A method for optimizing unit commitment in power system based on variable-scale time period aggregation

Abstract

The present invention discloses a method for optimizing the unit combination of a power system based on variable-scale time period aggregation, the method comprising: constructing a variable-scale time period aggregation optimization model and solving a variable-scale time period sequence, generating a variable-scale time period sequence by aggregation, obtaining basic data of economic operation of the power system, constructing a power system unit combination model suitable for a variable-scale time period, and calculating a variable-scale time period unit combination result, solving the power system unit combination of the variable-scale time period, expanding the variable-scale time period unit combination solution and repairing the original unit combination solution, so as to perform variable-scale time period unit combination post-processing. The present invention aggregates the calculation time period according to the data characteristics of the net load of the power system, reduces the number of 0-1 variables of the mixed integer linear programming problem, thereby effectively improving the calculation efficiency of the power system unit combination problem, and is conducive to improving the ability of the power system to cope with the challenges of the increase in the number of subjects, the expansion of the network scale, and the improvement of the time resolution in the optimized operation.

Description

Technical Field

[0001] The invention relates to a method and a device for optimizing the unit combination of an electric power system based on variable-scale time period aggregation, and belongs to the technical field of optimized operation of electric power systems. Background Art

[0002] The power system unit combination model can be solved according to different optimization objectives to obtain the start and stop status of each unit under various constraints, which is an important technical basis for the optimized operation of the power system. With the steady advancement of the construction of my country's new power system, the continuous increase in the penetration rate of fluctuating renewable energy, the gradual access of distributed power sources, and the further enhancement of source-load interaction will bring challenges such as an increase in the number of entities, an expansion of the network scale, and an improvement in time resolution to the optimized operation of the power system, resulting in an increase in the scale of the power system unit combination problem and a significant increase in the difficulty of solving it. In this context, how to reduce the calculation time of the power system unit combination while ensuring the feasibility and economy of the decision has become a practical problem that urgently needs to be solved for the optimized operation of the power system.

[0003] The unit combination of power system is usually modeled as a mixed integer programming problem, which is solved by methods such as branch and bound method, and has a high solution complexity. In order to reduce the solution time, some literatures apply heuristic methods to mixed integer programming from the perspective of optimizing the solution method to reduce the search scale. Some studies use the relaxed neighborhood search method to search for better feasible solutions in the neighborhood of the optimal solution of the relaxed linear programming, which effectively reduces the search space. Some studies add induction terms to the objective function to guide the integer variable values ​​to be close to the rounded values ​​of the optimal solution of the relaxed linear programming, significantly reducing the number of branch and bound. In terms of unit combination modeling, some studies start from the unit dimension and aggregate the generator units according to their parameter characteristics, thereby reducing the number of integer variables in the unit combination problem, effectively reducing the problem scale and reducing the calculation time. From the time dimension, some studies cluster the calculation time periods according to the characteristics of the boundary condition data, which also reduces the number of integer variables in the unit combination problem, but the clustering standard does not consider the unit operation constraint characteristics, and it is difficult to guarantee the feasibility of the results.

[0004] The existing research on unit commitment acceleration does not explore the multi-period aggregation in the time dimension sufficiently and carefully, and there is no literature that combines the operating characteristics of generator units with the characteristics of boundary condition data in multi-period aggregation. Summary of the invention

[0005] The purpose of the present invention is to propose a method for optimizing the unit combination of a power system based on variable-scale time period aggregation, which aggregates the calculation time periods according to the data characteristics of the net load of the power system, reduces the number of 0-1 variables in the mixed integer linear programming problem, and thus effectively improves the computational efficiency of the power system unit combination problem.

[0006] Another object of the present invention is to propose a power system unit combination optimization device based on variable-scale time period aggregation.

