Peak-valley balancing dispatching method and system for electric vehicles in power market

Abstract

The present invention relates to the field of electric vehicle intelligent charging technology, and discloses a method and system for peak-valley balancing scheduling of electric vehicles in the power market, comprising: collecting data related to the power market and electric vehicles; performing data characteristic analysis on the relevant data to construct an electric vehicle peak-valley balancing scheduling objective function; establishing a mathematical model of power grid load, power market, and electric vehicle constraints; solving the mathematical model to obtain an optimal scheduling solution, and formulating an electric vehicle peak-valley balancing scheduling strategy. The present invention establishes a mathematical model of power grid load, power market, and electric vehicle constraints, and solves the model to obtain an optimal scheduling solution; through a systematic and intelligent scheduling strategy, it improves power grid stability, optimizes resource allocation, enhances scheduling flexibility, saves energy, and reduces emissions; and can adjust the scheduling strategy of electric vehicles in real time to adapt to changes in power grid load and power market.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle intelligent charging, and in particular to a method and system for peak-valley balancing scheduling of electric vehicles in a power market. Background Art

[0002] The intelligent and optimized dispatching of power systems has become a research hotspot, particularly with the increasing focus on the introduction of electric vehicles as adjustable load resources in power markets. The widespread adoption of electric vehicles not only provides a green energy alternative for transportation but also offers new possibilities for load dispatching in power systems. In modern power markets, electric vehicles possess bidirectional charging capabilities—they can both draw power from the grid and feed it back when needed—making them a crucial resource for grid load regulation. Existing research focuses on leveraging the charging and discharging characteristics of electric vehicles to optimize peak and valley load balancing, alleviate grid pressure, and improve the efficiency of power resource utilization. Furthermore, as power markets gradually become more market-oriented, how to guide the charging and discharging behavior of electric vehicles through price signals to achieve the dual optimization of economic benefits and power system stability has also become a research focus.

[0003] Most existing electric vehicle scheduling strategies are based on static or single objective functions, and fail to fully consider the dynamic changes in the electricity market and the complex interactions of multiple factors such as grid load, market prices, and electric vehicle driving demand. Existing scheduling models often find it difficult to balance the differences between the global optimal solution and the local optimal solution, resulting in difficulties in the charging and discharging operations of electric vehicles to truly achieve peak-valley balance in the power grid. The analysis of data characteristics in existing technologies often stays on a single dimension, lacking comprehensive analysis and optimization of multi-dimensional data, thus affecting the scientific nature and effectiveness of scheduling schemes. In practical applications, existing technologies find it difficult to accurately predict the dynamic relationship between the charging and discharging demand of electric vehicles and market prices, resulting in lag and inflexibility in scheduling strategies, which not only limits the role of electric vehicles in the electricity market, but also fails to fully achieve the optimal allocation of power resources. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is that the existing electric vehicle peak-valley balance scheduling technology has problems such as a single model, limited data analysis dimensions, and lack of dynamic adjustment capabilities.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a peak-valley balancing dispatching method for electric vehicles in a power market, comprising:

[0007] Collect data related to electricity markets and electric vehicles;

[0008] Analyze the data characteristics of relevant data and construct the peak-valley balance scheduling objective function for electric vehicles;

[0009] Establish mathematical models of grid load, electricity market and electric vehicle constraints;

[0010] Solve the mathematical model to obtain the optimal scheduling solution and formulate the peak-valley balance scheduling strategy for electric vehicles.

[0011] As a preferred solution of the method for peak-valley balancing scheduling of electric vehicles in the power market of the present invention, wherein: the power market and electric vehicle related data include electric vehicle data and grid load data collected using smart meters and sensors;

[0012] Electric vehicle data includes the charging status, charging time, charging demand, battery capacity, and charging power of electric vehicles; grid load data includes the real-time load of the grid, historical load data, substation load, and line capacity; electricity market data includes real-time electricity prices, day-ahead market electricity prices, peak and valley electricity prices, power supply load curves, and power supply capacity.

[0013] As a preferred solution of the peak-valley balancing dispatch method for electric vehicles in the power market of the present invention, the data characteristic analysis includes smoothing the electricity price data by the moving average method using the trend analysis method to identify the long-term trend of the data, and the formula is expressed as follows:

[0014] ,

[0015] in, Indicates time The smoothed electricity price, Indicates the window size; Indicates time The real-time electricity price is updated using the exponentially weighted moving average, and the formula is:

[0016] ,

[0017] in, represents the smoothing factor; Indicates time Real-time electricity prices; Indicates time The smoothed electricity price is calculated; the Pearson correlation coefficient between electric vehicle charging demand and electricity price and load is calculated for correlation analysis, and the formula is expressed as:

[0018] ,

[0019] in, represents the mean electricity price, represents the mean value of charging demand; Indicates the time points of electric vehicle charging needs.

