Timing operation simulation method and system based on daily power balance and intraday verification

Abstract

The present invention discloses a sequential operation simulation method and system based on daily power balance and intraday verification. On the basis of comprehensively considering the long-term and short-term uncertainties of renewable energy output and load demand, a daily energy balance model and an intraday verification model are established, realizing the stochastic sequential operation simulation of the power system on an annual time scale.

Description

Technical Field

[0001] The present invention belongs to the field of chronological operation simulation of power systems, and particularly relates to a chronological operation simulation method and system based on daily power balance and intraday verification. Background Art

[0002] Facing the increasingly severe climate and energy problems, China needs to build a low-carbon, clean, safe and efficient energy system, and continuously increase the grid-connected installed capacity of renewable energy. However, the randomness and volatility of wind and photovoltaic power generation pose challenges to the power system. To better analyze the impact of renewable energy and flexible regulation resources on system planning, it is necessary to study the chronological operation simulation problem on a long time scale and a fine time granularity.

[0003] The chronological operation simulation model usually relies on the SCUC model for modeling. However, the annual time scale chronological operation simulation model of a large power system contains too many control variables and constraint conditions, and it is difficult to directly solve the problem considering multiple chronological scenarios throughout the year. Therefore, it is necessary to study its simplified algorithm.

[0004] In view of the above problems, based on simultaneously considering the long-term and short-term uncertainties of renewable energy output and load demand, this patent proposes a stochastic chronological operation simulation algorithm for power systems based on daily power balance and intraday verification. It simultaneously considers the long-term and short-term uncertainties of the system and realizes the solution of the annual time scale stochastic operation simulation problem.

[0005] Prior Art One: A panoramic operation simulation model based on multi-cycle coordination. In the long-term stage, the unit maintenance plan and annual power plan are determined. In the medium-term and short-term stages, the daily power generation is compiled, the unit start-stop plan and output plan are specified, and when solving, the medium-term operation simulation problem for 12 months is serially solved based on the long-term operation simulation.

[0006] Disadvantage One: In the long-term operation simulation stage, multiple random chronological scenarios of wind, light, and load are not considered, which affects the accuracy of the annual power plan in the long-term simulation stage, and further affects the operation simulation in the medium-term and short-term stages.

[0007] Disadvantage Two: The serial rolling calculation method cannot utilize parallel computing to accelerate the model solution speed.

[0008] Prior Art Two: An hourly operation simulation model at the annual time scale is proposed. The original chronological sequence of each month is clustered to obtain 12 groups of typical days, and each group of typical days represents the medium-term source-load uncertainty. The operation simulation model is modeled according to the network constraint relaxation cluster unit combination model, and is close to the solution result of the SCUC model and has a better solution speed advantage, and the overall operation simulation model can be quickly solved.

[0009] Disadvantages: Representing the uncertainty of a month or several months through a set of typical daily scenarios results in a relatively rough modeling of the uncertainty of the power source and load, and it is unable to consider the impact of extreme scenarios such as continuous low output of wind and light for multiple days on the system operation that may occur. Summary of the Invention

[0010] To solve the above problems, the present invention provides a sequential operation simulation method and system based on daily power balance and intra-day verification. Based on considering the long-term and short-term uncertainties of renewable energy output and load demand, a daily energy balance model and an intra-day verification model are established, realizing the solution to the problem of stochastic sequential operation simulation of the power system on an annual time scale.

[0011] To solve the technical problems, the technical solution of the present invention is as follows:

[0012] A sequential operation simulation method based on daily power balance and intra-day verification, the method comprising:

[0013] Within the daily power generation range of each thermal power unit, calculate the daily power generation cost coefficient of the thermal power unit by sampling and linearization;

[0014] Based on the daily power generation cost coefficient of the thermal power unit, construct a daily power balance model for long-term simulation, simulate the daily power generation plan of the unit under each scenario, and obtain the simulation results;

[0015] Solve the intra-day verification problems under each scenario in parallel, establish an intra-day verification model with the minimum daily operation cost of the system as the objective function, and verify the simulation results through the intra-day verification model;

[0016] Enter the next round of loop until the convergence condition is met, and obtain the daily power generation simulation results and the annual operation cost of the system.

