Unit Optimal Scheduling Method Considering the Joint Constraints of “Deep Peak Loading and Coal Consumption”

Abstract

The present invention belongs to the field of steady-state operation of power grids, and in particular, relates to a unit optimization dispatching method considering the joint constraint of "deep peak shaving and coal consumption". It includes establishing the objective function of the unit combination optimization model of the joint constraint of "deep peak shaving and coal consumption" of thermal power units; establishing the constraint conditions of the unit combination optimization model of the joint constraint of "deep peak shaving and coal consumption" of thermal power units; solving examples and analyzing results. The present invention can meet the increasing demand for peak shaving, and study the economic feasibility of deep peak shaving of thermal power units under the condition of large-scale wind power grid connection from multiple perspectives of thermal power companies, wind power companies and society, fully consider the current deep peak shaving capacity of thermal power units and coal consumption cost control strategy, establish a unit combination optimization model of the joint constraint of "deep peak shaving and coal consumption" of thermal power units, and coordinate the coordinated operation plan of each thermal power unit after the wind power unit is connected to the system. It can greatly reduce the operating cost of thermal power units, reduce the system energy consumption of thermal power units and the emission of fuel pollutants.

Description

Technical Field

[0001] The present invention belongs to the technical field of steady-state operation of power grids, and in particular relates to a unit optimization scheduling method considering the joint constraints of "deep peak load regulation and coal consumption". Background Art

[0002] The peak-shaving capacity of the power system generally refers to the difference between the output of the normally operating unit and the minimum technical output of the operating unit. During system operation, this difference needs to meet the requirements of the peak-to-valley difference of the system load. The peak-shaving capacity is closely related to the capacity and regulation capacity of each type of unit in the system, and is affected by factors such as the peak-to-valley difference of the load and the power regulation capacity of the interconnection line. Wind power output has the characteristics of intermittency and randomness. The grid connection of wind power will increase the peak-to-valley difference of the system load, and the peak-shaving pressure of the system will become increasingly heavy. The capacity of thermal power units in my country accounts for too large a proportion, and the power structure is unreasonable. The increased peak-shaving task after large-scale wind power grid connection is mainly undertaken by thermal power units. With the continuous increase in the scale of wind power grid connection, the society's call for deep peak-shaving of thermal power units is getting higher and higher.

[0003] In order to meet the growing demand for peak load regulation, the frequency and depth of deep peak load regulation of thermal power units are increasing. However, the thermal power units in the deep peak load regulation stage have significantly reduced their operating cost efficiency. Their operating costs include not only explicit costs such as fuel, but also some implicit costs.

[0004] Judging from the current operating conditions, on the one hand, operators of thermal power units lack the willingness to carry out deep peak-shaving, especially deep peak-shaving; on the other hand, deep peak-shaving of thermal power units will significantly increase system energy consumption and fuel pollutant emissions. Summary of the invention

[0005] In view of the shortcomings of the above-mentioned prior art, the present invention provides a unit optimization scheduling method considering the joint constraints of "deep peak load regulation and coal consumption". The purpose is to provide an invention that can consider the deep peak load regulation capability and coal consumption cost control strategy of the current thermal power units, and coordinate the coordinated operation plan of each thermal power unit after the wind turbine unit is connected to the system.

[0006] The technical solution adopted by the present invention to achieve the above-mentioned purpose is:

[0007] The unit optimization dispatching method considering the joint constraint of "deep peak load regulation and coal consumption" includes the following steps:

[0008] Step 1. Establish the objective function of the unit combination optimization model with the joint constraints of "deep peak regulation and coal consumption" of thermal power units;

[0009] Step 2. Establish the constraints of the unit combination optimization model of the joint constraints of "deep peak load regulation and coal consumption" for thermal power units;

[0010] Step 3. Example solution and result analysis.