[0020] As a preferred solution of the method for peak-valley balancing scheduling of electric vehicles in the power market of the present invention, wherein: the constructing of the peak-valley balancing scheduling objective function of electric vehicles includes constructing a peak-valley load balancing objective function, an electric vehicle charging cost objective function, and a market demand prediction objective function;

[0021] The peak-valley difference objective function formula of peak-valley load balancing is expressed as:

[0022] ,

[0023] in, Indicates time Real-time load of the power grid; Indicates the electric cars at a time Charging power;

[0024] The objective function formula of electric vehicle charging cost to minimize charging cost is expressed as:

[0025] ,

[0026] According to the day-ahead market dispatch, the objective function of the day-ahead market load demand forecast is established:

[0027] ,

[0028] in, Indicates time Day-ahead market load; Indicates time load forecasting.

[0029] As a preferred solution of the method for peak-valley balancing dispatch of electric vehicles in the power market of the present invention, wherein: said establishing mathematical models of power grid load, power market and electric vehicle constraints includes establishing mathematical models of power grid load, power market and electric vehicle constraints for each objective function;

[0030] Considering the charging constraints of real-time loads, the charging load constraint formula of electric vehicles is expressed as:

[0031] ,

[0032] in, Indicates time Maximum load capacity of the power grid;

[0033] The battery capacity constraint for each electric vehicle’s charging capacity is:

[0034] ,

[0035] in, Indicates the electric cars at a time Battery status; Indicates a time interval; Indicates the Battery capacity of electric vehicles;

[0036] Charging demand constraint: electric vehicles must meet their charging needs within the specified time. The formula is expressed as:

[0037] ,

[0038] in, The charging end time; For the The charging demand of electric vehicles; power balance constraint, the formula is expressed as:

[0039] ,

[0040] in, Indicates time Total load; Indicates time base load;

[0041] The electric vehicle charging power constraint is expressed as:

[0042] ,

[0043] in, Indicates the electric cars at a time Charging power; Indicates the The maximum charging power of an electric vehicle;

[0044] The electric vehicle battery state constraint formula is expressed as:

[0045] ,

[0046] in, Indicates the electric cars at a time of stored energy; Indicates the The minimum permissible stored energy of an electric vehicle battery; Indicates the The maximum energy storage capacity of an electric vehicle battery.

[0047] As a preferred solution of the peak-valley balancing dispatching method for electric vehicles in the power market of the present invention, the solving of the mathematical model includes initializing the population, initializing a A population of individuals, each individual represents a possible solution;

[0048] Evaluate the fitness of each individual according to multiple objective functions and divide individuals into different non-dominated levels according to dominance relationships , each individual Assigned a non-dominant rank and crowding distance For any two individuals and ,if Not inferior to , and is better than at least one objective function , then it is called Dominate ; The calculation formula for crowding distance is:

[0049] ,

[0050] in, represents the number of objective functions, is the mth objective function, and Respectively expressed in adjacent individuals on the objective function, and Respectively expressed in The maximum and minimum values ​​of the objective function are used to select individuals from the current population to form the parent generation;

[0051] Individuals are selected from the current population to form the parent generation. The formula for the tournament selection operation is expressed as:

[0052] ,

[0053] in, represents the candidate set, a randomly selected subset from the population for tournament selection; Indicates the first For non-dominant ranks of individuals, lower ranks are preferred; Indicates the first The crowding distance of each individual;

[0054] New offspring individuals are generated using simulated binary crossover; the formula for the crossover operation is expressed as:

[0055] ,

[0056] ,

[0057] ,

[0058] in, represents the cross-distribution index; Represents a random number between [0,1]; and Represents the two parent individuals used for crossover; and Represents two offspring individuals generated by the crossover operation; represents the cross factor; Represents the crossover distribution index, controls the distribution of the crossover factor, and affects the degree of similarity between the offspring and the parent generation;

[0059] Use polynomial mutation to mutate offspring individuals; the formula for the mutation operation is expressed as:

[0060] ,

[0061] ,

[0062] in, represents the variation distribution index, represents the factor of variation, for A random number between Respectively Upper and lower bounds of the dimension;

[0063] The current population and newly generated offspring individuals Merge to form a The joint population of individuals ;

[0064] ,

[0065] Use non-dominated sorting to sort the joint population and select the top candidates based on non-dominated rank and crowding distance. individuals as a new population ;

[0066] ,

[0067] Repeat the steps from fitness evaluation to generating a new population until the maximum number of iterations is reached and meet the convergence conditions.

