A medium and long term unit commitment calculation method and system based on operation constraint tightening

Abstract

This invention discloses a method and system for calculating medium- and long-term unit combination based on operational constraint tightening, belonging to the field of power system planning and evaluation technology. The method includes: acquiring basic technical data of a power system containing new energy sources; constructing a unit combination model and performing linear relaxation solutions; based on the linear relaxation solutions, performing state constraint tightening based on inter-unit coupling relationships and temporal coupling relationships, as well as power range constraint tightening based on dynamic time windows, to construct the original unit combination sub-problem model; and solving the sub-problem to obtain the output results. This invention, through a constraint tightening strategy, transforms the original model into a mixed-integer linear programming sub-problem with a more compact feasible region and containing high-quality solutions, thereby accelerating the calculation of medium- and long-term unit combination and effectively solving the technical problem of low efficiency in solving large-scale system unit combination problems.

Description

Technical Field

[0001] This invention belongs to the field of power system planning and evaluation technology, specifically relating to a method and system for calculating medium- and long-term unit combination based on tightened operational constraints. Background Technology

[0002] In the past, when solving medium- and long-term unit combination optimization problems, the periodic characteristics of the boundary conditions were not fully utilized. Calculations were typically performed directly using optimization solvers or employing decomposition and coordination-based approaches. However, direct solutions are extremely inefficient and memory-intensive for large-scale real-world system examples. Decomposition-based algorithms struggle to handle temporal coupling constraints across rolling cycles and suffer from error accumulation in medium- and long-term calculations, often failing to converge in large-scale systems. With the large-scale integration of renewable energy into systems, unit combination models are becoming increasingly complex. Considerations such as cross-regional power exchange, maintenance optimization constraints, and long-term energy storage place higher demands on the solution timeframe and scale of unit combination. Therefore, establishing efficient computational methods with tightened operational constraints for medium- and long-term unit combination is becoming particularly important.

[0003] As the penetration rate of new energy sources in the power system continues to increase, the factors that need to be considered in unit combination models are becoming increasingly complex, such as cross-regional power exchange, coordinated optimization of maintenance plans and unit combination, and long-term energy storage operation characteristics. This makes the solution period of unit combination problems longer, the scale larger, and the coupling relationship between variables tighter. Traditional direct solution methods of mixed-integer linear programming often face the "combinatorial explosion" problem when dealing with such large-scale, long-term unit combination problems, with computation time increasing exponentially, and even failing to solve due to insufficient memory. While decomposition-based algorithms, such as Benders decomposition and Lagrange relaxation, can alleviate the computational pressure to some extent, they often suffer from decomposition difficulties, poor coordination convergence, and large deviations in suboptimal solutions when dealing with strong temporal coupling constraints such as unit start-up and shutdown and minimum start-up and shutdown times. Therefore, developing a medium- and long-term unit combination calculation method that can ensure solution accuracy while significantly improving computational efficiency has become a key technical challenge that urgently needs to be solved in the field of power system optimization operation. Summary of the Invention

[0004] The technical problem to be solved by this invention is to address the shortcomings of the prior art by providing a method and system for calculating medium- and long-term unit combination based on tightened operational constraints. This method addresses the technical problems of low computational efficiency and excessive memory consumption in direct solution of medium- and long-term unit combination optimization problems in large-scale power systems, especially in the context of high proportion of new energy access, due to the complexity of the model, the large number of variables, and the strong temporal coupling constraints. Traditional decomposition algorithms are also unable to effectively handle cross-cycle coupling constraints, resulting in error accumulation and even difficulty in convergence.

[0005] The present invention adopts the following technical solution:

[0006] A method for calculating medium- and long-term unit combination based on tightened operational constraints includes the following steps:

[0007] S1. Obtain basic technical data for power systems containing new energy sources;

[0008] S2. Based on the basic technical data of the power system including new energy obtained in S1, construct a unit combination model, and perform linear relaxation solution on the unit combination model to obtain the linear relaxation solution of the unit combination model;

[0009] S3. Based on the linear relaxation solution of the unit combination model obtained in S2, tighten the state constraints based on the coupling relationship between units and the temporal coupling relationship between units, and tighten the power range constraints based on the dynamic time window to construct the original unit combination subproblem model.

[0010] S4. Solve the original unit combination sub-problem model obtained from S3 to obtain the output results including system operation information, objective function value, and model solution time, thereby realizing accelerated calculation of medium and long-term unit combination.

[0011] Preferably, in S2, the unit combination model includes the unit state logic constraints, ramping constraints, processing range constraints, and shortest start-up and shutdown time constraints of thermal power units, processing constraints of new energy units, system power balance constraints, reserve constraints, and power flow constraints.

[0012] The linear relaxation solution involves relaxing the 0-1 variables representing the state and start / stop signals into continuous variables, correspondingly relaxing the associated constraints, and thus relaxing the original mixed-integer linear programming problem into a linear programming problem for solution.

[0013] Preferably, in S3, the tightening of state constraints based on inter-unit coupling relationships and unit timing coupling relationships specifically includes:

[0014] For the state solutions in the linear relaxation solution of unit combination, based on the relative relationship between unit output and unit output range, a power transfer check is constructed, and a constraint propagation rule for the coupling relationship between units is established;

[0015] Based on the combination relationship between fractional state sequences and adjacent integer state sequences, a propagation rule for unit time-series coupling constraints is constructed; wherein, the time interval in which the state variable, start signal, and shutdown signal of the thermal power unit are not simultaneously integers is denoted as a fractional state interval, and the state sequence in the fractional state interval is called a fractional state sequence.

[0016] Preferably, the construction of power transfer verification and the establishment of inter-unit coupling relationship constraint propagation rules specifically include:

[0017] The unit's output range is divided into three sets;

[0018] A power transfer identification factor is constructed, comprising a power upward transfer identification factor and a power downward transfer identification factor. The power upward transfer identification factor is used to determine whether other generating units can take over the output of the fractional-state generating units by adjusting upwards. When the power upward transfer identification factor > 0, the other generating units can take over the output of the fractional-state generating units by adjusting upwards. The power downward transfer identification factor is used to determine whether other generating units can take over the output of the fractional-state generating units by adjusting downwards. When the power downward transfer identification factor > 0, the other generating units can take over the output of the fractional-state generating units by adjusting downwards.

