Urban transportation and electricity supply are two of the most important pillars in the transition toward a low-carbon society. In many large Chinese cities, electricity consumption has continued to grow while road transport has become electrified at a remarkable pace. Because the power sector increasingly relies on renewable generation, one promising option is to combine decentralized rooftop photovoltaic plants with electric cars. In such a coupled system, an electric car can behave not only as a travel vehicle but also as a mobile storage device. When a house or a community produces more solar energy than it consumes, the surplus can be stored in the car battery and discharged later in the evening or on cloudy days. This configuration is often described as rooftop photovoltaic plus electric cars, or PV+EV for short.
My research is motivated by a practical question: how attractive is the PV+EV system economically for a high-density urban community? The economic answer is not as obvious as the environmental one. Adding electric-car storage to a rooftop solar plant increases both the initial investment and the annual operating expenditures. At the same time, the system may generate additional revenue by shifting solar electricity to hours with higher household consumption or higher retail prices. The final result depends on many local factors, including sunshine conditions, building load profiles, electricity pricing, the number of vehicles available for vehicle-to-home services, battery degradation, and future cost reductions driven by technological learning. My study quantitatively compares the photovoltaic-only plant and the PV+EV plant in a representative dense residential neighborhood in southern China, using the site of a signed 3 MW rooftop photovoltaic program as the main data source. I pay particular attention to uncertainties arising from user behavior, technological progress, battery lifetime, and household demand growth.
I developed an integrated modeling framework that covers four stages. First, I simulate the hourly electricity balance between solar generation, household load, and battery storage for 8,760 hours per year. Second, I calculate the main technical indicators, such as the photovoltaic self-consumption rate and the self-sufficiency rate. Third, I translate the simulated energy flows into a full life-cycle cost–benefit model, producing levelized cost of electricity, net present value, internal rate of return, and discounted payback period. Fourth, I define scenario families around four influence mechanisms and compare the outcomes through controlled scenario decompositions. This procedure allows me to rank the factors, identify thresholds for profitable operation, and formulate policy recommendations that are tailored to dense urban villages and similar communities.
1. Conceptual Framework of PV+EV Economic Assessment
The economic assessment of a rooftop photovoltaic system coupled with electric cars must be based on an explicit model of the physical electricity flow. In the photovoltaic-only configuration, solar electricity directly serves the building; surplus power is exported to the grid and deficit power is imported from the grid. In the PV+EV configuration, an additional pathway exists: solar electricity can charge the batteries of electric cars connected at home or in the community parking area. During periods of high household demand, the battery can be discharged to satisfy the load. This pathway changes the revenue streams because the self-consumed fraction replaces retail electricity that would otherwise be purchased, whereas exported power is valued at the much lower feed-in tariff.
The PV+EV system can be decomposed into four interacting modules:
- Generation module: rooftop photovoltaic arrays, inverter characteristics, module performance under local irradiance and temperature, and annual module degradation.
- Demand module: hourly residential electricity consumption, including weekday/weekend patterns and seasonal cooling demand.
- Storage module: battery capacity, state-of-charge constraints, charging/discharging efficiency, charging power limits, and capacity fade over cycles.
- Economic module: investment, operation and maintenance, battery replacement, retail tariff savings, and grid export revenues.
From a system-analysis perspective, these four modules are linked through time series. Because an electric car has a dual function, the user must choose when and how often the car can serve as grid-connected storage. I therefore distinguish between the ownership stock of electric cars and the share of that stock that is actually available for photovoltaic storage during the day. The daily utilization rate of private cars is the core behavioral uncertainty in my scenario design.
The complete model is shown by the hourly power balance:
$$ P_{pv}(t)+P_{batt,d}(t)+P_{grid,imp}(t)=L(t)+P_{batt,c}(t)+P_{grid,exp}(t) $$
where Ppv is the photovoltaic production, Pbatt,d and Pbatt,c are the battery discharging and charging powers, Pgrid,imp and Pgrid,exp are the imported and exported powers, and L is the residential load. The equation is solved at every hour. From this solution I derive the two most important technical performance metrics: photovoltaic self-consumption rate (SC) and load self-sufficiency rate (SS).
$$ SC = \frac{\sum_{t} \big[ P_{pv,load}(t)+P_{batt,c}(t) \big]}{\sum_{t}P_{pv}(t)} \times 100\% $$
$$ SS = \frac{\sum_{t} \big[ P_{pv,load}(t)+P_{batt,d,to\;load}(t) \big]}{\sum_{t}L(t)} \times 100\% $$
In the PV-only benchmark, battery terms are omitted. The common purpose of household electricity storage is to raise SC because exported photovoltaic electricity is usually less valuable than electricity that displaces a retail purchase. In the long run, SC and SS determine how much monetary value a photovoltaic array can create for a given household or community.
