Day-ahead optimal dispatching decision method and device considering difference in ramp rate of thermal power generating units

Abstract

A kind of day-ahead optimization scheduling decision method and device considering the difference of thermal power unit climbing rate, method includes: extending thermal power unit optimization scheduling decision period;Thermal power unit operation process is modeled using 0-1 variable to different climbing rate peak shaving interval;Thermal power unit optimization scheduling objective function considering different state operation cost and deep peak shaving compensation cost is established, and the operation constraint condition of thermal power unit considering different working condition start-stop, segmented climbing rate, start-stop process output and standby climbing ability reservation;Combined with hydropower operation, system balance, new energy operation and grid safety constraint, build high proportion new energy power grid unit commitment optimization scheduling decision model;Access planned day grid topology model and operation boundary data, generate day-ahead unit commitment plan and output plan based on the unit commitment optimization scheduling decision model.The application can reduce the grid safety risk caused by insufficient climbing ability, and promote new energy consumption.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system dispatch automation, in particular to a day-ahead optimal dispatch decision method and device considering the difference in climbing rate of thermal power generating units. BACKGROUND

[0002] With the widespread implementation of flexible transformation of thermal power generating units, the depth of peak regulation, rapid start-stop and climbing ability of thermal power generating units have been significantly improved, and deep peak regulation and start-stop peak regulation of thermal power generating units have become an important means to improve the flexibility of new power systems and promote new energy consumption.

[0003] The prior art proposes a security constrained unit commitment (SCUC) algorithm, which mainly models thermal power generating units in only two states of operation / stop based on traditional peak regulation requirements, but does not consider the optimization requirements of the operation and regulation capacity of thermal power generating units under a high proportion of new energy environment, and the main deficiencies are as follows:

[0004] 1) The prior art models the climbing ability of the unit according to a single climbing rate and does not consider the reserve climbing ability, and there are significant differences in the climbing performance of the unit at different peak regulation stages after flexible transformation, and the demand for climbing ability is greater with a high proportion of new energy, so only considering the single climbing rate of the unit output and the reserve plan is easy to lead to insufficient climbing ability of the system in real-time operation, causing frequency safety risks, or not fully utilizing the climbing ability of the unit, hindering new energy consumption.

[0005] 2) The difference in output and regulation capacity of the unit during the start-stop process under different conditions, such as hot, warm and cold conditions, is ignored, and when the start-stop peak regulation of thermal power generating units is more, there is a large deviation between the output and regulation capacity of the unit during the start-stop process and the actual demand, affecting real-time power balance and new energy consumption.

[0006] Therefore, there is an urgent need for a new day-ahead optimal dispatch decision method. SUMMARY

[0007] To solve the deficiencies in the prior art, the present application provides a day-ahead optimal dispatch decision method and device considering the difference in climbing rate of thermal power generating units, which more accurately and effectively optimizes the deep peak regulation of the unit, the start-stop of the unit, the output plan, the reserve capacity and the climbing ability, reduces the safety risks of the power grid caused by insufficient climbing ability, and promotes new energy consumption.

[0008] The present application adopts the following technical solutions.

[0009] The first aspect of the present application provides a day-ahead optimal dispatch decision method considering the difference in climbing rate of thermal power generating units, comprising:

[0010] The decision-making period of the optimal scheduling of the thermal power unit is extended, and the future plan of the unit is connected with the known state in the past.

[0011] The operation process of the thermal power unit and the peak regulation interval with different ramp rates are modeled by using 0-1 variables to obtain a thermal power unit operation model, wherein the operation process includes a grid-connected stage, a warm-up stage, a disconnection stage and a scheduling stage.

[0012] An optimal scheduling objective function of the thermal power unit is established by considering the operation cost in different states and the compensation cost of deep peak regulation, and based on the thermal power unit operation model and the extended decision-making period, a thermal power unit operation constraint is established, which is composed of a unit combination state constraint, a start-up working condition constraint, a minimum start-stop interval constraint, a grid-connected stage constraint, a warm-up stage constraint, a disconnection stage constraint, a scheduling stage constraint and a unit standby constraint.

[0013] The thermal power unit operation constraint is combined with a hydropower operation constraint, a system balance constraint, a new energy operation constraint and a power grid safety constraint, and combined with the optimal scheduling objective function of the thermal power unit to construct a unit combination optimization scheduling decision-making model of a high-proportion new energy power grid.

[0014] The day-ahead unit combination plan and output plan are generated based on the unit combination optimization scheduling decision-making model of the high-proportion new energy power grid by accessing the planned day power grid topology model and operation boundary data.

[0015] Optionally, the extension of the decision-making period of the optimal scheduling of the thermal power unit connects the future plan of the unit with the known state in the past, and includes:

[0016] The maximum value of the sum of the warm-up time interval, the minimum shutdown time, the minimum operation time and the grid-connected and warm-up time of all thermal power units is confirmed as the extended past period, which represents the known state in the past.

[0017] The extended past period is combined with the initial state of the system and the unit before the start of the first planning period and the future planning period of the unit to obtain the extended decision-making period.

[0018] Optionally, in the scheduling stage, the unit output range is divided into a plurality of peak regulation interval sets M according to the difference in the ramp rate, and 0-1 variables are used to represent whether the output of the unit i at the t period is within the peak regulation interval m, wherein, .

[0019] Optionally, the establishment of the optimal scheduling objective function of the thermal power unit considering the operation cost in different states and the compensation cost of deep peak regulation includes:

[0020] The optimal scheduling objective function of the thermal power unit is as follows:

[0021] (1)

[0022] wherein, is the optimal dispatch objective function value of the thermal power unit;

[0023] is the set of thermal power units, is the future planning period, and T is the set of future planning periods;

[0024] is the length of each period;

[0025] is the number of unit output cost segments;

[0026] is the segment output price of unit i at period t;

[0027] is the planned power component of unit i at period t in price segment s;

[0028] is the no-load cost of unit i;

[0029] indicates whether unit i is in the warm-up phase at period t;

[0030] indicates whether unit i is in the dispatch phase at period t;

[0031] indicates whether unit i is in the de-parallel phase at period t;

[0032] is the start-up cost of unit i at start-up condition j;

[0033] is the shutdown cost of unit i at start-up condition j;

[0034] is the deep peak regulation compensation cost of unit i at period t;

[0035] is the number of start-up conditions of the thermal power unit;

[0036] is a 0-1 variable indicating whether the start-up of condition j of unit i occurs at period t, indicates the start-up of condition j of unit i at period t;

[0037] is a 0-1 variable indicating whether shutdown occurs at period t, indicates shutdown.

