Unit commitment optimization method and system containing transient voltage security constraint

Abstract

The invention discloses a unit commitment optimization method and system containing transient voltage security constraints, and belongs to the field of transient security evaluation and control of a power system. The method comprises the following steps: constructing a unit commitment model according to transient voltage safety constraint conditions; simplifying an embedded differential-algebraic equation set constraint in the unit commitment model through a trajectory sensitivity algorithm to obtain an optimization model under transient voltage under multiple time scales; decomposing the optimization model according to a generalized Benders decomposition algorithm to obtain a mixed integer linear programming problem which does not contain alternating current power flow calculation and a nonlinear programming problem which meets transient voltage constraint after iteration; and respectively solving the mixed integer linear programming problem and the nonlinear programming problem through a solver to obtain an optimal solution of the unit commitment model. Therefore, by simplifying the constraint of the embedded differential-algebraic equation set, the model is decomposed into the main problem and the sub-problem with different emphasis, and the main problem and the sub-problem are solved respectively, so that the efficiency of solving the original problem is greatly improved.

Description

Technical Field

[0001] The present application relates to the field of transient voltage safety assessment and control of power systems, and in particular to a unit combination optimization method and system containing transient voltage safety constraints. Background Art

[0002] In recent years, with the rapid increase in installed capacity and annual power generation of renewable energy, synchronous machines in the system have been gradually replaced by renewable energy units. As the proportion of renewable energy units increases, the system's dynamic reactive power support capacity decreases, and the transient voltage stability problem caused by faults becomes more prominent. When a short circuit fault occurs in the AC system, it may cause transient voltage limit problems, causing large-scale disconnection of renewable energy units and causing significant losses. Therefore, it is necessary to constrain transient voltage in the unit combination problem containing renewable energy units.

[0003] Due to the large-scale access of new energy units, the power grid contains a high proportion of power electronic equipment, which makes the transient voltage characteristics of the power grid during faults very complex and difficult to analyze. In addition, the constructed unit combination optimization model is a mixed integer nonlinear programming (MINLP) problem with embedded differential-algebraic equations (DAEs), which has a very low solution efficiency and is difficult to obtain a feasible solution within an acceptable time; therefore, it is necessary to simplify the model and propose a complete solution process; currently there is no good optimization solution in the industry. Summary of the invention

[0004] The purpose of this application is to provide a unit combination optimization method and system with transient voltage safety constraints, by first constructing a unit combination optimization model including transient voltage safety constraints, then simplifying the DAEs constraints through a trajectory sensitivity algorithm, and then decomposing the problem into a MILP main problem and a nonlinear programming (NLP) sub-problem, and finally solving them separately, thereby achieving an efficient solution to the original model.

[0005] To achieve the above-mentioned purpose, the present application provides a unit combination optimization method with transient voltage safety constraints, the method comprising: constructing a unit combination model according to transient voltage safety constraints; simplifying the embedded differential-algebraic equation group constraints in the unit combination model through a trajectory sensitivity algorithm to obtain an optimization model under transient voltage at multiple time scales; decomposing the optimization model according to a generalized Benders decomposition algorithm to obtain an iterative mixed integer linear programming problem that does not include AC power flow calculation and a nonlinear programming problem that satisfies transient voltage constraints; and solving the mixed integer linear programming problem and the nonlinear programming problem respectively through a solver to obtain the optimal solution of the unit combination model.

[0006] In the above-mentioned unit combination optimization method containing transient voltage safety constraints, optionally, constructing a unit combination model according to transient voltage safety constraints includes: constructing a unit model according to the start and stop status of the unit in each time period, the reactive output of the synchronous machine, the reactive output of the wind turbine and the fault-crossing reactive current proportional coefficient; and optimizing and combining the unit models according to a preset objective function and preset constraints to generate a unit combination model.