[0011] Furthermore, the objective function of the unit combination optimization model for the joint constraints of “deep peak regulation and coal consumption” of thermal power units is established in step 1, including:

[0012] Considering the impact of wind power grid connection on system peak load regulation, a model considering the deep peak load regulation capability of conventional units is established;

[0013] Assume that the dispatching cycle is 24 hours, the number of units dispatched in the system is n, and the load at each dispatching time point is set to P lt , represents the total power supply load power at time t, requiring the system to meet the principle of maximum peak load regulation capacity of unit combination and minimum power generation cost at any time within 24 hours;

[0014] The multi-objective function is as follows:

[0015]

[0016] and

[0017]

[0018] Where: α i =(P imax -P imin ) / P imax ;

[0019] In the above formula, F t represents the coal consumption and start-up and shutdown costs of the unit, X t Indicates the peak load regulation capability of the unit; α i represents the peak load depth coefficient of the i-th unit; P it represents the active power output of the i-th unit at time t; P imax represents the rated maximum output of the i-th unit, P imin represents the minimum technical output for stable combustion of the i-th unit; β it Set to two values, 0 and 1, β it =0 means that the i-th unit is in shutdown state at time t, β it =1 means that the i-th unit is in operation at time t; F(P it ) is the coal consumption of the i-th unit at time t, S coal represents coal price; f istart and f ishut They represent the start-up and shutdown costs of the i-th unit, a i , b i , c i They represent the coefficients of each order of the fuel consumption-power characteristic of thermal power units;

[0020] The objective function reflects the goal of economic, efficient and stable operation required by the coordinated dispatching strategy of “deep peak load regulation and coal consumption”. The peak load regulation capacity of the unit is X t The larger it is, the greater the peak load regulation capacity of the unit will be at its maximum while minimizing the unit operating cost at time t.

[0021] Furthermore, the establishment of the unit combination optimization model constraint conditions of the “deep peak regulation and absorption-coal consumption” joint constraint of the thermal power unit in step 2 includes the following steps:

[0022] Step (1) power balance constraint;

[0023] Step (2) Spinning reserve constraint;

[0024] Step (3) upper and lower limit constraints on unit output;

[0025] Step (4) unit ramping and start-stop power constraints;

[0026] Step (5) wind power output constraint;

[0027] Step (6) obtains the objective function and constraint conditions of the unit optimization scheduling mathematical model according to steps (1) to (5).

[0028] Furthermore, the power balance constraint includes:

[0029] At every dispatching moment, the total power generation and total load power are balanced;

[0030] The power balance constraint equation is:

[0031]

[0032] Where P wt P represents the grid-connected output required by wind power at time t; lt Represents the total power supply load power at time t.

[0033] Furthermore, the spinning reserve constraint includes:

[0034] Considering the spinning reserve capacity at 7% of the total system load, P lt +P xt =1.07P lt , then

[0035]

[0036] Where P xt P is the system's spinning reserve capacity; imax is the maximum upper limit of the power generation capacity of the i-th unit.

[0037] Furthermore, the upper and lower limit constraints of the unit output include:

[0038] P imin ≤P it ≤P imax i=1,2,...,n (5)

[0039] In the above formula, P imin and P imax They represent the upper and lower limits of the output of the i-th unit respectively.

[0040] Furthermore, the unit ramping and start-stop power constraints include:

[0041] P it -P it-1 ≤β it-1 ·A iup +(β it -β it-1 )·A istart (6)

[0042] P it-1 -P it ≤β it ·A idown +(β it-1 -β it )·A ishut (7)

[0043] Where A iup and A idown are the up and down climbing power limits of unit i respectively; A istart and A ishut are the startup and shutdown power limits of unit i respectively.

[0044] Furthermore, the wind power output constraint includes:

[0045]

[0046] Where P wt represents the actual output of wind power at time t; Indicates the maximum predicted wind power output at time t.

[0047] Furthermore, the objective function and constraint conditions of the unit optimization scheduling mathematical model obtained according to steps (1) to (5) are as follows:

[0048]

[0049] and

[0050]

[0051]

[0052] The above mathematical model is a nonlinear programming problem.