[0038] Optionally, the deep peak regulation compensation cost is calculated as follows:

[0039] (2)

[0040] (3)

[0041] (4)

[0042] (5)

[0043] (6)

[0044] (7)

[0045] (8)

[0046] In the formula, is the deep peak regulation compensation cost of unit i at time period t;

[0047] represents the deep peak regulation compensation cost of thermal power unit i at time period t if the output is located in the peak regulation interval m;

[0048] is the maximum power component of unit i at time period t in price segment s;

[0049] is whether the output of unit i at time period t is located in the peak regulation interval m;

[0050] is the planned power of unit i at time period t, which is a variable;

[0051] is the planned power component of unit i at time period t in price segment s;

[0052] is the set of deep peak regulation intervals to be compensated, wherein,

[0053] is the lower limit of the basic peak regulation output of unit i;

[0054] is the compensation price of unit i at time period t when the output is located in the peak regulation interval m;

[0055] is the maximum technical output limit of unit i at time period t.

[0056] ​Optionally, the thermal power unit operation constraint is combined with the hydropower operation constraint, system balance constraint, new energy operation constraint, and grid safety constraint, and combined with the thermal power unit optimization scheduling objective function to construct a high-proportion new energy grid unit commitment optimization scheduling decision model, which includes:

[0057] The high-proportion new energy grid unit commitment optimization scheduling decision model includes a high-proportion new energy grid unit commitment optimization scheduling objective function and a high-proportion new energy grid unit commitment optimization scheduling constraint condition;

[0058] The high-proportion new energy grid unit commitment optimization scheduling objective function is the sum of the thermal power unit optimization scheduling objective function and the hydropower unit output power purchase cost and the new energy unit output power purchase cost.

[0059] The thermal power unit operation constraint condition, the hydropower unit operation constraint, the new energy unit operation constraint, the system power and energy balance constraint, and the grid safety constraint constitute the high-proportion new energy grid unit commitment optimization scheduling constraint condition.

[0060] Optionally, the access plan day grid topology model and operation boundary data are used to generate a day-ahead unit commitment plan and output plan based on the high-proportion new energy grid unit commitment optimization scheduling decision model, which includes:

[0061] The access plan day grid topology model and operation boundary data are used to call a mixed integer linear programming algorithm to minimize the high-proportion new energy grid unit commitment optimization scheduling objective function based on the high-proportion new energy grid unit commitment optimization scheduling decision model to obtain an optimal day-ahead unit commitment plan and output plan.

[0062] Based on the accessed day-ahead market grid topology data, operation boundary data, and the solved day-ahead unit commitment plan and output plan, a safety check analysis is performed. If there are section and / or branch flow out-of-limit and / or device flow out-of-limit, the out-of-limit section information and / or branch information and / or device information are returned as new grid operation constraint conditions added to the high-proportion new energy grid unit commitment optimization scheduling constraint condition, and the plan day day-ahead unit commitment plan and output plan are re-solved until all flow out-of-limit conditions are eliminated or a specified number of iterations is reached.

[0063] The second aspect of the present application provides a day-ahead optimization scheduling decision device considering the difference in thermal power unit ramping rate, which includes:

[0064] The extension module is used to extend the thermal power unit optimization scheduling decision period, and connect the future unit plan with the past known state.

[0065] The first construction module is configured to model the operation process of the thermal power unit and the peak regulation interval of different ramping rates by using 0-1 variables, and obtain a thermal power unit operation model; the operation process includes a grid connection stage, a warm-up stage, a disconnection stage, and a dispatching stage.

[0066] The second construction module is configured to consider the optimization scheduling target of the thermal power unit in different state operation costs and deep peak regulation compensation costs, and establish a thermal power unit operation constraint based on the thermal power unit operation model, the thermal power unit operation constraint including a unit combination state constraint, a start-up working condition constraint, a minimum start-stop interval constraint, a grid connection stage constraint, a warm-up stage constraint, a disconnection stage constraint, a dispatching stage constraint, and a unit standby constraint.

[0067] The third construction module is configured to combine the thermal power unit operation constraint with a hydropower operation constraint, a system balance constraint, a new energy operation constraint, and a power grid safety constraint, and combine the thermal power unit optimization scheduling target to construct a high-proportion new energy power grid unit combination optimization scheduling decision model.

[0068] The day-ahead unit combination plan optimization solving module is configured to access a planning day power grid topology model and operation boundary data, and generate a day-ahead unit combination plan and an output plan in an extended thermal power unit optimization scheduling decision period based on the high-proportion new energy power grid unit combination optimization scheduling decision model.

[0069] Optionally, the device further includes:

[0070] The day-ahead plan safety checking module is configured to perform safety checking analysis based on the accessed day-ahead market power grid topology data, operation data, and the solved day-ahead unit combination plan and output plan, and if there is a cross-section and / or branch flow out-of-limit and / or equipment flow out-of-limit, return the out-of-limit cross-section information and / or branch information and / or equipment information, add the out-of-limit cross-section information and / or branch information and / or equipment information as new power grid operation constraint conditions to the high-proportion new energy power grid unit combination optimization scheduling constraint conditions, and re-solve the day-ahead unit combination plan and output plan of the planning day, until all flow out-of-limit conditions are eliminated or a specified number of iterations is reached.

[0071] The third aspect of the present application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being loaded into the processor to implement the method of the day-ahead optimization scheduling decision considering the difference in ramping rate of the thermal power unit.

[0072] The fourth aspect of the present application provides a computer readable storage medium, the computer readable storage medium storing a computer program, the computer program being executed by a processor to implement the method of the day-ahead optimization scheduling decision considering the difference in ramping rate of the thermal power unit.