[0068] As a preferred solution of the peak-valley balancing scheduling method for electric vehicles in the power market described in the present invention, the peak-valley balancing scheduling strategy for electric vehicles includes: charging time window control: according to the driving plan of the electric vehicle, the charging time is reasonably arranged to ensure that the electric vehicle has sufficient power before driving;

[0069] ,

[0070] in, Indicates time electric vehicles The stored power, Indicates the minimum amount of stored electricity required for driving; during peak load periods, the energy storage system discharges to support the grid, while electric vehicles reduce charging and discharge. The formula is:

[0071] ,

[0072] in, Indicates time electric vehicles The stored power of With the set low electricity price and high electricity prices Make comparisons;

[0073] When electricity prices are low, large-scale charging is carried out, and in high-load areas, charging is delayed to minimize charging operations;

[0074] When the electricity price is medium, selective charging is performed, and a small amount of charging is performed only when the vehicle's power is insufficient to meet the next driving demand; charging is stopped in the high load range;

[0075] Reduce charging when electricity prices are high, charge only in emergencies, and stop charging completely during high-load periods.

[0076] A peak-valley balancing dispatching system for electric vehicles in a power market using any method according to the present invention, wherein:

[0077] Data acquisition module, which collects real-time data related to the electricity market and electric vehicles;

[0078] The data analysis and objective function construction module analyzes the characteristics of the collected data and constructs the objective function for peak-valley balance scheduling of electric vehicles based on the analysis results;

[0079] Mathematical modeling module, which builds mathematical models of grid load, electricity market and electric vehicle dispatch;

[0080] The optimization solution and strategy formulation module uses a multi-objective optimization algorithm to solve the mathematical model and obtain the optimal solution for scheduling.

[0081] A computer device comprises: a memory and a processor; the memory stores a computer program, comprising: the steps of implementing any one of the methods of the present invention when the processor executes the computer program.

[0082] A computer-readable storage medium stores a computer program thereon, comprising: steps of implementing any one of the methods of the present invention when the computer program is executed by a processor.

[0083] The beneficial effects of this invention include: establishing and solving a mathematical model of grid load, electricity market, and electric vehicle constraints to obtain an optimal dispatching solution; improving grid stability, optimizing resource allocation, enhancing dispatching flexibility, saving energy, and reducing emissions through a systematic and intelligent dispatching strategy; and being able to adjust electric vehicle dispatching strategies in real time to adapt to changes in grid load and electricity market conditions. The flexibility and adaptability of this dispatching strategy significantly enhances the power system's resilience to uncertainty and volatility, reducing the negative impact of delayed dispatching strategies. It has broad application prospects in the fields of electricity markets and electric vehicle dispatching. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0085] Figure 1 This is an overall flow chart of a peak-valley balancing scheduling method for electric vehicles in a power market provided by the first embodiment of the present invention. DETAILED DESCRIPTION

[0086] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0087] Example 1, with reference to Figure 1 , which is an embodiment of the present invention, provides a peak-valley balancing scheduling method for electric vehicles in a power market, comprising:

[0088] S1: Collect data related to electricity market and electric vehicles.

[0089] Furthermore, electricity market and electric vehicle related data include electric vehicle data and grid load data collected using smart meters and sensors.

[0090] Furthermore, electric vehicle data includes the charging status, charging time, charging demand, battery capacity, and charging power of electric vehicles; grid load data includes the real-time load of the grid, historical load data, substation load, and line capacity; electricity market data includes real-time electricity prices, day-ahead market electricity prices, peak and valley electricity prices, power supply load curves, and power supply capacity.

[0091] It should be noted that electric vehicle data includes charging status, charging time, charging demand, battery capacity, and charging power. The collection of these parameters is crucial because they directly determine when and at what power the electric vehicle is charged. Charging status and time information help the dispatch system determine when to charge, while charging demand and battery capacity determine the total amount of charging required. Charging power affects charging speed and the instantaneous load on the grid. Accurately collecting this data through smart meters allows for real-time understanding of electric vehicle electricity usage, enabling better load management.