[0053] Furthermore, the example solution and result analysis include:

[0054] Step 1: Analyze the total load of a certain grid and the total active power of wind power, and select 0:00-24:00 on a typical day as an example for calculation analysis, with each hour as a dispatch period;

[0055] Step 2: By analyzing the peak-shaving capacity and ramp power of each cogeneration unit in a certain place, an intelligent optimization algorithm is used to obtain the optimal unit combination plan for each scheduling moment; units with large rated capacity and large peak-shaving capacity are given priority for power supply, and units with small rated capacity and small peak-shaving capacity are given priority when the load is reduced; the "deep peak-shaving and absorption" model is used to optimize the unit combination scheduling in each scheduling time period;

[0056] Step 3: For thermal power companies, within the peak load regulation capacity of the units, as the peak load regulation depth increases, according to the unit consumption characteristics, the unit's coal consumption cost decreases, the number of starts and stops also decreases significantly, and the start and stop costs decrease accordingly, so the total operating cost decreases; the unit combination scheme considering the peak load regulation capacity has a lower coal consumption cost than the traditional unit combination scheme when adopting "deep peak load regulation":

[0057]

[0058] Where P Git It represents the active output of each thermal power unit in each dispatching time during the “deep peak load regulation” of the traditional unit combination scheme, P Dit It represents the active output of each thermal power unit in each dispatching time during the "deep peak load regulation" of the unit combination scheme taking into account the peak load regulation capacity; T is 24, which represents the total number of dispatching points; n is 47, which represents the number of thermal power units;

[0059] Step 4: For wind power companies, as the peak load depth of thermal power units increases, the amount of wind power on-grid increases; the revenue of wind power companies is positively correlated with the utilization rate of wind power, and wind power companies are the most direct beneficiaries of deep peak load of thermal power units; in the case of "deep peak load", the combination scheme of thermal power units that takes into account the peak load capacity is compared with the combination scheme of traditional thermal power units. The economic benefits of wind power are:

[0060]

[0061] In the formula, S u , S c Respectively represent the on-grid electricity price and cost electricity price of wind power; unit: Yuan / (KWh);

[0062] In the case of "deep peak load regulation", compared with the traditional dispatching scheme, the unit combination scheme that takes peak load regulation capacity into consideration reduces the coal consumption cost of thermal power units, increases the profits of wind power companies, and maximizes the overall benefits;

[0063] Step 5: Compared with the traditional scheme, the unit combination scheme considering the peak load regulation capability not only reduces the amount of wind power abandoned during the deep peak load regulation process, but also improves the ability of thermal power units to cope with the sharp decline of wind power under high load; compared with the traditional unit combination, the unit combination considering the peak load regulation capability increases the space for smoothing wind power fluctuations at maximum output:

[0064]

[0065] In the formula, X it It represents the operating status of the i-th unit at time t under the traditional unit combination operation mode; when the load is high, the unit combination scheme considering the peak load regulation capacity increases the space for smoothing wind power fluctuations to 30566MW.

[0066] The present invention has the following beneficial effects and advantages:

[0067] The present invention is a deep peak regulation characteristic analysis. It studies the economics of deep peak regulation of thermal power units under the condition of large-scale wind power grid connection from multiple perspectives of thermal power enterprises, wind power enterprises and society. It fully considers the deep peak regulation capability and coal consumption cost control strategy of current thermal power units, establishes a unit combination optimization model of "deep peak regulation and absorption-coal consumption" joint constraints of thermal power units, and comprehensively arranges the coordinated operation plan of each thermal power unit after the wind power unit is connected to the system. It can meet the increasing peak regulation demand, greatly reduce the explicit and implicit costs of thermal power units such as fuel, and reduce the system energy consumption and fuel pollutant emissions of thermal power units.

[0068] The present invention will break through the traditional unit optimization scheduling of the power system containing wind farms, fully consider the current deep peak-shaving capacity of thermal power units and the coordination strategy of coal consumption cost, and arrange the coordinated operation plan of each thermal power unit in the system after the wind farm is connected. The principle followed by the overall optimization model design is: at each scheduling moment, the rated peak-shaving capacity of all running units is guaranteed to be the maximum while the coal consumption cost is minimized, and a mathematical model of the economic objective function of the deep peak-shaving stage of the thermal power units is constructed. According to the network architecture and power grid characteristics, a unit combination optimization model with joint constraints of "deep peak-shaving and absorption-coal consumption" of thermal power units is established, and the start-stop scheduling plan of the thermal power units is arranged. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0070] Figure 1 This is the total load curve of a certain place on September 22, 2017;

[0071] Figure 2 It is the optimization dispatch result of the thermal power unit considering the peak load regulation capability of the present invention;

[0072] Figure 3 This is a comparison chart of the present invention and two traditional scheduling schemes when performing "deep peak load regulation";

[0073] Figure 4 This is the space used by the present invention to smooth out wind power fluctuations. DETAILED DESCRIPTION

[0074] In order to more clearly understand the above-mentioned objectives, features and advantages of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0075] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0076] Refer to the following Figure 1-Figure 4 The technical solutions of some embodiments of the present invention are described.