[0007] The power system unit commitment optimization method based on variable-scale time period aggregation proposed in the present invention comprises the following steps:

[0008] S1, constructing a variable scale period aggregation optimization model and solving the variable scale period sequence to aggregate and generate the variable scale period sequence;

[0009] S2, obtaining basic data of economic operation of the power system, constructing a power system unit combination model suitable for the variable scale period, and calculating the unit combination results of the variable scale period, so as to solve the power system unit combination of the variable scale period;

[0010] S3, expand the unit combination solution in the variable scale period and repair the original unit combination solution to perform post-processing of the unit combination in the variable scale period.

[0011] The power system unit combination optimization method based on variable-scale time period aggregation proposed in the present invention designs an aggregation target according to the normalized regulation capability of the units within the variable-scale time period, aggregates the calculation time period according to the data characteristics of the net load of the power system, thereby reducing the number of 0-1 variables in the mixed integer linear programming problem, and uses the repair optimization method to ensure the feasibility of the result, which is beneficial to improving the calculation efficiency of the power system unit combination while ensuring feasibility and economy, and enhancing the ability of the power system optimization operation to cope with challenges such as the increase in the number of entities, the expansion of the network scale, and the improvement of the time resolution.

[0012] To achieve the above object, the present invention proposes, on the other hand, a device for optimizing the unit combination of a power system based on variable-scale time period aggregation, comprising:

[0013] Aggregation module, used to construct variable scale period aggregation optimization model and solve variable scale period sequence to aggregate and generate variable scale period sequence;

[0014] A solution module is used to obtain basic data of economic operation of the power system, construct a power system unit combination model suitable for variable-scale periods, and calculate the unit combination results of the variable-scale periods to solve the power system unit combination of the variable-scale periods;

[0015] The expansion and repair module is used to expand the unit combination solution in the variable scale period and repair the original unit combination solution to perform post-processing of the unit combination in the variable scale period.

[0016] The power system unit combination optimization device based on variable-scale time period aggregation of the embodiment of the present invention is conducive to improving the calculation efficiency of the power system unit combination while ensuring feasibility and economy, and enhancing the ability of the power system optimization operation to cope with challenges such as the increase in the number of entities, the expansion of the network scale, and the improvement of time resolution.

[0017] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0019] Figure 1 A flow chart of a method for optimizing the unit commitment of a power system based on variable-scale time period aggregation according to an embodiment of the present invention;

[0020] Figure 2 It is a framework diagram of a power system unit combination optimization method based on variable-scale time period aggregation according to an embodiment of the present invention;

[0021] Figure 3 Schematic diagram of the structure of a power system unit combination optimization device based on variable-scale time period aggregation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0023] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0024] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0025] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0026] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may include that the first and second features are in direct contact, or may include that the first and second features are not in direct contact but are in contact through another feature between them. Moreover, a first feature being "above", "above" and "above" a second feature includes that the first feature is directly above and obliquely above the second feature, or simply indicates that the first feature is higher in level than the second feature. A first feature being "below", "below" and "below" a second feature includes that the first feature is directly above and obliquely above the second feature, or simply indicates that the first feature is lower in level than the second feature.

[0027] The following describes a method for optimizing power system unit combination based on variable-scale time period aggregation according to an embodiment of the present invention with reference to the accompanying drawings.

[0028] Figure 1 It is a flow chart of a method for optimizing power system unit combination based on variable-scale time period aggregation according to an embodiment of the present invention.

[0029] like Figure 1 As shown, the method includes but is not limited to the following steps:

[0030] S1, construct a variable-scale period aggregation optimization model and solve the variable-scale period sequence to aggregate and generate the variable-scale period sequence.

