EV and 5G Base Station Storage Unite for Grid Flexibility in New Study
A groundbreaking study published in Power System Technology explores a novel approach to enhancing grid stability and economic efficiency by integrating two rapidly expanding distributed energy resources: electric vehicles (EVs) and 5G base station energy storage systems. As the world accelerates toward electrification and digital connectivity, the dual growth of EV fleets and 5G infrastructure presents a unique opportunity for synergy in the energy sector. Researchers from the School of Electrical Engineering at Southeast University have developed an innovative decision-making framework that enables these distinct yet complementary assets to be jointly managed by an aggregator participating in electricity markets.
The study, led by corresponding author Zhang Xinyu, addresses a critical challenge in modern power systems: how to effectively harness the flexibility of distributed, small-scale energy resources that individually lack the scale to participate in wholesale markets. With over 10 million pure electric vehicles and more than 2.3 million 5G base stations deployed across China by the end of 2022, the collective potential of their onboard batteries is immense. However, their value has remained largely untapped due to fragmentation and uncertainty. The research introduces a new optimization model—target robust optimization—that allows aggregators to make more resilient and profitable bidding decisions in the face of unpredictable market conditions and user behaviors.
The core idea behind the study is simple yet powerful: combine the strengths of two different types of mobile and stationary storage to create a more flexible, reliable, and economically viable resource for the grid. While both EV batteries and 5G base station backup batteries are electrochemical storage systems, their operational profiles differ significantly. EVs are mobile, with charging patterns dictated by user behavior, travel schedules, and battery state-of-charge requirements. In contrast, 5G base stations operate on fixed locations with predictable load profiles tied to communication traffic, but they must maintain a minimum reserve capacity to ensure uninterrupted service during power outages.
This divergence in behavior creates a temporal complementarity. When one resource is constrained, the other may have available headroom for grid support. For example, during evening hours when 5G traffic peaks and base station storage must preserve reserve capacity, EVs returning home from daily commutes are often available for charging and can provide upward regulation services. Conversely, during late-night off-peak hours when EVs are mostly parked and charging, 5G base stations may have surplus storage capacity that can be used for downward regulation or energy arbitrage.
The research team recognized that traditional optimization methods used in energy aggregation—such as stochastic optimization, robust optimization, and distributionally robust optimization—each have significant limitations. Stochastic optimization relies heavily on historical data and assumes known probability distributions, making it vulnerable to model misspecification. Robust optimization, while safeguarding against worst-case scenarios, often leads to overly conservative strategies that sacrifice profitability. Distributionally robust optimization attempts to balance these extremes by considering a range of possible distributions, but it still requires precise estimation of uncertainty sets, which can be challenging in practice.
To overcome these shortcomings, the authors propose a target robust (RS) optimization framework. Unlike conventional methods that focus on maximizing expected profit under uncertainty, the RS model flips the paradigm: it starts with a predefined acceptable target profit and then seeks to maximize the model’s robustness—the ability to withstand deviations from historical patterns. This approach does not require detailed knowledge of probability distributions or the construction of uncertainty sets. Instead, it introduces a concept called the “adversarial impact factor,” which quantifies how sensitive the system’s performance is to unexpected changes in market prices, user behavior, or equipment availability.
By embedding this factor directly into the objective function, the model ensures that even if real-world conditions diverge significantly from forecasts, the aggregator’s performance remains close to the desired target. This makes the strategy inherently more resilient to black swan events, forecasting errors, and behavioral shifts—common challenges in dynamic electricity markets.
The implications of this methodological shift are profound. In simulation tests using real-world data from the PJM ancillary services market and modeled after Chinese urban conditions, the target robust model outperformed all three benchmark approaches. It achieved the highest average net revenue while simultaneously exhibiting the lowest variance in performance across 1,000 test scenarios. Notably, its revenue was 76.92% higher than that of the distributionally robust optimization (DRO) model, with a standard deviation 11.05% lower. Even more striking, the DRO model’s performance, when evaluated under the same conditions, fell short of the RS model by 130.79% in terms of actual realized gains.
The robustness of the RS model was further demonstrated through its superior frequency regulation performance. Frequency regulation, a critical ancillary service that maintains grid stability by balancing supply and demand in real time, is typically compensated based on both capacity and accuracy. The RS model not only secured higher revenues but also maintained a high regulation accuracy score—averaging 91.27%—with minimal fluctuation. In comparison, the stochastic optimization model showed the lowest accuracy and highest volatility, making it unsuitable for markets with strict performance penalties.
One of the most compelling findings of the study is the economic benefit of co-optimizing EV and 5G storage. The researchers compared three scenarios: EVs alone, 5G base stations alone, and a combined fleet of both. When operated independently, the total revenue from the two resources amounted to $1,548.89. However, when aggregated and jointly optimized, the combined system generated $2,012.47—a 29.93% increase. This substantial uplift underscores the value of synergy and highlights the importance of holistic resource management in future smart grids.
