An optimization method, device, equipment and storage medium for peak-valley electricity price and time period

Abstract

The present invention discloses an optimization method for peak-valley electricity prices and time periods. The method includes: establishing a two-layer joint optimization model with peak-valley electricity prices and peak-valley time periods as independent variables; generating the number of hours corresponding to the peak-valley time periods, and initializing the generated peak-valley time periods, and starting the iterative calculation of the peak-valley time periods: optimizing the peak-valley electricity prices in the two-layer joint optimization model to obtain the first objective function value of the peak-valley optimized electricity prices, and optimizing the peak-valley time periods in the two-layer joint optimization model to obtain the second objective function value of the peak-valley optimized time periods. When the first objective function value is the same as the second objective function value, record the peak-valley optimized electricity prices, peak-valley optimized time periods and the second objective function. Until the number of iterations reaches the preset number, output the optimization schemes recorded after each iteration; thereby outputting the optimal second objective function value and its corresponding peak-valley electricity prices and peak-valley time periods. The present invention solves the technical problems of unreasonable peak-valley electricity price design and peak-valley time period division in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and in particular, to an optimization method, device, equipment and storage medium for peak-valley electricity prices and time periods. Background Technique

[0002] Existing peak-valley time-of-use electricity price design methods usually first divide peak-valley time periods, and then formulate peak-valley time-of-use electricity prices. The peak-valley time periods are separately divided according to the numerical values of the load curve. For example, by using methods such as fuzzy membership functions, the peak-valley time periods and their corresponding hours are optimized according to the numerical values of the load curve, and then the peak-valley electricity prices are formulated according to the divided peak-valley time periods, or the hours corresponding to the peak, flat, and valley time periods are first set, and then the numerical values of the load curve are sorted, and the load is divided into peak time periods, flat time periods, and valley time periods in descending order; then the peak-valley electricity prices are formulated according to the divided peak-valley time periods.

[0003] However, the existing peak-valley time periods are separately divided only considering the numerical values of the load curve, ignoring the interaction mechanism with peak-valley electricity price design and user response behavior and the impact on the implementation effect of time-of-use electricity prices, resulting in the fact that the effect of the time-of-use electricity price mechanism cannot reach the optimal, and the values of the hours corresponding to the peak-valley time periods completely depend on expert experience or the numerical values of each time period of the load, lacking scientificity.

[0004] Therefore, there is an urgent need for an optimization method that can make the utility of time-of-use electricity prices better and the design of peak-valley electricity prices and the division of peak-valley time periods more scientific and reasonable. Summary of the Invention

[0005] The present invention provides an optimization method, device, equipment and storage medium for peak-valley electricity prices and time periods to solve the technical problems of low utility of time-of-use electricity prices and unreasonable design of peak-valley electricity prices and division of peak-valley time periods in the prior art.

[0006] To solve the above technical problems, an embodiment of the present invention provides an optimization method for peak-valley electricity prices and time periods, including:

[0007] Establish a two-layer joint optimization model with peak-valley electricity prices and peak-valley time periods as independent variables;

[0008] Generate the number of hours corresponding to the peak and valley periods, and initialize the generated peak and valley periods according to the grid electricity load. Start the iterative calculation of the peak-valley electricity price and the peak-valley periods. In each iterative calculation of the peak-valley electricity price and the peak-valley periods, optimize the peak-valley electricity price in the double-layer joint optimization model to obtain the first objective function value of the optimized peak-valley electricity price, and optimize the peak-valley periods in the double-layer joint optimization model to obtain the second objective function value of the optimized peak-valley periods. Thus, when the first objective function value is the same as the second objective function value, record the optimized peak-valley electricity price, the optimized peak-valley periods, and the second objective function as the optimization scheme for the current iteration. Until the number of iterations reaches the preset number, output the optimization schemes recorded after each iteration;

[0009] Compare the second objective function values in all the optimization schemes, and thus output the optimal second objective function value and its corresponding peak-valley electricity price and peak-valley periods.

[0010] As a preferred solution, the establishment of the double-layer joint optimization model with the peak-valley electricity price and the peak-valley periods as independent variables is specifically as follows:

[0011] According to the peak-valley electricity price and the peak-valley periods under the time-of-use electricity price mechanism, using the peak-valley electricity price and the peak-valley periods as independent variables, construct a double-layer joint optimization model with the peak-valley electricity price and the peak-valley periods as independent variables, and impose constraints on the peak-valley electricity price and the peak-valley periods: F x x≥f x ,F y y≥f y ,G x x=g x ,G y y=g y ,U x x+U y y≥u,V x x+V y y=v;where M(x,y,m,i)≥Μ i i=1,2,3,...,N(x,y,n,j)=Π j j=1,2,3,...;

[0012] Among them, w is the objective function value; x is the variable vector of the peak-valley electricity price; y is the variable vector of the peak-valley periods; c x 、c y are the coefficient vectors corresponding to x and y respectively; is the coefficient constant; F x 、F y 、G x 、G y 、U x 、U y 、Vx , V y are both coefficient matrices; f x , f y , g x , g y , u, v, Μ i , Π j are all constant vectors; are all coefficient constants.

