Solving method and device for unit commitment optimization model

Abstract

The invention provides a solving method and device for a unit commitment optimization model, and relates to the technical field of new energy power grid optimization. The method comprises the following steps: constructing a unit commitment optimization model containing short-circuit ratio constraint; performing inequality scaling on the short-circuit ratio constraint, and performing discretization processing on the short-circuit ratio constraint subjected to inequality scaling; and performing matrix multiplication expansion on the discretized short-circuit ratio constraint, processing an obtained continuous variable and discrete variable product term by using a large M method to obtain an MILP relaxation problem, and solving the MILP relaxation problem. The device executes the method. According to the solving method and device of the unit commitment optimization model provided by the embodiment of the invention, the power grid strength can be improved through the unit commitment optimization model, the voltage instability risk can be reduced, and the system output power can be effectively improved through the model solving method.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy power grid optimization, and in particular to a method and device for solving a unit combination optimization model. Background Art

[0002] As the installed capacity of renewable energy is increasing rapidly, it poses new challenges to the security of the power grid. For example, the transmission of wind and thermal bundles through AC channels, since wind farms are usually located at the end of the grid with weak grid structure, when a large number of thermal power units are replaced by wind turbines, it is easy to have insufficient short-circuit capacity at the wind farm grid connection point and limited power delivery. In order to monitor the voltage support strength of the grid connection point, the existing technology proposes the concept of multi-station short-circuit ratio (MRSCR) based on the short-circuit ratio index, and has been applied in actual projects. By calculating the MRSCR of each station grid connection point, the weak links in the power grid can be quickly identified.

[0003] The existing unit combination optimization model uses an approximate method to establish relevant constraints. It cannot accurately limit the strength of the power grid when optimizing the transmitted power, which may lead to a decrease in the strength of the power grid and an increased risk of voltage instability. Summary of the invention

[0004] In view of the problems in the prior art, an embodiment of the present invention provides a method and device for solving a unit combination optimization model, which can at least partially solve the problems in the prior art.

[0005] On the one hand, the present invention provides a method for solving a unit commitment optimization model, comprising:

[0006] Construct a unit commitment optimization model including short-circuit ratio constraints;

[0007] Performing inequality scaling on the short-circuit ratio constraint, and discretizing the short-circuit ratio constraint after inequality scaling;

[0008] The short-circuit ratio constraint that has been discretized is expanded using matrix multiplication, and the product term of the continuous variable and the discrete variable is processed using the big-M method to obtain a MILP relaxation problem, and the MILP relaxation problem is solved.

[0009] The construction of the unit commitment optimization model including the short-circuit ratio constraint includes:

[0010] The unit commitment optimization model is expressed according to the following expression:

[0011]

[0012] Among them, S T represents the set of all moments, S w represents the set of all wind turbines, V N,wIndicates the bus voltage of wind turbine w at the grid-connected bus N, represents the conjugate value of the voltage of wind turbine w at time t, represents the equivalent impedance between fans w and j at time t, represents the equivalent impedance between fan w and fan w at time t, represents the conjugate value of the voltage of wind turbine j at time t, represents the per-unit value of the complex power generated by wind turbine j at time t Piecewise linearization of MRSCR th Indicates the short-circuit ratio threshold of multiple stations, Z t and Y t They represent the system node impedance matrix and node admittance matrix at time t, respectively. Y0 represents the fixed component obtained by splitting the node admittance matrix. t Indicates the start / stop state variables that affect the system network topology, x" g,d Represents the d-axis component of the g-th transient reactance of the thermal power unit.

[0013] The step of performing inequality scaling on the short circuit ratio constraint includes:

[0014] The short-circuit ratio constraint for inequality scaling is expressed as follows:

[0015]

[0016] Among them, N w represents the number of wind turbines in the system, represents the active output of wind turbine j at time t, After piecewise linearization V w,max Indicates the maximum value of the voltage of fan w.