[0073] Compared with the prior art, the beneficial effects of the present application at least include:

[0074] The present application divides the operation process of the thermal power unit into four stages of grid connection, warm-up, dispatching and disconnection by adding a 0-1 variable, and divides the output of the thermal power unit into multiple peak regulation intervals according to the difference in ramping rate by considering the cold, steady and hot state start-stop time interval and the output during the start-stop process, extends the modeling period to support the connection of different working conditions of the unit during the start-stop process, and according to the business demand of start-stop peak regulation and deep peak regulation of the thermal power unit after the flexibility reconstruction of the high proportion of new energy and new type power system, the modeling method of different operating states, output intervals, start-stop working conditions and ramping rate difference of the thermal power unit is improved, a high proportion of new energy power grid unit combination optimization scheduling model considering deep peak regulation compensation cost, segmented ramping rate, different working conditions of start-stop, output during start-stop process and reserve ramping capacity reservation is established, and the day-ahead unit combination plan and output plan generated based on the method and device can more accurately and effectively optimize the deep peak regulation of the unit, the start-stop of the unit, the output plan, the reserve capacity and the ramping capacity, reduce the safety risk of the power grid caused by insufficient ramping capacity, and promote new energy consumption. BRIEF DESCRIPTION OF DRAWINGS

[0075] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor. Among them:

[0076] Figure 1 It is a day-ahead optimization scheduling decision method flowchart considering the ramping rate difference of the thermal power unit provided by the embodiment of the present application;

[0077] Figure 2 It is a modeling schematic diagram of the dispatching operation process of the thermal power unit provided by the embodiment of the present application;

[0078] Figure 3 It is a certain planned day power balance schematic diagram of a certain provincial power grid provided by the embodiment of the present application;

[0079] Figure 4 It is a comparison schematic diagram of the start-stop and output plan of the thermal power unit provided by the embodiment of the present application;

[0080] Figure 5 It is a comparison schematic diagram of the thermal power up-regulation capacity and ramping capacity provided by the embodiment of the present application. DETAILED DESCRIPTION

[0081] In order to make the purposes, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described clearly and completely below in combination with the accompanying drawings in the embodiments of the present application. The embodiments described in the present application are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art without creative labor based on the spirit of the present application shall fall within the protection scope of the present application.

[0082] With reference to Figure 1 Embodiment 1 of the present application provides a day-ahead optimal scheduling decision-making method considering the difference in climbing rate of thermal power generating units, comprising:

[0083] S1: extending the optimal scheduling decision-making period of the thermal power generating unit, connecting the future plan and the past known state of the unit.

[0084] S1 specifically comprises: dividing the future plan time into a plurality of plan periods according to a certain time interval of 15 minutes or 1 hour , T is a set of future plan periods, and NT is the number of periods in the future plan period; a “0” period is introduced to represent the initial state of the system and the unit before the start of the first plan period.

[0085] The present application extends the optimal scheduling modeling period to the past time, realizes the unified modeling of the whole process state of the unit operation, and the extended scheduling modeling period set is as follows:

[0086]

[0087]

[0088] In the formula,

[0089] NK is the number of extended past periods, is the warm-up time interval of the thermal power generating unit i; is the minimum shutdown time of the thermal power generating unit i, is the minimum operation time of the thermal power generating unit, is the grid connection time of the thermal power generating unit i starting with the working condition j; is the warm-up time of the thermal power generating unit i starting with the working condition j; denotes the starting working condition of the thermal power generating unit, wherein, denotes the hot-state starting, denotes the warm-state starting, denotes the cold-state starting working condition.

[0090] S2: modeling the operation process of the thermal power generating unit and the peak regulation interval of different climbing rates by using 0-1 variables, to obtain a generating unit operation model. S2 specifically comprises:

[0091] S2.1: The experience of putting thermal power unit i into operation is divided into four consecutive operation stages, including grid connection stage, warm-up stage, scheduling stage and split stage.

[0092] As shown in Figure 2 : the grid connection stage does not carry load; the synchronous load increases from 0 to the minimum technical output in the warm-up stage ; the output decreases from the minimum technical output to zero in the split stage.

[0093] S2.2: 0-1 variables , , , are used to represent whether unit i is in the operation state of the above four stages in the planning period t.

[0094] Preferably but not limitedly, the unit output range is divided into a plurality of peak regulation interval sets M according to the difference in unit ramp rate, and m represents a single peak regulation interval of set M, i.e. ; 0-1 variables represent whether the output of unit i in period t is within the range of peak regulation interval m, and the lower limit of the output of unit in peak regulation interval m is defined as , and the upper limit of the output is .

[0095] In this embodiment, by dividing the unit output range according to the difference in unit ramp rate, the modeling period is expanded to support the connection of unit start-stop processes in different working conditions, considering that the ramp rate of the scheduling stage is related to the output, considering that the ramp rate of the unit decreases with the increase of the depth of peak regulation, and the regulation performance is poor, and the generated day-ahead unit combination plan and output plan are more accurate.

[0096] S3: Establishing a thermal power unit optimization scheduling objective function considering different state operation costs and deep peak regulation compensation costs.

[0097] Preferably but not limitedly, the calculation method of the thermal power unit optimization scheduling objective function is as shown below:

[0098] (1)

[0099] (2)

[0100] (3)

[0101] (4)

[0102] (5)

[0103] (6)

[0104] (7)

[0105] (8)

[0106] wherein, is the value of the objective function of the optimal dispatch of thermal power units;

[0107] is the set of thermal power units, is the future planning period;

[0108] is the length of each period, which can be in hours;

[0109] is the number of future planning periods;

[0110] is the number of unit output price or cost segments;

[0111] is the maximum power component of unit i at period t in price segment s;

[0112] is the planned power of unit i at period t, which is a variable,

[0113] is the planned power component of unit i at period t in price segment s;

[0114] is the number of start-up conditions of thermal power units;

[0115] is the segmented output price of unit i at period t;

[0116] is the no-load cost of unit i;

[0117] and are the start-up cost and shutdown cost of unit i in different start-up conditions j, respectively;

[0118] is a 0-1 variable indicating whether the start-up of condition j of unit i occurs at period t, indicates the start-up of condition j of unit i at period t;

[0119] is a 0-1 variable indicating whether shutdown occurs at period t for unit i, indicates shutdown; is the set of deep peak regulation intervals that need to be compensated;

[0120] is the basic peak load of the unit i, which can be set as 50% of the rated capacity of the unit i;

[0121] is the compensation price of the unit i when the output of the unit i is in the peak interval m at the t time period;

[0122] is an auxiliary variable, which represents the peak compensation cost of the thermal power unit i if the output of the unit i is in the peak interval m at the t time period;

[0123] is the deep peak compensation cost of the unit i at the t time period;

[0124] is the maximum technical output limit of the unit i at the t time period;

[0125] represents whether the unit i is in the warm-up stage at the t time period;

[0126] represents whether the unit i is in the dispatch stage at the t time period;

[0127] represents whether the unit i is in the tripping stage at the t time period;

[0128] represents whether the unit i is in the dispatch stage if the output of the unit i is in the peak interval m at the t time period.

