Electric vehicle resource optimization load regulation and control method, device and equipment and storage medium

Abstract

The invention discloses an electric vehicle resource optimization load regulation and control method, device and equipment and a storage medium, and the method comprises the steps: carrying out the quantification of the operation characteristics of adjustable resources of a to-be-regulated and controlled electric vehicle, and obtaining an evaluation index; according to the evaluation index and the performance index, constructing a virtual power plant distributed electric vehicle adjustable resource cooperative operation model; and according to the cooperative operation model, the adjustable resource cooperative load of the to-be-regulated electric vehicle is optimized, so that the flexibility of electric vehicle resource optimization load regulation and control can be improved, efficient scheduling and optimization of resources are realized, the economic benefit of a virtual power plant is enhanced, and the real-time requirements of an electricity market and a frequency modulation market are met. According to the method, efficient operation and intelligent load regulation of the electric vehicle in the virtual power plant are realized, the resource utilization efficiency is improved, the supply and demand balance is ensured by promoting linkage and cooperative operation mechanisms among resources, and the speed and efficiency of resource optimization load regulation and control of the electric vehicle are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power supply and distribution regulation, and particularly to a method, device, equipment and storage medium for optimizing the load regulation of electric vehicle resources. Background Art

[0002] A virtual power plant is a power coordination management system that realizes the aggregation and coordinated optimization of distributed energy sources such as distributed power sources, energy storage systems, and controllable loads through advanced information and communication technologies and software systems, and participates in the power market and grid operation as a special power plant.

[0003] With the increasing prominence of problems such as world energy shortage and environmental pollution, the application of renewable energy is becoming more and more widespread. Green energy sources such as wind and solar energy, which are clean, low-carbon, and pollution-free, are increasingly appearing in the power generation field. However, despite the prominent advantages of distributed energy sources, there are still many problems.

[0004] Wind power generation generates electrical energy by the natural wind blowing the blades of a wind turbine. Due to the intermittent nature of the wind, the electrical energy output by wind power generation has the characteristic of intermittency. Similarly, photovoltaic power generation is affected by sunlight, and its power generation fluctuates greatly and has strong randomness. The power grid requires the electrical energy input to it to be stable and smooth. However, due to their intermittency and volatility, wind power and photovoltaic power generation obviously do not meet this requirement. After being connected to the power grid, they will impact the power grid, making the power grid operation unstable and bringing potential safety hazards.

[0005] The sales volume of new energy electric vehicles has increased sharply, and a large number of electric vehicles are continuously penetrating into the distribution network. The disorderly power consumption of electric vehicles has also brought unprecedented pressure to the current power system. Therefore, the virtual power plant (VPP), as an energy coordination management system that can integrate conventional energy, renewable energy, energy storage systems, flexible loads, and demand-side response resources, has become an effective means to support the efficient operation of the regional power grid. However, in the context of the existing large-scale access of electric vehicles to the power grid, the energy coordination management efficiency of electric vehicles is low, lacking stability and security. Summary of the Invention

[0006] The main purpose of the present invention is to provide a method, device, equipment and storage medium for optimizing the load regulation of electric vehicle resources, aiming to solve the technical problems of low energy coordination management efficiency, lack of stability and security of electric vehicles in the prior art.

[0007] In a first aspect, the present invention provides a method for optimizing the load regulation of electric vehicle resources, and the method for optimizing the load regulation of electric vehicle resources includes the following steps:

[0008] Quantify the operating characteristics of the adjustable resources of the electric vehicle to be regulated to obtain an evaluation index;

[0009] Construct a collaborative operation model for the adjustable resources of distributed electric vehicles in a virtual power plant according to the evaluation index and the performance index;

[0010] Optimize the collaborative load of the adjustable resources of the electric vehicles to be regulated according to the collaborative operation model.

[0011] Optionally, quantify the operating characteristics of the adjustable resources of the electric vehicles to be regulated to obtain evaluation indexes, including:

[0012] Obtain the adjustable capacity of the single-vehicle temperature control load of the electric vehicles to be regulated at the current moment;

[0013] Continue to aggregate the adjustable capacity of the single-vehicle temperature control load to obtain the adjustable capacity of the cluster temperature control load;

[0014] Generate a flexibility index for evaluating the charging and discharging flexibility of the electric vehicles to be regulated according to the adjustable capacity of the cluster temperature control load, and obtain a performance index for evaluating the dynamic response performance of the electric vehicles to be regulated to the grid dispatching signal.

[0015] Optionally, the obtaining of the adjustable capacity of the single-vehicle temperature control load of the electric vehicles to be regulated at the current moment includes:

[0016] Obtain the adjustable capacity of the single-vehicle temperature control load of the electric vehicles to be regulated at the current moment through the following formula:

[0017]

[0018] where, is the probability that the adjustable power reduction of the temperature control load of the i-th electric vehicle at the current moment is equal to its rated power, is the probability that the adjustable power reduction of the temperature control load of the i-th electric vehicle at the current moment is equal to its rated power, p rated,i is the rated power of the temperature control load of the i-th electric vehicle, τ ON,i is the opening time period of the temperature control system of the i-th electric vehicle, t lock is the locking time, τ OFF,i is the closing time period of the temperature control system of the i-th electric vehicle, is the probability that the temperature control load of the electric vehicle cannot be adjusted, is the mathematical expectation of the adjustable power reduction, is the variance of the adjustable power reduction.

[0019] Optionally, the continuing to aggregate the adjustable capacity of the single-vehicle temperature control load to obtain the adjustable capacity of the cluster temperature control load includes:

[0020] Aggregate the adjustable capacity of the single-temperature-controlled load continuously, and obtain the adjustable capacity of the cluster temperature-controlled load through the following formula:

[0021]

[0022] wherein, is the expected adjustable power of the cluster temperature-controlled load, is the adjustable power random variable of the cluster temperature-controlled load, P(t) is the total rated power of the entire load cluster, n is the number of temperature-controlled load devices, τ ON,i is the on-duration of the temperature-controlled load, τ OFF,i is the off-duration of the temperature-controlled load, t lock is the locking time, is the variance of the adjustable power of the cluster temperature-controlled load.

[0023] Optionally, generate a flexibility index for evaluating the charging and discharging flexibility of the to-be-regulated electric vehicle according to the adjustable capacity of the cluster temperature-controlled load, and obtain a performance index for evaluating the dynamic response performance of the to-be-regulated electric vehicle to the grid dispatching signal, including:

[0024] Generate a flexibility index for evaluating the charging and discharging flexibility of the to-be-regulated electric vehicle through the following formula according to the adjustable capacity of the cluster temperature-controlled load:

[0025]

[0026] wherein, CFI t is the evaluation index of the charging and discharging flexibility of the to-be-regulated electric vehicle at time t, P max,EV,t is the maximum charging power of the to-be-regulated electric vehicle, P min,Ev,t is the minimum charging power of the to-be-regulated electric vehicle, α is the battery charge flexibility coefficient, ΔE t is the difference between the current state of the battery of the to-be-regulated electric vehicle and the required power, E max is the maximum capacity of the battery of the to-be-regulated electric vehicle, P nominal,EV is the nominal power of the to-be-regulated electric vehicle, is the expected value of the adjustable power of the cluster temperature-controlled load, γ is the temperature-controlled load comfort flexibility coefficient, ΔT t is the difference between the current temperature of the temperature-controlled load and the target temperature, T max is the maximum allowable temperature deviation, P nominal,thermal is the nominal power of the temperature-controlled load, β is the sensitivity coefficient of the response speed, τ t is the response speed of the electric vehicle or the temperature-controlled load to the dispatching signal;

[0027] Obtain a performance index reflecting the dynamic response performance of the electric vehicle to be regulated to the grid dispatching signal. The performance index includes an accuracy index, a correlation index, and a delay index, as follows:

[0028]

[0029] where x a is the accuracy index, AGC(t) is the desired power regulation value, p(t) is the power regulation value actually provided by the electric vehicle load, t0 is the starting time, n is the number of time steps, Δt is the time interval, and x c is the correlation index, δ is the time offset, and AGC(t + δ·Δt) is the AGC signal value considering a certain time offset δ·Δt when calculating the correlation. Also, x d is the delay index, t pg is the evaluation duration.