[0017] The short circuit ratio constraint for completing the inequality scaling is discretized, including:

[0018] The short-circuit ratio constraint of the discretization process is expressed as follows:

[0019]

[0020] Among them, N d Indicates the total number of bits used to represent the wind power curtailment variable in binary format. represents the predicted active power of wind turbine j at time t, represents the continuous variable after the wind power abandonment is discretized, A constant representing the discretized wind power abandonment.

[0021] Among them, the short-circuit ratio constraint that has been discretized is expanded by matrix multiplication to obtain the product term of continuous variables and discrete variables, including:

[0022] The product of continuous variables and discrete variables can be expressed as follows

[0023]

[0024] Among them, g represents the thermal power unit, Indicates that there is no thermal power unit connected to the synchronous machine bus number. Indicates the 01 variable related to the start and stop status of the unit, S g represents the synchronous machine bus set, S b S g Denotes the busbar set S b For synchronous machine busbar set S g Take the complement set, k represents the kth busbar, Y represents the equivalent impedance between the bus where wind turbine w is located and bus k at time t, kg,0 It represents the equivalent admittance between bus k and the bus where thermal power unit g is located, Indicates that bus k is not connected to the thermal power unit with synchronous machine bus number The equivalent admittance between the buses.

[0025] The obtained product term of continuous variable and discrete variable is processed by big M method to obtain MILP relaxation problem, including:

[0026] The MILP relaxation problem is expressed as follows:

[0027]

[0028] Where M represents the value given by the Big M method, represents the start-stop state variable of thermal power unit g at time t, It represents the equivalent impedance between the busbar where the wind turbine w is located and the busbar where the thermal power unit g is located at time t, S G Represents the set of all thermal power units.

[0029] Wherein, solving the MILP relaxation problem includes:

[0030] The commercial solver GUROBI is used to solve the MILP relaxation problem.

[0031] On the one hand, the present invention provides a device for solving a unit combination optimization model, comprising:

[0032] A construction unit, used for constructing a unit commitment optimization model including short-circuit ratio constraints;

[0033] A processing unit, used for performing inequality scaling on the short circuit ratio constraint, and performing discretization processing on the short circuit ratio constraint after the inequality scaling is completed;

[0034] The solving unit is used to expand the short-circuit ratio constraint after discretization by using matrix multiplication, process the obtained product term of continuous variable and discrete variable by using big M method, obtain MILP relaxation problem, and solve the MILP relaxation problem.

[0035] In another aspect, an embodiment of the present invention provides an electronic device, including: a processor, a memory, and a bus, wherein:

[0036] The processor and the memory communicate with each other via the bus;

[0037] The memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the following method:

[0038] Construct a unit commitment optimization model including short-circuit ratio constraints;

[0039] Performing inequality scaling on the short-circuit ratio constraint, and discretizing the short-circuit ratio constraint after inequality scaling;

[0040] The short-circuit ratio constraint that has been discretized is expanded using matrix multiplication, and the product term of the continuous variable and the discrete variable is processed using the big-M method to obtain a MILP relaxation problem, and the MILP relaxation problem is solved.

[0041] An embodiment of the present invention provides a non-transitory computer-readable storage medium, including:

[0042] The non-transitory computer-readable storage medium stores computer instructions, which cause the computer to execute the following method:

[0043] Construct a unit commitment optimization model including short-circuit ratio constraints;

[0044] Performing inequality scaling on the short-circuit ratio constraint, and discretizing the short-circuit ratio constraint after inequality scaling;

[0045] The short-circuit ratio constraint that has been discretized is expanded using matrix multiplication, and the product term of the continuous variable and the discrete variable is processed using the big-M method to obtain a MILP relaxation problem, and the MILP relaxation problem is solved.

