Introduction
With the increasing popularity of electric vehicles, the personal charging demand within communities is also showing a rapid growth trend. If users’ charging behaviors lack standardization and coordination, it not only poses serious safety risks to the community’s power grid, but also fails to ensure users’ basic charging needs and economic benefits. Therefore, how to manage and optimize electric vehicle charging behaviors has become an important issue.
This article proposes an intelligent scheduling algorithm for orderly charging, which aims to achieve reasonable allocation and efficient management of charging resources through intelligent technology. With this algorithm, we hope to improve the charging efficiency of electric vehicles, further optimize users’ charging experience, and provide strong support for the popularization and sustainable development of electric vehicles.
Business Background
According to data released by the China Association of Automobile Manufacturers, in March 2024, the sales of new energy vehicles reached an astonishing 883,000 units, with a penetration rate of 32.8% for new cars.This significant figure undoubtedly proves the rapid growth momentum of the electric vehicle market, which also means a continuous rise in charging demand.
For community residents who have purchased new energy vehicles, their charging needs often concentrate during specific time periods, such as after work in the evening or on weekends. This phenomenon of concentrated charging causes the community’s power load to surge during these specific periods, potentially exceeding the community’s power supply capacity, thus triggering the risk of power load overload.To effectively alleviate the problem of power load overload in the community, orderly charging becomes particularly important.
Orderly charging mainly optimizes charging strategies through algorithms to achieve maximum efficiency and stability in the charging process. However, traditional orderly charging algorithm scheduling methods are too simplistic and do not comprehensively consider user needs, vehicle requirements, and power grid load conditions.The main issues include:(1) Long waiting times:If the algorithm only considers the order of starting charging without adequately considering factors such as battery status and charging rate, it may lead to deviations in the calculation results, causing some users to wait for a long time.(2) Static scheduling:Some traditional orderly charging algorithms are static, meaning that the charging plan is predetermined without real-time adjustments. This makes it difficult for the system to adapt to dynamic changes in charging demand, reducing the system’s robustness and efficiency.(3) Energy management:Some traditional orderly charging algorithms may overlook the importance of energy management, failing to fully utilize renewable energy or charge during peak and valley periods, leading to negative environmental impacts and increased energy costs.Based on this, we need to develop an efficient model algorithm to achieve coordination and scheduling between charging piles,which should be based on the communication specifications of charging piles and real-time data, comprehensively considering various factors such as charging demand, power grid load, and charging efficiency, to formulate the best charging plan. Through this approach, we can effectively avoid issues such as charging pile overload, resource waste, and poor user experience.This article proposes a real-time algorithm for intelligent scheduling of orderly charging, focusing on intelligent scheduling, aiming to manage the start and stop of charging piles and output power in an intelligent manner. By optimizing the use of charging facilities, we achieve more flexible and efficient energy distribution, further promoting the sustainable development of electric vehicles.
Concept of the Orderly Charging Intelligent Scheduling Algorithm
In implementing orderly charging in residential communities for electric vehicles, algorithms such as first-come-first-served, earliest departure priority, and remaining charge priority are typically used. This article proposes an innovative matrix priority algorithm to more effectively manage the charging order and power distribution of charging piles. The core of this algorithm is to construct a priority matrix between charging piles, which determines the charging order and power distribution based on various factors (such as charging demand, remaining power, etc.).The specific idea is for the system to obtain user charging demand information to determine the priority coefficients of each charging pile, calculate the transformer’s available charging power, and allocate the load gap of each charging pile based on the priority coefficients. If the user load demand is greater than the allocated load gap and exceeds the preset minimum value, charging will proceed; otherwise, charging will be paused.Through this method, we can more intelligently manage the charging order of charging piles, improving charging efficiency and user experience. The steps include:(1) Calculate the priority coefficient:In the matrix priority algorithm, we set a priority for each charging pile, which is derived from a comprehensive consideration of various factors. These factors may include the remaining power of the vehicle, the urgency of charging demand, etc.Table 1 Example of Matrix Priority Algorithm Coefficients
Since the significance of each indicator is different, it is usually necessary to standardize the indicators, that is, convert the values of each indicator into relative values and use the weighted arithmetic mean method:① Zn = ∑( Yi * Wi)Where Zn is the charging priority coefficient of a certain charging pile, Yi is the i-th indicator, Wi is the weight of the i-th indicator, and ∑ is the summation symbol.(2) Calculate the transformer’s available charging power:While ensuring the residents’ electricity consumption, electric vehicle charging must fully utilize renewable energy and ensure that the transformer operates within the safe load rate range[1]. During renewable energy generation periods, set a relatively high transformer load rate, allowing the system to actively absorb more renewable energy for charging, maximizing its utilization, as shown in Table 2.② Available charging power M = Maximum output power of the transformer – Residential electricity consumption powerWhere the maximum output power of the transformer is calculated based on the upper limit of the load rate, and the residential electricity consumption power can be obtained through the smart integration terminal of the distribution area.Table 2 Upper Limit of Transformer Load Rate
(3) Allocate load gaps:The system calculates the load gaps of each charging pile based on the charging priority coefficients and the available charging power of the transformer. The specific steps are as follows:③ Power gap value S = Total charging pile demand power P – Available charging power of the transformer MIf the power gap of the transformer is negative, it is set to 0, where P is the total charging pile demand power, obtained by collecting charging pile information.④ Power gap allocation coefficient Xn=c/ZnIn the calculation of the charging priority coefficient Zn, a larger value indicates a higher charging priority. Since this algorithm requires the allocation of gap power, it needs to be converted into the form of gap allocation coefficient Xn=c/Zn. Here, c represents a constant that satisfies the constraint ∑(c/Zn)=1, and Xn is the power gap allocation coefficient for a certain charging pile.(4) Determine charging power and order:Based on the charging pile power gap allocation coefficients, the system calculates the charging order and power distribution of the charging piles to maximize user demand satisfaction while ensuring fairness among charging piles. The specific steps are as follows:⑤ Power allocation for a certain charging pile Gn=Pn – S * XnWhere Pn is the demand power of a certain charging pile, S is the power gap value of the transformer, and Xn is the power gap allocation coefficient for the charging pile.⑥ If the power allocation Gn for a certain charging pile exceeds the preset minimum value, charging will proceed according to the allocated power; otherwise, charging will be paused.Through the above orderly charging algorithm, community charging piles can more intelligently manage power distribution and charging order, thereby improving charging efficiency and user experience. This algorithm not only reduces competition and conflicts between charging piles but also better utilizes electrical resources, achieving efficient operation of community charging piles.