[0013] As a preferred solution, the number of hours corresponding to the generated peak and valley periods is generated, and the generated peak and valley periods are initialized according to the power grid load, specifically:

[0014] According to the power grid load curve, the peak periods are selected from the largest to the smallest load until all the hours satisfying the peak periods are obtained, and the valley periods are selected from the smallest to the largest load until all the hours satisfying the valley periods are obtained, and the remaining periods are used as the normal periods, thus completing the initialization of the peak and valley periods.

[0015] As a preferred solution, the first objective function value of the peak and valley optimized electricity price is obtained by optimizing the peak and valley electricity price in the double-layer joint optimization model, specifically:

[0016] According to the initialized peak and valley periods, the peak and valley electricity price in the double-layer joint optimization model is optimized and calculated: F x x ≥ f x , G x x = g x , U x x + U y y ≥ u, V x x + V y y = v; where, M(x, y, m, i) ≥ Μ i i = 1, 2, 3,...,, N(x, y, n, j) = Π j j = 1, 2, 3,...; thus obtaining the first objective function value of the peak and valley optimized electricity price.

[0017] As a preferred solution, the second objective function value of the peak and valley optimized periods is obtained by optimizing the peak and valley periods in the double-layer joint optimization model, specifically:

[0018] According to the peak and valley optimized electricity price, the peak and valley periods in the double-layer joint optimization model are optimized and calculated: F y y ≥ f y , G y y = g y , U x x + U y y ≥ u, V xx + V y y = v; where M(x, y, m, i) ≥ Μ i i = 1, 2, 3,..., N(x, y, n, j) = Π j j = 1, 2, 3,...; thereby obtaining the second objective function value of the peak - valley optimized period.

[0019] As a preferred solution, after optimizing the peak - valley period in the double - layer joint optimization model to obtain the second objective function value of the peak - valley optimized period, it further includes:

[0020] When the first objective function value is different from the second objective function value, re - optimize the peak - valley electricity price and optimize the peak - valley period until the first objective function value obtained from the optimization calculation is equal to the current second objective function value.

[0021] As a preferred solution, comparing the second objective function values in all optimization solutions, and thereby outputting the optimal second objective function value and its corresponding peak - valley electricity price and peak - valley period, specifically:

[0022] Compare the second objective function values in all optimization solutions, obtain the minimum second objective function value, and output the peak - valley electricity price and peak - valley period corresponding to the obtained minimum second objective function value.

[0023] Correspondingly, the present invention also provides an optimization device for peak - valley electricity price and period, including: a modeling module, an optimization module, and an output module;

[0024] The modeling module is used to establish a double - layer joint optimization model with peak - valley electricity price and peak - valley period as independent variables;

[0025] The optimization module is used to generate the number of hours corresponding to the peak - valley period, initialize the generated peak - valley period according to the power grid load, start the iterative calculation of peak - valley electricity price and peak - valley period. In each iterative calculation of peak - valley electricity price and peak - valley period, optimize the peak - valley electricity price in the double - layer joint optimization model to obtain the first objective function value of the peak - valley optimized electricity price, and optimize the peak - valley period in the double - layer joint optimization model to obtain the second objective function value of the peak - valley optimized period. Thus, when the first objective function value is the same as the second objective function value, record the peak - valley optimized electricity price, the peak - valley optimized period, and the second objective function as the optimization solution of the current iteration. Until the number of iterations reaches the preset number, output the optimization solutions recorded after each iteration;

[0026] The output module is used to compare the second objective function values in all optimization solutions, and thereby output the optimal second objective function value and its corresponding peak - valley electricity price and peak - valley period.

[0027] As an optimal solution, the establishment of a two - layer joint optimization model with peak - valley electricity price and peak - valley time period as independent variables is specifically as follows:

[0028] According to the peak - valley electricity price and peak - valley time period under the time - of - use electricity price mechanism, taking the peak - valley electricity price and peak - valley time period as independent variables, construct a two - layer joint optimization model with peak - valley electricity price and peak - valley time period as independent variables, and impose constraints on the peak - valley electricity price and peak - valley time period: F x x≥f x ,F y y≥f y ,G x x=g x ,G y y=g y ,U x x+U y y≥u,V x x+V y y=v;where M(x,y,m,i)≥Μ i i=1,2,3,...,N(x,y,n,j)=Π j j=1,2,3,...;

[0029] Among them, w is the objective function value; x is the variable vector of peak - valley electricity price; y is the variable vector of peak - valley time period; c x 、c y are the coefficient vectors corresponding to x and y respectively; is the coefficient constant; F x 、F y 、G x 、G y 、U x 、U y 、V x 、V y are all coefficient matrices; f x 、f y 、g x 、g y 、u、v、Μ i 、Π j are all constant vectors; are all coefficient constants.

[0030] As an optimal solution, generate the number of hours corresponding to the peak - valley time period, and initialize the generated peak - valley time period according to the power grid load, specifically as follows:

[0031] According to the power consumption load curve of the power grid, take the peak periods in descending order of load until all the hours that meet the peak periods are obtained, and take the valley periods in ascending order of load until all the hours that meet the valley periods are obtained, and take the remaining periods as the normal periods, so as to complete the initialization of the peak-valley periods.