[0046] The method and device for solving the unit combination optimization model provided in the embodiment of the present invention construct a unit combination optimization model including a short-circuit ratio constraint; perform inequality scaling on the short-circuit ratio constraint, and discretize the short-circuit ratio constraint that has completed the inequality scaling; expand the short-circuit ratio constraint that has completed the discretization processing by matrix multiplication, and process the obtained product term of continuous variables and discrete variables by the big M method to obtain a MILP relaxation problem, and solve the MILP relaxation problem. The unit combination optimization model can improve the strength of the power grid and reduce the risk of voltage instability, and the system output power can be effectively improved by solving the model method. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0048] Figure 1 It is a flow chart of a method for solving a unit combination optimization model provided in one embodiment of the present invention.

[0049] Figure 2 It is a structural schematic diagram of a device for solving a unit combination optimization model provided by an embodiment of the present invention.

[0050] Figure 3 A schematic diagram of the physical structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention. It should be noted that, in the absence of conflict, the embodiments in this application and the features in the embodiments can be combined with each other arbitrarily.

[0052] Figure 1 is a flow chart of a method for solving a unit combination optimization model provided by an embodiment of the present invention, such as Figure 1 As shown, the method for solving the unit commitment optimization model provided by the embodiment of the present invention includes:

[0053] Step S1: Construct a unit commitment optimization model including short-circuit ratio constraints.

[0054] Step S2: performing inequality scaling on the short circuit ratio constraint, and discretizing the short circuit ratio constraint after inequality scaling.

[0055] Step S3: Expand the short-circuit ratio constraint after discretization by matrix multiplication, process the obtained product term of continuous variable and discrete variable by big-M method, obtain MILP relaxation problem, and solve the MILP relaxation problem.

[0056] In the above step S1, the device constructs a unit combination optimization model including short circuit ratio constraints. The device may be a computer device that executes the method, for example, may include a server. It should be noted that the acquisition and analysis of data involved in the embodiment of the present invention is authorized by the user.

[0057] The unit commitment optimization model is described as follows:

[0058] (1) Construct a unit combination optimization model with short-circuit ratio constraints: By optimizing the start-stop state variables of thermal power units g at different time periods Active power output of thermal power units and the active output of wind turbines To minimize the total cost of operation. The complete model is as follows:

[0059]

[0060] The mathematical nature of this model is a mixed integer linear programming (MILP) problem. It should be pointed out that since the main problem does not involve reactive voltage, the main problem adopts the active power flow constraint that ignores network loss. The accurate power flow model will be considered in the sub-problem. In order to ensure the feasibility of the optimization result, only the start and stop of the unit and the active output of the unbalanced machine are fixed in the sub-problem, and the active output of the balanced machine is used as the optimization variable. Although this will sacrifice a certain degree of optimality, for online applications, it is more practical to quickly find a feasible solution.

[0061] 1) Objective function:

[0062]

[0063] The generation cost is modeled using a piecewise linearization method. represents the power generation cost of the thermal power unit g generating active power P at time t, represents the start / stop state variable of thermal power unit g at time t, F g,m It represents the slope of the power generation cost of thermal power unit g in the mth segment with respect to active power, represents the power generation of thermal power unit g in the mth segment at time t, P g,min Indicates the lower limit of active output of thermal power unit g, f n (P g,min ) represents the initial point of the piecewise linearization, the power generation cost when the thermal power unit g generates the lower limit of active output, M n is the number of segments, and They represent the startup cost and shutdown cost of thermal power unit g at time t, respectively, and C g,su and C g,sd They represent the startup cost coefficient and shutdown cost coefficient of thermal power unit g, S G represents the set of all thermal power units, S T Represents the set of all moments.

[0064] 2) Piecewise linear active power constraint:

[0065]

[0066] in, represents the active output of thermal power unit g at time t, After piecewise linearization ΔP g,m,max It represents the maximum active output of thermal power unit g in the mth segment.

[0067] 3) Minimum startup / downtime constraints:

[0068]

[0069] Among them, T g,on and T g,off They respectively represent the minimum startup time interval and the minimum shutdown time interval of the thermal power unit g.

[0070] 4) Climbing constraints:

[0071]

[0072] Among them, RD g and RU g They respectively represent the upward climbing rate limit and downward climbing rate limit of the thermal power unit g.