[0007] In the above-mentioned unit combination optimization method with transient voltage safety constraints, optionally, the constraints include: piecewise linearized active power constraints, power flow constraints under steady state, initial conditions of dynamic equations, minimum start-up and shutdown times, ramp constraints, system spare capacity constraints, station grid connection point voltage constraints, synchronous machine capacity constraints, wind turbine capacity limitations and reactive current coefficient range, dynamic models, voltage safety constraints during transient processes, and wind turbine capacity limitations during transient processes, or a combination of one or more thereof.

[0008] In the above-mentioned unit commitment optimization method with transient voltage safety constraints, optionally, the expected fault machine in the dynamic model is a three-phase ground short circuit fault occurring at the beginning or end of the AC system line.

[0009] In the above-mentioned unit combination optimization method containing transient voltage safety constraints, optionally, simplifying the embedded differential-algebraic equation group constraints in the unit combination model through a trajectory sensitivity algorithm to obtain an optimization model for transient voltage at multiple time scales includes: establishing corresponding constraints according to the voltage amplitude at the time of fault in the transient process, and obtaining the sensitivity of the voltage amplitude at the time of fault to the controllable quantity through vector calculation composed of controllable quantities of the fault-associated dynamic reactive source; and obtaining the optimization model for transient voltage at multiple time scales according to the distance between the power grid and the safety and stability boundary and the sensitivity.

[0010] In the above-mentioned unit combination optimization method with transient voltage safety constraints, optionally, the mixed integer linear programming problem that does not include AC power flow calculation includes: generating a main problem model by minimizing the operating cost by optimizing the start and stop of units and the active output in different time periods; obtaining the mixed integer linear programming problem that does not include AC power flow calculation by constraining the main problem model with active power balance constraints and feasible cut constraints.

[0011] In the above-mentioned unit combination optimization method containing transient voltage safety constraints, optionally, the nonlinear programming problem that satisfies the transient voltage constraints includes: fixing the results of unit start-up and shutdown and active output according to the optimization results of the mixed integer linear programming problem that does not include AC power flow calculation; obtaining a sub-problem model by optimizing the unit reactive output and fan control parameters in each time period according to the results of unit start-up and shutdown and active output to meet the transient voltage safety constraints; obtaining a nonlinear programming problem that satisfies the transient voltage constraints through the sub-problem model of coupling constraints, voltage safety constraints at the moment of fault occurrence, steady-state voltage safety constraints during faults, and voltage safety constraints at the moment of fault clearing.

[0012] In the above-mentioned unit combination optimization method with transient voltage safety constraints, optionally, solving the mixed integer linear programming problem and the nonlinear programming problem respectively by a solver to obtain the optimal solution of the unit combination model includes: solving the mixed integer linear programming problem by a solver for solving mixed integer linear programming to obtain a first optimal solution; solving the nonlinear programming problem by a solver constructed by an interior point method to obtain a second optimal solution of the sub-problem; and obtaining the optimal solution of the unit combination model according to the first optimal solution and the second optimal solution.

[0013] The present application also provides a unit combination optimization system with transient voltage safety constraints, the system comprising a construction module, a simplification module, a decomposition module and a solution module; the construction module is used to construct a unit combination model according to transient voltage safety constraints; the simplification module is used to simplify the embedded differential-algebraic equation group constraints in the unit combination model through a trajectory sensitivity algorithm to obtain an optimization model under transient voltage at multiple time scales; the decomposition module is used to decompose the optimization model according to a generalized Benders decomposition algorithm to obtain an iterative mixed integer linear programming problem that does not include AC power flow calculation and a nonlinear programming problem that satisfies transient voltage constraints; the solution module is used to solve the mixed integer linear programming problem and the nonlinear programming problem respectively through a solver to obtain the optimal solution of the unit combination model.

[0014] The present application also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above method when executing the computer program.

[0015] The present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for executing the above method.

[0016] The present application also provides a computer program product, comprising a computer program / instruction, which implements the steps of the above method when executed by a processor.

