Method and system for real-time day-ahead climbing resource checking and collaborative optimization scheduling

Abstract

The application discloses a day-ahead and day-ahead climbing resource checking and collaborative optimization scheduling method and system, and the method comprises the following steps: checking the day-ahead climbing ability on D-1 day, D being the actual operation day of the power grid, determining the starting capacity of the thermal power unit, so that the day-ahead maximum climbing demand on D day is less than the day-ahead minimum climbing ability on D day; checking the day-ahead climbing ability on D day, and iteratively optimizing the output of various types of climbing resources by using the day-ahead collaborative optimization upper and lower layer model of the climbing resources. Through the safety checking of the day-ahead and day-ahead climbing resources, the application ensures that the system has sufficient static climbing ability during the real-time scheduling in the day, and meets the system operation safety boundary requirement; through the collaborative rolling optimization of the day-ahead climbing resources, the economic scheduling of various climbing resources is realized under the premise of ensuring the system frequency safety, and the dynamic climbing ability of the system is improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of large power grid dispatching operation, and particularly relates to a day-ahead and day-ahead climbing resource checking and collaborative optimization dispatching method and system. BACKGROUND

[0002] According to relevant research, the total installed capacity of power supply in China is expected to reach 8 billion kilowatts by 2060, and the installed capacity of new energy such as wind power and photovoltaic power will account for more than 60%. This development trend indicates that the dominant position of traditional thermal power, hydropower and other synchronous power sources in the power grid will change fundamentally. High proportion of new energy access and large-scale application of power electronic equipment will become a significant feature of future power grids. With the rapid increase of new energy generation proportion, the demand for climbing capacity of large power grids has become unprecedentedly urgent. The so-called "climbing capacity" refers to the rate at which a power generation unit adjusts its output power, that is, the ability to increase or decrease power generation within a unit of time.

[0003] In the traditional power grid, the load changes relatively gently, and mainly relies on controllable power sources such as coal-fired power to track and adjust. However, wind and solar power generation has significant intermittency and volatility, and its output will increase or decrease sharply due to weather changes, resulting in steep peaks and valleys in the "net load" curve of the power grid. This requires that the system must have sufficient and rapid flexible climbing resources to quickly fill the power shortage caused by cloud cover or wind speed drop within minutes or even seconds, or quickly reduce the output of traditional units to avoid wind and light abandonment when their power generation increases sharply, so as to maintain real-time balance of power generation and consumption at all times and ensure safe and stable operation of the power grid. Therefore, strong climbing capacity has become an indispensable core element of new power systems.

[0004] Existing research mainly involves the field of economic dispatching of generating units, and does not consider the long-time climbing supply and demand imbalance and low-frequency problems under the scenario of large fluctuations of new energy. SUMMARY

[0005] The purpose of the present application is to provide a day-ahead and day-ahead climbing resource checking and collaborative optimization dispatching method and system, which can solve the problem of insufficient system climbing capacity caused by large fluctuations of new energy.

[0006] In order to achieve the above purpose, the solution of the present application is:

[0007] A day-ahead and day-ahead climbing resource checking and collaborative optimization dispatching method, comprising,

[0008] On D-1 day, the day-ahead climbing capacity is checked, D is the actual operation day of the power grid, the on-line capacity of the thermal power unit is determined, so that the maximum climbing demand of D day before the day is less than the minimum climbing capacity of D day before the day;

[0009] On day D, the intraday climbing capacity was checked, and the output of each type of climbing resource was iteratively optimized using the intraday collaborative optimization model of climbing resources.

[0010] In the intraday collaborative optimization model for climbing resources, the objective function L of the upper-level model is... global for,

[0011] ,

[0012] In the formula, For the set of all cross-sections of the system Section numbering within, The objective function of the lower-level model;

[0013] The constraints of the upper-level model include the system ramp rate deficit constraint.

[0014] The objective function L of the lower-level model s for,

[0015] ,

[0016] In the formula, The types of installed power sources include thermal power units, hydropower units, pumped storage units, photovoltaic units, and wind power units; For a set of nodes Node number within; A collection of times within a day; cross-section internal nodes The Power supply at all times of effort, For the first Power-like moments The cost of output, It lasts for 15 minutes; For a certain cross section At any moment Lagrange multipliers, cross-section The distribution coefficient between the output of the indoor unit and the cross-sectional transmitted power; cross-section The distribution coefficient between internal load and cross-sectional transmitted power; cross-section internal nodes At any moment Forecasted load, cross-section Maximum permissible transmission power;

[0017] The constraint conditions of the lower layer model include upper and lower limits of unit output, upper and lower limits of unit ramping rate, and power balance constraint.