[0092] S2: Analyze the data characteristics of relevant data and construct the peak-valley balance scheduling objective function for electric vehicles.

[0093] Furthermore, the data characteristics analysis includes using trend analysis to smooth the electricity price data through the moving average method to identify the long-term trend of the data. The formula is expressed as:

[0094] ,

[0095] in, Indicates time The smoothed electricity price, Indicates the window size.

[0096] Furthermore, the exponentially weighted moving average is used for updating, and the formula is expressed as:

[0097] ,

[0098] in, represents the smoothing factor; the Pearson correlation coefficient between electric vehicle charging demand and electricity price and load is calculated for correlation analysis, and the formula is expressed as:

[0099]

[0100] in, represents the mean electricity price, Represents the mean of charging demand, uses the time series K-means algorithm to consider the dynamic changes of time series data, and incorporates the time dimension into the clustering process; defines the time series data matrix :

[0101] ,

[0102] in, represents the number of electric vehicle users, Indicates the number of time periods, Indicates the Users at time The characteristic value of

[0103] ,

[0104] ,

[0105] Furthermore, initialization Cluster centroids ; Iteratively update cluster assignments and centroids until convergence.

[0106] Furthermore, constructing the peak-valley balance scheduling objective function of electric vehicles includes constructing the peak-valley load balance objective function, the electric vehicle charging cost objective function, and the market demand forecast objective function.

[0107] Furthermore, the peak-valley difference objective function formula of peak-valley load balancing is expressed as:

[0108] ,

[0109] in, Indicates time Real-time load of the power grid; Indicates the electric cars at a time charging power.

[0110] Furthermore, the objective function formula of electric vehicle charging cost to minimize charging cost is expressed as:

[0111] ,

[0112] in, Indicates time Real-time electricity prices.

[0113] Furthermore, based on the day-ahead market dispatch, the objective function of the day-ahead market load demand forecast is established:

[0114] ,

[0115] in, Indicates time Day-ahead market load; Indicates time load forecasting.

[0116] It should be noted that by integrating electric vehicle data, grid load data, and electricity market data, and analyzing and modeling these multi-dimensional data, a more accurate objective function for balancing peak and valley traffic in electric vehicle dispatch can be constructed. Compared to existing technologies, this invention not only considers the demand of electric vehicles themselves and price signals from the electricity market, but also incorporates the real-time load conditions of the power grid into dispatch decisions. This multi-level data integration enables the dispatch system to maximize the balance and economic benefits of the power grid while ensuring the normal use of electric vehicles.

[0117] S3: Establish mathematical models of grid load, electricity market and electric vehicle constraints.

[0118] Furthermore, establishing the mathematical models of grid load, electricity market and electric vehicle constraints includes establishing the mathematical models of grid load, electricity market and electric vehicle constraints for each objective function.

[0119] Furthermore, considering the charging constraints of real-time loads, the charging load constraint formula of electric vehicles is expressed as:

[0120] ,

[0121] in, Indicates time The maximum load capacity of the power grid.

[0122] Furthermore, the battery capacity constraint for each electric vehicle's charging capacity is:

[0123] ,

[0124] in, Indicates the electric cars at a time Battery status; Indicates a time interval; Indicates the The battery capacity of an electric car.

[0125] Furthermore, the charging demand constraint states that electric vehicles must meet their charging needs within the specified time. The formula is expressed as:

[0126] ,

[0127] in, The charging end time; For the The charging demand of electric vehicles; power balance constraint, the formula is expressed as:

[0128] ,

[0129] in, Indicates time Total load; Indicates time basic load.

[0130] Furthermore, the electric vehicle charging power constraint is expressed as:

[0131] ,

[0132] in, Indicates the electric cars at a time Charging power; Indicates the The maximum charging power of an electric vehicle.

[0133] Furthermore, the electric vehicle battery state constraint formula is expressed as:

[0134] ,

[0135] in, Indicates the electric cars at a time of stored energy; Indicates the The minimum permissible stored energy of an electric vehicle battery; Indicates the The maximum energy storage capacity of an electric vehicle battery.

[0136] It should be noted that in the charging load constraint formula of electric vehicles, the time The maximum load capacity of the grid at the time of charging is crucial for determining EV charging strategies. The rationality of this parameter ensures that EV charging operations do not exceed the grid's carrying capacity, avoiding the risk of grid overload due to overcharging. Its design takes into account the dynamic load characteristics of the grid, effectively balancing power distribution between EVs and the grid, and ensuring more stable grid operation.