2. Influencing Factors and Scenario Identification
I identify four categories of economic factors that are particularly relevant for urban rooftop photovoltaic systems coupled with electric cars: (i) social and economic development, expressed here by the growth of residential electricity consumption; (ii) technical change, expressed by the falling costs of photovoltaic components, power batteries, and vehicle-to-home chargers; (iii) user behavior, expressed by the share of electric cars available for vehicle-to-home storage; and (iv) battery aging, expressed by the number of cycles after which the battery must be replaced. Table 1 summarizes the conceptual factor groups.
| Factor group | Concrete variable | Economic mechanism | Scenario values |
|---|---|---|---|
| User behavior | Electric-cars availability rate | Defines usable battery capacity and affects self-consumption, battery cycling depth, and replacement cost | 20%, 35%, 50% |
| Technological progress | Photovoltaic capital cost, battery pack cost, vehicle-to-home equipment cost, operation/maintenance cost | Reduces initial capital, replacement, and annual fixed costs | 2023 baseline; 2030 slow progress; 2030 fast progress |
| Battery degradation | Cycle life to 80% retained capacity | Determines whether, when, and how many times the battery is replaced during the 25-year photovoltaic lifetime | 750 cycles; 900 cycles |
| Social/economic development | Residential electricity consumption | Changes the load level and the amount of photovoltaic electricity that can be self-consumed | 2023 level; 2030 predicted level; low-load sensitivity |
According to local travel-survey evidence, about half of all registered cars in the reference city are driven on an average day. If all vehicles are electric, the maximum vehicle-to-home availability would be roughly 50%. Yet actual daily availability may be much lower because not every electric car is parked near the controlled photovoltaic site, and some users are not willing to let their battery be used for home storage. I therefore use 50% as an upper bound, 35% as an intermediate value, and 20% as a more conservative availability benchmark.
Technical progress is represented by three cost trajectories. The 2023 capital cost of a complete rooftop photovoltaic plant is taken as 5.79 CNY per watt-peak. Under the slow 2030 trajectory, the cost falls to about 3.45 CNY per watt-peak, a 40% reduction. Under the fast 2030 trajectory, the cost falls to 2.57 CNY per watt-peak, a reduction of roughly 56%. Battery pack prices follow an even stronger learning curve: the 2023 replacement cost is 1,200 CNY per kilowatt-hour, while the slow and fast 2030 trajectories lead to 550 and 370 CNY per kilowatt-hour, respectively. Operating and maintenance costs also decline at different speeds, though less dramatically.
Battery degradation is typically expressed by the total discharge energy before retirement. Chinese national standards require an automotive power battery to retain at least 80% capacity and to survive at least 1,000 full cycles. However, in practical driving, many power batteries reach the retirement threshold earlier. Measurements and field data suggest that a typical electric-car battery reaches 80% retained capacity after 750 to 900 equivalent full cycles. In my model, I only count the cycles caused by surplus photovoltaic charging and household discharging. Thus a lower availability rate increases the average cycling depth per available car, causing earlier replacement; a higher demand for household electricity reduces the amount of photovoltaic surplus available for storage, which slows down the battery aging process. For the 2030 scenario, household demand is 23% higher than in 2023, and this tends to reduce battery replacement frequency because annual photovoltaic surplus becomes smaller relative to load.