[0031] Specifically, the aggregation generates the variable-scale time period sequence, including two steps: S1.1 constructing the variable-scale time period aggregation optimization model and S1.2 solving the variable-scale time period sequence;

[0032] S1.1, build a variable-scale time period aggregation optimization model:

[0033] S1.11, establish the time period aggregation constraint condition, the expression is as follows:

[0034]

[0035] 1=t1 <t2<…<t N ≤T (2)

[0036] Where (1) is the integer constraint of the starting point of the time period; where N is the number of time periods after aggregation, t1, t2, …, t N is the starting point of each variable scale period in the original period sequence, and the value is an integer;

[0037] Formula (2) is the order constraint of the starting point of the time period, which stipulates that the i-th scaled time period is the original time period t i to i+1 -1(i=1,…N-1), the Nth scaled period is the original period t N to T; where T is the number of original time periods, T ≥ N;

[0038] S1.12, determine the time period aggregation objective function, the expression is as follows:

[0039]

[0040] Where (3) is the objective function, i.e., minimizing the difference between the maximum and minimum system net loads in all time periods divided by the sum of the maximum system net loads in the time period; where f(T,N) is the minimum objective function for optimally aggregating the original time periods 1 to T into N scaled time periods; D t is the net load value of the system in the original period t; let t N+1 -1 = T;

[0041] S1.2, solving the variable scale time period sequence; the present invention adopts dynamic programming as the solution method, the steps are:

[0042] S1.21, determine the state transition equation and boundary, the expression is as follows:

[0043]

[0044]

[0045]

[0046]

[0047]

[0048] Where (4) is the state transfer equation; where f(t,n) is the minimum objective function for optimally aggregating the original time period 1 to t into n scaled time periods, which is recursively calculated by the value function (8);

[0049] Formula (5) is the equation for the starting point of the variable scale period; where τ t,n The starting point of the last scaled period for optimally aggregating the original periods 1 to t into n scaled periods;

[0050] Formula (6) is the boundary state equation, which stipulates that when the number of variable-scale time periods is 1, the aggregate minimum objective function is the value of the value function (8) in the original time period 1 to t;

[0051] Formula (7) is the boundary starting point equation, which stipulates that the starting point of the first variable scale period is 1;

[0052] Formula (8) is the value function, which is defined as S is the starting point, t F The difference between the maximum and minimum net load values ​​of the system during the variable scale period of the end point is divided by the maximum net load value of the system during the period;

[0053] S1.22, recursively solve the optimal value of the objective function: starting from f(1,1), enumerate t=1,2,…T, enumerate n=1,2,…,min{t,N}, and solve f(T,N) and τ according to equations (4) to (8) t,n ;

[0054] S1.23, recursively solve the variable scale time series, the expression is as follows:

[0055] t N =τ T,N (9)

[0056]

[0057]

[0058] Where (9) is the recursive initial value of the starting point of the variable scale period, τ T,N is the starting point of the Nth variable scale period;

[0059] Formula (10) is the recursive equation for the starting point of the variable scale period. The starting point of the i-th variable scale period is the original period 1 to t i+1 -1 The optimal aggregation is the starting point of the last scaling period of i scaling periods;

[0060] Formula (11) is the variable scale time period length equation; where d i is the number of original time periods included in the scaled time period i.

[0061] Step S2, obtaining basic data of economic operation of the power system, constructing a power system unit combination model suitable for the variable scale period, and calculating the unit combination result of the variable scale period, so as to solve the power system unit combination of the variable scale period.

[0062] Specifically, solving the unit combination of the power system in the variable-scale period includes three steps: S2.1 obtaining the basic data of the economic operation of the power system, S2.2 constructing a unit combination model of the power system suitable for the variable-scale period, and S2.3 calculating the unit combination results of the variable-scale period.