[0018] wherein, on D-1 day, the day-ahead ramping capacity is checked, D is the actual operation day of the power grid, and the on-line capacity of the thermal power unit is determined so that the day-ahead maximum ramping demand on D day is less than the day-ahead minimum ramping capacity on D day, including,

[0019] The 96-point new energy day-ahead prediction value, 96-point load day-ahead prediction value, 96-point cross-region DC planned value, and 96-point thermal power on-line capacity on D day are obtained.

[0020] The day-ahead maximum ramping demand on D day and the day-ahead minimum ramping capacity on D day are calculated.

[0021] The day-ahead maximum ramping demand on D day and the day-ahead minimum ramping capacity on D day are compared, and if the day-ahead maximum ramping demand on D day is greater than the day-ahead minimum ramping capacity on D day, the day-ahead ramping safety check is not passed, and the on-line capacity of the thermal power unit is increased until the day-ahead maximum ramping demand on D day is less than the day-ahead minimum ramping capacity on D day.

[0022] wherein, the day-ahead maximum ramping demand on D day and the day-ahead minimum ramping capacity on D day are calculated, including,

[0023] The day-ahead maximum ramping demand on D day is calculated according to the following formula ,

[0024] ,

[0025] In the formula, is the type of the day-ahead ramping demand on D day, including the day-ahead new energy ramping demand on D day, the day-ahead load ramping demand on D day, and the day-ahead cross-region DC ramping demand on D day; is the first type of 96-point ramping demand value on D day;

[0026] The day-ahead minimum ramping capacity on D day is calculated according to the following formula ,

[0027] ,

[0028] In the formula, is the 96-point thermal power on-line capacity on D day determined on D-1 day, is the thermal power ramping rate.

[0029] wherein, the constraint conditions of the upper layer model include the system ramping rate shortage constraint, and the expression is

[0030] ,

[0031] In the formula, For a set of nodes The node number within, The types of installed power sources include thermal power units, hydropower units, pumped storage units, photovoltaic units, and wind power units; cross-section internal nodes The Power supply at all times of effort, cross-section internal nodes The Power supply at all times The amount of adjustment between the generated power and the predicted power; cross-section internal nodes The Power supply at all times of effort, cross-section internal nodes The Power supply at all times The amount of adjustment between the generated power and the predicted power; cross-section internal nodes At any moment Forecasted load, cross-section internal nodes At any moment The adjustment amount of the load compared to the predicted load; cross-section internal nodes At any moment Forecasted load, cross-section internal nodes At any moment The adjustment amount of the load compared to the predicted load;

[0032] The constraints of the lower-level model include upper and lower limits of unit output constraints, specifically as follows:

[0033] ,

[0034] In the formula, Cross-sections internal nodes The Power supply at all times The upper and lower limits of the emitted power;

[0035] The constraints of the lower-level model include upper and lower limits of the unit's ramp rate constraints, specifically as follows:

[0036] ,

[0037] In the formula, is the node of the maximum ramp rate of the same type unit group;

[0038] The constraint condition of the lower model includes a power balance constraint, which is specifically,

[0039] .

[0040] Wherein, on day D, the intra-day ramping capability checking is performed, and the output of each type of ramping resource is iteratively optimized by using the intra-day collaborative optimization upper and lower model of the ramping resource, including,

[0041] Obtaining the new energy ultra-short-term prediction value from T to T+4 hours on day D, the load ultra-short-term prediction value from T to T+4 hours on day D, the cross-region DC planned value after stacking the intra-day spot on day D from T to T+4 hours, and the thermal power start-up capacity from T to T+4 hours on day D, T being an integer time of the actual operation day of the power grid;

[0042] Calculating the maximum ramping demand from T to T+4 hours on day D and the minimum ramping capability from T to T+4 hours on day D;

[0043] Comparing the maximum ramping demand from T to T+4 hours on day D with the minimum ramping capability from T to T+4 hours on day D, if the maximum ramping demand from T to T+4 hours on day D is greater than the minimum ramping capability from T to T+4 hours on day D, the intra-day ramping safety checking is not passed, and the cross-region DC planned value after stacking the spot is reduced until the maximum ramping demand from T to T+4 hours on day D is less than the minimum ramping capability from T to T+4 hours on day D;

[0044] Building an intra-day collaborative optimization upper and lower model of the ramping resource, and optimizing the output of each type of ramping resource in the ultra-short-term prediction period from T to T+4 hours on day D before T hours;