[0019] Based on the power transfer identification factor, constrained propagation rules for inter-unit coupling relationships are established according to different cases, and the unit state constraints are tightened.

[0020] Preferably, the construction of the unit timing coupling constraint propagation rule specifically includes:

[0021] The fractional state sequences are divided into independent fractional state sequences and associated fractional state sequences. The independent fractional state sequence is characterized by containing only one fractional sequence in the corresponding unit state solution vector, and the state constraints constructed based on the latter fractional sequence are not affected by the former fractional sequence. The associated fractional state sequence is characterized by changing the integer state results on the left side of the next fractional sequence when constructing state constraints based on the former fractional sequence.

[0022] The propagation rules of unit time-series coupling constraints are constructed by adopting a backward correction method. For the state of the fractional sequence that has already met the minimum start-up and shutdown constraints in the previous time period, no correction is made. Only the state constraints of the fractional sequence in the next time period are corrected.

[0023] For independent fractional state sequences, constraints are tightened based on the 0-1 combinations and durations of their adjacent integer state sequences, combined with the check equation. For associated fractional state sequences, a time set is constructed, and constraints are constructed on the time set to form a constraint set, thereby achieving constraint tightening.

[0024] Preferably, in S3, the power range constraint tightening based on the dynamic time window specifically includes:

[0025] Based on the idea of ​​similarity aggregation, dynamic time windows are divided according to the temporal similarity of the linear relaxation solutions of unit combination and the construction basis. The construction basis uses an ordered array as the reference data for generating dynamic time windows, including the assessment coefficients of adjacent deviations and the assessment coefficients of mean deviation, as well as the start time of the current dynamic time window.

[0026] Based on the defined dynamic time windows, the output range constraints for thermal power units and new energy units are tightened respectively;

[0027] For thermal power units, within a dynamic time window, a power output range constraint is constructed based on the minimum power output value, maximum power output value, and adjustable parameters of the thermal power unit within that dynamic time window, thereby tightening the power output range constraint of the thermal power unit; wherein, the adjustable parameters are used to control the degree of conservatism in the solution.

[0028] For new energy units, the utilization rate of new energy is used as the dividing line to generate a dynamic time window. Based on the minimum utilization rate of new energy units, adjustable parameters, and the upper limit of output of new energy units under given resource conditions within the time window, the output range constraint is constructed to tighten the output range constraint of new energy units.

[0029] Preferably, the output range of the thermal power unit is constrained as follows:

[0030]

[0031]

[0032] in, A collection of thermal power units; , thermal power units No. The start time and duration of the time window; A dynamic time window index set; For thermal power units At any moment The running state variables; For thermal power units At any moment Actual output variables; For thermal power units The maximum output of technology; For the first Thermal power units within a dynamic time window The minimum output value; These are adjustable parameters; For thermal power units At any moment The optimal solution for linear relaxation yields the force value; For thermal power units In the Start time of segment dynamic time window The next moment; For thermal power units In the The output variable at the end of a dynamic time window.

[0033] Preferably, the output range constraint of the new energy unit is:

[0034]

[0035]

[0036] in, A collection of new energy generating units; For the first New energy units within a dynamic time window Minimum utilization rate; These are adjustable parameters; Represents new energy power units under given resource conditions The upper limit of output; Indicates new energy power units contribution; Represents the optimal solution for linear relaxation in new energy power units. In time The output value; For new energy units The actual output variable at the start of the s-th dynamic time window; For new energy units The actual output variable at the time following the start of the s-th dynamic time window; For new energy units The actual output variable at the end of the s-th dynamic time window; For new energy units The upper limit of output at the beginning of the s-th dynamic time window; For new energy units The upper limit of output at the moment following the start of the s-th dynamic time window; For new energy units The maximum output at the end of the s-th dynamic time window; For new energy units No. The start time of a dynamic time window; For new energy units No. The duration of the dynamic time window. This is a correction method for the base length of the dynamic time window of new energy units; This is a dynamic time window index set.

[0037] Preferably, in S1, the basic technical data of the power system containing new energy sources includes: operating parameters of various generating units, predicted values ​​of new energy sources and loads, and system reserve information.

[0038] Secondly, embodiments of the present invention provide a medium- to long-term unit combination calculation system based on tightened operational constraints, comprising:

[0039] The data module acquires basic technical data for power systems including new energy sources;

[0040] The construction module constructs a unit combination model based on the basic technical data of the power system containing new energy obtained from the data module, and performs linear relaxation solution on the unit combination model to obtain the linear relaxation solution of the unit combination model.

[0041] The constraint module, based on the linear relaxation solution of the unit combination model obtained from the construction module, performs state constraint tightening based on the coupling relationship between units and the temporal coupling relationship between units, as well as power range constraint tightening based on the dynamic time window, and constructs the original unit combination subproblem model;

[0042] The output module, which solves the constraint module, generates the original unit combination subproblem model and outputs the results including system operation information, objective function value, and model solution time, thereby accelerating the calculation of medium- and long-term unit combination.

[0043] Thirdly, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the above-described method for calculating medium- and long-term unit combinations based on tightened operational constraints.

[0044] Fourthly, embodiments of the present invention provide a computer-readable storage medium including a computer program, which, when executed by a processor, implements the steps of the above-described method for calculating medium- and long-term unit combinations based on tightened operational constraints.

[0045] Fifthly, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for calculating medium- and long-term unit combinations based on tightened operational constraints.

[0046] In a sixth aspect, embodiments of the present invention provide an electronic device, including a computer program, which, when executed by the electronic device, implements the steps of the above-described method for calculating medium- and long-term unit combinations based on tightened operational constraints.