[0032] As a preferred solution, the first objective function value of the peak-valley optimized electricity price obtained by optimizing the peak-valley electricity price in the double-layer joint optimization model is specifically:

[0033] According to the initialized peak-valley periods, optimize and calculate the peak-valley electricity price in the double-layer joint optimization model: F x x≥f x ,G x x=g x ,U x x+U y y≥u,V x x+V y y=v;where M(x,y,m,i)≥Μ i i=1,2,3,...,N(x,y,n,j)=Π j j=1,2,3,...;thus obtaining the first objective function value of the peak-valley optimized electricity price.

[0034] As a preferred solution, the second objective function value of the peak-valley optimized periods obtained by optimizing the peak-valley periods in the double-layer joint optimization model is specifically:

[0035] According to the peak-valley optimized electricity price, optimize and calculate the peak-valley periods in the double-layer joint optimization model: F y y≥f y ,G y y=g y ,U x x+U y y≥u,V x x+V y y=v;where M(x,y,m,i)≥Μ i i=1,2,3,...,N(x,y,n,j)=Π j j=1,2,3,...;thus obtaining the second objective function value of the peak-valley optimized periods.

[0036] As a preferred solution, after obtaining the second objective function value of the peak-valley optimized periods by optimizing the peak-valley periods in the double-layer joint optimization model, it further includes:

[0037] When the first objective function value is different from the second objective function value, re-optimize the peak-valley electricity price and optimize the peak-valley time period until the first objective function value obtained from the optimization calculation is equal to the current second objective function value.

[0038] As a preferred solution, compare the second objective function values in all the optimization solutions, and thus output the optimal second objective function value and its corresponding peak-valley electricity price and peak-valley time period, specifically:

[0039] Compare the second objective function values in all the optimization solutions, obtain the minimum second objective function value, and output the peak-valley electricity price and peak-valley time period corresponding to the obtained minimum second objective function value.

[0040] Correspondingly, the present invention further provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the optimization method of peak-valley electricity price and time period as described in any one of the above.

[0041] Correspondingly, the present invention further provides a computer-readable storage medium. The computer-readable storage medium includes a stored computer program; wherein, when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the optimization method of peak-valley electricity price and time period as described in any one of the above.

[0042] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0043] The technical solution of the present invention can fully consider the interaction mechanism of peak-valley time periods, peak-valley electricity prices, and user response behaviors by establishing a two-layer joint optimization model with peak-valley electricity prices and peak-valley time periods as independent variables, and initialize and generate peak-valley time periods so that the peak-valley time periods exactly correspond to the load sizes, reducing the number of iterations, thereby quickly obtaining the optimal result. Furthermore, perform iterations on the number of hours of peak-valley time periods to obtain the first objective function value and the second objective function value in the iterative calculation, so as to realize the joint optimization of the number of hours of peak-valley time periods, peak-valley electricity prices, and peak-valley time period distributions. Furthermore, after the number of iterations reaches the preset value, compare the second objective function values in the optimization solutions after each iteration, and then output the optimal second objective function value and its corresponding peak-valley electricity price and peak-valley time period, making the time-of-use electricity price utility better and the design of peak-valley electricity prices and the division of peak-valley time periods more scientific and reasonable. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 : is a step flow chart of an optimization method for peak-valley electricity price and time period provided by an embodiment of the present invention;

[0045] Figure 2: Flow chart of the optimization method for the double - layer optimization model of peak - valley time periods and peak - valley electricity prices designed considering the number of peak - valley hours provided by the embodiments of the present invention;

[0046] Figure 3 : Structural schematic diagram of an optimization device for peak - valley electricity prices and time periods provided by the embodiments of the present invention. Detailed implementation manners

[0047] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0048] Embodiment 1

[0049] Please refer to Figure 1 , an optimization method for peak - valley electricity prices and time periods provided by the embodiments of the present invention, including the following steps S101 - S103:

[0050] Step S101: Establish a double - layer joint optimization model with peak - valley electricity prices and peak - valley time periods as independent variables.

[0051] As a preferred solution of this embodiment, the establishment of the double - layer joint optimization model with peak - valley electricity prices and peak - valley time periods as independent variables is specifically:

[0052] According to the peak - valley electricity prices and peak - valley time periods under the time - of - use electricity price mechanism, using the peak - valley electricity prices and peak - valley time periods as independent variables, construct a double - layer joint optimization model with peak - valley electricity prices and peak - valley time periods as independent variables, and impose constraints on the peak - valley electricity prices and peak - valley time periods: G x x = g x G y y = g y U x x+U y y≥u, V x x+V y y = v; where M(x, y, m, i)≥Μ i i = 1, 2, 3,... N(x, y, n, j)=Π j j = 1, 2, 3,...; where w is the objective function value; x is the variable vector of peak - valley electricity prices; y is the variable vector of peak - valley time periods; c x 、c y are the coefficient vectors corresponding to x and y respectively; c xa,yb is the coefficient constant; F x 、Fy 、G x 、G y 、U x 、U y 、V x 、V y are all coefficient matrices; f x 、f y 、g x 、g y 、u, v, Μ i 、Π j are all constant vectors;

[0053]

[0054] are all coefficient constants.