[0030] Optionally, the collaborative operation model of the adjustable resources of the electric vehicles distributed in the virtual power plant constructed according to the evaluation index and the performance index includes:

[0031] Obtain the evaluation scores of different electric vehicle adjustable resources on different evaluation indexes and different performance indexes;

[0032] Obtain the comprehensive performance score through the following formula according to the evaluation score and the corresponding evaluation index weight:

[0033]

[0034] where PS i is the comprehensive performance score, m is the number of evaluation indexes, w j is the weight of the jth evaluation index, and I i,j is the score of the ith resource on the jth evaluation index;

[0035] Select an electric vehicle power model that meets the preset score requirement as the target power model from the feasible set of the charging and discharging trajectories of the electric vehicle to be regulated according to the comprehensive performance score;

[0036] Determine the minimum value of the grid load through the following formula according to the target power model:

[0037]

[0038] where T is the total number of time steps, C operation (t) is the operating cost, λ is the balancing coefficient, P load (t) is the total load requirement, and P target (t) is the load target to be achieved;

[0039] Construct a load forecasting model according to the minimum value of the grid load through the following formula:

[0040]

[0041] Wherein, is the load forecast value for the future kth moment, P load (t) is the actual grid load value at the current moment t, P weather (t) is the weather factor, and D(t) is the electricity price;

[0042] Determine a resource dynamic adjustment scheduling plan according to the load forecasting model and the current user demand, and construct a collaborative operation model of the adjustable resources of electric vehicles distributed in a virtual power plant according to the resource dynamic adjustment scheduling plan.

[0043] Optionally, optimizing the collaborative load of the adjustable resources of the electric vehicle to be regulated according to the collaborative operation model includes:

[0044] Determine the charging and discharging strategy of the electric vehicle to be regulated according to the collaborative operation model, and optimize the collaborative load of the adjustable resources of the electric vehicle to be regulated through the following formula:

[0045] P i ={P1(t), P2(t), …, P N (t)}

[0046] P min ≤P i (t) ≤ P max

[0047]

[0048] V i (t + 1) = ω·V i (t) + c1·r1·(P bast,i -P i (t)) + c2·r2·(G best -P i (t))

[0049] P i (t + 1) = P i (t) + V i (t + 1)

[0050]

[0051]

[0052] G best = argmin i Fitness(Pi )

[0053] Among them, P i is the i-th charge and discharge strategy, and P N (t) is the charge and discharge strategies of all electric vehicles. Fitness(P i ) is the fitness function for evaluating the quality of the strategy, C operation (t) is the operating cost of charge and discharge, T is the cumulative calculation period for optimizing the fitness function, λ is the balance parameter for weighing the proportion of economic cost and load smoothing effect, P load (t) is the actual load of the power grid at the current moment, P target (t) is the target load of the power grid, V i (t + 1) is the speed of the i-th electric vehicle charge and discharge strategy at the moment t + 1, ω is the inertia weight, V i (t) is the speed update amount of the charge and discharge strategy of the to-be-regulated electric vehicle, c1 and c2 are learning factors, r1 and r2 are random numbers for exploring the charge and discharge strategy, P bast,i is the historical optimal strategy, P i (t) is the power decision of the i-th charge and discharge strategy at the time t, G best is the global optimal charge and discharge strategy, P i (t + 1) is the power decision of the i-th electric vehicle charge and discharge strategy at the moment t + 1, P min is the minimum power limit of the to-be-regulated electric vehicle during charge and discharge, P max is the maximum power limit of the to-be-regulated electric vehicle during charge and discharge, argmin i is to select the optimal charge and discharge strategy.

[0054] Secondly, to achieve the above object, the present invention also proposes an electric vehicle resource optimized load regulation device, and the electric vehicle resource optimized load regulation device includes:

[0055] A quantization module, configured to quantify the operating characteristics of the adjustable resources of the to-be-regulated electric vehicle to obtain evaluation indexes;

[0056] A model construction module, configured to construct a collaborative operation model of the adjustable resources of the electric vehicle in a distributed virtual power plant according to the evaluation indexes and the performance indexes;

[0057] An optimization regulation module, configured to optimize the collaborative load of the adjustable resources of the to-be-regulated electric vehicle according to the collaborative operation model.

[0058] In a third aspect, to achieve the above object, the present invention further provides an electric vehicle resource optimization load regulation device, where the electric vehicle resource optimization load regulation device includes: a memory, a processor, and an electric vehicle resource optimization load regulation program stored on the memory and executable on the processor, and the electric vehicle resource optimization load regulation program is configured to implement the steps of the electric vehicle resource optimization load regulation method as described above.

[0059] In a fourth aspect, to achieve the above object, the present invention further provides a storage medium, where an electric vehicle resource optimization load regulation program is stored on the storage medium, and when the electric vehicle resource optimization load regulation program is executed by a processor, the steps of the electric vehicle resource optimization load regulation method as described above are implemented.

[0060] The electric vehicle resource optimization load regulation method proposed by the present invention quantifies the operating characteristics of the adjustable resources of the electric vehicle to be regulated to obtain evaluation indicators; constructs a collaborative operation model of the adjustable resources of the electric vehicle distributed in the virtual power plant according to the evaluation indicators and the performance indicators; optimizes the collaborative load of the adjustable resources of the electric vehicle to be regulated according to the collaborative operation model, which can improve the flexibility of the electric vehicle resource optimization load regulation, achieve efficient scheduling and optimization of resources, maximize the economic benefits of the virtual power plant and the power grid service capacity through load forecasting and rolling optimization, enhance the economic benefits and resource utilization rate through real-time feedback, enhance the power grid stability and operation efficiency, meet the power market and frequency modulation requirements, improve the power grid stability, enhance the economic benefits of the virtual power plant, and meet the real-time requirements of the power market and the frequency modulation market, realize the efficient operation and intelligent load regulation of the electric vehicle in the virtual power plant, improve the resource utilization efficiency, and ensure the supply-demand balance by promoting the linkage and collaborative operation mechanism between resources, and improve the speed and efficiency of the electric vehicle resource optimization load regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a schematic diagram of the device structure of the hardware operating environment related to the solution of the embodiment of the present invention;

[0062] Figure 2 It is a schematic flowchart of the first embodiment of the electric vehicle resource optimization load regulation method of the present invention;

[0063] Figure 3 It is a schematic flowchart of the second embodiment of the electric vehicle resource optimization load regulation method of the present invention;

[0064] Figure 4 It is a functional module diagram of the first embodiment of the electric vehicle resource optimization load regulation device of the present invention.

[0065] The realization, functional features and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed implementation manners

[0066] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0067] The solution of the embodiment of the present invention is mainly as follows: by quantifying the operating characteristics of the adjustable resources of the electric vehicle to be regulated, evaluation indexes are obtained; a collaborative operation model of the adjustable resources of the distributed electric vehicles in the virtual power plant is constructed according to the evaluation indexes and the performance indexes; the collaborative load of the adjustable resources of the electric vehicle to be regulated is optimized according to the collaborative operation model, which can improve the flexibility of the optimized load regulation of the electric vehicle resources, realize the efficient scheduling and optimization of resources, maximize the economic benefits and grid service capabilities of the virtual power plant through load forecasting and rolling optimization, enhance the economic benefits and resource utilization rate through real-time feedback, enhance the grid stability and operation efficiency, meet the power market and frequency modulation requirements, improve the grid stability, enhance the economic benefits of the virtual power plant, and meet the real-time requirements of the power market and the frequency modulation market, realize the efficient operation and intelligent load regulation of the electric vehicle in the virtual power plant, improve the resource utilization efficiency, and ensure the supply-demand balance by promoting the linkage and collaborative operation mechanism among resources, improve the speed and efficiency of the optimized load regulation of the electric vehicle resources, and solve the technical problems of low energy coordination management efficiency, lack of stability and security of electric vehicles in the prior art.

