Multi-Electricity-Price Demand Response Scheduling System for Peak Shifting Phenomenon Based on Genetic Algorithm

Abstract

An embodiment of the present invention relates to the technical field of power systems, and discloses a multi - electricity - price demand - response pricing system for peak - shifting phenomena based on a genetic algorithm. The system includes: a electricity - price - load optimization module, which is used to determine the optimal equipment load and the optimal electricity consumption cost based on the user equipment information of the auxiliary user group and the optimal multi - electricity - price curve group determined by the multi - pricing formulation module; a power grid scheduling module, which is used to determine the optimal power generation cost based on the total power grid load and the basic information of the power grid; a multi - pricing formulation module, which is used to calculate the optimal multi - electricity - price curve group by using the genetic algorithm based on the optimal equipment load, the optimal electricity consumption cost, and the optimal power generation cost, and send the optimal multi - electricity - price curve group to the auxiliary user group. The present invention solves for the optimal electricity - price combination through the genetic algorithm, and this electricity - price combination can ultimately effectively achieve peak shaving and valley filling, and improve the social energy efficiency level.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and particularly relates to a multi - electricity - price demand - response scheduling system for peak - shifting phenomena based on a genetic algorithm. Background Art

[0002] Smart grid is the future development direction of the construction of power systems in various countries. Demand response is one of the main topics in the development of smart grid, which plays a role in new - energy power generation, resource - allocation optimization, scientific load management and other fields, enabling demand - side management to play a greater role. In China, demand - side management is one of the important development directions in the energy and power industry. The essence of demand - side response is to change the user's own power - consumption pattern through electricity prices or incentives, and actively participate in the energy interaction of grid operation, so as to achieve the purposes of peak - shaving and valley - filling, improving the operation safety and stability of the power system, and improving the overall social operation efficiency.

[0003] Demand Response (DR for short) is the abbreviation of power demand response. It refers to the short - term behavior that when the power wholesale - market price rises or the system reliability is threatened, after receiving a direct - compensation notice of induced load reduction or a power - price increase signal sent by the power - supply side, power users change their inherent habitual power - consumption patterns to reduce or shift the power load in a certain period to respond to the power supply, thus ensuring the stability of the power grid and suppressing the rise of electricity prices. It is one of the solutions for demand - side management (DSM). Demand response can be divided into two categories: the first is time - based demand response; the second is incentive - based demand response. Incentive - based demand response works according to time. Through advanced contract rules, the customer's consumption behavior will change temporarily. In response to the load change, the power company gives corresponding compensation or punishment. Time - based demand response is also called price - based demand response, which means that users adjust their power demand according to the received price signals, including time - of - use price response, real - time price response, peak - cutting price response, etc.

[0004] Currently, most cities in China adopt peak - valley electricity prices. For large - industrial electricity users, the peak - valley electricity prices are as follows: the peak period is from 8:00 to 12:00 and from 17:00 to 21:00; the flat period is from 12:00 to 17:00 and from 21:00 to 24:00; the valley period is from 0:00 to 8:00, as Figure 1As shown in the figure. For cities with a relatively large proportion of flexible large users (referring to users with large electricity consumption and whose electricity load can be flexibly transferred during off-peak electricity price periods, generally large industrial users), a "peak shifting phenomenon" will occur. Flexible large users will avoid starting production equipment during peak electricity consumption periods and thus prefer to start equipment during off-peak electricity price periods. However, the duration of off-peak periods often cannot meet production requirements, so they will continue to choose to start during flat electricity price periods. When the loads of most large users in the city shift to off-peak periods, the peak shifting phenomenon will occur. That is, the peak electricity consumption period always moves with the off-peak electricity price period, weakening the effect of peak shaving and valley filling. Summary of the Invention

[0005] To solve the problem that the "peak shaving and valley filling" effect is weakened due to the same time-of-use electricity price for all users in the region under the traditional time-of-use electricity price model, a multi-electricity price demand response scheduling mechanism for the peak shifting phenomenon in large industrial cities is provided.

[0006] The embodiment of the present invention discloses a multi-electricity price demand response scheduling system for the peak shifting phenomenon based on a genetic algorithm, including:

[0007] An electricity price-load optimization module, configured to determine the optimal equipment load and the optimal electricity consumption cost based on the user equipment information of the auxiliary user group and the optimal multi-electricity price curve group determined by the multi-electricity price formulation module;

[0008] A power grid scheduling module, configured to determine the optimal power generation cost based on the total power grid load and the basic information of the power grid;

[0009] A multi-electricity price formulation module, configured to calculate the optimal multi-electricity price curve group by using a genetic algorithm based on the optimal equipment load, the optimal electricity consumption cost, and the optimal power generation cost, and send the optimal multi-electricity price curve group to the auxiliary user group.

[0010] As an optional implementation manner, the multi-electricity price formulation module includes a first objective function unit, a first index constraint unit, and a first algorithm optimization unit; the electricity price-load optimization module includes a second objective function unit, a second index constraint unit, and a second algorithm optimization unit; the power grid scheduling module includes a third objective function unit, a third index constraint unit, and a third algorithm optimization unit; where:

[0011] The second objective function unit is configured to construct a second objective function model based on the optimal multi-electricity price curve group output by the multi-electricity price formulation module; the second index constraint unit is configured to construct a second index constraint model according to the user equipment information; the second algorithm optimization unit is configured to calculate the optimal equipment load and the optimal electricity consumption cost according to the second objective function model and the second index constraint model;

[0012] The third objective function unit is used to construct a third objective function model; the third index constraint unit is used to construct a third index constraint model according to the total grid load and grid basic information; the third algorithm optimization unit is used to calculate the optimal generation cost according to the third objective function model and the third index constraint model.

[0013] The first objective function unit is used to construct a first objective function model based on the optimal equipment load, the optimal electricity consumption cost, and the optimal generation cost; the first index constraint unit is used to construct a first index constraint model according to the optimal equipment load and the optimal electricity consumption cost; the first algorithm optimization unit is used to calculate the optimal multi-tariff curve group according to the first objective function model and the first index constraint model by using a genetic algorithm.

[0014] As an optional implementation manner, the second objective function unit is used to construct a second objective function model based on the optimal equipment load, the optimal electricity consumption cost, and the optimal generation cost, including:

[0015] Construct a second objective function model:

[0016]

[0017] Where:

[0018]

[0019] In the formula: Bill and Obj1(MS) are the total electricity bills of all auxiliary user groups; NU is the number of users in the auxiliary user group; NT is the number of optimized minimum time periods; NDev is the number of transferable devices of all users in the auxiliary user group; e x =[1,…,1] represents a row vector with all 1s in 1 row and x columns; P is the tariff curve group of the auxiliary user group, and p k,j is the electricity price of the kth user in the jth time period; MS is the start-up state of the transferable device, where ms i,j represents the start-up state of the ith device in the jth time period. If it is 1, the device starts; otherwise, the device is turned off; DP represents the device power, and dp i represents the power of the ith device; UL is the total load of non-transferable devices, and ul k,j represents the total load of non-transferable devices of the kth user in the jth time period; the element in the kth row and jth column of SCA is 1, indicating that the jth device in MS belongs to the kth user, otherwise it is 0.

[0020] The second index constraint unit is used to construct a second index constraint model according to the user equipment information, including:

[0021] Use the total startup time as the second index to constrain the second constraint function of the model:

[0022] The total daily power consumption duration of each device is equal to the time required for the user to complete a certain production task:

[0023]

[0024] In the formula: ms i,j is the power consumption duration of the i-th device in the j-th time period, and wt i is the total startup time required for the i-th device;

[0025] Use the startup status as the second index to constrain the second constraint function of the model:

[0026] ms i,j = 1 or 0 (4)

[0027] In the formula: it means that the startup status of the i-th device in the j-th time period can only be startup or shutdown;

[0028] Use the non-transferable constraint as the second index to constrain the second constraint function of the model:

[0029] Assume that the i-th device needs to be continuously started or started at a specific time, then the i-th device is non-transferable; regard the load of the i-th device as a constant, that is, UL in formula (1), and it does not participate in the optimization process.