[0129] It should be noted that formula (1) is an optimization scheduling objective function of the thermal power unit, and the optimization objective of the thermal power unit is composed of the output cost, the idle cost, the start-stop cost and the deep peak compensation cost of the thermal power unit; formula (2, 3) supplements the definition of the output segmentation of the thermal power unit, that is, the thermal power unit corresponds to different prices at different output segmentations as shown in formula (1); formula (4) defines the calculation method of the deep peak compensation cost of the thermal power unit, and formula (5-8) supplements the range of the value of the variable in formula (4), and formula (4-8) jointly define the deep peak compensation cost of the unit as the product of the less generated power and the compensation price of the unit in the deep peak stage relative to the basic peak lower limit output.

[0130] As can be seen from formula (4), the peak compensation cost of the deep peak interval set of the unit i at the t time period which needs to be compensated is which includes: multiplying whether the unit i is in the dispatch stage when the output of the unit i is in the peak interval m at the t time period and the basic peak load of the unit i, and then subtracting the peak compensation cost of the thermal power unit i when the output of the unit i is in the peak interval m at the t time period from the compensation price of the unit i when the output of the unit i is in the peak interval m at the t time period S4.2: Construct the minimum start-stop interval constraint. The minimum start-stop interval constraint is used to define the minimum start-stop interval of the unit, and the specific constraint formula is as follows: S4.3: Construct the grid-connection phase constraint. The grid-connection phase constraint is used to define the grid-connection phase of the unit, and the specific constraint formula is as follows: .

[0131] S4.4: Construct the warming-up phase constraint. The warming-up phase constraint is used to define the warming-up phase of the unit, and the specific constraint formula is as follows:

[0132] As one of the prominent substantive features of the present application, the thermal power unit operation constraint established by the present application considers different working conditions start-stop, segmented climbing rate, output during start-stop process and standby climbing capacity reservation, and the constraint condition adopts operation process, peak regulation interval and extended time period. Compared with the constraint condition of the prior art, the unit operation process is considered more finely and comprehensively, and therefore, the day-ahead unit combination plan and output plan obtained by solving are more accurate.

[0133] For any thermal power unit and a planned time period , the following operation constraints need to be met: unit combination state constraint, start-up working condition constraint, minimum start-stop interval constraint, grid-connection phase constraint, warming-up phase constraint, de-parallel operation phase constraint, dispatching phase constraint and unit standby constraint. The construction of the thermal power unit operation constraint in S4 specifically includes:

[0134] S4.1: Construct the unit combination state constraint. The unit combination state is used to define the relationship of four running states of the unit and the relationship of the start-stop time period of the unit, and the specific constraint formula is as follows:

[0135] (9)

[0136] (10)

[0137] (11)

[0138] In the formula, denotes the running state of the unit i in the time period t, denotes whether the unit i is in the grid-connection phase in the time period t, denotes whether the unit i is in the warming-up phase in the time period t, denotes whether the unit i is in the dispatching phase in the time period t, denotes whether the unit i is in the de-parallel operation phase in the time period t; denotes whether the unit i is started in the time period t, denotes whether the unit i is stopped in the time period t, denotes the operation state of unit i at time period t-1.

[0139] It can be understood that formula (9) indicates that the unit is in one of the four stages of grid connection, warm-up, dispatching and disconnection once it is running, formula (10) defines the correlation between the start-stop state and the running state of the unit, and formula (11) indicates that the unit cannot start and stop at the same time at the same time period.

[0140] S4.2: Build start-up condition constraints. The start-up condition constraints are used to define the relationship between various start-up conditions of the unit at each time period and the hot, warm and cold start-up constraints. The specific constraint formula is as follows:

[0141] (12)

[0142] (13)

[0143] (14)

[0144] (15)

[0145] In the formula, denotes whether the start-up of unit i at time period t is a hot start-up, denotes whether the start-up of unit i at time period t is a warm start-up, denotes whether the start-up of unit i at time period t is a cold start-up; denotes the hot start-up time interval of unit i, denotes the warm start-up time interval of unit i; denotes the time period, , is the extended dispatching modeling time period set, , , denote whether the stop of unit i at time period , , occurs.

[0146] S4.3: Build minimum start-stop interval constraints. The minimum start-stop interval constraints are used to define the time that the thermal power unit needs to run or stop after starting or stopping. The minimum start-stop interval constraints are expressed as follows:

[0147] (16)

[0148] (17)

[0149] In the formula, denotes the operation state of unit i at time period t, denotes the minimum on period of unit i after it is started, denotes the minimum on period of unit i after it is started, denotes the minimum on period of unit i after it is started, denotes the minimum on period of unit i after it is started, denotes the minimum on period of unit i after it is started, denotes the minimum on period of unit i after it is started, denotes the minimum on period of unit i after it is started, denotes the minimum on period of unit i after it is started, denotes the minimum on period of unit i after it is started, denotes the minimum on period of unit i after it is started, denotes the minimum on period of unit i after it is started, denotes the minimum on period of unit i after it is started, denotes the minimum on period of unit i after it is started, denotes the minimum on period of unit i after it is started,

[0150] S4.4: Construct the grid-connection period constraint. The grid-connection period constraint is used to define the grid-connection period of unit i after it is started. The grid-connection period constraint is expressed as follows:

[0151] (18)

[0152] wherein, denotes whether unit i is in the grid-connection period at time period t, denotes the minimum grid-connection period of unit i after it is started, denotes the minimum grid-connection period of unit i after it is started, denotes the minimum grid-connection period of unit i after it is started, denotes the minimum grid-connection period of unit i after it is started, denotes the minimum grid-connection period of unit i after it is started.

[0153] S4.5: Construct the warm-up period constraint. The warm-up period constraint is used to define the warm-up period of unit i after it is started and the output of unit i in the warm-up period. The warm-up period constraint is expressed as follows:

[0154] (19)

[0155] (20)

[0156] wherein, denotes whether unit i is in the warm-up period at time period t, denotes the minimum warm-up period of unit i after it is started, denotes the output of unit i in the warm-up period at time period t, denotes the time period, denotes the minimum warm-up period of unit i after it is started, If the unit i starts at period t, its output parameter at period t in the warm-up stage is set according to the start-up curve of the unit i and the time relationship between t and t. Equation (19) shows that if the unit i starts at period t, it is in the warm-up stage from period t to period t+. If the unit i starts at period t, its output parameter at period t in the warm-up stage is set according to the start-up curve of the unit i and the time relationship between t and t. Equation (19) shows that if the unit i starts at period t, it is in the warm-up stage from period t to period t+.