[0017] The beneficial technical effect of this application is that: a unit combination optimization model of the system is constructed, and transient voltage safety is listed as a constraint condition. The model can effectively avoid the system transient voltage exceeding the limit. Based on the trajectory sensitivity analysis method, the DAEs constraints that are difficult to calculate in the model are simplified, and an algebraic expression of the transient voltage is given, which greatly reduces the difficulty of calculation. The generalized Benders decomposition algorithm is used to decompose the optimization model into the main problem of mixed integer linear programming and the sub-problems of nonlinear programming, so as to achieve efficient solution using solvers respectively. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application, constitute a part of the present application, and do not constitute a limitation of the present application. In the drawings:

[0019] Figure 1 A flow chart of a unit commitment optimization method with transient voltage safety constraints provided in one embodiment of the present application;

[0020] Figure 2 A schematic diagram of a construction process of a unit combination model provided in an embodiment of the present application;

[0021] Figure 3 A schematic diagram of an optimization model generation process provided in an embodiment of the present application;

[0022] Figure 4 A schematic diagram of a construction process of a mixed integer linear programming problem provided in an embodiment of the present application;

[0023] Figure 5 A schematic diagram of a flow chart for constructing a nonlinear programming problem satisfying transient voltage constraints provided in an embodiment of the present application;

[0024] Figure 6 A schematic diagram of the optimal solution acquisition process provided by an embodiment of the present application;

[0025] Figure 7 A schematic diagram of the structure of a unit combination optimization device with transient voltage safety constraints provided in one embodiment of the present application;

[0026] Figure 8 A schematic diagram of the structure of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0027] The following will describe the implementation methods of the present application in detail in conjunction with the accompanying drawings and embodiments, so that the implementation process of how the present application applies technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly. It should be noted that as long as there is no conflict, the various embodiments in the present application and the various features in the embodiments can be combined with each other, and the technical solutions formed are all within the protection scope of the present application.

[0028] In addition, the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be performed in an order different from that shown here.

[0029] Please refer to Figure 1 As shown, the present application provides a unit commitment optimization method with transient voltage safety constraints, the method comprising:

[0030] S101 builds a unit combination model based on transient voltage safety constraints;

[0031] S102 simplifies the embedded differential-algebraic equation constraints in the unit combination model by using a trajectory sensitivity algorithm to obtain an optimization model under transient voltage at multiple time scales;

[0032] S103 decomposes the optimization model according to the generalized Benders decomposition algorithm to obtain an iterative mixed integer linear programming problem that does not include AC power flow calculation and a nonlinear programming problem that satisfies transient voltage constraints;

[0033] S104: Solving the mixed integer linear programming problem and the nonlinear programming problem respectively by a solver to obtain an optimal solution for the unit commitment model.

[0034] In the above embodiment, the unit combination optimization method with transient voltage safety constraints provided by the present application first uses the trajectory sensitivity method to simplify the DAEs in the original constraints, and obtains an algebraic expression of transient voltages that is easy to calculate under multiple time scales; further, the generalized Benders decomposition algorithm is used to decompose the original problem into an iterative main problem and sub-problems, so that the main problem is a MILP problem that does not include AC power flow calculations, and the sub-problem is an NLP problem that satisfies transient voltage constraints, so that different solvers are used to efficiently solve the two problems respectively, and ultimately greatly improve the efficiency of solving the original problem. The specific implementation process of each step will be described in detail in the subsequent embodiments, and will not be listed one by one here.

[0035] Please refer to Figure 2 As shown, in one embodiment of the present application, constructing a unit combination model according to transient voltage safety constraints includes:

[0036] S201 constructs a unit model according to the start and stop status of the unit in each period, the reactive output of the synchronous machine, the reactive output of the fan and the proportional coefficient of the reactive current of the fault ride-through;

[0037] S202 optimizes and combines the unit models according to a preset objective function and preset constraints to generate a unit combination model.