[0030] As an optional implementation manner, the second algorithm optimization unit is used to calculate the optimal device load and the optimal power consumption cost according to the second objective function model and the second index constraint model, including:

[0031] Express the startup status of the i-th device in the first time period in terms of the total startup time and the startup status of the remaining time periods, that is, obtain formula (5) according to formula (3):

[0032]

[0033] where, ms i,1 is the startup status of the i-th device in the first time period, and tms i,j is the startup status of the i-th device in the j-th time period;

[0034] Express MS linearly through TMS, where the first column elements of TMS are all 0, and the other elements are equal to the corresponding elements of MS, as shown in formula (6); therefore, the optimization variable of the first objective function model in formula (1) changes from MS to TMS:

[0035]

[0036] Discretize formula (4):

[0037] The second constraint function of formula (4) represents ms i,j as a 0-1 discrete variable. Based on the idea of step-by-step optimization, convert ms in formula (4) i,j from a 0-1 variable to a continuous variable and optimize it in two steps:

[0038] First, introduce the following three auxiliary penalty functions as shown in formula (7); where the parameter α is as large as possible, and the parameters β and γ are adjusted according to the actual situation; the auxiliary penalty function f is used to handle inequality constraints, and the inequality constraints are added to the objective function in the form of penalty terms to convert it into an unconstrained optimization; the processing method for inequality constraints in the form of c1 ≤ x ≤ c2 is as follows: split the inequality constraint form of c1 ≤ x ≤ c2 into two inequalities x ≤ c2 and c1 ≤ x, where x is the variable and c1, c2 are constants; then, with the help of the function f, write x ≤ c2 and c1 ≤ x as f(d·(x - c2)) and f(-d·(x + c1)) respectively and add them to the objective function, where k is a parameter and its size is adjusted according to actual applications:

[0039]

[0040] First-step optimization:

[0041] Rewrite formula (4) as shown in formula (8), and let ms i,j always iterate and update within the range of 0 to 1 during optimization; use the function f in formula (7) to respectively impose penalty terms on ms i,j ≤ 1 and 0 ≤ ms i,j as shown in formulas (9) and (10) respectively:

[0042] 0 ≤ ms i,j ≤ 1 (8)

[0043]

[0044] Therefore, the first objective function model of the first-step optimization is as shown in formula (11) below; since ms i,j is linearly expressed by tms i,j and Obj1, l dr1 , l dr2 are also expressed by tms i,j , the linear relationship between ms i,j and tms i,j is as shown in formulas (5) and (6); by finding the partial derivative of PObj1 with respect to tms i,j and iteratively optimizing through the gradient method until convergence, the first-step optimization ends:

[0045] ​

[0046] The second-step optimization:

[0047] After the first-step optimization is completed, an additional penalty term is added based on formula (11). This penalty term makes the ms staying between 0 and 1 i,j continue to iterate in the direction of 0 or 1. The penalty function is shown in formula (12). For the sake of convenient expression, a temporary variable r is introduced i,j ; The second objective function model after the second-step optimization is shown in formula (13):

[0048]

[0049] The solution TMS that minimizes PObj1 is obtained by the gradient method as the optimal solution; thus, the optimal device startup state MS is obtained.

[0050] As an alternative implementation, the third objective function unit is used to construct a third objective function model, including:

[0051] Construct a third objective function model based on minimizing the total power generation cost:

[0052]

[0053] In the formula: Cost is the power generation cost; NG is the number of generating units; a i , b i , c i are the coal consumption curve parameters of the i-th unit; g i,j represents the power generation power of the i-th unit in the j-th time period, and Obj2(G) is the minimum total power generation cost;

[0054] The third index constraint unit is used to construct a third index constraint model according to the total grid load and grid basic information, including:

[0055] Use the power balance constraint as the third-one constraint function of the third index constraint model, as shown in formula (15):

[0056]

[0057] In the formula: ND is the number of the total grid load; D is the total grid load, and d i,j represents the power consumption power of the i-th load in the j-th time period;

[0058] Use the maximum / minimum power generation power constraint of the unit as the third-two constraint function of the third index constraint model, as shown in formula (16):

[0059] g min,i ≤g i,j ≤gmax,i (16)

[0060] Where: g min,i and g max,i represent the minimum and maximum power generation of the i-th unit respectively, and g i,j represents the power generation of the i-th unit at the j-th time period;

[0061] Use the unit ramp power constraint as the third constraint function of the third index constraint model, as shown in formula (17):

[0062]

[0063] Where: R max,i is the maximum ramp power of the i-th unit;

[0064] Use the line power flow constraint as the third-fourth constraint function of the third index constraint model, as shown in formulas (18) and (19):

[0065] -pl max,i ≤ pl i,j ≤ pl max,i (18)

[0066]

[0067] Where: pl i,j represents the power flow through the i-th line at the j-th time period; pl max,i represents the maximum power flow allowed through the i-th line; PL is the line power flow matrix; SF is the transfer factor matrix; XB is the admittance matrix, and xb i represents the admittance of the i-th line; KL is the incidence matrix of the line; KG is the incidence matrix of the generator set; KD is the incidence matrix of the load.

[0068] As an alternative implementation, the third algorithm optimization unit is used to calculate the optimal power generation cost according to the third objective function model and the third index constraint model, including:

[0069] Express the power generation of the first unit of the device by subtracting the power generation of the remaining units from the total load power, that is, rewrite formula (15) as formula (20), G can be linearly expressed by TG, where the first row elements of TG are all 0, and other elements are equal to the corresponding elements of G, as shown in formula (21), and change the optimization variable of the third objective function model in formula (14) from G to TG:

[0070]

[0071] The third two constraint functions, the third three constraint functions, and the third four constraint functions are all inequality constraints. With the help of the auxiliary penalty function f, the inequality constraints in equations (16), (17), and (18) are respectively written as the penalty terms in formulas (22)-(27), and each penalty term is added to the third objective function model in formula (14), as shown in formula (28):

[0072]

[0073] Since G can be represented by TG, formulas (22)-(28) are also functions with TG as the independent variable; therefore, by finding the partial derivative of PObj2 with respect to TG in formula (28), it can be quickly solved by the gradient method.

[0074] As an alternative implementation, the first objective function unit is used to construct a first objective function model based on the optimal equipment load, the optimal electricity consumption cost, and the optimal power generation cost, including:

[0075] The multi-tariff formulation module is used to find the tariff curves of a group of auxiliary user groups. The users in the auxiliary user groups obtain the optimal equipment start-up state MS after being optimized by the tariff-load optimization module according to their respective tariff curves, and obtain the total electricity bill Bill of the auxiliary user groups, and then calculate the total load D; as shown in formula (29); after calculating the total load D, the power grid obtains the optimal power generation cost unit power generation power combination G of the units through the optimization of the power grid dispatching module according to the total load D, and obtains the power generation cost Cost of the day; by adjusting the tariff, the peak shaving and valley filling are realized, thereby reducing the power generation cost Cost; therefore, Bill and Cost respectively correspond to the income and expenditure of the power grid and are regarded as the variable costs of the power grid; therefore, the first objective function model is as shown in formula (30), minimizing the variable costs of the power grid; that is, a part of the increased income from peak shaving and valley filling is distributed to the auxiliary user groups to meet their fee reduction standards, and the rest belongs to the power grid:

[0076]

[0077] Construct the first objective function model based on the lowest variable cost of the power grid:

[0078]

[0079] In the formula: BL is the total load of other users excluding the auxiliary user groups from the load D, that is, the base load; bl i,j represents the base load size of the i-th total load in the j-th time period; ML is the total load of the transferable equipment of each user in the auxiliary user group, ml i,j represents the total load of the transferable equipment of the i-th user in the auxiliary user group in the j-th time period; the element in the i-th row and j-th column of KM represents that the j-th auxiliary user belongs to the i-th total load.

