Security constraint unit commitment problem processing method and device based on time decoupling, computer equipment and storage medium

Abstract

The invention relates to a security constraint unit commitment problem processing method and device based on time decoupling, computer equipment and a storage medium. Relates to the technical field of power systems. The method comprises the steps of determining a plurality of target coupling time points in a to-be-optimized time period of a power system through a pre-constructed comprehensive evaluation model; dividing the to-be-optimized time period into a plurality of sub time periods based on the plurality of target coupling time points; decomposing a long-time-scale security constraint unit commitment problem to obtain a sub-problem optimization model corresponding to each sub-time period; and according to each sub-problem optimization model, solving to obtain a unit commitment optimization result of the power system. By adopting the method, the constraint calculation efficiency of the unit combination in the large-scale power system can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and particularly to a method, device, computer device, computer-readable storage medium, and computer program product for processing security-constrained unit commitment problems based on time decoupling. Background Art

[0002] With the expansion of the scale of power systems and the popularization of renewable energy, the long-term planning and real-time scheduling of power systems are particularly important. Optimization problems with a long time span involve a large number of variables and constraints, resulting in extremely high computational complexity. However, existing production cost models cannot efficiently handle the long-term planning and real-time scheduling problems of large-scale unit commitment.

[0003] Currently, the long-time-scale decomposition methods for unit commitment include the rolling horizon method, but this method cannot effectively handle time-coupling constraints, such as the minimum start-up and shut-down times and ramp rate limits of unit commitment, resulting in the solutions of the decomposed sub-problems not satisfying the global constraints and unable to further carry out the long-term planning and real-time scheduling of unit commitment, affecting the management efficiency of power systems. Therefore, there is currently a problem of low efficiency in calculating the constraints of unit commitment in large-scale power systems. Summary of the Invention

[0004] Based on this, in view of the above technical problem of low efficiency in calculating the constraints of unit commitment in large-scale power systems, it is necessary to provide a method, device, computer device, computer-readable storage medium, and computer program product for processing security-constrained unit commitment problems based on time decoupling.

[0005] In a first aspect, the present application provides a method for processing security-constrained unit commitment problems based on time decoupling, including:

[0006] Determining a plurality of target coupling time points in the time period to be optimized of the power system through a pre-constructed comprehensive evaluation model;

[0007] Dividing the time period to be optimized into a plurality of sub-time periods based on the plurality of target coupling time points;

[0008] Decomposing the long-time-scale security-constrained unit commitment problem to obtain sub-problem optimization models corresponding to the respective sub-time periods;

[0009] Solving to obtain the unit commitment optimization result of the power system according to the respective sub-problem optimization models.

[0010] In one embodiment, determining multiple target coupling time points in the time period to be optimized of the power system through a pre-constructed comprehensive evaluation model includes: obtaining the index values of each time point in the time period to be optimized under multiple coupling strength indexes and the weight coefficients of each coupling strength index; the coupling strength indexes include load change rate, number of unit state switches, renewable energy volatility, and ramping demand pressure; performing weighted fusion processing on the index values of each coupling strength index through the weight coefficients of each coupling strength index to obtain the coupling strength scores of each time point; screening out the time points with coupling strength scores less than the threshold from each time point as the target coupling time points.

[0011] In one embodiment, solving the unit commitment optimization result of the power system according to each sub-problem optimization model includes: setting consistency constraint conditions between adjacent sub-time periods; the consistency constraint conditions include consistency constraints on multiple pairs of shared coupling variables; applying the consistency constraint conditions of each pair of shared coupling variables to each sub-problem optimization model to solve for the unit commitment optimization result of the power system.

[0012] In one embodiment, the shared coupling variables include unit output variables, unit state variables, and auxiliary counting variables. The unit output variable represents the output of the unit in the sub-problem at a preset moment; the unit state variable represents the state of the unit in the sub-problem at the preset moment; the auxiliary counting variable is used to record the start-stop time of the generator within the coupling time interval; the method further includes: taking the same values of each pair of shared coupling variables as the goal, performing iterative solution on each sub-problem optimization model to obtain the unit commitment optimization result of the power system.

[0013] In one embodiment, taking the same values of each pair of shared coupling variables as the goal, performing iterative solution on each sub-problem optimization model to obtain the unit commitment optimization result of the power system includes: respectively obtaining multiple shared coupling variables and first consistency constraints corresponding to multiple sub-problems based on the accelerated analysis target cascading algorithm; inputting the multiple shared coupling variables into the master problem of the power system to obtain multiple auxiliary variables, and determining second consistency constraints of multiple sub-problems based on the multiple shared coupling variables and multiple auxiliary variables; obtaining the unit commitment optimization result of the power system when the difference between the second consistency constraint and the first consistency constraint is less than a preset value.

[0014] In one embodiment, determining the second consistency constraint of the multiple sub-problems based on the multiple shared coupling variables and the multiple auxiliary variables includes: respectively obtaining the differences between the multiple shared coupling variables and the multiple auxiliary variables, and determining the differences as the second consistency constraint of the multiple sub-problems.

