In recent years, the rapid development of electric vehicle (EV) technology in China has opened new avenues for energy management in buildings. As a mobile energy storage unit, electric vehicles can interact with building energy systems through vehicle-to-building (V2B) technology, which allows bidirectional energy flow. This interaction is particularly relevant under China’s evolving electricity pricing policies, which aim to balance grid demand and integrate renewable sources. In this analysis, I explore the application value and development potential of V2B systems under various electricity price scenarios, focusing on economic benefits, self-sufficiency rates, and optimization strategies. The electric vehicle market in China, often referred to as China EV, has seen exponential growth, driven by government policies and technological advancements. This growth positions electric vehicles as key players in building energy management, especially in urban settings where office buildings consume significant electricity.
The integration of electric vehicles into building microgrids involves complex optimization to minimize costs while ensuring energy reliability. I employ a mixed-integer linear programming (MILP) model to schedule the charging and discharging of electric vehicles, considering factors such as state of charge (SOC), battery degradation, and time-varying electricity prices. The objective function aims to minimize daily operational costs, which include building electricity expenses, EV battery depreciation, and user electricity fees. For instance, the daily cost \( C_d \) is defined as:
$$ C_d = C_{d,B,ele} + C_{d,EV,bat} + C_{d,EV,ele} $$
where \( C_{d,B,ele} \) represents the building’s electricity cost, \( C_{d,EV,bat} \) is the battery depreciation cost for electric vehicles, and \( C_{d,EV,ele} \) covers the electricity costs for EV charging and compensation for discharging. The battery depreciation cost accounts for additional wear from V2B operations, calculated based on the number of charge-discharge cycles and the battery’s lifecycle. Specifically, the depreciation for each electric vehicle \( i \) is given by:
$$ I(i) = \frac{\sum_{t=t_{\text{arr}}}^{t_{\text{dep}}} \left( E_{\text{EV,cha}}(i,t) + E_{\text{EV,dis}}(i,t) \right) \cdot \left( S_{\text{EV}}(i,t_{\text{dep}}) – S_{\text{EV}}(i,t_{\text{arr}}) \right)}{2 \times D \times R_{\text{rated}}(i)} $$
Here, \( E_{\text{EV,cha}}(i,t) \) and \( E_{\text{EV,dis}}(i,t) \) denote the charging and discharging energy of electric vehicle \( i \) at time \( t \), \( S_{\text{EV}} \) is the state of charge, \( D \) is the depth of discharge (set to 80%), and \( R_{\text{rated}}(i) \) is the rated battery lifecycle. The energy balance constraint ensures that the total energy from the grid, photovoltaic (PV) systems, and EV discharging matches the building load and EV charging:
$$ E_{\text{buy}}(t) + E_{\text{PV}}(t) + \sum_{i=1}^{N} E_{\text{EV,dis}}(i,t) = E_{\text{load}}(t) + E_{\text{dis}}(t) + \sum_{i=1}^{N} E_{\text{EV,cha}}(i,t) $$
where \( E_{\text{buy}}(t) \) is the energy purchased from the grid, \( E_{\text{PV}}(t) \) is PV generation, \( E_{\text{load}}(t) \) is the building load, and \( E_{\text{dis}}(t) \) is dissipated energy. To model the behavior of electric vehicles, I use probabilistic distributions for arrival times, departure times, initial SOC, and battery capacity, based on data from China’s EV market. For example, arrival times follow a normal distribution with a mean of 8 AM and a standard deviation of 0.41 hours, reflecting typical commuting patterns in Chinese cities.
The case study focuses on a typical office building in Beijing, with an area of 8,702 m² and a 400 kW PV system. This building represents a common scenario in China’s urban landscape, where electric vehicle adoption is high. The PV system, consisting of 1,000 monocrystalline silicon panels, generates approximately 395,000 kWh annually, while the building consumes about 401,800 kWh. Despite the annual balance, real-time mismatches between PV generation and building demand necessitate energy storage solutions, such as those provided by electric vehicles. The China EV fleet in this study comprises 20 vehicles, each with a battery capacity between 60 and 80 kWh, and a maximum charging/discharging power of 7 kW to simulate slow charging, which is prevalent in office settings.
I examine two primary electricity price policies prevalent in China. Policy 1 features a peak-off-peak-flat-valley structure, common in regions like Beijing and Shanghai, while Policy 2 includes an additional deep-valley period, as seen in Shandong and Xinjiang, to better accommodate renewable energy integration. The time-of-use tariffs for these policies are summarized in Table 1, showing the price differentials that influence V2B economics. For instance, in Policy 2, the deep-valley period during midday hours offers lower prices, encouraging EV charging when PV generation is high.
| Policy Type | Peak (¥/kWh) | Off-Peak (¥/kWh) | Flat (¥/kWh) | Valley (¥/kWh) | Deep Valley (¥/kWh) |
|---|---|---|---|---|---|
| Policy 1 | 1.295 | 1.152 | 0.835 | 0.558 | – |
| Policy 2 | 1.252 | 1.100 | 0.742 | 0.385 | 0.283 |
Under these policies, the optimization model schedules electric vehicle charging and discharging to minimize costs. The results demonstrate significant economic benefits: with V2B integration, the annual economic cost per square meter decreases by 36.96% under Policy 1 and 43.53% under Policy 2. This translates to savings of approximately ¥6.14/m² and ¥6.38/m², respectively. Moreover, the self-sufficiency rate (SSR) and self-consumption rate (SCR) improve notably. SSR, which measures the proportion of building energy covered by on-site generation, increases from 58.71% to over 64%, while SCR, indicating the utilization of PV energy, rises from 68.59% to over 91%. These metrics highlight how electric vehicles enhance energy resilience in buildings, a crucial aspect for China’s push toward carbon neutrality.