[0068] Refer to Figure 1 , Figure 1 which is a schematic diagram of the device structure of the hardware operating environment involved in the solution of the embodiment of the present invention.

[0069] As Figure 1 shown, the device may include: a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display) and an input unit such as a keyboard (Keyboard). Optionally, the user interface 1003 may further include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a Wi-Fi interface). The memory 1005 may be a high-speed RAM memory or a stable memory (Non-Volatile Memory), such as a disk memory. Optionally, the memory 1005 may also be a storage device independent of the foregoing processor 1001.

[0070] Those skilled in the art can understand, Figure 1The device structure shown does not constitute a limitation on the device, and may include more or fewer components than shown, or combine certain components, or have a different component arrangement.

[0071] As Figure 1 shown, the memory 1005, as a storage medium, may include an operating device, a network communication module, a user interface module, and an electric vehicle resource optimization load regulation program.

[0072] The device of the present invention calls the electric vehicle resource optimization load regulation program stored in the memory 1005 through the processor 1001, and performs the following operations:

[0073] Quantify the operating characteristics of the adjustable resources of the electric vehicle to be regulated to obtain evaluation indicators;

[0074] Construct a collaborative operation model of the adjustable resources of the distributed electric vehicles in the virtual power plant according to the evaluation indicators and the performance indicators;

[0075] Optimize the collaborative load of the adjustable resources of the electric vehicle to be regulated according to the collaborative operation model.

[0076] The device of the present invention calls the electric vehicle resource optimization load regulation program stored in the memory 1005 through the processor 1001, and also performs the following operations:

[0077] Obtain the adjustable capacity of the single-temperature control load of the electric vehicle to be regulated at the current moment;

[0078] Continue to aggregate the adjustable capacity of the single-temperature control load to obtain the adjustable capacity of the cluster temperature control load;

[0079] Generate a flexibility index for evaluating the charging and discharging flexibility of the electric vehicle to be regulated according to the adjustable capacity of the cluster temperature control load, and obtain a performance index for evaluating the dynamic response performance of the electric vehicle to be regulated to the grid dispatching signal.

[0080] The device of the present invention calls the electric vehicle resource optimization load regulation program stored in the memory 1005 through the processor 1001, and also performs the following operations:

[0081] Obtain the adjustable capacity of the single-temperature control load of the electric vehicle to be regulated at the current moment through the following formula:

[0082]

[0083] where is the probability that the power that can be adjusted downward of the temperature control load of the i-th electric vehicle at the current moment is equal to its rated power, is the probability that the power that can be adjusted downward of the temperature control load of the i-th electric vehicle at the current moment is equal to its rated power, prated,i is the rated power of the temperature control load of the i-th electric vehicle, τ ON,i is the start time period of the temperature control system of the i-th electric vehicle, t lock is the locking time, τ OFF,i is the shutdown time period of the temperature control system of the i-th electric vehicle, is the probability that the temperature control load of the electric vehicle is non-adjustable, is the mathematical expectation of the adjustable power reduction, is the variance of the adjustable power reduction.

[0084] The device of the present invention calls the electric vehicle resource optimization load control program stored in the memory 1005 through the processor 1001, and also performs the following operations:

[0085] Continue to aggregate the adjustable capacity of the single-body temperature control load, and obtain the adjustable capacity of the cluster temperature control load through the following formula:

[0086]

[0087] Among them, is the expected adjustable power of the cluster temperature control load, is the adjustable power random variable of the cluster temperature control load, P(t) is the total rated power of the entire load cluster, n is the number of temperature control load devices, τ ON,i is the on-duration of the temperature control load, τ OFF,i is the off-duration of the temperature control load, t lock is the locking time, is the variance of the adjustable power of the cluster temperature control load.

[0088] The device of the present invention calls the electric vehicle resource optimization load control program stored in the memory 1005 through the processor 1001, and also performs the following operations:

[0089] Generate a flexibility index for evaluating the charging and discharging flexibility of the to-be-regulated electric vehicle through the following formula according to the adjustable capacity of the cluster temperature control load:

[0090]

[0091] Among them, CFI t is the evaluation index of the charging and discharging flexibility of the to-be-regulated electric vehicle at time t, P max,EV,t is the maximum charging power of the to-be-regulated electric vehicle, P min,EV,t is the minimum charging power of the to-be-regulated electric vehicle, α is the battery charge flexibility coefficient, ΔE t is the difference between the current state of the battery of the to-be-regulated electric vehicle and the required battery charge, E max is the maximum capacity of the battery of the to-be-regulated electric vehicle, Pnominal,EV is the nominal power of the electric vehicle to be regulated, is the expected value of the adjustable power of the cluster temperature control load, γ is the comfort flexibility coefficient of the temperature control load, and ΔT t is the difference between the current temperature and the target temperature of the temperature control load, and T max is the maximum allowable temperature deviation, and P nominal,Thermal is the nominal power of the temperature control load, β is the sensitivity coefficient of the response speed, and τ t is the response speed of the electric vehicle or the temperature control load to the scheduling signal;

[0092] The performance index reflecting the dynamic response performance of the electric vehicle to be regulated to the grid scheduling signal is obtained through the following formula. The performance index includes an accuracy index, a correlation index, and a delay index:

[0093]

[0094] where x a is the accuracy index, AGC(t) is the expected power adjustment value, p(t) is the actual power adjustment value provided by the electric vehicle load, t0 is the starting time, n is the number of time steps, Δt is the time interval, and x c is the correlation index, δ is the time offset, and AGC(t + δ·Δt) is the AGC signal value after considering a certain time offset δ·Δt when calculating the correlation, and x d is the delay index, and t pg is the evaluation duration.

[0095] The device of the present invention calls the electric vehicle resource optimization load regulation program stored in the memory 1005 through the processor 1001 and also performs the following operations:

[0096] Obtain the evaluation scores of different adjustable resources of electric vehicles on different evaluation indexes and different performance indexes;

[0097] Obtain the comprehensive performance score through the following formula according to the evaluation score and the corresponding evaluation index weight;

[0098]

[0099] where PS i is the comprehensive performance score, m is the number of evaluation indexes, and w j is the weight of the jth evaluation index, and I i,j is the score of the ith resource on the jth evaluation index;

[0100] Select an electric vehicle power model that meets the preset score requirement from the feasible set of the charging and discharging trajectories of the electric vehicle to be regulated as the target power model;

[0101] Determine the minimum value of the grid load according to the target power model through the following formula:

[0102]

[0103] where T is the total number of time steps, C operation (t) is the operating cost, λ is the balancing coefficient, P load (t) is the total load requirement, P target (t) is the load target to be achieved;

[0104] Construct a load forecasting model according to the minimum value of the grid load through the following formula:

[0105]

[0106] where, is the predicted value of the load at the future kth moment, P load (t) is the actual grid load value at the current moment t, P weather (t) is the weather factor, D(t) is the electricity price;

[0107] Determine a resource dynamic adjustment scheduling plan according to the load forecasting model and the current user demand, and construct a collaborative operation model of the adjustable resources of distributed electric vehicles in a virtual power plant according to the resource dynamic adjustment scheduling plan.

