The rapid growth of electric car adoption, particularly in regions like China EV markets, has spurred interest in vehicle-to-building (V2B) technology as a solution for managing energy fluctuations in buildings. This study explores the economic and operational benefits of integrating electric car systems with building microgrids under varying electricity price policies. By employing optimization models, we assess how different tariff structures influence the scheduling of electric car charging and discharging, highlighting the potential for cost savings and enhanced renewable energy utilization. The focus is on China EV contexts, where policy-driven electricity pricing can significantly impact the viability of V2B applications.
In recent years, the push for decarbonization in the building sector has accelerated, with electric car technologies playing a pivotal role. China EV initiatives have promoted the integration of renewable energy sources, such as solar photovoltaics (PV), into building systems. However, the intermittent nature of renewables poses challenges for energy balance. V2B technology, which allows electric car batteries to serve as mobile storage units, offers a promising approach to mitigate these issues by enabling bidirectional energy flow between electric car and buildings. This study investigates how different electricity price policies affect the economic performance of V2B systems, using a mixed-integer linear programming (MILP) model to optimize electric car operations.
The building microgrid system comprises the building itself, a PV generation system, and V2B charging points for electric car. Energy is sourced from the grid and PV, with priority given to PV generation. Excess PV energy is not fed back to the grid, adhering to a “no export” strategy. The electric car connected via V2B chargers can charge from the grid or PV and discharge to the microgrid when needed. An energy management system coordinates all energy interactions, ensuring optimal scheduling based on real-time conditions and electricity prices. This setup is particularly relevant for China EV scenarios, where office buildings with high daytime energy demands can benefit from electric car flexibility.
To model electric car behavior, we consider arrival times, departure times, state of charge (SOC), and battery capacity. The arrival and departure times for electric car follow a normal distribution, as derived from data on China EV usage patterns. The SOC at arrival is modeled using two groups: one with higher initial SOC (mean 80%, standard deviation 5) for short-distance commuters and another with lower SOC (mean 70%, standard deviation 5) for long-distance users. Battery capacities for electric car are assumed to be uniformly distributed between 60 and 80 kW·h, reflecting common China EV models. The SOC dynamics are governed by the following equation:
$$ S_{EV}(i,t) = S_{EV}(i,t-1) + \frac{E_{EV,cha}(i,t) – E_{EV,dis}(i,t)}{I(i)} \times X $$
where \( S_{EV}(i,t) \) is the SOC of electric car \( i \) at time \( t \), \( E_{EV,cha}(i,t) \) and \( E_{EV,dis}(i,t) \) are the charging and discharging energies, \( I(i) \) is the battery capacity, and \( X \) is the efficiency factor. Constraints ensure that charging and discharging do not occur simultaneously and that SOC remains within safe limits (10% to 90%).
The optimization model uses MILP to minimize daily operational costs, which include building electricity expenses, electric car battery degradation costs, and electric car electricity costs. The objective function is formulated as:
$$ \min J = C_d $$
where \( C_d = C_{d,B,ele} + C_{d,EV,bat} + C_{d,EV,ele} \). Here, \( C_{d,B,ele} \) is the building electricity cost calculated from grid purchases and time-of-use tariffs, \( C_{d,EV,bat} \) compensates for electric car battery degradation due to additional cycling, and \( C_{d,EV,ele} \) accounts for electric car charging and discharging costs. The battery degradation cost is estimated as:
$$ C_{d,EV,bat} = \sum_{i=1}^{N} \frac{P_{EV,bat} \cdot I(i) \cdot R(i)}{R_{rated}(i)} $$
with \( R(i) \) representing the additional battery cycles from V2B participation. The electric car electricity cost is:
$$ C_{d,EV,ele} = \sum_{t=1}^{24} \sum_{i=1}^{N} \left( P_{EV,dis}(t) \cdot E_{EV,dis}(i,t) – P_{EV,cha}(t) \cdot E_{EV,cha}(i,t) \right) $$
Energy balance constraints ensure that supply meets demand at all times:
$$ E_{buy}(t) + E_{PV}(t) + \sum_{i=1}^{N} E_{EV,dis}(i,t) = E_{load}(t) + E_{dis}(t) + \sum_{i=1}^{N} E_{EV,cha}(i,t) $$
where \( E_{buy}(t) \) is grid purchase, \( E_{PV}(t) \) is PV generation, \( E_{load}(t) \) is building load, and \( E_{dis}(t) \) is dissipated energy.