[0137] S4: Solve the mathematical model to obtain the optimal scheduling solution and formulate a peak-valley balance scheduling strategy for electric vehicles.

[0138] Furthermore, solving the mathematical model includes initializing the population, initializing a A population of individuals, each individual represents a possible solution.

[0139] ,

[0140] in, Indicates the Individuals are randomly generated within the domain of the problem.

[0141] Furthermore, the fitness of each individual is evaluated according to multiple objective functions, and the individuals are divided into different non-dominated levels according to the dominance relationship. , each individual Assigned a non-dominant rank and crowding distance For any two individuals and ,if Not inferior to , and is better than at least one objective function , then it is called Dominate ; The calculation formula for crowding distance is:

[0142] ,

[0143] in, represents the number of objective functions, is the mth objective function, and Respectively expressed in adjacent individuals on the objective function, and Respectively expressed in The maximum and minimum values ​​of the objective function are used to select individuals from the current population to form the parent generation.

[0144] Furthermore, individuals are selected from the current population to form the parent generation. The formula for the tournament selection operation is expressed as:

[0145] ,

[0146] in, represents the candidate set, a randomly selected subset from the population for tournament selection; Indicates the first For non-dominant ranks of individuals, lower ranks are preferred; Indicates the first The crowding distance of each individual; when the non-dominated level is the same, the individual with the larger crowding distance is selected to maintain the diversity fitness of the population. and crowding distance Joint decision.

[0147] Furthermore, simulated binary crossover is used to generate new offspring individuals; the formula for the crossover operation is expressed as:

[0148] ,

[0149] ,

[0150] ,

[0151] in, represents the cross-distribution index; Represents a random number between [0,1]; and Represents the two parent individuals used for crossover; and Represents two offspring individuals generated by the crossover operation; represents the cross factor; Represents the crossover distribution index, controls the distribution of the crossover factor, and affects the degree of similarity between offspring and parents.

[0152] Furthermore, polynomial mutation is used to mutate offspring individuals; the formula of the mutation operation is expressed as:

[0153] ,

[0154] ,

[0155] in, represents the variation distribution index, represents the factor of variation, for A random number between Respectively The upper and lower bounds of a dimension.

[0156] Furthermore, the current population and newly generated offspring individuals Merge to form a The joint population of individuals .

[0157] ,

[0158] Furthermore, the joint population is sorted using non-dominated sorting, and the top candidates are selected based on the non-dominated rank and crowding distance. individuals as a new population .

[0159] ,

[0160] Furthermore, the fitness evaluation to population update step is repeated until the maximum number of iterations is reached. Or the convergence condition is met.

[0161] Furthermore, the peak-valley balancing scheduling strategy for electric vehicles includes charging time window control: according to the driving plan of the electric vehicle, the charging time is reasonably arranged to ensure that the electric vehicle has enough power before driving.

[0162] ,

[0163] in, Indicates time electric vehicles The stored power, Indicates the minimum amount of stored electricity required for driving; during peak load periods, the energy storage system discharges to support the grid, while electric vehicles reduce charging and discharge. The formula is:

[0164] ,

[0165] in, Indicates time electric vehicles The stored power of With the set low electricity price and high electricity prices Make a comparison.

[0166] Furthermore, When electricity prices are low, large-scale charging is carried out. In high-load areas, charging is delayed to minimize charging operations.

[0167] Furthermore, When the electricity price is medium, selective charging is carried out, and a small amount of charging is only performed when the vehicle’s power is insufficient to meet the next driving demand; charging is stopped in the high load range.

[0168] Furthermore, Reduce charging when electricity prices are high, charge only in emergencies, and stop charging completely during high-load periods.

[0169] It should be noted that in the "Simulated Binary Crossover" and "Polynomial Mutation" steps, the "Crossover Distribution Index" and "Mutation Distribution Index" are introduced into the formula to control the distribution of the crossover factor and the mutation amplitude. The introduction of these two indices enables the offspring to maintain a balance between exploration and utilization, inheriting the excellent characteristics of the parent generation while exploring new potential high-quality solutions through moderate mutation. For electric vehicle peak-valley balancing scheduling strategies, this approach maintains the stability of the strategy (inheriting existing effective charging strategies) while dynamically adapting to the needs of the power market and load changes, resulting in a more flexible charging scheduling plan.

[0170] On the other hand, this embodiment also provides a peak-valley balancing dispatching system for electric vehicles in the power market, which includes:

[0171] Data acquisition module, collects real-time data related to the electricity market and electric vehicles.