[0108] The device of the present invention calls the electric vehicle resource optimization load regulation program stored in the memory 1005 through the processor 1001, and also performs the following operations:

[0109] Determine the charging and discharging strategy of the electric vehicle to be regulated according to the collaborative operation model, and optimize the collaborative load of the adjustable resources of the electric vehicle to be regulated through the following formula according to the charging and discharging strategy:

[0110] P i ={P1(t), P2(t), …, P N (t)}

[0111] P min ≤P i (t)≤P max

[0112]

[0113] V i (t + 1)=ω·V i (t)+c1·r1·(P bast,i -P i (t))+c2·r2·(G best -P i (t))

[0114] P i (t + 1) = P i (t) + V i (t + 1)

[0115]

[0116]

[0117] G best = argmin i Fitness(P i )

[0118] Where P i is the i-th charge and discharge strategy, P N (t) is the charge and discharge strategies of all electric vehicles, Fitness(P i ) is the fitness function for evaluating the quality of the strategy, C operation (t) is the operating cost of charge and discharge, T is the cumulative calculation period for optimizing the fitness function, λ is the balance parameter for weighing the proportion of economic cost and load smoothing effect, P load (t) is the actual load of the power grid at the current moment, P target (t) is the target load of the power grid, V i (t + 1) is the speed of the i-th electric vehicle charge and discharge strategy at time t + 1, ω is the inertia weight, V i (t) is the speed update amount of the charge and discharge strategy of the electric vehicle to be regulated, c1 and c2 are learning factors, r1 and r2 are random numbers for exploring the charge and discharge strategy, P bast,i is the historical optimal strategy, P i (t) is the power decision of the i-th charge and discharge strategy at time t, G best is the global optimal charge and discharge strategy, P i (t + 1) is the power decision of the i-th electric vehicle charge and discharge strategy at time t + 1, P min is the minimum power limit of the electric vehicle to be regulated during charge and discharge, P max is the maximum power limit of the electric vehicle to be regulated during charge and discharge, argmin i is to select the optimal charge and discharge strategy.

[0119] In this embodiment, through the above solution, by quantifying the operating characteristics of the adjustable resources of the electric vehicle to be regulated, evaluation indicators are obtained; according to the evaluation indicators and the performance indicators, a collaborative operation model of the adjustable resources of the electric vehicle distributed in the virtual power plant is constructed; according to the collaborative operation model, the collaborative load of the adjustable resources of the electric vehicle to be regulated is optimized, which can improve the flexibility of the optimized load regulation of electric vehicle resources, achieve efficient scheduling and optimization of resources, maximize the economic benefits and grid service capabilities of the virtual power plant through load forecasting and rolling optimization, enhance the economic benefits and resource utilization rate through real-time feedback, enhance the grid stability and operation efficiency, meet the power market and frequency modulation requirements, improve the grid stability, enhance the economic benefits of the virtual power plant, and meet the real-time requirements of the power market and frequency modulation market, realize the efficient operation and intelligent load regulation of electric vehicles in the virtual power plant, improve the resource utilization efficiency, and ensure the supply-demand balance by promoting the linkage and collaborative operation mechanism among resources, and improve the speed and efficiency of the optimized load regulation of electric vehicle resources.

[0120] Based on the above hardware structure, an embodiment of the method for optimizing the load regulation of electric vehicle resources of the present invention is proposed.

[0121] Refer to Figure 2 , Figure 2 which is a schematic flowchart of the first embodiment of the method for optimizing the load regulation of electric vehicle resources of the present invention.

[0122] In the first embodiment, the method for optimizing the load regulation of electric vehicle resources includes the following steps:

[0123] Step S10: Quantify the operating characteristics of the adjustable resources of the electric vehicle to be regulated to obtain evaluation indicators.

[0124] It should be noted that the operating characteristics of the adjustable resources of the electric vehicle to be regulated can be quantified to obtain corresponding evaluation indicators.

[0125] Step S20: Construct a collaborative operation model of the adjustable resources of the electric vehicle distributed in the virtual power plant according to the evaluation indicators and the performance indicators.

[0126] It should be understood that according to the evaluation indicators and the performance indicators, a collaborative operation model of the adjustable resources of the electric vehicle distributed in the virtual power plant can be constructed, which can achieve efficient scheduling and optimization of resources.

[0127] Step S30: Optimize the collaborative load of the adjustable resources of the electric vehicle to be regulated according to the collaborative operation model.

[0128] It can be understood that according to the collaborative operation model, the adjustable resource collaborative load of the electric vehicle to be regulated can be optimized, which can ensure precise scheduling, improve economic benefits and resource utilization rate through real-time feedback, enhance grid stability and operation efficiency, and meet the power market and frequency modulation requirements.

[0129] In this embodiment, through the above scheme, the operation characteristics of the adjustable resources of the electric vehicle to be regulated are quantified to obtain evaluation indicators; a collaborative operation model of the adjustable resources of the distributed electric vehicles in the virtual power plant is constructed according to the evaluation indicators and the performance indicators; the collaborative load of the adjustable resources of the electric vehicle to be regulated is optimized according to the collaborative operation model, which can improve the flexibility of the optimized load regulation of electric vehicle resources, realize the efficient scheduling and optimization of resources, maximize the economic benefits of the virtual power plant and the grid service capacity through load forecasting and rolling optimization, improve economic benefits and resource utilization rate through real-time feedback, enhance grid stability and operation efficiency, meet the power market and frequency modulation requirements, improve grid stability, enhance the economic benefits of the virtual power plant, and meet the real-time requirements of the power market and the frequency modulation market, realize the efficient operation and intelligent load regulation of electric vehicles in the virtual power plant, improve resource utilization efficiency, and ensure the balance between supply and demand by promoting the linkage and collaborative operation mechanism between resources, and improve the speed and efficiency of the optimized load regulation of electric vehicle resources.

[0130] Furthermore, Figure 3 is a schematic flowchart of the second embodiment of the method for optimizing the load regulation of electric vehicle resources according to the present invention. As Figure 3 shown, the second embodiment of the method for optimizing the load regulation of electric vehicle resources according to the present invention is proposed based on the first embodiment. In this embodiment, the step S10 specifically includes the following steps:

[0131] Step S11: Obtain the adjustable capacity of the single-body temperature control load of the electric vehicle to be regulated at the current moment.

[0132] It should be noted that when obtaining the adjustable capacity of the single-body temperature control load of the electric vehicle to be regulated at the current moment, the relative position of the current moment within a switching cycle follows a uniform distribution, and the downward adjustable capacity of the single-body load at the current moment follows a binomial distribution.

[0133] Furthermore, the step S11 specifically includes the following steps:

[0134] The adjustable capacity of the single-body temperature control load of the electric vehicle to be regulated at the current moment is obtained by the following formula:

[0135]

[0136] where The probability that the adjustable power reduction of the temperature control load of the i-th electric vehicle at the current moment is equal to its rated power, The probability that the adjustable power reduction of the temperature control load of the i-th electric vehicle at the current moment is equal to its rated power, p rated,i The rated power of the temperature control load of the i-th electric vehicle, τ ON,i The opening time period of the temperature control system of the i-th electric vehicle, t lock The locking time, τ OFF,i The closing time period of the temperature control system of the i-th electric vehicle, The probability that the temperature control load of the electric vehicle is not adjustable, The mathematical expectation of the adjustable power reduction, The variance of the adjustable power reduction.

[0137] It should be understood that the probability distribution, expectation and variance formula of the adjustable capacity of the single-temperature control load are shown above, and the expression of aggregating the adjustable capacity of the single-temperature control load into the adjustable capacity of the cluster temperature control load is shown above.

[0138] Step S12: Continuously aggregate the adjustable capacity of the single-temperature control load to obtain the adjustable capacity of the cluster temperature control load.

[0139] It can be understood that after continuously aggregating the adjustable capacity of the single-temperature control load, the adjustable capacity of the cluster temperature control load can be obtained.

