Smart Charging Strategy Balances Grid Stability and User Savings in Solar-Storage-EV Communities
A groundbreaking study introduces a new smart charging framework designed to optimize electric vehicle (EV) charging in communities equipped with solar power, energy storage, and EV charging infrastructure. The research, led by Kang Tong from the State Grid Hunan Electric Power Company Limited Research Institute, presents a dual-layer, multi-objective charging strategy that simultaneously reduces strain on the power grid and lowers electricity costs for EV owners. Published in Power System Protection and Control, the work offers a scalable solution for managing the growing integration of EVs into urban energy systems, particularly in residential areas where uncontrolled charging can lead to grid instability and higher electricity bills.
As the global shift toward electrified transportation accelerates, urban communities face mounting challenges in managing the energy demands of a rapidly expanding EV fleet. While EVs offer environmental benefits by reducing reliance on fossil fuels, their charging patterns—especially when uncoordinated—can exacerbate peak electricity demand, widen the gap between peak and off-peak loads, and threaten the reliability of local distribution networks. This phenomenon, often referred to as “peak stacking,” occurs when large numbers of EVs charge simultaneously during high-demand periods, pushing local transformers beyond their capacity and increasing wear and tear on grid infrastructure.
To address these issues, researchers have explored various forms of “orderly charging,” where EV charging is scheduled based on grid conditions, electricity pricing, or renewable energy availability. However, many existing strategies focus on a single objective—either grid stability or user cost savings—often at the expense of the other. Some approaches fail to incorporate on-site renewable generation or energy storage, while others rely on centralized control systems that struggle to scale as EV adoption grows.
The new research by Kang Tong and colleagues tackles these limitations by proposing a comprehensive framework tailored for photovoltaic-storage-charging (PVSC) integrated communities. These are residential areas where solar panels, battery storage systems, and EV chargers are interconnected, allowing for greater energy self-sufficiency and more flexible load management. The team’s approach introduces a dual-layer optimization model that balances the priorities of both the power grid operator and individual EV users.
At the core of the strategy is a two-tiered decision-making process. The upper layer, referred to as the “grid layer,” focuses on minimizing the difference between peak and off-peak electricity demand—known as the peak-to-valley difference. This metric is critical for maintaining grid stability, as large fluctuations in load can lead to voltage instability, equipment overheating, and increased operational costs for utilities. By flattening the community’s overall load curve, the grid layer ensures that the local transformer operates within its rated capacity, avoiding overloads that could lead to outages or premature equipment failure.
The lower layer, or “user layer,” aims to minimize the electricity cost incurred by individual EV owners. This is achieved by shifting charging to periods when electricity prices are low—typically during off-peak hours—or when solar generation is abundant and essentially free. The model incorporates time-of-use (TOU) pricing, a common utility rate structure that charges higher rates during peak demand periods and lower rates during off-peak times. By aligning charging with low-cost periods, users can significantly reduce their monthly electricity bills.
What sets this strategy apart is how the two layers interact. The grid layer first determines an optimal charging schedule that minimizes peak load. This schedule is then used as a constraint for the user layer, which fine-tunes the timing to further reduce costs without violating the grid’s stability requirements. This hierarchical approach ensures that user savings are achieved without compromising the safety and reliability of the power distribution system.
To implement this dual-layer model in real-world conditions, the researchers developed a cloud-edge collaborative architecture. In this setup, the computationally intensive grid-layer optimization is performed on a central cloud server, which has access to comprehensive data on community load, solar generation forecasts, and user charging requests. Meanwhile, the user-layer optimization is executed on local edge devices—such as smart meters or energy management systems—installed within the community or individual homes.
This distributed computing model offers several advantages. First, it reduces the computational burden on the central cloud, enabling faster response times and scalability to accommodate thousands of EVs. Second, it enhances data privacy, as sensitive user information—such as exact charging times and energy consumption patterns—does not need to be transmitted to a remote server. Third, it improves system resilience; even if the cloud connection is temporarily lost, the edge devices can continue to operate based on the latest available instructions.
The strategy was tested through simulations using data from a real-world community in Hunan, China, featuring 150 EVs, a 200-kilowatt solar array, and a 200-kilowatt-hour battery storage system. The researchers compared five different charging scenarios: uncontrolled charging in a conventional community, uncontrolled charging in a PVSC community, grid-layer-only optimization, user-layer-only optimization, and the proposed dual-layer strategy.