[0015] In one embodiment, the method further includes: when the difference between the second consistency constraint and the first consistency constraint is greater than or equal to a preset value, re-determining the second consistency constraint of the multiple sub-problems.

[0016] In a second aspect, the present application further provides a device for processing a security-constrained unit commitment problem based on time decoupling, including:

[0017] A time determination module, configured to determine multiple target coupling time points in the time period to be optimized of the power system through a pre-constructed comprehensive evaluation model;

[0018] A time division module, configured to divide the time period to be optimized into multiple sub-time periods based on the multiple target coupling time points;

[0019] A problem decomposition module, configured to decompose the security-constrained unit commitment problem with a long time scale to obtain sub-problem optimization models corresponding to the respective sub-time periods;

[0020] A result acquisition module, configured to solve and obtain the unit commitment optimization result of the power system according to the respective sub-problem optimization models.

[0021] In a third aspect, the present application further provides a computer device, where the computer device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0022] Determine multiple target coupling time points in the time period to be optimized of the power system through a pre-constructed comprehensive evaluation model;

[0023] Divide the time period to be optimized into multiple sub-time periods based on the multiple target coupling time points;

[0024] Decompose the security-constrained unit commitment problem with a long time scale to obtain sub-problem optimization models corresponding to the respective sub-time periods;

[0025] Solve and obtain the unit commitment optimization result of the power system according to the respective sub-problem optimization models.

[0026] Fourthly, the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0027] Determine multiple target coupling time points in the time period to be optimized of the power system through a pre-constructed comprehensive evaluation model;

[0028] Divide the time period to be optimized into multiple sub-time periods based on the multiple target coupling time points;

[0029] Decompose the security-constrained unit commitment problem with a long time scale to obtain a sub-problem optimization model corresponding to each sub-time period;

[0030] Solve to obtain the unit commitment optimization result of the power system according to each sub-problem optimization model.

[0031] Fifthly, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0032] Determine multiple target coupling time points in the time period to be optimized of the power system through a pre-constructed comprehensive evaluation model;

[0033] Divide the time period to be optimized into multiple sub-time periods based on the multiple target coupling time points;

[0034] Decompose the security-constrained unit commitment problem with a long time scale to obtain a sub-problem optimization model corresponding to each sub-time period;

[0035] Solve to obtain the unit commitment optimization result of the power system according to each sub-problem optimization model.

[0036] The above-mentioned method, device, computer equipment, storage medium and computer program product for processing the security-constrained unit commitment problem based on time decoupling, in the process of processing the security-constrained unit commitment problem based on time decoupling, first determines multiple target coupling time points in the to-be-optimized time period of the power system through a pre-constructed comprehensive evaluation model; then divides the to-be-optimized time period into multiple sub-time periods based on the multiple target coupling time points; then decomposes the long-time-scale security-constrained unit commitment problem to obtain sub-problem optimization models corresponding to each sub-time period; finally, according to each sub-problem optimization model, solves to obtain the unit commitment optimization result of the power system. In the above process, a scientific basis is provided for the decision result through the pre-established comprehensive evaluation model, improving the overall operation efficiency of the power system, and by decomposing the complex long-time-scale problem into multiple sub-problems, the calculation process is simplified, enabling the optimization algorithm to find a feasible solution faster. Therefore, through clear security constraints and optimization models, it is ensured that the power system can stably and safely meet the load demand during operation, thereby improving the constraint calculation efficiency for unit commitment in large-scale power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0038] Figure 1 It is a schematic flowchart of the method for processing the security-constrained unit commitment problem based on time decoupling in an embodiment;

[0039] Figure 2 It is a schematic flowchart of the step of determining multiple target coupling time points in the to-be-optimized time period of the power system in an embodiment;

[0040] Figure 3 It is a schematic flowchart of the step of obtaining the unit commitment optimization result of the power system in an embodiment;

[0041] Figure 4 It is a schematic flowchart of the method for processing the security-constrained unit commitment problem based on time decoupling in another embodiment;

[0042] Figure 5 It is a schematic diagram of continuous sub-time periods determined in an embodiment;

[0043] Figure 6 It is a structural block diagram of the device for processing the security-constrained unit commitment problem based on time decoupling in an embodiment;

[0044] Figure 7 It is the internal structure diagram of a computer device in an embodiment. Specific implementation manners

[0045] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0046] In one embodiment, as Figure 1 shown, a method for processing a security-constrained unit commitment problem based on time decoupling is provided. In this embodiment, the method is exemplified by being applied to a server. It can be understood that the method can also be applied to a terminal, and can also be applied to a system including a terminal and a server, and is realized through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0047] Step S102, determine multiple target coupling time points in the time period to be optimized of the power system through a pre-constructed comprehensive evaluation model.

[0048] Among them, the comprehensive evaluation model refers to a mathematical model constructed by considering multiple factors and used for decision-making of target coupling time points; the time period to be optimized refers to a specific time range that needs to be optimized in the power system. The power generation and power consumption demands within this time period may change, so unit commitment and scheduling need to be optimized. The time period to be optimized can be a time period of any length; the target coupling time point refers to a specific moment within the time period to be optimized. The target coupling time point is a key node for realizing the optimization of the power system and can also be called a weak coupling time point.