[0073] 5) System spare capacity constraints:

[0074]

[0075] in, and They represent the upward reserve demand and downward reserve demand of the system at time t, respectively, P g,max Indicates the upper limit of active output of thermal power unit g.

[0076] 6) Short circuit ratio constraint:

[0077]

[0078] Among them, S T represents the set of all moments, Sw represents the set of all wind turbines, V N,w Indicates the bus voltage of wind turbine w at the grid-connected bus N, represents the conjugate value of the voltage of wind turbine w at time t, represents the equivalent impedance between fans w and j at time t, represents the equivalent impedance between fan w and fan w at time t, represents the conjugate value of the voltage of wind turbine j at time t, represents the per-unit value of the complex power generated by wind turbine j at time t Piecewise linearization of MRSCR th Indicates the short-circuit ratio threshold of multiple stations, Z t and Y t They represent the system node impedance matrix and node admittance matrix at time t, respectively. Y0 represents the fixed component obtained by splitting the node admittance matrix. t Indicates the start / stop state variables that affect the system network topology, x" g,d Represents the d-axis component of the g-th transient reactance of the thermal power unit.

[0079] 7) Synchronous machine capacity constraints:

[0080]

[0081] 8) Fan capacity limitation constraints:

[0082]

[0083] in, and They represent the predicted active power and abandoned wind power of wind turbine w at time t respectively.

[0084] 9) Active power balance constraints:

[0085]

[0086] Among them, P l t represents the load active power at time t, S LD Represents a collection of load nodes.

[0087] In the above step S2, the device performs inequality scaling on the short circuit ratio constraint, and discretizes the short circuit ratio constraint after inequality scaling.

[0088] (2) Scale the short-circuit ratio constraint by inequality: In order to avoid the main problem (MP k) introduces reactive voltage related variables, firstly, the denominator in the definition of short circuit ratio (9) is scaled by inequality, as shown in formula (14). Since the reactance in the high voltage transmission network is generally much larger than the resistance, formula (14) is generally valid. At this time, the short circuit ratio constraint (9) is relaxed to formula (15).

[0089]

[0090] in, represents the conjugate value of the voltage of wind turbine j at time t, N w Represents the number of wind turbines in the system, MRSCR th Represents the short-circuit ratio threshold of multiple stations. Since there is still a voltage variable in this expression, let the numerator and denominator be multiplied by By moving the terms and rearranging them, we can get equation (16). Considering that the voltage at the wind farm grid connection point has an upper and lower limit [V w,min ,V w,max ], scaling the inequality of equation (16) and moving the terms, and rearranging it to get equation (17).

[0091]

[0092] Among them, N w represents the number of wind turbines in the system, represents the active output of wind turbine j at time t, After piecewise linearization V w,max Indicates the maximum value of the voltage of fan w.

[0093] (3) Using discretization method:

[0094] Since the short-circuit ratio constraint contains bilinear terms, the wind power abandonment power is processed by discretization method, as shown in formula (18):

[0095]

[0096] The product terms of continuous variables and discrete variables are calculated by the Big M method. After processing, we get formula (19):

[0097]

[0098] Substituting equation (18) into equation (17), we can get equation (20). and are the 01 variable, continuous variable and constant related to the discrete wind power abandonment, N d It is the total number of bits used to represent the wind power curtailment variable in binary format.

[0099]

[0100] Among them, N d Indicates the total number of bits used to represent the wind power curtailment variable in binary format. represents the predicted active power of wind turbine j at time t, represents the continuous variable after the wind power abandonment is discretized, A constant representing the discretized wind power abandonment.

[0101] In the above step S3, the device uses matrix multiplication to expand the short-circuit ratio constraint that has completed the discretization processing, uses the big M method to process the obtained product term of the continuous variable and the discrete variable, obtains the MILP relaxation problem, and solves the MILP relaxation problem.