[0055] It should be noted that during the process of constructing the double - layer joint optimization model, the optimization calculation feasibility of the model is not considered first, so as to avoid the influence of multiple and complex parameters on the construction process of the double - layer joint optimization model. Thus, during the modeling process, an optimization model with peak - valley electricity prices and peak - valley periods as independent variables can be quickly established. The objective function value of the model is jointly determined by the peak - valley electricity prices and peak - valley periods, and the peak - valley electricity prices and peak - valley periods are constrained within a certain range.

[0056] It can be understood that by constructing the double - layer joint optimization model of peak - valley electricity prices and peak - valley periods, the interaction mechanism among peak - valley periods, peak - valley electricity prices, and user response behaviors can be fully considered, so that both the peak - valley periods and peak - valley electricity prices can fully consider the user response behaviors and their influence on the objective function, and the design of peak - valley electricity prices and the division of peak - valley periods are more scientific and reasonable.

[0057] Step S102: According to the power grid electricity load, generate the number of hours corresponding to the peak - valley periods, and initialize the generated peak - valley periods, and start the iterative calculation of the peak - valley periods, so that in each iterative calculation of the peak - valley periods, optimize the peak - valley electricity prices in the double - layer joint optimization model to obtain the first objective function value of the peak - valley optimized electricity prices, and optimize the peak - valley periods in the double - layer joint optimization model to obtain the second objective function value of the peak - valley optimized periods. Thus, when the first objective function value is the same as the second objective function value, record the peak - valley optimized electricity prices, the peak - valley optimized periods, and the second objective function as the optimization scheme for the current iteration. Until the number of iterations reaches the preset number, output the optimization schemes recorded after each iteration.

[0058] As a preferred solution of this embodiment, the generating the number of hours corresponding to the peak - valley periods according to the power grid electricity load and initializing the generated peak - valley periods is specifically:

[0059] According to the power grid load curve, the peak periods are taken from the largest to the smallest load until all the hours that meet the peak periods are obtained, and the valley periods are taken from the smallest to the largest load until all the hours that meet the valley periods are obtained, and the remaining periods are used as the normal periods, thus completing the initialization of the peak and valley periods.

[0060] It should be noted that the peak and valley periods of the initial iteration of the double-layer joint optimization model are initialized and generated by the numerical sorting method according to the input peak, normal, and valley hours. Exemplarily, for the peak periods, starting from the largest to the smallest power consumption load for each hour, the peak periods are taken until all the hours that meet the peak periods are obtained. For example, for 24 hours a day, the average power consumption load for each hour can be visually obtained according to the power grid load. Then, the power consumption loads for each hour are sorted from largest to smallest, and the two periods of 10:00 - 14:00 and 18:00 - 21:00 are the peak periods, that is, the number of hours corresponding to the peak periods is 7 hours. For the valley periods, starting from the smallest to the largest power consumption load for each hour, the valley periods are taken until all the hours that meet the valley periods are obtained. For example, the power consumption loads for each hour are sorted from smallest to largest, and the period of 0:00 - 7:00 is the valley period, that is, the number of hours corresponding to the valley period is 7 hours. Then the remaining periods are used as the normal periods. The peak and valley periods initialized and generated in this way fully correspond to the load size, can reduce the number of iterations, and quickly obtain the optimal result.

[0061] It can be understood that since the peak and valley periods initialized and generated by the numerical sorting method or generated according to the membership function only consider the numerical size of the load and do not consider the system situation and load response behavior, etc., the peak and valley periods are further optimized in the lower layer, so that the formulation of the peak and valley electricity prices and the peak and valley periods can consider each other and can fully consider the system situation and load response behavior, etc., making the time-of-use electricity price effect better.

[0062] As a preferred solution of this embodiment, the first objective function value for optimizing the peak and valley electricity prices in the double-layer joint optimization model is specifically:

[0063] According to the initialized peak and valley periods, as well as the transmission cost and generation cost, the peak and valley electricity prices in the double-layer joint optimization model are optimized and calculated: G x x = g x ,U x x + U y y ≥ u,V x x + V y y = v; where, M(x, y, m, i) ≥ Μ i i = 1, 2, 3,...,N(x, y, n, j) = Π jj = 1, 2, 3,...; thus, the first objective function value of the peak-valley optimized electricity price is obtained.

[0064] In this embodiment, since the peak-valley time periods have been divided and the peak-valley time period y is a known quantity, the peak-valley electricity price in the double-layer joint optimization model can be directly optimized and calculated, and the first objective function value w of the peak-valley optimized electricity price x is obtained after optimization. x 。

[0065] As a preferred solution of this embodiment, the second objective function value of the peak-valley optimized time period obtained by optimizing the peak-valley time period in the double-layer joint optimization model is specifically:

[0066] According to the peak-valley optimized electricity price, the peak-valley time period in the double-layer joint optimization model is optimized and calculated: F y y≥f y ,G y y = g y ,U x x + U y y≥u,V x x + V y y = v; where M(x, y, m, i)≥Μ i i = 1, 2, 3,...,N(x, y, n, j) = Π j j = 1, 2, 3,...; thus, the second objective function value of the peak-valley optimized time period is obtained.