[0045] Solving the lower model of the intra-day collaborative optimization upper and lower model of the ramping resource to obtain the optimal output , the optimal solution and pass it to the upper model; then solving the upper model to obtain the optimal solution ;

[0046] Judging whether the update deviation of the Lagrange multiplier and the deviation of the global objective function satisfy the convergence condition, if the convergence condition is satisfied, outputting the optimization result from T to T+4 hours on day D; if not, re-solving the lower model of the intra-day collaborative optimization upper and lower model of the ramping resource until the convergence condition is satisfied, and the convergence condition is that

[0047] ,

[0048] wherein, , are the Lagrange multipliers of the first and the first iterations, , are the global objective function values of the first and the first iterations, , is a preset accuracy threshold;

[0049] In the optimization results of D day T to T+4 hours, only the optimization results of D day T to T+1 hour are executed, and before D day T+1 hour, the intra-day climbing ability checking is restarted.

[0050] wherein, the maximum climbing demand of D day T to T+4 hours and the minimum climbing ability of D day T to T+4 hours are calculated, including,

[0051] The maximum climbing demand of D day T to T+4 hours is calculated according to the following formula ,

[0052] ,

[0053] wherein, is the type of the maximum climbing demand of D day T to T+4 hours, including the D day new energy ultra-short-term prediction value, the D day load ultra-short-term prediction value, and the D day superimposed intra-day spot cross-region direct current plan value, is the first type T to T+4 hour maximum climbing demand value;

[0054] The minimum climbing ability of D day T to T+4 hours is calculated according to the following formula ,

[0055] ,

[0056] wherein, is the effective thermal power start-up capacity within D day T+4 hours determined before D day T, is the thermal power climbing rate.

[0057] wherein, the thermal power start-up capacity of D day T to T+4 hours is obtained, including,

[0058] The effective thermal power start-up capacity within D day T+4 hours is calculated according to the following formula ,

[0059]

[0060] wherein, D is the on-line capacity of thermal power on day D determined on day D-1, and T is the off-line capacity of thermal power on day D at the minimum time; D is the on-line capacity of thermal power on day D determined on day D-1, and T is the off-line capacity of thermal power on day D at the minimum time due to the inability to increase the output of the thermal power unit caused by the section or the unit itself.

[0061] A day-ahead and day-ahead climbing resource checking and collaborative optimization scheduling system, comprising,

[0062] The day-ahead climbing capacity checking module is configured to check the day-ahead climbing capacity on day D-1, D being the actual operation day of the power grid, to determine the on-line capacity of the thermal power unit, so that the day-ahead maximum climbing demand on day D is less than the day-ahead minimum climbing capacity on day D;

[0063] The day-ahead climbing capacity checking module is configured to check the day-ahead climbing capacity on day D; and,

[0064] The iterative optimization module is configured to perform iterative optimization on the output of each type of climbing resource by using the climbing resource day-ahead collaborative optimization upper and lower model;

[0065] The objective function L of the upper model in the climbing resource day-ahead collaborative optimization upper and lower model is, global ,

[0066] ,

[0067] wherein, is the section number in the system all section set , is the objective function of the lower model;

[0068] The constraint condition of the upper model includes the system climbing rate shortage constraint;

[0069] The objective function L of the lower model is, s ,

[0070] ,

[0071] wherein, is the type of installed power, including thermal power unit, hydropower unit, pumped storage unit, photovoltaic unit and wind power unit; is the node number in the node set , is the time set in a day; is the output of the first type power of the node in the section at time , is the output cost of the power supply of the first type at the moment , is 15 minutes; is the cross section at the moment , is the distribution coefficient between the unit output and the cross section transmission power in the cross section ; is the distribution coefficient between the load and the cross section transmission power in the cross section ; is the node in the cross section at the moment , is the predicted load at the moment ;

[0072] The constraint conditions of the lower model include upper and lower limits of the unit output, upper and lower limits of the climbing rate of the unit and power balance constraints.

[0073] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor; the processor implements the steps of the day-ahead and day-ahead climbing resource checking and collaborative optimization scheduling method as described above when executing the computer program.

[0074] A computer readable storage medium stores a computer program; the computer program is executed by a processor to implement the steps of the day-ahead and day-ahead climbing resource checking and collaborative optimization scheduling method as described above.