For the reference community, the stated protocol refers to an actual urban village with high population density and an expected photovoltaic installation of 3 MW. The annual output is about 3.88 million kilowatt-hours in the first year, which is close to the officially estimated value. Household electricity demand is derived by scaling municipal residential statistics to the number of households participating in the photovoltaic program. The load curves are built from standard residential appliance schedules. The main economic parameters used in the study are listed in Table 2.
| Parameter | 2023 baseline | 2030 slow progress | 2030 fast progress |
|---|---|---|---|
| Photovoltaic capital cost, CNY/Wp | 5.79 | 3.45 | 2.57 |
| Photovoltaic O&M cost, CNY/Wp/year | 0.055 | 0.050 | 0.041 |
| Battery replacement cost, CNY/kWh | 1,200 | 550 | 370 |
| Retail electricity price, CNY/kWh | 0.663 | 0.663 | 0.663 |
| Feed-in electricity price, CNY/kWh | 0.463 | 0.463 | 0.463 |
| Discount rate | 8% | 8% | 8% |
| System lifetime, years | 25 | 25 | 25 |
Combining all factors creates a large scenario space. In total, I simulate 54 cases, including the 36 main combinations of two demand levels, three availability rates, two battery degradation rates, and three technology-cost trajectories, plus additional low-demand sensitivity runs that are useful for exploring the conditions under which the PV+EV system overtakes the photovoltaic-only system. Every case is simulated over a full year in hourly resolution and then translated into cash flows over a 25-year project lifetime.
3. Economic Assessment Method
The cost structure of the photovoltaic-only and the PV+EV system is different. For the photovoltaic-only case, the total life-cycle cost contains the initial photovoltaic investment and the annual operation and maintenance expenditure. For the PV+EV case, the initial capital cost also includes the purchase and installation of bidirectional chargers, and the maintenance account must be augmented by battery replacement costs. Since all scenarios use existing electric cars, the purchase cost of electric cars themselves is not included in the initial investment, except for the charger interface and any necessary grid connection upgrades.
The annual revenue in both configurations follows the same physical logic:
$$ R(n)=p_{retail}\cdot E_{self}(n)+p_{feed}\cdot E_{export}(n) $$
where Eself is the photovoltaic electricity directly consumed on site, Eexport is the electricity exported to the grid, and pretail and pfeed are the retail and feed-in prices. A photovoltaic unit consumed locally replaces a retail purchase and is therefore more valuable than a photovoltaic unit sold to the utility. This is exactly why electric-car batteries can improve the economics of rooftop solar: they shift generation from low-value export to high-value self-consumption. The annual net cash flow is:
$$ CF(n)=R(n)-C_{OM}(n)-C_{batt,repl}(n) $$
The levelized cost of electricity is calculated over the full life cycle as:
$$ LCOE=\frac{\sum_{n=1}^{N}\frac{C_{inv}+C_{OM}(n)+C_{batt,repl}(n)}{(1+r)^{n}}}{\sum_{n=1}^{N}\frac{E_{pv}(n)}{(1+r)^{n}}} $$
where Cinv is the initial capital cost including photovoltaic modules, inverters, installation, and bidirectional charging equipment, Epv is the gross photovoltaic generation, and r is the discount rate. I compare the resulting LCOE with two benchmarks: the retail price paid by residents and the feed-in price paid by the grid. If the LCOE is below the retail tariff, photovoltaic self-consumption yields a positive financial margin; if it is also below the feed-in tariff, grid export is profitable as well.
I also compute the net present value and internal rate of return:
$$ NPV=\sum_{n=1}^{N}\frac{CF(n)}{(1+r)^{n}}-C_{inv} $$
$$ IRR = r^* \; \text{such that} \; \sum_{n=1}^{N}\frac{CF(n)}{(1+r^*)^{n}}=C_{inv} $$
In this study the discount rate is 8%, so an IRR greater than 8% is considered financially acceptable. A project with an IRR between 8% and 15% is regarded as having moderate profitability, while an IRR above 15% indicates strong profitability, especially for a small distributed-energy asset.
4. Baseline Results for the Photovoltaic-Only System
I first evaluate the photovoltaic-only system in the benchmark year 2023. Because the reference community is very dense and its daytime load is relatively substantial, the photovoltaic generator achieves a self-consumption rate as high as 91.6% in the first year. The self-sufficiency rate is 32.8%, meaning that rooftop solar supplies about one-third of the community load. Such a high self-consumption rate is not typical for single-family houses; it occurs because the demand from many residential floors is high during midday, especially for cooking, cooling, and appliances.
The levelized cost of electricity of the photovoltaic-only system is 0.43 CNY/kWh. This value is lower than the Shenzhen feed-in benchmark of about 0.46 CNY/kWh and is far below the residential retail price of 0.66 CNY/kWh. Therefore, the photovoltaic-only system already reaches both user-side and generation-side grid parity in 2023. The PV plant produces a positive net present value of about 9.57 million CNY, has an internal rate of return close to 14.6%, and a discounted payback period of about 11.2 years. In short, the baseline photovoltaic-only project is technically feasible and financially attractive without any subsidy.