[0047] Compared with the prior art, the present invention has at least the following beneficial effects:

[0048] A method for calculating medium- and long-term unit combination based on operational constraint tightening is proposed. This method acquires power system data, constructs a unit combination model, and performs linear relaxation solving. Then, based on the relaxed solutions, it tightens state constraints and power range constraints, finally solving subproblems to obtain optimized results. This method significantly narrows the feasible region of the problem through constraint tightening strategies, avoiding the computational bottleneck of directly solving large-scale MILP problems, and achieving a qualitative improvement in computational efficiency.

[0049] Furthermore, by relaxing the 0-1 variables into continuous variables, the complex MILP problem is transformed into an easily solvable linear programming (LP) problem, providing a high-quality initial solution for subsequent constraint tightening, thus ensuring the effectiveness of the tightening strategy and the quality of the final solution.

[0050] Furthermore, through power transfer verification and unit time-series coupling analysis, refined constraint propagation is performed on the fractional state sequence, effectively eliminating a large number of infeasible solutions and constructing a compact set of state constraints. This mechanism-based tightening method ensures the feasibility of the solution while greatly improving computational efficiency.

[0051] Furthermore, time windows are dynamically divided based on the temporal similarity of the relaxed solutions, and the output range of thermal power and new energy units is tightened separately within the windows. This dynamic adaptive tightening strategy fully explores the temporal characteristics of unit output and further compresses the solution space.

[0052] Furthermore, state constraint tightening and power range constraint tightening replace the corresponding constraints of the original MILP problem by constructing compact sets of state constraints and power range constraints, which is the basis for generating MILP subproblems.

[0053] Furthermore, based on the similarity aggregation approach, dynamic time windows are divided, and combined with temporal similarity and assessment coefficients (adjacent deviation, mean deviation) to make the time window division more closely match the power output variation pattern of the units, avoiding the inapplicability of constraints caused by fixed time windows. Differentiated constraints are formulated for the characteristics of thermal power units and renewable energy units. For thermal power units, the constraints are tightened based on the power output extreme value and adjustable parameters within the time window, while for renewable energy units, the constraints are tightened based on the utilization rate. This takes into account the operating characteristics of both types of units, solves the problem of excessively large feasible regions caused by the loose power constraints in traditional methods, improves the solution efficiency, ensures the actual needs of high-priority renewable energy power generation, and adapts to scenarios with large-scale renewable energy integration.

[0054] Furthermore, by analyzing the relative relationship between unit output and unit output range in the LP relaxation solution, a power transfer check is constructed, and a constraint propagation rule for coupling relationship between units is established. At the same time, based on the combination relationship between fractional state sequences and adjacent integer state sequences, a backward correction method is adopted for different combinations of fractional state sequences and adjacent integer state sequences to construct a constraint propagation rule for unit temporal coupling, thereby tightening the state constraints of the unit combination problem.

[0055] Furthermore, by adopting the idea of ​​similarity aggregation, considering the similarity of linear branch subproblems, dynamic time windows are divided according to the temporal similarity of relaxed solutions, and the constraint set of unit output range is dynamically tightened based on this.

[0056] Furthermore, by replacing the corresponding constraints in the original unit combination problem with the tightened constraint set, a MILP subproblem with a more compact feasible region containing the optimal solution or a high-quality suboptimal solution is constructed, thereby achieving accelerated computation of medium- and long-term unit combination.

[0057] It is understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0058] In summary, the method of this invention is based on the optimal solution of a linear relaxation model, discusses the combination of fractions and integers in the state solution vector, and considers the coordination capability between units for verification, constructing a compact set of state constraints. Simultaneously, the power vector of the linear relaxation solution is used as an empirical parameter to construct a dynamically tightened set of unit output range constraints in the form of time windows. Replacing the corresponding parts of the original mixed-integer linear programming model of unit combination with the constructed constraint set results in subproblems containing high-quality feasible solutions, achieving efficient calculation of high-precision solutions for medium- and long-term unit combination.

[0059] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0060] Figure 1 This is a schematic diagram of the process of the present invention;

[0061] Figure 2 Flowchart for tightening operational constraints for unit combination problems;

[0062] Figure 3 A schematic diagram illustrating the combination of a state-constrained fractional sequence and its neighboring integer sequences;

[0063] Figure 4 A schematic diagram of a computer device provided in an embodiment of the present invention;

[0064] Figure 5 This is a block diagram of a chip provided according to an embodiment of the present invention.

[0065] Among them, 60. Computer equipment; 61. Processor; 62. Memory; 63. Computer program; 600. Electronic device; 610. Processing unit; 620. Storage unit; 6201. Random access memory unit; 6202. Cache memory unit; 6203. Read-only memory unit; 6204. Program / utility; 6205. Program module; 630. Bus; 640. Display unit; 650. Input / output interface; 660. Network adapter; 700. External device. Detailed Implementation

[0066] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0067] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0068] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0069] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0070] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0071] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0072] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0073] This invention provides a method for calculating medium- and long-term unit combination based on tightened operational constraints. It employs a compact state constraint set construction strategy based on linearly relaxed solutions: from the perspective of inter-unit coordination, a state constraint verification method based on power constraint transfer is constructed; from the perspective of temporal coupling, possible fractional-integer combinations are comprehensively classified and discussed; combining the verification method and temporal coupling characteristics, a more compact state constraint set is constructed. Considering the similarity of linear branch subproblems, the power vector of the linearly relaxed solution is used as an empirical parameter to dynamically generate a more compact unit output range constraint set in the form of a time window. Based on the two-stage solution framework of Linear Programming-Mixed-Integer Linear Programming (LP-MILP), the original unit combination model is transformed into a mixed-integer linear programming (MILP) subproblem with a more compact feasible region containing optimal or high-quality suboptimal solutions, achieving accelerated calculation of medium- and long-term unit combination, and exhibiting greater efficiency advantages as the solution period and system scale increase.

