Smart Charging Strategy Boosts EV and Grid Efficiency
As electric vehicles (EVs) surge in popularity across major Chinese cities, a new challenge has emerged: managing the growing strain their charging demands place on urban power grids. With millions of EVs now on the road, uncoordinated charging patterns risk overwhelming local distribution networks, leading to voltage instability, higher operational costs, and increased carbon emissions during peak hours. In response, researchers from Zhongyuan University of Technology have developed an innovative optimization strategy that not only enhances the profitability of charging infrastructure but also promotes a more balanced and sustainable energy ecosystem.
The solution, detailed in a recent study published in Power System Protection and Control, introduces a novel framework known as the charging-discharging-storage integrated station (CDSIS). Unlike conventional charging stations that simply draw power from the grid, CDSIS facilities are equipped with advanced energy storage systems, enabling them to both consume and supply electricity. This bidirectional capability transforms them from passive load points into active participants in grid management. By strategically storing energy during off-peak hours when electricity is cheap and abundant, and discharging it back to the grid during periods of high demand, CDSIS units can effectively “shave” peak loads, reduce strain on infrastructure, and lower overall system costs.
The research team, led by Professor Zhu Yongsheng, has taken this concept a step further by integrating it with real-world urban dynamics. Their approach is built on a sophisticated “vehicle-road-grid” interaction model, which recognizes that EV charging behavior is not random but deeply influenced by human mobility patterns. To capture this complexity, the team employed Origin-Destination (OD) matrix analysis, a method widely used in transportation planning to estimate travel flows between different locations. By analyzing historical traffic data, they were able to construct a dynamic road network model that simulates how EVs move through a city over a 24-hour period.
This model accounts for various factors that affect travel time, such as road length, traffic congestion, and signalized intersections. By incorporating a road impedance function that calculates travel time based on real-time traffic flow, the researchers could predict not just when EVs are likely to charge, but also where. This spatial-temporal forecasting is a critical advancement, as it allows for a more granular understanding of charging demand. Instead of treating the city as a single, homogeneous load, the model identifies specific nodes—intersections or neighborhoods—where charging pressure is likely to spike. This level of detail is essential for utilities and station operators to plan infrastructure investments and implement targeted demand response programs.
The heart of the study lies in its application of game theory to optimize the interaction between the CDSIS operator and EV users. The researchers adopted a Stackelberg game framework, a type of hierarchical decision-making model where one player, the “leader,” makes the first move, and the other players, the “followers,” respond accordingly. In this case, the CDSIS acts as the leader, setting dynamic electricity prices, while individual EV owners are the followers, choosing when and where to charge based on those prices to minimize their own costs.
This strategic pricing is not arbitrary. The model is designed to achieve a multi-objective optimization, balancing the profit of the CDSIS operator with the cost savings for EV users. The CDSIS’s revenue comes from four sources: selling stored energy back to the grid at peak prices, charging EV users, purchasing energy from the day-ahead market, and buying from the real-time market. The goal is to maximize net profit, which means buying low, selling high, and efficiently managing its storage assets. For the EV user, the objective is straightforward: minimize the total cost of charging, which includes both the price per kilowatt-hour and any time-related costs associated with waiting.
The brilliance of the Stackelberg approach is that it creates a stable equilibrium. The CDSIS cannot simply set sky-high prices during peak hours, as this would deter users from charging, reducing its revenue. Conversely, if it sets prices too low, it might attract a flood of users but at the expense of its own profitability. The model calculates the optimal price point where the CDSIS maximizes its profit, knowing full well that rational EV users will respond by shifting their charging behavior to the most economical times. This often means charging late at night or during midday when solar generation is high and demand is low.
To make this model practical, the researchers had to address the non-linearities inherent in such a system. The interaction between price and demand is inherently complex; a small change in price can lead to a large, non-proportional change in user behavior. To solve this, the team used the Karush-Kuhn-Tucker (KKT) conditions and duality theory to transform the bi-level optimization problem into a single-level mixed-integer linear program (MILP). This mathematical reformulation is crucial, as it allows the problem to be solved efficiently using standard commercial solvers like CPLEX, making the strategy feasible for real-world implementation.