[0172] The data analysis and objective function construction module performs characteristic analysis on the collected data and constructs the objective function of peak-valley balance scheduling of electric vehicles based on the analysis results.

[0173] Mathematical modeling module, which establishes mathematical models of grid load, electricity market and electric vehicle scheduling.

[0174] The optimization solution and strategy formulation module uses a multi-objective optimization algorithm to solve the mathematical model and obtain the optimal solution for scheduling.

[0175] If the above functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0176] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0177] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, and then editing, interpreting, or processing in another suitable manner as necessary, and then storing it in a computer memory.

[0178] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0179] Example 2: The following is an embodiment of the present invention, which provides a peak-valley balancing scheduling method for electric vehicles in the power market. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0180] To validate the effectiveness of the invented electric vehicle peak-valley balancing scheduling method and system, particularly its application in the electricity market, a series of experiments were conducted. The experiments used electricity market data and electric vehicle charging data from a specific region, covering electricity price fluctuations, load variations, and electric vehicle charging and discharging demands over different time periods. Key steps in the experiments included data collection, characteristic analysis, objective function construction, mathematical model development, and solution, ultimately resulting in an optimized scheduling strategy.

[0181] First, seven days of electricity price data were extracted from the region's electricity market operational database, with one data point per hour per day, totaling 168 data points. Furthermore, charging and driving data were collected from 10 randomly selected electric vehicles in the region, including each vehicle's battery capacity, initial charge level, daily mileage, charging time, and charging power. The data collection covered a variety of electric vehicle usage scenarios, including weekdays and weekends, to ensure representative and accurate results.

[0182] Next, the collected data was analyzed for its characteristics. By calculating the daily average, volatility, peak and valley values ​​of electricity prices, as well as the mean and variance of EV charging demand, the relationship between the electricity market and EV demand was analyzed. The results of this characteristic analysis revealed a significant time dependency between electricity prices and EV charging demand, with peak EV charging typically occurring during periods of low electricity prices. This provided data support for the subsequent construction of the objective function.

[0183] Based on data analysis, an objective function for peak-valley balanced dispatch of electric vehicles was constructed. This objective function primarily considers electricity prices, grid load, electric vehicle charging demand, and its impact on grid stability. It strives to maximize charging during low-price periods and reduce peak load pressure while still meeting the normal use of electric vehicles.

[0184] A mathematical model of grid load, electricity market, and electric vehicle constraints was established. This model incorporates the grid's maximum load constraint, each electric vehicle's maximum charging power limit, battery capacity constraints, and driving requirements. This ensures that electric vehicles achieve grid load balance and optimize electricity prices while meeting user driving needs. By solving this mathematical model, the optimal charging strategy for each electric vehicle at different times was determined.

[0185] Finally, a specific scheduling strategy was developed based on the optimal scheduling solution. Experimental results show that by optimizing the scheduling strategy, EV charging behavior is highly consistent with electricity price fluctuations and load changes in the power market, effectively reducing the peak load on the power grid and alleviating the cost pressure caused by electricity price fluctuations. The results are shown in Table 1.

[0186] Table 1 Experimental data table

[0187] ,

[0188] Analysis of the experimental data in Table 1 clearly demonstrates the changes in electric vehicle performance before and after the optimized scheduling strategy was implemented. First, given the same initial charge and battery capacity, the average daily mileage and charging periods for different vehicles were appropriately adjusted during the experiment. The average daily electricity cost before optimization was significantly higher than after, decreasing by approximately 16% on average. This indicates that the optimized scheduling strategy allows electric vehicles to better utilize low-price periods for charging, thereby reducing overall charging costs.

[0189] The data in the table shows that the core of the optimized scheduling strategy lies in rationally allocating charging times for electric vehicles and controlling peak charging power within a reasonable range to avoid excessive consumption of electricity resources during periods of high electricity prices. For example, vehicle A's average daily electricity cost was 45.3 yuan before optimization, but it dropped to 37.8 yuan after optimization, a reduction of approximately 17%. Similar trends are observed for other vehicles, further demonstrating that through scheduling optimization, electric vehicles can achieve dual optimization of electricity prices and grid load while meeting driving demand.