[0063] S2.1 Obtain basic data on economic operation of power system:

[0064] The basic data of economic operation of power system include upper and lower limits of unit output, upper and lower limits of unit ramp rate, minimum continuous start and stop time of unit, unit operation cost function, unit startup cost, power grid power flow transfer distribution factor, line transmission capacity, and system positive and negative reserve rate;

[0065] S2.2, construct a power system unit combination model suitable for variable-scale time periods:

[0066] S2.21, establish the variable-scale period power system operation constraints based on DC power flow, the expression is as follows:

[0067]

[0068]

[0069]

[0070]

[0071]

[0072]

[0073]

[0074]

[0075]

[0076]

[0077]

[0078] Where (12) is the system power balance constraint; is the output variable of generator set j in variable scale period i, N G is the number of units; is the average net load of the system in the variable scale period i, and the calculation formula is as follows:

[0079] Formula (13) and Formula (14) are the positive and negative reserve constraints of the system respectively; is a 0-1 variable, indicating the start and stop status of generator set j in variable scale period i, 0 means shutdown and 1 means startup; P j are the upper and lower limits of the output of generator set j; r + 、r -are the positive and negative reserve rates of the system respectively; D (i) are the maximum and minimum values ​​of the net load of the system in the variable scale period i, respectively, and the calculation formulas are as shown in formula (24) and formula (25);

[0080] Formula (15) and Formula (16) are the system positive and negative regulation reserve relaxation constraints respectively; where RU j , RD j They are the upper limit of the output increase and decrease of generator set j within 1 hour, Δt is the original time interval in hours; and are the maximum increase and decrease of the net load of the system in the adjacent original time period within the variable scale time period i, and the calculation formulas are as shown in formula (26) and formula (27);

[0081] Formula (17) is the line power flow constraint; where F l is the transmission capacity of line l, N L is the number of lines; are the power flow transfer distribution factors of line l corresponding to generator unit j and node k respectively; is the average net load of node k in variable scale period i, calculated as formula (28), where is the net load value of node k in the original period t;

[0082]

[0083]

[0084]

[0085]

[0086]

[0087]

[0088] Formula (18) is the generator set output constraint;

[0089] Formula (19) and Formula (20) are the constraints on the increase and decrease output rate of each unit respectively; are the upper limits of the output increase and decrease of generator set j in variable scale period i, respectively, and the calculation formulas are as follows: (29) and (30); are the upper and lower limits of the starting output of generator set j in variable scale period i, respectively, and the calculation formulas are as follows: (31), (32), and (33); Generator set j is started in variable scale period i, and the output increases from the minimum value during the period P j To increase the output limit RU j Increase to maximum The required number of original time periods is calculated as in formula (34); is the output of generator set j from the maximum value in variable scale period i before it is shut down in variable scale period i+1 Reduce the output limit RD j Reduce to minimum P j The required number of original time periods is calculated as in formula (35);

[0090]

[0091]

[0092]

[0093]

[0094]

[0095]

[0096]

[0097] Formula (21) and Formula (22) are the minimum continuous start-stop time constraints of the unit respectively; where T(i, DT) is the minimum time period function, defined as Formula (36), where TU j , TD j are the minimum continuous on / off hours of generator set j;

[0098]

[0099] If there is a generator set j, whose initial start-stop state is on and the initial continuous start-up time is less than its minimum continuous start-up time, or whose initial start-stop state is off and the initial continuous stop time is less than its minimum continuous stop time, then the initial minimum continuous start-stop time constraint of the unit should also be added, such as formula (37) and formula (38); are the initial continuous start and stop hours of generator set j;

[0100]

[0101]

[0102] In the above constraints, unless otherwise specified, and are the initial output and start / stop status of generator set j,

[0103] S2.22, determine the target function of the power system unit combination in the variable scale period, the expression is as follows:

[0104]

[0105]

[0106]

[0107] Where (39) is the objective function, i.e., minimizing the sum of total operating cost and startup cost, including the generator set operating cost (40) and the generator set startup cost (41);

[0108] Formula (40) is the operating cost function of the generator set, using M j +1 segment piecewise function form, is the slope of each segment, is the intercept of each segment, is the segmentation point;

[0109] Formula (41) is the generator start-up cost function, where is the startup cost of generator set j.