Although the self-consumption rate is already high, roughly 8.4% of the photovoltaic generation is still exported to the grid. The gap between the exported value and the price of grid electricity that must be bought back in the evening creates a niche for storage. The PV+EV system is designed to fill that niche. Whether it is economically justified is the core question of my analysis.
5. Effect of Electric-Car Availability
The availability of electric-car batteries is perhaps the most distinctive behavioral factor in the PV+EV system. If 50% of electric cars are available, the usable battery capacity is large; if only 20% are available, the storage capacity is small. The effect on technical performance is shown in Figure 1 within the text; the PV+EV matching curve clearly indicates that battery charging follows the photovoltaic surplus curve.
Increasing electric-car availability from 20% to 35% raises the total annual battery charging and discharging throughput by about 15.3%–15.6%, depending on the load level and battery degradation. However, increasing availability from 35% to 50% only adds another 2.8%–4.4% to the battery throughput. This diminishing marginal effect is due mainly to the single-peak shape of solar radiation. By early afternoon, the photovoltaic output has already charged the available battery fleet to its upper state-of-charge limit; additional battery capacity remains idle for many hours. Consequently, the marginal improvement of self-consumption falls rapidly as more electric cars are connected to the system.
On the revenue side, more electric-car availability implies a greater self-consumption rate. In the scenarios with high electric-car availability, the self-consumption is 0.03–0.93 percentage points higher than in the low-availability scenario. Because the residents pay about 0.66 CNY/kWh for retail electricity but receive only 0.46 CNY/kWh for exported solar power, shifting power from export to self-consumption raises gross revenue. Yet the revenue increase is modest when the baseline self-consumption is already near 90%.
On the cost side, adding more bidirectional chargers increases the vehicle-to-home installation cost, which is proportional to the installed charger power. At the same time, a larger usable battery capacity lowers the cycling depth per car, which lengthens battery life and may reduce or delay battery replacement expenditures. The interaction between these two cost components is crucial. For example, in the battery-degradation-slow scenario, the 20% availability case requires one battery replacement, while the 50% availability case requires no replacement. The avoided replacement cost partially compensates for the added charger expense. In the battery-degradation-fast scenario, the 20% and 35% cases have similar net present values, while the 50% case is slightly less attractive because the charger investment outweighs the remaining battery-saving benefit.
| Configuration | Discounted total cost (104 CNY) | Discounted total revenue (104 CNY) | Net benefit (104 CNY) |
|---|---|---|---|
| PV-only, 2023 | 1,957 | 2,914 | 957 |
| PV+EV, 50% availability, slow degradation | 2,206 | 2,932 | 726 |
| PV+EV, 35% availability, slow degradation | 2,309 | 2,896 | 587 |
| PV+EV, 20% availability, slow degradation | 2,245 | 2,915 | 670 |
| PV+EV, 50% availability, fast degradation | 2,503 | 2,833 | 330 |
| PV+EV, 35% availability, fast degradation | 2,461 | 2,890 | 429 |
| PV+EV, 20% availability, fast degradation | 2,462 | 2,880 | 418 |
In the 2023 baseline-cost world, the highest IRR of the PV+EV system is obtained in the scenario with 50% availability and slow degradation, but the difference is strongly scenario-dependent. In several other cases, adding more electric cars lowers the IRR rather than raising it because the incremental cost of bidirectional chargers exceeds the incremental net revenue created by the additional stored solar energy. Table 3 illustrates the non-monotonic relationship.
| Cost scenario | PV-only IRR | PV+EV 50% | PV+EV 35% | PV+EV 20% |
|---|---|---|---|---|
| 2023 baseline | 14.6% | 11%–13% | 12%–14% | 12%–14% |
| 2030 slow progress | 21%–23% | 23%–24% | 23%–25% | 23%–25% |
| 2030 fast progress | 30%–31% | 31%–32% | 32%–33% | 32%–33% |
Overall, I find that electric-car availability should not be increased blindly. In a dense community where the PV-only self-consumption is already high, the first units of battery storage add only small energy-shifting benefits. The optimal number of electric cars depends on the battery replacement schedule and on the price of bidirectional chargers. When the energy storage need is modest and battery replacement is expensive, a smaller fleet of electric cars may be economically better than a larger fleet.