[0049] Step S104, divide the time period to be optimized into multiple sub-time periods based on the multiple target coupling time points.

[0050] Among them, the sub-time period refers to dividing the time period to be optimized into multiple smaller time periods according to the target coupling time points, so as to perform more accurate and efficient optimization analysis.

[0051] As an example, if the time period to be optimized is 24 hours, the 24 hours can be divided into 24 one-hour time periods according to the target coupling time points.

[0052] Step S106, decompose the security-constrained unit commitment problem with a long time scale to obtain a sub-problem optimization model corresponding to each sub-time period.

[0053] Among them, Security-Constrained Unit Commitment (SCUC) refers to the conditions and restrictions that need to be followed to ensure the security and stability of the power system during operation; the unit commitment problem refers to the problem of how to select and schedule generating units to meet power demand and follow relevant constraints within a specific time period; the sub-problem optimization model can be a mathematical model.

[0054] As an example, security-constrained unit commitment can include power load, generation capacity, fault handling, etc.

[0055] Step S108: Solve according to each sub-problem optimization model to obtain the unit commitment optimization result of the power system.

[0056] Among them, the optimization result refers to obtaining the optimal unit commitment plan and unit output plan for the overall SCUC problem.

[0057] In the above method for dealing with the security-constrained unit commitment problem based on time decoupling, first, determine multiple target coupling time points in the to-be-optimized time period of the power system through a pre-constructed comprehensive evaluation model; then, based on the multiple target coupling time points, divide the to-be-optimized time period into multiple sub-time periods; next, decompose the security-constrained unit commitment problem with a long time scale to obtain sub-problem optimization models corresponding to each sub-time period; finally, solve according to each sub-problem optimization model to obtain the unit commitment optimization result of the power system. In the above process, the pre-established comprehensive evaluation model provides a scientific basis for the decision-making result, improves the overall operation efficiency of the power system, and simplifies the calculation process by decomposing the complex problem with a long time scale into multiple sub-problems, enabling the optimization algorithm to find a feasible solution faster. Therefore, through clear security constraints and optimization models, it is ensured that the power system can stably and safely meet the load demand during operation, thereby improving the constraint calculation efficiency for unit commitment in a large-scale power system.

[0058] In an exemplary embodiment, as Figure 2 shown, step S102 determining multiple target coupling time points in the to-be-optimized time period of the power system through a pre-constructed comprehensive evaluation model includes steps S202 to S206, where:

[0059] Step S202: Obtain the index values of each time point in the time period to be optimized under multiple coupling strength indices and the weight coefficients of each coupling strength index; the coupling strength indices include load change rate, number of unit state switches, renewable energy volatility, and ramping demand pressure; Step S204: Through the weight coefficients of each coupling strength index, perform weighted fusion processing on the index values of each coupling strength index to obtain the coupling strength score of each time point; Step S206: Screen out the time points with coupling strength scores less than the threshold from each time point as the target coupling time points.

[0060] Among them, the load change rate refers to the change amplitude of the power load within a specific time period; the number of unit state switches refers to the number of start-stop times of the generator set during the optimization process; the renewable energy volatility refers to the power generation fluctuation caused by renewable energies such as wind energy and solar energy; the ramping demand pressure refers to the ability of the system to respond quickly when the load demand rises rapidly; the weighted fusion processing refers to the process of obtaining a comprehensive score by multiplying the index value of each coupling strength index by the corresponding weight coefficient and summing them; the threshold refers to a pre-set score boundary used to classify the coupling strength scores, and the time points below this threshold are regarded as target coupling time points.

[0061] In this embodiment, through the comprehensive scoring of factors such as load change, unit state switch, and renewable energy fluctuation, the optimization process can identify which time points have a low coupling strength, that is, obtain the target coupling time points. These time points usually correspond to a relatively small power system pressure. Optimization adjustments are made at these moments, which can improve the stability of the power system and reduce power supply interruptions caused by load fluctuations.

[0062] Further, in one embodiment, according to each sub-problem optimization model, the unit commitment optimization result of the power system is solved, including:

[0063] Set the consistency constraint conditions between adjacent sub-time periods; the consistency constraint conditions include the consistency constraints of multiple pairs of shared coupling variables; apply the consistency constraint conditions of each pair of shared coupling variables to each sub-problem optimization model to solve the unit commitment optimization result of the power system.

[0064] Among them, adjacent sub - time periods refer to consecutive time periods in a time series, such as hours, days, or weeks. In power system optimization, the load demand of a day is usually divided into multiple hours or smaller time periods for analysis; the consistency constraint condition refers to the constraint condition that ensures the decisions and variables within adjacent sub - time periods are consistent, avoiding resource waste or power supply instability caused by inconsistent decisions between time periods; the shared coupling variables refer to variables shared among multiple sub - time periods, such as the output of generating units, load demand, and the power generation of renewable energy sources; the consistency constraint refers to the constraint condition set for each pair of shared coupling variables to ensure that the values or state changes of these variables are coordinated within adjacent time periods; the sub - problem optimization model means that the optimization problem of each sub - time period can be regarded as an independent sub - problem, usually considering the load demand, generating unit status, and other constraint conditions within that time period.

