A scheduling method for high energy consumption loads participating in peak load regulation based on variational mode decomposition

Abstract

A dispatching method for high-energy-consuming loads to participate in peak-shaving based on variational modal decomposition, by decomposing the equivalent load curve to obtain the components corresponding to different central frequencies, and classifying and adjusting the components corresponding to different central frequencies according to the frequency, introducing high-energy-consuming enterprises to join the adjustment and using the variational modal decomposition results as a reference, and using the gray wolf algorithm to obtain the optimal solution to obtain the optimal peak-shaving method. A dispatching method for high-energy-consuming loads to participate in peak-shaving based on variational modal decomposition has better effects than the traditional single adjustment method of thermal power units, solves the problem of wind power consumption under the condition of large-scale wind power access, plays a positive role in reducing wind power abandonment, improves wind power utilization, and has high economic and environmental benefits.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power absorption capacity methods, and in particular to a method for dispatching high energy consumption loads participating in peak load regulation based on variational mode decomposition. Background Art

[0002] At present, global energy consumption is too high and environmental problems are becoming more and more serious. Under this severe background, vigorously developing various clean energy technologies and realizing the transformation of energy production to green, environmentally friendly and renewable energy are necessary technical measures for the sustainable development of energy and economy in all countries in the world today, and are also a long-term plan for all countries to take the path of sustainable development. According to the latest global renewable energy installed capacity data released by the International New Energy Network Agency, the global renewable energy installed capacity reached 2,802 GW in 2020, an increase of 10.2% over 2019. Among them, hydropower installed capacity was 1,333 GW (accounting for 47.6% of renewable energy installed capacity), wind power installed capacity was 732 GW (26.1%), and photovoltaic installed capacity was 709 GW (25.3%). Among the global renewable energy installed capacity, China accounts for 31.9%. Especially in wind power and photovoltaic installed capacity, China accounts for 38.5% and 35.8% respectively, far ahead of the world.

[0003] The generation, supply and use of the power system are completed at the same time, and the power load shows obvious time-varying characteristics. At present, the peak-to-valley difference of regional power grids in my country has reached about 30%, and it is gradually expanding. A basic condition for the smooth operation of the power system is that the system regulation capacity must be greater than the change in load. Due to the resource characteristics of wind itself, it is random and volatile. And it shows certain anti-peak characteristics. When the output of new energy exceeds the system regulation range, the output must be controlled to ensure the dynamic balance of the system. At this time, it is inevitable that wind abandonment will occur. If improvements are made from the load side of the power system, the upgrade of the power system structure can be promoted to achieve the purpose of improving wind power consumption. High-energy-consuming loads have large power consumption and high load utilization. High-energy-consuming loads are used to participate in power peak regulation to reduce peak and valley filling and reduce wind abandonment, or battery energy storage systems can be added to wind power plants, and the charging and discharging functions of the added energy storage batteries can be used to increase the wind abandonment power. This is of great significance to improving the peak regulation capacity of the entire power grid. Summary of the invention

[0004] In view of the problems and technical requirements of the prior art, the purpose of the present invention is to provide a scheduling method for high-energy-consuming loads participating in peak-shaving based on variational modal decomposition. The equivalent load curve is decomposed to obtain components corresponding to different center frequencies, and the components corresponding to different center frequencies are classified and adjusted according to the frequency. High-energy-consuming enterprises are introduced to join the adjustment and the variational modal decomposition results are used as a reference. At the same time, the gray wolf algorithm is used to obtain the optimal solution to obtain the optimal peak-shaving method.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] 1. A method for dispatching high energy consumption loads to participate in peak load regulation based on variational mode decomposition, comprising the following steps:

[0007] (S1) obtaining a wind power generation prediction curve and a load power prediction curve for the next day in the power system, obtaining an equivalent load curve, and using the thermal power unit to adjust the equivalent load curve separately, judging whether the adjusted equivalent load curve meets the output range of the thermal power unit, and predicting whether the power system will have wind curtailment;

[0008] (S2) Decomposing the equivalent load curve by using variational modal decomposition to obtain components corresponding to modal curves with different center frequencies, wherein the components include low-frequency components, medium-frequency components, and high-frequency components, and adjusting the components by using high-energy-consuming loads and thermal power units;

[0009] (S3) Taking the prediction of the minimum amount of wind power abandoned in the power system as the objective function, a model for the coordinated consumption of wind power by high energy-consuming loads and thermal power units is established;

[0010] (S4) The objective function is solved by the grey wolf algorithm to obtain the high energy consumption load regulation power, the thermal power unit regulation power and the wind power output of the next day.