[0110] S2.3, use a mixed integer linear programming solver (such as commercial software such as Gurobi) to solve the model defined in S2.2 and calculate the unit combination results for the variable scale period; if the model has no solution, add slack variables to constraints (12) to (17) and constraints (19) to (20), and add them to the objective function as penalty items, and re-solve the model to obtain the unit combination results for the variable scale period.

[0111] Step S3, expanding the unit combination solution for the variable scale period and repairing the original unit combination solution to perform post-processing of the unit combination for the variable scale period.

[0112] Specifically, the post-processing of the unit combination in the variable scale period includes: S3.1 expanding the unit combination solution in the variable scale period and S3.2 repairing the original unit combination solution;

[0113] S3.1, expand the variable scale period unit combination solution, the expression is as follows:

[0114]

[0115] Formula (42) is the solution expansion equation of the variable-scale period unit combination. The start and stop status of each generator unit in the original period t is the start and stop status of each generator unit in the variable-scale period t. i to i+1 -1 start-stop state; is the start and stop value of generator set j in the original time period t obtained by expanding the variable scale time period solution; if If the original unit combination is feasible, it is the power system unit combination solution based on variable-scale time period aggregation; if it is not feasible, perform S3.2 to repair the original unit combination solution;

[0116] S3.2, repair the original unit combination solution:

[0117] S3.21, among the start and stop values ​​of the unit obtained by expanding S3.1, the start and stop values ​​before and after the state switching are relaxed as optimization variables;

[0118] S3.22, build a repair optimization model:

[0119] The objective function expression is as follows:

[0120]

[0121] The constraint expression is as follows:

[0122]

[0123]

[0124]

[0125]

[0126]

[0127]

[0128]

[0129]

[0130]

[0131]

[0132]

[0133] in are the start / stop status and output of generator set j in the original period t, For its initial start-stop state, For its initial output;

[0134] Formula (43) is the objective function of the repair optimization problem, which includes the sum of the total operating cost and the startup cost, as well as the slack variable penalty term; to are the slack variables corresponding to constraints (44) to (47), (49) and (50), and their values ​​are non-negative; R1 to R7 are the penalty coefficients corresponding to the above slack variables, and their values ​​are relatively large;

[0135] Formula (44) to Formula (54) are the constraints of the repair optimization problem, which correspond to Formula (12) to Formula (22), Formula (37) and Formula (38) respectively;

[0136] S3.23, for the currently relaxed optimization variables, a mixed integer linear programming solver (such as commercial software such as Gurobi) is used to solve the repair optimization model defined in S3.22; if the values ​​of all relaxation variables are 0, the start and stop values ​​of the units at this time are the power system unit combination solutions based on variable scale time period aggregation; otherwise, proceed to S3.24;

[0137] S3.24 Based on the current relaxed variables, for to If there is a slack variable with a positive value, then all the start and stop values ​​of the units in the original period t are relaxed as variables; and If there is a slack variable whose value is a positive number, the start and stop values ​​of the corresponding unit j in the original time period t are relaxed as variables; if the variables before and after relaxation are the same, the adjacent start and stop values ​​of all current variables are additionally relaxed; after the variables are relaxed, proceed to S3.23.

[0138] Furthermore, if Figure 2 As shown, the present invention generates variable-scale time period sequences through aggregation, solves the power system unit combination of the variable-scale time period, and performs post-processing of the unit combination of the variable-scale time period. Therefore, the present invention designs an aggregation target according to the normalized regulation capability of the unit within the variable-scale time period, aggregates the calculation time period according to the data characteristics of the net load of the power system, thereby reducing the number of 0-1 variables of the mixed integer linear programming problem, and uses the repair optimization method to ensure the feasibility of the result, which is conducive to improving the calculation efficiency of the power system unit combination under the premise of ensuring feasibility and economy, and enhancing the ability of the power system optimization operation to cope with the challenges of an increase in the number of entities, an expansion of the network scale, and an increase in the time resolution.

