A time-of-use electricity price determination method and terminal considering load characteristics and balanced benefits

Abstract

The application discloses a time-of-use electricity price determination method and terminal considering load characteristics and balanced benefits, establishes a demand response evaluation model considering the benefits of both residents and power grids based on the obtained load characteristics and constructed resident electricity price elasticity matrix, establishes a first target function of maximizing electricity sales income and a second target function of minimizing average charging cost based on the demand response evaluation model, solves the first target function and the second target function by using an improved great white shark optimization algorithm based on grid side constraint conditions and user side constraint conditions, obtains an optimal time-of-use electricity price scheme, and ensures mutual benefits of both power supply and demand based on the two target functions, effectively alleviates the power grid pressure during peak periods, reduces system operation cost, improves the satisfaction and participation of residents, and thus improves the accuracy of the electricity price scheme determination.

Description

Technical Field

[0001] The present invention relates to the technical field of electricity price setting, and in particular to a method and terminal for determining a time-of-use electricity price taking into account load characteristics and balanced benefits. Background Art

[0002] With the increasing popularity of electric vehicles (EVs), the development of charging infrastructure has become crucial. This includes public charging stations, home charging stations, and vehicle management systems, which are responsible for collecting charging data, monitoring charging status, and providing services to users. These facilities typically support fast and slow charging at different charging powers to suit different scenarios and user needs. To balance grid load and reduce peak electricity demand, many countries and regions have implemented dynamic electricity pricing or time-of-use pricing strategies. These strategies set different electricity prices according to different times of day, encouraging users to charge during off-peak hours, thereby alleviating grid pressure. However, traditional strategies often lack personalized design tailored to the characteristics of EV users. Demand response (DR), as a market mechanism, encourages electricity users to adjust their electricity usage behavior based on electricity price signals to respond to grid demand. In the EV sector, using incentives to encourage users to charge during off-peak hours is an effective way to alleviate grid pressure. User behavior research helps understand user responses to electricity price changes and how to design incentive mechanisms to promote user participation.

[0003] In summary, the background technology provides a solid foundation for the research on electric vehicle charging management and electricity price strategy design. However, it also has its shortcomings, and the accuracy of the determined electricity price scheme is low. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to improve a time-of-use electricity price determination method and terminal that considers load characteristics and balanced benefits, thereby improving the accuracy of determining an electricity price scheme.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] A method for determining a time-of-use electricity price taking into account load characteristics and balanced benefits comprises the following steps:

[0007] Collecting usage data of residential electric vehicles, and performing load characteristic analysis based on the usage data of residential electric vehicles to obtain load characteristics;

[0008] Constructing a residential electricity price elasticity matrix, and establishing a demand response evaluation model that considers the benefits of both residents and the power grid based on the load characteristics and the residential electricity price elasticity matrix;

[0009] Establishing a first objective function for maximizing electricity sales revenue and a second objective function for minimizing average charging costs based on the demand response evaluation model, and establishing grid-side constraints corresponding to the first objective function and user-side constraints corresponding to the second objective function;

[0010] Based on the grid-side constraints and the user-side constraints, the first objective function and the second objective function are solved using an improved great white shark optimization algorithm to obtain an optimal time-of-use electricity price scheme, where the improved great white shark optimization algorithm is improved using a gray wolf optimization algorithm.

[0011] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0012] A time-of-use electricity price determination terminal that considers load characteristics and balanced benefits includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0013] Collecting usage data of residential electric vehicles, and performing load characteristic analysis based on the usage data of residential electric vehicles to obtain load characteristics;

[0014] Constructing a residential electricity price elasticity matrix, and establishing a demand response evaluation model that considers the benefits of both residents and the power grid based on the load characteristics and the residential electricity price elasticity matrix;

[0015] Establishing a first objective function for maximizing electricity sales revenue and a second objective function for minimizing average charging costs based on the demand response evaluation model, and establishing grid-side constraints corresponding to the first objective function and user-side constraints corresponding to the second objective function;

[0016] Based on the grid-side constraints and the user-side constraints, the first objective function and the second objective function are solved using an improved great white shark optimization algorithm to obtain an optimal time-of-use electricity price scheme, where the improved great white shark optimization algorithm is improved using a gray wolf optimization algorithm.