The revenue boost stems from several factors. First, the combined system has greater overall flexibility in both energy and power domains. Second, the temporal complementarity allows the aggregator to shift the type and timing of services offered to the market. For instance, during periods of high downward regulation prices (when the grid needs to absorb excess generation), the aggregator can leverage the 5G base stations’ ability to absorb energy without compromising their primary function. Similarly, during peak demand periods when upward regulation is valuable, EVs with sufficient charge can discharge to support the grid.
Moreover, the joint operation enables strategic adjustments to the baseline power profile—the amount of energy the aggregator plans to consume or supply in the day-ahead market. Conventional strategies focus on energy arbitrage, buying low and selling high. However, the RS model reveals that adjusting the baseline not only for price differences but also to enable more profitable frequency regulation bids can yield superior results. For example, increasing discharge during low-price periods may seem counterintuitive, but if it allows the aggregator to bid more downward regulation capacity when prices are high, the net benefit outweighs the energy cost.
This strategic maneuvering is only possible because the combined system provides redundancy and balancing capability. When one component absorbs or releases energy for regulation purposes, the other can compensate, preventing either from violating operational constraints such as minimum state-of-charge or maximum charging power. This internal balancing reduces the need for external energy purchases to correct imbalances, thereby lowering transaction costs and improving overall efficiency.
The study also investigates how different risk preferences affect bidding behavior. By setting varying target profit levels, the aggregator can tailor its strategy to its risk appetite. A higher target profit leads to more aggressive bidding, which increases potential rewards but also exposes the system to greater performance variability and lower regulation accuracy. Conversely, a more conservative target results in a more stable and reliable operation, with higher average accuracy and lower standard deviation in outcomes.
This tunability is a major advantage for real-world deployment. Aggregators with access to capital and a higher risk tolerance can aim for maximum returns, while those prioritizing reliability—such as utilities or public entities—can adopt a more cautious stance. The model thus provides a flexible tool that can be adapted to diverse business models and regulatory environments.
Another key insight is the role of communication infrastructure in the energy transition. 5G base stations, traditionally viewed as energy consumers, are increasingly being recognized as potential grid assets. Many operators are already repurposing retired EV batteries for base station backup, creating a circular economy link between transportation and telecommunications. This study adds a new dimension by showing that these batteries can actively participate in energy markets, generating additional revenue streams for telecom companies while supporting grid decarbonization.
For EV owners, the implications are equally significant. While the study does not directly model user incentives, it lays the groundwork for future platforms where drivers can earn passive income by allowing their vehicles’ batteries to provide grid services. The success of such programs depends on ensuring that vehicle availability and charge levels meet user needs—a challenge that the target robust model helps address by incorporating uncertainty and risk management into the planning process.
From a policy perspective, the research supports the case for regulatory frameworks that enable and encourage the aggregation of distributed energy resources. As grids face increasing penetration of variable renewable energy, the need for fast-responding, flexible resources like EVs and battery storage will only grow. Policies that facilitate third-party aggregators, standardize communication protocols, and establish fair compensation mechanisms will be essential to unlocking this potential.
The study also highlights the importance of data-driven approaches in modern energy systems. By leveraging historical data on electricity prices, frequency signals, and usage patterns, the model can learn and adapt over time. However, it does so without assuming perfect knowledge of future conditions, making it more practical than models that require precise probabilistic forecasts.
Despite its strengths, the authors acknowledge limitations. The current model assumes full control over all aggregated resources, without accounting for user response rates or willingness to participate. In reality, not all EV owners may opt in, and some 5G operators may be hesitant to expose their backup systems to grid demands. Future work will need to incorporate behavioral models and game-theoretic approaches to design incentive schemes that align the interests of aggregators, end-users, and network operators.
Additionally, the equitable distribution of revenues among participants remains an open question. If an EV provides most of the regulation energy while a 5G station merely maintains balance, how should profits be shared? Developing transparent and fair allocation mechanisms will be crucial for building trust and encouraging widespread adoption.
Nonetheless, the study represents a significant step forward in the integration of transportation and energy systems. It demonstrates that by thinking beyond silos and embracing cross-sector collaboration, we can unlock new sources of value and resilience in the clean energy transition. The convergence of EVs and 5G is not just a technological trend—it is a strategic opportunity to build smarter, more sustainable cities.
As 5G networks continue to expand and EV adoption accelerates, the number of potential grid-supporting batteries will grow exponentially. The framework proposed by Zhang Xinyu and colleagues offers a roadmap for turning this distributed potential into tangible benefits for consumers, businesses, and the grid. By combining advanced optimization techniques with a deep understanding of resource synergies, the study paves the way for a more integrated, efficient, and resilient energy future.
The research was conducted at the School of Electrical Engineering, Southeast University, and published in the peer-reviewed journal Power System Technology. Its findings are expected to influence both academic research and industry practice in the fields of smart grids, distributed energy resources, and demand-side management.
Zhang Xinyu, School of Electrical Engineering, Southeast University, Power System Technology, DOI: 10.13335/j.1000-3673.pst.2023.2232