Source-load resource regulation method and device applied to integrated source-network-load-storage

Abstract

The present application discloses a source-load resource regulation method and device applied to source-network-load-storage integration. The method includes: determining the power generation optimization parameter and load optimization parameter for each first time unit according to the new energy power generation prediction parameter and load prediction parameter for each first time unit within a preset duration; determining the new energy power generation power optimization curve according to the power generation optimization parameter for each first time unit, and determining the load optimization curve according to the load optimization parameter for each first time unit; regulating the source-load resources in the first time period according to the new energy power generation power optimization curve and the load optimization curve. Through the above method, according to the new energy power generation power optimization curve and the load optimization curve, the source-load resources in the first time period can be regulated to minimize the difference between the new energy power generation power parameter and the load parameter, so that the source-load resource regulation amount matches the source-load resource regulation rule.

Description

Technical Field

[0001] The present invention relates to the field of power technology, and in particular to a method and device for regulating source-load resources applied to the integration of power generation, grid, load and energy storage. Background Art

[0002] With the advancement of the integration project of power generation, grid, load and energy storage, rules for regulating source-load resources have been proposed, and it is required that the new energy power station side regulates the new energy output power according to the rules for regulating source-load resources, and the park load side regulates the park load according to the rules for regulating source-load resources. However, currently, the resource regulation on the new energy power station side and the park load side usually cannot be carried out in accordance with the rules for regulating source-load resources, which makes the amount of source-load resource regulation unable to match the rules for regulating source-load resources. The prior art has not yet proposed a technical solution that can be used to achieve the matching of the amount of source-load resource regulation with the rules for regulating source-load resources, resulting in poor source-load resource regulation effects. Summary of the Invention

[0003] Embodiments of the present application provide a method and device for regulating source-load resources applied to the integration of power generation, grid, load and energy storage to solve the problem that the amount of source-load resource regulation cannot match the rules for regulating source-load resources.

[0004] To solve the above technical problems, the present application is implemented as follows:

[0005] In a first aspect, embodiments of the present application provide a method for regulating source-load resources applied to the integration of power generation, grid, load and energy storage, including:

[0006] Determining the power generation power optimization parameter and the load optimization parameter for each of the first time units according to the new energy power generation power prediction parameter and the load prediction parameter for each of the first time units within a preset duration;

[0007] Determining an optimized new energy power generation power curve according to the power generation power optimization parameter for each of the first time units, and determining an optimized load curve according to the load optimization parameter for each of the first time units;

[0008] Regulating the source-load resources in the first time period according to the optimized new energy power generation power curve and the optimized load curve.

[0009] In a second aspect, embodiments of the present application provide a device for regulating source-load resources applied to the integration of power generation, grid, load and energy storage, including:

[0010] A first determination module, configured to determine the power generation power optimization parameter and the load optimization parameter for each of the first time units according to the new energy power generation power prediction parameter and the load prediction parameter for each of the first time units within a preset duration;

[0011] A second determination module, configured to determine a new energy power generation optimization curve according to the power generation power optimization parameters of each of the first time units, and determine a load optimization curve according to the load optimization parameters of each of the first time units;

[0012] A first regulation module, configured to regulate the source-load resources in the first time period according to the new energy power generation optimization curve and the load optimization curve.

[0013] In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor, a memory, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, the steps of the source-load resource regulation method applied to source-network-load-storage integration as described in the first aspect are implemented.

[0014] In a fourth aspect, an embodiment of the present application provides a readable storage medium, which is characterized in that a program or instruction is stored on the readable storage medium. When the program or instruction is executed by a processor, the steps of the source-load resource regulation method applied to source-network-load-storage integration as described in the first aspect are implemented.

[0015] In the embodiment of the present application, according to the new energy power generation prediction parameters and load prediction parameters of each first time unit within a preset duration, the power generation power optimization parameters and load optimization parameters of each first time unit are determined. It can be understood that within the range of the new energy power generation prediction parameters and the load prediction parameters, the optimized new energy power generation parameters and the optimized load parameters are determined, so that the difference between the new energy power generation parameters and the load parameters is minimized, thereby matching the source-load resource regulation rules. According to the power generation power optimization parameters of each first time unit, a new energy power generation optimization curve can be determined, and according to the load optimization parameters of each first time unit, a load optimization curve can be determined. In this way, according to the above new energy power generation optimization curve and the load optimization curve, the source-load resources in the first time period can be regulated, so that the source-load resource regulation amount matches the source-load resource regulation rules. Description of the Drawings

[0016] Figure 1 is a flowchart of the source-load resource regulation method applied to source-network-load-storage integration provided by an embodiment of the present application;

[0017] Figure 2 is a graph of the monthly new energy power generation optimization curve and the monthly load optimization curve provided by an embodiment of the present application;

[0018] Figure 3 is a graph of the difference between the power generation power optimization parameters and the load optimization parameters provided by an embodiment of the present application;

[0019] Figure 4 It is an operation diagram of the participation of the power source, grid, load, and energy storage in the medium- and long-term power market provided by an embodiment of the present application;

[0020] Figure 5 It is a partial new energy power generation power optimization curve and a new energy power generation power optimization curve diagram for a preset duration provided by an embodiment of the present application;

[0021] Figure 6 It is a difference curve diagram of the daily first deviation provided by an embodiment of the present application;

[0022] Figure 7 It is a flowchart for determining the new energy power generation power optimization parameters at t+T points provided by an embodiment of the present application;

[0023] Figure 8 It is a partial new energy power generation power optimization curve and a new energy power generation power optimization curve diagram for a preset duration updated every 15 minutes provided by an embodiment of the present application;

[0024] Figure 9 It is a daily first deviation difference curve diagram updated every 15 minutes provided by an embodiment of the present application;

[0025] Figure 10 It is a daily load optimization curve and a partial load optimization curve diagram for a preset duration provided by an embodiment of the present application;

[0026] Figure 11 It is a difference curve diagram of the daily second deviation provided by an embodiment of the present application;

[0027] Figure 12 It is a flowchart for determining the load optimization parameters at t+T points provided by an embodiment of the present application;

[0028] Figure 13 It is a daily load optimization curve and a partial load optimization curve diagram for a preset duration updated every 15 minutes provided by an embodiment of the present application;

[0029] Figure 14 It is a difference curve diagram of the daily second deviation updated every 15 minutes provided by an embodiment of the present application;

[0030] Figure 15 It is a source and load resource regulation device applied to the integration of power source, grid, load, and energy storage provided by an embodiment of the present application;

[0031] Figure 16 It is an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0032] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0033] See Figure 1 , Figure 1 which is a flowchart of a source-load resource regulation method applied to source-network-load-storage integration provided by an embodiment of the present application. The method includes the following steps:

[0034] Step 101: Determine the power generation power optimization parameter and the load optimization parameter for each of the first time units according to the new energy power generation power prediction parameter and the load prediction parameter for each of the first time units within a preset duration.