[0011] The step (S1) comprises the following steps:

[0012] (S11) obtaining a wind power generation prediction curve for the next day by predicting the wind power generation power in the power system;

[0013] (S12) obtaining a load power forecast curve for the next day by forecasting the load power forecast in the power system;

[0014] (S13) taking the wind power generation power prediction curve of the next day as a negative load power curve, and superimposing it with the load power prediction curve of the next day to obtain an equivalent load curve;

[0015] (S14) adjusting the equivalent load curve individually by the thermal power unit;

[0016] (S15) determining whether the adjusted equivalent load curve is within the predicted technical output range of the power system; if so, no wind power abandonment occurs; otherwise, wind power abandonment occurs.

[0017] The step (S2) decomposes the equivalent load curve using variational modal decomposition, and comprises the following steps:

[0018] (S21) Initialization λ k , and let n = 1, where k is the number of modes to be decomposed, is the decomposed single-component AM / FM signal, is the center frequency of each single-component AM / FM signal, λ is the Lagrange multiplication operator, and n is the number of cycles;

[0019] (S22) Set n=n+1 and start the loop;

[0020] (S23) Update according to VMD algorithm λ k , until the number of decompositions reaches k and the cycle stops;

[0021] (S24) Determine whether the accuracy ε satisfies the stop loop condition. If yes, stop the loop. Otherwise, jump to step (S22), where the accuracy ε is determined as follows:

[0022] The low-frequency component, medium-frequency component and high-frequency component in the step (S2), wherein the center frequency range of the low-frequency component is less than 0.04, the center frequency range of the medium-frequency component is 0.04-0.13, and the center frequency range of the high-frequency component is greater than 0.13, the low-frequency component and the high-frequency component are adjusted by the thermal power unit, and the medium-frequency component is adjusted by the high-energy consumption load.

[0023] In the step (S3), the objective function is to predict the minimum amount of wind abandoned in the power system:

[0024]

[0025] Where W q To predict the total wind curtailment in the power system, T is the dispatch period, N W is the number of wind farms, is the predicted value of wind power active output of the ath wind farm at time t, N G is the number of thermal power units, S G_b is the start-stop variable of the thermal power unit. G_b Set to 1, when stopped, S G_b Set to 0, is the active power output value of the bth thermal power unit at time t, N L is the number of high energy-consuming enterprises, A Boolean variable for high energy consuming enterprises, when running Set to 1, when stopped Set to 0, The operating power of the cth high energy consumption enterprise at time t, N M is the number of normal loads, is the planned active power value of the dth common load at time t, and Δt is the time interval.

[0026] The step (S3) of coordinating the wind power consumption model between high energy consumption loads and thermal power units includes a discrete high energy consumption constraint, a transferable high energy consumption constraint, a power system operation power balance constraint, a spinning reserve constraint for balancing wind power, a wind power output constraint, a wind farm active output change constraint in two adjacent time periods, and upper and lower limit constraints on the output power of thermal power units;

[0027] The high energy consumption constraint can be discretized, namely:

[0028]

[0029] Where P DL(i),min is the lower limit value of the discrete high energy consumption load adjustment, is the discrete high energy consumption load value, P DL(i),max is the upper limit of the discrete high energy consumption load regulation, r DL(i),down is the lower limit of the climbing constraint of the discrete high energy consumption load, r DL(i),up is the upper limit of the climbing constraint of the discretizable high energy consumption load, the decision variable at the ζ constraint moment, P DL(i),rated is the rated power of the discrete high energy load, T keep(i) is the duration of the discrete high energy consumption load, is the decision variable for the discrete high energy consumption time t, T g(i) To continuously adjust the time constraint of discrete high energy load, M SL(i) is the maximum allowable adjustment times of the discrete high energy load within the dispatching cycle;