[0140] Further, step S12 specifically includes the following steps:

[0141] Continuously aggregate the adjustable capacity of the single-temperature control load, and obtain the adjustable capacity of the cluster temperature control load through the following formula:

[0142]

[0143] Among them, The expected adjustable power of the cluster temperature control load, The adjustable power random variable of the cluster temperature control load, P(t) is the total rated power of the entire load cluster, n is the number of temperature control load devices, τ ON,i The opening duration of the temperature control load, τ OFF,i The closing duration of the temperature control load, t lock The locking time, The variance of the adjustable power of the cluster temperature control load.

[0144] It should be understood that the expression of aggregating the adjustable capacity of the single-temperature control load into the adjustable capacity of the cluster temperature control load is shown above.

[0145] Step S13: Generate a flexibility index for evaluating the charging and discharging flexibility of the to-be-regulated electric vehicles according to the adjustable capacity of the cluster temperature control load, and obtain a performance index for evaluating the dynamic response performance of the to-be-regulated electric vehicles to grid dispatching signals.

[0146] It should be understood that a flexibility index for evaluating the charging and discharging flexibility of the to-be-regulated electric vehicles can be generated according to the adjustable capacity of the cluster temperature control load, and a performance index for evaluating the dynamic response performance of the to-be-regulated electric vehicles to grid dispatching signals can be obtained.

[0147] Further, step S13 specifically includes the following steps:

[0148] Generating a flexibility index for evaluating the charging and discharging flexibility of the to-be-regulated electric vehicles according to the adjustable capacity of the cluster temperature control load, and obtaining a performance index for evaluating the dynamic response performance of the to-be-regulated electric vehicles to grid dispatching signals includes:

[0149] Generate a flexibility index for evaluating the charging and discharging flexibility of the to-be-regulated electric vehicles according to the adjustable capacity of the cluster temperature control load through the following formula:

[0150]

[0151] where CFI t is the evaluation index of the charging and discharging flexibility of the to-be-regulated electric vehicles at time t, P max,EV,t is the maximum charging power of the to-be-regulated electric vehicles, P min,EV,t is the minimum charging power of the to-be-regulated electric vehicles, α is the battery charge flexibility coefficient, ΔE t is the difference between the current state and the required charge of the battery of the to-be-regulated electric vehicles, E max is the maximum capacity of the battery of the to-be-regulated electric vehicles, P nominal,EV is the nominal power of the to-be-regulated electric vehicles, is the expected value of the adjustable power of the cluster temperature control load, γ is the temperature control load comfort flexibility coefficient, ΔT t is the difference between the current temperature and the target temperature of the temperature control load, T max is the maximum allowable temperature deviation, P nominal,Thermal is the nominal power of the temperature control load, β is the sensitivity coefficient of the response speed, τ t is the response speed of the electric vehicle or the temperature control load to the dispatching signal;

[0152] Obtain a performance index reflecting the dynamic response performance of the to-be-regulated electric vehicles to grid dispatching signals through the following formula. The performance index includes an accuracy index, a correlation index, and a delay index:

[0153]

[0154] where x a is the accuracy index, AGC(t) is the desired power regulation value, p(t) is the actual power regulation value provided by the electric vehicle load, t0 is the starting time, n is the number of time steps, Δt is the time interval, and x c is the correlation index, δ is the time offset, and AGC(t + δ·Δt) is the AGC signal value after considering a certain time offset δ·Δt when calculating the correlation, and x d is the delay index, and t pg is the evaluation duration.

[0155] It can be understood that taking the adjustable capacity of the cluster temperature control load as an important factor affecting the charging and discharging flexibility of electric vehicles, the dynamic response performance index reflects the response speed and accuracy of electric vehicles to grid dispatching signals, and mainly considers the accuracy index x a is the mean absolute error between the actual response power p(t) and the frequency modulation command AGC(t); the correlation index x c reflects the similarity in shape between the above two signals and can calculate the delay of the response signal relative to the frequency modulation signal; the delay index x d is the relative gap between the actual calculated delay and the evaluation duration (such as 300s or other values, which are not limited in this embodiment).

[0156] It should be understood that the economic index is used to calculate the relationship between the charging and discharging power and the running time of electric vehicles at different electricity price periods to maximize cost - effectiveness; the load regulation capacity index is used to measure the contribution of electric vehicles to regulating the grid load fluctuation through charging and discharging during peak - shaving and valley - filling. By quantifying the economic benefits and load regulation capacity of electric vehicles, it provides efficient dispatching optimization support for virtual power plants.

[0157] In specific implementation, in the coordinated dispatching of a virtual power plant, the fast response ability of the temperature control load can help balance short - term load fluctuations, while electric vehicles can perform long - term flexible dispatching. By comprehensively dispatching the charging and discharging of electric vehicles and the temperature control load, power competition can be avoided, peak - shaving and valley - filling strategies can be optimized, economic benefits can be improved, and user comfort can be maintained at the same time.

[0158] Correspondingly, step S20 specifically includes the following steps:

[0159] Obtain the evaluation scores of different adjustable resources of electric vehicles on different evaluation indicators and different performance indicators;

[0160] Obtain the comprehensive performance score according to the evaluation scores and the corresponding evaluation index weights through the following formula;

[0161]

[0162] Among them, PS i is the comprehensive performance score, m is the number of evaluation indicators, and w j is the weight of the j-th evaluation indicator, and I i,j is the score of the i-th resource on the j-th evaluation indicator;

[0163] Select an electric vehicle power model that meets the preset score requirements from the feasible set of the charging and discharging trajectories of the to-be-regulated electric vehicle according to the comprehensive performance score as the target power model;

[0164] Determine the minimum grid load according to the target power model through the following formula:

[0165]

[0166] Among them, T is the total number of time steps, C operation (t) is the operating cost, λ is the balance coefficient, and P load (t) is the total load requirement, and P target (t) is the load target to be achieved;

[0167] Construct a load prediction model according to the minimum grid load through the following formula:

[0168]

[0169] Among them, is the load prediction value for the future k moment, and P load (t) is the actual grid load value at the current moment t, and P weather (t) is the weather factor, and D(t) is the electricity price;

[0170] Determine a resource dynamic adjustment scheduling plan according to the load prediction model and the current user demand, and construct a collaborative operation model for the adjustable resources of distributed electric vehicles in the virtual power plant according to the resource dynamic adjustment scheduling plan.

[0171] It should be noted that to construct a quantitative evaluation index for the operating characteristics of distributed adjustable resources in a virtual power plant under the electric vehicle charging and discharging scenario, its comprehensive performance score PS i is the weighted average of the above single indicators. This index is used to measure and evaluate the regulation performance and economy of electric vehicles and other distributed resources, provides an important tool for the resource optimization scheduling of the virtual power plant, and promotes the improvement of grid stability, efficient utilization of resources, and maximization of economic benefits.

[0172] It is understandable that, in order to optimize the utilization efficiency of heterogeneous distributed adjustable resources in a virtual power plant under the scenario of electric vehicle charging and discharging, a collaborative operation model is constructed in this embodiment. Through three collaborative operation modes of source-source complementarity, load-load negotiation, and source-load interaction, the efficient scheduling and optimization of electric vehicle resources are realized. The model aims to maximize the resource utilization rate, reduce the overall operation cost, and improve the system flexibility and stability through real-time dynamic scheduling and load forecasting, ensuring the supply-demand balance and economic benefit optimization of the virtual power plant in a changing power grid environment.

[0173] In the specific implementation, the characteristics definition and quantitative modeling of heterogeneous distributed adjustable resources in the virtual power plant: The feasible region of electric vehicle charging and discharging is represented by the boundaries of its electricity quantity and power to represent the feasible set of all possible charging and discharging trajectories.

