})+\left(T_{E_{n}G}\left(t\right)/{\eta }_{disch}\right)+\left(T_{E_n{B}_1}\left(t\right)/{\delta }_{de}\right)+\left(T_{E_n{B}_2}\left(t\right)/{\delta }_{de}\right)+{(T}_{E_n{B}_3}\left(t\right)/{\delta }_{de}))*{\varphi }_{E_n}(t);\forall (n) \end{gathered}$$

(25)

$$({\delta }_{ce}\ast T_{{GE}_n}\left(t\right))\le \epsilon ;\forall \left(n\right)$$

(26)

$$({\delta }_{ce}\ast T_{{SE}_n}(t\left)\right)\le \epsilon ;\forall \left(n\right)$$

(27)

$$({\delta }_{ce}\ast T_{{BE}_n}\left(t\right)/{\delta }_{db})\le \epsilon ;\forall \left(n\right)$$

(28)

$$\left(T_{E_{n}G}\left(t\right)/{\delta }_{de}\right)\le \epsilon ;\forall \left(n\right)$$

(29)

$$({\delta }_{cb}\ast T_{E_{n}B}\left(t\right)/{\delta }_{de})\le \epsilon ;\forall \left(n\right)$$

(30)

$$\left(T_{E_n{B}_I}\left(t\right)/{\delta }_{de}\right)\le \epsilon ;\forall \left(n\right),i=\mathrm{1,2},3$$

(31)

$$\left({\delta }_{cb}\ast T_{E_n{E}_n}\left(t\right)/{\delta }_{de}\right)\le \epsilon ;\forall \left(n\right),(n\ne n)$$

(32)

3.2.4. Solar Constraint

The total amount of electricity sold to the utility, and distributed to the battery storage system, the EVs’ fleet, smart building and non-primary building is equal to the solar output modeled as 15 equality constraints, as shown by Equation (33):

$$T_{S{B}_1}\left(t\right)+T_{S{B}_3}\left(t\right)+T_{S{B}_3}\left(t\right)+T_{SG}\left(t\right)+T_{SB}\left(t\right)+T_{{SE}_n}\left(t\right)=a\left(t\right) \forall \left(n\right)$$

(33)

3.3. Solution Methodology

There are limitations to non-negativity for each state variable. The model is created as a mixed-integer linear program (MILP), solved using the CPLEX solver. The associated input data, including the daily load demands for each building, solar production, tariff details, and contractual tariffs, are readily accessible for assessing the technique and are further described in our prior work [17].

4. Results and Discussion (Case Studies)

The daily earnings and savings for smart and non-primary buildings are assessed in three cases. Figure 3 illustrates the utility’s on-peak and off-peak pricing, contract rates for non-primary buildings and sales/purchase prices for EV charging/discharging. For both non-primary buildings, identical contract rates are used, which are less expensive than the utility price. A minimum contractual load of 5 kW is maintained during the simulations. Further, it is assumed that the utility can buy power at a price that is one-third of that of the utility and that EVs can be charged at a cheaper rate with solar power and batteries rather than with the utility. Additionally, EVs offer a power trade at slightly discounted prices compared to the utility prices to every participant (V2X trade).

4.1. Reference Case (Business As Usual)

Rooftop solar power, battery storage system, or electric vehicle charging facilities are absent from any of the buildings in this case. Our study shows that smart and non-primary buildings (B₂ and B₃) spend USD 50, USD 21, and USD 72, respectively, on buying all of the power from the utility. Furthermore, it is estimated that EVs will cost the system USD 9.5 in total to buy electricity.

4.2. Case 1

In this case, the smart building has 400 kWp rooftop solar power and a parking station (no battery storage system storage). Figure 4a–c displays the results of the optimized power transactions between the utility, solar, buildings, and electric vehicles. As seen in Figure 4a, solar energy powers electric vehicles and delivers power to smart and non-primary buildings to the greatest extent possible. It is seen from Figure 4b that EVs are charged from the utility when solar power is unavailable. Additionally, Figure 4c shows that buildings purchase power from the utility when solar power is unavailable. Additionally, Figure 5 shows that EVs exchange power among themselves depending upon the SOC and stay duration of each EV.

4.3. Case 2

In this case, the smart building is installed with solar power, a battery storage system (400 kWh) and a parking station. Figure 6a depicts the optimized power transactions from solar power to each building, EVs, and the utility. Further, Figure 6b illustrates the battery storage system’s changing power levels. The findings show that the battery storage system is charged during the off-peak hours and drains between the on-peak hours of 09:00 a.m. and 12:00 p.m. and 07:00 p.m. and 12:00 a.m, respectively.

Furthermore, Figure 7a shows that a sizable portion of the EVs’ charging occurs between the hours of 2:00 p.m. and 5:00 p.m. using either solar power or the battery storage system. The utility’s optimized power transactions with smart and non-primary buildings are depicted in Figure 7b. It has been noted that neither the smart building nor non-primary buildings purchase any power from the utility between 9:00 a.m. and 12:00 p.m., which is the on-peak price time. Additionally, Figure 8 shows the power trade between EVs (V2V). However, owing to the battery storage system’s availability during on-peak hours, EVs trade less frequently with each other compared to in this case compared to in case 1 when only solar power was available.