[0065] In this embodiment, by setting the consistency constraint conditions between adjacent sub - time periods, not only can the scheduling efficiency be improved, avoiding resource waste caused by inconsistent decisions between adjacent time periods, but also the power supply stability can be guaranteed, ensuring a smooth change in generating capacity between different time periods and reducing the risk of power supply interruption.

[0066] In one embodiment, the shared coupling variables include unit output variables, unit status variables, and auxiliary counting variables. The unit output variable represents the output of the unit at a preset moment in the sub - problem; the unit status variable represents the status of the unit at a preset moment in the sub - problem; the auxiliary counting variable is used to record the start - stop time of the generator within the coupling time interval. The method further includes: aiming at each pair of shared coupling variables reaching the same value, iteratively solving each sub - problem optimization model to obtain the unit commitment optimization result of the power system.

[0067] Among them, the unit status variable can include running or shutdown; iterative solution means that in the optimization process, by iteratively adjusting the values of the shared coupling variables of each sub - problem multiple times, after each iteration, re - evaluating and optimizing the sub - problem according to the new variable values until the consistency constraint conditions are met.

[0068] In this embodiment, by optimizing the unit output and status, the generation cost can be reduced and the overall economic benefit can be improved; by recording with the auxiliary counting variable, analyzing the start - stop frequency of the unit, thereby optimizing the scheduling strategy of the unit and improving the resource utilization rate.

[0069] In one embodiment, as Figure 3 shown, aiming at each pair of shared coupling variables reaching the same value, iteratively solving each sub - problem optimization model to obtain the unit commitment optimization result of the power system, including:

[0070] Step S302: Based on the accelerated analysis objective cascading algorithm, obtain multiple shared coupling variables and first consistency constraints corresponding to multiple sub-problems respectively; Step S304: Input the multiple shared coupling variables into the master problem of the power system to obtain multiple auxiliary variables, and determine the second consistency constraints of the multiple sub-problems based on the multiple shared coupling variables and the multiple auxiliary variables; Step S306: When the difference between the second consistency constraint and the first consistency constraint is less than a preset value, obtain the unit commitment optimization result of the power system.

[0071] Among them, the accelerated analysis objective cascading algorithm refers to an algorithm for solving complex optimization problems. By gradually analyzing and optimizing each objective, it quickly converges to the optimal solution; the first consistency constraint refers to the initial constraint set in the sub-problem to ensure the consistency of the shared coupling variables between adjacent sub-time periods, that is, it requires the unit output and status in different time periods to reach a certain consistency standard; the master problem refers to the problem corresponding to the time period to be optimized; the second consistency constraint refers to the additional constraint formed based on the multiple shared coupling variables and auxiliary variables.

[0072] As an example, based on the accelerated analysis objective cascading algorithm, the consistency problem between sub-problems is solved by adjusting the deviation between sub-problems; specifically, the SCUC master problem is the first level, and the divided sub-problems are the second level. In collaborative optimization, when each sub-problem is optimized, the connection between sub-problems at the same level is not considered temporarily and is optimized independently. The optimization goal is to minimize the difference between the optimization result of the sub-problem and the goal provided by the upper-level optimization. After collecting the optimization results of each sub-problem, the inconsistency of the optimization results of each sub-problem is coordinated by the upper-level optimization.

[0073] In this embodiment, through the application of the accelerated analysis objective cascading algorithm, the solution speed is improved, making the overall optimization of processing multiple sub-problems more efficient; by setting multiple consistency constraints, each sub-problem of the power system can maintain good coordination, ensuring the consistency of the dispatching decisions of the generating units in different time periods; according to the analysis of multiple shared coupling variables and auxiliary variables, a better resource allocation strategy can be formulated, thereby improving the overall economic benefits.

[0074] Furthermore, in one embodiment, determining the second consistency constraints of multiple sub-problems based on multiple shared coupling variables and multiple auxiliary variables includes:

[0075] Respectively obtain the differences between multiple shared coupling variables and multiple auxiliary variables, and determine the differences as the second consistency constraints of the multiple sub-problems.

[0076] Among them, the second consistency constraint refers to the constraint condition set based on the calculated differences to ensure that the differences between relevant shared coupling variables and auxiliary variables in multiple sub-problems remain within an acceptable range.

[0077] In this embodiment, by ensuring the consistency of shared coupling variables and auxiliary variables, the decision-making differences between different sub-problems can be reduced during the optimization process, improving the overall operating efficiency of the power system; the second consistency constraint helps to achieve a common optimization goal on the premise of meeting economy and reliability, thereby improving the overall effect of unit commitment optimization in the power system.

[0078] In an exemplary embodiment, when the difference between the second consistency constraint and the first consistency constraint is greater than or equal to a preset value, the second consistency constraint of multiple sub-problems is re-determined.

[0079] Among them, the preset value refers to a threshold set during the optimization process, which is used to judge the acceptable range of the difference between the first and second consistency constraints; if the difference is greater than or equal to the preset value, it means that the current constraint conditions fail to effectively coordinate the decisions of each sub-problem, which may lead to unexpected operating results or low efficiency.