[0038] Among them, the constraints include: piecewise linear active power constraints, power flow constraints in steady state, initial conditions of dynamic equations, minimum startup and shutdown time, ramp constraints, system spare capacity constraints, station grid connection point voltage constraints, synchronous machine capacity constraints, wind turbine capacity limitations and reactive current coefficient range, dynamic models, voltage safety constraints in transient processes, and wind turbine capacity limitations in transient processes. The expected fault machine in the dynamic model is a three-phase ground short circuit fault at the beginning or end of the AC system line. Specifically, constructing a unit combination model in actual work is to construct a collaborative optimization model, and its process is as follows:

[0039] The complete unit commitment model considering transient voltage safety constraints is shown in equations (1)-(23). t Represents the variable of the tth period. s represents the variable under the sth fault. 0 Indicates the initial value of a variable. k Indicates the value of the variable in the kth iteration. k Represents the variable at time τ k The value of . d ,(·) q ,(·) x ,(·) y Indicates the d-axis, q-axis, x-axis, and y-axis components of the variable. The meaning of each variable is explained below.

[0040]

[0041] st(2)-(23);

[0042] Among them, the optimization variables are the start and stop states of the units in each period Synchronous machine active output Reactive power output Active output of fan Reactive power output Fault Ride-Through Reactive Current Proportional Coefficient S T ,S G is the number set of each time period and the number set of synchronous machines. The optimization goal is to minimize the operating cost of each unit g within a day, including the actual power generation cost Actual startup cost and actual downtime costs In addition, the wind power curtailment in each period is included in the optimization target As the penalty term, M is the penalty coefficient. The specific expressions of the objective function and constraints are shown below.

[0043] 1) Objective function:

[0044]

[0045] The generation cost is modeled using a piecewise linearization method. su ,C sd Indicates the startup and shutdown cost coefficient. g,m It is the slope of the power generation cost of unit g in the mth segment with respect to active power. g,m Indicates the power generation of unit g in the mth segment. n is the number of segments. g,min Indicates the lower limit of the active output of unit g.

[0046] 2) Piecewise linear active power constraint:

[0047]

[0048] Among them, y g Indicates the start and stop status of the unit g, P g Indicates the active output of unit g, ΔP g,m,max It indicates the maximum active output of unit g in the mth segment.

[0049] 3) Power flow constraints in steady state:

[0050]

[0051] Where I and V represent the vectors of node injection current and node voltage respectively. G and B represent the real and imaginary parts of the node admittance matrix. b ,Q b They represent the active power and reactive power injected into node b, V b ,I b They represent the node b voltage and node injection current respectively. bus It should be noted that equations (4)-(6) are equivalent to the constraints of the power flow equation in the rectangular coordinate system.

[0052] 4) Initial conditions of the dynamic equations:

[0053]

[0054] Among them, E g represents the g-time transient potential of the synchronous machine, x" g,d is the direct-axis subtransient reactance, V g ,I g They represent the node voltage and injection current of the synchronous machine g respectively. g ,T wRespectively represent the coordinate transformation matrix of the synchronous machine g and the fan w variables from the xy axis to the dq axis. g ,θ w They represent the synchronous machine g power angle and the wind turbine w grid connection point voltage phase angle respectively. g,dq ,I g,dq Respectively represent the vectors composed of the dq axis components of the synchronous machine terminal voltage and the injected current, V g,xy ,I g,xy Represents the vectors composed of the x-axis components of the synchronous machine terminal voltage and the injected current, V w,dq ,I w,dq They represent the vectors composed of the voltage at the grid connection point of wind turbine w and the dq axis components of the injected current, V w,xy ,I w,xy Respectively represent the vectors composed of the xy axis components of the voltage at the grid connection point of wind turbine w and the injected current. W Represents a collection of fan numbers.

[0055] 5) Minimum startup / downtime:

[0056]

[0057] Among them, T on ,T off Indicates the minimum startup and shutdown time interval.

[0058] 6) Climbing constraints:

[0059]

[0060] Among them, RD and RU represent the upward and downward climbing rate limits.