[0075] After the above scheme is adopted, the beneficial effects of the present application are:

[0076] The present application ensures that the system has sufficient static climbing ability during real-time scheduling in the day, and meets the system operation safety boundary requirements, through the safety checking of the day-ahead and day-ahead climbing resources; and realizes the economic scheduling of various climbing resources under the premise of ensuring the system frequency safety, and improves the dynamic climbing ability of the system, through the day-ahead and day-ahead climbing resource collaborative rolling optimization. BRIEF DESCRIPTION OF DRAWINGS

[0077] Figure 1 is the flowchart of the present application;

[0078] Figure 2 is a schematic diagram of a typical grid structure;

[0079] Figure 3 is a comparison diagram of climbing demand and climbing ability before and after optimization;

[0080] Figure 4 is the adjustable power load rate characteristic diagram before and after optimization. DETAILED DESCRIPTION

[0081] The technical solutions and beneficial effects of the present application will be described in detail below with reference to the drawings.

[0082] The present application provides a day-ahead and intra-day climbing resource checking and collaborative optimization scheduling method, comprising the following steps:

[0083] S1: on D-1 day, day-ahead climbing ability checking is performed, D is the actual operation day of the power grid, and the specific steps comprise:

[0084] S101: 96-point new energy day-ahead prediction value, 96-point load day-ahead prediction value, 96-point cross-region direct current planned value, and 96-point thermal power unit capacity on D day are obtained; the 96 points are data obtained with a sampling interval of 15 minutes;

[0085] S102: D-day day-ahead maximum climbing demand and D-day day-ahead minimum climbing ability are calculated;

[0086] S103: the D-day day-ahead maximum climbing demand and the D-day day-ahead minimum climbing ability are compared, if the D-day day-ahead maximum climbing demand is greater than the D-day day-ahead minimum climbing ability, the day-ahead climbing safety checking is not passed, and the thermal power unit capacity is arranged to be increased until the D-day day-ahead maximum climbing demand is less than the D-day day-ahead minimum climbing ability;

[0087] S2: on D day, intra-day climbing ability checking is performed, and an intra-day collaborative optimization upper and lower layer model is constructed to perform iterative optimization, and the specific steps comprise:

[0088] S201: 96-point new energy ultra-short-term prediction value on D day, 96-point load ultra-short-term prediction value on D day, 96-point cross-region direct current planned value after stacking intra-day spot, and 96-point thermal power unit capacity on D day are obtained, T is an integer time of the actual operation day of the power grid;

[0089] S202: D-day T to T+4-hour maximum climbing demand and D-day T to T+4-hour minimum climbing ability are calculated;

[0090] S203: the D-day T to T+4-hour maximum climbing demand and the D-day T to T+4-hour minimum climbing ability are compared, if the D-day T to T+4-hour maximum climbing demand is greater than the D-day T to T+4-hour minimum climbing ability, the intra-day climbing safety checking is not passed, and the cross-region direct current planned value after stacking the spot is adjusted and reduced until the D-day T to T+4-hour maximum climbing demand is less than the D-day T to T+4-hour minimum climbing ability;

[0091] S204: Construct an intraday collaborative optimization model for climbing resources, optimizing the output of various types of climbing resources for an ultra-short-term forecast period from day T to T+4 hours before day D. The objective function of the upper-level model is L. global for:

[0092]

[0093] In the formula, For the set of all cross-sections of the system Section numbering within, The objective function of the lower-level model;

[0094] The constraints of the upper-level model include the system ramp rate deficit constraint:

[0095]

[0096] In the formula, For a set of nodes The node number within, The types of installed power sources include thermal power units, hydropower units, pumped storage units, photovoltaic units, and wind power units. cross-section internal nodes The Power supply at all times of efforts, cross-section internal nodes The Power supply at all times The amount of adjustment between the generated power and the predicted power; cross-section internal nodes At any moment Forecasted load, cross-section internal nodes At any moment The adjustment amount of the load compared to the predicted load;

[0097] The objective function L of the lower-level model s for:

[0098]

[0099] In the formula, For a certain cross section At any moment Lagrange multipliers, cross-section Maximum permissible transmission power; cross-section Distribution coefficient between internal unit output and cross-section transmission power; For cross-section Distribution coefficient between internal load and cross-section transmission power; For a set of times within a day, For 15 minutes; For the Class power supply moment Output cost;

[0100] The constraint condition of the lower layer model includes the upper and lower limit constraints of unit output, which are specifically:

[0101]

[0102] In the formula, For cross-section Internal node The upper and lower limits of the power output by the Class power supply at time ;

[0103] The constraint condition of the lower layer model also includes the upper and lower limit constraints of unit ramp rate, which are specifically:

[0104]

[0105] In the formula, The maximum ramp rate of the Class unit of node ;

[0106] The constraint condition of the lower layer model also includes the power balance constraint, which is specifically:

[0107]

[0108] S205: Solve the lower layer model to obtain optimal output , optimal solution and pass to the upper layer model; then solve the upper layer model to obtain optimal solution ;

[0109] S206: Judge whether the update deviation of Lagrange multiplier and the deviation of global objective function satisfy the convergence condition, if the convergence condition is satisfied, output the optimization result of D days T to T+4 hours; if not, return to step S205 to solve again until the convergence condition is satisfied, the convergence condition is:

[0110]

[0111] In the formula, , The first Next and first The Lagrange multipliers of the next iteration, , The first Next and first The global objective function value of the next iteration. , Preset precision threshold;

[0112] S207: In the optimization results from day D to T+4, only the optimization results from day D to T+1 are executed. Before day D T+1, return to S201 for rolling calculation.