To delve deeper, I analyze typical days with high and low PV generation. On a day with insufficient PV, under Policy 1, electric vehicles primarily charge during low-price periods without discharging, due to small price differentials that don’t justify battery wear. In contrast, Policy 2’s larger price gaps—such as a ¥0.817/kWh difference between peak and deep-valley periods—encourage discharging during peak hours, yielding arbitrage benefits. For example, shifting 209.05 kWh from peak to deep-valley periods under Policy 2 saves ¥170.61 in grid costs, offsetting ¥100.34 in battery compensation for a net gain of ¥70.27. On days with abundant PV, both policies show discharging during low-generation periods, leveraging stored energy to reduce grid dependence. This flexibility underscores the adaptability of electric vehicles in varying scenarios, emphasizing their role in China’s energy transition.
The economic advantages of V2B systems are further amplified under future electricity price scenarios. As China’s grid evolves, price differentials are expected to widen to manage demand and integrate more renewables. I simulate increases in peak-valley price gaps by 10%, 20%, and 30%, and the results, summarized in Table 2, show progressive cost savings. For instance, a 10% increase in the price gap boosts annual savings to ¥7.35/m², a 15.11% improvement over the base case, while a 30% increase raises savings to ¥7.63/m², though the rate of improvement slows to 19.37%, indicating diminishing marginal returns. This trend suggests that while larger price differentials enhance V2B profitability, other factors like battery technology and infrastructure costs will become increasingly important.
| Price Gap Increase | Annual Savings (¥/m²) | Improvement Over Base Case (%) |
|---|---|---|
| 0% (Base) | 6.39 | 0.00 |
| 10% | 7.35 | 15.11 |
| 20% | 7.56 | 18.11 |
| 30% | 7.63 | 19.37 |
Additionally, the cost of bidirectional charging infrastructure is a critical factor for V2B adoption. Currently, a 7 kW bidirectional charger costs around ¥20,000, compared to ¥2,000-3,000 for a unidirectional one, but as the China EV market matures, economies of scale are expected to reduce these costs. I model scenarios where charger costs decrease by 10%, 20%, and 30%, and find that each 10% reduction increases annual savings by approximately 4.8%. For example, a 30% cost reduction elevates savings to ¥7.31/m², a 14.4% improvement. This underscores the potential for cost reductions to accelerate V2B deployment, making it a viable option for more buildings in China.
The optimization model also incorporates constraints to ensure practical operation, such as limits on EV SOC to prevent battery damage. The SOC for each electric vehicle \( i \) at time \( t \) is governed by:
$$ S_{\text{EV}}(i,t) = S_{\text{EV}}(i,t-1) – \frac{E_{\text{EV,dis}}(i,t) + E_{\text{EV,cha}}(i,t)}{I(i)} \cdot X $$
where \( X \) is the charging/discharging efficiency, and \( I(i) \) is the battery capacity. The SOC is constrained between 10% and 90% to maintain battery health. Furthermore, the model ensures that EVs have a minimum SOC of 80% upon departure to meet user travel needs, a critical consideration for real-world adoption in China’s EV ecosystem.
In terms of environmental impact, V2B systems contribute to reducing carbon emissions by maximizing the use of renewable energy. In the case study, the integration of electric vehicles increases the annual consumption of PV energy by over 7,000 kWh, reducing reliance on grid electricity, which in China is still largely fossil-fuel-based. This aligns with national goals for carbon peak and neutrality, highlighting the synergistic potential of electric vehicles and buildings. The China EV initiative, supported by policies and incentives, can thus drive broader energy sustainability.
Looking ahead, the development potential of V2B in China is substantial. As electricity prices become more dynamic and charger costs decline, the return on investment for V2B systems will improve. However, challenges remain, such as the need for standardized protocols and user acceptance. Future research could explore larger-scale integrations, such as vehicle-to-grid (V2G) applications, or the impact of emerging technologies like solid-state batteries on V2B economics. In conclusion, electric vehicle-building energy interaction offers a promising path for enhancing building energy efficiency and supporting grid stability in China. With continued policy support and technological advancements, the China EV market can play a pivotal role in achieving a sustainable energy future.
To summarize the key findings, I present a comparative table of performance metrics under different policies and scenarios. This includes annual cost savings, SSR, SCR, and the impact of price variations. Such data provides a comprehensive view of how electric vehicles can transform building energy management in China.
| Scenario | Annual Cost Savings (¥/m²) | SSR (%) | SCR (%) | Additional PV Use (kWh/m²) |
|---|---|---|---|---|
| Policy 1 | 6.14 | 64.27 | 91.25 | 7.82 |
| Policy 2 | 6.38 | 64.12 | 91.06 | 7.74 |
| Policy 2 +10% Price Gap | 7.35 | 64.50 | 91.40 | 7.90 |
| Policy 2 +30% Price Gap | 7.63 | 64.80 | 91.60 | 8.00 |
In essence, the interplay between electric vehicles and building energy systems under varying electricity prices reveals a robust framework for cost savings and sustainability. The China EV revolution is not just about transportation; it’s about reimagining energy infrastructures for a greener tomorrow. As I continue to refine these models, the focus will be on real-world implementations that leverage the flexibility of electric vehicles to create more resilient and efficient buildings across China.