[0035] Step 102: Determine the new energy power generation power optimization curve according to the power generation power optimization parameter for each of the first time units, and determine the load optimization curve according to the load optimization parameter for each of the first time units.

[0036] Step 103: Regulate the source-load resources in the first time period according to the new energy power generation power optimization curve and the load optimization curve.

[0037] In step 101, the regulation device determines the power generation power optimization parameter and the load optimization parameter for each of the first time units according to the new energy power generation power prediction parameter and the load prediction parameter for each of the first time units within a preset duration. It can be understood that within the range of the new energy power generation power prediction parameter and the load prediction parameter, the optimized new energy power generation power parameter and the optimized load parameter are determined to minimize the difference between the new energy power generation power parameter and the load parameter, so as to match the source-load resource regulation rule. The above preset duration includes different time scales such as N annual durations, N monthly durations, N daily durations, and N hourly durations, where N is an integer greater than or equal to 1.

[0038] The regulation device can determine the new energy power generation power optimization curve according to the power generation power optimization parameter for each of the first time units, and can determine the load optimization curve according to the load optimization parameter for each of the first time units. The above new energy power generation power optimization curve and load optimization curve are used to declare to the medium- and long-term power market, and the regulation device regulates the new energy power generation power and the load side load according to the declared curves. That is, the new energy power generation power optimization curve and the load optimization curve are used to regulate the source-load resources, so that the source-load resource regulation amount matches the source-load resource regulation rule. Among them, the source-load resource regulation rule can be understood as the rule of minimizing the difference between the source and load resources.

[0039] Taking the preset duration as one year as Example 1, the control device determines the power generation optimization parameters and load optimization parameters for each first time unit within a year according to the new energy power generation power prediction parameters and load prediction parameters for each first time unit within a year. Among them, the first time unit can be a time unit such as month, day, hour or minute. If the first time unit is month, the control device determines the power generation optimization parameters and load optimization parameters for each month within a year; if the first time unit is hour, the control device determines the new energy power generation optimization parameters and load optimization parameters for each hour within a year. The control device determines the new energy annual power generation optimization curve according to the power generation optimization parameters for each first time unit. This annual new energy power generation optimization curve is used to declare to the medium- and long-term power market, and the control device regulates the new energy power generation for this year according to the new energy annual power generation optimization curve. The control device can determine the annual load optimization curve according to the load optimization parameters for each first time unit. This annual load optimization curve is used to declare to the medium- and long-term power market, and the control device regulates the load parameters for this year according to the annual load optimization curve.

[0040] Taking the preset duration as one month as Example 2, the new energy power generation curve and load optimization curve for this month can be determined. The control device determines the power generation optimization parameters and load optimization parameters for each first time unit within this month according to the new energy power generation power prediction parameters and load prediction parameters for each first time unit within this month. If the first time unit is day, the control device determines the power generation optimization parameters and load optimization parameters for each day within this month; if the first time unit is hour, the control device determines the power generation optimization parameters and load optimization parameters for each hour within this month. The control device determines the new energy monthly power generation optimization curve according to the power generation optimization parameters for each first time unit. This monthly new energy power generation optimization curve is used to declare to the medium- and long-term power market, and the control device regulates the new energy power generation for this month according to the new energy monthly power generation optimization curve. The control device can determine the monthly load optimization curve according to the load optimization parameters for each first time unit. This monthly load optimization curve is used to declare to the medium- and long-term power market, and the control device regulates the load parameters for this month according to the monthly load optimization curve.

[0041] According to the new energy annual power generation optimization curve in Example 1, the monthly new energy power generation optimization curve for each month of this year can be determined. According to the annual load optimization curve, the monthly load optimization curve for each month of this year can be determined. In Example 2, when the preset duration is taken as one month, the monthly power generation optimization curve and the monthly load optimization curve for this month can be directly determined. It is easy to understand that the shorter the preset duration, the higher the accuracy of the new energy power generation prediction parameters and the load prediction parameters determined, so the accuracy of the power generation optimization parameters and the load optimization parameters is also higher. In this way, after determining the annual power generation optimization curve and the annual load optimization curve, the preset duration can be taken as one month duration, and the control device can determine the monthly power generation optimization curve and the monthly load optimization curve again every month, so as to update the annual power generation optimization curve and the annual load optimization curve. It should be noted that the control device may not update the annual power generation optimization curve and the annual load optimization curve either, and the embodiments of the present application do not limit this.

[0042] Optionally, determining the power generation optimization parameter and the load optimization parameter for each first time unit according to the new energy power generation prediction parameter and the load prediction parameter for each first time unit within the preset duration includes:

[0043] Determining the objective function within the preset duration according to the difference between the new energy power generation prediction parameter and the load prediction parameter for each first time unit within the preset duration;

[0044] Determining the power generation optimization parameter and the load optimization parameter for each first time unit according to the sequential quadratic programming algorithm and the objective function within the preset duration.

[0045] In this embodiment, the objective function within the preset duration is solved by the sequential quadratic programming method, so that the power generation optimization parameter and the load optimization parameter for each first time unit can be determined. The objective function within the preset duration is the difference between the new energy power generation prediction parameter and the load prediction parameter for each first time unit, and can be expressed as:

[0046]

[0047] where F1 is the objective function within the preset duration, P a,t is the new energy power generation prediction parameter at time t, and P b,t is the load prediction parameter at time t.

[0048] The value range of t is from 1 to M. The duration from 1 to M is the preset duration, and the value of M is associated with the first time unit and the preset duration. Taking the preset duration as one year as an example, if the first time unit is month, then the value of M is 12; if the first time unit is hour, then the value of M is 8760.

[0049] Solve the objective function F1 by sequential quadratic programming method, where the constraint conditions are: the optimized power generation parameters at time t are within the range of the predicted power generation parameters; the optimized load parameters at time t are within the range of the predicted load parameters at time t.

