Revolutionary EV Charging-Discharging Scheduling Strategy: A Breakthrough in V2G Technology
The rapid proliferation of electric vehicles (EVs) has emerged as a cornerstone in the global transition towards sustainable energy, offering a promising solution to mitigate energy scarcity and environmental pollution. However, the uncoordinated charging of a large number of EVs poses significant challenges to the stability and reliability of power grids, leading to increased peak-valley load differences and reduced load rates. Addressing these issues, a groundbreaking study titled “Charge and Discharge Scheduling Strategies for Electric Vehicle Double-Layer Optimization Models” presents a novel approach that harmonizes the needs of both power grids and EV users, marking a significant advancement in Vehicle-to-Grid (V2G) technology.
The study delves into the critical problem of unordered EV charging, which has been a persistent concern for grid operators. As EV adoption surges—with China’s new energy vehicle ownership reaching 20.41 million by the end of 2023, accounting for 6.07% of total vehicle ownership, and pure electric vehicles making up 76.04% of that figure—the impact of unregulated charging on grid stability has become increasingly pronounced. Traditional time-of-use (TOU) tariff strategies, while attempting to guide users to charge during off-peak hours, often result in new load peaks when a large number of EVs converge on these low-tariff periods. Meanwhile, existing multi-objective optimization strategies have struggled to achieve satisfactory peak-shaving and valley-filling effects, with issues such as suboptimal weight coefficients in objective function weighting and low user participation plaguing their effectiveness.
To overcome these limitations, the research team proposed a double-layer optimization model that takes into account the dual demands of the power grid and EV users. The first layer of the model focuses on minimizing the daily load variance of the power grid, aiming to flatten the load curve and reduce peak-valley differences. This is crucial for maintaining grid stability, as excessive fluctuations can strain grid infrastructure and increase the risk of outages. The second layer centers on the user perspective, with the dual objectives of minimizing charging costs for EV owners and ensuring sufficient state of charge (SOC) to meet their travel needs. By considering both aspects, the strategy seeks to create a win-win scenario where grid reliability is enhanced while user costs are reduced, thereby boosting user willingness to participate in V2G programs.
A key innovation of this study lies in its use of an improved particle swarm optimization-simulated annealing (PSO-SA) algorithm to solve the double-layer optimization model. The standard PSO algorithm, while efficient, is prone to falling into local optima, especially when dealing with a large number of EVs. On the other hand, the simulated annealing (SA) algorithm excels at escaping local optima but has a slower convergence rate. By combining the strengths of both algorithms, the improved PSO-SA algorithm first uses PSO to quickly converge to a near-optimal solution, then applies SA to perturb and optimize this solution, effectively jumping out of local optima to find a better global solution. This hybrid approach not only improves the efficiency of the optimization process but also enhances the accuracy of the results, making it well-suited for handling the complex and large-scale problem of EV charging-discharging scheduling.
To validate the effectiveness of the proposed strategy, the researchers conducted simulations using Matlab, based on parameters from a district in Chongqing. The simulation involved 1,500 EVs with a battery capacity of 35 kWh, a charging power of 7 kW, and a charging efficiency of 90%. The time-of-use electricity prices for Chongqing were used as a reference, with peak, flat, and valley periods defined, and corresponding prices set at 0.64 yuan/(kWh), 0.54 yuan/(kWh), and 0.36 yuan/(kWh) respectively. The Monte Carlo method was employed to simulate the charging and discharging scenarios of the 1,500 EVs across 96 time segments (each 15 minutes) over a day.
The simulation results revealed striking improvements when comparing the double-layer optimization strategy with existing approaches. In terms of grid performance, the peak-valley difference of the daily load curve, a key indicator of grid stability, was significantly reduced. The time-of-use strategy resulted in a peak-valley difference of 3,251.44 kW, while the multi-objective optimization strategy brought it down to 2,340.90 kW. However, the double-layer optimization strategy achieved a much lower peak-valley difference of 1,682.79 kW, representing a 48.24% reduction compared to the time-of-use strategy and a 28% reduction compared to the multi-objective strategy. Additionally, the daily load variance, which measures the degree of load fluctuation, was reduced by 51.6% compared to the time-of-use strategy and 19.75% compared to the multi-objective strategy, indicating a much flatter and more stable load curve.