[0030] Transferable high energy consumption constraints, namely:

[0031]

[0032] Where ΔQ TL(i) (t, t′) is the time shift of the i-th transferable high energy consumption load from time t to time t′, X TL(i) (t, t′) is the adjustment decision variable of the ith high energy load, Q TL(i),anit is the i-th transferable high-energy base load, is the maximum load at time t, is the original load at time t, is the decision variable that can transfer high energy consumption at time t, L (i) To continuously adjust the time constraints for the transfer of high energy loads, is the time shift value of the i-th transferable high energy consumption load at time t;

[0033] Power system operation power balance constraints, namely:

[0034]

[0035] In the formula is the active power output value of the wind farm, is the active power value of conventional load;

[0036] The spinning reserve constraint for balancing wind power is:

[0037]

[0038] In the formula is the output upper limit of the bth thermal power unit at time t, is the lower limit of the output of the bth thermal power unit at time t, For spinning reserve on the system, It is a spinning reserve for the system;

[0039] Wind power output constraints, namely:

[0040]

[0041] In the formula is the predicted value of wind power active output at time t;

[0042] The active output change constraint of the wind farm in two adjacent time periods is:

[0043]

[0044] Where P wind_down is the maximum downward adjustment value of the active output of the wind farm in adjacent periods, P wind_up It is the maximum upward adjustment value of the active output of the wind farm in adjacent time periods;

[0045] The upper and lower limits of the output power of thermal power units are:

[0046]

[0047] Where P G_b_min is the minimum active output of the bth thermal power unit, P G_b_max It is the maximum value of the active output of the bth thermal power unit.

[0048] The step (S4) of solving the objective function using the grey wolf algorithm comprises the following steps:

[0049] (S41) Initialization of the gray wolf population, including the number of groups N, the maximum number of iterations Maxlter, the convergence factor a, and the coefficient vectors A and C;

[0050] (S42) randomly initializing the position X of the individual gray wolf according to the upper and lower bounds of the variable;

[0051] (S43) Calculate the fitness value of each wolf, and save the location information of the wolf with the best fitness value in the population α , save the location information of the wolf with the second best fitness value in the population as X β , save the location information of the gray wolf with the third best fitness in the population as X δ ;

[0052] (S44) updating the position of the gray wolf individual X;

[0053] (S45) updating the convergence factor a, coefficient vectors A and C;

[0054] (S46) calculating the fitness value of each gray wolf and updating the optimal position of the three alpha wolves;

[0055] (S47) Determine whether the maximum number of iterations Maxlter is reached, if so, the Gray Wolf Algorithm stops and outputs X α The value of is taken as the final optimal solution, otherwise jump to step (S44).

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] The present invention discloses a scheduling method for high-energy-consuming loads to participate in peak regulation based on variational modal decomposition. By decomposing an equivalent load curve, components corresponding to different central frequencies are obtained, and components corresponding to different central frequencies are classified and adjusted according to the high and low frequencies. High-energy-consuming enterprises are introduced to join the adjustment and the variational modal decomposition results are used as a reference. Compared with the traditional single adjustment method of thermal power units, the method has better effect, solves the problem of wind power consumption under the condition of large-scale wind power access, plays a positive role in reducing abandoned wind power, improves the utilization rate of wind power, and has higher economic and environmental benefits.

[0058] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention so that it can be implemented in accordance with the contents of the specification, and in order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation method of the present invention is listed below.

[0059] Based on the detailed description of the specific embodiments of the present invention in combination with the accompanying drawings below, those skilled in the art will become more aware of the above and other objects, features and advantages of the present invention, but this does not limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a schematic diagram of the component adjustment of the present invention;

[0061] Figure 2 It is a flowchart of the solution of the grey wolf algorithm of the present invention;

[0062] Figure 3 It is a wind power generation power curve, load power curve and equivalent load curve of a wind farm at a certain place on a certain day in an embodiment of the present invention;

[0063] Figure 4 Schematic diagram of the result of adjusting the output of a thermal power unit individually in an embodiment of the present invention;