[0080] As an alternative implementation, the first index constraint unit is configured to construct a first index constraint model based on the optimal device load and the optimal electricity cost, including:

[0081] Based on the percentage constraint of the reduction of the electricity bill of the users in the auxiliary user group as the first constraint function of the first index constraint model, as shown in formula (31):

[0082]

[0083] In the formula: bill i represents the minimum electricity cost of the users in the i-th auxiliary user group under the new electricity price; bill init,i represents the minimum electricity cost of the users in the i-th auxiliary user group under the original electricity price; δ i is the percentage of fee reduction negotiated by the users in the i-th auxiliary user group with the power grid, and bill init,i ·δ i is the target electricity bill of the users in the i-th auxiliary user group;

[0084] Based on the maximum / minimum value constraint of the electricity price as the second constraint function of the first index constraint model, as shown in formula (32):

[0085] p min ≤p i,j ≤p max (32)

[0086] In the formula: p min is the minimum value allowed for the electricity price, p max is the maximum value allowed for the electricity price, p i,j is the electricity price of the users in the i-th auxiliary user group at the j-th time period.

[0087] As an alternative implementation, the first algorithm optimization unit is configured to calculate the optimal device load and the optimal electricity cost by using a genetic algorithm according to the first objective function model and the first index constraint model, including:

[0088] Since the first objective function model of formula (30) is not a function of the electricity price P, it cannot be solved by the gradient method; Bill and Cost in formula (30) correspond to the minimum electricity bill of the user and the minimum power generation cost of the power grid respectively, and are obtained through the electricity price-load optimization module and the power grid scheduling module respectively; therefore, the first objective function model, that is, formula (30), is solved by means of a genetic algorithm:

[0089] The first constraint function of formula (31) uses the auxiliary penalty function f to rewrite formula (31) as the penalty term of formula (33), and adds this penalty term to the first objective function model of formula (30), which is the objective function of the genetic algorithm, as shown in formula (34):

[0090]

[0091] For the second constraint function of formula (32), it is made to be satisfied during the initialization of the genetic algorithm population; and after mutation, it is judged through the program whether it satisfies the second constraint function of formula (32), otherwise re - mutation is carried out;

[0092] The core task of the multi - electricity - price demand response scheduling system is to achieve peak shaving and valley filling to the greatest extent and reduce the power generation cost to the greatest extent; therefore, the objective function of the genetic algorithm in formula (34) is rewritten in the form of formula (35); where σ and τ are small constant coefficients; when the genetic algorithm progresses to the later stage, Obj2 is close to the optimal value, and thereafter, when selecting individuals, the difference between Obj1 and l p1 among individuals plays a decisive role:

[0093]

[0094] The solution steps of the genetic algorithm include:

[0095] 1) Population initialization:

[0096] The electricity price P is the solution variable of the genetic algorithm, and the gene of each individual is P, obtaining a matrix of size NU×NT through random numbers; and when initializing the electricity price P, it should satisfy the second constraint function of formula (32);

[0097] 2) Fitness calculation:

[0098] According to the electricity price matrix P of each individual, through the electricity price - load optimization module, the optimal start - up state MS of each individual is obtained, and Bill is calculated; then the total load D is calculated according to MS; then through the power grid scheduling module, the optimal power generation cost Cost of the power grid is calculated; finally, formula (35) is calculated and the reciprocal is taken respectively, as shown in formula (36):

[0099] fit i = 1 / PGCost i (36)

[0100] In the formula: fit i is the fitness of the i - th individual, and the higher the value of fit i , the more excellent the i - th individual represents;

[0101] 3) Natural selection:

[0102] First, directly select the NF individuals with the highest fitness, and call them elite individuals; then the remaining individuals participate in roulette wheel selection, and the higher the fitness, the higher the survival rate. The selected individuals are used as the parental generation; before participating in roulette wheel selection, their fitness values are all subtracted by the minimum fitness value of the population, as shown in formula (37), and tfit i is used to represent the individuals participating in roulette wheel selection, where υ is a constant parameter that is as close to 1 as possible but less than 1;

[0103] tfit i = fit i - υ·min{fit1, fit2, …, fit N} (37)

[0104] 4) Crossover inheritance:

[0105] When generating offspring, there is a certain probability that the parental generation crosses gene information, or directly replicates to obtain offspring; when crossing, only a certain row of genes can be selected from any two parental generations for crossing, as shown in formula (38); in the formula, P i m represents the i-th row gene of the m-th individual in the parental generation;

[0106]

[0107] 5) Gene mutation:

[0108] Each offspring has a certain probability of mutating, and the number and position of the mutated genes are randomly selected; after mutation, check whether the first and second constraint functions of formula (32) are satisfied; if not, re-mutate until satisfied.

[0109] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0110] The present invention screens out high-component flexible users, who are characterized by large electricity consumption, sensitivity to electricity price changes, and a large number of devices that can be flexibly transferred. Among the screened flexible users, through negotiation on profit distribution with the power grid company, the users participating in the mechanism proposed by the present invention are finally determined, and are called auxiliary users. The present invention constructs an electricity price-load optimization module and a power grid scheduling module for auxiliary users, and at the same time gives an optimization algorithm. Then, a multi-electricity price formulation module model of the electricity price mechanism of the present invention is established. Combining the results output by the electricity price-load optimization module and the power grid scheduling module, the optimal electricity price combination is solved through a genetic algorithm. This electricity price can finally effectively achieve peak shaving and valley filling, improve the social energy efficiency level, and bring more profits to both auxiliary users and the power grid company, achieving a win-win situation. Description of the Drawings

[0111] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings required in the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0112] Figure 1 It is the structural schematic diagram of a multi - electricity - price demand response scheduling system for peak - shifting phenomena based on genetic algorithms disclosed in the embodiments of the present invention;

[0113] Figure 2 It is the structural schematic diagram of the multi - electricity - price formulation module disclosed in the embodiments of the present invention;

[0114] Figure 3 It is the structural schematic diagram of the electricity - price - load optimization module disclosed in the embodiments of the present invention;

[0115] Figure 4 It is the structural schematic diagram of the power grid scheduling module disclosed in the embodiments of the present invention;

[0116] Figure 5 It is the flow chart of the genetic algorithm of the first algorithm optimization unit in total disclosed in the embodiments of the present invention. Specific Embodiments

[0117] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0118] It should be noted that the terms "first", "second", "third", "fourth", etc. in the description and claims of the present invention are used to distinguish different objects, rather than to describe a specific order. The terms "including" and "having" in the embodiments of the present invention and any of their variations are intended to cover non - exclusive inclusion. Exemplarily, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0119] Embodiment

[0120] Please refer to Figure 1 , Figure 1 It is the structural schematic diagram of a multi - electricity - price demand response scheduling system for peak - shifting phenomena based on genetic algorithms disclosed in the embodiments of the present invention. As Figure 1As shown in the figure, the multi-tariff demand response scheduling system for peak shifting based on genetic algorithm includes: a multi-tariff formulation module 1, a tariff-load optimization module 2, a power grid scheduling module 3, and an auxiliary user group 4; among which: the multi-tariff formulation module 1 is connected to the tariff-load optimization module 2; the multi-tariff formulation module 1 is connected to the power grid scheduling module 3; the multi-tariff formulation module 1 is connected to the auxiliary user group 4; the tariff-load optimization module 2 is connected to the auxiliary user group 4. The auxiliary user group specifically refers to the high-component flexible user group that has been screened and participated in the mechanism proposed in the present invention after negotiating the benefit distribution with the power grid. Their characteristics include huge electricity consumption and a large amount of load that can be flexibly transferred along with the low-tariff period.

[0121] The main functions of each part are as follows: The tariff-load optimization module 2 is used to determine the optimal equipment load and the optimal electricity consumption cost based on the user equipment information of the auxiliary user group and the optimal multi-tariff curve group determined by the multi-tariff formulation module. The power grid scheduling module 3 is used to determine the optimal power generation cost based on the total power grid load and the basic information of the power grid; the multi-tariff formulation module 1 is used to calculate the optimal multi-tariff curve group by using the genetic algorithm based on the optimal equipment load, the optimal electricity consumption cost, and the optimal power generation cost, and send the optimal multi-tariff curve group to the auxiliary user group.