[0102] (4) Expand using matrix multiplication: The variable Z in the short-circuit ratio constraint above needs to satisfy constraint (10), and the Y matrix contains 01 variables This is essentially a symbolic matrix inversion operation, which is difficult to model analytically. Considering that Z is the inverse matrix of Y, the two need to satisfy ZY=I. Expanding each element in the formula according to matrix multiplication, we can get formula (21):

[0103]

[0104] Among them, g represents the thermal power unit, Indicates that there is no thermal power unit connected to the synchronous machine bus number. Indicates the 01 variable related to the start and stop status of the unit, S g represents the synchronous machine bus set, S b S g Denotes the busbar set S b For synchronous machine busbar set S g Take the complement set, k represents the kth busbar, Y represents the equivalent impedance between the bus where wind turbine w is located and bus k at time t, kg,0 It represents the equivalent admittance between bus k and the bus where thermal power unit g is located, Indicates that bus k is not connected to the thermal power unit with synchronous machine bus number The equivalent admittance between the buses.

[0105] (5) Use the Big M method to deal with product terms: for the product terms of continuous variables and discrete variables ( represents the equivalent impedance between the bus where the wind turbine w is located and the bus where the thermal power unit g is located at time t), and the equivalent conversion is performed using the large M method to obtain equation (22). Among them, the variable It is a 01 variable related to the start and stop status of the unit. Indicates the thermal power unit with no synchronous machine bus number connected. g represents the synchronous machine bus set, S b S g Denotes the busbar set S b For synchronous machine busbar set S g Take the complement set.

[0106]

[0107] Where M represents the value given by the Big M method, represents the start-stop state variable of thermal power unit g at time t, It represents the equivalent impedance between the busbar where the wind turbine w is located and the busbar where the thermal power unit g is located at time t, S G Represents the set of all thermal power units.

[0108] The solving of the MILP relaxation problem comprises:

[0109] The commercial solver GUROBI is used to solve the MILP relaxation problem.

[0110] The method for solving the unit commitment optimization model provided by the embodiment of the present invention has the following advantages:

[0111] 1. The method of the present invention takes into account the short-circuit ratio index of multiple stations in the system, and the constructed model can quickly and effectively identify the weak links in the power grid and avoid insufficient short-circuit capacity at the grid connection point.

[0112] 2. The method of the present invention avoids introducing reactive voltage into the short-circuit ratio constraint by scaling the inequality (by using the complex power per unit value The piecewise linearization of the problem is thus maintained as a mixed integer linear programming problem, which is conducive to efficient solution.

[0113] 3. The method of the present invention utilizes steps such as discretization method, matrix multiplication expansion and big M method to relax the short-circuit ratio constraint, realizes the linearization of the short-circuit ratio constraint containing discrete variables, and reduces the difficulty of solving.

[0114] The method for solving the unit combination optimization model provided in the embodiment of the present invention constructs a unit combination optimization model including a short-circuit ratio constraint; performs inequality scaling on the short-circuit ratio constraint, and adopts discretization processing for the short-circuit ratio constraint that completes the inequality scaling; adopts matrix multiplication to expand the short-circuit ratio constraint that completes the discretization processing, adopts the big-M method to process the obtained product term of continuous variables and discrete variables, obtains the MILP relaxation problem, solves the MILP relaxation problem, and can improve the strength of the power grid and reduce the risk of voltage instability through the unit combination optimization model, and can effectively improve the system output power through the model solving method.

[0115] Furthermore, the construction of the unit commitment optimization model including the short-circuit ratio constraint includes:

[0116] The unit commitment optimization model is expressed according to the following expression:

[0117]

[0118] Among them, S T represents the set of all moments, S w represents the set of all wind turbines, V N,w Indicates the bus voltage of wind turbine w at the grid-connected bus N, represents the conjugate value of the voltage of wind turbine w at time t, represents the equivalent impedance between fans w and j at time t, represents the equivalent impedance between fan w and fan w at time t, represents the conjugate value of the voltage of wind turbine j at time t, represents the per-unit value of the complex power generated by wind turbine j at time t Piecewise linearization of MRSCR th Indicates the short-circuit ratio threshold of multiple stations, Z t and Y t They represent the system node impedance matrix and node admittance matrix at time t, respectively. Y0 represents the fixed component obtained by splitting the node admittance matrix. t Indicates the start / stop state variables that affect the system network topology, x" g,d The d-axis component of the g-order transient reactance of the thermal power unit can be referred to the above embodiment for explanation and will not be described in detail.