[0061] 7) System spare capacity constraints:

[0062]

[0063] Among them, PR U , P.R. D Indicates the system's upward and downward reserve requirements. g,max Indicates the maximum output of the synchronous machine.

[0064] 8) Voltage constraints at the station grid connection point:

[0065]

[0066] Among them, V w ,V w,min ,V w,max Indicates the grid connection point voltage of wind turbine w and its minimum and maximum values.

[0067] 9) Synchronous machine capacity constraints:

[0068]

[0069] Among them, Q g ,Q g,min ,Q g,max Indicates the reactive output of the synchronous machine g and its minimum and maximum values.

[0070] 10) Fan capacity limit and reactive current coefficient range:

[0071]

[0072] Among them, P w,pre ,P w,cur They represent the predicted active output value and abandoned wind power of wind turbine w respectively. w Indicates the reactive power output of the fan w, S w,N Indicates the rated capacity of the fan w. k w,q It indicates the reactive current proportional coefficient of wind turbine w under low voltage ride through.

[0073] 11) Dynamic Models (DAEs):

[0074] dx t,s (τ) / dτ=f t,s (u t ,x t,s (τ),y t,s (τ)); (20)

[0075]

[0076] Where x represents the state variable, such as the sub-transient electromotive force of the synchronous machine, the injection current of the fan, etc. y represents the algebraic variable, such as the voltage amplitude, voltage phase, etc. u represents the control variable, such as the start and stop of the unit, the reactive current proportional coefficient of the fan fault ride-through, etc. flt represents the expected fault set. The fault considered in this application is a three-phase ground short circuit fault at the beginning or end of the AC system line. τ1 and τ2 represent the fault occurrence time and clearing time respectively.

[0077] 12) Voltage safety constraints during transient processes:

[0078]

[0079] Among them, V LVRT,th ,V HVRT,th They respectively represent the lowest and highest voltage amplitudes that the wind turbine can withstand during low voltage ride-through and high voltage ride-through.

[0080] 13) Fan capacity limitation during transient process:

[0081]

[0082] Among them, I w ,I N,w They represent the fan injection current and its rated value respectively.

[0083] Please refer to Figure 3 As shown, in one embodiment of the present application, the embedded differential-algebraic equations in the unit combination model are simplified by a trajectory sensitivity algorithm to obtain an optimization model for transient voltages at multiple time scales, including:

[0084] S301 establishes corresponding constraints according to the voltage amplitude at the time of the fault in the transient process, and obtains the sensitivity of the voltage amplitude at the time of the fault to the controllable quantity through vector calculation composed of the controllable quantity of the fault-associated dynamic reactive source;

[0085] S302 obtains an optimization model for transient voltage at multiple time scales according to the distance between the power grid and the safety and stability boundary and the sensitivity.

[0086] Specifically, in actual work, trajectory sensitivity is mainly used to simplify the transient voltage safety constraints (20)-(23): the voltage amplitudes at three key moments, namely, the moment of fault occurrence, the "steady state" during the fault, and the moment of fault clearing, are extracted from the transient process to establish relevant constraints. For a dynamic reactive source, its controllable quantity is related to the node type. For example, if the bus where the reactive source is located is a PV type node, the controllable quantity is voltage; if the bus where the reactive source is located is a PQ type node, the controllable quantity is reactive power. Let the vector of controllable quantities of the fault-associated dynamic reactive source be denoted as u, then the sensitivity w of the voltage amplitude at the moment of fault occurrence, the "steady state" during the fault, and the moment of fault clearing to the controllable quantity u can be calculated. u , the calculation expression is as follows:

[0087]

