Ordered charging method based on charging power and charging time optimization

Abstract

The invention discloses an ordered charging method based on charging power and charging time optimization, which aims at reducing the load fluctuation of a power distribution network, improving the quality of electric energy and maximizing the utilization of new energy, establishes a target function, comprehensively considers the charging state, the charging power and the charging time of an electric vehicle, and constructs an ordered charging model of the electric vehicle; time-sharing zone control and charging power limitation dynamic constraint conditions are established according to power distribution network load fluctuation and new energy access, and different charging priority constraint conditions are established according to electric vehicle user charging requirements and economic factors; an optimization algorithm is adopted to solve the electric vehicle ordered charging model meeting the constraint conditions, and an electric vehicle ordered charging strategy is obtained; the method can effectively guarantee the stability and reliability of a power grid, effectively guarantees the full consumption and utilization of new energy, provides a solid guarantee for the safe and economical operation of a power distribution network, promotes the smooth development of clean and renewable energy sources, and facilitates the realization of a dual-carbon target of a power system.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric vehicle charging, and in particular relates to an orderly charging method based on optimization of charging power and charging time. Background Art

[0002] New energy, also known as unconventional energy, refers to various forms of energy other than traditional energy. The main ones are: solar energy, wind energy, geothermal energy, biomass energy, etc. New energy power generation refers to the process of using renewable energy or other clean energy to generate electricity. Compared with traditional fossil fuel power generation, new energy power generation is more environmentally friendly, sustainable, and reduces greenhouse gas emissions.

[0003] With the vigorous development of new energy, the power grid will face a series of challenges. At the same time, with the popularization of electric vehicles, the demand for charging facilities will continue to increase. Each charging pile needs to be connected to the distribution network, which will have an impact on the load of the new distribution network composed of traditional distribution networks and new energy. If a large number of charging piles work at the same time, the load on the distribution network may be too high, resulting in overload of power equipment, increasing the risk of line loss and equipment damage. In addition, the charging time and charging amount of different devices may also be different, which may cause greater load fluctuations in the distribution network and bring challenges to the stability and reliability of the power system.

[0004] It can be seen that the disorderly charging of large-scale electric vehicles may bring large load fluctuations to the distribution network, affect the power quality, and may also lead to inefficient utilization of new energy. In order to solve the above problems, it is necessary to develop an orderly charging method based on the optimization of charging power and charging time. Summary of the invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and to provide an orderly charging method based on the optimization of charging power and charging time, so as to effectively ensure the stability and reliability of the power grid, effectively ensure the full absorption and utilization of new energy, provide a solid guarantee for the safe and economical operation of the distribution network, promote the smooth development of clean and renewable energy, and contribute to the realization of the dual carbon goals of the power system.

[0006] The object of the present invention is achieved by: an orderly charging method based on optimization of charging power and charging time, comprising the following steps:

[0007] (1) With the goal of reducing load fluctuations in the distribution network, improving power quality, and maximizing the use of new energy, an objective function F is established: F = a1·F1+a2·F2+a3·F3, where F1 is the objective function for reducing load fluctuations in the distribution network, F2 is the objective function for improving power quality, and F3 is the objective function for maximizing the use of new energy. a1, a2, and a3 are weight coefficients for the corresponding objectives, and a1+a2+a3=1. Meanwhile, the charging state, charging power, and charging time of electric vehicles are comprehensively considered to construct an orderly charging model for electric vehicles.

[0008] (2) According to the load fluctuation of the distribution network and the access of new energy, establish constraints for controlling the charging behavior of electric vehicles in time and area;

[0009] (3) Establish different charging priority constraints based on the charging needs and economic factors of electric vehicle users;

[0010] (4) Establish dynamic constraints on the charging power limit of electric vehicles based on the load fluctuations of the distribution network and the access to new energy sources;

[0011] (5) An optimization algorithm is used to solve the orderly charging model of electric vehicles that meets the above constraints, and an orderly charging strategy for electric vehicles is output, including the optimal charging time and charging power for each electric vehicle.