From the user perspective, the benefits were equally impressive. The average charging cost per EV under unordered charging was 12.01 yuan. The multi-objective optimization strategy reduced this to 9.97 yuan, while the double-layer optimization strategy further lowered it to 9.05 yuan, a 24.63% reduction compared to unordered charging and a 9.18% reduction compared to the multi-objective strategy. This significant cost savings not only benefits individual users but also incentivizes greater participation in V2G programs, which is essential for the widespread adoption of such strategies.
Another notable advantage of the double-layer optimization strategy is its ability to avoid the creation of new load peaks, a common drawback of the time-of-use strategy. The simulation showed that while the time-of-use strategy did have some peak-shaving effect during the 18:00-21:00 peak period, it led to a new load peak between 10:15 and 3:00 the next day. In contrast, the double-layer optimization strategy effectively smoothed out these fluctuations, ensuring that no new peaks were formed and that the overall load curve was more balanced.
The success of the double-layer optimization model can be attributed to its structured approach to addressing the conflicting needs of the grid and users. By separating the optimization objectives into two layers and iteratively feeding back results between them, the model ensures that neither the grid’s stability nor the users’ economic interests are compromised. The first layer’s focus on minimizing load variance ensures that the grid operates within safe and efficient parameters, while the second layer’s emphasis on cost minimization and meeting travel needs ensures that users have a tangible incentive to participate. This two-pronged approach creates a mutually beneficial cycle where grid reliability is enhanced, user costs are reduced, and participation rates increase, leading to even better grid performance.
The improved PSO-SA algorithm played a crucial role in achieving these results. Compared to the standard PSO and SA algorithms, the hybrid algorithm demonstrated superior performance in terms of both convergence speed and optimization accuracy. The PSO algorithm converged quickly but got stuck in local optima, while the SA algorithm was more accurate but slower. The PSO-SA algorithm, however, combined the speed of PSO with the accuracy of SA, resulting in a more efficient and effective optimization process. This makes it a valuable tool for handling the complex and dynamic nature of EV charging-discharging scheduling, where large numbers of variables and constraints must be considered.
Looking ahead, this research has important implications for the future development of V2G technology and the integration of EVs into smart grids. As EV adoption continues to grow—with projections suggesting that new energy vehicles will account for more than 50% of the market share by 2026—effective charging-discharging scheduling strategies will become increasingly critical. The double-layer optimization strategy presented in this study offers a viable solution that can be scaled up to accommodate larger numbers of EVs and adapted to different regional electricity price structures and grid conditions.
Moreover, the inclusion of battery degradation costs in the second layer of the optimization model adds a realistic dimension to the strategy. By accounting for the additional costs associated with battery wear and tear due to charging and discharging cycles, the model ensures that the long-term economic impact on users is considered. This not only makes the strategy more accurate but also helps in developing more sustainable and user-friendly V2G policies.
In conclusion, the study by MA Yongxiang, WANG Xixin, YAN Qunmin, KONG Zhizhan, and DAN Wenguo from Shaanxi University of Technology, Shaanxi Electric Power Company, and Ulanqab Electric Power Bureau represents a significant step forward in EV charging-discharging scheduling. The proposed double-layer optimization strategy, coupled with the improved PSO-SA algorithm, effectively balances the needs of power grids and users, reduces load peak-valley differences, lowers user costs, and avoids the creation of new load peaks.
This research, published in the Journal of Chongqing University of Technology (Natural Science) (Vol. 38, No. 2, 2024, doi: 10.3969/j.issn.1674-8425(z).2024.02.029), provides a practical and efficient solution for integrating large numbers of EVs into the power grid while ensuring its stability and economic operation. As the world continues to transition towards a low-carbon future, such innovations will play a pivotal role in maximizing the benefits of EVs and advancing the development of smart and sustainable energy systems.