[0139] In order to implement the above embodiment, Figure 3 As shown, this embodiment also provides a power system unit combination optimization device 10 based on variable-scale time period aggregation, and the device 10 includes: an aggregation module 100, a solution module 200 and an expansion and repair module 300.

[0140] Aggregation module 100, used to construct a variable scale period aggregation optimization model and solve the variable scale period sequence to aggregate and generate the variable scale period sequence;

[0141] The solution module 200 is used to obtain basic data of economic operation of the power system, construct a power system unit combination model suitable for the variable scale period, and calculate the unit combination result of the variable scale period to solve the power system unit combination of the variable scale period;

[0142] The expansion and repair module 300 is used to expand the unit combination solution of the variable scale period and repair the original unit combination solution to perform post-processing of the unit combination of the variable scale period.

[0143] According to the power system unit combination optimization device based on variable-scale time period aggregation according to the embodiment of the present invention, the calculation time periods are aggregated according to the data characteristics of the net load of the power system, thereby reducing the number of 0-1 variables of the mixed integer linear programming problem, and using the repair optimization method to ensure the feasibility of the result, which is conducive to improving the calculation efficiency of the power system unit combination while ensuring feasibility and economy, and enhancing the ability of the power system optimization operation to cope with challenges such as the increase in the number of entities, the expansion of the network scale, and the improvement of the time resolution.

[0144] It should be noted that the aforementioned explanation of the embodiment of the method for optimizing the unit combination of a power system based on variable-scale time period aggregation is also applicable to the device for optimizing the unit combination of a power system based on variable-scale time period aggregation of this embodiment, and will not be repeated here.

[0145] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0146] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0147] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0148] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0149] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0150] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0151] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0152] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention without departing from the principles and intent of the present invention.

Claims

1. A method for optimizing power system unit commitment based on variable-scale time period aggregation, characterized in that: The following steps are involved: S1, constructing a variable scale period aggregation optimization model and solving the variable scale period sequence to aggregate and generate the variable scale period sequence; S2, obtaining basic data of economic operation of the power system, constructing a power system unit combination model suitable for the variable-scale period, and calculating the unit combination results of the variable-scale period, so as to solve the power system unit combination of the variable-scale period; S3, expand the unit combination solution of the variable scale period and repair the original unit combination solution to perform post-processing of the unit combination of the variable scale period; Said S1 comprises: S1.1, constructing a variable-scale time period aggregation optimization model, including: S1.11, establish the time period aggregation constraint condition, the expression is as follows: (2) Among them, (1) is the integer constraint of the starting point of the time period, where is the number of scaled time periods after aggregation, is the starting point of each variable-scale period in the original period sequence, which is an integer. Formula (2) is the order constraint of the starting point of the period, which stipulates that The scaled period is the original period to , No. The scaled period is the original period to , where is the original time period number, ; S1.12, determine the time period aggregation objective function, the expression is as follows: (3) Among them, (3) is the objective function, which minimizes the difference between the maximum and minimum system net load in all time periods divided by the sum of the maximum system net load in the period, where To convert the original period 1 to The optimal aggregation is The minimum objective function of the variable scale period, For the original period System net load value; ; S1.2, dynamic programming is used as a solution method to solve the variable-scale time period sequence; The S2 comprises: S2.1, obtain basic data on economic operation of the power system; including: obtaining upper and lower limits of unit output, upper and lower limits of unit ramp rate, minimum continuous start-up and shutdown time of the unit, unit operation cost function, unit startup cost, power grid power flow transfer distribution factor, line transmission capacity and system positive and negative reserve rate; S2.2, construct a power system unit combination model suitable for variable-scale time periods; S2.3, calculate the unit combination results during the variable scale period.