[0080] In this embodiment, by dynamically updating the second consistency constraint, it helps to reduce possible decision-making conflicts between different sub-problems and ensure the stable operation of the power system.

[0081] This application provides a method for processing security-constrained unit commitment problems based on time decoupling. To better understand the process of the above method for processing security-constrained unit commitment problems based on time decoupling, in combination with Figure 4 As shown below, the specific process of a method for processing security-constrained unit commitment problems based on time decoupling in this application is elaborated in detail, including the following steps:

[0082] Step S402, dividing time into multiple sub-time periods by using weakly coupled time points (target coupling time points).

[0083] Among them, a comprehensive evaluation model of weakly coupled time points is proposed. By comprehensively evaluating multiple factors to identify weakly coupled time points, the master problem is decomposed at the weakly coupled time points, significantly reducing the coupling degree between sub-problems, thereby improving the convergence speed and calculation efficiency of the Lagrangian relaxation method.

[0084] As an example, the overall scheduling time period is decomposed into multiple smaller sub-time periods, and an SCUC sub-problem is formulated for each sub-time period. The time point coupling strength scoring function of the comprehensive evaluation model of weakly coupled time points is:

[0085]

[0086] Among them, represents the load change rate, represents the number of unit state switches, Represents the volatility of renewable energy, Represents the ramp demand pressure, To Are the weight coefficients of each factor.

[0087] The criterion for identifying the weakly coupled time point is that when The time point t is identified as a "weakly coupled time point". Where Is a threshold value, which can be determined in the following way:

[0088]

[0089] Where Is the safety factor, taking values from 0.8 to 1.2, Is the percentile, taking 10%, indicating the calculation of the coupling strength scores for all time points (t = 1 to T) Of Percentile value, that is, arranging all time points of In ascending order and taking the value at the βth percentile.

[0090] Step S404, relax the coupling constraints of the security-constrained unit commitment problem, and construct security-constrained unit commitment sub-problems based on different sub-time periods.

[0091] Among them, the unit ramp rate constraint and the minimum up / down time constraint of the unit in the SCUC problem are defined as two types of time coupling constraints. The variables to be solved in the time coupling constraints are time coupling variables, using the subscript To distinguish the time coupling variables for the modeling transition between sub-problems. The time coupling variable represented by the subscript Is the decision variable obtained by solving the left sub-problem.

[0092] As an example, Is the time coupling variable Between sub-problem And sub-problem , The value of Depends on sub-problem And is obtained by optimizing and solving in

[0093] As another example, if the overall scheduling time period (the time period to be optimized) is T, taking the decomposition of the centralized long-time scale SCUC problem into two sub-problems as an example, then the two consecutive sub-time periods And The corresponding sub-problems , The optimization model of Assign the first interval to the SCUC sub-problem Of sub-time period​

[0094]

[0095]

[0096] Among them, is the variable to be solved corresponding to time points from 1 to t1-1, is the active power generated by unit u at time point t, is the power and electricity balance constraint, represents the upper or lower limit of unit output, line capacity constraint, and the internal unit ramp rate constraint and unit minimum start-stop time constraint within the time period.

[0097] Similarly, for the sub-time period the SCUC sub-problem is defined as:

[0098]

[0099]

[0100] Among them, is the variable corresponding to time points from t1 to T. Since the sub-problems and are smaller than the original centralized long-time scale SCUC problem, the calculation speed is fast.

[0101] In addition, the sub-problems and only consider the time coupling constraints within their respective time periods ( and ), and do not consider the time coupling constraints between the sub-problems and ( ). Therefore, it is necessary to further process and consider the time coupling constraints between sub-problems within the sub-problems.

[0102] Step S406, k = 1, initialize the Lagrange multiplier and penalty factor.

[0103] Among them, the Lagrange multiplier is represented by λ, and the penalty factor is represented by ρ.

[0104] Step S408, solve the k-th security-constrained unit commitment sub-problem in parallel.

[0105] Among them, based on the Lagrangian relaxation time decoupling method, the time coupling relationship between the divided continuous sub-problems is converted into a set of shared coupling variables, and at the same time, consistency constraints are proposed to ensure that from the perspective of the two sub-problems, each pair of shared coupling variables can reach the same value.

[0106] As an example, the augmented Lagrangian relaxation method is adopted, and the consistency constraint penalty is taken as part of the sub-problem objective function. During the iteration process, the difference between each pair of shared coupling variables gradually approaches 0. The coupling variables introduced in the sub-problem can include: the shared generator output variable : the generator in the sub-problem at time. This variable is optimized and solved in the sub-problem and the result is sent to the sub-problem ; the shared generator status variable : the generator in the sub-problem at time. 0 represents shutdown and 1 represents startup. This variable is optimized and solved in the sub-problem and the result is sent to the sub-problem ; the auxiliary counting variables and are used to record the startup and shutdown times of the generator during the coupling time interval. The corresponding logical expressions are as follows:

[0107]

[0108] where is the request to keep the generator running for hours, is the request to shut down the generator for hours, is the minimum on-time of the generator . is the minimum shutdown time of the generator , is the auxiliary variable used to calculate , is the auxiliary variable used to calculate .