[0050] The specific steps for solving the objective function F1 by sequential quadratic programming method are as follows:

[0051] Step 1001: Given the initial point X 0 , convergence accuracy ε, let H 0 = I, set k = 0, where X is the decision variable, that is, the predicted new energy power generation parameters and load prediction parameters, and H is a matrix;

[0052] Step 1002: Simplify the above determined new energy power generation optimization parameters and load optimization parameters into a quadratic programming problem at the iteration point X k :

[0053]

[0054] s.t. AS = -B

[0055] A eq S = -B eq

[0056] where S = X - X k ,

[0057] Step 1003: Solve the above quadratic programming problem and let S k = S * ;

[0058] Step 1004: Conduct a constrained one-dimensional search on the original problem objective function in the direction of S k to obtain the next iteration point X k +1 ;

[0059] Step 1005: Termination judgment: If X k+1 meets the termination criterion of the given accuracy, the given accuracy can be, for example, e×10 -16 , then take X k+1 as the optimal solution, that is, the new energy power generation optimization parameters and load optimization parameters, and f(X k+1 ) as the optimal cost of the objective function, that is, the minimum value of the objective function within the preset duration, and terminate the calculation; otherwise, go to Step 1006;

[0060] Step 1006: According to the modified Lagrangian matrix H k+1 , let k = k + 1, and go back to Step 1002.

[0061] The objective function F1 is solved by the above sequential quadratic programming method, so as to determine the power generation power optimization parameters and load optimization parameters for each first time unit.

[0062] Taking a preset duration of one month as an example, and assuming the current month has 30 days, if the first unit time length is one day, the objective function can be expressed as:

[0063]

[0064] where F1 is the objective function for this month, P a,t is the new energy power generation power prediction parameter for the t-th day of this month, and P b,t is the load prediction parameter for the t-th day of this month. By solving the above objective function F1 through the sequential quadratic programming method, the new energy power generation power optimization parameters and load optimization parameters for each day of the current month can be determined.

[0065] Taking a preset duration of one month as an example, and assuming the current month has 30 days, if the first unit time length is one hour, the objective function can be expressed as:

[0066]

[0067] where F1 is the objective function for this month, P a,t is the new energy power generation power prediction parameter for the t-th hour of this month, and P b,t is the load prediction parameter for the t-th hour of this month. By solving the above objective function through the sequential quadratic programming method, the new energy power generation power optimization parameters and load optimization parameters for each hour of the current month can be determined. The control device determines the energy power generation power optimization curve and load optimization curve for this month according to the new energy power generation power optimization parameters and load optimization parameters for each hour. The example above is verified by numerical simulation, Figure 2 and Figure 3 The curves shown are the results of the numerical simulation. See Figure 2 , Figure 2 The curves in are the new energy monthly power generation power optimization curve 1 and monthly load optimization curve 2 determined through numerical simulation. See Figure 3 , Figure 3 The curve in is the curve 3 determined according to the difference between the power generation power optimization parameters and the load optimization parameters.

[0068] Optionally, the new energy power generation power prediction parameters include new energy output power prediction parameters and new energy side energy storage power prediction parameters;

[0069] Determining the power generation power optimization parameters for each first time unit according to the new energy power generation power prediction parameters for each first time unit within the preset duration includes:

[0070] Determine the new energy output power optimization parameter and the new energy side energy storage power optimization parameter for each first time unit within a preset duration according to the new energy output power prediction parameter and the new energy side energy storage power prediction parameter of each first time unit.

[0071] Determine the new energy power generation optimization curve according to the power generation optimization parameter of each first time unit, including:

[0072] Determine the new energy output power optimization curve and the new energy side energy storage power optimization curve according to the new energy output power optimization parameter and the new energy side energy storage power prediction parameter of each first time unit.

[0073] In this embodiment, the control device can determine the new energy output power optimization curve and the new energy side energy storage power optimization curve, so that the control device can regulate the new energy output power in the first time period according to the new energy output power optimization curve, and can also regulate the energy storage power on the new energy side according to the new energy side energy storage power optimization curve.

[0074] The control device determines the new energy output power optimization parameter and the new energy side energy storage power optimization parameter for each first time unit according to the new energy output power prediction parameter and the new energy side energy storage power prediction parameter of each first time unit within a preset duration. The target function F1 can be solved by the sequential quadratic programming algorithm to determine the new energy output power optimization parameter and the new energy side energy storage power optimization parameter for each first time unit. For the specific process of solving the target function F1 by the sequential quadratic programming algorithm, reference can be made to the above embodiment. To avoid repetition, it will not be elaborated here. The target function F1 can be expressed as:

[0075]

[0076] where F1 is the target function within the preset duration at time t, P G,t is the new energy output power prediction parameter at time t, P PES,t is the new energy side energy storage power prediction parameter, and P b,t is the load prediction parameter at time t.

[0077] The value range of t is from 1 to M. The duration from 1 to M is the preset duration, and the value of M is associated with the first time unit and the preset duration.

[0078] Optionally, the load prediction parameter includes a computing power load prediction parameter, a heating and cooling equipment load prediction parameter, a charging facility load prediction parameter, and a load side energy storage power prediction parameter;

[0079] Determine the load optimization parameters for each first time unit according to the load prediction parameters for each first time unit within a preset duration, including:

[0080] Determine the computing power load optimization parameters, heating and cooling equipment load optimization parameters, charging facility load optimization parameters, and load-side energy storage power optimization parameters for each first time unit according to the computing power load prediction parameters, heating and cooling equipment load prediction parameters, charging facility load prediction parameters, and load-side energy storage power prediction parameters for each first time unit within a preset duration.

[0081] Determine the load optimization curve according to the load optimization parameters for each first time unit, including:

[0082] Determine the computing power load optimization curve, heating and cooling equipment load optimization curve, charging facility load optimization curve, and load-side energy storage power optimization curve according to the computing power load optimization parameters, heating and cooling equipment load optimization parameters, charging facility load optimization parameters, and load-side energy storage power optimization parameters for each first time unit.

[0083] In this embodiment, the regulation device can determine the computing power load optimization curve, heating and cooling equipment load optimization curve, charging facility load optimization curve, and load-side energy storage power optimization curve according to the computing power load optimization parameters, heating and cooling equipment load optimization parameters, charging facility load optimization parameters, and load-side energy storage power optimization parameters for each first time unit. Thus, the regulation device can regulate the computing power load in the first time period according to the computing power load optimization curve; the regulation device can regulate the heating and cooling equipment load in the first time period according to the heating and cooling equipment load optimization curve; the regulation device can regulate the charging facility load in the first time period according to the charging facility load optimization curve; the regulation device can regulate the load-side energy storage power in the first time period according to the load-side energy storage power optimization curve.

[0084] The load prediction parameters include computing power load prediction parameters, heating and cooling equipment load prediction parameters, charging facility load prediction parameters, and load-side energy storage power prediction parameters. Thus, the objective function F1 can be expressed as:

[0085]

[0086] where F1 is the objective function within the preset duration at time t, P G,t is the predicted new energy output power parameter at time t, P PES,t is the new energy side energy storage power prediction parameter, P CT,t is the computing power load prediction parameter at time t, P LES,t is the load-side energy storage power prediction parameter at time t, P CL,t is the heating and cooling equipment load prediction parameter at time t, P EV,t is the charging facility load prediction parameter at time t.