[0074] Please see Figure 1 This invention provides a method for calculating medium- and long-term unit combination based on tightened operational constraints, comprising the following steps:

[0075] S1. Obtain basic technical data for power systems containing new energy sources;

[0076] Basic technical data for power systems that include new energy sources include: operating parameters of various generating units, predicted values ​​of new energy sources and loads, and system reserve information.

[0077] S2. Based on the basic technical data of the power system including new energy sources obtained in S1, construct the unit combination model and solve it using linear relaxation.

[0078] The unit combination model includes: unit state logic constraints, ramp-up constraints, processing range constraints, and minimum start-up and shutdown time constraints for thermal power units; processing constraints for new energy units; system power balance constraints; reserve constraints; and power flow constraints.

[0079] Linear relaxation refers to relaxing 0-1 variables, such as state and start / stop signals, into continuous variables, and correspondingly relaxing the associated constraints, thereby relaxing the original mixed-integer linear programming problem into a linear programming problem and solving it.

[0080] S3. Based on the linear relaxation solution of the unit combination model obtained in S2, tighten the state constraints based on the coupling relationship between units and the temporal coupling relationship between units, and tighten the power range constraints based on the dynamic time window to construct the original unit combination subproblem model.

[0081] For the state solutions in the linear relaxation solution of the unit combination, the state constraints of non-integer states are tightened, specifically including:

[0082] Based on the relative relationship between unit output and unit output range, a power transfer verification is constructed to establish a constraint propagation rule for coupling relationship between units;

[0083] Based on the combination relationship between fractional state sequences and adjacent integer state sequences, a propagation rule for unit time-series coupling constraints is constructed.

[0084] For the power solution in the linear relaxation solution of the unit combination, a power range constraint tightening is established.

[0085] Please see Figure 2 The process for tightening constraints on unit combination problems includes tightening constraints on inter-unit coupling relationships, tightening constraints on unit timing coupling, and tightening constraints on power range.

[0086] Among them, thermal power units In time state variables Start signal Stop signal Time intervals that are not both integers are denoted as In the interval The sequence of states on the sphere is called a fractional state sequence.

[0087] When constructing the constraint propagation rules for coupling relationships between units, considering that the unit output in the linear relaxation model may take a value less than the technical output, the output range is divided into three sets:

[0088]

[0089]

[0090]

[0091] in, Indicates the start time of the current fractional state sequence; To represent the duration of a fractional state, the time interval of the fractional state can be represented as: ; This is the first set of power output ranges for thermal power units. For thermal power units At any moment The linear relaxation solution force value; For thermal power units At any moment The minimum technical output; It is a set of fractional state intervals; This serves as the index identifier for the time interval of the fractional state. The second set of power output ranges for thermal power units; This is the third set of the power output range of thermal power units.

[0092] Subsequently, a power transfer identification factor was constructed:

[0093]

[0094]

[0095] in, This represents the set of powered-on units and currently powered-on units whose status is an integer. This represents the set of powered-on units whose status is an integer and the set of units that can be shut down at present. For thermal power units Maximum output; For thermal power units At any moment of efforts, For thermal power units At any moment The optimal solution for linear relaxation yields the force value. For thermal power units At any moment Actual output variables; For thermal power units The rate of upward climb; For thermal power units At any moment contribution; For thermal power units At any moment Actual output variables; power upward transfer identification factor ,when A value >0 indicates that the remaining units can take over the output of the fractional-state units through upward adjustments (climbing the ramp, starting up). Similarly, the power downward transfer identification factor... ,when A value >0 indicates that the remaining units can take over the output of the fractional-state units through (ramp-down, shutdown). Based on the transfer factor, the propagation rule for the coupling relationship constraints between units is:

[0096] for , superior Establishment status:

[0097] (1) , judged as in hour ;

[0098] (2) and , judged as in hour ;

[0099] (3) and , judged as in Shangshi .

[0100] for hour Establishment status:

[0101] (1) , judged as in hour ;

[0102] (2) and , judged as in hour ;

[0103] (3) and , judged as in Add state constraints .

[0104] for hour All of the above are considered valid. .

[0105] When constructing the propagation rules for unit time-series coupling constraints, different combinations of fractional state sequences and adjacent integer state sequences are divided into independent fractional state sequences and associated fractional state sequences. A backward correction method is adopted, that is, the state of the fractional sequence that has already met the shortest start-up and shutdown constraint in the previous time period is not corrected, and only the state constraint of the fractional sequence in the next time period is corrected.

[0106] The specific characteristics that an independent fractional state sequence should have are as follows:

[0107] 1) The corresponding unit state solution vector contains only one fractional sequence.

[0108] 2) When constructing state constraints based on the latter fraction sequence, it is not affected by the former fraction sequence.

[0109] Its verification equation is expressed as:

[0110]

[0111]

[0112] in, , They represent thermal power units Minimum startup time / minimum downtime; For fractional sequences The start time, For thermal power units At any moment Startup status indicator For fractional sequences At the end of the day, It is the set of fractional state intervals for thermal power units.

[0113] For a fractional sequence that meets the conditions, the time interval of its left-hand adjacent integer state already satisfies the shortest start-stop constraint, and subsequent start-stop states can be freely determined. Then, based on the 0-1 combinations and duration of the adjacent integer state sequences of the independent fractional state sequence, a tightening constraint is applied. The tightening constraint is as follows:

[0114]

[0115]

[0116]

[0117] The characteristic of an associated fractional state sequence is that the state constraints constructed by the previous fractional sequence change the integer state results on the left side of the next fractional sequence. A time set is constructed accordingly. :

[0118]

[0119] Wherein, the starting time of the s-th segment of the fractional sequence is The duration of the integer sequence to the left of sequence s Construct constraints to constraint sets on top of it:

[0120]

[0121] When establishing power range constraint tightening, a dynamic time window is defined based on the temporal similarity of the relaxed solutions, using the similarity of the solutions as the basis for constraint tightening. The dynamic time window is constructed based on the following:

[0122]

[0123]

[0124] Among them, ordered array As reference data for the generation of dynamic time windows The evaluation coefficient representing the adjacent deviations. The assessment coefficient representing the deviation from the mean. This is the start time of the current dynamic time window. Large coal-fired units typically exhibit output variations within a narrower range compared to the original unit combination model, assuming minimal changes in the source load scenario. Therefore, it is advisable to tighten the output range constraint within the dynamic time window.