[0157] S4.6: Build the constraint of the decoupling stage. The constraint of the decoupling stage is used to define the required running time of the unit after preparing to shut down until the output of the unit is 0 and the output of the decoupling stage. The constraint of the decoupling stage is expressed as follows:

[0158] (21)

[0159] (22)

[0160] In the formula, t indicates whether the unit i is in the decoupling stage at period t, is the output of the unit i at period t when the unit i is in the decoupling stage, If the unit i is shut down at period t, its output parameter at period t in the decoupling stage is set according to the shut-down curve of the unit i and the time relationship between t and t. Equation (21) shows that if the unit i is shut down at period t, it enters the decoupling stage in advance by period t.

[0161] S4.7: Build the constraint of the scheduling stage as follows:

[0162] (23)

[0163] (24)

[0164] (25)

[0165] (26)

[0166] (27)

[0167] (28)

[0168] (29) ​​​​​​​​​

[0169] (30)

[0170] (31)

[0171] (32)

[0172] wherein, is a 0-1 variable, indicating whether unit i can provide frequency regulation at time period t, i.e., whether it can be in AGC (Automatic Generation Control) state;

[0173] denotes the set of peak regulation intervals in which frequency regulation can be provided, wherein, for example, under deep peak regulation, unit i can not provide frequency regulation; denotes the number of peak regulation intervals;

[0174] denotes the lower output limit of unit i in peak regulation interval m, denotes the upper output limit of unit i in peak regulation interval m;

[0175] and denote the upper and lower frequency regulation reserve reserved by unit i at time period t, respectively;

[0176] and denote the upper and lower spinning reserve reserved by unit i at time period t, respectively;

[0177] denotes the planned power of unit i at time period t;

[0178] denotes the planned power of unit i at time period t-1;

[0179] denotes the minimum technical output limit of unit i at time period t;

[0180] denotes the maximum technical output limit of unit i at time period t;

[0181] and are the upward and downward ramping rates of unit i when its output is in peak regulation interval m, respectively;

[0182] It should be noted that formula (23) shows that if the unit is in the dispatching stage, it must run in one of the peak regulation intervals, formula (24) shows that the unit output can only provide frequency regulation when it is in the peak regulation interval that can provide frequency regulation, and formulas (29) and (30) define the upper and lower limits of the unit output when it can or cannot provide frequency regulation; Formulas (25) and (26) define the unit output in different peak regulation intervals and operating stages in combination with the operating state of the unit; Formulas (27) and (28) show that the sum of the planned output and the reserve of the unit cannot exceed the upper and lower limit values of the unit output; Formulas (31) and (32) show that the rising or falling rate of the unit output and reserve in any adjacent period cannot exceed the upward or downward ramping rate corresponding to the output peak regulation interval, and the output change in the non-dispatching stage is relaxed.

[0183] As one of the prominent substantive features of the present application, the unit output adjusts to the load and new energy output and undertakes frequency regulation and rotating reserve in the dispatching operation stage. The dispatching stage of the thermal power unit not only considers the difference in the ramping capacity of the unit in different peak regulation intervals, but also considers the reserved ramping capacity, which can adapt to the volatility and randomness of high-proportion new energy output and ensure the actual reserve resource output capacity when the unit is running.

[0184] S4.8: Construct a unit reserve constraint for defining the limit value of the thermal power unit providing frequency regulation and rotating reserve in each period. Specifically, it is expressed as follows:

[0185] (33)

[0186] (34)

[0187] (35)

[0188] (36)

[0189] (37)

[0190] (38)

[0191] In the formula, represents the upward frequency regulation of the unit in the peak regulation interval m, represents the downward frequency regulation of the unit in the peak regulation interval m, represents the upper limit value of the rotating reserve capacity of the unit in the peak regulation interval m, represents the lower limit value of the rotating reserve capacity of the unit in the peak regulation interval m, wherein the frequency regulation reserve capacity is only borne by the frequency regulation unit and meets the output segmented frequency regulation availability and capacity limit value.

[0192] S5: combine the thermal power unit operation constraint with the hydropower operation constraint, system balance constraint, new energy operation constraint and power grid safety constraint, and combine the thermal power unit optimization scheduling objective function to construct a high proportion of new energy power grid unit commitment optimization scheduling decision model.

[0193] S5 specifically includes:

[0194] The sum of the thermal power unit optimization scheduling objective function and the hydropower unit output power purchase cost and the new energy unit output power purchase cost is the high proportion of new energy power grid unit commitment optimization scheduling objective function, the thermal power unit operation constraint defined in the application, the hydropower unit operation constraint, the new energy unit operation constraint, the system power and energy balance constraint and the power grid safety constraint are comprehensively considered, and a high proportion of new energy power grid unit commitment optimization scheduling decision model is generated.

[0195] S6: access the planned daily power grid topology model and operation boundary data, and generate the day-ahead unit commitment plan and output plan based on the high proportion of new energy power grid unit commitment optimization scheduling decision model. The process includes:

[0196] Access the planned daily power grid topology model, power grid topology model parameters, thermal power, hydropower and new energy unit operation parameters, wind and solar new energy short-term power prediction, unit maintenance plan, stability section definition and limit, system load prediction and bus load prediction, and other planned daily operation boundary data;

[0197] The sum of the thermal power unit optimization scheduling objective and the hydropower unit and new energy unit output power purchase cost is the high proportion of new energy power grid unit commitment optimization scheduling objective, the thermal power unit operation constraint defined in the application, the hydropower unit operation constraint, the new energy unit operation constraint, the system power and energy balance constraint and the power grid safety constraint are comprehensively considered, and a high proportion of new energy power grid unit commitment optimization scheduling decision model is constructed;

[0198] Using the accessed planned daily power grid topology model and operation boundary data, based on the high proportion of new energy power grid unit commitment optimization scheduling decision model constructed in the application, calling CPLEX or GUROBI or other mixed integer linear programming algorithm package to solve the optimal day-ahead unit commitment plan and output plan;

[0199] Based on the accessed day-ahead market power grid topology data, operation data and the solved day-ahead unit commitment plan and output plan, safety checking analysis is carried out, if there are section, branch and device flow out-of-limit conditions, the out-of-limit section, branch and device information is returned as new power grid operation constraint condition added to the high proportion of new energy power grid unit commitment optimization scheduling decision model, and the planned daily day-ahead unit commitment plan and output plan is re-solved until all flow out-of-limit conditions are eliminated or a specified number of iterations is reached.