[0087] The value range of t is from 1 to M. The duration from 1 to M is a preset duration, and the value of M is associated with the first time unit and the preset duration.

[0088] Optionally, the preset duration includes multiple sub-preset durations, and the method further includes:

[0089] Determine the new energy power generation power prediction parameters for each second time unit within the first sub-preset duration;

[0090] Determine the new energy power generation power optimization parameters for each second time unit according to the new energy power generation power optimization curve of the preset duration;

[0091] Determine the first difference between the new energy power generation power prediction parameters and the new energy power generation power optimization parameters for each second time unit;

[0092] Determine the new energy side objective function within the first sub-preset duration according to the product of the first difference and the first deviation assessment. The first deviation assessment is the deviation assessment cost per unit of electricity on the new energy side;

[0093] Determine the updated new energy power generation power optimization parameters for each second time unit according to the new energy side objective function within the first sub-preset duration and the sequential quadratic programming algorithm.

[0094] Determine the new energy power generation power optimization curve of the first sub-preset duration according to the updated new energy power generation power optimization parameters for each second time unit, and regulate the new energy power generation power in the second time period according to the new energy power generation power optimization curve of the first sub-preset duration.

[0095] In this embodiment, the regulation device determines the new energy power generation power prediction parameters for each second time unit within the first sub-preset duration, and the first sub-preset duration is one of the multiple sub-durations within the preset duration. The new energy power generation power parameters may be affected by some conditions. Understandably, when predicting the new energy power generation power parameters of the preset duration, the shorter the preset duration, the more accurate the predicted new energy power generation power parameters. Based on this, determining the new energy power generation power prediction parameters for each second time unit within the first sub-preset duration has higher accuracy than determining the new energy power generation power prediction parameters of the preset duration.

[0096] The regulation device determines the first difference between the new energy power generation power prediction parameters and the new energy power generation power optimization parameters for each second time unit. See Figure 4 , Figure 4It is an operation diagram of the participation of the power source, grid, load, and energy storage in the medium- and long-term power market. Among them, on the new energy side, also known as the power station side, it includes wind power output power parameters, photovoltaic power output power parameters, and electrical energy storage power parameters. On the load side, also known as the park side, it includes computing power load, park-side electrical energy storage, heating and cooling load, and charging facility load. The optimized curve of the new energy power generation power for a preset duration can be used to declare to the medium- and long-term power market, and the control equipment adjusts the new energy power generation power according to the optimized curve of the new energy power generation power. If the actual new energy power generation power parameter is inconsistent with the optimized power generation parameter at a certain moment, a new energy power generation deviation assessment fee will be generated. Therefore, if there is a difference between the actual new energy power generation power parameter and the new energy power generation optimized parameter in a certain second time unit, a new energy power generation deviation assessment fee will be generated. Based on this, within the range of the new energy power generation power prediction parameters for each second time unit, the control equipment determines the actual new energy power generation power parameter for each second time unit, that is, determines the updated new energy power generation optimized parameter for each second time unit, so as to minimize the total new energy side power generation deviation assessment cost.

[0097] The sequential quadratic programming algorithm can be used to solve the objective function F2, so as to determine the updated new energy power generation optimized parameter for each second time unit. For the specific process of using the sequential quadratic programming algorithm to solve the objective function F2, reference can be made to the above-mentioned embodiment. To avoid repetition, it will not be elaborated here. The objective function F2 on the new energy side within the first sub-preset duration can be expressed as:

[0098]

[0099] Among them, F2 is the objective function on the new energy side within the first sub-preset duration, P a,t is the new energy power generation power prediction parameter at time t, P PD,t is the new energy side power generation optimized parameter at time t, C PD,t is the new energy side unit power deviation assessment fee.

[0100] The value range of t is from 1 to M. The duration from 1 to M is the first sub-preset duration, and the value of M is associated with the second time unit and the first sub-preset duration. For example: if the first sub-preset duration is one month and the second time unit is day, then M takes the value of 30; if the first sub-preset duration is one day and the second time unit is hour, then M takes the value of 24.

[0101] By solving the new energy side objective function F2 within the above-mentioned first sub preset duration through sequential quadratic programming method, the optimized parameters of new energy power generation for each updated second time unit can be determined. The regulation device determines the optimized curve of new energy power generation for the first sub preset duration according to the optimized parameters of new energy power generation for each updated second time unit, and regulates the new energy output power in the second time period according to the optimized curve of new energy power generation for the first sub preset duration, so as to minimize the total cost of new energy side power generation deviation assessment. Wherein, the duration of the second time period is equal to the first sub preset duration, and the second time period is a sub time period of the first time period.

[0102] Considering that the new energy power generation prediction parameters can include new energy output power prediction parameters and new energy side energy storage power prediction parameters, the objective function can be expressed as:

[0103]

[0104] Among them, P G,t is the new energy output power prediction parameter at time t, P PES,t is the new energy side energy storage power prediction parameter at time t, P PD,t is the optimized parameter of new energy side power generation at time t, and C PD,t is the deviation assessment cost per unit of electricity on the new energy side.

[0105] The value range of t is from 1 to M. The duration from 1 to M is the first sub preset duration, and the value of M is associated with the second time unit and the first sub preset duration.

[0106] By solving F2 through sequential quadratic programming method, the optimized parameters of new energy output power and new energy side energy storage power for each updated second time unit can be determined, so that the optimized curve of new energy output power and the optimized curve of new energy side energy storage power for the first sub preset duration can be determined. The regulation device can regulate the new energy output power and the new energy side energy storage power respectively according to the optimized curve of new energy output power and the optimized curve of new energy side energy storage power.

[0107] Taking a monthly preset duration as an example, the first preset sub duration is a daily duration within this month. If the second time unit is an hour, the objective function F2 of the first preset sub duration can be expressed as:

[0108]

[0109] Among them, the calculation method of C PD,t can be as follows:

[0110] When P G,t +P PES,t >P PD,t , C PD,t =CG,t

[0111] When P G,t +P PES,t <P PD,t is the case, C PD,t = 0.15C G,t

[0112] Among them, P G,t is the predicted parameter of the new energy output power at the t-th hour of the day, P PES,t is the predicted parameter of the energy storage power on the new energy side at the t-th hour of the day, P PD,t is the optimization parameter of the power generation power on the new energy side at the t-th hour, and C PD,t is the deviation assessment cost per unit of electricity on the new energy side.

[0113] C G,t is the electricity purchase price for a certain power grid enterprise to act as an agent for other users at time t.