[0119] Further, the inequality scaling of the short circuit ratio constraint includes:

[0120] The short-circuit ratio constraint for inequality scaling is expressed as follows:

[0121]

[0122] Among them, N w represents the number of wind turbines in the system, represents the active output of wind turbine j at time t, After piecewise linearization V w,max Indicates the maximum value of the voltage of the fan w. The above description can be referred to in the above embodiment and will not be repeated here.

[0123] Furthermore, the short circuit ratio constraint for completing the inequality scaling is discretized, including:

[0124] The short-circuit ratio constraint of the discretization process is expressed as follows:

[0125]

[0126] Among them, N d Indicates the total number of bits used to represent the wind power curtailment variable in binary format. represents the predicted active power of wind turbine j at time t, represents the continuous variable after the wind power abandonment is discretized, The constant representing the discretized wind power abandonment power can be referred to the above embodiment for explanation and will not be described in detail.

[0127] Furthermore, the short-circuit ratio constraint that has been discretized is expanded using matrix multiplication to obtain the product term of the continuous variable and the discrete variable, including:

[0128] The product of continuous variables and discrete variables can be expressed as follows

[0129]

[0130] Among them, g represents the thermal power unit, Indicates that there is no thermal power unit connected to the synchronous machine bus number. Indicates the 01 variable related to the start and stop status of the unit, S g represents the synchronous machine bus set, S b S g Denotes the busbar set S b For synchronous machine busbar set S g Take the complement set, k represents the kth busbar, Y represents the equivalent impedance between the bus where wind turbine w is located and bus k at time t, kg,0 It represents the equivalent admittance between bus k and the bus where thermal power unit g is located, Indicates that bus k is not connected to the thermal power unit with synchronous machine bus number The equivalent admittance between the buses can be described with reference to the above embodiment and will not be described in detail.

[0131] Furthermore, the obtained product term of the continuous variable and the discrete variable is processed by the big M method to obtain the MILP relaxation problem, including:

[0132] The MILP relaxation problem is expressed as follows:

[0133]

[0134] Where M represents the value given by the Big M method, represents the start-stop state variable of thermal power unit g at time t, It represents the equivalent impedance between the busbar where the wind turbine w is located and the busbar where the thermal power unit g is located at time t, S G Represents the set of all thermal power units. Please refer to the above embodiment for description, which will not be repeated here.

[0135] Further, solving the MILP relaxation problem includes:

[0136] The commercial solver GUROBI is used to solve the MILP relaxation problem. Please refer to the above embodiment for explanation, which will not be repeated here.

[0137] Figure 2 FIG. 1 is a schematic diagram of a structure of a device for solving a unit combination optimization model provided by an embodiment of the present invention. Figure 2 As shown, the device for solving the unit combination optimization model provided by the embodiment of the present invention includes a construction unit 201, a processing unit 202 and a solving unit 203, wherein:

[0138] The construction unit 201 is used to construct a unit combination optimization model including a short-circuit ratio constraint; the processing unit 202 is used to perform inequality scaling on the short-circuit ratio constraint, and discretize the short-circuit ratio constraint that has completed the inequality scaling; the solution unit 203 is used to expand the short-circuit ratio constraint that has completed the discretization processing by using matrix multiplication, and use the big-M method to process the obtained product term of the continuous variable and the discrete variable to obtain a MILP relaxation problem, and solve the MILP relaxation problem.