[0200] Finally, the generated day-ahead unit commitment plan and output plan are sent to the smart grid dispatch automation system for dispatch execution.

[0201] Embodiment 2 of the present application provides a day-ahead optimal dispatch decision device considering the ramp rate difference of thermal power units, which runs the day-ahead optimal dispatch decision method considering the ramp rate difference of thermal power units as described in Embodiment 1, and the device comprises:

[0202] an extension module for extending the optimal dispatch decision period of the thermal power units, connecting the future plan of the units with the known past state;

[0203] a first construction module for modeling the operation process of the thermal power units, the peak regulation interval of different ramp rates by using 0-1 variables, to obtain a thermal power unit operation model; the operation process includes a grid connection stage, a warm-up stage, a disconnection stage, and a dispatch stage;

[0204] a second construction module for considering the optimal dispatch target of the thermal power units in view of the operation cost in different states and the compensation cost of deep peak regulation, and based on the thermal power unit operation model, establishing a thermal power unit operation constraint composed of unit commitment state constraints, start-up working condition constraints, minimum start-stop interval constraints, grid connection stage constraints, warm-up stage constraints, disconnection stage constraints, dispatch stage constraints, and unit standby constraints;

[0205] a third construction module for combining the thermal power unit operation constraint with the hydropower operation constraint, the system balance constraint, the new energy operation constraint, and the grid safety constraint, and combining the optimal dispatch target of the thermal power units, to construct a high-proportion new energy grid unit commitment optimization dispatch decision model;

[0206] a day-ahead unit commitment plan optimization solving module for accessing the grid topology model and the operation boundary data of the planning day, and generating a day-ahead unit commitment plan and an output plan in the extended optimal dispatch decision period of the thermal power units based on the high-proportion new energy grid unit commitment optimization dispatch decision model.

[0207] Preferably but not limitatively, the device further comprises:

[0208] a day-ahead plan safety checking module for performing safety checking on the optimal day-ahead unit commitment plan and output plan obtained based on the high-proportion new energy grid unit commitment optimization dispatch decision model, and iteratively closing the loop with the day-ahead unit commitment optimization solving module until all the power flow overruns are eliminated or the specified number of iterations is reached.

[0209] Specifically, the day-ahead plan safety checking module is configured to perform safety checking analysis based on the accessed day-ahead market power grid topology data, operation data, and the solved day-ahead unit commitment plan and output plan, and if there are section and / or branch flow out-of-limit and / or device flow out-of-limit, the out-of-limit section information and / or branch information and / or device information are returned as new power grid operation constraint conditions added to the high-proportion new energy power grid unit commitment optimization scheduling constraint conditions, and the day-ahead unit commitment plan and output plan of the planning day are solved again until all flow out-of-limit conditions are eliminated or the specified number of iterations is reached.

[0210] The device further comprises:

[0211] The data access module is configured to access the planning day power grid topology model, power grid topology model parameters, and planning day operation boundary data from a business system, wherein the business system comprises an intelligent power grid control system and a new energy power prediction system, the planning day operation boundary data comprises unit operation parameters, short-term power prediction of wind, light, and new energy, unit maintenance plan, stable section definition and limit, system load prediction, and bus load prediction, and the unit operation parameters comprise thermal power, hydropower, and new energy operation parameters.

[0212] The device further comprises:

[0213] The day-ahead plan execution sending module is configured to send the day-ahead unit commitment plan and output plan of the planning day to an intelligent power grid dispatching automation system for dispatching execution.

[0214] As to the system in the above-described embodiments, the specific manner in which each unit performs operations has been described in detail in the embodiments related to the method, and thus will not be described in detail here.

[0215] Embodiment 3 of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when loaded into the processor, implements the day-ahead optimization scheduling decision method considering the difference in ramping rate of thermal power units as described in Embodiment 1.

[0216] Embodiment 4 of the present application provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program, when executed by a processor, implements a day-ahead optimization scheduling decision method considering the difference in ramping rate of thermal power units according to Embodiment 1.

[0217] It should be understood that the size of the serial number of each step in the above-described embodiments does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0218] Embodiment 5 of the present application provides a verification test of the day-ahead optimal scheduling decision method and device considering the difference in climbing rate of thermal power units, to verify the technical effects adopted in the method.

[0219] In this embodiment, the day-ahead unit commitment optimization of a certain provincial power grid on a certain day is taken as an example to verify that the method of the present application can better promote new energy consumption and guarantee the safe operation of a high-proportion new energy power grid. On the planning day, the grid has 54 adjustable thermal power units with a total capacity of 17575MW. Due to the influence of low water inflow and comprehensive utilization, the water power output is low and has been optimized in advance in coordination with the new energy output. The equivalent load demand, fixed output plan, wind power and photovoltaic power prediction on the planning day are shown in FIG. 1. Figure 3

[0220] The operation data such as the adjustable output range of the units, the start-stop time interval, the start-stop cost and the power generation cost on the planning day come from the day-ahead spot market declaration. The unit output interval is divided into a basic peak shaving interval [50%-100%], a deep peak shaving interval 1 [40%-50%] and a deep peak shaving interval 2 [30%-40%]. The climbing / slide rate of the deep peak shaving interval 1 and the deep peak shaving interval 2 is set to 1% and 0.5% of the rated capacity, respectively. The deep peak shaving compensation prices are 50 yuan / MWh and 100 yuan / MWh, respectively.

[0221] Algorithm 1 is the unit commitment optimization method adopted by the existing intelligent power grid regulation and control system D5000 day-ahead plan application of the grid regulation and control center; and algorithm 2 is the day-ahead optimal scheduling decision method considering the difference in climbing rate of thermal power units. The following compares and analyzes the results of different algorithms.

[0222] The optimal scheduling results of algorithm 1 and algorithm 2 and the average calculation time of three times are shown in Table 1. The execution environment of algorithm 1 and algorithm 2 is ThinkPad T14, the processor uses i7-10510U, the memory is 16G, and the algorithm software is CPLEX12.6. As shown in Table 1, the total scheduling cost of thermal power units of algorithm 2 is slightly higher than that of algorithm 1. Because the climbing rate in different peak shaving intervals and the start-stop conditions are considered, the number of unit start-stop increases, the start-stop cost rises, and because the power generation price increases gradually, the power generation cost slightly decreases. The deep peak shaving compensation cost of algorithm 1 is calculated after the event, while the deep peak shaving cost is included in the optimization target of algorithm 2, so algorithm 2 further optimizes the deep peak shaving and reduces the cost. In terms of calculation efficiency, algorithm 2 has a slight decrease in calculation efficiency because more integer variables, more complex objective functions and constraints are introduced, but it still meets the actual application requirements.