[0017] The beneficial effects of the present invention are: based on the load characteristics obtained by analysis and the constructed residential electricity price elasticity matrix, a demand response evaluation model that considers the benefits of both residents and the power grid is established; based on the demand response evaluation model, a first objective function that maximizes electricity sales revenue and a second objective function that minimizes average charging costs are established, and grid-side constraints and user-side constraints are established. Based on the grid-side constraints and user-side constraints, the first objective function and the second objective function are solved using the improved great white shark optimization algorithm to obtain the optimal time-of-use electricity price scheme. The residential electricity price elasticity matrix dynamically reflects the sensitivity of residents to electricity price changes in different seasons and time periods. The demand response evaluation model takes into account the benefits of both residents and the power grid. The two objective functions established based on this can also ensure mutual benefit and win-win results for both supply and demand. The time-of-use electricity price scheme obtained by this solution can effectively alleviate the pressure on the power grid during peak hours, reduce system operating costs, and at the same time improve residents' satisfaction and participation, thereby improving the accuracy of the electricity price scheme determination. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flowchart of a method for determining a time-of-use electricity price that takes into account load characteristics and balanced benefits according to an embodiment of the present invention;

[0019] Figure 2 This is a structural diagram of a terminal for determining a time-of-use electricity price that takes load characteristics and balanced benefits into consideration according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] To illustrate the technical content, achieved objectives and effects of the present invention in detail, the following description is given in conjunction with the embodiments and accompanying drawings.

[0021] Please refer to Figure 1 A method for determining a time-of-use electricity price taking into account load characteristics and balanced benefits comprises the following steps:

[0022] Collecting usage data of residential electric vehicles, and performing load characteristic analysis based on the usage data of residential electric vehicles to obtain load characteristics;

[0023] Constructing a residential electricity price elasticity matrix, and establishing a demand response evaluation model that considers the benefits of both residents and the power grid based on the load characteristics and the residential electricity price elasticity matrix;

[0024] Establishing a first objective function for maximizing electricity sales revenue and a second objective function for minimizing average charging costs based on the demand response evaluation model, and establishing grid-side constraints corresponding to the first objective function and user-side constraints corresponding to the second objective function;

[0025] Based on the grid-side constraints and the user-side constraints, the first objective function and the second objective function are solved using an improved great white shark optimization algorithm to obtain an optimal time-of-use electricity price scheme, where the improved great white shark optimization algorithm is improved using a gray wolf optimization algorithm.

[0026] From the above description, it can be seen that the beneficial effects of the present invention are: based on the load characteristics obtained by analysis and the constructed residential electricity price elasticity matrix, a demand response evaluation model that takes into account the benefits of both residents and the power grid is established; based on the demand response evaluation model, a first objective function for maximizing electricity sales revenue and a second objective function for minimizing average charging costs are established, and grid-side constraints and user-side constraints are established. Based on the grid-side constraints and user-side constraints, the first objective function and the second objective function are solved using the improved great white shark optimization algorithm to obtain the optimal time-of-use electricity price scheme. The residential electricity price elasticity matrix dynamically reflects the sensitivity of residents to electricity price changes in different seasons and time periods. The demand response evaluation model takes into account the benefits of both residents and the power grid. The two objective functions established based on this can also ensure mutual benefit and win-win results for both supply and demand. The time-of-use electricity price scheme obtained by this solution can effectively alleviate the pressure on the power grid during peak hours, reduce system operating costs, and at the same time improve residents' satisfaction and participation, thereby improving the accuracy of the electricity price scheme determination.

[0027] Furthermore, the load characteristics analysis is performed based on the residential electric vehicle usage data to obtain the load characteristics including:

[0028] Correcting abnormal data in the residential electric vehicle usage data using the quartile method to obtain corrected residential electric vehicle usage data;

[0029] The load characteristics are analyzed on the corrected residential electric vehicle usage data to obtain load characteristics.

[0030] From the above description, it can be seen that the use of the quartile method to correct abnormal data in the residential electric vehicle usage data ensures the accuracy of the subsequent load characteristic analysis and can effectively reveal the seasonal changes in residents' charging patterns, thereby improving the accuracy of the electricity price plan.

[0031] Furthermore, the residential electricity price elasticity matrix is:

[0032]

[0033]

[0034]

[0035] Where E represents the residential electricity price elasticity matrix, ε i,iIndicates that the change in charging cost in time period i affects the electricity demand in time period i, ε i,j It refers to the response of the change in electricity consumption in time period i to the change in charging cost in time period j, Q i represents the original electricity demand in time period i, P i represents the original charging cost in time period i, P j represents the original charging cost in time period j, represents the change in electricity demand caused by the change in charging cost in time period i, represents the change in charging cost within time period i, represents the change in charging cost during time period j.

[0036] From the above description, it can be seen that there are two situations in which buyers respond based on electricity prices: first, single-period response, that is, responding during a specific period when electricity prices change, such as immediately turning off some power-consuming appliances after the electricity price increases during this period. This response method is similar to the principle of price elasticity of demand for traditional commodities; second, multi-period response, that is, according to the changes in electricity prices during this period, the load demand for this period and other periods is changed, such as reducing the load during high-price periods for use during low-price periods. However, when responding to a single period, users will also pay attention to past changes in electricity prices and consider that the load value during this period is not worth increasing or reducing. Therefore, the residents' electricity price-energy interaction relationship will not be related only to the electricity price in a single period. Therefore, the above-mentioned residential electricity price elasticity matrix is ​​constructed to effectively reflect the changing relationship between electricity price and energy.