[0064] Figure 5 is a center frequency modal curve diagram corresponding to the low-frequency component in an embodiment of the present invention;

[0065] Figure 6 is a center frequency modal curve diagram corresponding to the intermediate frequency component in an embodiment of the present invention;

[0066] Figure 7 is a center frequency modal curve diagram corresponding to the high frequency component in an embodiment of the present invention;

[0067] Figure 8 is a curve diagram of power generation change of a thermal power unit in accordance with an embodiment of the present invention;

[0068] Fig. 9 is an operation curve diagram of a high energy consumption enterprise in an embodiment of the present invention;

[0069] Fig.10 It is a power variation curve diagram after coordinated adjustment of high energy-consuming enterprises of thermal power units by variational mode decomposition in an embodiment of the present invention. DETAILED DESCRIPTION

[0070] In order to facilitate the understanding of the present invention, the present invention will be described more fully below. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present invention more thorough and comprehensive.

[0071] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0072] The specific implementation modes provided by the present invention are described in detail below with reference to the accompanying drawings.

[0073] like Figure 1 As shown, a schematic diagram of component adjustment of a scheduling method for high energy consumption loads participating in peak load regulation based on variational mode decomposition includes the following steps:

[0074] (S1) obtaining a wind power generation prediction curve and a load power prediction curve for the next day in the power system, obtaining an equivalent load curve, and using the thermal power unit to adjust the equivalent load curve separately, judging whether the adjusted equivalent load curve meets the output range of the thermal power unit, and predicting whether the power system will have wind curtailment;

[0075] (S2) Decomposing the equivalent load curve by using variational modal decomposition to obtain components corresponding to modal curves with different center frequencies, wherein the components include low-frequency components, medium-frequency components, and high-frequency components, and adjusting the components by using high-energy-consuming loads and thermal power units;

[0076] (S3) Taking the prediction of the minimum amount of wind power abandoned in the power system as the objective function, a model for the coordinated consumption of wind power by high energy-consuming loads and thermal power units is established;

[0077] (S4) The objective function is solved by the grey wolf algorithm to obtain the high energy consumption load regulation power, the thermal power unit regulation power and the wind power output of the next day.

[0078] The step (S1) comprises the following steps:

[0079] (S11) obtaining a wind power generation prediction curve for the next day by predicting the wind power generation power in the power system;

[0080] (S12) obtaining a load power forecast curve for the next day by forecasting the load power forecast in the power system;

[0081] (S13) taking the wind power generation power prediction curve of the next day as a negative load power curve, and superimposing it with the load power prediction curve of the next day to obtain an equivalent load curve;

[0082] (S14) adjusting the equivalent load curve individually by the thermal power unit;

[0083] (S15) determining whether the adjusted equivalent load curve is within the predicted technical output range of the power system; if so, no wind power abandonment occurs; otherwise, wind power abandonment occurs.

[0084] The step (S2) decomposes the equivalent load curve using variational modal decomposition, and comprises the following steps:

[0085] (S21) Initialization λ k , and let n = 1, where k is the number of modes to be decomposed, is the decomposed single-component AM / FM signal, is the center frequency of each single-component AM / FM signal, λ is the Lagrange multiplication operator, and n is the number of cycles;

[0086] (S22) Set n=n+1 and start the loop;

[0087] (S23) Update according to VMD algorithm λ k , until the number of decompositions reaches k and the cycle stops;

[0088] (S24) Determine whether the accuracy ε satisfies the stop loop condition. If yes, stop the loop. Otherwise, jump to step (S22), where the accuracy ε is determined as follows:

[0089] Among them, the low-frequency component, medium-frequency component and high-frequency component in step (S2), wherein the center frequency range of the low-frequency component is less than 0.04, the center frequency range of the medium-frequency component is 0.04-0.13, and the center frequency range of the high-frequency component is greater than 0.13, the low-frequency component and the high-frequency component are regulated by the thermal power unit, and the medium-frequency component is regulated by the high-energy-consuming load.

