Smart Charging Pricing Strategy Cuts Emissions and Boosts Profits in Urban Networks
As cities worldwide accelerate their transition to electric mobility, a new study from researchers at Liaoning Technical University presents a groundbreaking approach to managing the complex interplay between urban transportation and power systems. The research, published in the journal Electrical Engineering, introduces an innovative pricing model that enables charging network operators (CNOs) to simultaneously increase profitability and reduce carbon emissions across interconnected urban grids.
With electric vehicle (EV) sales projected to reach 56 million annually by 2040—representing over 30% of the global vehicle fleet—the integration of EVs into existing infrastructure has become a critical challenge. While EVs offer a promising pathway to reduce fossil fuel dependence and urban pollution, their widespread adoption intensifies the coupling between transportation networks and power distribution systems. This growing interdependence creates both challenges and opportunities, particularly in how charging behavior influences grid load, congestion, and overall emissions.
Traditional models have often treated charging stations as passive nodes, simply passing on electricity prices from the grid to consumers. However, this approach overlooks the strategic role that CNOs can play in shaping user behavior and optimizing system-wide performance. The new research, led by Aoyang Liu and Jianchen Liu, challenges this status quo by positioning the CNO as an active participant in the energy-transportation nexus, capable of driving both economic and environmental improvements through intelligent pricing.
At the heart of the study is a novel framework that integrates three key components: the urban transportation network (UTN), the power distribution network (PDN), and the charging network operator (CNO). Unlike previous models that either ignored CNO profitability or failed to account for carbon emissions comprehensively, this approach treats the CNO as a profit-driven entity whose pricing decisions ripple through both the road and power systems. By setting differentiated prices across charging stations, the CNO can influence where and when EV owners choose to charge, thereby redistributing load across the grid and alleviating congestion on both roads and power lines.
The researchers emphasize that EVs are not inherently zero-emission vehicles—their environmental impact depends heavily on the carbon intensity of the electricity used for charging. In regions where power generation relies on fossil fuels, EVs can generate more emissions over their lifecycle than internal combustion engine vehicles. This reality underscores the importance of incorporating both direct emissions from gasoline vehicles (GVs) and indirect emissions from EVs into any holistic urban mobility strategy. The model developed by Liu and Liu explicitly accounts for both, using a carbon emission flow (CEF) theory to quantify the carbon footprint at each charging node.
One of the most significant contributions of the study is its decentralized solution method, which respects the operational privacy of each network operator. In real-world urban environments, the UTN, PDN, and CNO are typically managed by separate entities with distinct objectives and confidential data. A centralized control system would require full transparency, which is neither practical nor desirable. Instead, the researchers employ a best-response iterative algorithm that allows each party to optimize its own objectives while exchanging only limited information—such as energy prices and traffic flow forecasts—with the others. This ensures that sensitive operational data remain protected while still enabling system-wide coordination.
The algorithm operates in a cyclical manner. First, the UTN calculates traffic flows based on current charging prices, determining how EVs distribute themselves across the road network. Then, the PDN uses this charging load data to compute optimal energy prices, factoring in grid congestion, renewable generation availability, and carbon intensity. Finally, the CNO takes these energy prices and traffic patterns to set new charging prices that maximize its profit. This loop repeats until a stable equilibrium is reached, where no party can improve its outcome by unilaterally changing its strategy.
To validate their model, the researchers applied it to a modified version of the Nguyen-Dupuis transportation network coupled with the Baran & Wu 33-node distribution system. The simulation included four EV charging stations connected to two different PDNs, one of which hosts a photovoltaic (PV) plant. This setup allowed the team to study how pricing strategies affect the spatial distribution of charging demand between high-carbon and low-carbon grid zones.
The results were striking. Under a non-discriminatory pricing scheme—where all stations charge the same rate—EV charging behavior is largely driven by convenience and proximity, leading to suboptimal load distribution. When the CNO adopts a simple markup over grid prices (LMP-based pricing), some load shifting occurs, but it often directs EVs to stations with lower electricity costs, which may coincide with higher carbon intensity. In contrast, when carbon taxes are incorporated into the energy price (EP-based pricing), the model incentivizes charging at low-emission nodes, particularly those powered by solar energy.