[0088] in, It means that when other controllable quantities remain unchanged, the controllable quantity u of the ith dynamic reactive power source is i Increase Δu i The tth component of TVSI index at time ; It means that when other controllable quantities remain unchanged, the controllable quantity u of the ith dynamic reactive power source is i Increase or decrease Δu i The component of the voltage amplitude at the tth critical moment. It is the voltage amplitude t component to the controllable quantity u i If the controllable quantity u is the initial value u 0 The voltage amplitude is V 0 , the voltage amplitude safety threshold is V L ,V H , you can use u0 The following expression is defined in the vicinity:

[0089]

[0090] in, is the vector of the sensitivity of the tth component of the voltage amplitude to the controllable quantity vector u. Equation (25) is the transient voltage safety constraint. Among them, the sensitivity vector It reflects the strength of the reactive power source control effect and can be used to determine the reactive power source that needs to be adjusted; V t 0 With V L ,V H The difference reflects the distance between the power grid and the safety and stability boundary, which can be used to determine whether the reactive power source needs to be adjusted. Note that since different faults have different associated reactive power sources, the transient voltage safety constraint is fault-dependent; V t 0 They are all divided into multiple time scales, so the transient voltage safety constraints are multi-time scales.

[0091] In one embodiment of the present application, the generalized Benders decomposition algorithm is mainly used to decompose the problem. Specifically, in order to be able to directly call the existing mature solver to solve the model, the above-mentioned converted MINLP problem is decomposed using the generalized Benders decomposition algorithm. The mathematical model corresponding to the master problem (Master Problem, MP) and sub-problem (Sub-Problem, SP) of the kth iteration is as follows. k is the main problem of the kth iteration, SP t,k is the t-th subproblem of the k-th iteration.

[0092] Please refer to Figure 4 As shown, in one embodiment of the present application, the mixed integer linear programming problem that does not include AC power flow calculation includes:

[0093] S401 generates the main problem model by optimizing the start and stop of units and active output in different periods to minimize the operating cost;

[0094] S402 obtains a mixed integer linear programming problem that does not include AC power flow calculation by using the main problem model with active power balance constraints and feasible cut constraints.

[0095] Specifically, in actual work, the main problem (MP k ) that is, the mixed integer linear programming problem that does not include AC power flow calculation is mainly solved by optimizing the start and stop of units in different time periods. and active contribution To minimize the total operating cost. The complete model is as follows. The mathematical properties of this model are MILP problems. It should be pointed out that since the main problem does not involve reactive voltage, the main problem adopts active power flow constraints that ignore network losses. The precise power flow model will be considered in the sub-problem. In order to ensure the feasibility of the optimization results, 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.

[0096]

[0097] st(2)(3),(9)-(13),(15)(17);

[0098] In addition to the above constraints, you also need to add the following constraints:

[0099] 1) Active power balance constraints:

[0100]

[0101] Among them, P l Indicates the load active power. S LD Represents a collection of load nodes.

[0102] 2) Feasible cutting:

[0103]

[0104] in, is the Lagrange multiplier corresponding to constraint (30), PO t,k The objective function value calculated at the kth iteration for the subproblem corresponding to the tth time period.

[0105] Please refer to Figure 5 As shown, in one embodiment of the present application, the nonlinear programming problem that satisfies the transient voltage constraint includes:

[0106] S501 fixes the start and stop and active output results of the unit according to the optimization results of the mixed integer linear programming problem that does not include AC power flow calculation;

[0107] S502 obtains a sub-problem model by optimizing the reactive output of the unit and the fan control parameters in each period according to the results of the unit start-up and shutdown and the active output to meet the transient voltage safety constraint;

[0108] S503 obtains a nonlinear programming problem that satisfies transient voltage constraints through the sub-problem models of coupling constraints, voltage safety constraints at the moment of fault occurrence, voltage safety constraints in a steady state during a fault, and voltage safety constraints at the moment of fault clearing.