For the case study, we consider a typical office building in Beijing with an area of 8,702 m² and a 400 kW PV system. The building’s annual electricity consumption is 401,800 kW·h, and PV generation is 395,000 kW·h, showing a mismatch between supply and demand. We simulate two electricity price policies common in China EV regions: Policy 1 (peak-high-mid-low tariffs) and Policy 2 (peak-high-mid-low-deep low tariffs). The tariffs and time periods are summarized in Table 1.
| Policy | Peak (¥/kW·h) | High (¥/kW·h) | Mid (¥/kW·h) | Low (¥/kW·h) | Deep Low (¥/kW·h) |
|---|---|---|---|---|---|
| 1 | 1.295 | 1.152 | 0.835 | 0.558 | – |
| 2 | 1.252 | 1.100 | 0.742 | 0.385 | 0.283 |
The time-of-use periods vary by month, with Policy 2 including deep low periods during high solar output hours to encourage PV consumption. This is especially relevant for China EV strategies aimed at reducing curtailment.
Using k-means clustering, we analyze 16 typical days from a year of simulation data. With 20 electric car integrated, the results show significant cost savings under both policies. Under Policy 1, V2B reduces the annual economic cost per unit area by ¥6.14/m² (36.96% savings), while Policy 2 achieves ¥6.38/m² (43.53% savings). The self-consumption rate (SCR) and self-sufficiency rate (SSR) also improve, as shown in Table 2.
| Scenario | SCR (%) | SSR (%) | Grid Electricity (kW·h/m²·a) | PV Electricity (kW·h/m²·a) |
|---|---|---|---|---|
| Base Case | 68.59 | 58.71 | 40.30 | 23.66 |
| Policy 1 | 91.25 | 64.27 | 34.50 | 31.48 |
| Policy 2 | 91.06 | 64.12 | 34.50 | 31.41 |
On days with low PV generation, electric car charging is concentrated in low-tariff periods, and discharging occurs during high-tariff times under Policy 2 due to larger price differentials. For example, shifting 209.05 kW·h from peak to deep low periods yields a net benefit of ¥70.27 after accounting for battery costs. On high-PV days, electric car discharge during low-PV periods helps utilize excess solar energy, transferring 55-71 kW·h and saving ¥12-19 per day. This demonstrates the adaptability of electric car scheduling to price signals.
To assess future potential, we examine the impact of increasing peak-to-valley price differences and decreasing V2B charger costs. As the price gap widens, the economic benefits of V2B grow, though at a diminishing rate. For instance, a 10% increase in the price differential boosts savings by 15.11%, while a 30% increase raises it by 19.37%. Similarly, a 10% reduction in charger investment cost improves savings by 4.8%, with a 30% reduction leading to a 14.4% gain. These trends are summarized in Table 3, highlighting the synergy between China EV adoption and evolving tariff structures.
| Change | Savings Increase (%) | Annual Economic Cost (¥/m²) |
|---|---|---|
| Base Policy 2 | 0 | 6.39 |
| Price Gap +10% | 15.11 | 7.35 |
| Price Gap +20% | 18.11 | 7.56 |
| Price Gap +30% | 19.37 | 7.63 |
| Charger Cost -10% | 4.80 | 6.69 |
| Charger Cost -20% | 9.60 | 7.00 |
| Charger Cost -30% | 14.40 | 7.31 |
In conclusion, V2B technology offers substantial economic benefits for building microgrids, particularly in China EV contexts with dynamic electricity pricing. Policies with larger peak-to-valley differences, such as Policy 2, enhance the value of electric car flexibility by enabling profitable energy arbitrage. As electricity tariffs evolve and charger costs decline, the adoption of electric car in V2B systems is poised to grow, supporting grid stability and renewable integration. Future work should explore real-world implementations and regulatory frameworks to maximize the potential of electric car in building energy management.