[0113] In step S102, the maximum climbing demand before day D is... The calculation formula is:

[0114]

[0115] In the formula, The types of day-ahead ramp-up demand on day D include day-ahead ramp-up demand for renewable energy, day-ahead ramp-up demand for load, and day-ahead ramp-up demand for inter-regional DC transmission. For the first The current ramp-up demand value for Class D is 96 points.

[0116] The minimum climbing ability before day D The calculation formula is:

[0117]

[0118] In the formula, The thermal power unit operating capacity at 96:00 on day D, as determined on day D-1. This refers to the ramp-up rate of thermal power plants.

[0119] In step S202, the maximum ramp requirement from day D to day T+4 is... The calculation formula is:

[0120]

[0121] In the formula, This refers to the type of maximum intraday demand ramp-up from day T to day T+4, including the ultra-short-term forecast value of new energy on day D, the ultra-short-term forecast value of load on day D, and the inter-regional DC planned value after superimposing intraday spot prices on day D. For the first Maximum daily ramp-up demand value from T to T+4 hours;

[0122] The minimum climbing ability from D day T to T+4 hours The calculation formula is:

[0123]

[0124] In the formula, The effective operating capacity of thermal power plants within 4 hours of D day T, determined before time T on day D.

[0125] In step S201, the effective operating capacity of thermal power plants within the 4 hours of day D (T+4) is... The calculation formula is:

[0126]

[0127] In the formula, The thermal power plant operating capacity determined on day D-1 and the thermal power plant non-outage unit capacity during time T on day D are given. The thermal power unit capacity determined on day D-1 is the thermal power unit capacity that cannot increase its output due to cross-section or unit-specific reasons during the T-hour period on day D.

[0128] This invention also provides a system for checking and collaboratively optimizing intraday ramp-up resources, including:

[0129] The day-ahead ramping capacity verification module is configured to perform day-ahead ramping capacity verification on day D-1, where D is the actual day of grid operation, to determine the operating capacity of thermal power units, so that the maximum day-ahead ramping demand on day D is less than the minimum day-ahead ramping capacity on day D.

[0130] The intraday climbing ability verification module is configured to perform intraday climbing ability verification on day D; and...

[0131] The iterative optimization module is configured to iteratively optimize the output of various types of climbing resources using an intraday collaborative optimization model of climbing resources at both upper and lower levels.

[0132] In the intraday collaborative optimization model for climbing resources, the objective function L of the upper-level model is... global for,

[0133] ,

[0134] In the formula, For the set of all cross-sections of the system Section numbering within, The objective function of the lower-level model;

[0135] The constraints of the upper-level model include the system ramp rate deficit constraint.

[0136] The objective function L of the lower-level model s for,

[0137] ,

[0138] In the formula, is the type of installed power, including thermal power units, hydropower units, pumped storage units, photovoltaic units and wind power units; is the node set in the node number; is the time set in a day; is the cross section of the node in the first type power at time , is the output cost of the first type power at time , is 15 minutes; is the Lagrange multiplier of a certain cross section at time , is the distribution coefficient between unit output and cross section transmission power in the cross section ; is the distribution coefficient between load and cross section transmission power in the cross section ; is the predicted load of the node in the cross section at time , is the maximum transmission power allowed in the cross section ;

[0139] The constraint conditions of the lower model include upper and lower limits of unit output, upper and lower limits of unit ramp rate and power balance constraints.

[0140] Based on the above technical scheme, the actual network frame of a certain place in China is selected as the model case application object, and is simplified accordingly. In the typical network frame case, application analysis is carried out, as shown in Figure 2 , which includes 2 pieces of power supply and power receiving partitions, involves 4 sets of cross section sets, and each partition or cross section contains thermal power units, hydropower units, pumped storage units, wind power units, photovoltaic power stations, energy storage power stations and loads and other units.