[0109] Moreover, the shared coupling variables introduced in the sub-problems and must reach the same value during the iteration process. Otherwise, from the perspective of the entire scheduling period, the obtained solution is infeasible. For example, the output of the generator at time is solved in , while the output of the generator At the output at time , for the overall SCUC problem the generating unit at the output at time cannot be both equal to 100 and 50. To ensure and the consistency between

[0110]

[0111] where CC represents consistency constraints. The first equation can ensure the consistency of the output of the generating unit in at the output at time in sub - problem and the output of the generating unit at the output at time in sub - problem so that in sub - problem it can be ensured that the ramping constraint ( and ) between sub - problems is satisfied through and The second to fourth equations are used to ensure the minimum up - down time constraint between sub - problems

[0112] By introducing an auxiliary counting variable to record the up - down time of the generator within the boundary interval and using logical expressions to ensure the satisfaction of the minimum up - down time constraint, this auxiliary variable helps to track the up - down time of the generating unit and ensure that its minimum up - down time requirement is met in adjacent time intervals.

[0113] As an example, if a generating unit is started within a certain time period, the auxiliary variable can count and indicate that the unit cannot be stopped again in the following several time periods. By introducing coupling variables, between adjacent sub - time periods, the start - stop sequence and time of the generating unit can be controlled to avoid violating the minimum up - down time constraint due to too fast state switching, and it can also ensure that the generating unit follows the ramping limit during start - up and shut - down and gradually adjusts the output power.

[0114] Step S410: Based on the accelerated objective cascading algorithm, improve the convergence speed.

[0115] Among them, the Accelerated Analytical Target Cascading (A-ATC) algorithm proposed for coordinating SCUC sub-problems is essentially an acceleration technique and initialization strategy to enhance the convergence performance of ATC, considering the divided sub-problems. And , the shared coupling variables can be represented in vector format. For example, of , using the parallel ATC coordination sub-problem based on augmented Lagrangian relaxation, the SCUC sub-problem is at the second level, and the coordinator serves as the SCUC master problem at the first level. The shared coupling variables received from each sub-problem at each iteration are copied to obtain a set of auxiliary variables . is the consistency constraint between the coordinator and the sub-problems, and violating the consistency constraint will be penalized. At the iteration of the relaxed sub-problem is:

[0116]

[0117] Wherein, is the variable set belonging to sub-problem at the th iteration, is the sub-problem at the th iteration, and the ~ shared variables calculated between is the active power generated by unit at time, is the start-stop state of unit at time, and are the Lagrange multiplier and penalty multiplier respectively. The superscript is the transpose operator, is the target of the shared coupling variable at the (k - 1)th iteration sent by the coordinator to , is the sub-problem in response to calculated by the sub-problem and shared variables between.

[0118] And, P1 can be expressed as:

[0119]

[0120] Wherein, a key task of the Lagrangian relaxation method is to obtain the Lagrange multiplier , considering using the proximal bundle method to obtain the best Lagrangian dual bound. The Lagrangian dual function can be approximated from the outside by adding a set of linear inequalities and having a proximal term in the objective function. The coordinator receives and solves the following problem to determine the shared coupling variable objective for the iteration:

[0121]

[0122] For the penalty term with λ being a positive number, the Lagrange multiplier is updated as follows:

[0123]

[0124] For the penalty term with λ being a negative number, the Lagrange multiplier is updated as follows:

[0125]

[0126] The convergence condition is:

[0127]

[0128] Furthermore, the above formula can be generalized to multiple sub-problems. λ and ρ need to be initialized within an acceptable range. If ρ is chosen too large, the convergence speed may increase. Choosing a smaller ρ can improve the solution accuracy, but the convergence speed will also decrease. Therefore, it is necessary to solve the problem multiple times and use the effective historical information obtained from past practices of distributed SCUC to obtain the appropriate value ranges of λ and ρ.

[0129] Step S412, update the shared coupling variable objective and the Lagrange multiplier.

[0130] Step S414, output the optimal unit commitment plan and unit output plan for the security-constrained unit commitment problem.

[0131] where the convergence condition is ;

[0132] Moreover, in one embodiment, the divided time periods are as Figure 5 shown. According to the weak coupling time point identification criterion, k weak coupling time points are identified. If the set of identified weak coupling time points is , the original SCUC problem can be decomposed into K + 1 sub-problems ~ and the corresponding time periods:

[0133]

