Shared Energy Storage Cuts EV Charging Costs
A groundbreaking study from researchers at Nanjing Institute of Technology and North China Electric Power University reveals a powerful new strategy to dramatically reduce the operational costs of electric vehicle (EV) charging stations. By integrating a “shared energy storage” model, the research demonstrates significant savings in both investment and daily running expenses, offering a compelling economic blueprint for the future of EV infrastructure. The findings, published in the journal Electric Power Engineering Technology, provide a detailed, two-layer optimization framework that could reshape how charging networks are planned and operated.
As the global transition to electric mobility accelerates, the demand for robust and efficient charging infrastructure is surging. However, the rapid growth in EV ownership presents a significant challenge: the unpredictable and often peaky nature of charging demand can strain local power grids and lead to high electricity bills for station operators. Traditional solutions, such as installing individual, privately-owned battery systems at each station, are capital-intensive and often underutilized, making them a less-than-ideal economic proposition. This is where the concept of shared energy storage comes in, moving away from a siloed approach to a more collaborative, community-based model.
The research, led by Dr. Haihong Bian from Nanjing Institute of Technology, tackles this challenge head-on by proposing a comprehensive double-layer optimization model. The core idea is to replace or supplement individual station batteries with a single, centralized energy storage facility that serves multiple charging stations within a local area. This shared battery, managed by a dedicated operator, acts as a communal energy bank. Instead of each station bearing the full cost of a large battery, operators pay a service fee to access the shared resource, significantly lowering their upfront investment. The study’s model is designed to answer two critical questions: first, what is the optimal size and power capacity for this shared battery (the planning layer)? And second, how should the battery be used on a day-to-day basis to minimize operating costs (the operational layer)?
The upper layer of the model focuses on long-term planning. Its primary goal is to minimize the total annual cost, which is a combination of the initial investment cost for the shared battery and the ongoing operational costs. The investment cost is calculated based on the battery’s total energy capacity (in kilowatt-hours) and its maximum power output (in kilowatts), factoring in the cost of equipment and installation, amortized over its expected lifespan. The operational cost, which is derived from the results of the lower layer, includes the daily maintenance cost of the battery and the cumulative cost of electricity purchased from the main grid over the year. This layer essentially determines the ideal “size” of the shared resource that provides the best balance between capital expenditure and long-term savings.
The lower layer of the model shifts the focus to short-term, daily operations. Its objective is to minimize the total operating cost for a typical day. This cost is primarily driven by the station’s electricity bill, which is heavily influenced by time-of-use pricing. Electricity is much cheaper during off-peak hours (e.g., overnight) and significantly more expensive during peak demand periods (e.g., mid-afternoon and early evening). The model uses sophisticated optimization to schedule the charging and discharging cycles of the shared battery, as well as the charging of the EVs themselves, to exploit these price differences. For instance, the system will charge the shared battery with cheap grid power or excess renewable energy (from on-site solar or wind) during low-cost periods. Later, during expensive peak hours, the battery discharges to power the charging stations, reducing or even eliminating the need to draw expensive power from the grid. This process, known as “peak shaving and valley filling,” is a key mechanism for cost reduction.
To validate their model, the researchers conducted a detailed simulation involving three distinct EV charging stations, each with unique characteristics. One station was equipped with solar power generation, while the other two relied on wind power. The stations also had different daily loads, simulating a realistic scenario where some stations serve more customers than others. The study compared four different operational scenarios to isolate the benefits of the shared storage model.
The first scenario served as a baseline: each station operated with its own private, distributed battery and allowed EVs to charge in an uncontrolled, “random” manner. This scenario, while common, proved to be the most expensive. The uncontrolled charging of EVs often coincided with peak electricity prices, leading to high grid purchase costs. To compensate, the model allocated large, expensive batteries to each station, which further increased the total investment. The second scenario improved upon this by introducing “ordered charging,” where the charging of EVs was scheduled to occur during cheaper off-peak hours. This alone led to a substantial reduction in operating costs, demonstrating the power of demand-side management.
The third scenario introduced “interconnected energy storage,” where the three stations, each with their own private battery, were allowed to share power with each other over a local network. This model showed further improvements, as a station with excess renewable energy could charge its neighbors’ batteries, increasing overall efficiency. However, the cost of building the interconnecting power lines and the continued need for three separate battery systems limited the potential savings.
The fourth and final scenario implemented the proposed “shared energy storage” model. Here, the three stations were connected to a single, centralized battery. The results were striking. Compared to the distributed storage model with ordered charging, the shared storage model achieved a 12.5% reduction in total operating costs and a 7.9% reduction in the total amount of electricity purchased from the grid. The most dramatic difference was in the required battery capacity. The shared model needed a battery that was 61.2% smaller in energy capacity and 63.0% smaller in power output than the combined capacity of the three separate batteries in the distributed model. This massive reduction in required hardware translates directly into a huge savings on the initial capital investment, which is the biggest barrier to deploying energy storage at scale.