[0037] Furthermore, establishing a demand response evaluation model that considers the benefits of both residents and the power grid based on the load characteristics and the residential electricity price elasticity matrix includes:

[0038]

[0039]

[0040]

[0041]

[0042] FG=q max -q min ;

[0043]

[0044] Where I' represents the total amount of user consumption before the implementation of time-of-use electricity pricing, N represents the total number of time periods in a day, p' represents the original residential electric vehicle charging price, q i ′ represents the charging power of the original residents’ electric vehicles in period i, I represents the total consumption of users after the implementation of time-of-use electricity prices, and pi represents the charging electricity price of residential electric vehicles in period i after the implementation of time-of-use electricity prices, q i It represents the charging power of residents’ electric vehicles in period i after the implementation of time-of-use electricity price. represents the expected charging capacity of residents’ electric vehicles after the grid implements time-of-use electricity prices, represents the change in electricity price during period i, B represents the social benefits after the implementation of time-of-use electricity price, represents the unit cost of electricity purchased by the power grid from the power generation enterprise during period i, FG represents the peak-to-valley difference, LF represents the load factor, and q max represents the maximum load of residential electric vehicles during the dispatch period, q min Indicates the minimum load of residential electric vehicles during the scheduling period.

[0045] From the above description, it can be seen that based on the load characteristics and the residential electricity price elasticity matrix, a demand response evaluation model that considers the benefits of both residents and the power grid is established. From this, we can understand how the charging power of residents in the province in each period is affected by the time-of-use electricity price level in the current period and the demand response effect achieved by the time-of-use electricity price, thereby better guiding the determination of subsequent electricity price plans.

[0046] Furthermore, the first objective function of maximizing electricity sales revenue is:

[0047]

[0048] Where Y represents the revenue from electricity sales.

[0049] From the above description, we can see that, generally speaking, the power grid hopes to maximize its own electricity sales revenue while implementing time-of-use electricity price scheduling for residential electric vehicle users to charge and swap batteries. Therefore, one of the objective functions is established to maximize electricity sales revenue.

[0050] Furthermore, the second objective function of minimizing the average charging cost is:

[0051]

[0052] Where C represents the average charging cost.

[0053] As can be seen from the above description, residential electric vehicle users will subjectively adjust their charging and swapping behavior based on time-of-use electricity prices, with the goal of minimizing the average charging cost of their electric vehicles over a certain period. Obviously, the grid's goal is to set the highest possible electricity price to ensure its own electricity sales revenue. However, rising electricity prices will inevitably lead to an increase in the average charging cost for residential electric vehicle users. Therefore, in addition to establishing the first objective function, a second objective function to minimize the average charging cost is also required to ensure that the final electricity pricing plan achieves mutual benefits for both supply and demand.

[0054] Furthermore, the grid-side constraints are:

[0055] I≤I′;

[0056] p g ≤p p ≤p f ;

[0057]

[0058]

[0059] Where p g represents the off-peak electricity price for residential electric vehicles, p p represents the electricity price of residential electric vehicles during normal hours, p f Represents the peak-time electricity price for residential electric vehicles.

[0060] From the above description, it can be seen that the grid-side constraints of the first objective function are established. These constraints stipulate that the user expenditure after the implementation of the time-of-use electricity price scheme cannot be higher than the original expenditure, and the peak electricity price is higher than the flat electricity price, and the flat electricity price is higher than the valley electricity price. In addition, it is also required that the floating ratio of the peak period electricity price and the valley period electricity price is within a certain range, so as to ensure the accuracy and effectiveness of the electricity price scheme finally solved.

[0061] Furthermore, the user-side constraint condition is:

[0062] Y f =p f q f -p′q f ;

[0063] Y g =p g q g -p′q g ;

[0064] Where Y f Indicates the change in power grid benefits during peak hours, Y g Indicates the change in grid efficiency during valley period, q f Indicates the peak period electricity consumption, q g Indicates the electricity consumption during off-peak hours.

[0065] From the above description, it can be seen that the implementation of time-of-use electricity prices can effectively improve the distribution of charging and swapping loads of residential electric vehicles. Taking the optimized load as the benchmark, the impact of the original electricity price of electric vehicles and the time-of-use electricity price on the grid benefits during peak and valley periods is examined, and thus user-side constraints are constructed.

[0066] Furthermore, it also includes:

[0067] A random disturbance term is introduced into the auditory and olfactory movement stages of the great white shark optimization algorithm, and three sharks are introduced into the stage of schooling behavior to update the population position, thereby obtaining an improved great white shark optimization algorithm, wherein the three sharks have the best suitability function.