[0134] Furthermore, in one embodiment, a weakly coupled time point comprehensive evaluation model is proposed. By comprehensively considering various indicators such as load changes, unit state switching, renewable energy fluctuations, and ramping requirements, the weighted scoring method is used to mark key time periods. If the comprehensive score of a certain time point is lower than a certain threshold, it is marked as a weakly coupled time point. Based on the marked weakly coupled time points, the long-time scale SCUC problem is disconnected from these time points and divided into multiple time periods. The load fluctuations in the time periods before and after the weakly coupled time points are small, and the output fluctuations of the units to meet the load demand are small, so there is a greater probability of meeting the unit start-stop constraints and ramping constraints. Therefore, such a division can ensure that the coupling constraints between subsequent sub-problems are satisfied as much as possible and reduce the number of iterations. Using the divided time periods, a time decoupling method based on Lagrangian relaxation is proposed to decouple the ramping constraint and the minimum start-stop time constraint in the long-time scale SCUC problem. The minimum start-stop time constraint is added with an auxiliary counting variable for auxiliary calculation, and the ramping constraint and the minimum start-stop time constraint together constitute the consistency constraint. Finally, for the solution and coordination of sub-problems, an accelerated analytical target cascading algorithm is used to coordinate the SCUC sub-problems to enhance the convergence performance of the algorithm and achieve faster convergence of the SCUC problem to the optimal value.

[0135] Among them, the start-stop constraint means that the unit cannot stop immediately after starting and cannot start immediately after stopping; the ramping constraint means that the output of the unit cannot change rapidly and significantly. If the load fluctuations and renewable energy fluctuations are very small during this time period, then the output of the unit will not change violently. Therefore, it can ensure that the time coupling constraint does not exceed the limit as much as possible and does not require repeated iterative solutions, so the number of iterations can be reduced.

[0136] Through the above embodiments, a new time decoupling strategy is proposed, which decomposes the long-time span optimization problem into multiple short-time span sub-problems, significantly reducing the computational complexity. By introducing the coupled time interval and the auxiliary counting variable, the ramping limit and the minimum start-stop time constraint of the unit between adjacent sub-time spans are effectively handled. Moreover, by using an accelerated target cascading algorithm (A-ATC) suitable for the time decoupled SCUC model and introducing the momentum term and the prediction correction step, the convergence speed of the algorithm is significantly improved. The A-ATC algorithm can greatly reduce the number of iterations while ensuring the optimization accuracy and is applicable to the distributed optimization of large-scale power systems.

[0137] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are sequentially shown according to the indications of the arrows, these steps are not necessarily executed sequentially in the order indicated by the arrows. Unless there is a clear indication in this article, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0138] Based on the same inventive concept, an embodiment of the present application also provides a device for processing a security-constrained unit commitment problem based on time decoupling for implementing the above-mentioned security-constrained unit commitment problem processing method based on time decoupling. The implementation solution provided by this device to solve the problem is similar to the implementation solution described in the above method. Therefore, the specific limitations in one or more embodiments of the device for processing a security-constrained unit commitment problem based on time decoupling provided below can refer to the limitations on the security-constrained unit commitment problem processing method based on time decoupling in the above text, and will not be repeated here.

[0139] In an exemplary embodiment, as Figure 6 shown, a device for processing a security-constrained unit commitment problem based on time decoupling is provided, including: a time determination module 601, a time division module 602, a problem decomposition module 603, and a result acquisition module 604, where:

[0140] The time determination module 601 is configured to determine a plurality of target coupling time points in the time period to be optimized of the power system through a pre-constructed comprehensive evaluation model.

[0141] The time division module 602 is configured to divide the time period to be optimized into a plurality of sub-time periods based on the plurality of target coupling time points.

[0142] The problem decomposition module 603 is configured to decompose the long-term security-constrained unit commitment problem to obtain a sub-problem optimization model corresponding to each sub-time period.

[0143] The result acquisition module 604 is configured to solve and obtain the unit commitment optimization result of the power system according to each sub-problem optimization model.

[0144] Further, in one embodiment, the time determination module 601 is further configured to obtain the index values of each time point in the time period to be optimized under multiple coupling strength indexes and the weight coefficients of each coupling strength index; the coupling strength indexes include load change rate, number of unit state switches, renewable energy volatility, and ramp demand pressure; perform weighted fusion processing on the index values of each coupling strength index through the weight coefficients of each coupling strength index to obtain the coupling strength score of each time point; screen out the time points with a coupling strength score less than the threshold from each time point as the target coupling time points.

[0145] Further, in one embodiment, the result acquisition module 604 is further configured to set the consistency constraint conditions between adjacent sub-time periods; the consistency constraint conditions include the consistency constraints of multiple pairs of shared coupling variables; apply the consistency constraint conditions of each pair of shared coupling variables to each sub-problem optimization model, and solve to obtain the unit commitment optimization result of the power system.

[0146] Further, in one embodiment, the result acquisition module 604 is further configured to iteratively solve each sub-problem optimization model with the goal that each pair of shared coupling variables reaches the same value to obtain the unit commitment optimization result of the power system.

[0147] Further, in one embodiment, the result acquisition module 604 is further configured to respectively obtain multiple shared coupling variables and the first consistency constraints corresponding to multiple sub-problems based on the accelerated analysis target cascading algorithm; input the multiple shared coupling variables into the master problem of the power system to obtain multiple auxiliary variables, and determine the second consistency constraints of the multiple sub-problems based on the multiple shared coupling variables and the multiple auxiliary variables; when the difference between the second consistency constraint and the first consistency constraint is less than a preset value, obtain the unit commitment optimization result of the power system.