[0122] As Figure 2 shown in the figure, the multi-tariff formulation module 1 includes a first objective function unit 11, a first index constraint unit 12, and a first optimization algorithm unit 13. Among which: one input end of the first objective function unit 11 is connected to the output end of the tariff-load optimization module 2, and the other input end of the first objective function unit 11 is connected to the output end of the power grid scheduling module 3, and is used to construct a first objective function model based on the optimal equipment load, the optimal electricity consumption cost, and the optimal power generation cost. The input end of the first index constraint unit 12 is connected to the output end of the tariff-load optimization module 2, and is used to construct a first index constraint model according to the optimal equipment load and the optimal electricity consumption cost. The two input ends of the first optimization algorithm unit 13 are respectively connected to the output end of the first objective function unit 11 and the output end of the first index constraint unit 12, and are used to calculate the optimal multi-tariff curve group by using the genetic algorithm according to the first objective function model and the first index constraint model.

[0123] As Figure 3As shown, the electricity price - load optimization module 2 includes a second objective function unit 21, a second index constraint unit 22, and a second algorithm optimization unit 23. Among them: The input end of the second objective function unit 21 is connected to the output end of the multi - electricity - price formulation module 1, and is used to construct a second objective function model based on the optimal multi - electricity - price curve group output by the multi - electricity - price formulation module. The second index constraint unit is used to construct a second index constraint model according to the user equipment information. The two input ends of the second algorithm optimization unit 23 are respectively connected to the output end of the second objective function unit 21 and the output end of the second index constraint unit 22, and are used to calculate the optimal equipment load and the optimal electricity cost according to the second objective function model and the second index constraint model.

[0124] As Figure 4 shown, the power grid scheduling module 3 includes a third objective function unit 31, a third index constraint unit 32, and a third algorithm optimization unit 33. Among them: The third objective function unit is used to construct a third objective function model. The input end of the third index constraint unit 32 is connected to the output end of the electricity price - load optimization module 2, and is used to construct a third index constraint model according to the total power grid load and the basic power grid information; The two input ends of the third algorithm optimization unit 33 are respectively connected to the output end of the third objective function unit 31 and the output end of the third index constraint unit 32, and are used to calculate the optimal power generation cost according to the third objective function model and the third index constraint model.

[0125] In the above - mentioned solution, for the electricity price - load optimization module 2, it receives the electricity - price group output by the multi - electricity - price formulation module, and according to the equipment parameters, the second objective function unit 21 and the second index constraint unit 22 establish models and calculate through the second algorithm optimization unit 23 to output the optimal compliance behavior and the optimal electricity cost of each auxiliary user group 4 respectively.

[0126] In the above - mentioned solution, for the power grid scheduling module 3, it receives the optimal load behavior output by the electricity price - load optimization module 2, calculates the total power grid load, and according to the grid structure information and the unit information, the third objective function unit 31 and the third index constraint unit 32 are established and calculated through the third algorithm optimization unit 33 to calculate the optimal power generation cost.

[0127] In the above - mentioned solution, for the multi - electricity - price formulation module 1, it receives the optimal electricity cost output by the electricity price - load optimization module 2 and the optimal power generation cost output by the power grid scheduling module 3, establishes the first objective function unit 11 and the first index constraint unit 12 and calculates through the first optimization algorithm unit 13 to calculate the optimal electricity - price combination.

[0128] In the above solution, first, high-component flexible users who meet the electricity price mechanism proposed in the present invention are selected as auxiliary users, and this group of auxiliary users mainly plays a role in peak shaving. For a multi-electricity-price demand response pricing mechanism for the phenomenon of peak shifting in large industrial cities: First, the multi-electricity-price formulation module outputs a group of electricity prices, and this group of electricity prices is input to the second objective function unit 21. The second index constraint unit 22 is responsible for collecting user equipment information, such as equipment power, total equipment startup time, whether the equipment can be transferred, etc. And through the second algorithm optimization unit 23, the optimal load behavior and the optimal electricity consumption cost are obtained. Among them, the optimal electricity consumption behavior is input to the third index constraint unit 32. The third index constraint unit 32 simultaneously collects grid information and unit information, and combines with the third objective function unit 31. Through the third algorithm optimization unit 33, the optimal unit power generation power combination and generation cost are calculated. For the multi-electricity-price formulation module, it receives the user electricity consumption cost output by the electricity price-load optimization module 2 and the generation cost output by the power grid dispatching module. Through the genetic algorithm, it outputs a group of electricity prices to the electricity price-load optimization module 2 again. This is repeated until a group of electricity prices is obtained. After the auxiliary users (users in the group of auxiliary users, hereinafter referred to as auxiliary users or users for short in the group of auxiliary users) generate a response, their electricity consumption cost can be reduced to the agreed value, and at the same time, peak shaving and valley filling can be maximally achieved, making the power grid generation cost the lowest.

[0129] Specifically, the second objective function unit is used to construct a second objective function model based on the optimal equipment load, the optimal electricity consumption cost, and the optimal generation cost, including:

[0130] Construct the second objective function model:

[0131]

[0132] Bill and Obj1(MS) are the total electricity bills of all auxiliary user groups. Since each user takes the minimum electricity bill as the optimization goal of the load behavior. Therefore, the minimum electricity bill of each user is equivalent to the objective function of the minimum electricity bill of all users. NU is the number of users in the auxiliary user group; NT is the number of the smallest optimized time periods. If a day is divided into 24 hours, then NT = 24; NDev is the number of transferable devices of all users in the auxiliary user group; e x =[1,…,1] represents a row vector with all 1s in 1 row and x columns. It can be understood that e NU is a column vector with all 1s in 1 row and NU columns, is a row vector with all 1s in NT rows and 1 column; P is the electricity price curve group of the auxiliary user group, also called the electricity price group or electricity price. MS is the startup state of the transferable device. DP represents the equipment power, dp iIt represents the power of the i-th device. UL is the total load of non-transferable devices. The element in the i-th row and j-th column of SCA is 1, indicating that the j-th device in MS belongs to the i-th user, otherwise it is 0.

[0133] Where:

[0134]

[0135] In the formula: p k,j is the electricity price of the k-th user at the j-th time period; ms i,j represents the startup status of the i-th device at the j-th time period. If it is 1, the device starts; otherwise, the device is off; dp i represents the power of the i-th device; ul k,j represents the total load of non-transferable devices of the k-th user at the j-th time period.

[0136] The second index constraint unit is used to construct a second index constraint model according to the user device information, including:

[0137] Using the total startup time as the second one constraint function of the second index constraint model:

[0138] The daily electricity consumption of each device (the total daily electricity consumption duration of each device) needs to be equal to the time required to meet a certain production task of the user:

[0139]

[0140] In the formula: ms i,j is the electricity consumption duration of the i-th device at the j-th time period, wt i is the total startup time required for the i-th device;

[0141] Using the startup status as the second two constraint function of the second index constraint model:

[0142] ms i,j = 1 or 0 (4)

[0143] In the formula: it means that the startup status of the i-th device at the j-th time period can only be startup or off;

[0144] Using the non-transferable constraint as the second three constraint function of the second index constraint model:

[0145] Assume that the i-th device needs to start all the time or start at a specific time, then the i-th device is non-transferable; take the load of the i-th device as a constant, which is UL in formula (1) and does not participate in the optimization process.

[0146] In the above solution, each user takes the lowest electricity cost as the response target and preferentially starts the equipment during the period with relatively low electricity prices. At the same time, the total equipment startup time constraint is considered, that is, according to the production plan, each piece of equipment needs to be started for a certain period of time to meet the production capacity requirements. Moreover, this model divides a day into NT periods, so taking 24 / NT hours as the minimum unit, the equipment can only be started or shut down in one period. Usually, NT is set to 24. If a more accurate model is to be established, NT can be set to a larger value to subdivide each period and improve the response accuracy. For some equipment that cannot be started and stopped casually, it is regarded as a constant and does not participate in the optimization, that is, UL in the first objective function model.