[0109] Specifically, in actual work, the sub-problem (SP t,k ) is a nonlinear programming problem that satisfies transient voltage constraints. It mainly starts and stops fixed units based on the optimization results of the main problem. and active contribution The result is (30), and then the reactive power output of the units in each period is optimized. Fan control parameters To meet the transient voltage safety constraint. The mathematical nature of this model is an NLP problem. Since the transient voltage is affected by the start and stop of the unit, the optimization result of the main problem may not be feasible for the sub-problem. Therefore, constraint (22) is relaxed to obtain equations (31)-(33), and non-negative slack variables are added to the optimization objective in the form of penalty terms, as shown in equation (29), forming a relaxed sub-problem. Among them, Non-negative slack variables introduced to ensure the feasibility of the subproblem.

[0110]

[0111] st(4)-(8),(14),(16),(18)-(19),(23);

[0112] In addition to the above constraints, you also need to add the following constraints:

[0113] 1) Coupling constraints:

[0114]

[0115] 2) Voltage safety constraints at the moment of fault occurrence:

[0116]

[0117] 3) Voltage safety constraints in “steady state” during faults:

[0118]

[0119] 4) Voltage safety constraints at the moment of fault clearing:

[0120]

[0121] When there is a sub-problem corresponding to a certain time period that does not satisfy the constraints, the Benders cut will be returned to the main problem, that is, formula (28). When all sub-problems satisfy the constraints, the algorithm converges.

[0122] Please refer to Figure 6 As shown, in one embodiment of the present application, respectively solving the mixed integer linear programming problem and the nonlinear programming problem by a solver to obtain the optimal solution of the unit commitment model includes:

[0123] S601: solving the mixed integer linear programming problem by a solver for solving mixed integer linear programming to obtain a first optimal solution;

[0124] S602: solving the nonlinear programming problem by using a solver constructed by an interior point method to obtain a second optimal solution to the subproblem;

[0125] S603 obtains an optimal solution of the unit combination model according to the first optimal solution and the second optimal solution.

[0126] Specifically, in actual work, appropriate solvers are selected according to the types of the main problem and sub-problems for solving. This process does not involve too much technical difficulty, and users can choose to use it according to actual needs. This application does not impose further restrictions on this.

[0127] Please refer to Figure 7 As shown, the present application also provides a unit combination optimization system with transient voltage safety constraints, the system comprising a construction module, a simplification module, a decomposition module and a solution module; the construction module is used to construct a unit combination model according to transient voltage safety constraints; the simplification module is used to simplify the embedded differential-algebraic equation group constraints in the unit combination model through a trajectory sensitivity algorithm to obtain an optimization model under transient voltage under multiple time scales; the decomposition module is used to decompose the optimization model according to the generalized Benders decomposition algorithm to obtain an iterative mixed integer linear programming problem that does not include AC power flow calculation and a nonlinear programming problem that satisfies transient voltage constraints; the solution module is used to solve the mixed integer linear programming problem and the nonlinear programming problem respectively through a solver to obtain the optimal solution of the unit combination model. Since the principle of solving the problem of the device is similar to that of the unit combination optimization method with transient voltage safety constraints, the implementation of the device can refer to the implementation of the unit combination optimization method with transient voltage safety constraints, and the repeated parts will not be repeated.

[0128] The beneficial technical effect of this application is that: a unit combination optimization model of the system is constructed, and transient voltage safety is listed as a constraint condition. The model can effectively avoid the system transient voltage exceeding the limit. Based on the trajectory sensitivity analysis method, the DAEs constraints that are difficult to calculate in the model are simplified, and an algebraic expression of the transient voltage is given, which greatly reduces the difficulty of calculation. The generalized Benders decomposition algorithm is used to decompose the optimization model into the main problem of mixed integer linear programming and the sub-problems of nonlinear programming, so as to achieve efficient solution using solvers respectively.

[0129] The present application also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above method when executing the computer program.

[0130] The present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for executing the above method.

[0131] The present application also provides a computer program product, comprising a computer program / instruction, which implements the steps of the above method when executed by a processor.