[0114] By solving the objective function F2 of the new energy side under the above one-day period through the sequential quadratic programming method, the updated optimization parameters of the new energy power generation power for each hour of this day can be determined, and thus the optimization curve of the new energy power generation power for this day can be determined. For the above example, a numerical example simulation verification is carried out, Figure 5 and Figure 6 are the results of the numerical example simulation. See Figure 5 , Figure 5 The curve in is the daily new energy power generation power optimization curve 4 obtained through the numerical example simulation operation, and part of the new energy power generation power optimization curve 5 for a preset duration. See Figure 6 , Figure 6 The curve in is the difference curve 6 determined according to multiple first deviations in a day.

[0115] Taking a preset duration of one month as an example, the first preset sub-duration is one day in this month. If the second time unit is 15 minutes, the objective function F2 of the new energy side for the first preset sub-duration can be expressed as:

[0116]

[0117] C PD,t The calculation method of can refer to the above embodiment, and will not be repeated here to avoid duplication.

[0118] Among them, P G,t is the predicted parameter of the new energy output power at the t-th 15-minute of the day, P PES,t is the predicted parameter of the energy storage power on the new energy side at the t-th 15-minute of the day, P PD,t is the optimization parameter of the power generation power on the new energy side at the t-th 15-minute, and C PD,t is the deviation assessment cost per unit of electricity on the new energy side.

[0119] By solving the new - energy - side objective function F2 for the above - mentioned one - day period using the sequential quadratic programming method, the optimized parameters of the new - energy power generation for each 15 - minute period in this day can be determined, and thus the optimized curve of the new - energy power generation for this day can be determined.

[0120] P G,t is the predicted parameter of the new - energy output power for the t - th 15 - minute period of the day, and P PES,t is the predicted parameter of the new - energy - side energy storage for the t - th 15 - minute period of the day, where the value range of t is from 1 to 96. The new - energy power generation may be affected by some unpredictable situations, such as sudden weather changes. Therefore, the predicted parameters of the new - energy power generation for the next t + T periods can be obtained again at each t point to make the predicted parameters of the new - energy power generation more accurate, where the value of t + T is less than or equal to 96. See Figure 7 , Figure 7 is the flow chart for determining the optimized parameters of the new - energy power generation at t + T points. The initial value of t is 1. At each t point, the predicted parameters of the new - energy power generation for the next t + T periods are obtained, and the predicted parameters of the new - energy power generation for the next t + T periods are input into the objective function F2. It is easy to understand that the predicted parameters of the new - energy power generation for the next t + T periods determined again at each t point are more accurate than directly determining the predicted parameters of the new - energy power generation at 96 points. Input the predicted parameters of the new - energy power generation for the next t + T periods into the objective function F2, and use the sequential quadratic programming method to solve the objective function F2, so that the optimized parameters of the new - energy power generation at t + T points can be determined. According to the optimized parameters of the new - energy power generation at t + T points, the optimized curve of the new - energy power generation for the period corresponding to t to T can be determined, and this optimized curve of the new - energy power generation can be used to update the optimized curve of the daily new - energy power generation.

[0121] It should be noted that, in order to make the value of the objective function F2 more accurate, that is, the total cost of the new - energy - side power generation deviation assessment more accurate, before the t point, the actual new - energy power generation parameter can be input instead of the predicted parameter of the new - energy power generation; at the t point and after the t point, the predicted parameters of the new - energy power generation for t + T points are input. The simulation of this embodiment is carried out through the numerical simulation technology, and the simulation results are shown in Figure 8 and Figure 9 , Figure 8 is the optimized curve 7 of the daily new - energy power generation updated every 15 minutes, and part of the optimized curve 8 of the new - energy power generation for the preset duration. See Figure 9 , Figure 9 is the difference curve 9 determined according to the multiple daily first - order deviations updated every 15 minutes.

[0122] Optionally, the preset duration includes multiple sub-preset durations, and the method further includes:

[0123] Determine the load prediction parameters for each second time unit within the first sub-preset duration;

[0124] Determine the new load optimization parameters for each second time unit according to the load optimization curve of the preset duration;

[0125] Determine the second difference between the load prediction parameter and the load optimization parameter for each of the second time units;

[0126] Determine the load-side objective function within the first sub-preset duration according to the product of the second difference and the second deviation assessment, where the second deviation assessment is the deviation assessment cost per unit of electricity on the load side;

[0127] Determine the updated load optimization parameters for each second time unit according to the sequential quadratic programming algorithm and the load-side objective function within the first sub-preset duration.

[0128] Determine the load optimization curve of the first sub-preset duration according to the updated load optimization parameters for each of the second time units, and regulate the load parameters of the second time period according to the load optimization curve of the first sub-preset duration.

[0129] The regulating device determines the load prediction parameters for each second time unit within the first sub-preset duration, and the first sub-preset duration is one of the multiple sub-durations within the preset duration. It is easy to understand that when predicting the load parameters of the preset duration, the shorter the preset duration, the more accurate the predicted load parameters. Based on this, determining the load prediction parameters for each second time unit within the first sub-preset duration results in higher accuracy of the determined load prediction parameters compared to determining the load prediction parameters of the preset duration.

[0130] The regulating device determines the second difference between the load prediction parameter and the load optimization parameter for each second time unit. The load optimization curve of the preset duration can be used to declare to the medium- and long-term electricity market, and the regulating device regulates the load parameters according to the load optimization curve. If the actual load parameters at a certain moment are inconsistent with the load optimization parameters, a load-side load deviation assessment cost will be generated. Therefore, if there is a difference between the actual load parameters and the load optimization parameters of a certain second time unit, a load-side load deviation assessment cost will be generated. Based on this, within the range of the load prediction parameters for each second time unit, the regulating device determines the actual load parameters for each second time unit, that is, determines the updated load optimization parameters for each second time unit, so as to minimize the total load-side deviation assessment cost.

[0131] The sequential quadratic programming algorithm can be used to solve the objective function F3, so as to determine the load optimization parameters for each updated second time unit. For the specific process of solving the objective function F3 by the sequential quadratic programming algorithm, reference can be made to the above-mentioned embodiments. To avoid repetition, it will not be elaborated here. The load-side objective function F3 within the first sub-predetermined duration can be expressed as:

[0132]

[0133] where F3 is the load-side objective function within the first sub-predetermined duration, P b,t is the load prediction parameter at time t, P LD,t is the load optimization parameter at time t, C LD,t is the deviation assessment cost per unit electricity of the load side.

[0134] The value range of t is from 1 to M. The duration from 1 to M is the first sub-predetermined duration, and the value of M is associated with the second time unit and the first sub-predetermined duration.