[0147] The second algorithm optimization unit is used to calculate the optimal equipment load and the optimal electricity cost according to the second objective function model and the second index constraint model, including:

[0148] Express the startup state of the i-th equipment in the first period by the total startup time and the startup states of the remaining periods, that is, obtain formula (5) according to formula (3):

[0149]

[0150] where, ms i,1 is the startup state of the i-th equipment in the first period, and tms i,j is the startup state of the i-th equipment in the j-th period;

[0151] Express MS linearly through TMS, where the first column elements of TMS are all 0, and the other elements are equal to the corresponding elements of MS, as shown in formula (6); therefore, the optimization variable of the first objective function model in formula (1) changes from MS to TMS:

[0152]

[0153] Discretize formula (4):

[0154] The second constraint function of formula (4) indicates that ms i,j is a 0-1 discrete variable, and the optimal solution needs to be found from 2 NDev×NT combinations. Generally, global search intelligent algorithms such as genetic algorithms, particle swarm algorithms, and simulated annealing methods are used for solving. However, since this electricity price-load optimization module is only a sub-optimization in this mechanism and has a nested relationship with the power grid scheduling module 3 and the multi-electricity price formulation module 1, if a global search intelligent algorithm is used for solving, it will consume a large amount of computing power and time. Therefore, based on the idea of step-by-step optimization, convert ms i,j in formula (4) from a 0-1 variable to a continuous variable and optimize it in two steps:

[0155] First, introduce the following three auxiliary penalty functions as shown in formula (7); among them, the parameter α is as large as possible, and the parameters β and γ are adjusted according to the actual situation; the auxiliary penalty function f is used to handle inequality constraints, and the inequality constraints are added to the objective function in the form of penalty terms to transform it into an unconstrained optimization; the processing method for inequality constraints in the form of c1 ≤ x ≤ c2 is as follows: split the inequality constraint form of c1 ≤ x ≤ c2 into two inequalities x ≤ c2 and c1 ≤ x, where x is a variable and c1, c2 are constants; then, with the help of the function f, write x ≤ c2 and c1 ≤ x as f(d·(x - c2)) and f(-d·(x + c1)) respectively, and add them to the objective function, where k is a parameter and is adjusted according to the actual application:

[0156]

[0157] The first-step optimization:

[0158] Rewrite formula (4) as shown in formula (8) to make ms i,j During the optimization, always iterate and update within the range of 0 to 1; through the function f in formula (7), respectively for ms i,j ≤ 1 and 0 ≤ ms i,j Introduce penalty terms, as shown in formulas (9) and (10) respectively:

[0159] 0 ≤ ms i,j ≤ 1 (8)

[0160]

[0161] Therefore, the first objective function model for the first-step optimization is as shown in formula (11); since ms i,j is linearly expressed through tms i,j Obj1, l dr1 and l dr2 are also expressed through tms i,j The linear relationship between ms i,j and tms i,j is as shown in formulas (5) and (6); by finding the partial derivative of PObj1 with respect to tms i,j Iteratively optimize through the gradient method until convergence, then the first-step optimization ends:

[0162]

[0163]

[0163] The second-step optimization:

[0164] After the first-step optimization is completed, add another penalty term on the basis of formula (11). This penalty term is to make ms that stays between 0 and 1 i,jContinue to iterate in the direction of 0 or 1. The penalty function is shown in formula (12). For convenience of expression, a temporary variable r is introduced. i,j ; The second objective function model after the second-step optimization is shown in formula (13):

[0165]

[0166] The solution TMS that minimizes PObj1 is obtained by the gradient method and is the optimal solution; thus, the optimal device startup state MS is obtained.

[0167] In the above solution, the equality constraint of the total device startup time can be processed into a form where one variable can be expressed by other variables, which can reduce the optimization variables. The inequality processing converts the inequality constraint into a penalty term by means of f and adds it to the objective function. The principle of f is composed of an approximation to the step function fg and a slope function fp outside the adjustment boundary. When the variable breaks the constraint, the value of the objective function will become very large, and the slope of f outside the constraint guides the variable to move inside the constraint.

[0168] The third objective function unit is used to construct a third objective function model, including:

[0169] Construct a third objective function model based on minimizing the total power generation cost:

[0170]

[0171] In the formula: Cost is the power generation cost; NG is the number of generating units; a i , b i , c i are the coal consumption curve parameters of the i-th unit; g i,j represents the power generation power of the i-th unit in the j-th time period, and Obj2(G) is the minimum total power generation cost;

[0172] The third index constraint unit is used to construct a third index constraint model according to the total grid load and grid basic information, including:

[0173] Use the power balance constraint as the third-one constraint function of the third index constraint model, as shown in formula (15):

[0174]

[0175] In the formula: ND is the number of the total grid load; D is the total grid load, and d i,j represents the power consumption power of the i-th load in the j-th time period;

[0176] Use the maximum / minimum power generation power constraint of the unit as the third-two constraint function of the third index constraint model, as shown in formula (16):

[0177] gmin,i ≤ g i,j ≤ g max,i (16)

[0178] Where: g min,i , g max,i respectively represent the minimum and maximum power generation of the i-th unit, and g i,j represents the power generation of the i-th unit at the j-th time period;

[0179] Use the unit ramp power constraint as the third constraint function of the third index constraint model, as shown in formula (17):

[0180]

[0181] Where: R max,i is the maximum ramp power of the i-th unit. Since the mechanism proposed in the present invention temporarily considers the optimization within one day and assumes that the total load in the first time period of the next day is similar to the total load in the first time period of the current day. Therefore, the term with j = NT in formula (17) represents the ramp power at the NT-th time period, which is expressed as the power generation at the first time period of the current day minus the power generation at the NT-th time period.

[0182] Use the line power flow constraint as the third constraint function of the third index constraint model, as shown in formulas (18) and (19):

[0183] -pl max,i ≤ pl i,j ≤ pl max,i (18)

[0184]

[0185] Where: pl i,j represents the power flow through the i-th line at the j-th time period; pl max,i represents the maximum power flow allowed through the i-th line; PL is the line power flow matrix; SF is the transfer factor matrix; XB is the admittance matrix, and xb i represents the admittance of the i-th line; KL is the incidence matrix of the line; KG is the incidence matrix of the generator set; KD is the incidence matrix of the load.

[0186] In the above solution, the power grid dispatching aims at the optimal overall power generation cost of the generators. At the same time, it satisfies the power balance constraint, the maximum / minimum power constraint of the generators, the ramp power constraint, and the line power flow constraint. Among them, the power balance constraint means that the total load of the whole network at each time period is equal to the power generation at each time period, and the total load of the whole network is affected by the load of the auxiliary users. Therefore, if the auxiliary users adjust the load to make the total load curve smoother, the power generation cost will be lower under the same load.

[0187] The third algorithm optimization unit is configured to calculate the optimal power generation cost according to the third objective function model and the third index constraint model, including:

[0188] Express the power generation power of the first unit of the device by subtracting the power generation powers of the remaining units from the total load power, that is, rewrite formula (15) as formula (20). G can be linearly expressed by TG, where the first row elements of TG are all 0, and the other elements are equal to the corresponding elements of G, as shown in formula (21). Change the optimization variable of the third objective function model in formula (14) from G to TG:

[0189]

[0190] The third two-constraint function, the third three-constraint function, and the third four-constraint function are all inequality constraints. With the help of the auxiliary penalty function f, write the inequality constraints in formulas (16), (17), and (18) as penalty terms in formulas (22)-(27) respectively, and add each penalty term to the third objective function model in formula (14), as shown in formula (28):

[0191]

[0192] Since G can be expressed by TG, formulas (22)-(28) are also functions with TG as the independent variable. Therefore, find the partial derivative of PObj2 with respect to TG in formula (28), and it can be quickly solved by the gradient method.

[0193] In the above solution, the power balance equality constraint can be converted into a form where one variable is expressed by the remaining variables, which can reduce the number of variables. For inequality constraints, they can be converted into penalty terms and added to the objective function through f, that is, it can be converted into unconstrained optimization.