[0141] On D-1 day, it is calculated that the maximum demand value of D-day pre-day ramping is 3 million kilowatts / 15 minutes, the pre-day ramping capacity of D-day is 5.2 million kilowatts / 15 minutes, and the maximum demand value of D-day pre-day ramping is less than the pre-day ramping capacity of D-day. The pre-day safety check is passed.

[0142] Before 15:00 on D day, considering new energy, short-term load forecasting and cross-regional DC planning value after superimposing spot, the maximum demand value of the intra-day ramping is increased to 3.2 million kilowatts from 15:00 to 19:00, and considering the non-stop of the intra-day thermal power unit and the thermal power unit unable to increase the output due to the obstruction of the cross-section, the intra-day ramping capacity is reduced to 5 million kilowatts / 15min, the maximum demand value of the intra-day ramping on D day is less than the intra-day ramping capacity on D day, and the intra-day safety check is passed.

[0143] Figure 3 The system ramping demand and the actual ramping capacity of the system before and after optimization are compared. The results show that the system ramping capacity after optimization is increased by about 19% compared with that before optimization at the moment of maximum ramping demand, and always meets the ramping demand. It is ensured that the system has sufficient ramping capacity at any moment, thereby fundamentally avoiding the operation risk caused by power shortage.

[0144] Figure 4 The load rate change of the adjustable power source in the system after optimization is reflected. After optimization calculation, slow units are preferentially called from 15:00, and the load rate is 68.4% at 16:00, which is increased by 6.7% compared with that before optimization. The load rate of the fast unit is 39.1%, which is decreased by 13.7% compared with that before optimization to cope with the subsequent fast ramping demand. When the ramping demand reaches the maximum at 16:00, the fast and slow adjustable power sources start to increase the load rate to meet the ramping demand.

[0145] The embodiment of the present application also provides another computer device, which comprises a processor and a memory configured to store a computer program capable of running on the processor; wherein the processor is configured to run the computer program to execute the method steps in the foregoing embodiments.

[0146] In practical application, the processor includes a field programmable gate array (FPGA), and the processor can be a central processing unit (CPU) or a digital signal processor (DSP). It can be understood that for different devices, the electronic device for realizing the function of the processor can also be other devices, and the embodiment of the present application is not limited specifically.

[0147] The above-mentioned memory can be a volatile memory (volatile memory), such as a random access memory (RAM, Random-Access Memory); or a non-volatile memory (non-volatile memory), such as a read-only memory (ROM, Read-Only Memory), a flash memory, a hard disk (HDD, Hard Disk Drive) or a solid state disk (SSD, Solid-State Drive); or a combination of the above-mentioned kinds of memories, and provides instructions and data to the processor.

[0148] In an exemplary embodiment, the present embodiment also provides a computer readable storage medium for storing a computer program.

[0149] Optionally, the computer readable storage medium can be applied to any one of the methods in the embodiments of the present application, and the computer program causes the computer to execute the corresponding processes realized by the processor in each method of the embodiments of the present application. For the sake of brevity, it will not be repeated here.

[0150] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages, such as object-oriented programming language Java and interpreted scripting language JavaScript.

[0151] The present application is described with reference to 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 flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be realized by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing apparatus to produce a machine, so that the instructions executed by the computer or other programmable data processing apparatus produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The device that implements the functions specified in one flow or multiple flows and / or blocks Figure 1 The device that implements the functions specified in one flow or multiple flows and / or blocks

[0152] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0153] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions that are executed on the computer or other programmable apparatus provide steps for implementing the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0154] While the preferred embodiments of the application have been described, additional variations and modifications can be made to the embodiments by those of skill in the art once they have the benefit of the present disclosure. Therefore, the appended claims are intended to encompass within their scope all possible variations and modifications of the preferred embodiments. 1

[0155] It is apparent that a person skilled in the art can make a variety of changes and modifications to the application without departing from the spirit and scope thereof. Thus, if these modifications and changes fall within the scope of the claims and their equivalents, it is intended to include them in the application.