The study’s findings are compelling. In a simulation based on a real-world urban area in China, which was modeled using the IEEE 33-node distribution system and a 31-node road network, the proposed strategy demonstrated significant benefits. The results showed that the CDS7IS could achieve a daily profit of 4,823 yuan, a substantial improvement over a traditional charging station, which, without storage capabilities, could only achieve a profit of 3,191 yuan under the same conditions. This 51% increase in profitability highlights the immense value of integrated energy storage.
For EV users, the benefits are equally tangible. By responding to the optimized pricing signals, users were able to significantly reduce their charging costs. The model successfully shifted charging demand away from two major peak periods: 8:00-9:00 AM and 6:00-7:00 PM. During the morning peak, the strategy managed to transfer 427 kWh of demand, saving users 245 yuan. In the evening, the impact was even greater, with 1,123 kWh shifted and 645 yuan in savings. This “peak shaving” not only benefits the users and the station operator but also provides a valuable service to the entire power grid by reducing the need for expensive and often carbon-intensive peaking power plants.
The research also delved into the sensitivity of the system to key parameters. One critical finding was the impact of the minimum pricing limit. The model requires that the average daily price charged to users remains constant, a constraint likely imposed by regulatory or market fairness concerns. The study found that setting a very low minimum price (e.g., 50% of the day-ahead market rate) allowed the CDSIS to maximize its profit by creating a larger spread between low and high prices. However, this came at a cost: users faced higher prices during the peak charging periods, increasing their overall costs. As the minimum price was raised, both CDSIS profit and user costs decreased. The most balanced outcome, a true “win-win,” was achieved when the minimum price was set at a moderate level, ensuring that the benefits of the system were shared fairly.
Another key parameter is the size of the CDSIS’s energy storage system. The simulation showed that increasing the storage capacity from 4,000 kWh to 10,000 kWh led to a dramatic increase in profit, from 4,170 yuan to 7,302 yuan. This is because a larger battery allows the station to store more energy when prices are low and sell more when prices are high. However, the returns began to diminish beyond 10,000 kWh. Once the storage was large enough to fully capture all the arbitrage opportunities presented by the daily price cycle and the EV charging demand, adding more capacity provided no additional benefit. This insight is crucial for investors, as it suggests there is an optimal, cost-effective size for these systems, preventing over-investment in oversized and underutilized batteries.
The implications of this research extend far beyond a single city or a single type of charging station. It presents a blueprint for a new generation of intelligent, grid-supportive EV infrastructure. As China and other nations push for deeper electrification of their transportation sectors, the integration of storage and smart control will be essential. This model could be scaled up to manage fleets of charging stations across an entire metropolitan area, creating a virtual power plant that can provide a range of grid services, from frequency regulation to emergency backup.
Moreover, the “vehicle-road-grid” framework is highly adaptable. It could be integrated with other data sources, such as real-time weather forecasts to predict solar generation, or with ride-sharing platforms to better predict vehicle availability. The core concept of using economic signals to guide user behavior is a powerful tool for managing complex, decentralized systems. It respects user autonomy—drivers are free to make their own choices—while gently nudging the collective outcome toward a more efficient and sustainable equilibrium.
The work by Zhu Yongsheng and his colleagues at Zhongyuan University of Technology represents a significant step forward in the field of smart grid and EV integration. It moves beyond theoretical models to provide a practical, computationally tractable solution that addresses the real-world challenges of urban energy management. By combining advanced modeling of human mobility with sophisticated game-theoretic optimization, they have created a strategy that is not only technically sound but also economically viable. This research provides a compelling case for policymakers and utilities to invest in next-generation charging infrastructure that is not just a place to plug in, but an active, intelligent node in a smarter, more resilient energy network.
The success of this strategy hinges on collaboration. It requires charging station operators to adopt new business models, utilities to provide the necessary market signals, and EV drivers to be willing to shift their charging habits for financial gain. The study shows that when these parties are aligned through a well-designed incentive structure, the result can be a system that is more efficient, more profitable, and ultimately, more sustainable for everyone involved. As the world transitions to a zero-carbon future, such innovative, multi-stakeholder solutions will be key to unlocking the full potential of electric mobility.
Zhu Yongsheng, Chang Wen, Wu Dongya, Wang Geng, Peng Sheng, Zhang Shibo, College of Electronic and Information Engineering, Zhongyuan University of Technology, Power System Protection and Control, DOI: 10.19783/j.cnki.pspc.231253