[0139] Specifically, the construction unit 201 in the device is used to construct a unit combination optimization model including a short-circuit ratio constraint; the processing unit 202 is used to perform inequality scaling on the short-circuit ratio constraint, and discretize the short-circuit ratio constraint that has completed the inequality scaling; the solving unit 203 is used to expand the short-circuit ratio constraint that has completed the discretization processing by using matrix multiplication, and use the big-M method to process the obtained product term of the continuous variable and the discrete variable to obtain a MILP relaxation problem, and solve the MILP relaxation problem.

[0140] The device for solving the unit combination optimization model provided in the embodiment of the present invention constructs a unit combination optimization model including a short-circuit ratio constraint; performs inequality scaling on the short-circuit ratio constraint, and adopts discretization processing for the short-circuit ratio constraint that completes the inequality scaling; adopts matrix multiplication to expand the short-circuit ratio constraint that completes the discretization processing, adopts the big-M method to process the obtained product term of continuous variables and discrete variables, obtains the MILP relaxation problem, solves the MILP relaxation problem, and can improve the strength of the power grid and reduce the risk of voltage instability through the unit combination optimization model, and can effectively improve the system output power through the model solving method.

[0141] The embodiment of the present invention provides an embodiment of a solution device for a unit combination optimization model, which can be specifically used to execute the processing flow of the above-mentioned method embodiments. Its functions are not repeated here, and reference can be made to the detailed description of the above-mentioned method embodiments.

[0142] Figure 3 A schematic diagram of the physical structure of an electronic device provided in an embodiment of the present invention, such as Figure 3 As shown, the electronic device includes: a processor (processor) 301, a memory (memory) 302 and a bus 303;

[0143] The processor 301 and the memory 302 communicate with each other via a bus 303;

[0144] The processor 301 is used to call the program instructions in the memory 302 to execute the methods provided by the above method embodiments, for example, including:

[0145] Construct a unit commitment optimization model including short-circuit ratio constraints;

[0146] Performing inequality scaling on the short-circuit ratio constraint, and discretizing the short-circuit ratio constraint after inequality scaling;

[0147] The short-circuit ratio constraint that has been discretized is expanded using matrix multiplication, and the product term of the continuous variable and the discrete variable is processed using the big-M method to obtain a MILP relaxation problem, and the MILP relaxation problem is solved.

[0148] This embodiment discloses a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can perform the methods provided by the above method embodiments, for example, including:

[0149] Construct a unit commitment optimization model including short-circuit ratio constraints;

[0150] Performing inequality scaling on the short-circuit ratio constraint, and discretizing the short-circuit ratio constraint after inequality scaling;

[0151] The short-circuit ratio constraint that has been discretized is expanded using matrix multiplication, and the product term of the continuous variable and the discrete variable is processed using the big-M method to obtain a MILP relaxation problem, and the MILP relaxation problem is solved.

[0152] This embodiment provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein the computer program enables the computer to execute the methods provided by the above method embodiments, for example, including:

[0153] Construct a unit commitment optimization model including short-circuit ratio constraints;

[0154] Performing inequality scaling on the short-circuit ratio constraint, and discretizing the short-circuit ratio constraint after inequality scaling;

[0155] The short-circuit ratio constraint that has been discretized is expanded using matrix multiplication, and the product term of the continuous variable and the discrete variable is processed using the big-M method to obtain a MILP relaxation problem, and the MILP relaxation problem is solved.

[0156] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0157] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0158] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0159] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1The steps for the functions specified in one or more boxes.

[0160] In the description of this specification, the description with reference to the terms "one embodiment", "a specific embodiment", "some embodiments", "for example", "example", "specific example", 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.

[0161] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for solving a unit combination optimization model, characterized in that: include: Construct a unit commitment optimization model including short-circuit ratio constraints; Performing inequality scaling on the short-circuit ratio constraint, and discretizing the short-circuit ratio constraint after inequality scaling; The short-circuit ratio constraint that has been discretized is expanded using matrix multiplication, and the product term of the continuous variable and the discrete variable is processed using the big-M method to obtain a MILP relaxation problem, and the MILP relaxation problem is solved.