[0194] The first objective function unit is configured to construct a first objective function model based on the optimal device load, the optimal power consumption cost, and the optimal power generation cost, including:

[0195] The multi - electricity - price formulation module is used to obtain the electricity - price curves of a group of auxiliary user groups. Users in the auxiliary user groups, according to their respective electricity - price curves, obtain the optimal device startup state MS after being optimized by the electricity - price - load optimization module, and obtain the total electricity bill Bill of the auxiliary user groups. Furthermore, the total load D is calculated as shown in formula (29). After calculating the total load D, the power grid, based on the total load D, optimizes through the power - grid dispatching module to obtain the generator power combination G with the lowest power - generation cost and obtains the power - generation cost Cost for the day. By adjusting the electricity price, peak shaving and valley filling are realized, thereby reducing the power - generation cost Cost. Therefore, Bill and Cost respectively correspond to the income and expenditure of the power grid and are regarded as the variable costs of the power grid. Therefore, the first objective - function model is as shown in formula (30), which minimizes the variable costs of the power grid. That is, part of the increased revenue from peak shaving and valley filling is distributed to the auxiliary user groups to meet their cost - reduction standards, and the rest belongs to the power grid:

[0196]

[0197] Construct the first objective - function model based on the lowest variable costs of the power grid:

[0198]

[0199] In the formula: BL is the total load of other users excluding the auxiliary user groups from the load D, that is, the base load; bl i,j represents the base - load size of the i - th total load in the j - th time period; ML is the total load of the transferable devices of each user in the auxiliary user groups, ml i,j represents the total load of the transferable devices of the i - th user in the auxiliary user groups in the j - th time period; The element in the i - th row and j - th column of KM represents that the j - th auxiliary user belongs to the i - th total load.

[0200] The first - index constraint unit is used to construct the first - index constraint model based on the optimal device load and the optimal electricity - consumption cost, including:

[0201] Based on the percentage constraint of the reduction in the electricity bills of the users in the auxiliary user groups (the constraint that after each auxiliary user participates in the new mechanism, their electricity bills are reduced by a pre - agreed percentage compared to the electricity bills under the original electricity price) as the first - one constraint function of the first - index constraint model, as shown in formula (31):

[0202]

[0203] In the formula: bill i represents the minimum electricity - consumption cost of the i - th user in the auxiliary user groups under the new electricity price; bill init,i represents the minimum electricity - consumption cost of the i - th user in the auxiliary user groups under the original electricity price; δ i is the pre - negotiated cost - reduction percentage of the i - th user in the auxiliary user groups with the power grid, and bill init,i·δ i is the target electricity bill for users in the i-th auxiliary user group;

[0204] Based on the maximum / minimum electricity price constraint as the first and second constraint functions of the first index constraint model, upper and lower limits are set for each electricity price, as shown in formula (32):

[0205] p min ≤p i,j ≤p max (32)

[0206] In the formula: p min is the minimum value allowed for the electricity price, p max is the maximum value allowed for the electricity price, p i,j is the electricity price of users in the i-th auxiliary user group at the j-th time period.

[0207] In the above solution, it means that the power grid hopes that the price set by the pricing system can enable the auxiliary users to just pay the electricity bill of init,i ·δ i . Among them, δ of each user i should not be too high. If δ i is too high, it is not enough to attract users to participate in this mechanism. If δ i is too low, the increased profit of the power grid company is not enough to attract the power grid company to participate in this mechanism. Generally, δ i is set to about 0.98 - 0.99. At the same time, the number of auxiliary user groups should not be too high, and the benefits brought by peak shaving and valley filling do not have a linear relationship with the total load of auxiliary users. When the peak-valley difference gradually decreases, the additional benefits obtained from peak shaving and valley filling will decrease.

[0208] The first algorithm optimization unit is used to calculate the optimal equipment load and the optimal electricity consumption cost according to the first objective function model and the first index constraint model by using the genetic algorithm, including:

[0209] Since the first objective function model of formula (30) is not a function of the electricity price P, it cannot be solved by the gradient method; in formula (30), Bill and Cost correspond to the small electricity bills of users and the minimum power generation cost of the power grid respectively, and are obtained through the electricity price-load optimization module and the power grid scheduling module respectively; therefore, the first objective function model, that is, formula (30), is solved by using the genetic algorithm:

[0210] The first constraint function of formula (31) all uses the auxiliary penalty function f to write formula (31) as the penalty term of formula (33), and adds this penalty term to the first objective function model of formula (30), which is the objective function of the genetic algorithm, as shown in formula (34):

[0211]

[0212] The first and second constraint functions of formula (32) are made to be satisfied during the initialization of the genetic algorithm population; and after mutation, it is determined by a program whether the first and second constraint functions of formula (32) are satisfied, otherwise re-mutation is performed;

[0213] In the new electricity price mechanism proposed by the present invention, the core task is to achieve peak shaving and valley filling to the greatest extent and reduce the power generation cost to the greatest extent; therefore, the objective function of the genetic algorithm in formula (34) is rewritten in the form of formula (35); where σ and τ are relatively small constant coefficients; when the genetic algorithm progresses to the later stage, Obj2 is already close to the optimal value, and thereafter, when individuals are selected, the difference between Obj1 and l among individuals plays a decisive role: p1

[0214]

[0215] For the solution process of the genetic algorithm, please refer to Figure 5 as shown below, which includes the following steps:

[0216] 1) Population initialization:

[0217] The electricity price P is the solution variable of the genetic algorithm, and the gene of each individual is P. A matrix of size NU×NT is obtained through random numbers; and when initializing the electricity price P, it should satisfy the first and second constraint functions of formula (32);

[0218] 2) Fitness calculation:

[0219] According to the electricity price P of each individual, through the electricity price - load optimization module, the optimal start state MS of each individual is obtained, and Bill is calculated; then the total load D is calculated based on MS; then through the power grid scheduling module, the optimal power generation cost Cost of the power grid is calculated; finally, formula (35) is calculated and the reciprocal is taken respectively, as shown in formula (36):

[0220] fit i = 1 / PGCost i (36)

[0221] In the formula: fit i is the fitness of the i-th individual, and the higher the value of fit i , the more excellent the i-th individual represents;

[0222] 3) Natural selection:

[0223] First, directly select the NF individuals with the highest fitness, and call them elite individuals; then the remaining individuals participate in roulette selection, and the higher the fitness, the higher the survival rate, and the selected individuals are used as the parental generation; before participating in roulette selection, their fitness values are all subtracted by the minimum fitness of the population, as shown in formula (37), using tfit iDenote the individuals participating in roulette, where υ is a constant parameter that is as close as possible to 1 but less than 1;

[0224] tfit i = fit i - υ·min{fit1, fit2, …, fit N} (37)

[0225] 4) Crossover inheritance:

[0226] When generating offspring, there is a certain probability that the parental generation crosses gene information, or directly replicates to obtain offspring; when crossing over, only a certain row of genes can be selected from any two parental generations for crossing, as shown in formula (38); where P i m represents the i-th row gene of the m-th individual in the parental generation;

[0227]

[0228] 5) Gene mutation:

[0229] Each offspring has a certain probability of mutating, and the number and position of the mutated genes are randomly selected; after mutation, check whether the first and second constraint functions of formula (32) are satisfied; if not, re-mutate until satisfied.

[0230] In the above scheme, the optimal electricity price combination is solved by the genetic algorithm. Each individual in the genetic algorithm is a set of electricity price combinations. Through the electricity price - load optimization module, the optimal load behavior and electricity consumption cost under each electricity price group are calculated, and then the optimal power generation cost is calculated through the power grid dispatching model. Finally, the fitness of each individual is calculated based on the electricity consumption cost and power generation cost, etc., so as to perform iterative optimization in the genetic algorithm until a set of optimal electricity prices is obtained.

[0231] The above has introduced in detail the multi - electricity - price demand response dispatching system based on the genetic algorithm for the peak - shifting phenomenon disclosed in the embodiments of the present invention. Specific examples are used in this article to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation on the present invention.