[0068] From the above description, we can see that by drawing on the concept of the gray wolf optimization algorithm, random disturbance terms are introduced into the auditory and olfactory movement stages of the great white shark optimization algorithm, and three sharks are introduced to update the population position during the schooling behavior stage to optimize the convergence speed and global search capability of the great white shark optimization algorithm, ensuring that the electricity price solution obtained is the optimal result.

[0069] Please refer to Figure 2 Another embodiment of the present invention provides a terminal for determining time-of-use electricity prices that takes into account load characteristics and balanced benefits, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step in the above-mentioned method for determining time-of-use electricity prices that takes into account load characteristics and balanced benefits is implemented.

[0070] The above-mentioned method and terminal for determining time-of-use electricity prices taking into account load characteristics and balanced benefits of the present invention can be applied to time-of-use electricity price formulation scenarios, and are described below through specific implementation methods:

[0071] Please refer to Figure 1 , embodiment 1 of the present invention is:

[0072] A method for determining a time-of-use electricity price taking into account load characteristics and balanced benefits comprises the following steps:

[0073] S1. Collecting residential electric vehicle usage data, and performing load characteristic analysis based on the residential electric vehicle usage data to obtain load characteristics, specifically including S11-S12:

[0074] S11. Correct abnormal data in the residential electric vehicle usage data using the quartile method to obtain corrected residential electric vehicle usage data.

[0075] Considering the one-dimensionality of residential electric vehicle usage data and the applicability of the method, the box plot method in distribution detection was selected for quantitative evaluation of outliers. The box plot is a commonly used method for quickly identifying outliers in sample data. It can be used to judge outliers in sample data that follows a normal distribution, and it can also be used to judge outliers in sample data that does not follow a normal distribution. It has a wide range of applications. It is composed of the maximum value, minimum value, median, and two quartiles of the sample. The criteria for judging outliers Q by the box plot are as follows:

[0076]

[0077] Where Q LDenotes the lower quartile, Q U Denotes the upper quartile, Q IQR Denotes the interquartile range.

[0078] Specifically, the lower quartile, upper quartile, and interquartile range are calculated using the residential electric vehicle usage data. If there is data Q in the residential electric vehicle usage data that meets the above criteria, it indicates that it is abnormal data. Then, a reasonable interval distribution model is constructed based on Chebyshev's inequality to calculate the reasonable threshold of the data. Define the confidence level α such that at least α samples fall within k standard deviations, that is: σ represents the standard deviation, represents a real number, which is the observed value of a random variable, μ represents the expected value of the normal distribution. To obtain a suitable k value, an optimization algorithm is used with the difference between the interval length of the reasonable threshold and 90% as the objective function, that is, it is considered that when the interval length ratio approaches 90% infinitely, this interval is the reasonable interval for the cost, that is: Q(x) represents the entire sample interval, q(μ - kσ ≤ x ≤ μ + kσ) represents the interval length falling within (μ - kσ ≤ x ≤ μ + kσ), and the golden section method is used to optimize the parameter k. The average value of the interval is used as the correction value for the abnormal data to obtain the corrected residential electric vehicle usage data.

[0079] Among them, the specific steps to optimize the parameter k using the golden section method are as follows:

[0080] (1) Given the interval [a, b] (a < b) and a very small ε (ε > 0);

[0081] (2) Calculate k1 = a + 0.382(b - a), k2 = a + 0.618(b - a);

[0082] (3) If k2 - k1 < ε, then output k = (k1 + k2) / 2, and the calculation ends. Otherwise, go to the next step;

[0083] (4) If f(k2) > f(k1), then let b = k2 and transfer to (2). Otherwise, a = k1 and transfer to (2).

[0084] The golden section method can effectively approach the extreme point when reducing the search range. Its convergence speed is close to the optimal speed in optimization theory, that is, each iteration reduces the search interval to a certain proportion of the original, ensuring the efficiency and stability of the algorithm. And different from some optimization methods that require the derivative of the function (such as the gradient descent method), the golden section method is a pure iterative direct search method that does not require the derivative information of the objective function, which is very beneficial for complex systems or black-box functions (that is, functions with unknown internal mechanisms and can only be observed through input and output), reducing the computational complexity and implementation difficulty.

[0085] S12. Perform load characteristic analysis on the corrected residential electric vehicle usage data to obtain load characteristics.

[0086] S2. Construct a residential electricity price elasticity matrix, and establish a demand response evaluation model that considers the benefits of both residents and the power grid based on the load characteristics and the residential electricity price elasticity matrix.