[0017] Specifically, the calculation of the daily power generation cost coefficient of the thermal power unit specifically includes:

[0018] Uniformly sample within the daily power generation range of each thermal power unit to obtain a set of daily power generation demand values to construct a single-sample-point daily power cost calculation model, and substitute each into the constructed single-sample-point daily power cost calculation model, and solve to obtain the daily power generation cost corresponding to the daily power generation. and together constitute a complete sample point. Repeat the sampling and solution process to obtain the sample point set of all thermal power units, plot the scatter diagram of all sample points of a single unit, and perform piecewise linearization on the scatter diagram to obtain the daily power generation cost coefficient of the thermal power unit.

[0019] Specifically, taking the minimum daily power generation cost of the unit as the objective function, a daily power quantity cost calculation model at the sampling points is constructed, and the constraint conditions include: the output limit of thermal power units, the ramping constraint, and the start-stop constraint.

[0020] Specifically, the long-term simulation daily power balance model is constructed based on the daily power generation cost coefficient of thermal power units, which specifically includes:

[0021] Based on the daily power generation cost coefficient of thermal power units, taking the minimum annual operation cost as the objective function, considering the long-term uncertainties of renewable energy and load, a daily power balance model is established for long-term operation simulation;

[0022] Taking the minimum annual operation cost as the objective function includes: the unit power generation cost, the daily total start-stop cost, and the power generation deviation penalty term;

[0023] Among them, the power system constraints include: the daily power generation range constraint of the unit, the daily power generation correction constraint, the daily start-stop times constraint, and the daily-level system power balance constraint.

[0024] Specifically, the constraint conditions of the intra-day verification model include: the hourly-level system power balance constraint, the spinning reserve constraint, the line transmission capacity constraint, the output constraint of thermal power units, and the ramping and start-stop constraints of thermal power units.

[0025] Specifically, calculate the sum of the daily power generation correction amounts of all units, determine whether the model convergence requirement is met, and if not, perform the next round of iteration.

[0026] A sequential operation simulation system based on daily power balance and intra-day verification, the system includes:

[0027] A calculation module, used to calculate the daily power generation cost coefficient of thermal power units by sampling and linearization within the daily power generation range of each thermal power unit;

[0028] A first construction module, used to construct a long-term simulation daily power balance model based on the daily power generation cost coefficient of thermal power units, simulate the daily power generation schemes of units in each scenario, and obtain the simulation results;

[0029] A second construction module, used to parallelly solve the intra-day verification problems in each scenario, establish an intra-day verification model with the minimum daily operation cost of the system as the objective function, and verify the simulation results through the intra-day verification model;

[0030] A loop detection module, used to enter the next round of loop until the convergence condition is met, and obtain the daily power generation simulation results and the annual operation cost of the system.

[0031] Specifically, the calculation module includes:

[0032] The sampling unit is used to uniformly sample within the daily power generation range of each thermal power unit to obtain a set of daily power generation demand values. ;

[0033] The solving unit is used to construct a daily power cost calculation model for a single sample point, and then substitute each into the constructed daily power cost calculation model for a single sample point to solve and obtain the daily power generation cost corresponding to the daily power generation. and together constitute a complete sample point; repeat the sampling and solving process to obtain a set of sample points for all thermal power units;

[0034] The plotting processing unit is used to plot all sample points of a single unit as a scatter plot and perform piecewise linearization on the scatter plot to obtain the daily power generation cost coefficient of the thermal power unit.