However, the most effective strategy emerged when the CNO actively optimizes its pricing to balance profit and emissions. In this scenario, the operator sets lower prices at stations connected to the PV-rich PDN, attracting more EVs despite potentially higher grid-level costs. By doing so, the CNO not only reduces overall system emissions but also increases its own revenue through higher utilization rates. The study found that compared to uniform pricing, optimized pricing boosted CNO profits by 16.78% while reducing carbon emissions by approximately 27%.
This dual benefit arises from the flexibility inherent in EV charging behavior. Unlike gasoline vehicles, which refuel at the nearest available station, EV owners are often willing to detour for better prices or faster service. The model leverages this behavioral elasticity, using price signals to guide EVs toward underutilized, low-carbon charging infrastructure. This not only reduces strain on congested parts of the grid but also enhances the economic viability of renewable energy integration.
The time-of-use analysis further revealed the dynamic nature of the optimization. During nighttime hours, when solar generation is zero and traffic demand is low, the carbon benefits of optimized pricing are minimal. However, as daylight increases and PV output rises, the carbon intensity gap between the two PDNs widens, creating stronger incentives for load shifting. The midday peak, when both solar generation and traffic demand are high, becomes the most effective window for emissions reduction. At 11:00 a.m., the model achieved its highest profit (753.09 yuan) and lowest emissions (12.07 kg), demonstrating the synergy between economic and environmental goals.
Evening hours present a different challenge. As solar output declines but traffic demand remains high during the evening rush, the carbon advantage of the PV-integrated PDN diminishes. Nevertheless, the CNO can still optimize pricing to smooth demand and avoid peak load spikes, contributing to grid stability. The study shows that profit and emissions optimization follow similar temporal patterns, both driven by the interplay of renewable generation and travel behavior.
The implications of this research extend beyond academic interest. For city planners and utility regulators, it offers a practical tool for promoting sustainable mobility without sacrificing economic efficiency. By empowering CNOs to act as intelligent coordinators between transportation and energy systems, cities can achieve deeper decarbonization without relying solely on subsidies or mandates. The model suggests that market-based mechanisms, when properly designed, can align private profit motives with public environmental goals.
For charging network operators, the findings represent a strategic opportunity. Rather than viewing carbon reduction as a compliance cost, CNOs can integrate it into their core business model. By investing in data analytics and dynamic pricing systems, operators can turn environmental stewardship into a competitive advantage. Stations that offer lower carbon footprints can be marketed as “green” options, attracting environmentally conscious consumers willing to trade slight detours for cleaner energy.
Moreover, the decentralized nature of the solution makes it highly scalable and adaptable to different urban contexts. Whether in a dense metropolis with multiple CNOs or a smaller city with a single operator, the framework can be implemented without requiring centralized control. This flexibility is crucial for real-world deployment, where institutional and regulatory landscapes vary widely.
The research also opens new avenues for future innovation. One promising direction is the integration of energy storage at charging stations, allowing CNOs to arbitrage between high- and low-price periods while providing grid services. Another is the extension of the model to include vehicle-to-grid (V2G) capabilities, where EVs not only draw power but also feed it back to the grid during peak demand. Additionally, the authors suggest exploring competitive dynamics among multiple CNOs, which could lead to more sophisticated pricing games and market equilibria.
From a policy perspective, the study highlights the importance of enabling regulatory frameworks. For CNOs to implement such advanced pricing strategies, they must be granted sufficient autonomy in setting rates, within reasonable bounds to prevent monopolistic exploitation. Governments can support this transition by establishing average price caps—such as the constant C used in the model—while allowing flexibility for spatial and temporal differentiation.
In conclusion, the work of Liu and Liu demonstrates that the future of urban mobility lies not just in electrifying vehicles, but in intelligently managing the entire ecosystem in which they operate. By treating charging stations not as isolated facilities but as dynamic nodes in a coupled transportation-energy network, cities can unlock significant gains in efficiency, sustainability, and profitability. As the world moves toward a low-carbon future, such integrated, market-driven solutions will be essential for turning the promise of electric mobility into a tangible reality.
The study was conducted by Aoyang Liu and Jianchen Liu of the Faculty of Electrical and Control Engineering at Liaoning Technical University, and published in Electrical Engineering. Liu Aoyang, Liu Jianchen. Optimal pricing strategy of electrical transportation network under low-carbon target. Electrical Engineering, 2024, 25(5).