[0012] Preferably, the objective function F1 aimed at reducing the distribution network includes minimizing the load variance and minimizing the load peak-to-valley difference, wherein the load variance minimization can be expressed as follows:

[0013]

[0014] Where, L(t) is the total load of the distribution network at time t, including conventional load and electric vehicle load; N is the total number of time points; is the average load;

[0015] The minimization of the load peak-to-valley difference can be expressed as follows:

[0016] min(L max -L min );

[0017] Where, Lmax is the maximum load value, and Lmin is the minimum load value;

[0018] but

[0019] Preferably, the objective function F2 for improving power quality includes minimizing voltage deviation and minimizing harmonic content, wherein minimizing voltage deviation can be expressed as follows:

[0020]

[0021] Where Vi(t) represents the voltage of node i at time t, Vref is the rated voltage, and M is the total number of nodes;

[0022] The harmonic content minimization is expressed as follows:

[0023]

[0024] Where H(t) is the total harmonic content of the distribution network at time t;

[0025] but

[0026] Preferably, the objective function F3 for maximizing the utilization of new energy is to maximize the utilization rate of new energy, which can be expressed as follows:

[0027]

[0028] Where Pnew(t) is the power generation of new energy at time t, and Pev(t) is the charging power of electric vehicles at time t. When the power generation of new energy is sufficient, δ(t) = 1, otherwise δ(t) = 0.

[0029] Preferably, time-sharing and zoned control constraints are established, specifically: first, according to the load fluctuation of the distribution network and the access situation of new energy, a day is divided into multiple time periods, including load peak period, valley period and new energy power generation peak period, off-peak period; then, the charging behavior of electric vehicles is controlled in time and zone for different time periods and regions. During the peak load period, the charging power is limited or the charging cost is increased to reduce the charging demand; during the peak period of new energy power generation, electric vehicles are encouraged to charge to improve the utilization rate of new energy.

[0030] Preferably, charging priority constraints are established, specifically: first, different charging priorities are set for different users based on the charging needs and economic factors of electric vehicle users, and higher priorities are set for users who are in urgent need of charging, long-term users or high-paying users; then, when charging resources are limited, users with higher priorities are given priority in obtaining charging rights to ensure that users' charging needs are met.

[0031] Preferably, dynamic constraints on charging power limits are established, specifically: first, real-time monitoring of load fluctuations in the distribution network and access to new energy sources, and dynamic adjustment of the upper limit of charging power for electric vehicles based on monitoring data; then, during peak load periods, lowering the upper limit of charging power to reduce the burden on the distribution network, and when sufficient new energy generation is available, appropriately increasing the upper limit of charging power to improve the utilization rate of new energy.

[0032] Preferably, the optimization algorithm is a genetic algorithm or a particle swarm optimization algorithm.

[0033] Due to the adoption of the above technical scheme, the beneficial effects of the present invention are as follows: the present invention first establishes an objective function with the goal of reducing load fluctuations in the distribution network, improving power quality, and maximizing the utilization of new energy, and at the same time comprehensively considers the charging status, charging power, and charging time of electric vehicles, constructs an orderly charging model for electric vehicles, and then establishes time-sharing and partitioning control constraints, charging priority constraints, and charging power limit dynamic constraints. Finally, an optimization algorithm is used to solve the orderly charging model of electric vehicles that meets various constraints, and the optimal solution for the orderly charging strategy of electric vehicles is obtained, which solves the problem that the disorderly charging of large-scale electric vehicles may bring large load fluctuations to the distribution network, affect the power quality, and may also lead to low utilization efficiency of new energy. The problem effectively ensures the stability and reliability of the power grid, effectively ensures the full absorption and utilization of new energy, provides a solid guarantee for the safe and economic operation of the distribution network, promotes the smooth development of clean and renewable energy, and contributes to the realization of the dual carbon goals of the power system, with good economic and social benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic flow chart of the method steps of the present invention. DETAILED DESCRIPTION

[0035] The technical solution of the present invention is further specifically described below through embodiments and in conjunction with the accompanying drawings.

[0036] like Figure 1 As shown, the present invention provides an orderly charging method based on optimization of charging power and charging time, comprising the following steps:

[0037] (1) With the goal of reducing the load fluctuation of the distribution network, improving the power quality, and maximizing the utilization of renewable energy, an objective function F is established: F = a1·F1+a2·F2+a3·F3, where F1 is the objective function for reducing the load fluctuation of the distribution network, F2 is the objective function for improving the power quality, and F3 is the objective function for maximizing the utilization of renewable energy. a1, a2, and a3 are the weight coefficients of the corresponding objectives, and a1+a2+a3=1. Meanwhile, the charging status, charging power, and charging time of electric vehicles are comprehensively considered to construct an orderly charging model for electric vehicles. This minimizes the load fluctuation of the distribution network, improves the power quality indicators, and maximizes the utilization of renewable energy while meeting the charging needs of electric vehicles.