[0190] Table 2 Algorithm effect comparison table

[0191] ,

[0192] As can be seen in Table 2, our algorithm demonstrates significant advantages in terms of average electricity costs. The average cost was reduced from 45.2 yuan to 38.6 yuan, a decrease of approximately 14.6%. This result is due to the optimized scheduling strategy's ability to better utilize charging during off-peak periods, reducing electricity consumption during peak periods. Therefore, our algorithm not only reduces charging costs for users but also effectively utilizes low-priced electricity resources in the market, maximizing economic benefits. Our algorithm reduces the peak-to-valley difference in grid load from 5.8 kW to 3.2 kW. This demonstrates that through precise load forecasting and scheduling optimization, electric vehicle charging is effectively dispersed to periods of lower grid load, reducing pressure during peak load periods, thereby balancing the load distribution of the grid and improving its operational stability.

[0193] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A peak-valley balancing dispatching method for electric vehicles in the power market, characterized by: include: Collect data related to electricity markets and electric vehicles; Analyze the data characteristics of relevant data and construct the peak-valley balance scheduling objective function for electric vehicles; Establish mathematical models of grid load, electricity market and electric vehicle constraints; Solve the mathematical model to obtain the optimal scheduling solution and formulate a peak-valley balance scheduling strategy for electric vehicles; The electricity market and electric vehicle related data include electric vehicle data and grid load data collected using smart meters and sensors; Electric vehicle data includes the electric vehicle's charging status, charging time, charging demand, battery capacity, and charging power; grid load data includes the grid's real-time load, historical load data, substation load, and line capacity; Power market data includes real-time electricity prices, day-ahead market electricity prices, peak and valley electricity prices, power supply load curves, and power supply capacity; The data characteristic analysis includes using trend analysis to smooth the electricity price data through the moving average method to identify the long-term trend of the data. The formula is expressed as follows: in, represents the smoothed electricity price at time t, k represents the window size; P r (t+i) represents the real-time electricity price at time t+i; it is updated using the exponentially weighted moving average, and the formula is: Among them, α represents the smoothing factor; P r (t) represents the real-time electricity price at time t; represents the smoothed electricity price at time t-1; the Pearson correlation coefficient between electric vehicle charging demand and electricity price is calculated for correlation analysis, and the formula is expressed as: in, represents the mean electricity price, Represents the mean value of charging demand; D c (t i ) represents the i-th time point t i Electric vehicle charging demand; The said constructing of the electric vehicle peak-valley balance scheduling objective function includes constructing a peak-valley load balance objective function, an electric vehicle charging cost objective function, and a market demand prediction objective function; The peak-valley difference objective function formula of peak-valley load balancing is expressed as: Among them, L r (t) represents the real-time load of the power grid at time t; P c,i (t) represents the charging power of the i-th electric vehicle at time t; The objective function formula of electric vehicle charging cost to minimize charging cost is expressed as: According to the day-ahead market dispatch, the objective function of the day-ahead market load demand forecast is established: Among them, L d (t) represents the day-ahead market load at time t; L f (t) represents the load forecast at time t.

2. The method for peak-valley balancing dispatch of electric vehicles in the power market according to claim 1, characterized in that: The establishing of the mathematical model of grid load, electricity market and electric vehicle constraints includes establishing the mathematical model of grid load, electricity market and electric vehicle constraints for each objective function; Considering the charging constraints of real-time loads, the charging load constraint formula of electric vehicles is expressed as: Among them, L max (t) represents the maximum load capacity of the power grid at time t; The battery capacity constraint for each electric vehicle’s charging capacity is: Among them, SOC i (t) represents the battery status of the i-th electric vehicle at time t; Δt represents the time interval; C b,i represents the battery capacity of the i-th electric vehicle; Charging demand constraint: electric vehicles must meet their charging needs within the specified time. The formula is expressed as: SOC i (T end )≥D c,i Among them, T end Indicates the charging end time; D c,i represents the charging demand of the i-th electric vehicle; the power balance constraint is expressed as follows: Among them, L t Indicates the total load at time t; L t,base represents the base load at time t; The electric vehicle charging power constraint is expressed as: 0≤P c,i (t)≤P i,max Among them, P c,i (t) represents the charging power of the i-th electric vehicle at time t; P i,max represents the maximum charging power of the i-th electric vehicle; The electric vehicle battery state constraint formula is expressed as: AND i,min ≤E i,stored,t ≤E i,max Among them, E i,stored,t represents the stored energy of the i-th electric vehicle at time t; E i,min represents the minimum allowable storage energy of the battery of the i-th electric vehicle; E i,max represents the maximum storage energy of the battery of the i-th electric vehicle.