[0223] Table 1 ​

[0224] Algorithm Total cost of thermal power (ten thousand yuan) Thermal power generation cost (ten thousand yuan) Thermal power start-stop cost (ten thousand yuan) Deep peak compensation (ten thousand yuan) Average calculation time (seconds) Algorithm 1 12818.47 11877.42 870.00 71.06 389.25 Algorithm 2 12852.65 11823.65 1029.00 50.75 495.94

[0225] The unit start-stop and output are analyzed below.

[0226] The output plans of two units using different optimization algorithms are shown in the attached Figure 4 The maximum technical outputs of unit 1 and unit 2 are 350 MW and 220 MW respectively. Unit 1 is deeply 30% peaking during the peak period of new energy output. Algorithm 1 does not consider the difference in ramping rate in different peaking intervals, and the output of unit 1 is rapidly decreased / increased, and the maximum ramping rate reaches 5.95 MW / min, which is the ramping rate in the basic peaking interval. In actual operation, the unit cannot reach the ramping rate at the position, so there is an output deviation, which needs AGC to adjust the output of other units to make up for it. Algorithm 2 considers the difference in ramping rate, and adjusts the planned power through multiple time intervals, and the adjustment amount of each time interval does not exceed the ramping rate in the power interval, and the unit can actually track and execute.

[0227] Unit 2 is peaking during the start-stop of the planning day, and algorithm 1 does not consider the start-stop process and output. The planned output is directly decreased from 165 MW in the last time interval to 0 during the shutdown time interval, and the output is directly increased from 0 in the last time interval to 110 MW during the start-up time interval. Algorithm 2 models the start-stop process, and the output of the unit start-stop process is accurately reflected on the output plan, which improves the executability of the start-stop plan and reduces the output deviation during actual execution.

[0228] The ramping capacity of the unit is analyzed below.

[0229] The lower limit of each upward rotating reserve of the system and the 15-minute maximum upward adjustable capacity calculated according to the output and the single ramping rate, i.e. upward adjustable capacity 1, and the 15-minute maximum upward adjustable capacity calculated according to the segmented ramping rate, i.e. upward adjustable capacity 2, are shown in the attached Figure 5 Whether it is algorithm 1 or algorithm 2, during the deep peaking period of the unit, upward adjustable capacity 1 is significantly higher than upward adjustable capacity 2, that is, the upward adjustable capacity calculated by considering only the single ramping rate is overestimated, which is easy to lead to insufficient actual reserve of ramping capacity and affect the consumption and frequency safety when the power of new energy fluctuates rapidly. In addition, algorithm 1 does not consider the reserve of the reserve ramping capacity, so the actual upward adjustable capacity is lower than the lower limit of the reserve demand in some time intervals, and algorithm 2 does not have this problem.

[0230] The present disclosure can be a system, a method, and / or a computer program product. The computer program product can include a computer readable storage medium having computer readable program instructions loaded thereon, the computer readable program instructions being used to cause a processor to implement various aspects of the present disclosure.

[0231] It should be pointed out finally that the above embodiments are only used to illustrate the technical solutions of the present application but not to limit it, and although the present application has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that the specific embodiments of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and any modification or equivalent replacement without departing from the spirit and scope of the present application should be covered within the protection scope of the claims of the present application.

Claims

1. A day-ahead optimal scheduling decision method considering the difference of climbing rate of thermal power units, characterized in that, The method comprises the following steps: extending the decision-making period of the optimal scheduling of the thermal power unit, connecting the future plan of the unit with the past known state; adopting 0-1 variables to model the operation process of the thermal power unit and the peak regulation interval of different ramping rates, and obtaining a thermal power unit operation model; the operation process comprises a grid-connected stage, a warm-up stage, a disconnection stage and a scheduling stage; establishing a thermal power unit optimal scheduling objective function considering the operation cost in different states and the compensation cost of deep peak regulation, and based on the thermal power unit operation model and the extended decision-making period, establishing a thermal power unit operation constraint composed of a unit combination state constraint, a start-up working condition constraint, a minimum start-stop interval constraint, a grid-connected stage constraint, a warm-up stage constraint, a disconnection stage constraint, a scheduling stage constraint and a unit standby constraint; combining the thermal power unit operation constraint with a hydropower operation constraint, a system balance constraint, a new energy operation constraint and a power grid safety constraint, and combining the thermal power unit optimal scheduling objective function, a unit combination optimization scheduling decision-making model of a high-proportion new energy power grid is constructed; accessing a planned day power grid topology model and operation boundary data, and based on the unit combination optimization scheduling decision-making model of the high-proportion new energy power grid, a day-ahead unit combination plan and an output plan are generated; the step of establishing the thermal power unit optimal scheduling objective function considering the operation cost in different states and the compensation cost of deep peak regulation comprises: the thermal power unit optimal scheduling objective function is as follows: (1) In the formula, is the optimal scheduling objective function value of the thermal power unit; for a thermal power unit set, for a future planning period, T for a future planning period set; is the length of each time period; number of cost segments for unit output; Pi, the segmental output price of the unit i at the time period t; Ppi,s(t) is the planned power component for unit i at time period t for price segment s; Coi is the no-load cost for unit i; represents whether the unit i is in the warm-up phase at time period t; denotes whether the unit i is in the dispatch phase at time period t; represents whether the unit i is in a de- commissioning phase at time period t; Ci is the start-up cost for unit i at start-up condition j; Ci is the shutdown cost for unit i at start-up condition j; a deep peak shaving compensation cost for the unit i at time period t; The number of start-up conditions for a thermal power unit; is a 0-1 variable, indicating whether the unit i has a start-up of the state j at the time period t, is a 0-1 variable, indicating whether the unit i has a start-up of the state j at the time period t, is a 0-1 variable indicating whether a shutdown of unit i occurs at time period t, denotes a shutdown.

2. The day-ahead optimal scheduling decision-making method considering the difference in ramping rates of thermal power units according to claim 1, characterized in that: the step of extending the decision-making period of the optimal scheduling of the thermal power unit, connecting the future plan of the unit with the past known state comprises: confirming the maximum value of the sum of the warm-up start-up time interval of all thermal power units, the minimum shutdown time, the minimum operation time and the grid-connected and warm-up time as the extended past period, representing the past known state; taking the union of the extended past period, the initial state of the system and the unit before the start of the first planning period and the future planning period of the unit, to obtain the extended decision-making period.