Claims

1. A multi - electricity - price demand response scheduling system for peak - shifting phenomenon based on genetic algorithm, characterized in that, Including: An electricity price - load optimization module, which is used to determine the optimal equipment load and the optimal electricity consumption cost based on the user equipment information of the auxiliary user group and the optimal multi - electricity - price curve group determined by the multi - electricity - price formulation module; A power grid scheduling module, which is used to determine the optimal power generation cost based on the total power grid load and the power grid basic information; A multi - electricity - price formulation module, which is used to calculate the optimal multi - electricity - price curve group by using the genetic algorithm based on the optimal equipment load and the optimal electricity consumption cost, and the optimal power generation cost, and send the optimal multi - electricity - price curve group to the auxiliary user group; The electricity price - load optimization module includes a second objective function unit and a second index constraint unit; The second objective function unit is used to construct a second objective function model based on the optimal equipment load and the optimal electricity consumption cost, and the optimal power generation cost, including: Constructing a second objective function model: Wherein: Where: Bill and Obj1(MS) are the total electricity bills of all auxiliary user groups; NU is the number of users in the auxiliary user group; NT is the minimum number of optimized time periods; NDev is the number of transferable devices of all users in the auxiliary user group; e x =[1,…,1] represents a row vector of 1 row and x columns all of which are 1; P is the electricity price curve group of the auxiliary user group, p k,j is the electricity price of the k-th user in the j-th time period; MS is the start-up state of the transferable device, where ms i,j represents the start-up state of the i-th device in the j-th time period. If it is 1, the device starts up; otherwise, the device shuts down; DP represents the device power, dp i represents the power of the i-th device; UL is the total load of non-transferable devices, ul k,j represents the total load of non-transferable devices of the k-th user in the j-th time period; The element in the k-th row and j-th column of SCA is 1, indicating that the j-th device in MS belongs to the k-th user; otherwise, it is 0; The second index constraint unit is used to construct a second index constraint model according to the user equipment information, including: Using the total startup time as the second - one constraint function of the second index constraint model: The total daily electricity consumption duration of each device is equal to the time required under a certain production task of the user: where: wt i is the total startup time required for the i-th device; Using the startup state as the second - two constraint function of the second index constraint model: ms i,j = 1 or 0 (4) where: represents that the startup state of the i-th device in the j-th time period can only be startup or shutdown; Using the non - transferable constraint as the second - three constraint function of the second index constraint model: Assume that the i - th device is non - transferable because it needs to be always started or started at a specific time; take the load of the i - th device as a constant, that is, UL in formula (1), and it does not participate in the optimization process.

2. The multi - electricity - price demand response scheduling system for peak - shifting phenomenon based on genetic algorithm as claimed in claim 1, wherein The multi - electricity - price formulation module includes a first objective function unit, a first index constraint unit, and a first algorithm optimization unit; the electricity price - load optimization module further includes a second algorithm optimization unit; the power grid scheduling module includes a third objective function unit, a third index constraint unit, and a third algorithm optimization unit; wherein: The second algorithm optimization unit is used to calculate the optimal equipment load and the optimal electricity consumption cost according to the second objective function model and the second index constraint model; The third objective function unit is used to construct a third objective function model; the third index constraint unit is used to construct a third index constraint model according to the total power grid load and the power grid basic information; the third algorithm optimization unit is used to calculate the optimal power generation cost according to the third objective function model and the third index constraint model; The first objective function unit is used to construct a first objective function model based on the optimal equipment load and the optimal electricity consumption cost, and the optimal power generation cost; the first index constraint unit is used to construct a first index constraint model according to the optimal equipment load and the optimal electricity consumption cost; the first algorithm optimization unit is used to calculate the optimal multi - electricity - price curve group by using the genetic algorithm according to the first objective function model and the first index constraint model.

3. The multi-tariff demand response scheduling system for peak shifting based on genetic algorithm according to claim 2, characterized in that, The second algorithm optimization unit is used to calculate the optimal equipment load and the optimal electricity consumption cost according to the second objective function model and the second index constraint model, including: Express the start-up status of the i-th device in the first time period using the total start-up time and the start-up status of the remaining time periods, that is, obtain Equation (5) according to Equation (3): where, ms i,1 is the startup status of the i-th device in the first time period, and tms i,j is the startup status of the i-th device in the j-th time period; Linearly express MS through TMS, where the first column elements of TMS are all 0, and the other elements are equal to the corresponding elements of MS, as shown in Equation (6); therefore, the optimization variable of the first objective function model in Equation (1) changes from MS to TMS: Discretize Equation (4): The second two-constraint function of formula (4) represents ms i,j is a 0-1 discrete variable. Based on the idea of step-by-step optimization, ms in formula (4) i,j is converted from a 0-1 variable to a continuous variable and optimized in two steps: First, introduce the following three auxiliary penalty functions, as shown in Equation (7); among them, the parameter α is as large as possible, and the parameters β and γ are adjusted according to the actual situation; the auxiliary penalty function f is used to handle inequality constraints, and the inequality constraints are added to the objective function in the form of penalty terms to convert it into an unconstrained optimization; the processing method for inequality constraints in the form of c1 ≤ x ≤ c2 is as follows: split the inequality constraint form of c1 ≤ x ≤ c2 into two inequalities x ≤ c2 and c1 ≤ x, where x is a variable, and c1 and c2 are constants; then, with the help of the function f, write x ≤ c2 and c1 ≤ x as f(d·(x - c2)) and f(-d·(x + c1)) respectively, and add them to the objective function, where k is a parameter and is adjusted according to the actual application: The first step of optimization: Rewrite formula (4) as shown in formula (8) to make $m_s$ i,j always iterate and update within the range of 0 to 1 during optimization; for $m_s$ respectively through function $f$ in formula (7) i,j $\leq1$ and $0\leq m_s$ i,j Introduce penalty terms, as shown in formulas (9) and (10) respectively: 0 ≤ ms i,j ≤ 1(8) Therefore, in the first step, the first objective function model is optimized as shown in the following formula (11); since ms i,j Through tms i,j Linearly expressed, Obj1, l dr1 , l dr2 Are also expressed through tms i,j Expression, ms i,j And tms i,j The linear relationship is shown in formulas (5) and (6); by finding the partial derivative of PObj1 with respect to tms i,j And iteratively optimizing through the gradient method until convergence, then the first step of optimization ends: Through gradient method iteration optimization until convergence, then the first step of optimization ends: The second step of optimization: After the first-step optimization is completed, an additional penalty term is added based on formula (11). This penalty term makes \(m_s\) stay between 0 and 1. i,j Continue to iterate in the direction of 0 or 1. The penalty function is shown in formula (12). For convenience of expression, a temporary variable \(r\) is introduced. i,j ; The second objective function model after the second-step optimization is shown in formula (13): r i,j = -k·ms i,j ·(ms i,j - 1) The solution TMS that minimizes PObj1 is obtained by the gradient method as the optimal solution; thus, the optimal device start-up status MS is obtained.

4. The multi - electricity - price demand response scheduling system for peak shifting phenomenon based on genetic algorithm according to claim 3, characterized in that The third objective function unit is used to construct a third objective function model, including: Construct a third objective function model based on minimizing the total power generation cost: Where: Cost is the power generation cost; NG is the number of generator sets; a i , b i , c i are the coal consumption curve parameters of the i-th unit; g i,j represents the power generation power of the i-th unit in the j-th period, and Obj2(G) is the minimum total power generation cost; The third index constraint unit is used to construct a third index constraint model according to the total grid load and grid basic information, including: Use the power balance constraint as the third one constraint function of the third index constraint model, as shown in Equation (15): Where: ND is the quantity of the total network load; D is the total network load, and d i,j represents the power consumption of the i-th load at the j-th time period; Use the maximum / minimum power generation power constraint of the unit as the third two constraint function of the third index constraint model, as shown in Equation (16): g min,i ≤ g i,j ≤ g max,i (16) where: g min,i and g max,i respectively represent the minimum and maximum power generation of the i-th unit, and g i,j represents the power generation of the i-th unit in the j-th period; Use the unit ramp power constraint as the third three constraint function of the third index constraint model, as shown in Equation (17): Where: R max,i is the maximum ramp rate of the i-th unit; Use the line power flow constraint as the third four constraint function of the third index constraint model, as shown in Equations (18) and (19): -pl max,i ≤ pl i,j ≤ pl max,i (18) Where: pl i,j represents the power flow passing through the i-th line in the j-th time period; pl max,i represents the maximum power flow that the i-th line is allowed to pass through; PL is the line power flow matrix; SF is the transfer factor matrix; XB is the admittance matrix, and xb i represents the admittance of the i-th line; KL is the incidence matrix of the line; KG is the incidence matrix of the generator set; KD is the incidence matrix of the load.