[0125]

[0126]

[0127] in, A collection of thermal power units; , thermal power units No. The start time / duration of a dynamic time window; A dynamic time window index set; For thermal power units At any moment The running state variables; For thermal power units At any moment Actual output variables; For thermal power units The maximum output of technology; , For the first Thermal power units within a dynamic time window The minimum and maximum output values; This is an adjustable parameter used to control the degree of conservatism in the solution; Represents the optimal solution for linear relaxation units In time The output value; For thermal power units At any moment The optimal solution for linear relaxation yields the force value; For thermal power units In the Start time of segment dynamic time window The next moment; For thermal power units In the The output variable at the end of a dynamic time window.

[0128] Because new energy sources have lower costs and higher power generation priority, their output lower bound is often far greater than 0. In this case, the lower bound constraint only establishes a feasible range and has no impact on the optimal solution. Based on the above analysis, the utilization rate of new energy sources is used as reference data for generating dynamic time windows. Dynamic time windows are then generated, and output range constraints are constructed.

[0129]

[0130]

[0131] in, A collection of new energy generating units; For the first New energy units within a dynamic time window Minimum utilization rate; This is an adjustable parameter used to control the degree of conservatism in the solution; Represents new energy power units under given resource conditions The upper limit of output; Indicates new energy power units contribution; Represents the optimal solution for linear relaxation units In time The output value; For new energy units The actual output variable at the start of the s-th dynamic time window; For new energy units The actual output variable at the time following the start of the s-th dynamic time window; For new energy units The actual output variable at the end of the s-th dynamic time window; For new energy units The upper limit of output at the beginning of the s-th dynamic time window; For new energy units The upper limit of output at the moment following the start of the s-th dynamic time window; For new energy units The maximum output at the end of the s-th dynamic time window; For new energy units No. The start time of the dynamic time window; For new energy units No. The duration of the dynamic time window. This is a correction method for the base length of the dynamic time window of new energy units; This is a dynamic time window index set.

[0132] S4. Based on the original unit combination model sub-problem obtained in S3, solve the model to obtain output results including system operation information, objective function value, and model solution time.

[0133] In addition to measuring computation time, to quantify the effectiveness of the comparison methods, the optimal deviation iGap and speedup AR are defined:

[0134]

[0135]

[0136] in, , These are the original model's integrated solution and the objective function value that converges to the optimization gap using the method of this invention, respectively. , These represent the computation time required for the original model and the method of this invention to converge to the optimization gap, respectively.

[0137] Please see Figure 3 A schematic diagram illustrating different combinations of fractional state sequences and their neighboring integer sequences in the propagation of unit time-coupling constraints.

[0138] Case 1 to Case 4 represent four cases of independent fractional state sequences and their neighboring states. and For independent fractional states, the integer state to the left has already satisfied the minimum start-up / stop time, while the adjacent integer state to the right may last for a shorter time than the minimum start-up / stop time. The tightening strategy should also differ depending on the fractional sequence and the duration of the adjacent integer state to the right. Based on the difference in sequence duration, Case 1 is further divided into three cases: Case-1-A, Case-1-B, and Case-1-C.

[0139] 1) For Case-1-A, the following conditions are met: and condition, For thermal power units The duration of the left-hand integer states in the fractional state sequence. For thermal power units The shortest boot time For thermal power units The duration of the right-hand integer states in the fractional state sequence; if Furthermore, based on the state constraint verification method, the constraint construction form is determined to be as follows: To determine the shortest downtime for thermal power unit i, add the following formula to the constraint set:

[0140]

[0141] if Furthermore, based on the state constraint verification method, the constraint construction form is determined to be within the interval. exist Then add the following formula to the constraint set:

[0142]

[0143] 2) For Case-1-B, because ,if Then, the constraint construction form can be directly determined according to the state constraint verification method. If Furthermore, if the constraint construction form is determined to be the following expression based on the state constraint verification method, then the following expression is added to the state constraint set:

[0144]

[0145] if Furthermore, based on the state constraint verification method, within the time interval... The internal structure is as follows The constraints present a complex coordination between fractional and integer states. To ensure the quality and feasibility of the solution, The state constraints are modified as follows:

[0146]

[0147] 3) For Case-1-C, because If the state constraint check result satisfies the following formula, then add it to the state constraint set.

[0148]

[0149] If the state constraint check result is an interval for It involves and Partial coordination also requires consideration of coordination with subsequent states. For such extremely complex situations, using heuristic methods or verification approaches can lead to significant optimal deviations. Therefore, the time interval... The state constraints are constructed as follows:

[0150]

[0151] in, express and The larger value in the range.

[0152] Case-2 can also be divided into Case-2-A, Case-2-B, and Case-2-C. Case-2-A satisfies... and Condition, Case-2-B is satisfied , ,as well as Condition. Case-2-C satisfies. , and condition.

[0153] For Case-2-A, if Then, according to the state constraint verification method in Section 3.1, the constraint construction form is determined and added to the constraint set. If If the constraint construction form is determined to be the following expression based on the state constraint verification method, then the following expression is added to the constraint set:

[0154]

[0155] if Furthermore, based on the state constraint verification method, the constraint construction form is determined to be within the interval. exist Then add the following formula to the constraint set:

[0156]

[0157] For Case-2-B, because ,if If so, the constraint construction form can be directly determined according to the state constraint verification method and added to the constraint set; if Furthermore, the constraint construction form is determined based on the state constraint verification method. for Then add the constraint to the state constraint set; if the constraint construction form is determined according to the state constraint verification method... for At this point, there is a complex coordination between fractional and integer states. To ensure the quality and feasibility of the solution, we will... The state constraints are modified as follows:

[0158]

[0159] For Case-2-C, because Regardless of the constraint construction form determined by the state constraint verification method, it may lead to temporal inconsistencies. Therefore, the time interval... The state constraints are modified as follows:

[0160]

[0161] For Case-3 and Case-4, the situation is exactly the same as for Case-1 and Case-2.