[0090] In the step (S3), the objective function is to predict the minimum amount of wind abandoned in the power system:

[0091]

[0092] Where W q To predict the total wind curtailment in the power system, T is the dispatch period, N W is the number of wind farms, is the predicted value of wind power active output of the ath wind farm at time t, N G is the number of thermal power units, S G_b is the start-stop variable of the thermal power unit. G_b Set to 1, when stopped, S G_b Set to 0, is the active power output value of the bth thermal power unit at time t, N L is the number of high energy-consuming enterprises, A Boolean variable for high energy consuming enterprises, when running Set to 1, when stopped Set to 0, The operating power of the cth high energy consumption enterprise at time t, N M is the number of normal loads, is the planned active power value of the dth common load at time t, and Δt is the time interval.

[0093] Among them, step (S3) high energy consumption load and thermal power unit coordinated wind power consumption model, including discretizable high energy consumption constraints, transferable high energy consumption constraints, power system operation power balance constraints, balanced wind power rotation reserve constraints, wind power output constraints, active power output change constraints of wind farms in two adjacent time periods, and upper and lower limit constraints of thermal power unit output power;

[0094] The high energy consumption constraint can be discretized, namely:

[0095]

[0096] Where P DL(i),min is the lower limit value of the discrete high energy consumption load adjustment, is the discrete high energy consumption load value, P DL(i),max is the upper limit of the discrete high energy consumption load regulation, r DL(i),down is the lower limit of the climbing constraint of the discrete high energy consumption load, r DL(i),up is the upper limit of the climbing constraint of the discretizable high energy consumption load, the decision variable at the ζ constraint moment, P DL(i),rated is the rated power of the discrete high energy load, T keep(i) is the duration of the discrete high energy consumption load, is the decision variable that can discretize the high energy consumption time t, To continuously adjust the time constraint of discrete high energy load, M SL(i) is the maximum allowable adjustment times of the discrete high energy load within the dispatching cycle;

[0097] Transferable high energy consumption constraints, namely:

[0098]

[0099] Where ΔQ TL(i) (t, t′) is the time shift of the i-th transferable high energy consumption load from time t to time t′, X TL(i) (t, t′) is the adjustment decision variable of the ith high energy load, Q TL(i),anit is the i-th transferable high-energy base load, is the maximum load at time t, is the original load at time t, is the decision variable that can transfer high energy consumption at time t, L (i) To continuously adjust the time constraints for the transfer of high energy loads, is the time shift value of the i-th transferable high energy consumption load at time t;

[0100] Power system operation power balance constraints, namely:

[0101]

[0102] In the formula is the active power output value of the wind farm, is the active power value of conventional load;

[0103] The spinning reserve constraint for balancing wind power is:

[0104]

[0105] In the formula is the output upper limit of the bth thermal power unit at time t, is the lower limit of the output of the bth thermal power unit at time t, For spinning reserve on the system, It is a spinning reserve for the system;

[0106] Wind power output constraints, namely:

[0107]

[0108] In the formula is the predicted value of wind power active output at time t;

[0109] The active output change constraint of the wind farm in two adjacent time periods is:

[0110]

[0111] Where P wind_down is the maximum downward adjustment value of the active output of the wind farm in adjacent periods, P wind_up It is the maximum upward adjustment value of the active output of the wind farm in adjacent time periods;

[0112] The upper and lower limits of the output power of thermal power units are:

[0113]

[0114] Where P G_b_min is the minimum active output of the bth thermal power unit, P G_b_max It is the maximum value of the active output of the bth thermal power unit.

[0115] like Figure 2 As shown, a gray wolf algorithm solution flow chart of a scheduling method for high energy consumption loads participating in peak load regulation based on variational mode decomposition is shown. The gray wolf algorithm solves the objective function, including the following steps:

[0116] (S41) Initialization of the gray wolf population, including the number of groups N, the maximum number of iterations Maxlter, the convergence factor a, and the coefficient vectors A and C;

[0117] (S42) randomly initializing the position X of the individual gray wolf according to the upper and lower bounds of the variable;

[0118] (S43) Calculate the fitness value of each wolf, and save the location information of the wolf with the best fitness value in the population α , save the location information of the wolf with the second best fitness value in the population as X β , save the location information of the gray wolf with the third best fitness in the population as Xδ ;

[0119] (S44) updating the position of the gray wolf individual X;

[0120] (S45) updating the convergence factor a, coefficient vectors A and C;

[0121] (S46) calculating the fitness value of each gray wolf and updating the optimal position of the three alpha wolves;

[0122] (S47) Determine whether the maximum number of iterations Maxlter is reached, if so, the Gray Wolf Algorithm stops and outputs X α The value of is taken as the final optimal solution, otherwise jump to step (S44).