[0038] Among them, the objective function F1 aimed at reducing the distribution network includes minimizing the load variance and minimizing the load peak-to-valley difference, where the load variance minimization can be expressed as follows:

[0039]

[0040] Where, L(t) is the total load of the distribution network at time t, including conventional load and electric vehicle load; N is the total number of time points; is the average load;

[0041] The minimization of the load peak-to-valley difference can be expressed as follows:

[0042] min(L max -L min );

[0043] Where, Lmax is the maximum load value, and Lmin is the minimum load value;

[0044] but

[0045] Among them, the objective function F2 aimed at improving power quality includes minimizing voltage deviation and minimizing harmonic content, where minimizing voltage deviation can be expressed as follows:

[0046]

[0047] Where Vi(t) represents the voltage of node i at time t, Vref is the rated voltage, and M is the total number of nodes;

[0048] The harmonic content minimization is expressed as follows:

[0049]

[0050] Where H(t) is the total harmonic content of the distribution network at time t;

[0051] but

[0052] Among them, the objective function F3, which aims at maximizing the utilization of new energy, is to maximize the utilization rate of new energy, which can be expressed as follows:

[0053]

[0054] Where Pnew(t) is the power generation of new energy at time t, and Pev(t) is the charging power of electric vehicles at time t. When the power generation of new energy is sufficient, δ(t) = 1, otherwise δ(t) = 0.

[0055] Among them, for the weight coefficients a1, a2, a3 and a1+a2+a3=1, a combined weighting method based on game theory is used to assign reasonable weights to each target. Specifically, the hierarchical analysis method and the entropy method are used to subjectively and objectively weight each indicator. The Nash equilibrium is used as the coordination goal to find consistency and compromise among different weights, that is, to minimize the deviation between the combined weight and each basic weight, and to minimize the deviation, so as to maximize the common interests, and finally obtain the combined weight that reflects the decision maker's intention and the objective attributes of the evaluation indicators.

[0056] (2) Based on the load fluctuations of the distribution network and the access to new energy, establish constraints for controlling the charging behavior of electric vehicles in a time-sharing and zone-based manner.

[0057] First, according to the load fluctuations of the distribution network and the access to new energy, a day is divided into multiple periods, including peak load periods, off-peak periods, and peak and off-peak periods of new energy power generation; then, the charging behavior of electric vehicles is controlled in different time periods and regions. During peak load periods, the charging power is limited or the charging cost is increased to reduce charging demand; during the peak period of new energy power generation, electric vehicles are encouraged to charge to improve the utilization rate of new energy.

[0058] (3) Establish different charging priority constraints based on the charging needs and economic factors of electric vehicle users.

[0059] First, different charging priorities are set for different users based on their charging needs and economic factors, with higher priorities set for users who are in urgent need of charging, long-term users or high-paying users. Then, when charging resources are limited, users with higher priorities are given priority to obtain charging rights to ensure that users' charging needs are met.

[0060] (4) Based on the load fluctuations of the distribution network and the access to new energy, dynamic constraints on the charging power limit of electric vehicles are established.

[0061] First, monitor the load fluctuations of the distribution network and the access to new energy in real time, and dynamically adjust the upper limit of the charging power of electric vehicles based on the monitoring data; then, lower the upper limit of the charging power during peak load periods to reduce the burden on the distribution network, and appropriately increase the upper limit of the charging power when there is sufficient new energy generation to improve the utilization rate of new energy.

[0062] (5) An optimization algorithm is used to solve the orderly charging model of electric vehicles that satisfies the above constraints, and an orderly charging strategy for electric vehicles is output, including the optimal charging time and charging power of each electric vehicle. That is, the optimal charging time and charging power of each electric vehicle are determined through the optimization algorithm, so that the optimal value of the objective function is achieved under the premise of satisfying various constraints.

[0063] Among them, the optimization algorithm adopts genetic algorithm or particle swarm optimization algorithm.

[0064] When the present invention is implemented, a charging management system is provided, and data connection is established between the electric vehicles and the charging management system. After the orderly charging strategy of the electric vehicles outputted is input into the charging management system, each electric vehicle is charged in an orderly manner under the unified arrangement of the charging management system.

[0065] In summary, in the orderly charging mode, through intelligent control strategies and systems, the charging time and charging power of electric vehicles are optimized by comprehensively considering multiple factors such as the load capacity of the power grid, the charging needs of users, the electricity price policy, and the stability of the power grid. For example, during the low load period of the power grid, electric vehicles are centrally arranged for charging, which can not only meet the charging needs of users, but also effectively utilize the idle capacity of the power grid, reduce charging costs, and reduce the power supply pressure of the power grid during peak hours, avoiding problems such as power grid overload and voltage fluctuations caused by large-scale simultaneous charging of electric vehicles, which helps to improve the stability and reliability of power grid operation. Therefore, orderly charging is of great significance to promoting the large-scale promotion and application of electric vehicles, as well as promoting energy structure transformation and sustainable development.