[0162] In another embodiment of the present invention, a medium- and long-term unit combination calculation system based on tightened operational constraints is provided. This system can be used to implement the above-mentioned medium- and long-term unit combination calculation method based on tightened operational constraints. Specifically, the medium- and long-term unit combination calculation system based on tightened operational constraints includes a data module, a construction module, a constraint module, and an output module.

[0163] The data module acquires basic technical data of the power system, including new energy sources.

[0164] The construction module constructs a unit combination model based on the basic technical data of the power system containing new energy obtained from the data module, and performs linear relaxation solution on the unit combination model to obtain the linear relaxation solution of the unit combination model.

[0165] The constraint module, based on the linear relaxation solution of the unit combination model obtained from the construction module, performs state constraint tightening based on the coupling relationship between units and the temporal coupling relationship between units, as well as power range constraint tightening based on the dynamic time window, and constructs the original unit combination subproblem model;

[0166] The output module, which solves the constraint module, generates the original unit combination subproblem model and outputs the results including system operation information, objective function value, and model solution time, thereby accelerating the calculation of medium- and long-term unit combination.

[0167] This invention provides a terminal device comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve corresponding method flows or corresponding functions. The processor described in this embodiment can be used for operations based on a medium- and long-term unit combination calculation method with tightened operational constraints, including:

[0168] Acquire basic technical data of power systems including new energy sources; construct a unit combination model based on the obtained basic technical data of power systems including new energy sources, and perform linear relaxation solution on the unit combination model to obtain the linear relaxation solution of the unit combination model; based on the obtained linear relaxation solution of the unit combination model, perform state constraint tightening based on the coupling relationship between units and the temporal coupling relationship between units, as well as power range constraint tightening based on dynamic time window, to construct the original unit combination sub-problem model; solve the obtained original unit combination sub-problem model to obtain the output results including system operation information, objective function value, and model solution time, thereby realizing accelerated calculation of medium and long-term unit combination.

[0169] Please see Figure 4 The terminal device is a computer device. In this embodiment, the computer device 60 includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When executed by the processor 61, the computer program 63 implements the medium- and long-term unit combination calculation method based on tightened operational constraints as described in this embodiment. To avoid repetition, these details are not elaborated here. Alternatively, when executed by the processor 61, the computer program 63 implements the functions of each model / unit in the medium- and long-term unit combination calculation system based on tightened operational constraints as described in this embodiment. To avoid repetition, these details are not elaborated here.

[0170] Computer device 60 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Computer device 60 includes, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will understand that... Figure 4 This is merely an example of computer device 60 and does not constitute a limitation on computer device 60. It may include more or fewer components than shown, or combine certain components, or different components. For example, computer device may also include input / output devices, network access devices, buses, etc.

[0171] The processor 61 may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0172] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or RAM of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 60.

[0173] Furthermore, the memory 62 may include both internal storage units and external storage devices of the computer device 60. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0174] Please see Figure 5 The terminal device is an electronic device 600, which is manifested in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0175] The storage unit stores program code, which can be executed by the processing unit 610 to perform the steps described in the method section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 610 can perform actions such as... Figure 1 The steps are shown in the figure.

[0176] Storage unit 620 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include a read-only memory (ROM) 6203.

[0177] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0178] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0179] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem). This communication can be performed via input / output interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network, wide area network, and / or public network, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0180] Example 4

[0181] This invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It is understood that the computer-readable storage medium here can include both built-in storage media in the terminal device and extended storage media supported by the terminal device; it can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which can be one or more computer programs (including program code). More specific examples of the computer-readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical fiber, portable compact disk read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0182] Computer-readable storage media also include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium can also be any readable medium other than a readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, radio frequency, etc., or any suitable combination thereof.

[0183] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0184] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the medium- and long-term unit combination calculation method based on tightened operational constraints in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor in the following steps:

[0185] Acquire basic technical data of power systems including new energy sources; construct a unit combination model based on the obtained basic technical data of power systems including new energy sources, and perform linear relaxation solution on the unit combination model to obtain the linear relaxation solution of the unit combination model; based on the obtained linear relaxation solution of the unit combination model, perform state constraint tightening based on the coupling relationship between units and the temporal coupling relationship between units, as well as power range constraint tightening based on dynamic time window, to construct the original unit combination sub-problem model; solve the obtained original unit combination sub-problem model to obtain the output results including system operation information, objective function value, and model solution time, thereby realizing accelerated calculation of medium and long-term unit combination.

[0186] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0187] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0188] Case Analysis

[0189] To verify the effectiveness of the method of this invention, a virtual system and a real system were selected for example analysis. The virtual system contains 80 generating units, including 45 thermal power units and 35 hydropower units, with a total installed capacity of 21,050 MW and a maximum load of 17,922.7 MW. The real system contains 118 busbars, 186 transmission lines, 54 conventional generating units, and 92 renewable energy generating units. In this system, the installed capacity of renewable energy is 38.52%.

[0190] All calculations were performed in a C++ environment using CPLEX 12.80 as the solver, and the computer processor was an Intel(R) Xeon(R) Gold 6136 CPU @ 3.00GHz. The calculation time and efficiency after solving are as follows:

[0191] Table 1. Computational acceleration and efficiency for each case

[0192]

[0193] Among them, TS represents the model constructed using only the state tightening method, while TSP represents the model constructed using a combination of state tightening and power tightening methods. Computational efficiency and accuracy were measured for Cases 1 to 6, with a uniform solution period of 672 hours and γ=0.3. As shown in Table 1 after random generation, compared to Case 1, Cases 2 and 3 achieved speedup ratios exceeding 20, and their computational accuracy remained within 0.5%, with an average deviation of only 0.144%. In comparisons with Cases 4 and 5 / 6, a speedup ratio exceeding 7 and an optimal deviation below 0.15% were achieved. The constrained tightening models all demonstrated high computational efficiency and accuracy.