[0123] Using the above method, take the load curve and wind power plant output of a certain place on a certain day as an example. Figure 3 As shown in the figure, a method for dispatching high-energy-consuming loads participating in peak load regulation based on variational mode decomposition shows the wind power generation curve, load power curve and equivalent load curve of a wind farm in a certain place on a certain day. The trend of the original system load is relatively stable. When wind power is not connected to the system, although there are sometimes relatively large fluctuations, it still maintains a relatively stable trend overall. When wind power is connected to the system, Figure 3 As shown in the middle curve, from time 0 to time 10, the load change trend has changed significantly compared with the original load change trend, and between time 10 and time 15, it can be seen that the peak-to-valley difference is larger than before the wind power was connected to the system. When the equivalent load is simply adjusted by using the thermal power unit as shown in Table 1:

[0124] Table 1 Output parameters of thermal power units

[0125]

[0126]

[0127] The objective function is established by taking the wind power abandonment of the power system as the objective function, and the constraints on the thermal power units are brought into the solution. The optimization result is shown in Figure 4. At the beginning, due to the large equivalent load of wind power, the equivalent system load is less than the minimum output of the system, so the phenomenon of wind abandonment occurs. When the peak-to-valley difference of the equivalent load is large between 10 and 15 o'clock, only the thermal power units are used to adjust the equivalent load, and the effect is not very good. The wind abandonment volume within the day is 14.492 (pu*h).

[0128] The dispatching method of high energy consumption load participating in peak load regulation of wind farm in a certain place on a certain day based on variational mode decomposition:

[0129] First, we perform variational mode decomposition on the equivalent system load as follows Figure 5-7As shown in the figure, the low-frequency component mode, the medium-frequency component mode and the high-frequency component mode are decomposed in turn, and the corresponding center frequencies (decomposition modes 1 to 3) are: 0.000295, 0.0392, 0.129, and the curve in the figure shows its change trend. Figure 5 It can be seen that the low-frequency component mode is relatively smooth, while Figure 6 and Figure 7 The fluctuations of the intermediate frequency component mode and the high frequency component mode in the power grid are large, and the corresponding center frequencies are different, so the high energy consumption load is used to adjust the intermediate frequency component mode. The traditional thermal power unit adjusts the superposition of the low frequency sub-mode and the high frequency component mode, and the high energy consumption load parameters are shown in Table 2:

[0130] Table 2 High energy consumption load parameters

[0131]

[0132] The parameters of the thermal power units in the system are consistent with those in Table 1.

[0133] The same constraint parameters are used for thermal power units, and the constraint conditions of high energy consumption load are introduced at the same time. The objective function is established with the maximum daily wind power consumption. The results are as follows: Figure 8 , Fig. 9 shown.

[0134] Finally, the daily wind abandonment of the thermal power unit in the low-frequency component mode and high-frequency component mode is 3.6432 (pu*h), while the daily wind abandonment of the medium-frequency component mode of the high-load system is 5.627 (pu*h). The final daily wind abandonment of the system is 9.3622 (pu*h). The adjustment results are as follows Fig.10 shown.

[0135] like Fig.10 It can be seen that compared with the adjustment of a single thermal power unit, its daily wind abandonment volume is significantly reduced, and at the starting time, the high energy-consuming load is in the shutdown stage, which can effectively increase the equivalent load of the system, making the area of ​​the system equivalent load curve under the system minimum load value smaller, thereby reducing the system's wind abandonment volume.

[0136] The technical features of the above-mentioned examples can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned examples are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0137] The above examples only express the embodiments of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and non-substantial improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the attached claims.