[0067] In this embodiment, after the peak-valley optimized electricity price x is obtained through optimization and the peak-valley optimized electricity price x is a known quantity, the peak-valley time period in the double-layer joint optimization model can be directly optimized and calculated, and the second objective function value w of the peak-valley optimized time period y is obtained after optimization. y 。

[0068] It should be noted that before the iterative calculation of the peak-valley time period, the original parameters of the iterative calculation need to be input, including but not limited to data such as load, transmission cost, and generation cost, and input according to the objectives and constraints considered when formulating time-of-use electricity prices by grid enterprises and power sales enterprises, etc.

[0069] As a preferred solution of this embodiment, after the second objective function value of the peak-valley optimized time period obtained by optimizing the peak-valley time period in the double-layer joint optimization model, it further includes:

[0070] When the first objective function value is not the same as the second objective function value, the peak-valley electricity price is re-optimized and calculated, and the peak-valley time period is optimized and calculated until the first objective function value obtained by the optimization calculation is equal to the corresponding current second objective function value.

[0071] In this embodiment, when the first objective function value w x is the same as the second objective function value w y , that is, when w x = w y , end the inner loop of the double-layer optimization of the current peak-valley period and peak-valley electricity prices, and directly record the second objective function value w k = w y , the peak-valley optimized electricity price x k = x, and the peak-valley optimized period y k = y. When the first objective function value w x is not the same as the second objective function value w y , that is, when w x ≠ w y , return to the peak-valley electricity price optimization again, that is, re-optimize and calculate the peak-valley optimized electricity price and the corresponding first objective function value, and re-optimize the peak-valley period, that is, re-optimize and calculate the peak-valley optimized period and the corresponding second objective function value, until the first objective function value obtained by the optimization calculation is equal to the current second objective function value, ensuring the continuous interactive optimization of the peak-valley period and peak-valley electricity prices.

[0072] It should be noted that when the number of iterations has not reached the preset number, return to re-initialize the peak-valley period y, that is, the peak-valley periods under the curves of different grid electricity loads, and then use different peak-valley periods to perform iterative optimization calculations on the first objective function value and the second objective function value, so that the formulation of peak-valley electricity prices and peak-valley periods can consider each other and fully consider the system situation and load response behavior. It can be understood that, optionally, the preset number is the combination number of the peak, flat, and valley period hours acceptable when power grid enterprises and power sales enterprises formulate time-of-use electricity prices (the sum of the peak, flat, and valley period hours is 24h).

[0073] Step S103: Compare the second objective function values in all the optimization schemes, and thus output the optimal second objective function value and its corresponding peak-valley electricity price and peak-valley period.

[0074] As a preferred solution of this embodiment, the comparison of the second objective function values in all the optimization schemes to output the optimal second objective function value and its corresponding peak-valley electricity price and peak-valley period is specifically:

[0075] Compare the second objective function values in all the optimization schemes, obtain the minimum second objective function value, and output the peak-valley electricity price and peak-valley period corresponding to the obtained minimum second objective function value.

[0076] In this embodiment, by comparing the second objective function values in different optimization schemes and outputting the peak-valley electricity price and peak-valley time periods corresponding to the minimum second objective function value, it can be ensured that both the peak-valley electricity price and the time periods of the peak-valley are within the constraint conditions, and it is ensured that the relationship between the peak-valley electricity price and the peak-valley time periods reaches the optimum when the second objective function value is the smallest. Thus, the number of hours corresponding to the optimal peak-valley time periods is found, and no matter what the value of the number of hours of the peak-valley time periods is, the optimal peak-valley time periods can be divided, which is more in line with the supply and demand conditions at each moment of the power system, making the time-of-use electricity price more effective, and the formulation of the peak-valley electricity price and the division of the peak-valley time periods more scientific and reasonable.

[0077] In this embodiment, please refer to Figure 2 , which is a flowchart of the optimization method for the double-layer optimization model of the peak-valley time periods and the peak-valley electricity price designed considering the number of hours of the peak-valley time periods. First, a double-layer joint optimization model of the peak-valley electricity price and the peak-valley time periods is established, the number of loops is set to 0, that is, k = 0, and the original parameters are input. Then, the number of hours corresponding to the peak-valley time periods is generated, k = k + 1, and the peak-valley time periods are initially divided. The peak-valley electricity price is optimized to obtain the peak-valley optimized electricity price x and its objective function value w x , and the peak-valley time periods are optimized to obtain the peak-valley optimized time periods y and its objective function value w y ; judge whether w x is equal to w y . If they are not equal, the optimization of the peak-valley electricity price and the optimization of the peak-valley time periods are carried out again; if they are equal, the objective function value w k = w y , the peak-valley optimized electricity price x k = x, and the peak-valley optimized time periods y k = y are recorded; then, it is further judged whether the number of iterations k ≤ K. If k ≤ K, return to regenerate the number of hours corresponding to the peak-valley time periods to ensure that the number of hours of the peak-valley time periods corresponding to the remaining grid load curves all perform the iterative optimization calculation in the present invention; if k > K, it means that the iterative optimization calculation for all the numbers of hours of the peak-valley time periods has been completed, the objective function values obtained in each loop are output, that is, the optimization results, and the objective function values are compared, and then the optimal result is obtained. The optimal result includes: the optimal objective function value and its corresponding peak-valley electricity price and peak-valley time periods.