[0035] Specifically, the solving unit includes a construction subunit, which is used to take the minimum daily power generation cost of the unit as the objective function; construct a daily power cost calculation model at the sampling point, and the constraint conditions include: the output limit of the thermal power unit, the ramping constraint, and the start-stop constraint.

[0036] Specifically, the first construction module includes:

[0037] The first construction unit is used to establish a daily power balance model for long-term operation simulation based on the daily power generation cost coefficient of the thermal power unit, with the minimum annual operation cost as the objective function, considering the long-term uncertainty of renewable energy and load; where taking the minimum annual operation cost as the objective function includes: the power generation cost of the unit, the daily total start-stop cost, and the power generation deviation penalty term; where the power system constraints include: the daily power generation range constraint of the unit, the daily power generation correction constraint, the daily start-stop times constraint, and the daily-level system power balance constraint.

[0038] Specifically, the loop detection module includes a judgment unit, which is used to calculate the sum of the daily power generation correction values of all units and determine whether the model convergence requirement is met. If not, perform the next round of iteration.

[0039] A computer-readable storage medium, characterized in that the computer-readable storage medium stores computer execution instructions, and when the computer execution instructions are executed by a processor, they are used to implement the method described in any one of the above.

[0040] Compared with the prior art, the advantages of the present invention are:

[0041] The method of modeling based on the annual daily power source-load scenario set has obvious advantages. The daily power scenario includes the seasonal fluctuation characteristics of the source-load and the short-term extreme output characteristics of renewable energy. The chronological operation simulation model based on this scenario set can consider both the long-term and short-term uncertainties of the source-load. After significantly reducing the model scale, it can avoid the "curse of dimensionality" problem when considering multi-chronological scenarios. The idea of combining complex constraint decoupling and parallel computing proposed in the present invention can effectively control the scale of a single sub-problem to be solved and significantly improve the calculation efficiency.

[0042] Therefore, the stochastic chronological operation simulation method based on daily power balance and intra-day verification proposed in the present invention can efficiently solve the problem of stochastic chronological operation simulation of power systems on an annual time scale. Brief Description of the Drawings

[0043] Figure 1 The iterative process of the operation simulation method based on daily power balance and intra-day verification;

[0044] Figure 2 The calculation process of the daily power cost coefficient;

[0045] Figure 3 The schematic diagram of the daily power balance and intra-day verification process. Detailed Embodiments

[0046] The following describes the specific embodiments of the present invention in combination with embodiments:

[0047] It should be noted that the structures, ratios, sizes, etc. shown in this specification are only used to cooperate with the content disclosed in the specification for those skilled in this technology to understand and read, and are not used to limit the limiting conditions under which the present invention can be implemented. Any modification of the structure, change of the proportional relationship, or adjustment of the size, without affecting the effects that the present invention can produce and the purposes that can be achieved, should still fall within the scope covered by the technical content disclosed in the present invention.

[0048] At the same time, the terms such as "upper", "lower", "left", "right", "middle", and "one" cited in this specification are only for the convenience of clear narration and are not used to limit the scope under which the present invention can be implemented. The change or adjustment of their relative relationships, without substantial change in the technical content, should also be regarded as the scope within which the present invention can be implemented.

[0049] Embodiment 1:

[0050] Chronological operation simulation (COS)

[0051] Security-Constrainted Unit Commitment (SCUC)

[0052] Daily Electric Energy Balance (DEB) model

[0053] Intra - day Verification (IDV) model

[0054] Daily Electricity Cost Factor Calculation (DCC)

[0055] The long - term stochastic operation simulation method of the present invention includes two stages. In the first stage, the daily electricity cost factor of thermal power units needs to be calculated; in the second stage, a daily electric energy balance model and an intra - day verification model are established and iteratively solved. The algorithm iteration process is as Figure 1 shown.