[0174] It should be noted that for the collaborative operation model of heterogeneous distributed adjustable resources in the virtual power plant, this model adopts a multi-level control architecture combining a centralized control layer and a distributed control layer. The global controller of the virtual power plant is responsible for optimizing the total power output of the entire system. The goal is to balance the grid load through optimal scheduling decisions and maximize the economic benefit; in response to the load fluctuations of the grid and the uncertainty of resources, real-time dynamic scheduling and rolling optimization strategies are adopted. The goal of rolling optimization is to continuously update and optimize the decision according to the load forecast. At each time step, the scheduling plan is dynamically adjusted according to the latest load forecast, photovoltaic power generation forecast, and user demand forecast:

[0175]

[0176] Among them, To minimize the total cost from t = 1 to T, the subject is that at each time step t, the total supply power P total (t) must be equal to the load demand P load (t) is the constraint of this model. The constraints of this model are divided into source-load interaction constraints, source-source complementarity constraints, and load-load negotiation constraints. The source-load interaction constraint is the two-way regulation process between the electric vehicle distributed resources and the power grid. The resources respond in real time according to the grid demand to ensure grid stability and demand response. The source-source complementarity constraint is that the heterogeneous distributed energy of electric vehicles cooperates and complements each other. On the premise of meeting user needs, the overall load balance is achieved by flexibly scheduling electric vehicles.

[0177] Furthermore, the step S30 specifically includes the following steps:

[0178] Determine the charging and discharging strategy of the to-be-regulated electric vehicle according to the collaborative operation model, and optimize the collaborative load of the adjustable resources of the to-be-regulated electric vehicle through the following formula according to the charging and discharging strategy:

[0179] P i={P1(t), P2(t), …, P N (t)}

[0180] P min ≤P i (t) ≤ P max

[0181]

[0182] V i (t + 1) = ω·V i (t) + c1·r1·(P bast,i - P i (t)) + c2·r2·(G best - P i (t))

[0183] P i (t + 1) = P i (t) + V i (t + 1)

[0184]

[0185]

[0186] G best = argmin i Fitness(P i )

[0187] Among them, P i is the i-th charging and discharging strategy, P N (t) is the charging and discharging strategies of all electric vehicles, Fitness(P i ) is the fitness function for evaluating the quality of the strategy, C operation (t) is the operating cost of charging and discharging, T is the cumulative calculation period for optimizing the fitness function, λ is the balance parameter for weighing the proportion of economic cost and load smoothing effect, P load (t) is the actual load of the power grid at the current moment, P target (t) is the target load of the power grid, V i (t + 1) is the speed of the i-th electric vehicle charging and discharging strategy at time t + 1, ω is the inertia weight, V i (t) is the speed update amount of the charging and discharging strategy of the electric vehicle to be regulated, c1 and c2 are learning factors, r1 and r2 are random numbers for exploring the charging and discharging strategy, P bast,i is the historical optimal strategy, P i (t) is the power decision of the i-th charging and discharging strategy at time t, G best is the global optimal charging and discharging strategy, P i(t + 1) is the power decision of the i-th electric vehicle charging and discharging strategy at time t + 1, P min is the minimum power limit of the electric vehicle to be regulated during charging and discharging, P max is the maximum power limit of the electric vehicle to be regulated during charging and discharging, argmin i is to select the optimal charging and discharging strategy.

[0188] It should be understood that based on the load optimization control technology of quantization evaluation index and collaborative model, an intelligent algorithm load optimization control technology is constructed to achieve dynamic regulation. According to the real-time electricity price and load changes, the charging and discharging strategy is automatically adjusted, an adaptive algorithm is implemented, and the optimization result is updated in real time to ensure that the system is always in the best operating state under the power market and frequency modulation requirements; the proposed intelligent algorithm effectively realizes the collaborative load optimization control of distributed adjustable resources of virtual power plants in the electric vehicle charging and discharging scenario. This method shows excellent performance in optimizing operating costs and load regulation, significantly improving the economy of the power market and the stability of the power grid, enhancing the power grid stability, increasing the economic benefits of the virtual power plant, and meeting the real-time requirements of the power market and frequency modulation market, realizing the efficient operation and intelligent load regulation of electric vehicles in the virtual power plant. By quantifying several indicators and combining frequency modulation requirements, a quantization evaluation index system for the operating characteristics of electric vehicle adjustable resources can be constructed to accurately evaluate the operating characteristics of distributed electric vehicles in the virtual power plant.

[0189] In this embodiment, through the above scheme, by obtaining the adjustable capacity of the single-body temperature control load of the electric vehicle to be regulated at the current moment; aggregating the adjustable capacity of the single-body temperature control load to obtain the adjustable capacity of the cluster temperature control load; generating a flexibility index for evaluating the charging and discharging flexibility of the electric vehicle to be regulated according to the adjustable capacity of the cluster temperature control load, and obtaining a performance index for evaluating the dynamic response performance of the electric vehicle to be regulated to the grid dispatching signal, it can improve the flexibility of electric vehicle resource optimization load regulation, realize the efficient scheduling and optimization of resources, maximize the economic benefits and grid service capabilities of the virtual power plant through load forecasting and rolling optimization, enhance the economic benefits and resource utilization rate through real-time feedback, enhance the power grid stability and operation efficiency, and meet the power market and frequency modulation requirements.

[0190] Correspondingly, the present invention further provides an electric vehicle resource optimization load regulation device.

[0191] Referring to Figure 4 , Figure 4 is the functional module diagram of the first embodiment of the electric vehicle resource optimization load regulation device of the present invention.

[0192] In the first embodiment of the electric vehicle resource optimization load regulation device of the present invention, the electric vehicle resource optimization load regulation device includes:

[0193] The quantization module 10 is configured to quantify the operating characteristics of the adjustable resources of the electric vehicle to be regulated, and obtain evaluation indicators.

[0194] The model construction module 20 is configured to construct a collaborative operation model of the adjustable resources of the electric vehicle in the virtual power plant distributed according to the evaluation indicators and the performance indicators.

[0195] The optimal regulation module 30 is configured to optimize the collaborative load of the adjustable resources of the electric vehicle to be regulated according to the collaborative operation model.

[0196] The quantization module 10 is further configured to obtain the adjustable capacity of the single-temperature control load of the electric vehicle to be regulated at the current moment; continue to aggregate the adjustable capacity of the single-temperature control load to obtain the adjustable capacity of the cluster temperature control load; generate a flexibility index for evaluating the charging and discharging flexibility of the electric vehicle to be regulated according to the adjustable capacity of the cluster temperature control load, and obtain a performance index for evaluating the dynamic response performance of the electric vehicle to be regulated to the grid dispatching signal.

[0197] The quantization module 10 is further configured to obtain the adjustable capacity of the single-temperature control load of the electric vehicle to be regulated at the current moment through the following formula:

[0198]

[0199] where is the probability that the adjustable power-down of the temperature control load of the i-th electric vehicle at the current moment is equal to its rated power, is the probability that the adjustable power-down of the temperature control load of the i-th electric vehicle at the current moment is equal to its rated power, p rated,i is the rated power of the temperature control load of the i-th electric vehicle, τ ON,i is the opening time period of the temperature control system of the i-th electric vehicle, t lock is the locking time, τ OFF,i is the closing time period of the temperature control system of the i-th electric vehicle, is the probability that the temperature control load of the electric vehicle is not adjustable, is the mathematical expectation of the adjustable power-down, is the variance of the adjustable power-down.

[0200] The quantization module 10 is further configured to continue to aggregate the adjustable capacity of the single-temperature control load, and obtain the adjustable capacity of the cluster temperature control load through the following formula:

[0201]

[0202] where is the expected adjustable power of the cluster temperature control load, is the adjustable power random variable of the cluster temperature control load, P(t) is the total rated power of the entire load cluster, n is the number of temperature control load devices, and τ ON,i is the on-duration of the temperature control load, and τ OFF,i is the off-duration of the temperature control load, t lock is the locking time, is the variance of the adjustable power of the cluster temperature control load.