[0077] Example 1

[0078] The present invention is a unit optimization scheduling method that considers the joint constraints of "deep peak regulation and absorption-coal consumption". In order to meet the increasing peak regulation demand, it is urgent to study the economic efficiency of deep peak regulation of thermal power units under the condition of large-scale wind power grid connection from multiple perspectives of thermal power companies, wind power companies and society. The present invention fully considers the current deep peak regulation capability and coal consumption cost control strategy of thermal power units, considers the economic efficiency of the deep peak regulation stage of thermal power units, establishes a unit combination optimization model of the joint constraints of "deep peak regulation and absorption-coal consumption" of thermal power units, and comprehensively arranges the coordinated operation plan of each thermal power unit after the wind power unit is connected to the system.

[0079] The present invention specifically comprises the following steps:

[0080] Step 1. Establish the objective function of the unit combination optimization model with the joint constraints of “deep peak regulation and absorption-coal consumption” for thermal power units.

[0081] After wind power is connected to the system, its randomness may cause some units to shut down in a certain period of time. This behavior may lead to insufficient peak-shaving capacity of the operating units in the subsequent scheduling period, and the shut-down units do not have enough time to start up, resulting in system load loss. Furthermore, the startup of some units may lead to too many online units in the subsequent scheduling period and insufficient shutdown time, which will eventually lead to wind abandonment. Therefore, in the process of formulating the unit combination plan, if the peak-shaving capacity of the system is not considered, the energy-saving benefits will be lost and huge economic losses will be caused. Considering the impact of wind power on system peak-shaving after grid connection, a model considering the deep peak-shaving capacity of conventional units is established.

[0082] Assume that the dispatch cycle is 24 hours, the number of units dispatched in the system is n, and the load at each dispatch time point is set to P lt , represents the total power supply load power at time t, requiring the system to meet the principle of maximum peak-shaving capacity of unit combination and lowest power generation cost at any time within 24 hours.

[0083] The multi-objective function can be written as:

[0084]

[0085] and

[0086]

[0087] Where: α i =(P imax -P imin ) / P imax ;

[0088] In the above formula, F t represents the coal consumption and start-up and shutdown costs of the unit, X t Indicates the peak load regulation capability of the unit. i represents the peak load depth coefficient of the i-th unit; P it represents the active power output of the i-th unit at time t; P imax represents the rated maximum output of the i-th unit, P imin represents the minimum technical output for stable combustion of the i-th unit; β it Set to two values, 0 and 1, β it =0 means that the i-th unit is in shutdown state at time t, β it =1 means that the i-th unit is in operation at time t; F(P it ) is the coal consumption of the i-th unit at time t, S coal represents coal price; f istart and f ishut They represent the start-up and shutdown costs of the i-th unit, a i , b i , ci They respectively represent the coefficients of the fuel consumption-power characteristics of thermal power units.

[0089] The relevant functions and parameters in the formula have been explained. It can be seen that the objective function can reflect the goal of economic, efficient and stable operation required by the coordinated dispatching strategy of "deep peak regulation and coal consumption". The peak regulation capacity of the unit X t The larger the value is, the greater the peak load regulation capacity of the unit can be while minimizing the unit operating cost at time t.

[0090] Step 2. Establish the constraints of the unit combination optimization model of the joint constraints of "deep peak regulation and coal consumption" for thermal power units, including the following steps:

[0091] Step (1) Power balance constraint.

[0092] It is required that at each dispatching moment, the total power generation and the total load power must be balanced, so the power balance constraint equation is:

[0093]

[0094] Where P wt P represents the grid-connected output required by wind power at time t; lt Represents the total power supply load power at time t.

[0095] Step (2) Rotational standby constraint.

[0096] Considering the spinning reserve capacity at 7% of the total system load, P lt +P xt =1.07P lt , then

[0097]

[0098] Where P xt P is the system's spinning reserve capacity; imax is the maximum upper limit of the power generation capacity of the i-th unit.

[0099] Step (3) upper and lower limits of unit output.