[0056] (1) Process of calculating the daily electricity cost factor of thermal power units:

[0057] The independent variable of the consumption characteristic equation of thermal power units is the hourly output, which is not applicable to the scenario of daily electric energy balance. Therefore, it is necessary to calculate the cost factor of the daily electricity generation of the units in the daily electric energy balance model. This patent proposes a method for calculating this cost factor, and the principle is as Figure 2 shown.

[0058] Uniformly sample within the range of the daily electricity generation of each thermal power unit to obtain a set of daily electricity generation demand values Substitute each into the single - sample - point daily electricity cost calculation model (see Formulas (1) to (16)) to solve and obtain the daily power generation cost corresponding to this daily electricity generation. and together constitute a complete sample point. Repeat the sampling and solving process to obtain the sample point set of all thermal power units. The scatter plot of all sample points of a single unit can be drawn as Figure 2 shown in. To construct the daily electric energy balance model, it is necessary to perform piece - wise linearization on this scatter plot. The functions of each segment obtained by linearization form a convex hull line, which can be used to construct the daily - level model.

[0059] When given , the single - sample - point daily electricity cost calculation model is as follows, with the lowest daily power generation cost of the unit as the objective function:

[0060]

[0061] Where is the daily power generation cost of unit u at the j - th sampling point during the sampling process; is the hourly consumption characteristic equation of unit u; Pu,t is the output of unit u at time t; I u,t indicates the startup status of unit u at time t; HT represents the number of hours within a day; NU is the number of thermal power units; NJ is the number of sample points for a single unit.

[0062] The constraint conditions include:

[0063] 1) Sampling range constraint:

[0064] The daily power generation demand of the unit at the sampling point does not exceed the power generation range:

[0065]

[0066] In the formula, and are the upper and lower limits of the daily power generation range of unit u, respectively.

[0067] 2) Daily power balance constraint, the total daily power generation of the unit is equal to the daily power generation demand at the sample point:

[0068]

[0069] 3) Thermal power unit output limit:

[0070]

[0071] In the formula, and are the upper and lower limits of the output of unit u, respectively.

[0072] 4) Thermal power unit ramp rate constraint:

[0073] P u,t -P u,t-1 ≤UR i (1 - y u,t ) + P i min y u,t (5)

[0074] P u,t-1 -P u,t ≤DR i (1 - z u,t ) + P i min z u,t (6)

[0075] In the formula, UR u and DR u represent the ramp-up and ramp-down rate limits of unit u, respectively.

[0076] 5) Thermal power unit startup and shutdown constraint:

[0077]

[0078]

[0079]

[0080]

[0081]

[0082]

[0083] y u,t -z u,t -I u,t +I u,t-1 =0 (13)

[0084] y u,t +z u,t ≤1 (14)

[0085] UT u =max{0,min[HT,(T on,u -X on,u,0 )I u,0} (15)

[0086] DT u =max{0,min[HT,(T off,u -X off,u,0 )(1-I u,0 )]} (16)

[0087] In the formula, X on,u,t and X off,u,t respectively represent the running and stopped times of thermal power unit u at time t; T on,u and T off,u respectively represent the minimum start-up and shutdown times of thermal power unit u; UT u and DT u respectively represent the remaining required continuous running and shutdown times of thermal power unit u.

[0088] The daily power cost coefficient obtained by piecewise linearization for each unit is simplified as w i D for constructing the daily power balance model.

[0089] After obtaining the daily power cost coefficient of the unit, the modeling of the daily energy balance model and the in-day verification model is carried out, and the principle is as Figure 3As shown in the figure. The total output of each unit in every 24 hours in the time-series operation simulation problem is equivalent to the result of a daily power generation. With the system daily energy balance as the constraint condition at the daily level and the goal of minimizing the annual total operation cost, the daily power generation simulation results of each unit under each scenario throughout the year are optimized. Since this result gives priority to ensuring the optimal economy, a large number of system security constraints need to be considered during the actual operation process. It is necessary to transfer the daily power generation simulation results under each scenario to the intra-day model for verification and transfer the boundary conditions back to the annual daily-level model for correction. The annual operation cost of the daily-level model represents the total operation cost of the time-series operation simulation problem. Among them, the daily power balance model and the intra-day verification model need to be iteratively solved until the model converges, and the 365 intra-day verification problems under a single annual scenario can be solved much faster through parallel computing.