6. Effect of Technological Progress
Technological progress lowers the capital cost of photovoltaic modules, the purchase price of power batteries, the specific cost of bidirectional chargers, and the yearly operation/maintenance cost. Since these four cost elements account for most of the life-cycle expense of the PV+EV system, the rate of future technical change is the strongest force shaping the long-term economic attractiveness of the system.
Between 2023 and 2030, a slow technological trajectory reduces total discounted system costs by about 40%, whereas a fast trajectory reduces them by about 55%. The LCOE falls by 38%–44% from the 2023 baseline to the slow 2030 trajectory. In the fast 2030 trajectory, the LCOE is reduced by an additional 22%–26%, reaching values as low as 0.20 CNY/kWh, which is less than half of the residential retail tariff. In every 2030 scenario, the PV+EV system achieves both user-side and generation-side grid parity. At a fast technological pace, the internal rate of return rises above 32%, meaning that the project moves from a moderate-profitability asset in 2023 to a high-return asset in 2030.
The effect of technological progress is especially visible in the high-availability cases. In 2023, the initial vehicle-to-home investment is so expensive that connecting a large number of electric cars creates a serious financial burden. By 2030, as charger and battery prices decline, the same level of availability becomes much less costly. The IRR gap between the 20% and 50% availability scenarios narrows from several percentage points in 2023 to about one percentage point under the fast technological trajectory. Thus technological progress not only improves average economic performance but also reduces the economic penalty associated with high electric-car participation.
In the absence of technological learning, the PV+EV system is less profitable than the PV-only system in this dense community. When 2030 fast progress is reached, however, the PV+EV system IRR approaches or slightly exceeds the PV-only IRR in most availability scenarios. This finding has an important policy implication: waiting for the next phase of cost reduction, or accelerating it through innovation policy, is an effective way to make mobile batteries competitive with dedicated stationary storage.
7. Effect of Battery Degradation
Battery degradation is a technical constraint with direct economic consequences. When the remaining capacity of an electric-car battery falls below 80%, the battery is retired from vehicle service. In my analysis, all retired batteries are assumed to be replaced because the system still needs working storage. Since power batteries are costly, each replacement event produces a negative spike in the cash flow of the year.
Using 750 cycles and 900 cycles as two alternative degradation paths produces very different replacement schedules. Under the high-demand scenario of 2030, the surplus photovoltaic generation available for charging is small and therefore the battery cycles slowly. In that case, the battery may not need to be replaced at all during the 25-year project horizon. Under the lower-demand scenario of 2023, by contrast, more photovoltaic energy passes through the battery, and the replacement schedule may require one or even two battery changes. The replacement years depend on the available battery capacity: a low availability rate forces a few batteries to do all the cycling work, which accelerates aging and leads to earlier replacement. A high availability rate spreads the cycles over many batteries and therefore postpones or avoids replacement.
The economic impact is quantified by comparing systems with identical photovoltaic output and revenue but different battery aging rates. A faster battery degradation rate increases the discounted replacement cost and therefore raises the LCOE by 7.45%–57.02%, depending on the number of replacement events and the cost scenario. The effect on IRR is generally negative and can be especially large in 2023. For example, in the 50% availability case with baseline costs, the fast-degradation scenario has an IRR that is roughly 1.5 percentage points lower than the slow-degradation scenario; under the 2030 cost trajectory, the IRR penalty is smaller because battery prices are much lower.
Another important result is that a high availability of electric cars does not automatically reduce total battery-replacement cost. Increasing availability from 20% to 50% spreads the aging process and reduces replacement frequency, but the corresponding increase in the number of bidirectional chargers may dominate the benefit. The total cost ranking therefore changes across degradation assumptions. In the slow-degradation case, the 50% availability scenario has the lowest total cost among all PV+EV cases; in the fast-degradation case, the total cost of the 50% availability scenario is higher because the charger cost increases while some replacement still occurs.
These findings suggest that battery lifetime extension deserves public and industrial attention. Better thermal management, advanced lithium-iron-phosphate chemistries, smarter charging controls, and milder cycle depths can all raise the cycle life. From the perspective of social welfare, lengthening battery life is equivalent to a direct cost reduction, because it reduces the frequency of expensive replacement events in the PV+EV system.