2. The method according to claim 1, characterized in that S1.2, including: S1.21, determine the state transition equation and boundary, the expression is as follows: (4) (5) (6) (7) (8) Among them, formula (4) is the state transfer equation, where To convert the original period 1 to The optimal aggregation is The minimum objective function of the variable scale period is calculated recursively by the value function (8); Formula (5) is the equation for the starting point of the variable scale period, where To convert the original period 1 to The optimal aggregation is The last scaling period of the scaling period is the starting point of the scaling period; Formula (6) is the boundary state equation, which stipulates that when the number of scaling periods is 1, the aggregate minimum objective function is the value function (8) in the original period 1 to The value of; Formula (7) is the boundary starting point equation, which stipulates that the starting point of the first variable scale period is 1; Formula (8) is the value function, which is defined as As the starting point, The difference between the maximum and minimum net load values ​​of the system during the variable scale period of the end point is divided by the maximum net load value of the system during the period; S1.22, recursively solve the optimal value of the objective function: Departure, enumeration ,enumerate , according to equations (4) to (8), we can solve and ; S1.23, recursively solve the variable scale time series, the expression is as follows: (9) (10) (11) Among them, (9) is the recursive initial value of the starting point of the variable scale period, For the The starting point of the variable scale period; Formula (10) is the recursive equation of the starting point of the variable scale period, The starting point of the scaled period is to change the original period 1 to The optimal aggregation is The last variable scale period starting point of the variable scale period; Formula (11) is the variable scale period length equation, where For variable scale period The number of original time periods to include.

3. The method according to claim 1, characterized in that S2.2, including: S2.21, establish the power system operation constraints for variable-scale periods based on DC power flow; S2.22, determine the target function of the power system unit combination in the variable scale period.

4. The method according to claim 3, characterized in that The expression of S2.21 is: (13) (14) (15) (16) (18) (19) (20) (21) (22) Where (12) is the system power balance constraint; For generator sets In the variable scale period The output variable, is the number of units; For variable scale period The average value of the system net load is calculated as shown in formula (23); formula (13) and formula (14) are the positive and negative reserve constraints of the system respectively; Is a 0-1 variable, representing the generator set In the variable scale period The start and stop status, 0 means stop, 1 means start; , Generator sets The upper and lower limits of output; , are the positive and negative reserve rates of the system respectively; , Variable scale period The maximum and minimum values ​​of the system net load are calculated as shown in equations (24) and (25); equations (15) and (16) are the system positive and negative regulation reserve relaxation constraints respectively; , Generator sets Increase and decrease the output limit within 1 hour, is the original time interval in hours; and Variable scale period The maximum increase and decrease of the net load of the system in adjacent original time periods is calculated as shown in equations (26) and (27); equation (17) is the line flow constraint; where For Line The transmission capacity of is the number of lines; , Generator sets and nodes Corresponding lines The power flow transfer distribution factor; For Node In the variable scale period The net load average value is calculated as shown in formula (28), where For Node In the original period Net load value; (24) (25) (26) (27) Formula (18) is the output constraint of the generator set; Formula (19) and Formula (20) are the output rate constraints of each unit respectively; , Generator sets In the variable scale period Increase and decrease the output upper limit, the calculation formula is as shown in formula (29) and formula (30); , Generator sets In the variable scale period The upper and lower limits of the starting output are calculated as shown in equations (31), (32), and (33); For generator sets In the variable scale period Start, the output increases from the minimum value during the period To increase the output limit Increase to maximum The required number of original time periods is calculated as in formula (34); For generator sets In the variable scale period Before closing, during the variable scale period Internal output from maximum To reduce the output limit Reduce to minimum The required number of original time periods is calculated as in formula (35); (29) (30) (31) (32) (33) (34) (35) Formula (21) and Formula (22) are the minimum continuous start-stop time constraints of the unit respectively; is the minimum time period function, defined as in equation (36), where: , Generator sets Minimum continuous on / off hours; (36) If there is a generator , so that its initial start-stop state is on and its initial continuous start-up time is less than its minimum continuous start-up time, or its initial start-stop state is off and its initial continuous stop time is less than its minimum continuous stop time, then the unit's initial minimum continuous start-stop time constraint should also be added, such as formula (37) and formula (38); , Generator sets Initial continuous on / off hours; (37) (38) In the above constraints, and Generator sets The initial output and start-stop status, .