[0090] (2) Daily power balance model:

[0091] The daily power balance model takes the minimum annual operation cost of the system as the objective function, which includes the unit power generation cost, the total daily start-stop cost, and the power generation deviation penalty term to make the simulation result close to the verification result:

[0092]

[0093] In the formula, NS is the number of annual scenarios; π s is the probability of each scenario; ND is the number of days in a year; NI is the number of system nodes; is the daily power generation of the thermal power unit at node i on day d in scenario s; and are the unit power generation cost at the daily level, the total number of daily start-ups and shut-downs of the thermal power unit at node i respectively. is the correction deviation of the thermal power unit at node i on day d in scenario s, which is non-negative.

[0094] The constraint conditions include:

[0095] 1) Constraint on the range of daily power generation of thermal power units:

[0096] The lower limit of daily power generation is the minimum output of the unit, and the upper limit of daily power generation is the power generation when operating at full output for 24 hours.

[0097]

[0098] In the formula, represents the start-up state of the thermal power unit at node i on day d in scenario s; P i max 、P i min are the upper and lower limits of the output of the thermal power unit at node i respectively; HT is the number of hours within a day.

[0099] 2) Day-ahead generation correction constraint for thermal power units:

[0100] The day-ahead generation simulation of the units in this iteration needs to be corrected based on the results of the previous iteration combined with the intra-day verification results. By setting a penalty term, the long-term simulation results can be made close to the correction requirements.

[0101]

[0102] In the formula, and are the planned generation of the thermal power units at node i on day d in scenario s of the previous iteration, and the positive and negative correction amounts after intra-day verification, respectively.

[0103] 3) Total start-stop times constraint per day:

[0104]

[0105]

[0106] In the formula, are the start-stop actions of the thermal power units at node i at time t on day d in scenario s, respectively.

[0107] 4) System power balance constraint at the day level:

[0108]

[0109] In the formula, is the generation of the wind power units at node i on day d in scenario s, is the total system load demand on day d in scenario s.

[0110] (3) Intra-day verification model:

[0111] The intra-day verification model verifies the day-ahead generation simulation results for scenario s on day d. With the minimum daily operating cost of the system as the objective function, considering the intra-day system generation, start-stop costs, and the generation deviation penalty term for correcting the day-ahead generation simulation results:

[0112]

[0113] Among them, is the output of the thermal power units at node i at time t on day d in scenario s; w pen is the penalty coefficient of the constraint relaxation variable.

[0114] Constraints:

[0115] 1) Day-ahead power balance constraint:

[0116] When the unit is actually operating, the total daily generation is the corrected value of the day-ahead generation simulation result.

[0117]

[0118] 2) Hour - level system power balance constraint:

[0119]

[0120] Among them, is the output of the wind turbine at node i at time t on day d in scenario s, is the total system load demand at time t on day d in scenario s.

[0121] 3) Spinning reserve constraint:

[0122]

[0123]

[0124] In the formula, SR s,d,t,i is the spinning reserve that can be provided by the thermal power unit at node i at time t on day d in scenario s; I s,d,t,i represents the on - off state of the thermal power unit at node i at time t on day d in scenario s; is the positive spinning reserve of the system at time t on day d in scenario s.

[0125] 4) Line transmission capacity constraint:

[0126] P l min ≤P s,d,t,l ≤P l max (28)

[0127] In the formula, P l min and P l max are the upper and lower bounds of the allowable transmission capacity of branch l respectively; P s,d,t,l is the transmission power of branch l at time t on day d in scenario s.