[0203] The quantization module 10 is further configured to generate a flexibility index for evaluating the charging and discharging flexibility of the to-be-regulated electric vehicle according to the adjustable capacity of the cluster temperature control load through the following formula:

[0204]

[0205] where CFI t is the evaluation index of the charging and discharging flexibility of the to-be-regulated electric vehicle at time t, P max,EV,t is the maximum charging power of the to-be-regulated electric vehicle, P min,EV,t is the minimum charging power of the to-be-regulated electric vehicle, α is the battery charge flexibility coefficient, and ΔE t is the difference between the current state and the required charge of the battery of the to-be-regulated electric vehicle, E max is the maximum capacity of the battery of the to-be-regulated electric vehicle, P nominal,EV is the nominal power of the to-be-regulated electric vehicle, is the expected value of the adjustable power of the cluster temperature control load, γ is the temperature control load comfort flexibility coefficient, and ΔT t is the difference between the current temperature and the target temperature of the temperature control load, T max is the maximum allowable temperature deviation, P nominal,thermal is the nominal power of the temperature control load, β is the sensitivity coefficient of the response speed, and τ t is the response speed of the electric vehicle or the temperature control load to the scheduling signal;

[0206] Obtain a performance index reflecting the dynamic response performance of the to-be-regulated electric vehicle to the grid scheduling signal through the following formula. The performance index includes an accuracy index, a correlation index, and a delay index:

[0207]

[0208] where x a is the accuracy index, AGC(t) is the expected power adjustment value, p(t) is the actual power adjustment value provided by the electric vehicle load, t0 is the start time, n is the number of time steps, Δt is the time interval, and x c is the correlation index, δ is the time offset, AGC(t + δ·Δt) is the AGC signal value considering a certain time offset δ·Δt when calculating the correlation, and xd is the delay index, t pg is the evaluation duration.

[0209] The model construction module 20 is further configured to obtain the evaluation scores of different adjustable resources of electric vehicles on different evaluation indexes and different performance indexes;

[0210] Obtain the comprehensive performance score through the following formula according to the evaluation score and the corresponding evaluation index weight;

[0211]

[0212] where, PS i is the comprehensive performance score, m is the number of evaluation indexes, w j is the weight of the j-th evaluation index, I i,j is the score of the i-th resource on the j-th evaluation index;

[0213] Select an electric vehicle power model that meets the preset score requirement as the target power model from the feasible set of the charge and discharge trajectories of the to-be-regulated electric vehicle according to the comprehensive performance score;

[0214] Determine the minimum value of the grid load through the following formula according to the target power model:

[0215]

[0216] where, T is the total number of time steps, C operation (t) is the operating cost, λ is the balance coefficient, P load (t) is the total load requirement, P target (t) is the load target to be achieved;

[0217] Construct a load prediction model through the following formula according to the minimum value of the grid load:

[0218]

[0219] where, is the load prediction value for the future k-th moment, P load (t) is the actual grid load value at the current moment t, P wmather (t) is the weather factor, D(t) is the electricity price;

[0220] Determine a resource dynamic adjustment scheduling plan according to the load prediction model and the current user demand, and construct a collaborative operation model of the adjustable resources of electric vehicles distributed in the virtual power plant according to the resource dynamic adjustment scheduling plan.

[0221] The optimization and control module 30 is further configured to determine the charging and discharging strategy of the electric vehicle to be controlled according to the collaborative operation model, and optimize the collaborative load of the adjustable resources of the electric vehicle to be controlled according to the charging and discharging strategy through the following formula:

[0222] P i ={P1(t),P2(t),…,P N (t)}

[0223] P min ≤P i (t)≤P max

[0224]

[0225] V i (t + 1)=ω·V i (t)+c1·r1·(P bast,i -·r i (t))+c2*r2·(G best -P i (T))

[0226] P i (f + 1)=P i (t)+V i (t + 1)

[0227]

[0228]

[0229] G best =argmin i Fitness(P i )

[0230] Wherein, P i is the i-th charging and discharging strategy, P N (t) is the charging and discharging strategies of all electric vehicles, Fitness(P i ) is the fitness function for evaluating the quality of the strategy, C operation (t) is the operating cost of charging and discharging, T is the cumulative calculation period for optimizing the fitness function, λ is the balance parameter for weighing the proportion of economic cost and load smoothing effect, P load (t) is the actual load of the power grid at the current moment, P target (t) is the target load of the power grid, V i (t + 1) is the speed of the i-th electric vehicle charging and discharging strategy at the moment t + 1, ω is the inertia weight, V i(t) is the speed update amount of the charging and discharging strategy of the electric vehicle to be regulated, c1 and c2 are learning factors, r1 and r2 are random numbers for exploring the charging and discharging strategy, P bast,i is the historical optimal strategy, P i (t) is the power decision of the i-th charging and discharging strategy at time t, G best is the global optimal charging and discharging strategy, P i (t + 1) is the power decision of the i-th electric vehicle charging and discharging strategy at time t + 1, P min is the minimum power limit of the electric vehicle to be regulated during charging and discharging, P max is the maximum power limit of the electric vehicle to be regulated during charging and discharging, argmin i is to select the optimal charging and discharging strategy.

[0231] Among them, the steps implemented by each functional module of the electric vehicle resource optimization load regulation device can refer to each embodiment of the electric vehicle resource optimization load regulation method of the present invention, which will not be elaborated here.

[0232] In addition, an embodiment of the present invention also proposes a storage medium, on which an electric vehicle resource optimization load regulation program is stored. When the electric vehicle resource optimization load regulation program is executed by a processor, the operations in the embodiments of the electric vehicle resource optimization load regulation method described above are implemented.

[0233] Those skilled in the art can understand that all or part of the steps in implementing the above-mentioned method can be completed by instructing relevant hardware through a program. The program is stored in a storage medium, including several instructions to enable a device (which can be a single-chip microcomputer, a chip, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application; and the aforementioned storage medium is a computer-readable storage medium, including but not limited to: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks or optical disks and other media that can store program codes.

[0234] It should be noted that in this article, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such a process, method, article or device. Without further limitations, the element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or device including the element.

[0235] The serial numbers of the embodiments of the present invention above are only for description and do not represent the superiority or inferiority of the embodiments.

[0236] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. An electric vehicle resource optimization load regulation method, characterized in that, The electric vehicle resource optimization load regulation method includes: Quantify the operating characteristics of the adjustable resources of the electric vehicle to be regulated to obtain evaluation indicators; Construct a collaborative operation model of the adjustable resources of the electric vehicle in a virtual power plant distributed manner according to the evaluation indicators and the performance indicators; Optimize the collaborative load of the adjustable resources of the electric vehicle to be regulated according to the collaborative operation model.

2. The method for optimizing the load regulation of electric vehicle resources according to claim 1, characterized in that The quantification of the operating characteristics of the adjustable resources of the electric vehicle to be regulated to obtain evaluation indicators includes: Obtain the adjustable capacity of the single-temperature control load of the electric vehicle to be regulated at the current moment; Continuously aggregate the adjustable capacity of the single-temperature control load to obtain the adjustable capacity of the cluster temperature control load; Generate a flexibility index for evaluating the charging and discharging flexibility of the electric vehicle to be regulated according to the adjustable capacity of the cluster temperature control load, and obtain a performance index for evaluating the dynamic response performance of the electric vehicle to be regulated to the grid dispatching signal.

3. The method for optimizing the load regulation of electric vehicle resources according to claim 2, characterized in that, The obtaining of the adjustable capacity of the single-temperature control load of the electric vehicle to be regulated at the current moment includes: Obtain the adjustable capacity of the single-temperature control load of the electric vehicle to be regulated at the current moment through the following formula: Among them, The probability that the power that can be adjusted downward of the temperature control load of the i-th electric vehicle at the current moment is equal to its rated power, The probability that the power that can be adjusted downward of the temperature control load of the i-th electric vehicle at the current moment is equal to its rated power, p rated,i Is the rated power of the temperature control load of the i-th electric vehicle, τ ON,i Is the opening time period of the temperature control system of the i-th electric vehicle, t lock Is the locking time, τ OFF,i Is the closing time period of the temperature control system of the i-th electric vehicle, Is the probability that the temperature control load of the electric vehicle cannot be adjusted, Is the mathematical expectation of the power that can be adjusted downward, Is the variance of the power that can be adjusted downward.