[0087] Among them, electricity as a commodity has a different elasticity between demand and price than traditional commodities. There are two situations in which buyers respond based on electricity prices: first, single-period response, that is, responding during a specific period when electricity prices change, such as immediately shutting down some power-consuming appliances after the electricity price increases during that period. This response method is similar to the principle of demand price elasticity of traditional commodities; second, multi-period response, that is, changing the load demand for that period and other periods according to the changes in electricity prices during that period, such as reducing the load during high-price periods for use during low-price periods. However, when responding to a single period, users will also pay attention to past changes in electricity prices and consider that the load value during that period is not worth increasing or reducing. Therefore, the user's electricity price-electricity interaction relationship will not be related only to the electricity price of a single period, so the residential electricity price elasticity matrix is:

[0088]

[0089]

[0090]

[0091] Where E represents the residential electricity price elasticity matrix, ε i,i Indicates that the change in charging cost in time period i affects the electricity demand in time period i, ε i,j It refers to the response of the change in electricity consumption in time period i to the change in charging cost in time period j, Q i represents the original electricity demand in time period i, P i represents the original charging cost in time period i, P j represents the original charging cost in time period j, represents the change in electricity demand caused by the change in charging cost in time period i, represents the change in charging cost within time period i, represents the change in charging cost during time period j.

[0092] The power grid formulates a time-of-use electricity price strategy for residential electric vehicles to guide residential electric vehicle users to adjust their charging and swapping behaviors, and assist the power grid in peak shaving and valley filling, that is, moving peak loads to low periods.

[0093] When charging their electric vehicles, residential users will make various considerations and hope to keep their electricity costs as low as possible. If the user's electricity bill is higher than before after the implementation of time-of-use electricity prices, then residential users will choose other charging channels due to the increase in costs. The power grid will lose part of the income from residential electric vehicle charging fees, and social benefits will also be reduced. Therefore, the total expenditure of users cannot increase after the implementation of time-of-use electricity prices. Assuming that the total consumption of users before the implementation of time-of-use electricity prices is I' and the total consumption of users after the implementation of time-of-use electricity prices is I, the demand response evaluation model that considers the benefits of both residents and the power grid based on the load characteristics and the residential electricity price elasticity matrix includes:

[0094]

[0095]

[0096]

[0097]

[0098] FG=q max -q min ;

[0099]

[0100] Where I' represents the total amount of user consumption before the implementation of time-of-use electricity pricing, N represents the total number of time periods in a day, p' represents the original residential electric vehicle charging price, q i ′ represents the charging power of the original residents’ electric vehicles in period i, I represents the total consumption of users after the implementation of time-of-use electricity prices, and p i represents the charging electricity price of residential electric vehicles in period i after the implementation of time-of-use electricity prices, q i It represents the charging power of residents’ electric vehicles in period i after the implementation of time-of-use electricity price. represents the expected charging capacity of residents’ electric vehicles after the grid implements time-of-use electricity prices, represents the change in electricity price during period i, B represents the social benefits after the implementation of time-of-use electricity price, represents the unit cost of electricity purchased by the power grid from the power generation enterprise during period i, FG represents the peak-to-valley difference, LF represents the load factor, and q max represents the maximum load of residential electric vehicles during the dispatch period, q min Indicates the minimum load of residential electric vehicles during the scheduling period.

[0101] Residential electric vehicle charging and swapping behavior is highly sensitive to charging electricity prices. This means that the amount of electricity charged by residents across the province during each period is influenced by the level of TOU electricity prices. Therefore, the model above establishes the expected amount of charging and swapping for residents' electric vehicles during each period after TOU electricity prices are established. The demand response effect of TOU electricity pricing policies is primarily examined from two perspectives: social benefits and system safety. The social benefits are shown in the model above.

[0102] S3. Based on the demand response evaluation model, a first objective function for maximizing electricity sales revenue and a second objective function for minimizing average charging costs are established, and grid-side constraints corresponding to the first objective function and user-side constraints corresponding to the second objective function are established.

[0103] In order to ensure that the time-of-use electricity price policy is conducive to the recovery of the grid's charging and swapping profits and losses, the first objective function for maximizing electricity sales revenue is:

[0104]

[0105] Where Y represents the revenue from electricity sales.

[0106] Residential electric vehicle users adjust their charging and swapping behaviors based on the time-of-use electricity price policy. Their primary goal is to minimize their average charging cost. Therefore, the second objective function of minimizing the average charging cost is:

[0107]

[0108] Where C represents the average charging cost.