[0194] Table 2 Comparison of computational results for different scale examples

[0195]

[0196] In Table 2, For planning time periods, Table 2 shows the computational efficiency of the proposed method under different scale examples, with iGap representing the optimal solution time, iGap representing the optimal deviation, and AR representing the speedup ratio. In small-to-medium scale systems, the computational efficiency improvement within the test range reaches a maximum of 82.41%. On large-scale systems, the National Renewable Energy Laboratory 454-node Test System (NREL-454) requires 22,603 ​​seconds to obtain the optimal solution over a 672-hour solution period. The proposed method obtains the suboptimal solution in 1,164 seconds, with a relative difference of only 0.161% between the suboptimal and optimal solutions. The proposed method demonstrates significant efficiency in solving medium- and long-term unit combination problems, especially in computations involving long scheduling periods in large-scale systems, where the speedup ratio exceeds 10. As the solution period expands, the solution time of the constructed model increases less compared to the original model. Regarding computational accuracy, the optimal deviation of the proposed method in the examples is generally between 0.1% and 0.5%, which is acceptable for medium- and long-term unit combination calculations in engineering practice.

[0197] In summary, this invention presents a method and system for calculating medium- and long-term unit combination based on tightened operational constraints. It proposes a strategy for constructing a compact state constraint set based on linearly relaxed solutions: from the perspective of coordination relationships between units, a state constraint verification method based on power constraint transfer is constructed; from the perspective of temporal coupling relationships, a comprehensive classification and discussion of possible fractional-integer combinations is conducted; and by combining the verification method and temporal coupling characteristics, a more compact state constraint set is constructed. Considering the similarity of linear branch subproblems, the power vector of the linearly relaxed solution is used as an empirical parameter to dynamically generate a more compact unit output range constraint set in the form of a time window. Based on the LP-MILP two-stage solution framework, the original unit combination model is transformed into a MILP subproblem with a more compact feasible region and containing optimal or high-quality suboptimal solutions, achieving accelerated calculation of medium- and long-term unit combination, and exhibiting greater efficiency advantages as the solution period and system scale increase.

[0198] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0199] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0200] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0201] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0202] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0203] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0204] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random-access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0205] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0206] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0207] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0208] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for calculating medium- and long-term unit combination based on tightened operational constraints, characterized in that, Includes the following steps: S1. Obtain basic technical data for power systems containing new energy sources; S2. Based on the basic technical data of the power system including new energy obtained in S1, construct a unit combination model, and perform linear relaxation solution on the unit combination model to obtain the linear relaxation solution of the unit combination model; S3. Based on the linear relaxation solution of the unit combination model obtained in S2, tighten the state constraints based on the coupling relationship between units and the temporal coupling relationship between units, and tighten the power range constraints based on the dynamic time window to construct the original unit combination subproblem model. State constraint tightening based on inter-unit coupling relationships and unit timing coupling relationships specifically includes: For the state solutions in the linear relaxation solution of unit combination, based on the relative relationship between unit output and unit output range, a power transfer check is constructed, and a constraint propagation rule for the coupling relationship between units is established; Based on the combination relationship between fractional state sequences and adjacent integer state sequences, a propagation rule for unit time-series coupling constraints is constructed; wherein, the time interval in which the state variable, start signal and shutdown signal of the thermal power unit are not simultaneously integers is denoted as a fractional state interval, and the state sequence in the fractional state interval is called a fractional state sequence. Power range constraint tightening based on dynamic time windows specifically includes: Based on the idea of ​​similarity aggregation, dynamic time windows are divided according to the temporal similarity of the linear relaxation solutions of unit combination and the construction basis. The construction basis uses an ordered array as the reference data for generating dynamic time windows, including the assessment coefficients of adjacent deviations and the assessment coefficients of mean deviation, as well as the start time of the current dynamic time window. Based on the defined dynamic time windows, the output range constraints for thermal power units and new energy units are tightened respectively; For thermal power units, the output range constraint is constructed based on the minimum output value, maximum output value, and adjustable parameters of the thermal power unit within the dynamic time window, thereby tightening the output range constraint of the thermal power unit; wherein, the adjustable parameters are used to control the degree of conservatism in the solution. For new energy units, the utilization rate of new energy is used as the dividing criterion to generate a dynamic time window. Based on the minimum utilization rate of new energy units within the time window, adjustable parameters, and the upper limit of output of new energy units under given resource conditions, output range constraints are constructed to tighten the output range constraints of new energy units. S4. Solve the original unit combination subproblem model obtained in S3 to obtain the output results including system operation information, objective function value, and model solution time.

2. The method for calculating medium- and long-term unit combination based on tightened operational constraints according to claim 1, characterized in that, In S2, the unit combination model includes the unit state logic constraints, ramping constraints, processing range constraints, shortest start-up and shutdown time constraints of thermal power units, processing constraints of new energy units, system power balance constraints, reserve constraints, and power flow constraints. The linear relaxation solution involves relaxing the 0-1 variables representing the state and start / stop signals into continuous variables, correspondingly relaxing the associated constraints, and thus relaxing the original mixed-integer linear programming problem into a linear programming problem for solution.

3. The method for calculating medium- and long-term unit combination based on tightened operational constraints according to claim 1, characterized in that, Constructing power transfer verification and establishing inter-unit coupling relationship constraint propagation rules, specifically including: The unit's output range is divided into three sets; A power transfer identification factor is constructed, comprising a power upward transfer identification factor and a power downward transfer identification factor. The power upward transfer identification factor is used to determine whether other generating units can take over the output of the fractional-state generating units by adjusting upwards. When the power upward transfer identification factor > 0, the other generating units can take over the output of the fractional-state generating units by adjusting upwards. The power downward transfer identification factor is used to determine whether other generating units can take over the output of the fractional-state generating units by adjusting downwards. When the power downward transfer identification factor > 0, the other generating units can take over the output of the fractional-state generating units by adjusting downwards. Based on the power transfer identification factor, constrained propagation rules for inter-unit coupling relationships are established according to different cases, and the unit state constraints are tightened.