[0100] P imin ≤P it ≤P imax i=1,2,...,n (5)

[0101] In the above formula, P imin and P imax They represent the upper and lower limits of the output of the i-th unit respectively.

[0102] Step (4) Unit ramping and start-stop power constraints.

[0103] P it -P it-1 ≤β it-1 ·A iup +(β it -β it-1 )·A istart (6)

[0104] P it-1 -P it ≤β it ·A idown +(β it-1 -β it )·A ishut (7)

[0105] Where A iup and A idown are the up and down ramp power limits of unit i respectively; A istart and A ishut are the startup and shutdown power limits of unit i respectively.

[0106] Step (5) Wind power output constraints.

[0107]

[0108] Where P wt represents the actual output of wind power at time t; Indicates the maximum predicted wind power output at time t.

[0109] Step (6) obtains the objective function and constraint conditions of the unit optimization scheduling mathematical model according to steps (1) to (5).

[0110] The objective function and constraints of the mathematical model for unit optimization scheduling are as follows:

[0111]

[0112] and

[0113]

[0114]

[0115] The above mathematical model is a nonlinear programming problem.

[0116] Step 3. Example solution and result analysis.

[0117] The total load of a certain grid and the total active power of wind power from September 22 to 25, 2017 are analyzed. The typical day of September 22, 00:00-24:00, is selected as an example for analysis, with one hour as a dispatch period. Figure 1 As shown, Figure 1 It is the total grid load data curve of a certain place on September 22, 2017.

[0118] By analyzing the peak-shaving capacity and ramp power of each cogeneration unit in a certain location, an intelligent optimization algorithm is used to obtain the optimal unit combination plan for each scheduling moment.

[0119] The present invention considers that units with large rated capacity and large peak-shaving capacity are given priority in power supply, and units with small rated capacity and small peak-shaving capacity are given priority in cutting off when the load decreases. When the "deep peak-shaving and absorption" model is adopted, the optimized dispatching results of the unit combination in each dispatching time period are as follows: Figure 2 shown.

[0120] For thermal power companies, within the peak load regulation capacity of the units, as the peak load regulation depth increases, according to the unit consumption characteristics, the unit's coal consumption cost decreases, the number of starts and stops also decreases significantly, and the start and stop costs decrease accordingly, so the overall operating cost decreases. Figure 3 As shown, there is a comparison between the traditional scheduling scheme and the scheduling scheme taking peak-shaving capability into consideration during “deep peak-shaving” operation.

[0121] It can be seen that the coal consumption cost of the unit combination scheme considering the peak load regulation capability is lower than that of the traditional unit combination scheme when the “deep peak load regulation” is adopted:

[0122]

[0123] Where P Git It represents the active output of each thermal power unit in each dispatching time during the “deep peak load regulation” of the traditional unit combination scheme, P Dit It indicates the active output of each thermal power unit in each dispatching time when the unit combination scheme takes into account the peak load capacity in "deep peak load". T is 24, which indicates the total number of dispatching points. n is 47, which indicates the number of thermal power units.

[0124] For wind power companies, as the depth of peak load regulation of thermal power units increases, the amount of wind power connected to the grid increases. The revenue of wind power companies is directly proportional to the utilization rate of wind power, and wind power companies are the most direct beneficiaries of the deep peak load regulation of thermal power units.

[0125] In the case of "deep peak load regulation", the combination scheme of thermal power units taking into account the peak load regulation capability is compared with the combination scheme of traditional thermal power units. The economic benefits of wind power are as follows:

[0126]

[0127] In the formula, S u , S c They respectively represent the on-grid electricity price and cost electricity price of wind power; the unit is: Yuan / (KWh).

[0128] Table 1 Benefit Analysis of “Deep Peak Loading”

[0129]

[0130] As shown in Table 1, under the condition of "deep peak load regulation", compared with the traditional dispatching scheme, the unit combination scheme that considers the peak load regulation capacity not only reduces the coal consumption cost of thermal power units, but also increases the profits of wind power companies, thereby maximizing the overall benefits.

[0131] Compared with the traditional scheme, the unit combination scheme considering the peak-shaving capacity not only reduces the amount of wind power abandoned during the deep peak-shaving process, but also improves the ability of thermal power units to cope with the sharp drop in wind power under high load.