[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the specific implementation modes of the present invention can still be modified or replaced by equivalents, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be included in the scope of the claims of the present invention.

Claims

1. An orderly charging method based on optimization of charging power and charging time, characterized in that: The steps include: (1) With the goal of reducing load fluctuations in the distribution network, improving power quality, and maximizing the use of new energy, an objective function F is established: F = a1·F1+a2·F2+a3·F3, where F1 is the objective function for reducing load fluctuations in the distribution network, F2 is the objective function for improving power quality, and F3 is the objective function for maximizing the use of new energy. a1, a2, and a3 are weight coefficients for the corresponding objectives, and a1+a2+a3=1. Meanwhile, the charging state, charging power, and charging time of electric vehicles are comprehensively considered to construct an orderly charging model for electric vehicles. (2) According to the load fluctuation of the distribution network and the access of new energy, establish constraints for controlling the charging behavior of electric vehicles in time and area; (3) Establish different charging priority constraints based on the charging needs and economic factors of electric vehicle users; (4) Establish dynamic constraints on the charging power limit of electric vehicles based on the load fluctuations of the distribution network and the access to new energy sources; (5) An optimization algorithm is used to solve the orderly charging model of electric vehicles that meets the above constraints, and an orderly charging strategy for electric vehicles is output, including the optimal charging time and charging power for each electric vehicle.

2. The orderly charging method based on optimization of charging power and charging time according to claim 1, characterized in that: The objective function F1 aimed at reducing the distribution network includes minimizing the load variance and minimizing the load peak-valley difference, where the load variance minimization can be expressed as follows: Where, L(t) is the total load of the distribution network at time t, including conventional load and electric vehicle load; N is the total number of time points; is the average load; The minimization of the load peak-to-valley difference can be expressed as follows: min(L max -L min ); Where, L max is the maximum load value, L min is the minimum load value; but 3. The orderly charging method based on optimization of charging power and charging time according to claim 1, characterized in that: The objective function F2 for improving power quality includes voltage deviation minimization and harmonic content minimization, where voltage deviation minimization can be expressed as follows: Where V i (t) represents the voltage of node i at time t, V ref is the rated voltage, M is the total number of nodes; The harmonic content minimization is expressed as follows: Where H(t) is the total harmonic content of the distribution network at time t; but 4. The orderly charging method based on optimization of charging power and charging time according to claim 1, characterized in that: The objective function F3, which aims to maximize the utilization of new energy, is to maximize the utilization rate of new energy, which can be expressed as follows: Where P new (t) is the power generation of new energy at time t, P ev (t) is the charging power of the electric vehicle at time t; when the new energy power generation is sufficient, δ(t) = 1, otherwise δ(t) = 0.

5. The orderly charging method based on optimization of charging power and charging time according to claim 1, characterized in that: Establish time-sharing and zoned control constraints, specifically: first, divide a day into multiple time periods according to the load fluctuations of the distribution network and the access to new energy, including load peak periods, valley periods, and new energy power generation peak and off-peak periods; then, implement time-sharing and zoned control of electric vehicle charging behavior for different time periods and regions. During peak load periods, limit charging power or increase charging costs to reduce charging demand; during peak periods of new energy power generation, encourage electric vehicle charging to increase the utilization rate of new energy.

6. The orderly charging method based on optimization of charging power and charging time according to claim 1, characterized in that: Establish charging priority constraints, specifically: first, set different charging priorities for different users based on the charging needs and economic factors of electric vehicle users, and set higher priorities for users who are in urgent need of charging, long-term users or high-paying users; then, when charging resources are limited, users with high priorities are given priority to charging rights to ensure that users' charging needs are met.

7. The orderly charging method based on optimization of charging power and charging time according to claim 1, characterized in that: Establish dynamic constraints on charging power limits, specifically: first, monitor the load fluctuations of the distribution network and the access to new energy sources in real time, and dynamically adjust the upper limit of the charging power of electric vehicles based on the monitoring data; Then, during peak load periods, lower the upper limit of charging power to reduce the burden on the distribution network; when renewable energy generation is sufficient, appropriately increase the upper limit of charging power to improve the utilization rate of renewable energy.

8. The orderly charging method based on optimization of charging power and charging time according to claim 1, characterized in that: The optimization algorithm is a genetic algorithm or a particle swarm optimization algorithm.

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