[0078] It can be understood that the peak-valley time periods and the peak-valley electricity price are continuously interactively optimized to obtain the optimal peak-valley electricity price and peak-valley time periods under the setting of the number of hours of the peak-valley time periods, making the design of the peak-valley electricity price and the division of the peak-valley time periods more scientific and reasonable.

[0079] Implementing the above embodiments has the following effects:

[0080] The technical solution of the present invention can fully consider the interaction mechanism of peak-valley time periods, peak-valley electricity prices, and user response behaviors by establishing a two-layer joint optimization model with peak-valley electricity prices and peak-valley time periods as independent variables. The peak-valley time periods are initialized to exactly correspond to the load size, reducing the number of iterations, so as to quickly obtain the optimal result. Then, the number of hours of the peak-valley time periods is iterated to obtain the first objective function value and the second objective function value in the iterative calculation, thereby realizing the joint optimization of the number of hours of the peak-valley time periods, peak-valley electricity prices, and peak-valley time period distribution. Furthermore, after the number of iterations reaches the preset value, the second objective function values in the optimization schemes after each iteration are compared, and then the optimal second objective function value and its corresponding peak-valley electricity price and peak-valley time period are output, making the time-of-use electricity price utility better and the peak-valley electricity price design and peak-valley time period division more scientific and reasonable.

[0081] Embodiment 2

[0082] Please refer to Figure 3 , which is an optimization device for peak-valley electricity prices and time periods provided by the present invention, including: a modeling module 201, an optimization module 202, and an output module 203.

[0083] The modeling module 201 is used to establish a two-layer joint optimization model with peak-valley electricity prices and peak-valley time periods as independent variables.

[0084] The optimization module 202 is used to generate the number of hours corresponding to the peak-valley time periods, initialize the generation of the peak-valley time periods, and start the iterative calculation of the peak-valley electricity prices and peak-valley time periods, so that in each iterative calculation of the peak-valley electricity prices and peak-valley time periods, the peak-valley electricity price in the two-layer joint optimization model is optimized to obtain the first objective function value of the peak-valley optimized electricity price, and the peak-valley time period in the two-layer joint optimization model is optimized to obtain the second objective function value of the peak-valley optimized time period. Thus, when the first objective function value is the same as the second objective function value, the peak-valley optimized electricity price, the peak-valley optimized time period, and the second objective function are recorded as the optimization scheme for the current iteration. Until the number of iterations reaches the preset number of times, the optimization schemes recorded after each iteration are output.

[0085] The output module 203 is used to compare the second objective function values in all the optimization schemes, and thus output the optimal second objective function value and its corresponding peak-valley electricity price and peak-valley time period.

[0086] As a preferred solution of this embodiment, the establishment of the two-layer joint optimization model with peak-valley electricity prices and peak-valley time periods as independent variables is specifically as follows:

[0087] According to the peak-valley electricity price and peak-valley time periods under the time-of-use electricity price mechanism, using the peak-valley electricity price and peak-valley time periods as independent variables, construct a two-layer joint optimization model with the peak-valley electricity price and peak-valley time periods as independent variables, and impose constraints on the peak-valley electricity price and peak-valley time periods: F x x ≥ f x ,F y y ≥ f y ,G x x = g x ,G y y = g y ,U x x + U y y ≥ u,V x x + V y y = v; where M(x, y, m, i) ≥ Μ i i = 1, 2, 3,...,N(x, y, n, j) = Π j j = 1, 2, 3,...; where w is the objective function value; x is the variable vector of the peak-valley electricity price; y is the variable vector of the peak-valley time periods; c x 、c y are the coefficient vectors corresponding to x and y respectively; is the coefficient constant; F x 、F y 、G x 、G y 、U x 、U y 、V x 、V y are all coefficient matrices; f x 、f y 、g x 、g y 、u、v、Μ i 、Π j are all constant vectors;

[0088]

[0089] are all coefficient constants.

[0090] As a preferred solution of this embodiment, the method for generating the number of hours corresponding to the peak-valley time periods according to the power grid electricity load and initializing the generated peak-valley time periods is specifically as follows:

[0091] According to the power grid electricity load curve, take the peak time periods by arranging the load from large to small until all the hours satisfying the peak time periods are obtained, and take the valley time periods by arranging the load from small to large until all the hours satisfying the valley time periods are obtained, and use the remaining time periods as the normal time periods, thereby completing the initialization of the peak-valley time periods.

[0092] As a preferred solution of this embodiment, the first objective function value of the peak-valley optimized electricity price obtained by optimizing the peak-valley electricity price in the double-layer joint optimization model is specifically:

[0093] According to the initialized peak-valley periods, as well as the transmission cost and generation cost, optimize and calculate the peak-valley electricity price in the double-layer joint optimization model: F x x≥f x ,G x x=g x ,U x x+U y y≥u,V x x+V y y=v; where, M(x, y, m, i)≥Μ i i = 1, 2, 3,...,N(x, y, n, j)=Π j j = 1, 2, 3,...; thus obtaining the first objective function value of the peak-valley optimized electricity price.