[0128] 5) Output, ramping and start - stop constraints of thermal power units:

[0129] The constraint of a single unit is the same as formulas (4) to (16) in Section 2.1

[0130] 6) Model convergence condition:

[0131] The transfer of the daily power generation of units among units with similar consumption characteristics has little impact on the operating cost. When the total daily power generation correction amount of all thermal power units in the system is reduced to a small value, the iteration framework converges.

[0132]

[0133] ΔESum ≤ΔE Permit (30)

[0134] Wherein, ΔE Sum represents the sum of the corrected total power generation of all units throughout the year; ΔE Permit is the convergence threshold.

[0135] (4) Solution algorithm

[0136] The calculation steps of the daily power generation cost coefficient of thermal power units are as follows:

[0137]

[0138] After obtaining the cost coefficient w i D The solution steps of the sequential operation simulation algorithm based on daily power balance and in-day verification are as follows:

[0139]

[0140]

[0141] Example 2:

[0142] In this example, the IEEE-118 bus system is used for case analysis. The method proposed in the present invention is compared with the 8760-hour operation simulation algorithm based on SCUC in terms of solution results and solution speed to verify the effectiveness of the present method.

[0143] Table 1 Algorithm error and solution speed

[0144]

[0145] The algorithm of this patent converges after 2 iterations. As can be seen from Table 1, there is an error of about 0.1% in the system operation cost obtained by the algorithm of this patent compared with the 8760-hour operation simulation. Since the algorithm of this patent assumes that the initial state of each thermal power unit is the same every day and ignores the state transfer of thermal power units during the day, the actual operation state of the units is simplified compared with the 8760-hour sequential operation simulation problem. Therefore, there are certain differences in the optimal unit output strategies of the two algorithms. There are many units with similar consumption characteristics in the 118-bus system. Transferring the output among these units has little impact on the total system operation cost. By simplifying the daytime state transfer, the decoupling of the daily SCUC problem is achieved, which provides the possibility for accelerating the solution on the basis of a small solution error. The total time used by this algorithm is about 2% of the 8760-hour operation simulation.

[0146] Example 3:

[0147] To better implement the above method, this embodiment provides a timing operation simulation system based on daily power balance and in-day verification;

[0148] For example, a timing operation simulation system based on daily power balance and in-day verification, the system includes:

[0149] A calculation module, configured to calculate the daily power generation cost coefficient of a thermal power unit by sampling and linearization within the daily power generation range of each thermal power unit;

[0150] A first construction module, configured to construct a daily power balance model for long-term simulation based on the daily power generation cost coefficient of the thermal power unit, simulate the daily power generation plan of the unit under each scenario, and obtain a simulation result;

[0151] A second construction module, configured to solve the in-day verification problem under each scenario in parallel, establish an in-day verification model with the lowest system daily operation cost as the objective function, and verify the simulation result through the in-day verification model;

[0152] A loop detection module, configured to enter the next round of loop until the convergence condition is met, and obtain the daily power generation simulation result and the annual operation cost of the system.

[0153] Specifically, the calculation module includes:

[0154] The sampling unit is configured to uniformly sample within the daily power generation range of each thermal power unit to obtain a set of daily power generation demand values of the set;

[0155] The solution unit is configured to construct a single-sample-point daily power cost calculation model, and then substitute each into the constructed single-sample-point daily power cost calculation model to solve and obtain the daily power generation cost corresponding to the daily power generation, and together constitute a complete sample point; repeat the sampling and solution process to obtain a set of sample points for all thermal power units;

[0156] The plotting processing unit is configured to plot all sample points of a single unit as a scatter plot, and perform piecewise linearization on the scatter plot to obtain the daily power generation cost coefficient of the thermal power unit.

[0157] Specifically, the solution unit includes a construction subunit, configured to take the lowest daily power generation cost of the unit as the objective function; construct a daily power cost calculation model at the sampling point, and the constraint conditions include: thermal power unit output limit, ramp constraint and start-stop constraint.