[0109] Under the incentive of peak-valley time-of-use electricity prices, residential users will shift part of the charging load during peak hours to valley hours in order to reduce their electric vehicle charging electricity bills. Therefore, the user expenditure after implementation cannot be higher than the original expenditure. The reasonable setting of peak-valley electricity prices in time-of-use electricity prices can further support demand response and the "win-win" situation of the power grid and residential users. The purpose of promoting peak-valley time-of-use electricity prices is to use electricity prices to encourage users to charge their electric vehicles more in valley and flat periods, reduce peak electricity consumption, and thus achieve the purpose of shaving peaks and filling valleys and making load characteristics more perfect. Therefore, the peak electricity price should be higher than the flat electricity price, and the flat electricity price should be higher than the valley electricity price. In addition, when formulating peak-valley electricity prices, the floating ratio of peak electricity prices and valley electricity prices needs to be controlled within a reasonable data domain to avoid the phenomenon of peak-valley reversal of charging load or insufficient user response, which makes it impossible to achieve the purpose of smoothing the load curve. Therefore, the floating ratio of peak-period electricity prices and valley-period electricity prices is required to be within a certain range. Therefore, the grid-side constraints are:

[0110] I≤I′;

[0111] p g ≤p p≤p f ;

[0112]

[0113]

[0114] Where p g represents the off-peak electricity price for residential electric vehicles, p p represents the electricity price of residential electric vehicles during normal hours, p f Represents the peak-time electricity price for residential electric vehicles.

[0115] The user-side constraints are:

[0116] Y f =p f q f -p′q f ;

[0117] Y g =p g q g -p′q g ;

[0118] Where Y f Indicates the change in power grid benefits during peak hours, Y g Indicates the change in grid efficiency during valley period, q f Indicates the peak period electricity consumption, q g Indicates the electricity consumption during off-peak hours.

[0119] After the implementation of time-of-use electricity prices, in order to ensure that there is no "peak-valley inversion" or all residents concentrate on charging during low electricity price periods, the actual charging amount of residential electric vehicle users should fluctuate within the range allowed by the grid's expected charging amount, that is:

[0120]

[0121] Where α represents a known parameter, which limits the fluctuation range of the actual charging amount of residential electric vehicles after scheduling.

[0122] S4. Based on the grid-side constraints and the user-side constraints, the first objective function and the second objective function are solved using an improved great white shark optimization algorithm to obtain an optimal time-of-use electricity price scheme, where the improved great white shark optimization algorithm is improved using a gray wolf optimization algorithm.

[0123] The great white shark optimization algorithm can effectively solve some optimization problems in continuous search space, but it has problems such as being prone to falling into local optimal solutions and slow convergence speed.

[0124] The processing process of the existing great white shark optimization algorithm is as follows:

[0125] The Great White Shark optimization algorithm is inspired by the navigation and hunting techniques of white sharks. White sharks use their sensory organs to track prey and then use schooling behavior to capture them. This process mainly involves two aspects:

[0126] (1) Move towards the optimal shark through senses, i.e., schooling behavior. In the process of moving towards the prey, the position of each shark is: W i =(w i1 ,w i2 ,...,w id ), where w id Represents the value of the d-th dimension of the current i-th shark.

[0127] (2) Tracking prey by hearing and smell. The mathematical model of movement is:

[0128]

[0129]

[0130]

[0131] Where, represents the position of the i-th shark at the k+1th iteration, represents the position of the i-th shark at the k-th iteration, It means to negate the values ​​of each dimension after ω0 is converted to binary. ω0 means that there is an out-of-bounds dimension in the coordinates of the i-th shark. It consists of two parts, represented by a and b respectively. a represents W t The dimension that exceeds the upper bound u, b represents W i The dimension is smaller than the lower bound l, rand represents a random number, m n Indicates the maximum movement distance limit of the shark. represents the moving speed of the i-th shark in the k-th iteration, f represents the frequency parameter of the wave, which is a fixed constant, and m V Indicates the strategy selection threshold. For the choice of two movement situations, it is necessary to compare the strength of hearing and smell to determine the movement strategy with a smaller m V The value will trigger a local search move, and the larger the m V The value will trigger a global search move, represents the moving speed of the i-th shark in the k+1th iteration, μ represents the shrinkage factor, represents the moving speed of the i-th shark in the k-th iteration, and p1 represents the control right The proportion of the degree of influence of the change, p2 represents the control right The impact ratio of the change, Indicates the global optimal position found, c1 represents the first random number between 0 and 1, the larger the value, the more inclined Play a leading role, represents the optimal position that the i-th shark has visited.

[0132]

[0133]

[0134] Where, represents the speed of the i-th individual in the k+1-th iteration, Indicates the update result based on the next position and speed, r1 represents the first random number between [0,1], r2 represents the second random number between [0,1], r3 represents the third random number between [0,1], s s Represents the control factor, sgn represents the function that determines whether the sign is positive or negative, which is used to change whether the current movement is addition or subtraction.

[0135] When moving towards the optimal shark, a control factor s is set. s , which determines whether to move toward the optimal shark position and whether schooling behavior occurs to update the population position. However, during the iteration process, the control factor increases slowly, iterating from 0 to 0.0005 throughout the entire iteration process. Therefore, the probability of schooling behavior and movement toward the optimal shark in the original algorithm is low.