Algorithm System Verification
This system plan adopts a complete set of intelligent technologies, including deploying an application program with the orderly charging intelligent scheduling algorithm on the smart integration terminal of the distribution area to achieve real-time control of charging piles, and centralized monitoring through the orderly charging monitoring system. The system structure is shown in Figure 1.It consists of three parts: first, the smart integration terminal of the distribution area monitors the community’s electricity load and communicates with the charging piles; second, the orderly charging monitoring system can centrally monitor the electricity load of the distribution area and the charging status of the charging piles; finally, the orderly charging intelligent scheduling algorithm is the core of the entire orderly charging system, deployed on the smart integration terminal of the distribution area, responsible for ensuring orderly charging between charging piles.
Figure 1 Algorithm System Structure Diagram
To verify the effectiveness and feasibility of the proposed orderly charging intelligent scheduling algorithm, we conducted data simulations for a certain residential community. This community currently has 200 households, with 15 pure electric vehicles. Currently, the total capacity of the community’s transformer is 400kVA, and the charging power is relatively sufficient to meet the existing charging demand, as shown in the load curve in Figure 2 (excluding charging load).
Figure 2 Residential Electricity Load Curve
However, with the popularization of electric vehicles, assuming that the number of electric vehicles in this community reaches 100, and an unordered charging method is adopted, it will place enormous pressure on the power grid load, potentially leading to insufficient power grid load.
Therefore, we simulated the data situation of unordered charging and orderly charging for the future 100 vehicles based on the current charging data of 15 vehicles, as shown in Figure 3. From the figure, it can be seen that under the unordered charging method, the power grid load fluctuates significantly, with a high peak load, placing considerable pressure on the power grid. In contrast, after adopting the orderly charging method, the power grid load fluctuations are significantly reduced, and the peak load is also lowered. This indicates that orderly charging can reduce pressure on the power grid and distribute the power grid load more smoothly.
Figure 3 Simulated Load Curve for 100 Vehicles in the Community
Conclusion
In this article, the orderly charging intelligent scheduling algorithm comprehensively considers multiple key factors, including power grid load, renewable energy generation, user demand, and the status of charging piles. By comprehensively evaluating these factors, the algorithm can calculate the priority order and corresponding charging power distribution plan for each electric vehicle. Through reasonable scheduling and control, the orderly charging system helps effectively avoid excessive load on the power grid, thereby ensuring the stable operation of the power grid. It also helps improve users’ charging experience, reduce waiting times, and ensure they can conveniently charge when needed.In the future, the discharging capabilities of electric vehicles can be further considered to achieve a higher degree of integration between vehicles and the power grid. This means that the charging system can more intelligently coordinate the charging and discharging of electric vehicles in response to power grid demands, achieving more effective allocation of electrical resources. This vehicle-grid integration will bring greater benefits to the power system and electric vehicle users while promoting the development of sustainable energy and electric transportation.[Glossary][1] Transformer load rate: In China, there are regulations on the transformer load rate (load rate) for various building scenarios. For example, the “Standard for Electrical Design of Civil Buildings” GB 51348-2019 stipulates that the long-term working load rate of distribution transformers should not exceed 85%.[2] Smart integration terminal of the distribution area: Installed on the secondary side of transformers in distribution stations, box transformers, or pole transformers, it is an edge device in the smart IoT system’s “cloud management edge terminal” architecture, integrating functions such as power supply and consumption information collection, data collection from various terminals or energy meters, equipment status monitoring and communication networking, and edge computing.
Reviewed by: Wang Pingxi
Author: Zhang Qingren
Department: Langxin Technology Group Comprehensive Energy Business Department
END
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