[0158] Specifically, the first construction module includes:

[0159] The first construction unit is used to establish a daily power balance model for long-term operation simulation based on the daily power generation cost coefficient of thermal power units, with the lowest annual operation cost as the objective function, considering the long-term uncertainties of renewable energy and load. Among them, taking the lowest annual operation cost as the objective function includes: unit power generation cost, daily total start-stop cost, and power generation deviation penalty term. Among them, the power system constraints include: unit daily power generation range constraint, daily power generation correction constraint, daily start-stop times constraint, and daily-level system power balance constraint.

[0160] Specifically, the loop detection module includes: a judgment unit, which is used to calculate the sum of the daily power generation correction values of all units, determine whether the model convergence requirement is met, and if not, perform the next round of iteration.

[0161] Embodiment 4:

[0162] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructions, or by controlling relevant hardware through instructions. The instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0163] Therefore, an embodiment of the present invention provides a storage medium, in which multiple instructions are stored, and the instructions can be loaded by a processor to execute the steps in any of the time-series operation simulation methods based on daily power balance and intra-day verification provided by the embodiments of the present invention.

[0164] For example, the instructions can perform the following steps:

[0165] Within the daily power generation range of each thermal power unit, calculate the daily power generation cost coefficient of the thermal power unit by sampling and linearization;

[0166] Construct a daily power balance model for long-term simulation based on the daily power generation cost coefficient of the thermal power unit, simulate the daily power generation plan of the unit under each scenario, and obtain the simulation results;

[0167] Solve the intra-day verification problems under each scenario in parallel, establish an intra-day verification model with the lowest system daily operation cost as the objective function, and verify the simulation results through the intra-day verification model;

[0168] Enter the next round of loop until the convergence condition is met, and obtain the daily power generation simulation results and the annual operation cost of the system.

[0169] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general purpose computers, special purpose computers, embedded processors, or other programmable data processing devices to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 one or more blocks

[0170] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means for implementing the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 one or more blocks

[0171] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 one or more blocks

[0172] The preferred embodiments of the present invention have been described in detail above, but the present invention is not limited to the above embodiments. Within the knowledge of those of ordinary skill in the art, various changes can be made without departing from the spirit of the present invention.

[0173] Many other changes and modifications can be made without departing from the concept and scope of the present invention. It should be understood that the present invention is not limited to specific embodiments, and the scope of the present invention is defined by the appended claims.

Claims

1. A sequential operation simulation method based on daily power balance and intra-day verification, characterized in that The method includes: Within the daily power generation range of each thermal power unit, calculate the daily power generation cost coefficient of the thermal power unit through sampling and linearization; Based on the daily power generation cost coefficient of the thermal power unit, construct a long-term simulated daily power balance model, simulate the daily power generation plan of the unit under each scenario, and obtain the simulation results; Parallelly solve the intra-day verification problems under each scenario, establish an intra-day verification model with the minimum daily operating cost of the system as the objective function, and verify the simulation results through the intra-day verification model; Enter the next round of loop until the convergence condition is met, and obtain the daily power generation simulation results and the annual operating cost of the system; The constructing of the long-term simulated daily power balance model based on the daily power generation cost coefficient of the thermal power unit specifically includes: Based on the daily power generation cost coefficient of the thermal power unit, with the minimum annual operating cost as the objective function, considering the long-term uncertainties of renewable energy and load, establish a daily power balance model for long-term operation simulation; The objective function of minimizing the annual operating cost includes: unit power generation cost, total daily start-stop cost, and power generation deviation penalty term; Among them, the power system constraints include: unit daily power generation range constraint, daily power generation correction constraint, daily start-stop times constraint, and daily-level system power balance constraint; The constraint conditions of the intra-day verification model include: hourly-level system power balance constraint, spinning reserve constraint, line transmission capacity constraint, thermal power unit output constraint, and thermal power unit ramp-up and start-stop constraint.