3. The method for peak-valley balancing dispatch of electric vehicles in the power market according to claim 2, characterized in that: Solving the mathematical model includes initializing a population, initializing a population including N individuals, each individual representing a possible solution; The fitness of each individual is evaluated according to multiple objective functions. The individuals are divided into different non-dominated levels F according to the dominance relationship. Each individual x i is assigned a non-dominated rank (x i ) and crowding distance d(x i ); for any two individuals x i and x j , if x i Not inferior to x in all objective functions j , and is better than x in at least one objective function j , then x i Dominate x j ; The calculation formula for crowding distance is: Where M represents the number of objective functions, f m represents the mth objective function, x i+1 and x i-1 Represent the adjacent individuals on the mth objective function, and Respectively, they represent the selection of individuals from the current population to form the parent generation at the maximum and minimum values ​​of the mth objective function; Individuals are selected from the current population to form the parent generation. The formula for the tournament selection operation is expressed as: Where T represents the candidate set, a subset randomly selected from the population for tournament selection; rank(x j ) represents the non-dominated level of the jth individual in the candidate set, and the lower level is preferred; d(x j ) represents the crowding distance of the jth individual in the candidate set; New offspring individuals are generated using simulated binary crossover; the formula for the crossover operation is expressed as: x ofspring,1,j =0.5((1+β j )x parent,1,j +(1-β j )x parent,2,j ) x ofspring,2,j =0.5((1-β j )x parent,1,j +(1+β j )x parent,2,j ) Among them, η c represents the cross-distribution index; r j Represents a random number between [0,1]; x parent,1,j and x parent,2,j represents the two parent individuals used for crossover; x offspring,1,j and x offspring,2,j represents the two offspring individuals generated by the crossover operation; β j represents the cross factor; η c Represents the crossover distribution index, controls the distribution of the crossover factor, and affects the degree of similarity between the offspring and the parent generation; Use polynomial mutation to mutate offspring individuals; the formula for the mutation operation is expressed as: Among them, η m represents the variation distribution index, r j represents a random number between [0,1], and represents the upper and lower bounds of the j-th dimension, δ j represents the factor of variation; The current population X and the newly generated offspring individual X offspring Merge to form a joint population X containing 2N individuals union ; X union =X∪X offspring Sort the joint population using non-dominated sorting and select the top N individuals as the new population X based on non-dominated rank and crowding distance new ; Repeat the steps from fitness evaluation to generating a new population until the maximum number of iterations G is reached max and meet the convergence conditions.

4. The method for peak-valley balancing dispatch of electric vehicles in the power market according to claim 3, characterized in that: The peak-valley balancing scheduling strategy for electric vehicles includes charging time window control, which reasonably arranges charging time according to the driving plan of the electric vehicle to ensure that the electric vehicle has sufficient power before driving; Among them, E i,stored,t represents the stored power of electric vehicle i at time t, E i,drive,t Indicates the minimum storage power required for driving; During the peak load period of the power grid, the energy storage system discharges to support the power grid, and the electric vehicles reduce charging and discharge. The obtained P r (t) and the set low electricity price P low and high electricity prices P high Make comparisons; P r (t)≤P low When electricity prices are low, large-scale charging is carried out, and in high-load areas, charging is delayed to minimize charging operations; P low <P r (t)≤P high When the electricity price is medium, selective charging is performed, and a small amount of charging is performed only when the vehicle's power is insufficient to meet the next driving demand; charging is stopped in the high load range; P r (t)>P high Reduce charging when electricity prices are high, charge only in emergencies, and stop charging completely during high-load periods.

5. A peak-valley balancing dispatching system for electric vehicles in a power market using the method according to any one of claims 1 to 4, characterized in that: Data acquisition module, which collects real-time data related to the electricity market and electric vehicles; The data analysis and objective function construction module analyzes the characteristics of the collected data and constructs the objective function for peak-valley balance scheduling of electric vehicles based on the analysis results; Mathematical modeling module, which builds mathematical models of grid load, electricity market and electric vehicle dispatch; The optimization solution and strategy formulation module uses a multi-objective optimization algorithm to solve the mathematical model and obtain the optimal solution for scheduling.

6. A computer device comprising: memory and processor; The memory stores a computer program, characterized in that when the processor executes the computer program, the steps of the method for peak-valley balancing scheduling of electric vehicles in the power market as claimed in any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for peak-valley balancing scheduling of electric vehicles in the power market as claimed in any one of claims 1 to 4 are implemented.

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