The economic advantages of the shared model are multifaceted. First, it achieves a higher utilization rate for the battery. A single, larger battery serving multiple stations with different usage patterns is far less likely to sit idle than three smaller batteries, each subject to the random fluctuations of its own station’s demand. Second, it leverages the “diversity” of the charging load. When one station is experiencing a peak demand, another might be quiet, allowing the shared battery to efficiently balance the load across the entire network. Third, it simplifies maintenance and management, as a single operator is responsible for one large asset rather than multiple smaller ones.
A critical component of the shared storage model is the pricing mechanism for the service. The study proposes a novel internal pricing system based on the real-time supply and demand of electricity within the network of charging stations. When the collective renewable generation of the stations exceeds their total load, the system has a surplus, and the internal price for using the shared battery to store this excess energy is set lower. Conversely, when the collective load exceeds generation, the system is in deficit, and the internal price for drawing energy from the battery is set higher. This dynamic pricing incentivizes stations to use the shared resource efficiently, charging it when power is abundant and cheap, and discharging it when power is scarce and expensive. The research found that this internal transaction cost for users was lower under the shared model compared to the interconnected model, further enhancing its economic appeal.
The study also conducted a sensitivity analysis to understand how key variables impact the overall economics. One such variable is the service fee charged by the shared storage operator. The research found that as this fee increases, the total operating cost for the charging stations naturally rises. However, the relationship is not linear. At very low service fees, stations are incentivized to rely heavily on the shared battery, minimizing their grid purchases. As the fee increases, stations find it more economical to reduce their battery usage and instead purchase more power directly from the grid, especially during off-peak hours. This trade-off means that the optimal service fee is a balance that encourages efficient use of the shared asset without making it prohibitively expensive.
Another crucial factor is the cost associated with EV battery degradation. When an EV discharges its battery to power the grid or a charging station (a process known as Vehicle-to-Grid, or V2G), it causes wear and tear on the vehicle’s battery. For this service to be viable, EV owners must be compensated for this “discharge loss.” The study incorporated this cost into its model and found that as the compensation cost increases, the amount of energy drawn from EVs decreases. At a certain threshold, it becomes too expensive for the charging station to use V2G power, and the system relies more on the shared battery and the grid. This highlights a key challenge: the economic benefits of shared storage are maximized when V2G is available, but only if the compensation for battery wear is kept at a reasonable level.
The implications of this research are profound for the future of the EV charging industry. It provides a clear, data-driven argument that shared energy storage is not just a theoretical concept but a practical and highly economical solution. For charging station operators, it offers a path to profitability by drastically cutting their two largest expenses: electricity and capital investment. For utilities and grid operators, a network of charging stations using shared storage can act as a valuable grid resource, helping to smooth out demand and integrate more renewable energy. For city planners and policymakers, it presents a model for building a more resilient and sustainable urban energy infrastructure.
The model’s success hinges on collaboration. It requires a shift in mindset from individual station operators thinking only about their own costs to a more cooperative approach where multiple stakeholders benefit from a shared asset. This could be facilitated by a third-party energy service company that owns and operates the shared battery, or by a cooperative formed by the charging station owners themselves. The precise business model will need to be developed, but the underlying technical and economic feasibility has now been clearly demonstrated.
Furthermore, the integration of renewable energy sources like solar and wind is central to the model’s effectiveness. The shared battery acts as a buffer, storing excess renewable energy when the sun is shining or the wind is blowing and releasing it when it’s needed. This not only reduces costs but also increases the overall share of clean energy used to power EVs, amplifying the environmental benefits of electrification. The study shows that in the shared storage scenario, all renewable energy generated by the stations was fully utilized, with no “curtailment” or waste.
In conclusion, the work by Bian, Li, and Tong presents a transformative approach to EV charging station economics. By moving from a model of isolated, privately-owned storage to a collaborative, shared resource, the research unlocks significant cost savings and operational efficiencies. The double-layer optimization model provides a robust framework for designing and operating these systems, ensuring they are both economically viable and technically sound. As the world builds out its EV charging network, this research offers a compelling vision for a smarter, more efficient, and more sustainable future. The findings are a significant step forward in making electric vehicle ownership not only environmentally responsible but also economically sustainable for all stakeholders involved.
Haihong Bian, Nanjing Institute of Technology; Can Li, Nanjing Institute of Technology; Yuxuan Tong, North China Electric Power University. Electric Power Engineering Technology. DOI: 10.12158/j.2096-3203.2024.05.017