[0132] like Figure 4 As shown, Figure 4 Compared with the traditional unit combination, the unit combination considering the peak load regulation capability increases the space for smoothing wind power fluctuations at maximum output:

[0133]

[0134] In the formula, X it It represents the operating status of the i-th unit at time t under the traditional unit combination operation mode.

[0135] When the load is high, the unit combination scheme considering the peak-shaving capability increases the space for smoothing wind power fluctuations to 30,566MW.

[0136] In order to meet the increasing peak-shaving demand of thermal power units, it is necessary to study the economic feasibility of deep peak-shaving of thermal power units under the condition of large-scale wind power grid connection from multiple perspectives of thermal power companies, wind power companies and society. Based on the consideration of the deep peak-shaving capability of the current thermal power units and the coal consumption cost control strategy, the present invention establishes a unit combination optimization model of the joint constraint of "deep peak-shaving absorption-coal consumption" for thermal power units, so that the deep peak-shaving capability of the thermal power units is optimized while maintaining economic efficiency.

[0137] Example 2

[0138] Based on the same inventive concept, an embodiment of the present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed, it implements the steps of the unit optimization scheduling method considering the joint constraints of "deep peak regulation and absorption-coal consumption" as described in Example 1.

[0139] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0140] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0141] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0142] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0143] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. The unit optimization dispatching method considering the joint constraint of "deep peak load regulation and coal consumption" is characterized by: The following steps are involved: Step 1. Establish the objective function of the unit combination optimization model with the joint constraints of "deep peak regulation and coal consumption" for thermal power units, including: considering the impact of wind power grid connection on system peak regulation, establishing a model considering the deep peak regulation capacity of conventional units; assuming that the dispatching cycle is 24 hours, the number of units dispatched in the system is n, and the load at each dispatching time point is set to P lt , represents the total power supply load power at time t, requiring the system to meet the principle of maximum peak load regulation capacity of unit combination and minimum power generation cost at any time within 24 hours; the multi-objective function is as follows: and Where: αi=(P i max -P i min ) / P i max ; In the above formula, F t represents the coal consumption and start-up and shutdown costs of the unit, X t Indicates the peak load regulation capability of the unit; α i represents the peak load depth coefficient of the i-th unit; P it represents the active power output of the i-th unit at time t; P i max represents the rated maximum output of the i-th unit, P i min represents the minimum technical output for stable combustion of the i-th unit; β it Set to two values, 0 and 1, β it =0 means that the i-th unit is in shutdown state at time t, β it =1 means that the i-th unit is in operation at time t; F(P it ) is the coal consumption of the i-th unit at time t, S coal represents coal price; f istart and f ishut They represent the start-up and shutdown costs of the i-th unit, a i , b i , c i They represent the coefficients of the fuel consumption-power characteristics of thermal power units respectively; the formula shows that this objective function reflects the goal of economic, efficient and stable operation required by the coordinated dispatching strategy of "deep peak regulation and absorption-coal consumption". The peak regulation capacity of the unit X t The larger it is, the greater the peak load regulation capacity of the unit can be while ensuring the minimum unit operating cost at time t. Step 2. Establish the constraints of the unit combination optimization model with the joint constraints of "deep peak load regulation and coal consumption" of thermal power units, including: step (1) power balance constraint; step (2) spinning reserve constraint; step (3) upper and lower limit constraints of unit output; step (4) unit ramping and start-stop power constraints; step (5) wind power output constraint; step (6) according to steps (1) to (5), obtain the objective function and constraints of the mathematical model of unit optimization scheduling; Step 3. Example solution and result analysis.

2. The unit optimization scheduling method considering the joint constraint of "deep peak load regulation and coal consumption" according to claim 1 is characterized by: The power balance constraint includes: At every dispatching moment, the total power generation and total load power are balanced; The power balance constraint equation is: Where P wt P represents the grid-connected output required by wind power at time t; lt Represents the total power supply load power at time t.

3. The unit optimization scheduling method considering the joint constraint of "deep peak load regulation and coal consumption" according to claim 1 is characterized by: The spinning reserve constraint includes: Considering the spinning reserve capacity at 7% of the total system load, P lt +P xt =1.07Plt, then Where P xt P is the system's spinning reserve capacity; imax is the maximum upper limit of the power generation capacity of the i-th unit.