[0135] By solving the load-side objective function F3 within the above-mentioned first sub-predetermined duration through the sequential quadratic programming algorithm, the load optimization parameters for each updated second time unit can be determined. The regulation device determines the load optimization curve for the first sub-predetermined duration according to the load optimization parameters for each updated second time unit, and regulates the load in the second time period according to the load optimization curve for the first sub-predetermined duration, so as to minimize the total deviation assessment cost of the load side. The duration of the second time period is equal to the first sub-predetermined duration, and the second time period is a sub-time period of the first time period.

[0136] Considering that the load prediction parameters may include computing power load prediction parameters, heating and cooling equipment load prediction parameters, charging facility load prediction parameters, and load-side energy storage prediction parameters, the objective function can be expressed as:

[0137]

[0138] where P CT,t is the computing power load prediction parameter at time t, P LES,t is the load-side energy storage power prediction parameter at time t, P CL,t is the heating and cooling equipment load prediction parameter at time t, P EV,t is the charging facility load prediction parameter at time t, P LD,t is the load optimization parameter at time t, C LD,t is the load-side deviation assessment cost.

[0139] The value range of t is from 1 to M. The duration from 1 to M is the first sub-predetermined duration, and the value of M is associated with the second time unit and the first sub-predetermined duration.

[0140] Optionally, determine the load-side objective function within the first sub-predetermined duration according to the product of the second difference and the second deviation assessment, including:

[0141] Determine the additional revenue for each second time unit on the load side;

[0142] Determine the load-side objective function within the first sub-predetermined duration according to the difference between the additional revenue for each second time unit and the first product, where the first product is the product of the second difference and the second deviation assessment.

[0143] In this implementation manner, when the load optimization parameter updated for each second time unit is greater than the load optimization parameter, there may be additional revenue on the load side. For example, because the computing power task increases, the actual load parameter increases, and executing the increased computing power task will bring additional revenue; an increase in the load of the charging facility may also bring additional revenue. Therefore, the objective function F3 can be expressed as:

[0144] When P b,t >P LD,t At this time, ((P b,t -P LD,t )C LD,t -(P CT,t -P CTD,t )C CTP,t -(P EV,t -P EVD,t )C EVP,t );

[0145] When P b,t <P LD,t At this time, (P b,t -P LD,t )C LD,t

[0146] Among them, F3 is the load-side objective function within the first sub-predetermined duration at time t, F D Total deviation assessment cost on the load side, F P Is the additional revenue on the load side, P b,t Is the load prediction parameter at time t, P LD,t Is the load optimization parameter at time t, C LD,t Is the unit power deviation assessment cost on the load side, P CT,t Is the updated computing power load optimization parameter at time t, P CTD,t Is the computing power load optimization parameter, C CTP,t Is the additional revenue obtained per degree of electricity with increased computing power at time t, P EV,t Is the updated load optimization parameter of the charging facility at time t, P EVD,t Is the charging facility load optimization parameter at time t, C EVP,tThe additional revenue obtained per degree of electricity when charging facilities are in operation at time t.

[0147] The value range of t is from 1 to M. The time period from 1 to M is the first sub - preset time period, and the value of M is associated with the second time unit and the first sub - preset time period.

[0148] Taking a preset time period of one month as an example, the first preset sub - time period is one day within this month. If the second time unit is hours, the objective function can be expressed as:

[0149] When P b,t >P LD,t At this time, ((P b,t -P LD,t )C LD,t -(P CT,t -P CTD,t )C CTP,t -(P EV,t -P EVD,t )C EVP,t );

[0150] When P b,t <P LD,t At this time, (P b,t -P LD,t )C LD,t

[0151] Among them, the calculation method of C LD,t can be as follows:

[0152] When P b,t >P LD,t At this time, C PD,t =C G,t

[0153] When P b,t <P LD,t At this time, C PD,t =0.15C G,t

[0154] C G,t is the electricity purchase price of a certain power grid enterprise on behalf of other users at time t.

[0155] By solving the load - side objective function F3 for one day using the sequential quadratic programming algorithm, the updated load optimization parameters for each hour of this day can be determined. Based on the updated load optimization parameters for each hour, the load optimization curve for this day can be determined. For the above example, a case - study simulation verification is carried out. The simulation results are shown in Figure 10 and Figure 11 . See Figure 10 , Figure 10The curve in it is the daily load optimization curve 10 obtained through example simulation operations, and the partial load optimization curve 11 for a preset duration. See Figure 11 , Figure 11 the difference curve 12 determined according to multiple daily second deviations.

[0156] Taking a preset duration of one month as an example, the first preset sub-duration is one day in this month. If the second time unit is 15 minutes, the load-side objective function F3 for the first preset sub-duration can be expressed as:

[0157] When P b,t > P LD,t at that time, ((P b,t - P LD,t ) C LD,t -(P CT,t - P CTD,t ) C CTP,t -(P EV,t - P EVD,t ) C EVP,t );

[0158] When P b,t < P LD,t at that time, (P b,t - P LD,t ) C LD,t

[0159] The calculation method of C LD,t can refer to the above embodiments. To avoid repetition, it will not be elaborated here.

[0160] By solving the load-side objective function F3 for the above one day through the sequential quadratic programming method, the updated load optimization parameters for each 15 minutes in this day can be determined. According to the updated load optimization parameters for each 15 minutes, the load optimization curve for this day can be determined.

[0161] P b,t is the load prediction parameter for the t-th 15 minutes of the day, where the value range of t is from 1 to 96. The load prediction parameter may be affected by some unpredictable situations, such as sudden increase in computing power tasks. Therefore, the future load prediction parameters for t + T can be obtained again at each t point to make the load prediction parameter more accurate, where the value of t + T is less than or equal to 96. See Figure 12 , Figure 12It is a flowchart for determining the load optimization parameters of t+T points. The initial value of t is 1. At each t point, the future t+T load prediction parameters are obtained and input into the objective function F3. It is easy to understand that the future t+T load prediction parameters determined again at each t point are more accurate than directly determining the load prediction parameters of 96 points. The future t+T load prediction parameters are input into the objective function F3, and the sequential quadratic programming method is used to solve the objective function F3, so as to determine the load optimization parameters of t+T points. According to the load optimization parameters of t+T points, the load optimization curve corresponding to the time period from t to T can be determined, and this load optimization curve can be used to update the daily load optimization curve.

[0162] It should be noted that, in order to make the value of the objective function F3 more accurate, that is, the total cost of load deviation assessment on the load side more accurate, before the t point, the actual load parameters on the load side can be input instead of the load prediction parameters; at the t point and after the t point, the new load prediction parameters of t+T points are input. The example is simulated by the example simulation technology, and the simulation results are shown in Figure 13 and Figure 14 , Figure 13 is the daily load optimization curve 13 updated every 15 minutes after the example simulation operation, and a partial load optimization curve 14 with a preset duration. See Figure 14 , Figure 14 is the difference curve 15 determined according to multiple daily second deviations updated every 15 minutes.