5. The multi-tariff demand response scheduling system for peak shifting phenomenon based on genetic algorithm according to claim 4, wherein, The third algorithm optimization unit is used to calculate the optimal power generation cost according to the third objective function model and the third index constraint model, including: Express the power generation power of the first unit of the device by subtracting the power generation power of the remaining units from the total load power, that is, rewrite Equation (15) as Equation (20), G can be linearly expressed by TG, where the first row elements of TG are all 0, and the other elements are equal to the corresponding elements of G, as shown in Equation (21), and the optimization variable of the third objective function model in Equation (14) changes from G to TG: The third two constraint function, the third three constraint function, and the third four constraint function are all inequality constraints. With the help of the auxiliary penalty function f, write the inequality constraints in Equations (16), (17), and (18) as penalty terms in Equations (22)-(27) respectively, and add each penalty term to the third objective function model in Equation (14), as shown in Equation (28): Since G can be represented by TG, formulas (22)-(28) are also functions with TG as the independent variable. Therefore, by finding the partial derivative of PObj2 with respect to TG in formula (28), it can be quickly solved by the gradient method.

6. The multi - electricity - price demand response scheduling system for peak - shifting phenomenon based on genetic algorithm according to claim 5, characterized in that, The first objective function unit is used to construct a first objective function model based on the optimal equipment load, optimal electricity consumption cost, and optimal power generation cost, including: The multi-tariff formulation module is used to obtain the tariff curves of a group of auxiliary user groups. Users in the auxiliary user groups obtain the optimal equipment startup state MS after being optimized by the tariff-load optimization module according to their respective tariff curves, and obtain the total electricity bill Bill of the auxiliary user groups, and then calculate the total load D, as shown in formula (29). After calculating the total load D, the power grid, based on the total load D, optimizes through the power grid dispatching module to obtain the generator power combination G with the lowest power generation cost and obtain the power generation cost Cost for the day. By adjusting the tariff to achieve peak shaving and valley filling, the power generation cost Cost is reduced. Therefore, Bill and Cost respectively correspond to the income and expenditure of the power grid and are regarded as the variable costs of the power grid. Therefore, the first objective function model is as shown in formula (30), minimizing the variable costs of the power grid. That is, part of the increased revenue from peak shaving and valley filling is distributed to the auxiliary user groups to meet their fee reduction standards, and the rest belongs to the power grid: Construct the first objective function model based on the lowest variable costs of the power grid: Where: BL is the total load of other users excluding the auxiliary user group from the load D, i.e., the base load; bl i,j represents the base load of the i-th total load at the j-th time period; ML is the total load of the transferable devices of each user in the auxiliary user group, ml i,j represents the total load of the transferable devices of the user in the i-th auxiliary user group at the j-th time period; the element in the i-th row and j-th column of KM indicates that the j-th auxiliary user belongs to the i-th total load.

7. The multi - electricity - price demand response scheduling system for peak - shifting phenomenon based on genetic algorithm according to claim 6, characterized in that, The first index constraint unit is used to construct a first index constraint model based on the optimal equipment load and optimal electricity consumption cost, including: Based on the percentage constraint of the reduction in the electricity bills of users in the auxiliary user group as the first constraint function of the first index constraint model, as shown in formula (31): Where: bill i represents the minimum electricity consumption cost of the users in the i-th auxiliary user group under the new electricity price; bill init,i represents the minimum electricity consumption cost of the users in the i-th auxiliary user group under the original electricity price; δ i is the percentage of electricity cost reduction negotiated by the users in the i-th auxiliary user group with the power grid, and bill init,i ·δ i is the target electricity bill of the users in the i-th auxiliary user group; Based on the maximum / minimum value constraint of the tariff as the second constraint function of the first index constraint model, as shown in formula (32): p min ≤ p i,j ≤ p max (32) where: p min is the minimum value allowed for the electricity price, p max is the maximum value allowed for the electricity price, p i,j is the electricity price of the j-th period for the users in the i-th auxiliary user group.

8. The multi-tariff demand response scheduling system for peak shifting phenomenon based on genetic algorithm according to claim 7, wherein The first algorithm optimization unit is used to calculate the optimal equipment load and optimal electricity consumption cost using the genetic algorithm according to the first objective function model and the first index constraint model, including: Since the first objective function model of formula (30) is not a function of the tariff P, it cannot be solved by the gradient method. In formula (30), Bill and Cost respectively correspond to the electricity bills of users and the minimum power generation cost of the power grid, and are obtained through the tariff-load optimization module and the power grid dispatching module respectively. Therefore, the first objective function model, that is, formula (30), is solved using the genetic algorithm: All the first constraint functions of formula (31) use the auxiliary penalty function f, rewrite formula (31) as the penalty term in formula (33), and add this penalty term to the first objective function model of formula (30), which is the objective function of the genetic algorithm, as shown in formula (34): For the second constraint function of formula (32), it is made to be satisfied during the initialization of the genetic algorithm population. And after mutation, it is judged by the program whether it satisfies the second constraint function of formula (32), otherwise, re-mutation is performed; The core task of the multi - electricity - price demand response scheduling system is to achieve peak shaving and valley filling to the greatest extent and reduce the power generation cost to the greatest extent. Therefore, the objective function of the genetic algorithm in formula (34) is rewritten in the form of formula (35). Among them, σ and τ are relatively small constant coefficients. When the genetic algorithm progresses to the later stage, Obj2 is already close to the optimal value. After that, when selecting individuals, the difference between Obj1 and l p1 among individuals plays a decisive role: The solution steps of the genetic algorithm include: 1) Population initialization: The electricity price P is the solution variable of the genetic algorithm, and the gene of each individual is P. A matrix of size NU×NT is obtained through random numbers; when initializing the electricity price P, it should satisfy the first and second constraint functions of formula (32). 2) Fitness calculation: Based on the electricity price matrix P of each individual, through the electricity price-load optimization module, the optimal starting state MS of each individual is obtained, and Bill is calculated; then the total load D is calculated according to MS; then through the power grid dispatching module, the optimal power generation cost Cost of the power grid is calculated; finally, formula (35) is calculated, and the reciprocals are taken respectively, as shown in formula (36): fit i = 1 / PGCost i In equation (36): fit i is the fitness of the i-th individual. The higher the value of fit i , the better the i-th individual represents. 3) Natural selection: First, directly select the NF individuals with the highest fitness and call them elite individuals; then the remaining individuals participate in roulette wheel selection, where the higher the fitness, the higher the survival rate, and the selected individuals are used as the parental generation; before participating in roulette wheel selection, their fitness values are all subtracted by the minimum fitness of the population, as shown in formula (37), and tfit i represents the individuals participating in roulette wheel selection, where υ is a constant parameter that is as close to 1 as possible but less than 1; tfit i = fit i - υ·min{fit1, fit2, …, fit N} (37) 4) Crossover inheritance: When generating offspring, there is a certain probability that the parental generation crosses gene information or directly replicates to obtain offspring; when crossing, only a certain row of genes can be selected from any two parental generations for crossing, as shown in formula (38); where P i m represents the i-th row gene of the m-th individual in the parental generation; 5) Gene mutation: Each offspring has a certain probability of mutating, and the number and position of the mutated genes are randomly selected; after mutation, it is checked whether the first and second constraint functions of formula (32) are satisfied; if not, re-mutation is performed until it is satisfied.

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