The implemented business model proves to be feasible for smart buildings equipped with EV charging infrastructure (with V2V, V2B and V2G capabilities), as all buildings and the EVs’ fleet make revenue and savings compared to those in the business-as-usual case. This highlights the viability of the proposed model. Notably, there is a substantial price difference between on-peak and off-peak hours, encouraging peer-to-peer (P2P) energy trading within the grid-connected arrangement.

The optimization framework allows the computation of fiscal benefits for each interconnected building (B₁, B₂, and B₃) and the electric vehicles’ fleet, ensuring mutual benefits for all participants. The utilization of solar and storage resources by primary and non-primary buildings, as well as EVs, during on-peak hours, enables the shared demand on the electric grid. Additionally, the localized power sharing among non-primary buildings and the charging of electric vehicles using local resources contribute to reduced losses (I²R) and enhanced grid resilience.

The daily revenues and savings earned by the smart building, non-primary buildings and fleet of electric vehicles for cases 1 and 2 are shown in

Table 2. Daily revenues and savings (USD) earned by the smart building, non-primary buildings, and EVs.

Entity	Case 1 (without Storage)	Case 2 (with Storage)
Smart Building	30	79.1
Non-primary Building (B2)	1.0	1.8
Non-primary Building (B3)	3.1	5.4
Electric Vehicles	6.6	4.8

. It can be observed that that an annual revenue of USD 10,950 and USD 28,871.5 are earned by the smart building for cases 1 and 2, respectively. From Table 1, the total cost of the system for cases 1 and 2 can be computed as USD 100,500 and USD 158,500, respectively. Hence, the payback period for the smart building in cases 1 and 2 is calculated as 9.2 years and 5.5 years, respectively.

Table 3. Daily revenues and savings earned by smart, non-primary buildings, and EVs (in % ofg age).

Entity	Case 1 (without Storage)	Case 2 (with Storage)
Smart Building	60	158.2
Non-primary Building (B2)	4.8	8.6
Non-primary Building (B3)	4.3	7.5
Electric Vehicles	69.4	50.5

lists the percentage revenues made by smart buildings and the cost reductions for non-primary buildings for a typical summer day. The formulae to compute percentage financial gains are given in Appendix A. Depending upon the total load and local supply (from solar power and/or the battery storage system) to the load, the revenues and savings for the smart building and non-primary buildings vary from 60–158% and 4–9%, respectively. The revenues and savings earned by the respective buildings increase with the addition of battery storage system storage. The smart building raises its revenue by around 98% and non-primary buildings increase their savings by around 4% after the inclusion of battery storage system storage in the smart building. However, the savings of EVs decrease by 19% in case 2 due to there being less trade with EVs during on-peak hours.

5. Conclusions

The deployment of rooftop PV along with a battery storage system and parking station with V2X capability (including V2V, V2B and V2G) in the smart building is presented in this study as a new paradigm for maximizing financial benefits. The smart and non-primary buildings agree on a minimum contractual load and contract pricing(s) based on the non-primary building’s load shape. At the parking station located in the smart building, EVs can be charged via the utility, solar power, or the battery storage system. This paper provides consistent results and valuable insights that can be derived from the conducted research, including the following:

• The research demonstrates that bilateral arrangements between the smart building and non-primary building can be adjusted effectively to benefit all parties engaged in the trade.
• The profits and savings of each building and the fleet of electric vehicles are influenced by several key factors, including the design of bilateral contracts, time-of-use tariffs, and the diverse characteristics of load profiles. Additionally, the charging and buying rates for EVs also play a significant role in determining profitability and savings.
• Optimized charging strategies utilizing solar power, or storage technologies substantially reduce the reliance of EVs on the grid. Moreover, the on-peak hour load of both smart and non-primary buildings, as well as of EVs, is shared, further minimizing their dependency on grid power.
• Based on the power level, arrival, and departure times of each vehicle, EVs can sell electricity to any of the entities including the building load, battery storage system storage, and utility along with the bidirectional trade between EVs (V2X).
• The smart building (B₁) earns a daily revenue of up to 158%, while the non-primary buildings and EVs have daily savings of up to 9% and 69%, respectively, due to the optimized power scheduling during on-peak hours with the inclusion of battery storage system storage.
• The smart building is assured the highest financial gains due to the well-designed profit model. Additionally, the non-primary buildings and the fleet of electric vehicles achieve substantial cost savings in electricity expenses by leveraging the contracted load, pricing, and tariff mechanisms.

The proposed market architecture is applicable to a neighborhood comprising N buildings, where the smart building assumes responsibility for solar PV, storage, and charging infrastructure. By optimally involving N-1 non-primary buildings in the trading process, the aim is to maximize the revenues and savings for both the smart building and the non-primary buildings. The research findings demonstrate that the developed system enables and promotes interconnected buildings to connect while also providing convenient charging and discharging facilities to EVs within the smart building. In future, the suggested model can be extended to M smart buildings (with solar power, a battery storage system and charging facilities to support V2X operations (including V2V, V2B and V2G)) with bilateral contracts with N-M non-primary buildings.