2. The method for solving the unit commitment optimization model according to claim 1, characterized in that: The constructing of the unit commitment optimization model including the short-circuit ratio constraint comprises: The unit commitment optimization model is expressed according to the following expression: Among them, S T represents the set of all moments, S w represents the set of all wind turbines, V N，w Indicates the bus voltage of wind turbine w at the grid-connected bus N, represents the conjugate value of the voltage of wind turbine w at time t, represents the equivalent impedance between fans w and j at time t, represents the equivalent impedance between fan w and fan w at time t, represents the conjugate value of the voltage of wind turbine j at time t, represents the per-unit value of the complex power generated by wind turbine j at time t Piecewise linearization of MRSCR th Indicates the short-circuit ratio threshold of multiple stations, Z t and Y t They represent the system node impedance matrix and node admittance matrix at time t, respectively. Y0 represents the fixed component obtained by splitting the node admittance matrix. t Indicates the start / stop state variable that affects the system network topology, x″ g，d Represents the d-axis component of the g-th transient reactance of the thermal power unit.

3. The method for solving the unit commitment optimization model according to claim 2, characterized in that: The inequality scaling of the short circuit ratio constraint includes: The short-circuit ratio constraint for inequality scaling is expressed as follows: Among them, N w represents the number of wind turbines in the system, represents the active output of wind turbine j at time t, After piecewise linearization V w，max Indicates the maximum value of the voltage of fan w.

4. The method for solving the unit commitment optimization model according to claim 3, characterized in that: The short-circuit ratio constraint for completing the inequality scaling is discretized, including: The short-circuit ratio constraint of the discretization process is expressed as follows: Where Nd represents the total number of bits in the binary representation of the wind power curtailment variable. represents the predicted active power of wind turbine j at time t, represents the continuous variable after the wind power abandonment is discretized, A constant representing the discretized wind power abandonment.

5. The method for solving the unit commitment optimization model according to claim 4, characterized in that: The short-circuit ratio constraint that has been discretized is expanded using matrix multiplication to obtain the product term of continuous variables and discrete variables, including: The product of continuous variables and discrete variables can be expressed as follows Among them, g represents the thermal power unit, Indicates that there is no thermal power unit connected to the synchronous machine bus number. Indicates the 01 variable related to the start and stop status of the unit, S g represents the synchronous machine bus set, S b S g Denotes the busbar set S b For synchronous machine busbar set S g Take the complement set, k represents the kth busbar, Y represents the equivalent impedance between the bus where wind turbine w is located and bus k at time t, kg，0 It represents the equivalent admittance between bus k and the bus where thermal power unit g is located, Indicates that bus k is not connected to the thermal power unit with synchronous machine bus number The equivalent admittance between the buses.

6. The method for solving the unit commitment optimization model according to claim 5, characterized in that: The obtained product term of continuous variable and discrete variable is processed by big M method to obtain MILP relaxation problem, including: The MILP relaxation problem is expressed as follows: Where M represents the value given by the Big M method, represents the start-stop state variable of thermal power unit g at time t, It represents the equivalent impedance between the busbar where the wind turbine w is located and the busbar where the thermal power unit g is located at time t, S G Represents the set of all thermal power units.

7. The method for solving the unit commitment optimization model according to any one of claims 1 to 6, characterized in that: The solving of the MILP relaxation problem comprises: The commercial solver GUROBI is used to solve the MILP relaxation problem.

8. A device for solving a unit combination optimization model, characterized in that: include: A construction unit, used for constructing a unit commitment optimization model including short-circuit ratio constraints; A processing unit, used for performing inequality scaling on the short circuit ratio constraint, and performing discretization processing on the short circuit ratio constraint after the inequality scaling is completed; The solving unit is used to expand the short-circuit ratio constraint after discretization by using matrix multiplication, process the obtained product term of continuous variable and discrete variable by using big M method, obtain MILP relaxation problem, and solve the MILP relaxation problem.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: 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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