[0136] The existing gray wolf optimization algorithm is processed as follows:

[0137] In the gray wolf algorithm, there are n gray wolves in a gray wolf population. Each wolf has its own position. The first three wolves with the best fitness function are called α, β, and δ wolves. The remaining wolves update their positions based on the positions of these three wolves. The position of each wolf can be expressed as: X i =(x i1 ,x i2 ,...,x id ), where x id Indicates the d-th dimension value of the position of the i-th wolf.

[0138] D=|C·X p (t)-X(t)|;

[0139] X(t+1)=X p (t)-A·D;

[0140] Where D represents the vector distance from the current wolf position to the α wolf position, which is multiplied by the coefficient C and then applied to the absolute value to ensure the correct direction of the vector. C represents the decreasing linear coefficient, which controls the range and direction of the search. It usually decreases with the increase of the number of iterations to promote convergence. p (t) represents the position of the prey. In the actual execution process of the algorithm, this value is replaced by α, β, and δ wolves. X(t) represents the position of the current wolf, X(t+1) represents the position of the next wolf, and A represents the coefficient vector, including A1, A2, and A3.

[0141] The update strategy of the gray wolf optimization algorithm first updates the positions of the three current optimal wolves to obtain D α 、D β 、D δ , D α Indicates the distance between the current X(t) and α wolf, D β Indicates the distance between the current X(t) and β wolf, D δ The distance between the current X(t) and the δ wolf is obtained using the following formula: X1, X2, and X3. X1 represents the adjustment of the current wolf's position based on the position of the α wolf (the wolf with the highest fitness function value, which can be regarded as the leader), X2 represents the adjustment of the current wolf's position based on the position of the β wolf (the wolf with the second highest fitness function value), and X3 represents the adjustment of the current wolf's position based on the position of the δ wolf (the wolf with the third highest fitness function value). The current population position is updated by X(t+1) = (X1+X2+X3) / 3.

[0142] X1=X α -A1D α ;

[0143] X2=X β -A2D β ;

[0144] X3=X δ -A3D δ ;

[0145] Where, X α Indicates the location of wolf α, X β Indicates the location of β wolf, X δ Indicates the location of the δ wolf.

[0146] The improved great white shark optimization algorithm proposed in the present invention improves the encirclement and suppression scheme of the white shark in the great white shark optimization algorithm by combining the encirclement and suppression strategy of the alpha wolf in the gray wolf optimization algorithm, thereby achieving better local development capabilities.

[0147] In an optional embodiment, before S4, the step further includes:

[0148] A random disturbance term is introduced into the auditory and olfactory movement stages of the great white shark optimization algorithm, and three sharks are introduced into the stage of schooling behavior to update the population position, thereby obtaining an improved great white shark optimization algorithm, wherein the three sharks have the best suitability function.

[0149] Specifically, in the auditory and olfactory movement phase of the Great White Shark optimization algorithm, when rand<m v In this case, a random perturbation term is introduced to enhance the global optimization capability of the White Shark algorithm, specifically:

[0150]

[0151] Where rand 1×d represents the random disturbance term.

[0152] In the stage of schooling behavior, sharks α, β, and δ are introduced to update the population position, specifically:

[0153] X(t+1)==X(t)+0.01×((X1+X2+X3) / 3)

[0154] Please refer to Figure 2 , the second embodiment of the present invention is:

[0155] A time-of-use electricity price determination terminal that considers load characteristics and balanced benefits includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step of the method for determining a time-of-use electricity price that considers load characteristics and balanced benefits in embodiment 1 is implemented.

[0156] In summary, the present invention provides a method and terminal for determining time-of-use electricity prices that take into account load characteristics and balanced benefits. Based on the load characteristics obtained through analysis and the constructed residential electricity price elasticity matrix, a demand response evaluation model that takes into account the benefits of both residents and the power grid is established. Based on the demand response evaluation model, a first objective function that maximizes electricity sales revenue and a second objective function that minimizes average charging costs are established. Grid-side constraints and user-side constraints are established. Based on the grid-side constraints and user-side constraints, the improved great white shark optimization algorithm is used to solve the first objective function and the second objective function to obtain the optimal time-of-use electricity price solution, which dynamically reflects the different seasons and different times through the residential electricity price elasticity matrix. The sensitivity of residents to changes in electricity prices within the time period is taken into account. The demand response evaluation model takes into account the benefits of both residents and the power grid. The two objective functions established based on this can also ensure mutual benefit and win-win results for both supply and demand sides. The time-of-use electricity price scheme solved in this way can effectively alleviate the pressure on the power grid during peak hours, reduce system operating costs, and at the same time improve residents' satisfaction and participation, thereby improving the accuracy of the electricity price scheme determination; in addition, by drawing on the concept of the gray wolf optimization algorithm, random disturbance terms are introduced in the auditory and olfactory movement stages of the great white shark optimization algorithm, and three sharks are introduced to update the population position during the stage of fish school behavior to optimize the convergence speed and global search capability of the great white shark optimization algorithm, ensuring that the solved electricity price scheme is the optimal result.