[0163] The embodiment of the present application also provides a source-load resource regulation device 200 applied to the integration of the power grid, load and energy storage, hereinafter referred to as the regulation device 200. See Figure 15 The regulation device 200 includes:

[0164] The first determination module 201 is used to determine the power generation power optimization parameters and load optimization parameters of each first time unit according to the new energy power generation power prediction parameters and load prediction parameters of each first time unit within a preset duration;

[0165] The second determination module 202 is used to determine the new energy power generation power optimization curve according to the power generation power optimization parameters of each first time unit, and determine the load optimization curve according to the load optimization parameters of each first time unit;

[0166] The first regulation module 203 is used to regulate the source-load resources in the first time period according to the new energy power generation power optimization curve and the load optimization curve.

[0167] Optionally, the first determination module 201 is specifically configured to determine an objective function within a preset duration according to the difference between the new energy power generation power prediction parameter and the load prediction parameter for each first time unit within the preset duration; the first determination module is further specifically configured to determine the power generation power optimization parameter and the load optimization parameter for each first time unit according to the sequential quadratic programming algorithm and the objective function within the preset duration.

[0168] Optionally, the new energy power generation power prediction parameter includes a new energy output power prediction parameter and a new energy side energy storage power prediction parameter;

[0169] The first determination module 201 is specifically configured to determine the new energy output power optimization parameter and the new energy side energy storage power optimization parameter for each first time unit according to the new energy output power prediction parameter and the new energy side energy storage power prediction parameter for each first time unit within the preset duration;

[0170] The second determination module 202 is specifically configured to determine the new energy output power optimization curve and the new energy side energy storage power optimization curve according to the new energy output power optimization parameter and the energy side energy storage power prediction parameter for each of the first time units.

[0171] Optionally, the load prediction parameter includes a computing power load prediction parameter, a heating and cooling equipment load prediction parameter, a charging facility load prediction parameter, and a load side energy storage prediction parameter;

[0172] The first determination module 201 is specifically configured to determine the computing power load optimization parameter, the heating and cooling equipment load optimization parameter, the charging facility load optimization parameter, and the load side energy storage optimization parameter for each first time unit according to the computing power load prediction parameter, the heating and cooling equipment load prediction parameter, the charging facility load prediction parameter, and the load side energy storage prediction parameter for each first time unit within the preset duration.

[0173] The second determination module 202 is specifically configured to determine the computing power load optimization curve, the heating and cooling equipment load optimization curve, the charging facility load optimization curve, and the load side energy storage optimization curve according to the computing power load optimization parameter, the heating and cooling equipment load optimization parameter, the charging facility load optimization parameter, and the load side energy storage optimization parameter for each first time unit.

[0174] Optionally, the preset duration includes a plurality of sub-preset durations, and the control device 200 further includes:

[0175] A third determination module, configured to determine the new energy power generation power prediction parameter for each second time unit within the first sub-preset duration;

[0176] A fourth determination module, configured to determine the new energy power generation power optimization parameter for each second time unit according to the new energy power generation power optimization curve of the preset duration;

[0177] A fifth determination module, configured to determine a first difference between the new energy power generation power prediction parameter and the new energy power generation power optimization parameter for each second time unit;

[0178] A sixth determination module, configured to determine the new energy side objective function within a first sub-predetermined duration according to the product of the first difference and the first deviation assessment, where the first deviation assessment is the new energy side unit power deviation assessment cost;

[0179] A seventh determination module, configured to determine the updated new energy power generation power optimization parameter for each second time unit according to the new energy side objective function within the first sub-predetermined duration and the sequential quadratic programming algorithm;

[0180] A second regulation module, configured to determine the new energy power generation power optimization curve for the first sub-predetermined duration according to the updated new energy power generation power optimization parameter for each second time unit, and regulate the new energy power generation power for the second time period according to the new energy power generation power optimization curve for the first sub-predetermined duration.

[0181] Optionally, the predetermined duration includes multiple sub-predetermined durations, and the regulation device 200 further includes:

[0182] An eighth determination module, configured to determine the load prediction parameter for each second time unit within the first sub-predetermined duration;

[0183] A ninth determination module, configured to determine the load optimization parameter for each second time unit according to the load optimization curve for the predetermined duration;

[0184] A tenth determination module, configured to determine a second difference between the load prediction parameter and the load optimization parameter for each second time unit;

[0185] An eleventh determination module, configured to determine the load side objective function within the first sub-predetermined duration according to the product of the second difference and the second deviation assessment, where the second deviation assessment is the load side unit power deviation assessment cost;

[0186] A twelfth determination module, configured to determine the updated load optimization parameter for each second time unit according to the load side objective function within the first sub-predetermined duration and the sequential quadratic programming algorithm;

[0187] A third regulation module, configured to determine the load optimization curve for the first sub-predetermined duration according to the updated load optimization parameter for each second time unit, and regulate the load parameter for the second time period according to the load optimization curve for the first sub-predetermined duration.

[0188] Optionally, the twelfth determination module is specifically configured to determine the additional revenue for each second time unit on the load side;

[0189] The twelfth determination module is specifically configured to determine the target function on the load side within the first sub-preset duration according to the difference between the additional revenue of each second time unit and the first product, where the first product is the product of the second difference and the second deviation assessment.

[0190] It should be noted that the regulation device in the embodiments of the present application can implement each process of the source-load resource regulation method embodiment applied to the integrated source-network-load-storage described above and can achieve the same technical effects. To avoid repetition, it will not be elaborated here.

[0191] The embodiments of the present application also provide an electronic device 300. Refer to Figure 16 , including at least one processor 301, a memory 302, and a computer program stored on the memory 302 and executable on the processor 301. The computer program is executed by at least one processor 301 to implement each process of the source-load resource regulation method embodiment applied to the integrated source-network-load-storage described above and can achieve the same technical effects. To avoid repetition, it will not be elaborated here.

[0192] The embodiments of the present application also provide a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. The computer program is executed by the processor 301 to implement each process of the source-load resource regulation method embodiment applied to the integrated source-network-load-storage described above and can achieve the same technical effects. To avoid repetition, it will not be elaborated here. The computer-readable storage medium includes a read-only memory (ROM for short), a random access memory (RAM for short), a magnetic disk, an optical disc, or the like.