2. The sequential operation simulation method based on daily power balance and intra-day verification according to claim 1, characterized in that The calculating of the daily power generation cost coefficient of the thermal power unit specifically includes: Uniformly sample within the daily power generation range of each thermal power unit to obtain the daily power generation demand value to form a set, construct a single-sample-point daily power cost calculation model, and substitute each into the constructed single-sample-point daily power cost calculation model to solve for the daily power generation cost corresponding to the daily power generation and together constitute a complete sample point. Repeat the sampling and solution process to obtain the sample point set of all thermal power units. Plot all the sample points of a single unit as a scatter plot and perform piecewise linearization on the scatter plot to obtain the daily power generation cost coefficient of the thermal power unit.

3. The timing operation simulation method based on daily power balance and intra-day verification according to claim 2, characterized in that With the minimum daily power generation cost of the unit as the objective function; construct a daily power cost calculation model at the sampling points, and the constraint conditions include: thermal power unit output limit, ramp-up constraint, and start-stop constraint.

4. The sequential operation simulation method based on daily power balance and intra-day verification according to claim 1, characterized in that Calculate the sum of the daily power generation correction values of all units, determine whether the model convergence requirement is met, and if not, perform the next round of iteration.

5. A sequential operation simulation system based on daily power balance and intra-day verification, characterized in that, The system includes: A calculation module, which is used to calculate the daily power generation cost coefficient of the thermal power unit through sampling and linearization within the daily power generation range of each thermal power unit; A first construction module, which is used to construct a long-term simulated daily power balance model based on the daily power generation cost coefficient of the thermal power unit, simulate the daily power generation plan of the unit under each scenario, and obtain the simulation results; A second construction module, which is used to parallelly solve the intra-day verification problems under each scenario, establish an intra-day verification model with the minimum daily operating cost of the system as the objective function, and verify the simulation results through the intra-day verification model; A loop detection module, which is used to enter the next round of loop until the convergence condition is met, and obtain the daily power generation simulation results and the annual operating cost of the system; The first construction module includes: A first construction unit, which is used to establish a daily power balance model for long-term operation simulation based on the daily power generation cost coefficient of the thermal power unit, with the minimum annual operating cost as the objective function, considering the long-term uncertainties of renewable energy and load; among them, the objective function of minimizing the annual operating cost includes: unit power generation cost, total daily start-stop cost, and power generation deviation penalty term; among them, the power system constraints include: unit daily power generation range constraint, daily power generation correction constraint, daily start-stop times constraint, and daily-level system power balance constraint; In the second construction module, the constraint conditions of the intra-day verification model include: hourly system power balance constraint, spinning reserve constraint, line transmission capacity constraint, thermal power unit output constraint, thermal power unit ramp-up / down and start-stop constraint.

6. The time-sequence operation simulation system based on daily power balance and intra-day verification according to claim 5, characterized in that, The calculation module includes: A sampling unit, configured to uniformly sample within the daily power generation range of each thermal power unit to obtain a set of daily power generation demand values ; A solution unit, after constructing a daily power cost calculation model for a single sample point, substitutes each into the constructed daily power cost calculation model for a single sample point, and solves to obtain the daily power generation cost corresponding to the daily power generation volume and together form a complete sample point; repeat the sampling and solution processes to obtain a set of sample points for all thermal power units; A plotting processing unit, which is used to plot all sample points of a single unit into a scatter plot, and perform piecewise linearization on the scatter plot to obtain the daily power generation cost coefficient of the thermal power unit.

7. The time-sequential operation simulation system based on daily power balance and in-day verification according to claim 6, wherein The solving unit includes a construction subunit, which is used to take the minimum daily power generation cost of the unit as the objective function; Construct a daily power cost calculation model at the sampling points, and the constraint conditions include: thermal power unit output limit, ramp-up / down constraint and start-stop constraint.

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