[0148] Further, in one embodiment, the result acquisition module 604 is further configured to respectively obtain the differences between the multiple shared coupling variables and the multiple auxiliary variables, and determine the differences as the second consistency constraints of the multiple sub-problems.

[0149] Further, in one embodiment, the result acquisition module 604 is further configured to re-determine the second consistency constraints of the multiple sub-problems when the difference between the second consistency constraint and the first consistency constraint is greater than or equal to the preset value.

[0150] Each module in the above device for processing the security-constrained unit commitment problem based on time decoupling can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor of the computer device in hardware form or be independent of it, or be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0151] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structural diagram may be as shown in Figure 7 the figure. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data for processing the security-constrained unit commitment problem based on time decoupling. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it implements a method for processing the security-constrained unit commitment problem based on time decoupling.

[0152] Those skilled in the art can understand that Figure 7 the structure shown in is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0153] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0154] In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0155] In an embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0156] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0157] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAM), magnetoresistive random access memories (MRAM), ferroelectric random access memories (FRAM), phase change memories (PCM), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in this application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.

[0158] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0159] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A method for processing safety-constrained unit commitment problems based on time decoupling, characterized in that: The method comprises: Through the pre-built comprehensive evaluation model, multiple target coupling time points in the time period to be optimized of the power system are determined; Based on the multiple target coupling time points, dividing the time period to be optimized into multiple sub-time periods; Decomposing the long-time-scale safety-constrained unit commitment problem to obtain sub-problem optimization models corresponding to each of the sub-time periods; According to the optimization models of each of the sub-problems, the unit combination optimization result of the power system is solved and obtained.

2. The method according to claim 1, characterized in that The method of determining multiple target coupling time points in the time period to be optimized of the power system through the pre-built comprehensive evaluation model includes: Obtaining the index values ​​of multiple coupling strength indicators and the weight coefficients of each coupling strength indicator at each time point in the time period to be optimized; the coupling strength indicators include load change rate, number of unit state switching, renewable energy fluctuation rate and climbing demand pressure; By using the weight coefficients of the coupling strength indicators, weighted fusion processing is performed on the indicator values ​​of the coupling strength indicators to obtain the coupling strength score at each time point; The time points whose coupling strength scores are less than a threshold value are screened out from the various time points as the target coupling time points.

3. The method according to claim 1, characterized in that The optimizing model of each sub-problem is solved to obtain the unit combination optimization result of the power system, including: Setting consistency constraints between adjacent sub-time periods; the consistency constraints include consistency constraints on multiple pairs of shared coupling variables; The consistency constraints of each pair of the shared coupling variables are applied to each of the sub-problem optimization models to obtain the unit commitment optimization result of the power system.

4. The method according to claim 3, characterized in that The shared coupling variables include a unit output variable, a unit state variable and an auxiliary count variable, wherein the unit output variable represents the output of the unit in the sub-problem at a preset time; the unit state variable represents the state of the unit in the sub-problem at the preset time; The auxiliary counting variable is used to record the start and stop time of the generator within the coupling time interval; The method further comprises: With the goal of achieving the same value for each pair of the shared coupling variables, each sub-problem optimization model is iteratively solved to obtain the unit commitment optimization result of the power system.

5. The method according to claim 4, characterized in that The step of iteratively solving each sub-problem optimization model with the goal of achieving the same value for each pair of shared coupling variables to obtain the unit commitment optimization result of the power system includes: Based on the accelerated analysis target cascade algorithm, a plurality of shared coupling variables and a first consistency constraint corresponding to the plurality of sub-problems are respectively obtained; Inputting the plurality of shared coupling variables into the main problem of the power system to obtain a plurality of auxiliary variables, and determining the second consistency constraints of the plurality of sub-problems based on the plurality of shared coupling variables and the plurality of auxiliary variables; When the difference between the second consistency constraint and the first consistency constraint is less than a preset value, a unit commitment optimization result of the power system is obtained.

6. The method according to claim 5, characterized in that The determining, based on the plurality of shared coupling variables and the plurality of auxiliary variables, second consistency constraints of the plurality of sub-problems comprises: The difference values ​​between the multiple shared coupling variables and the multiple auxiliary variables are respectively obtained, and the difference values ​​are determined as the second consistency constraints of the multiple sub-problems.

7. The method according to claim 5, characterized in that The method further comprises: When the difference between the second consistency constraint and the first consistency constraint is greater than or equal to a preset value, the second consistency constraints of the plurality of sub-problems are re-determined.

8. A device for processing safety constraint unit commitment problems based on time decoupling, characterized in that: The device comprises: A time determination module, used to determine multiple target coupling time points in a time period to be optimized of the power system through a pre-built comprehensive evaluation model; A time division module, used for dividing the time period to be optimized into a plurality of sub-time periods based on a plurality of target coupling time points; A problem decomposition module is used to decompose the long-time-scale safety-constrained unit commitment problem to obtain a sub-problem optimization model corresponding to each of the sub-time periods; The result acquisition module is used to solve the unit combination optimization result of the power system according to the optimization models of each sub-problem.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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