8. Effect of Residential Electricity Demand Growth
Residential electricity demand is expected to grow as urban incomes rise, air-conditioning penetration increases, and household appliances multiply. In the reference city, historical data from 2010 to 2023 show a strong linear trend with a goodness of fit above 0.98. Projecting this trend to 2030 yields an electricity consumption level that is approximately 23% higher than in the 2023 base year. This demand growth has two effects. First, it raises the residential load that can be supplied by photovoltaic power, thus increasing the self-consumption share. Second, because photovoltaic capacity is fixed, a larger load means less photovoltaic surplus is available for charging electric-car batteries, which reduces the cycling and decelerates battery fading.
The revenue model attributes a very high value to self-consumed electricity because the retail tariff is much higher than the feed-in tariff. When household electricity consumption grows by 23%, the PV-only system increases its internal rate of return from about 14.6% to 14.9%, a relative rise of roughly 1.85%. For the PV+EV system, the increase in IRR is between 0.46% and 0.67%, or roughly 0.07–0.10 percentage points. The reason for this weaker response is that the PV+EV system already has a higher self-consumption rate in 2023; therefore, adding extra household load produces only a small marginal improvement. This is another illustration of the law of diminishing marginal returns: when self-consumption is already very high, demand growth cannot create much additional value.
To explore the boundary condition under which the PV+EV system becomes economically preferable to the PV-only system, I artificially lower the residential demand to one-half and one-third of the 2023 level. In these low-load settings, the photovoltaic-only system exports more electricity and becomes less profitable because much of its generation is sold at the low feed-in price. The PV+EV system, however, can shift the surplus solar output into the evening and exploit the retail price premium. If battery replacement costs were ignored, reducing the load to one-third of the baseline makes the PV+EV system more profitable than the PV-only system. Yet when realistic battery replacement is included, the higher cycling rate shortens battery life to seven years at half load and five years at one-third load. With three or four replacement events over the system lifetime, the PV+EV system loses its advantage.
This boundary analysis is particularly useful for the diffusion of PV+EV systems. A dense urban village with high building loads and high self-consumption may not need electric-car storage because the PV plant can directly supply almost all the daytime electricity. A lower-density residential neighborhood, where daytime load is small and photovoltaic generation would otherwise be exported, is exactly the market where vehicle-to-home storage creates the largest benefit. Therefore, the economically optimal system depends heavily on the balance between local rooftop generation and local consumption.
9. Multi-Factor Interactions and Economic Synthesis
After analyzing each factor separately, I evaluate how the factors interact with each other. Technological progress, battery aging, electric-car availability, and demand growth do not act independently; their effects may reinforce or offset each other.
The largest synergy is between technological progress and battery degradation. Faster battery-learning reduces not only the initial installation cost but also the replacement cost. Consequently, a future scenario with both fast technical progress and slow battery aging is particularly favorable. In such a scenario, the PV+EV system can achieve an internal rate of return above 32%, which is substantially higher than the PV-only system in the same cost world. By contrast, under high battery replacement frequency and slow technical change, the PV+EV system may become close to the financial break-even point.
Electric-car availability acts as a moderating factor. In high-demand communities, a high availability of electric cars raises capital cost without proportional operational benefit. In low-demand communities, however, a high availability rate is required to absorb midday photovoltaic generation; otherwise, the PV+EV system will export a large share of its output at the low feed-in tariff. The optimal availability share is therefore location-specific and depends strongly on the future price trajectory of vehicle-to-home chargers.
Table 4 provides a compact synthesis of the LCOE and IRR ranges for the main scenario groups.
| Scenario family | LCOE range (CNY/kWh) | IRR range | Grid parity |
|---|---|---|---|
| PV-only, 2023 | 0.43 | About 14.6% | Both user-side and generation-side |
| PV+EV, 2023 costs | 0.49–0.57 | 11%–14% | User-side only |
| PV+EV, 2030 slow progress | 0.29–0.33 | 23%–26% | Both sides |
| PV+EV, 2030 fast progress | 0.20–0.25 | 31%–34% | Both sides |
In the 2023 baseline, the PV+EV system does not reach generation-side grid parity because the additional storage cost raises the LCOE above the feed-in tariff. It does reach user-side grid parity, which means that every kilowatt-hour of PV+EV electricity used by the household is cheaper than the electricity purchased from the grid. By 2030, all modeled PV+EV cases achieve both parity conditions. The economic gap between the PV+EV and PV-only systems shrinks as costs fall.