3. The day-ahead optimal scheduling decision-making method considering the difference in ramping rates of thermal power units according to claim 1, characterized in that: In the dispatch stage, the unit output range is divided into multiple peak regulation interval sets M according to the difference in ramping rate, and 0-1 variables represent whether the output of unit i at period t is within the range of peak regulation interval m, where, .

4. The day-ahead optimal scheduling decision-making method considering the difference in ramping rates of thermal power units according to claim 1, characterized in that: the compensation cost of deep peak regulation is calculated according to the following formula: (2) (3) (4) (5) (6) (7) (8) In the formula, is the deep peak regulation compensation cost of the unit i at the time period t; represents the deep peak regulation compensation cost of the thermal power unit i at the time period t when the output is located in the peak regulation interval m; Pmax,i,s(t) is the maximum power component of the unit i for the time period t and price segment s; whether the output of the unit i at the time period t is located in the peak regulation interval m; Pit is the planned power of unit i at time period t, which is a variable; Ppi,s(t) is the planned power component of the unit i for the time period t at the price segment s; is a set of depth peak shaving intervals for which compensation is required, wherein ; B is the basic peak output lower limit of the unit i; is the compensation price for the unit i at the time period t when the output is in the peak regulation interval m; Pmax,i(t) is the maximum technical power limit for the unit i at time period t.

5. The day-ahead optimal scheduling decision-making method considering the difference in ramping rates of thermal power units according to claim 1, characterized in that: the step of combining the thermal power unit operation constraint with a hydropower operation constraint, a system balance constraint, a new energy operation constraint and a power grid safety constraint, and combining the thermal power unit optimal scheduling objective function, a unit combination optimization scheduling decision-making model of a high-proportion new energy power grid comprises: the unit combination optimization scheduling decision-making model of the high-proportion new energy power grid comprises a unit combination optimization scheduling objective function of the high-proportion new energy power grid and a unit combination optimization scheduling constraint condition of the high-proportion new energy power grid; wherein the sum of the thermal power unit optimal scheduling objective function and the hydropower unit output power purchase cost and the new energy unit output power purchase cost is the unit combination optimization scheduling objective function of the high-proportion new energy power grid. The operation constraint of the thermal power unit, and the operation constraint of the hydroelectric unit, the operation constraint of the new energy unit, the system power balance constraint, and the grid safety constraint, constitute the high-proportion new energy grid unit combination optimization scheduling constraint condition. 6.The day-ahead optimization scheduling decision method considering the difference of thermal power unit ramping rate, according to claim 5, characterized in that: The access of the planned daily grid topology model and the operation boundary data, based on the high-proportion new energy grid unit combination optimization scheduling decision model, generates the day-ahead unit combination plan and the output plan, which comprises: Accessing the planned daily grid topology model and the operation boundary data, based on the high-proportion new energy grid unit combination optimization scheduling decision model, calling the mixed integer linear programming algorithm to minimize the high-proportion new energy grid unit combination optimization scheduling objective function for solving, obtaining the optimal day-ahead unit combination plan and output plan; Based on the accessed day-ahead market grid topology data, the operation boundary data, and the solved day-ahead unit combination plan and output plan, safety checking analysis is carried out, if there are section and / or branch flow out-of-limit and / or equipment flow out-of-limit, the out-of-limit section information and / or branch information and / or equipment information are returned as new grid operation constraint conditions added to the high-proportion new energy grid unit combination optimization scheduling constraint conditions, the planned daily day-ahead unit combination plan and output plan are solved again until all the flow out-of-limit conditions are eliminated or the specified number of iterations is reached.

7. A device for day-ahead optimal scheduling decision taking into account the difference of climbing rate of thermal power units, using the method of day-ahead optimal scheduling decision taking into account the difference of climbing rate of thermal power units according to any one of claims 1-6, characterized in that, The device comprises: An extension module for extending the thermal power unit optimization scheduling decision period, connecting the future unit plan with the known past state; A first construction module for modeling the thermal power unit operation process, the peak regulation interval of different ramping rates by using 0-1 variables, obtaining the thermal power unit operation model; the operation process includes the grid connection stage, the warm-up stage, the split stage, and the dispatching stage; A second construction module for considering the thermal power unit optimization scheduling objective of different state operation cost and deep peak regulation compensation cost, and based on the thermal power unit operation model, establishing the thermal power unit operation constraint composed of unit combination state constraint, start-up working condition constraint, minimum start-stop interval constraint, grid connection stage constraint, warm-up stage constraint, split stage constraint, dispatching stage constraint, and unit standby constraint; A third construction module for combining the thermal power unit operation constraint with the hydroelectric operation constraint, the system balance constraint, the new energy operation constraint, and the grid safety constraint, and combining the thermal power unit optimization scheduling objective, constructing the high-proportion new energy grid unit combination optimization scheduling decision model; A day-ahead unit combination plan optimization solving module for accessing the planned daily grid topology model and the operation boundary data, based on the high-proportion new energy grid unit combination optimization scheduling decision model, generating the day-ahead unit combination plan and the output plan in the extended thermal power unit optimization scheduling decision period. 8.The day-ahead optimization scheduling decision device considering the difference of thermal power unit ramping rate, according to claim 7, characterized in that: The device further comprises: A day-ahead plan safety checking module is configured to perform safety checking analysis based on the accessed day-ahead market power grid topology data, operation data, and the solved day-ahead unit commitment plan and output plan, and if there are section and / or branch flow out-of-limit and / or equipment flow out-of-limit, the out-of-limit section information and / or branch information and / or equipment information are returned as new power grid operation constraint conditions added to the high-proportion new energy power grid unit commitment optimization scheduling constraint conditions, the day-ahead unit commitment plan and output plan are solved again until all flow out-of-limit conditions are eliminated or a specified number of iterations is reached. 9.An electronic device comprising a processor and a storage medium; characterized in that: the storage medium is configured to store instructions; the processor is configured to operate according to the instructions to perform the steps of the day-ahead optimization scheduling decision-making method considering the ramp rate difference of thermal power units according to any one of claims 1-6.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the steps of the day-ahead optimization scheduling decision-making method considering the ramp rate difference of thermal power units according to any one of claims 1-6.

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