[0157] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's description and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for determining time-of-use electricity prices taking into account load characteristics and balanced benefits, characterized in that: Including steps: Collecting usage data of residential electric vehicles, and performing load characteristic analysis based on the usage data of residential electric vehicles to obtain load characteristics; Constructing a residential electricity price elasticity matrix, and establishing a demand response evaluation model that considers the benefits of both residents and the power grid based on the load characteristics and the residential electricity price elasticity matrix; Establishing a first objective function for maximizing electricity sales revenue and a second objective function for minimizing average charging costs based on the demand response evaluation model, and establishing grid-side constraints corresponding to the first objective function and user-side constraints corresponding to the second objective function; Solving the first objective function and the second objective function based on the grid-side constraints and the user-side constraints using an improved Great White Shark optimization algorithm to obtain an optimal time-of-use electricity price solution, wherein the improved Great White Shark optimization algorithm is improved by the Grey Wolf optimization algorithm; The residential electricity price elasticity matrix is: ； ； ； Where E represents the residential electricity price elasticity matrix, Indicates that the change in charging cost in time period i affects the electricity demand in time period i, It refers to the response of the change in electricity consumption in time period i to the change in charging cost in time period j, Q i represents the original electricity demand in time period i, P i represents the original charging cost in time period i, P j represents the original charging cost in time period j, represents the change in electricity demand caused by the change in charging cost in time period i, represents the change in charging cost within time period i, represents the change in charging cost within time period j; The establishment of a demand response evaluation model that considers the benefits of both residents and the power grid based on the load characteristics and the residential electricity price elasticity matrix includes: ； ； ； ； ； ； Where I' represents the total amount of user consumption before the implementation of time-of-use electricity pricing, N represents the total number of time periods in a day, p' represents the original residential electric vehicle charging price, q i ′, i=1,2,…,24, represents the charging power of the original residents’ electric vehicles in period i, I represents the total consumption of users after the implementation of time-of-use electricity prices, p i represents the charging electricity price of residential electric vehicles in period i after the implementation of time-of-use electricity prices, q i It represents the charging power of residents’ electric vehicles in period i after the implementation of time-of-use electricity price. , i=1,2,…,24 represents the expected charging amount of residents’ electric vehicles after the grid implements time-of-use electricity pricing, , i=1,2,…,24 represents the change in electricity price during period i, B represents the social benefit after the implementation of time-of-use electricity price, represents the unit cost of electricity purchased by the power grid from the power generation enterprise during period i, FG represents the peak-to-valley difference, LF represents the load factor, and q max represents the maximum load of residential electric vehicles during the dispatch period, q min Indicates the minimum load of residential electric vehicles during the dispatch period; Also includes: A random disturbance term is introduced into the auditory and olfactory movement stages of the great white shark optimization algorithm, and three sharks are introduced into the stage of schooling behavior to update the population position, thereby obtaining an improved great white shark optimization algorithm, wherein the three sharks have the best suitability function.

2. A method for determining time-of-use electricity prices considering load characteristics and balanced benefits according to claim 1, characterized in that: The load characteristics analysis based on the residential electric vehicle usage data includes: Correcting abnormal data in the residential electric vehicle usage data using the quartile method to obtain corrected residential electric vehicle usage data; The load characteristics are analyzed on the corrected residential electric vehicle usage data to obtain load characteristics.

3. The method for determining time-of-use electricity prices considering load characteristics and balanced benefits according to claim 1, characterized in that: The first objective function for maximizing electricity sales revenue is: ； Where Y represents the revenue from electricity sales.

4. The method for determining time-of-use electricity prices considering load characteristics and balanced benefits according to claim 1, characterized in that: The second objective function of minimizing the average charging cost is: ； Where C represents the average charging cost.

5. The method for determining time-of-use electricity prices considering load characteristics and balanced benefits according to claim 1, characterized in that: The grid-side constraints are: ； ； ； ； Where p g represents the off-peak electricity price for residential electric vehicles, p p represents the electricity price of residential electric vehicles during normal hours, p f Represents the peak-time electricity price for residential electric vehicles.

6. A method for determining time-of-use electricity prices considering load characteristics and balanced benefits according to claim 5, characterized in that: The user-side constraints are: ； ； Where Y f Indicates the change in power grid benefits during peak hours, Y g Indicates the change in grid efficiency during valley period, q f Indicates the peak period electricity consumption, q g Indicates the electricity consumption during off-peak hours.

7. A time-of-use electricity price determination terminal that considers load characteristics and balanced benefits, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, each step of the method for determining a time-of-use electricity price considering load characteristics and balancing benefits according to any one of claims 1 to 6 is implemented.

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