I also observe a substitution effect between demand growth and battery degradation. Higher demand growth decreases the surplus photovoltaic energy that flows through electric-car batteries. Therefore, in growing urban areas, the same number of available electric cars will face lower cycle depth, last longer, and require fewer replacements. This explains why the 2030 demand-growth scenarios almost always have weaker battery aging effects than the 2023 scenarios.
10. Policy Implications
My results provide a clear basis for urban energy policy. The most important condition for a rapid deployment of rooftop photovoltaic systems coupled with electric cars is sustained technological improvement. A policy package that supports advanced photovoltaic cell manufacturing, battery material innovation, intelligent bidirectional chargers, and efficient power electronics can dramatically increase the economic competitiveness of the PV+EV system. The simulation shows that a fast progress trajectory can reduce the LCOE by approximately 55%–60% by 2030 and raise the IRR above 32%. Accelerating cost learning curves is therefore more effective than subsidizing end-user hardware at the current high price.
The second policy implication is that electric-vehicle availability should be promoted selectively. In communities where the rooftop photovoltaic self-consumption rate is already high, adding too many vehicle-to-home interfaces may not improve financial performance. In such cases, an optimal policy is to encourage moderate participation by residents whose electric cars are usually parked during peak solar hours. For low-density communities or districts with low daytime occupancy, high electric-car availability is much more valuable and should be actively supported.
The third policy implication concerns battery lifetime. Since battery replacement frequency is one of the main cost drivers of the PV+EV system, public research and development should focus on improving cycle life, durability, and repairability. Standards that require high-quality battery management and warranty conditions can reduce future replacement risks. If the cycle life of a power battery can be raised from 750 to 900 cycles, the discounted cost and the LCOE of the PV+EV system decline markedly. This improvement is equivalent to a large subsidy without any direct fiscal cost.
Retail tariff design also matters. The existence of a large price gap between retail electricity and feed-in tariffs gives storage an economic value. Time-of-use pricing, if designed carefully, could further encourage electric-car owners to charge during the solar noon and discharge during evening peaks. Combined with charging-control signals, such a tariff could help the grid absorb more renewable energy while also improving household finances.
11. Conclusions
This study develops an hourly simulation and life-cycle economic assessment framework for a rooftop photovoltaic system coupled with electric cars in a dense urban community. I use the actual scale of a 3 MW rooftop photovoltaic program and construct 54 scenarios across four influence dimensions: electric-car availability, technical progress, power-battery degradation, and residential electricity demand growth.
My main results can be summarized as follows. First, in the reference high-density community, a photovoltaic-only system already reaches grid parity in 2023, with an LCOE of 0.43 CNY/kWh and an IRR of 14.6%. The PV+EV system has a higher LCOE, 0.49–0.57 CNY/kWh in 2023, because the high existing load leaves little economic room for extra storage. Yet the PV+EV system still reaches user-side grid parity and offers moderate profitability, with an IRR of 11%–14%.
Second, technological progress is the single most powerful factor that can improve PV+EV economics. If fast technical progress persists to 2030, both photovoltaic module costs and power-battery costs decline substantially, the LCOE falls by 55%–60%, and the IRR rises above 32%. The PV+EV system then becomes competitive with the photovoltaic-only system and is able to achieve both generation-side and user-side grid parity.
Third, electric-car availability has an optimal range. A high availability rate can improve self-consumption, but it also increases the vehicle-to-home infrastructure cost. In the 2023 baseline-cost world, connecting too many electric cars can lower the IRR of the project. Under fast technical progress, however, the negative effect of charger cost becomes smaller, making high electric-car availability more attractive.
Fourth, battery degradation is a substantial economic risk. Faster aging creates earlier replacement events and increases the LCOE by 7%–57%, depending on the number of replacements. Longer-lasting batteries are a key enabler for distributed storage with electric cars.
Finally, electricity demand growth improves the economics of both the PV-only and the PV+EV configuration, but with diminishing returns. The PV+EV system can become superior to the PV-only system when the photovoltaic self-consumption rate is low enough, but only when battery replacement cycles are long enough to avoid excessive replacement costs. For communities whose solar generation is not synchronized with their local consumption, coupling rooftop photovoltaic with electric cars is a promising, low-carbon, and increasingly cost-effective option.