The results were striking. Compared to uncontrolled charging in a PVSC community, the dual-layer strategy reduced the peak-to-valley load difference by 40.47%, bringing it well within the safe operating range of the community’s 1,000-kilovolt-ampere transformer. Without intervention, the community’s peak load exceeded the transformer’s capacity, posing a risk of overloading. With the new strategy, peak demand was consistently kept below the limit, ensuring safe and stable operation.
Equally impressive was the impact on user costs. The dual-layer strategy reduced the average charging price by 52.63% compared to uncontrolled charging. While user-layer-only optimization achieved slightly lower costs, it did so at the expense of grid stability, causing a new peak in electricity demand during off-peak hours when many users shifted their charging simultaneously. The dual-layer approach avoided this pitfall by incorporating grid constraints, resulting in a more balanced and sustainable load profile.
The study also highlighted the importance of integrating solar generation and battery storage. In the PVSC community, solar power met a significant portion of the EV charging demand during daylight hours, reducing reliance on the grid and lowering overall energy costs. The battery system played a crucial role in smoothing out supply and demand imbalances, storing excess solar energy during the day and discharging it during evening peak hours when solar output was low but charging demand was high.
One of the key innovations of the research is the use of the Rat Swarm Optimizer (RSO), a nature-inspired metaheuristic algorithm, to solve the complex optimization problem. Unlike traditional optimization methods that can get stuck in local minima, RSO mimics the hunting behavior of rats to explore the solution space more effectively. The algorithm demonstrated strong performance in finding near-optimal charging schedules that satisfied all technical and economic constraints.
The implications of this research extend beyond individual communities. As cities worldwide strive to decarbonize their transportation and energy systems, scalable and intelligent EV charging solutions will be essential. The dual-layer model offers a blueprint for utilities, urban planners, and technology providers to design charging infrastructure that supports both grid reliability and consumer affordability.
Moreover, the cloud-edge architecture aligns with broader trends in smart grid development, where decentralized computing and real-time data processing are becoming increasingly important. By leveraging edge computing, the system can respond dynamically to changing conditions—such as sudden changes in solar output or unexpected EV charging requests—without relying on constant communication with a central server.
The researchers acknowledge that their model has limitations. For instance, it assumes a uniform EV fleet with identical battery capacities and charging rates, which may not reflect the diversity of real-world EV models. It also does not account for vehicle-to-grid (V2G) capabilities, where EVs can feed electricity back into the grid during peak periods. Future work will explore these aspects, as well as the integration of other flexible loads, such as smart thermostats and water heaters, to further enhance energy management.
Despite these limitations, the study represents a significant step forward in the field of EV charging optimization. It demonstrates that with the right combination of modeling, control architecture, and intelligent algorithms, it is possible to achieve a win-win outcome for both grid operators and consumers. As EV adoption continues to rise, such solutions will be critical for ensuring that the transition to electric mobility is not only environmentally sustainable but also technically and economically viable.
The research also underscores the importance of holistic system design. Rather than treating solar panels, batteries, and EV chargers as separate components, the PVSC integrated community approach views them as parts of a unified energy ecosystem. This systems-level perspective enables more efficient use of resources, reduces energy waste, and enhances resilience against supply disruptions.
From a policy standpoint, the findings suggest that incentives for EV adoption should be accompanied by investments in smart charging infrastructure and energy storage. Governments and utilities can play a key role in promoting the deployment of PVSC communities through targeted subsidies, favorable rate structures, and regulatory support for distributed energy resources.
For consumers, the message is clear: smart charging is not just about convenience—it’s about saving money and contributing to a more stable and sustainable energy future. By participating in orderly charging programs, EV owners can reduce their electricity bills, extend the life of grid infrastructure, and maximize the use of clean, renewable energy.
In conclusion, the work by Kang Tong and his team offers a practical and scalable solution to one of the most pressing challenges in the energy transition. By balancing the needs of the grid and the individual, their dual-layer charging strategy paves the way for a smarter, more resilient, and more equitable energy system. As cities around the world embrace electrified transportation, this research provides a valuable roadmap for integrating EVs into the grid in a way that benefits everyone.
Kang Tong, Zhu Jiran, Feng Churui, Fan Min, Ren Lei, Tang Haiguo, State Grid Hunan Electric Power Company Limited Research Institute, State Grid Corporation Laboratory of Intelligent Application Technology for Distribution Network, College of Automation, Chongqing University. Power System Protection and Control, DOI: 10.19783/j.cnki.pspc.230998