Claims

1. A scheduling method for high energy consumption loads to participate in peak load regulation based on variational mode decomposition, It is characterized in that The following steps are involved: (S1) obtaining a wind power generation prediction curve and a load power prediction curve for the next day in the power system, obtaining an equivalent load curve, and using the thermal power unit to adjust the equivalent load curve separately, judging whether the adjusted equivalent load curve meets the output range of the thermal power unit, and predicting whether the power system will have wind curtailment; (S2) Decomposing the equivalent load curve by using variational modal decomposition to obtain components corresponding to modal curves with different center frequencies, wherein the components include low-frequency components, medium-frequency components, and high-frequency components, and adjusting the components by using high-energy-consuming loads and thermal power units; (S3) Taking the prediction of the minimum amount of wind power abandoned in the power system as the objective function, a model for the coordinated consumption of wind power by high energy-consuming loads and thermal power units is established; (S4) solving the objective function by using the grey wolf algorithm to obtain the high energy consumption load regulation power, the thermal power unit regulation power and the wind power output of the next day; The step (S2) decomposes the equivalent load curve using variational modal decomposition, and includes the following steps: (S21) Initialization λ k , and let n = 1, where k is the number of modes to be decomposed, is the decomposed single-component AM / FM signal, is the center frequency of each single-component AM / FM signal, λ k is the Lagrange multiplication operator, n is the number of cycles; (S22) Set n=n+1 and start the loop; (S23) Update according to VMD algorithm λ k , until the number of decompositions reaches k, the loop stops; (S24) Determine whether the accuracy ε satisfies the stop loop condition. If yes, stop the loop. Otherwise, jump to step (S22), where the accuracy ε is determined as follows: The step (S4) of solving the objective function using the grey wolf algorithm comprises the following steps: (S41) Initialization of the gray wolf population, including the number of groups N, the maximum number of iterations Maxlter, the convergence factor a1, and the coefficient vectors A and C; (S42) randomly initializing the position X of the gray wolf individual according to the upper and lower bounds of the variable; (S43) Calculate the fitness value of each wolf, and save the location information of the wolf with the best fitness value in the population as X α , save the location information of the wolf with the second best fitness value in the population as X β , save the location information of the gray wolf with the third best fitness in the population as X δ ; (S44) updating the position of the gray wolf individual X; (S45) updating the convergence factor a1, coefficient vectors A and C; (S46) calculating the fitness value of each gray wolf and updating the optimal position of the three alpha wolves; (S47) Determine whether the maximum number of iterations Maxlter is reached, if so, the Gray Wolf Algorithm stops and outputs X α The value of is taken as the final optimal solution, otherwise jump to step (S44).

2. According to the method of scheduling high energy consumption loads participating in peak load regulation based on variational mode decomposition according to claim 1, It is characterized in that The step (S1) comprises the following steps: (S11) obtaining a wind power generation prediction curve for the next day by predicting the wind power generation power in the power system; (S12) obtaining a load power forecast curve for the next day by forecasting the load power forecast in the power system; (S13) taking the wind power generation power prediction curve of the next day as a negative load power curve, and superimposing it with the load power prediction curve of the next day to obtain an equivalent load curve; (S14) adjusting the equivalent load curve by the thermal power unit alone; (S15) determining whether the adjusted equivalent load curve is within the predicted technical output range of the power system; if so, no wind power abandonment occurs; otherwise, wind power abandonment occurs.

3. According to the method for dispatching high energy consumption loads participating in peak load regulation based on variational mode decomposition according to claim 1, It is characterized in that In the step (S3), the objective function is to predict the minimum amount of wind abandoned in the power system: Where W q To predict the total wind curtailment in the power system, T is the dispatch period, N W is the number of wind farms, is the predicted value of wind power active output of the ath wind farm at time t, N G is the number of thermal power units, S G_b is the start-stop variable of the thermal power unit. G_b Set to 1, when stopped, S G_b Set to 0, is the active power output value of the bth thermal power unit at time t, N L is the number of high energy-consuming enterprises, A Boolean variable for high energy consuming enterprises, when running Set to 1, when stopped Set to 0, The operating power of the cth high energy consumption enterprise at time t, N M is the number of normal loads, is the planned active power value of the dth common load at time t, and Δt is the time interval.

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