Claims

1. A method for day-ahead intraday ramp-up resource verification and collaborative optimization scheduling, characterized in that: include, On D-1, the day-ahead ramping capacity is checked, where D is the actual day of grid operation. The operating capacity of thermal power units is determined so that the maximum day-ahead ramping demand on D is less than the minimum day-ahead ramping capacity on D. On day D, the intraday climbing capacity was checked, and the output of each type of climbing resource was iteratively optimized using the intraday collaborative optimization model of climbing resources. In the intraday collaborative optimization model for climbing resources, the objective function L of the upper-level model is... global for, ， In the formula, For the set of all cross-sections of the system Section numbering within, The objective function of the lower-level model; The constraints of the upper-level model include the system ramp rate deficit constraint. The objective function L of the lower-level model s for, ， In the formula, The types of installed power sources include thermal power units, hydropower units, pumped storage units, photovoltaic units, and wind power units; For a set of nodes Node number within; A collection of times within a day; cross-section internal nodes The Power supply at all times of effort, For the first Power-like moments The cost of output, It lasts for 15 minutes; For a certain cross section At any moment Lagrange multipliers, cross-section The distribution coefficient between the output of the indoor unit and the cross-sectional transmitted power; cross-section The distribution coefficient between internal load and cross-sectional transmitted power; cross-section internal nodes At any moment Forecasted load, cross-section Maximum permissible transmission power; The constraints of the lower-level model include upper and lower limits of unit output, upper and lower limits of unit ramp rate, and power balance constraints.

2. The method for day-ahead intraday ramp resource verification and collaborative optimization scheduling as described in claim 1, characterized in that: On day D-1, a day-ahead ramping capacity check is performed, where D is the actual day the power grid is operating. The operating capacity of thermal power units is determined to ensure that the maximum day-ahead ramping demand on day D is less than the minimum day-ahead ramping capacity on day D, including... Obtain the day-ahead forecast values ​​for new energy sources at 96:00 on day D, the day-ahead forecast values ​​for load at 96:00, the planned values ​​for inter-regional DC transmission at 96:00 on day D, and the operating capacity of thermal power plants at 96:00 on day D; The maximum climbing demand and minimum climbing capacity before day D are calculated. Compare the maximum ramp requirement before day D with the minimum ramp capacity before day D. If the maximum ramp requirement before day D is greater than the minimum ramp capacity before day D, the ramp safety check fails, and the operating capacity of the thermal power unit is increased until the maximum ramp requirement before day D is less than the minimum ramp capacity before day D.

3. The method for day-ahead intraday ramp resource verification and collaborative optimization scheduling as described in claim 2, characterized in that: The maximum climbing demand and minimum climbing capacity before day D are calculated, including: The maximum climbing demand before day D is calculated using the following formula. , ， In the formula, The types of day-ahead ramp-up demand on D-day include day-ahead ramp-up demand for new energy sources on D-day, day-ahead ramp-up demand for loads on D-day, and day-ahead ramp-up demand for inter-regional DC transmission on D-day. For the first The current ramp-up demand value for Class D is 96 points. The minimum climbing ability before day D is calculated using the following formula. , ， In the formula, The thermal power unit operating capacity at 96:00 on day D, as determined on day D-1. This refers to the ramp-up rate of thermal power plants.

4. The method for day-ahead intraday ramp resource verification and collaborative optimization scheduling as described in claim 1, characterized in that: The constraints of the upper-level model include the system ramp rate deficit constraint, the expression of which is: ， In the formula, cross-section internal nodes The Power supply at all times The amount of adjustment between the generated power and the predicted power; cross-section internal nodes The Power supply at all times of effort, cross-section internal nodes The Power supply at all times The amount of adjustment between the generated power and the predicted power; cross-section internal nodes At any moment The adjustment amount of the load compared to the predicted load; cross-section internal nodes At any moment Forecasted load, cross-section internal nodes At any moment The adjustment amount of the load compared to the predicted load; The constraints of the lower-level model include upper and lower limits of unit output constraints, specifically as follows: ， In the formula, Cross-sections internal nodes The Power supply at all times The upper and lower limits of the emitted power; The constraints of the lower-level model include upper and lower limits of the unit's ramp rate constraints, specifically as follows: ， In the formula, For nodes of Maximum ramp rate for this type of unit; The constraints of the lower-level model include power balance constraints, specifically, 。 5. The method for day-ahead intraday ramp resource verification and collaborative optimization scheduling as described in claim 1, characterized in that: On day D, intraday climbing capacity verification was performed, and the output of various types of climbing resources was iteratively optimized using an intraday collaborative optimization model of climbing resources at both upper and lower levels, including: Obtain the ultra-short-term forecast values ​​of new energy from day T to T+4, the ultra-short-term forecast values ​​of load from day T to T+4, the cross-regional DC planned values ​​after superimposing intraday spot prices from day T to T+4, and the thermal power generation capacity from day T to T+4, where T is a certain integer moment on the actual day of grid operation; Calculate the maximum climbing requirement from day D to T+4 hours and the minimum climbing capacity from day D to T+4 hours. Compare the maximum ramp demand from day T to T+4 with the minimum ramp capacity from day T to T+4. If the maximum ramp demand from day T to T+4 is greater than the minimum ramp capacity from day T to T+4, the intraday ramp safety check fails, and the cross-regional DC plan value after adding spot prices is reduced until the maximum ramp demand from day T to T+4 is less than the minimum ramp capacity from day T to T+4. Construct an intraday collaborative optimization model for climbing resources, optimizing the output of various types of climbing resources for an ultra-short-term forecast period from day T to day T+4 hours before day D. Solve the lower-level model of the intraday collaborative optimization model for climbing resources to obtain the optimal output. Optimal solution The solution is then passed to the upper-level model; the upper-level model is then solved to obtain the optimal solution. ; Determine the update bias of Lagrange multipliers and the deviation of the global objective function If the convergence condition is met, output the optimization results from day T to hour T+4 of day D; if not, re-solve the lower-level model of the intraday collaborative optimization model for climbing resources until the convergence condition is met. The convergence condition is: ， In the formula, , The first Next and first The Lagrange multipliers of the next iteration, , The first Next and first The global objective function value of the next iteration. , Preset precision threshold; In the optimization results from T to T+4 hours on day D, only the optimization results from T to T+1 hours on day D are executed. Before T+1 hours on day D, the intraday ramp-up capability verification is restarted.