[0094] As a preferred solution of this embodiment, the second objective function value of the peak-valley optimized period obtained by optimizing the peak-valley period in the double-layer joint optimization model is specifically:

[0095] According to the peak-valley optimized electricity price, optimize and calculate the peak-valley period in the double-layer joint optimization model: F y y≥f y ,G y y=g y ,U x x+U y y≥u,V x x+V y y=v; where, M(x, y, m, i)≥Μ i i = 1, 2, 3,...,N(x, y, n, j)=Π j j = 1, 2, 3,...; thus obtaining the second objective function value of the peak-valley optimized period.

[0096] As a preferred solution of this embodiment, after obtaining the second objective function value of the peak-valley optimized period by optimizing the peak-valley period in the double-layer joint optimization model, it further includes:

[0097] When the first objective function value is not the same as the second objective function value, re-optimize and calculate the peak-valley electricity price, and optimize and calculate the peak-valley period until the first objective function value obtained by the optimization calculation is equal to the corresponding current second objective function value.

[0098] As a preferred solution of this embodiment, compare the second objective function values in all the optimization solutions, and then output the optimal second objective function value and its corresponding peak-valley electricity price and peak-valley time period. Specifically:

[0099] Compare the second objective function values in all the optimization solutions to obtain the minimum second objective function value, and output the peak-valley electricity price and peak-valley time period corresponding to the obtained minimum second objective function value.

[0100] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process of the above-described device can refer to the corresponding process in the foregoing method embodiment, and will not be elaborated here.

[0101] Implementing this embodiment has the following effects:

[0102] The technical solution of the present invention can fully consider the interaction mechanism of peak-valley time periods, peak-valley electricity prices, and user response behaviors by establishing a two-layer joint optimization model with peak-valley electricity prices and peak-valley time periods as independent variables, and initialize and generate peak-valley time periods so that the peak-valley time periods completely correspond to the load size, reduce the number of iterations, and thus quickly obtain the optimal result. Furthermore, perform iteration on the number of peak-valley time period hours to obtain the first objective function value and the second objective function value in the iterative calculation, so as to realize the joint optimization of the number of peak-valley time period hours, peak-valley electricity prices, and peak-valley time period distributions. Then, after the number of iterations reaches the preset value, compare the second objective function values in the optimization solutions after each iteration, and then output the optimal second objective function value and its corresponding peak-valley electricity price and peak-valley time period, making the time-of-use electricity price utility better and the peak-valley electricity price design and peak-valley time period division more scientific and reasonable.

[0103] Embodiment Three

[0104] Correspondingly, the present invention also provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the optimization method of peak-valley electricity price and time period described in any one of the above embodiments.

[0105] The terminal device of this embodiment includes: a processor, a memory, and a computer program and computer instructions stored in the memory and executable on the processor. When the processor executes the computer program, it implements each step in the above Embodiment One, such as Figure 1 Steps S101 to S103 shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above device embodiment, such as the modeling module 201.

[0106] Exemplarily, the computer program may be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program in the terminal device. For example, the modeling module 201 is used to establish a two-layer joint optimization model with peak-valley electricity prices and peak-valley periods as independent variables.

[0107] The terminal device may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the schematic diagram is only an example of the terminal device and does not constitute a limitation on the terminal device. It may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, the terminal device may further include input / output devices, network access devices, a bus, etc.

[0108] The so-called processor may be a central processing unit (CPU), or may also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the terminal device and connects various parts of the entire terminal device through various interfaces and lines.

[0109] The memory can be used to store the computer programs and / or modules. By running or executing the computer programs and / or modules stored in the memory, and invoking the data stored in the memory, the processor realizes various functions of the terminal device. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function, etc.; the data storage area can store data created according to the use of the mobile terminal, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0110] Among them, if the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present invention, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be realized. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disc, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0111] Embodiment 4

[0112] Correspondingly, the present invention also provides a computer-readable storage medium, which includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute the optimization method of peak-valley electricity price and time period described in any one of the above embodiments.