5. The method according to claim 3, characterized in that: The S2.22 is expressed as: (39) (40) (41) Among them, formula (39) is the objective function, which is to minimize the sum of total operating cost and startup cost, including the generator set operating cost (40) and the generator set startup cost (41); formula (40) is the generator set operating cost function, using The piecewise function form of the segment, is the slope of each segment, is the intercept of each segment, is the segmentation point; Formula (41) is the generator start-up cost function, where: For generator sets startup costs.

6. The method according to claim 4, characterized in that The S2.3 comprises: using a mixed integer linear programming solver to solve the power system unit combination model and calculate the unit combination results in the variable scale period; if the model has no solution, adding slack variables to constraints (12) to constraints (17) and constraints (19) to constraints (20), and adding them to the objective function as penalty items, and resolving the model to obtain the unit combination results in the variable scale period.

7. The method according to claim 1, characterized in that The S3 includes: S3.1, expand the variable scale period unit combination solution, the expression is: Among them, formula (42) is the expanded equation for the solution of the variable-scale period unit combination. Each generator unit in the original period The start-stop state is the The variable scale period to The start and stop state; is the generator set obtained by expanding the variable-scale time period solution In the original period The start and stop value of If the original unit combination is feasible, it is the power system unit combination solution based on variable-scale time period aggregation; if it is not feasible, perform S3.2 to repair the original unit combination solution.

8. The method according to claim 1, characterized in that The S3 further includes: S3.2, repair the original unit combination solution, including: S3.21, among the start and stop values ​​of the unit obtained by expanding S3.1, the start and stop values ​​before and after the state switching are relaxed as optimization variables; S3.22, build a repair optimization model: The objective function expression is as follows: (43) The constraint expression is as follows: (44) (45) (46) (47) (48) (49) (50) (51) (52) (53) (54) in, , Generator sets In the original period The start / stop status and output of For its initial start-stop state, is its initial output; Formula (43) is the objective function of the repair optimization problem, which includes the sum of the total operating cost and the startup cost, as well as the slack variable penalty; to are the slack variables corresponding to constraints (44) to (47), (49) and (50), and their values ​​are non-negative; to is the penalty coefficient corresponding to the above slack variable; Equations (44) to (54) are the constraints of the repair optimization problem, corresponding to Equations (12) to (22), (37) and (38) respectively; S3.23, for the currently relaxed optimization variables, a mixed integer linear programming solver is used to solve the repair optimization model defined in S3.22; if the values ​​of all the relaxation variables are 0, then the start and stop values ​​of the units at this time are the power system unit combination solutions based on variable scale time period aggregation; otherwise, proceed to S3.24; S3.24, based on the current relaxed variables, for to If there is a positive slack variable, then the original period All the start and stop values ​​of the units are relaxed to variables; and If there is a slack variable with a positive value, the corresponding unit Original time period The start and stop values ​​of are relaxed as variables; if the variables are the same before and after relaxation, the start and stop values ​​adjacent to all current variables are additionally relaxed; after the variables are relaxed, proceed to S3.

23.

9. A power system unit combination optimization device based on variable scale time period aggregation using the method as claimed in claim 1, characterized in that: include: Aggregation module, used to construct variable scale period aggregation optimization model and solve variable scale period sequence to aggregate and generate variable scale period sequence; A solution module is used to obtain basic data of economic operation of the power system, construct a power system unit combination model suitable for variable-scale periods, and calculate the unit combination results of the variable-scale periods to solve the power system unit combination of the variable-scale periods; The expansion and repair module is used to expand the unit combination solution in the variable scale period and repair the original unit combination solution to perform post-processing of the unit combination in the variable scale period.

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