4. The method for calculating medium- and long-term unit combination based on tightened operational constraints according to claim 1, characterized in that, Constructing unit timing coupling constraint propagation rules specifically includes: The fractional state sequences are divided into independent fractional state sequences and associated fractional state sequences. The independent fractional state sequence is characterized by containing only one fractional sequence in the corresponding unit state solution vector, and the state constraints constructed based on the latter fractional sequence are not affected by the former fractional sequence. The associated fractional state sequence is characterized by changing the integer state results on the left side of the next fractional sequence when constructing state constraints based on the former fractional sequence. The propagation rules of unit time-series coupling constraints are constructed by adopting a backward correction method. For the state that has already met the shortest start-up and shutdown constraints in the previous period of the fractional sequence, no correction is made. Only the state constraints in the next period of the fractional sequence are corrected. For independent fractional state sequences, constraints are tightened based on the 0-1 combinations and duration of their adjacent integer state sequences, combined with the check equation. For associated fractional state sequences, a time set is constructed, and constraints are constructed on the time set to form a constraint set, thereby achieving constraint tightening.

5. The method for calculating medium- and long-term unit combination based on tightened operational constraints according to claim 1, characterized in that, The output range constraints of thermal power units are as follows: in, A collection of thermal power units; , thermal power units No. The start time and duration of a dynamic time window; A dynamic time window index set; For thermal power units At any moment The running state variables; For thermal power units At any moment Actual output variables; For thermal power units The maximum output of technology; For the first Thermal power units within a dynamic time window The minimum output value; These are adjustable parameters; For thermal power units At any moment The optimal solution for linear relaxation yields the force value; For thermal power units In the Start time of segment dynamic time window The next moment; For thermal power units In the The output variable at the end of a dynamic time window.

6. The method for calculating medium- and long-term unit combination based on tightened operational constraints according to claim 1, characterized in that, The output range constraints of new energy generating units are: in, A collection of new energy generating units; For the first New energy units within a dynamic time window Minimum utilization rate; These are adjustable parameters; Represents new energy power units under given resource conditions The upper limit of output; Indicates new energy power units contribution; Represents the optimal solution for linear relaxation in new energy power units. In time The output value; For new energy units The actual output variable at the start of the s-th dynamic time window; For new energy units The actual output variable at the time following the start of the s-th dynamic time window; For new energy units The actual output variable at the end of the s-th dynamic time window; For new energy units The upper limit of output at the beginning of the s-th dynamic time window; For new energy units The upper limit of output at the moment following the start of the s-th dynamic time window; For new energy units The maximum output at the end of the s-th dynamic time window; For new energy units No. The start time of a dynamic time window; For new energy units No. The duration of the dynamic time window. This is a correction method for the base length of the dynamic time window of new energy units; This is a dynamic time window index set.

7. The method for calculating medium- and long-term unit combination based on tightened operational constraints according to claim 1, characterized in that, In S1, the basic technical data of the power system containing new energy sources includes: operating parameters of various generating units, predicted values ​​of new energy sources and loads, and system reserve information.

8. A medium- to long-term unit combination calculation system based on tightened operational constraints, characterized in that, include: The data module is used to acquire basic technical data of power systems including new energy sources; The construction module is used to construct a unit combination model based on the basic technical data of the power system containing new energy obtained from the data module, and to perform linear relaxation solution on the unit combination model to obtain the linear relaxation solution of the unit combination model. The constraint module is used to tighten state constraints based on the inter-unit coupling relationship and the unit temporal coupling relationship, as well as the power range constraint tightening based on the dynamic time window, based on the linear relaxation solution of the unit combination model obtained by the construction module, and to construct the original unit combination subproblem model. State constraint tightening based on inter-unit coupling relationships and unit timing coupling relationships specifically includes: For the state solutions in the linear relaxation solution of unit combination, based on the relative relationship between unit output and unit output range, a power transfer check is constructed, and a constraint propagation rule for the coupling relationship between units is established; Based on the combination relationship between fractional state sequences and adjacent integer state sequences, a propagation rule for unit time-series coupling constraints is constructed; wherein, the time interval in which the state variable, start signal and shutdown signal of the thermal power unit are not simultaneously integers is denoted as a fractional state interval, and the state sequence in the fractional state interval is called a fractional state sequence. Power range constraint tightening based on dynamic time windows specifically includes: Based on the idea of ​​similarity aggregation, dynamic time windows are divided according to the temporal similarity of the linear relaxation solutions of unit combination and the construction basis. The construction basis uses an ordered array as the reference data for generating dynamic time windows, including the assessment coefficients of adjacent deviations and the assessment coefficients of mean deviation, as well as the start time of the current dynamic time window. Based on the defined dynamic time windows, the output range constraints for thermal power units and new energy units are tightened respectively; For thermal power units, the output range constraint is constructed based on the minimum output value, maximum output value, and adjustable parameters of the thermal power unit within the dynamic time window, thereby tightening the output range constraint of the thermal power unit; wherein, the adjustable parameters are used to control the degree of conservatism in the solution. For new energy units, the utilization rate of new energy is used as the dividing criterion to generate a dynamic time window. Based on the minimum utilization rate of new energy units within the time window, adjustable parameters, and the upper limit of output of new energy units under given resource conditions, output range constraints are constructed to tighten the output range constraints of new energy units. The output module is used to solve the original unit combination subproblem model obtained by the constraint module, and obtain the output results including system operation information, objective function value, and model solution time, so as to realize accelerated calculation of medium and long-term unit combination.

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