6. The method for day-ahead intraday ramp resource verification and collaborative optimization scheduling as described in claim 5, characterized in that: Calculate the maximum climbing requirement and minimum climbing capacity from day D (T) to day T+4. The maximum climbing requirement from day D to day T+4 is calculated using the following formula. , ， In the formula, This refers to the type of maximum intraday demand ramp-up from day T to day T+4, including the ultra-short-term forecast value of new energy on day D, the ultra-short-term forecast value of load on day D, and the inter-regional DC planned value after superimposing intraday spot prices on day D. For the first Maximum daily ramp-up demand value from T to T+4 hours; The minimum climbing ability from day T to T+4 is calculated using the following formula. , ， In the formula, This refers to the effective operating capacity of thermal power plants within 4 hours of day T+4, determined before time T on day D. This refers to the ramp-up rate of thermal power plants.

7. The method for day-ahead intraday ramp resource verification and collaborative optimization scheduling as described in claim 5, characterized in that: Obtain the thermal power plant operating capacity from day D to day T+4 hours, including: The effective operating capacity of thermal power plants within 4 hours of day D (T+4) is calculated using the following formula. , ， In the formula, The thermal power plant operating capacity determined on day D-1 and the thermal power plant non-outage unit capacity during time T on day D. The thermal power unit capacity determined on D-1 day and the thermal power unit capacity that cannot increase output due to cross-section or unit-specific reasons during the T-hour period on D day.

8. A system for checking and collaboratively optimizing the scheduling of intraday and daytime ramp-up resources, characterized in that: include, The day-ahead ramping capacity verification module is configured to perform day-ahead ramping capacity verification on day D-1, where D is the actual day of grid operation, to determine the operating capacity of thermal power units, so that the maximum day-ahead ramping demand on day D is less than the minimum day-ahead ramping capacity on day D. The intraday climbing ability verification module is configured to perform intraday climbing ability verification on day D. as well as, The iterative optimization module is configured to iteratively optimize the output of various types of climbing resources using an intraday collaborative optimization model of climbing resources at both upper and lower levels. In the intraday collaborative optimization model for climbing resources, the objective function L of the upper-level model is... global for, ， In the formula, For the set of all cross-sections of the system Section numbering within, The objective function of the lower-level model; The constraints of the upper-level model include the system ramp rate deficit constraint. The objective function L of the lower-level model s for, ， In the formula, The types of installed power sources include thermal power units, hydropower units, pumped storage units, photovoltaic units, and wind power units; For a set of nodes Node number within; A collection of times within a day; cross-section internal nodes The Power supply at all times of effort, For the first Power-like moments The cost of output, It lasts for 15 minutes; For a certain cross section At any moment Lagrange multipliers, cross-section The distribution coefficient between the output of the indoor unit and the cross-sectional transmitted power; cross-section The distribution coefficient between internal load and cross-sectional transmitted power; cross-section internal nodes At any moment Forecasted load, cross-section Maximum permissible transmission power; The constraints of the lower-level model include upper and lower limits of unit output, upper and lower limits of unit ramp rate, and power balance constraints.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor; characterized in that: When the processor executes the computer program, it implements the steps of the day-to-day intraday ramp resource verification and collaborative optimization scheduling method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program; characterized in that: When the computer program is executed by the processor, it implements the steps of the day-to-day intraday ramp resource verification and collaborative optimization scheduling method as described in any one of claims 1 to 7.

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