4. The unit optimization scheduling method considering the joint constraint of "deep peak load regulation and coal consumption" according to claim 1 is characterized by: The upper and lower limit constraints of the unit output include: P imin ≤P it ≤P imax i=1,2,...,n (5) In the above formula, P i min and P i max They represent the upper and lower limits of the output of the i-th unit respectively.

5. The unit optimization scheduling method considering the joint constraint of "deep peak load regulation and coal consumption" according to claim 1 is characterized by: The unit ramping and start-stop power constraints include: P.S it -P it-1 ≤β it-1 ·A iup +(β it -β it-1 )·Start (6) P it-1 -P it ≤β it ·A idown +(β it-1 -b it )·A ishut (7) Where A iup and A idown are the up and down climbing power limits of unit i respectively; A istart and A ishut are the startup and shutdown power limits of unit i respectively.

6. The unit optimization scheduling method considering the joint constraint of "deep peak load regulation and coal consumption" according to claim 1 is characterized by: The wind power output constraints include: Where P wt represents the actual output of wind power at time t; Indicates the maximum predicted wind power output at time t.

7. The unit optimization scheduling method considering the joint constraint of "deep peak load regulation and coal consumption" according to claim 1 is characterized by: The objective function and constraint conditions of the unit optimization scheduling mathematical model are obtained according to steps (1) to (5), as follows: and The above mathematical model is a nonlinear programming problem.

8. The unit optimization scheduling method considering the joint constraint of "deep peak load regulation and coal consumption" according to claim 1 is characterized by: The example solution and result analysis include: Step a: Analyze the total load of a certain grid and the total active power of wind power, select 0:00-24:00 on a typical day as an example for calculation analysis, and take one hour as a dispatch period; Step b: By analyzing the peak-shaving capacity and ramp power of each cogeneration unit in a certain place, an intelligent optimization algorithm is used to obtain the optimal unit combination plan for each scheduling moment; units with large rated capacity and large peak-shaving capacity are given priority for power supply, and units with small rated capacity and small peak-shaving capacity are given priority when the load is reduced; the "deep peak-shaving and absorption" model is used to optimize the unit combination scheduling in each scheduling time period; Step c: For thermal power companies, within the peak load regulation capacity of the units, as the peak load regulation depth increases, according to the unit consumption characteristics, the unit's coal consumption cost decreases, the number of starts and stops also decreases significantly, and the start and stop costs decrease accordingly, so the total operating cost decreases; the unit combination scheme considering the peak load regulation capacity has a lower coal consumption cost than the traditional unit combination scheme when adopting "deep peak load regulation": Where P Gi t represents the active output of each thermal power unit in each dispatching time in the traditional unit combination scheme when "deep peak regulation" is used, P Di t represents the active output of each thermal power unit in each dispatching time when the unit combination scheme takes into account the peak-shaving capacity in "deep peak-shaving"; T is 24, which represents the total number of dispatching points; n is 47, which represents the number of thermal power units; Step d: For wind power companies, as the peak load depth of thermal power units increases, the amount of wind power on-grid increases; the revenue of wind power companies is positively correlated with the utilization rate of wind power, and wind power companies are the most direct beneficiaries of deep peak load of thermal power units; in the case of "deep peak load", the combination scheme of thermal power units that takes into account the peak load capacity is compared with the combination scheme of traditional thermal power units. The economic benefits of wind power are: In the formula, S u , S c Respectively represent the on-grid electricity price and cost electricity price of wind power; unit: Yuan / (KWh); In the case of "deep peak load regulation", compared with the traditional dispatching scheme, the unit combination scheme considering the peak load regulation capacity reduces the coal consumption cost of thermal power units, increases the income of wind power companies, and maximizes the overall benefits; Step e: Compared with the traditional scheme, the unit combination scheme considering the peak load regulation capability not only reduces the amount of wind power abandoned during the deep peak load regulation process, but also improves the ability of thermal power units to cope with the sharp decline of wind power under high load; compared with the traditional unit combination, the unit combination considering the peak load regulation capability increases the space for smoothing wind power fluctuations at maximum output: Where, X it It represents the operating status of the i-th unit at time t under the traditional unit combination operation mode; when the load is high, the unit combination scheme considering the peak load regulation capacity increases the space for smoothing wind power fluctuations to 30566MW.

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