[0193] It should be noted that in this article, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article or device. Without more limitations, an element defined by the statement "including a..." does not exclude the existence of another identical element in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed. It may also include performing functions in a substantially simultaneous manner or in a reverse order according to the functions involved. For example, the described methods may be performed in an order different from that described, and various steps may be added, omitted, or combined. Additionally, the features described with reference to certain examples may be combined in other examples.

[0194] The embodiments of the present application have been described above in conjunction with the accompanying drawings. However, the present application is not limited to the above specific embodiments. The above specific embodiments are merely illustrative rather than restrictive. Under the inspiration of the present application, those of ordinary skill in the art can also make many forms without departing from the purpose of the present application and the scope protected by the claims, and all of them fall within the protection scope of the present application.

Claims

1. A source-load resource regulation method applied to the integration of power generation, grid, load and energy storage, characterized in that The method includes: Determining the power generation optimization parameter and load optimization parameter for each of the first time units within a preset duration according to the new energy power generation prediction parameter and load prediction parameter for each of the first time units; Determining the new energy power generation optimization curve according to the power generation optimization parameter for each of the first time units, and determining the load optimization curve according to the load optimization parameter for each of the first time units; Regulating the source-load resources in the first time period according to the new energy power generation optimization curve and the load optimization curve; The preset duration includes a plurality of sub-preset durations, and the method further includes: Determining the new energy power generation prediction parameter for each of the second time units within the first sub-preset duration; Determining the new energy power generation optimization parameter for each of the second time units according to the new energy power generation optimization curve of the preset duration; Determining the first difference between the new energy power generation prediction parameter and the new energy power generation optimization parameter for each of the second time units; Determining the new energy side objective function within the first sub-preset duration according to the product of the first difference and the first deviation assessment, where the first deviation assessment is the new energy side unit power deviation assessment cost; Determining the updated new energy power generation optimization parameter for each of the second time units according to the new energy side objective function within the first sub-preset duration and the sequential quadratic programming algorithm; Determining the new energy power generation optimization curve of the first sub-preset duration according to the updated new energy power generation optimization parameter for each of the second time units, and regulating the new energy power generation power in the second time period according to the new energy power generation optimization curve of the first sub-preset duration; The preset duration includes a plurality of sub-preset durations, and the method further includes: Determining the load prediction parameter for each of the second time units within the first sub-preset duration; Determining the load optimization parameter for each of the second time units according to the load optimization curve of the preset duration; Determining the second difference between the load prediction parameter and the load optimization parameter for each of the second time units; Determining the load side objective function within the first sub-preset duration according to the product of the second difference and the second deviation assessment, where the second deviation assessment is the load side unit power deviation assessment cost; Determining the updated load optimization parameter for each of the second time units according to the load side objective function within the first sub-preset duration and the sequential quadratic programming algorithm; Determining the load optimization curve of the first sub-preset duration according to the updated load optimization parameter for each of the second time units, and regulating the load parameter in the second time period according to the load optimization curve of the first sub-preset duration.

2. The method according to claim 1, wherein The step of determining the power generation optimization parameter and load optimization parameter for each of the first time units according to the new energy power generation prediction parameter and load prediction parameter for each of the first time units within a preset duration includes: Determining the objective function within the preset duration according to the difference between the new energy power generation prediction parameter and the load prediction parameter for each of the first time units within the preset duration; Determine the power generation power optimization parameter and load optimization parameter for each of the first time units according to the sequential quadratic programming algorithm and the objective function within the preset duration.

3. The method according to claim 1, wherein: The new energy power generation power prediction parameter includes a new energy output power prediction parameter and a new energy side energy storage power prediction parameter; The step of determining the power generation power optimization parameter for each of the first time units according to the new energy power generation power prediction parameter for each of the first time units within the preset duration includes: Determine the new energy output power optimization parameter and the new energy side energy storage power optimization parameter for each of the first time units according to the new energy output power prediction parameter and the new energy side energy storage power prediction parameter for each of the first time units within the preset duration; The step of determining the new energy power generation power optimization curve according to the power generation power optimization parameter for each of the first time units includes: Determine the new energy output power optimization curve and the new energy side energy storage power optimization curve according to the new energy output power optimization parameter and the new energy side energy storage power prediction parameter for each of the first time units.

4. The method according to claim 1, wherein: The load prediction parameter includes a computing power load prediction parameter, a heating and cooling equipment load prediction parameter, a charging facility load prediction parameter, and a load side energy storage power prediction parameter; The step of determining the load optimization parameter for each of the first time units according to the load prediction parameter for each of the first time units within the preset duration includes: Determine the computing power load optimization parameter, the heating and cooling equipment load optimization parameter, the charging facility load optimization parameter, and the load side energy storage power optimization parameter for each of the first time units according to the computing power load prediction parameter, the heating and cooling equipment load prediction parameter, the charging facility load prediction parameter, and the load side energy storage power prediction parameter for each of the first time units within the preset duration; The step of determining the load optimization curve according to the load optimization parameter for each of the first time units includes: Determine the computing power load optimization curve, the heating and cooling equipment load optimization curve, the charging facility load optimization curve, and the load side energy storage power optimization curve according to the computing power load optimization parameter, the heating and cooling equipment load optimization parameter, the charging facility load optimization parameter, and the load side energy storage power optimization parameter for each of the first time units.

5. The method according to claim 1, wherein: The step of determining the load side objective function within the first sub-preset duration according to the product of the second difference and the second deviation assessment includes: Determine the additional income for each of the second time units on the load side; Determine the load side objective function within the first sub-preset duration according to the difference between the additional income for each of the second time units and the first product, where the first product is the product of the second difference and the second deviation assessment.

6. A source-load resource regulation device applied to the integration of power generation, grid, load and energy storage, characterized in that, The device is used to execute the source load resource regulation method applied to source-network-load-storage integration according to any one of claims 1 to 5, and the device includes: The first determination module is configured to determine the power generation optimization parameter and the load optimization parameter for each of the first time units according to the new energy power generation prediction parameter and the load prediction parameter for each of the first time units within a preset time period; The second determination module is configured to determine the new energy power generation optimization curve according to the power generation optimization parameter for each of the first time units, and determine the load optimization curve according to the load optimization parameter for each of the first time units; The first regulation module is configured to regulate the source-load resources in the first time period according to the new energy power generation optimization curve and the load optimization curve.

7. An electronic device, characterized in that, It includes a processor, a memory, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of the source-load resource regulation method applied to source-grid-load-storage integration as described in any one of claims 1 to 5.

8. A readable storage medium, characterized in that, A program or instruction is stored on the readable storage medium. When the program or instruction is executed by the processor, it implements the steps of the source-load resource regulation method applied to source-grid-load-storage integration as described in any one of claims 1 to 5.

Images