4. The method for optimizing the load regulation of electric vehicle resources according to claim 2, wherein The continuous aggregation of the adjustable capacity of the single-temperature control load to obtain the adjustable capacity of the cluster temperature control load includes: Continuously aggregate the adjustable capacity of the single-temperature control load and obtain the adjustable capacity of the cluster temperature control load through the following formula: Among them, is the expected adjustable power of the cluster temperature control load, is the adjustable power random variable of the cluster temperature control load, P(t) is the total rated power of the entire load cluster, n is the number of temperature control load devices, and τ ON,i is the on-duration of the temperature control load, and τ OFF,i is the off-duration of the temperature control load, and t lock is the locking time, is the variance of the adjustable power of the cluster temperature control load.

5. The method for optimizing the load regulation of electric vehicle resources according to claim 2, characterized in that, The generating of a flexibility index for evaluating the charging and discharging flexibility of the electric vehicle to be regulated according to the adjustable capacity of the cluster temperature control load, and the obtaining of a performance index for evaluating the dynamic response performance of the electric vehicle to be regulated to the grid dispatching signal includes: Generate a flexibility index for evaluating the charging and discharging flexibility of the electric vehicle to be regulated according to the adjustable capacity of the cluster temperature control load through the following formula: Among them, CFI t is the evaluation index of the charging and discharging flexibility of the electric vehicle to be regulated at time t, P max,EV,t is the maximum charging power of the electric vehicle to be regulated, P min,EV,t is the minimum charging power of the electric vehicle to be regulated, α is the battery charge flexibility coefficient, ΔE t is the difference between the current state of the battery of the electric vehicle to be regulated and the required power, E max is the maximum capacity of the battery of the electric vehicle to be regulated, P nominal,EV is the nominal power of the electric vehicle to be regulated, is the expected value of the adjustable power of the cluster temperature control load, γ is the temperature control load comfort flexibility coefficient, ΔT t is the difference between the current temperature of the temperature control load and the target temperature, T max is the maximum allowable temperature deviation, P nomibal,rhermal is the nominal power of the temperature control load, β is the sensitivity coefficient of the response speed, τ t is the response speed of the electric vehicle or temperature control load to the scheduling signal; Obtain a performance index reflecting the dynamic response performance of the electric vehicle to be regulated to the grid dispatching signal through the following formula, and the performance index includes an accuracy index, a correlation index, and a delay index: where x a is the accuracy index, AGC(t) is the desired power regulation value, p(t) is the actual power regulation value provided by the electric vehicle load, t0 is the starting time, n is the number of time steps, Δt is the time interval, and x c is the correlation index, δ is the time offset, AGC(t + δ·Δt) is the AGC signal value after considering a certain time offset δ·Δt when calculating the correlation, and x d is the delay index, t pg is the evaluation duration.

6. The method for optimizing the load regulation of electric vehicle resources according to claim 1, characterized in that, The constructing of a collaborative operation model of the adjustable resources of the electric vehicle in a virtual power plant distributed manner according to the evaluation indicators and the performance indicators includes: Obtain the evaluation scores of different electric vehicle adjustable resources on different evaluation indicators and different performance indicators; Obtain the comprehensive performance score through the following formula according to the evaluation score and the corresponding evaluation index weight; Among them, PS i is the comprehensive performance score, m is the number of evaluation indicators, and w j is the weight of the j-th evaluation indicator, and I i,j is the score of the i-th resource on the j-th evaluation indicator; Select an electric vehicle power model that meets the preset score requirements as the target power model from the feasible set of the charging and discharging trajectories of the electric vehicle to be regulated according to the comprehensive performance score; Determine the minimum value of the grid load through the following formula according to the target power model; Among them, T is the total number of time steps, C operation (t) is the operating cost, λ is the balancing coefficient, P load (t) is the total load requirement, P target (t) is the load target to be achieved; Construct a load prediction model through the following formula according to the minimum value of the grid load; Among them, is the predicted load value at the future k-th moment, P load (t) is the actual grid load value at the current moment t, P weather (t) is the weather factor, and D(t) is the electricity price; Determine a resource dynamic adjustment scheduling plan according to the load prediction model and the current user demand, and construct a collaborative operation model of the adjustable resources of the electric vehicle in a virtual power plant distributed manner according to the resource dynamic adjustment scheduling plan.

7. The method for optimizing the load regulation of electric vehicle resources according to claim 1, characterized in that, The optimizing of the collaborative load of the adjustable resources of the electric vehicle to be regulated according to the collaborative operation model includes: Determine the charging and discharging strategy of the electric vehicle to be regulated according to the collaborative operation model, and optimize the collaborative load of the adjustable resources of the electric vehicle to be regulated through the following formula according to the charging and discharging strategy: P i = {P1(t), P2(t), …, P N (t)} P min ≤P i (t)≤P max V i (t + 1)= ω·V i (t)+ c1·r1·(P bast,i -P i (t))+ c2·r2·(G best -P i (t)) P i (t + 1)= P i (t)+ V i (t + 1) G best = argmin i Fitness(P i ) Among them, P i is the i-th charge and discharge strategy, and P N (t) is the charge and discharge strategy of all electric vehicles. Fitness(P i ) is the fitness function for evaluating the quality of the strategy. C operation (t) is the operating cost of charge and discharge. T is the cumulative calculation period for optimizing the fitness function. λ is the balance parameter for weighing the proportion of economic cost and load smoothing effect. P load (t) is the actual load of the power grid at the current moment. P target (t) is the target load of the power grid. V i (t + 1) is the speed of the i-th electric vehicle charge and discharge strategy at the moment t + 1. ω is the inertia weight. V i (t) is the speed update amount of the charge and discharge strategy of the electric vehicle to be regulated. c1 and c2 are learning factors. r1 and r2 are random numbers for exploring the charge and discharge strategy. P bast,i is the historical optimal strategy. P i (t) is the power decision of the i-th charge and discharge strategy at the time t. G best is the global optimal charge and discharge strategy. P i (t + 1) is the power decision of the i-th electric vehicle charge and discharge strategy at the moment t + 1. P min is the minimum power limit of the electric vehicle to be regulated during charge and discharge. P max is the maximum power limit of the electric vehicle to be regulated during charge and discharge. argmin i is to select the optimal charge and discharge strategy.

8. An electric vehicle resource optimization load regulation device, characterized in that The electric vehicle resource optimization load regulation device includes: A quantization module, configured to quantify the operating characteristics of the adjustable resources of the electric vehicle to be regulated, and obtain evaluation indicators; A model construction module, configured to construct a collaborative operation model of the adjustable resources of the electric vehicle in a distributed virtual power plant according to the evaluation indicators and the performance indicators; An optimization regulation module, configured to optimize the collaborative load of the adjustable resources of the electric vehicle to be regulated according to the collaborative operation model.

9. An electric vehicle resource optimization load regulation device, characterized in that, The electric vehicle resource optimization load regulation equipment includes: a memory, a processor, and an electric vehicle resource optimization load regulation program stored on the memory and executable on the processor, and the electric vehicle resource optimization load regulation program is configured to implement the steps of the electric vehicle resource optimization load regulation method according to any one of claims 1 to 7.

10. A storage medium, characterized in that, An electric vehicle resource optimization load regulation program is stored on the storage medium, and when the electric vehicle resource optimization load regulation program is executed by a processor, the steps of the electric vehicle resource optimization load regulation method according to any one of claims 1 to 7 are implemented.

Images