[0113] The specific embodiments described above further elaborate on the object, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only for the specific embodiments of the present invention and is not used to limit the protection scope of the present invention. In particular, it is pointed out that for those skilled in the art, any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An optimization method for peak-valley electricity prices and time periods, characterized in that, Including: Establish a two - layer joint optimization model with peak - valley electricity price and peak - valley time period as independent variables; among them, according to the peak - valley electricity price and peak - valley time period under the time - of - use electricity price mechanism, taking the peak - valley electricity price and peak - valley time period as independent variables, construct a two - layer joint optimization model with peak - valley electricity price and peak - valley time period as independent variables, and impose constraints on the peak - valley electricity price and peak - valley time period: , , , , , , ; among them, , ; Among them, is the objective function value; is the variable vector of peak-valley electricity prices; is the variable vector of peak-valley time periods; and are respectively and corresponding coefficient vectors; , is a coefficient constant; and and and and and and and are all coefficient matrices; and and and and and and and are all constant vectors; , ; and and and and and are all coefficient constants; Generating the number of hours corresponding to peak and valley periods, initializing the generated peak and valley periods according to the grid electricity load, and starting the iterative calculation of peak-valley electricity prices and peak-valley periods. In each iterative calculation of peak-valley electricity prices and peak-valley periods, optimizing the peak-valley electricity prices in the double-layer joint optimization model to obtain the first objective function value of the optimized peak-valley electricity prices, and optimizing the peak-valley periods in the double-layer joint optimization model to obtain the second objective function value of the optimized peak-valley periods. Thus, when the first objective function value is the same as the second objective function value, recording the optimized peak-valley electricity prices, the optimized peak-valley periods, and the second objective function as the optimization scheme for the current iteration. Until the number of iterations reaches the preset number, outputting the optimization schemes recorded after each iteration; when the first objective function value is not the same as the second objective function value, re-optimizing the calculation of peak-valley electricity prices and optimizing the calculation of peak-valley periods until the first objective function value obtained from the optimization calculation is equal to the corresponding current second objective function value; Comparing the second objective function values in all the optimization schemes, and thus outputting the optimal second objective function value and its corresponding peak-valley electricity prices and peak-valley periods.

2. The optimization method of peak-valley electricity price and time period according to claim 1, characterized in that, The generating the number of hours corresponding to peak and valley periods and initializing the generated peak and valley periods according to the grid electricity load is specifically: According to the grid electricity load curve, taking the peak periods by arranging the loads from large to small until all the hours satisfying the peak periods are obtained, and taking the valley periods by arranging the loads from small to large until all the hours satisfying the valley periods are obtained, and taking the remaining periods as the normal periods, thereby completing the initialization of the peak and valley periods.

3. The optimization method of peak-valley electricity price and time period according to claim 2, characterized in that The optimizing the peak-valley electricity prices in the double-layer joint optimization model to obtain the first objective function value of the optimized peak-valley electricity prices is specifically: Optimize and calculate the peak-valley electricity price in the double-layer joint optimization model according to the initialized peak-valley periods: , , , , ; among which, , ; thus obtaining the first objective function value of the peak-valley optimized electricity price.

4. The optimization method of peak-valley electricity price and time period according to claim 2, characterized in that The optimizing the peak-valley periods in the double-layer joint optimization model to obtain the second objective function value of the optimized peak-valley periods is specifically: Optimize and calculate the peak-valley periods in the double-layer joint optimization model according to the peak-valley optimized electricity price: , , , , ; among which, , ; thereby obtaining the second objective function value of the peak-valley optimized period.

5. The optimization method of peak-valley electricity price and time period according to claim 3, characterized in that, The comparing the second objective function values in all the optimization schemes and thus outputting the optimal second objective function value and its corresponding peak-valley electricity prices and peak-valley periods is specifically: Comparing the second objective function values in all the optimization schemes, obtaining the minimum second objective function value, and outputting the peak-valley electricity prices and peak-valley periods corresponding to the obtained minimum second objective function value.

6. An optimization device for peak-valley electricity prices and time periods, characterized in that, Including: A modeling module, an optimization module, and an output module; The modeling module is used to establish a two-layer joint optimization model with peak-valley electricity prices and peak-valley periods as independent variables. Among them, according to the peak-valley electricity prices and peak-valley periods under the time-of-use electricity price mechanism, taking the peak-valley electricity prices and peak-valley periods as independent variables, a two-layer joint optimization model with peak-valley electricity prices and peak-valley periods as independent variables is constructed, and constraints are imposed on the peak-valley electricity prices and peak-valley periods: , , , , , , ; Among them, , ; Among them, is the objective function value; is the variable vector of peak-valley electricity prices; is the variable vector of peak-valley time periods; , are respectively , corresponding coefficient vectors; , is the coefficient constant; , , , , , , , are all coefficient matrices; , , , , , , , are all constant vectors; , ; , , , , , are all coefficient constants; The optimization module is used to generate the number of hours corresponding to peak and valley periods, initialize the generated peak and valley periods according to the power grid electricity load, and start the iterative calculation of peak-valley electricity prices and peak-valley periods. In each iterative calculation of peak-valley electricity prices and peak-valley periods, the peak-valley electricity prices in the double-layer joint optimization model are optimized to obtain the first objective function value of the peak-valley optimized electricity price, and the peak-valley periods in the double-layer joint optimization model are optimized to obtain the second objective function value of the peak-valley optimized period. Thus, when the first objective function value is the same as the second objective function value, the peak-valley optimized electricity price, the peak-valley optimized period, and the second objective function are recorded as the optimization scheme for the current iteration. Until the number of iterations reaches the preset number, the optimization schemes recorded after each iteration are output; when the first objective function value is not the same as the second objective function value, the peak-valley electricity price is re-optimized and the peak-valley period is re-optimized until the first objective function value obtained from the optimization calculation is equal to the corresponding second objective function value for the current time; The output module is used to compare the second objective function values in all the optimization schemes, so as to output the optimal second objective function value and its corresponding peak-valley electricity price and peak-valley period.

7. A terminal device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the optimization method of peak-valley electricity price and period as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program; wherein, when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the optimization method of peak-valley electricity price and period as described in any one of claims 1-5.

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