[0132] like Figure 8 As shown, the electronic device 600 may further include: a communication module 110, an input unit 120, an audio processor 130, a display 160, and a power supply 170. It is worth noting that the electronic device 600 does not necessarily have to include Figure 8 In addition, the electronic device 600 may also include Figure 8 For components not shown, reference may be made to the prior art.

[0133] like Figure 8 As shown, the central processor 100 is sometimes also referred to as a controller or an operation control, and may include a microprocessor or other processor devices and / or logic devices. The central processor 100 receives inputs and controls the operations of various components of the electronic device 600.

[0134] The memory 140 may be, for example, one or more of a cache, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory or other suitable devices. The above-mentioned information related to the failure may be stored, and a program for executing the relevant information may also be stored. The CPU 100 may execute the program stored in the memory 140 to implement information storage or processing.

[0135] The input unit 120 provides input to the CPU 100. The input unit 120 is, for example, a key or a touch input device. The power supply 170 is used to provide power to the electronic device 600. The display 160 is used to display display objects such as images and text. The display may be, for example, an LCD display, but is not limited thereto.

[0136] The memory 140 may be a solid-state memory, such as a read-only memory (ROM), a random access memory (RAM), a SIM card, etc. It may also be a memory that saves information even when the power is off, can be selectively erased, and is provided with more data, examples of which are sometimes referred to as EPROMs, etc. The memory 140 may also be some other type of device. The memory 140 includes a buffer memory 141 (sometimes referred to as a buffer). The memory 140 may include an application / function storage unit 142, which is used to store application programs and function programs or processes for executing the operation of the electronic device 600 through the central processor 100.

[0137] The memory 140 may also include a data storage unit (data 143) for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit (driver 144) of the memory 140 may include various drivers for communication functions of the electronic device and / or for executing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0138] The communication module 110 is a transmitter / receiver 110 that transmits and receives signals via an antenna 111. The communication module (transmitter / receiver) 110 is coupled to the central processor 100 to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.

[0139] Based on different communication technologies, multiple communication modules 110 may be provided in the same electronic device, such as a cellular network module, a Bluetooth module and / or a wireless LAN module. The communication module (transmitter / receiver) 110 is also coupled to a speaker 131 and a microphone 132 via an audio processor 130 to provide an audio output via the speaker 131 and receive an audio input from the microphone 132, thereby realizing a common telecommunication function. The audio processor 130 may include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor 130 is also coupled to the central processor 100, so that the sound can be recorded on the local machine through the microphone 132, and the sound stored on the local machine can be played through the speaker 131.

[0140] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0141] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes 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 generate 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 1A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0142] 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 including 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.

[0143] 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 1 The steps for the functions specified in one or more boxes.

[0144] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the scope of protection of the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A unit commitment optimization method with transient voltage safety constraints, characterized in that: The method comprises: Construct the unit combination model according to the transient voltage safety constraint conditions; The embedded differential-algebraic equations in the unit combination model are simplified by using a trajectory sensitivity algorithm to obtain an optimization model under transient voltage at multiple time scales; Decomposing the optimization model according to the generalized Benders decomposition algorithm to obtain an iterative mixed integer linear programming problem that does not include AC power flow calculation and a nonlinear programming problem that satisfies transient voltage constraints; The mixed integer linear programming problem and the nonlinear programming problem are solved respectively by a solver to obtain an optimal solution of the unit commitment model.

2. The unit commitment optimization method with transient voltage safety constraints according to claim 1 is characterized in that: The unit combination model constructed according to transient voltage safety constraints includes: The unit model is constructed according to the start and stop status of the unit in each period, the reactive output of the synchronous machine, the reactive output of the fan and the proportional coefficient of the reactive current of the fault ride-through; The unit models are optimized and combined according to the preset objective function and the preset constraint conditions to generate a unit combination model.

3. The unit commitment optimization method with transient voltage safety constraints according to claim 2 is characterized in that: The constraints include: piecewise linear active power constraints, power flow constraints in steady state, initial conditions of dynamic equations, minimum startup and shutdown time, ramp constraints, system spare capacity constraints, station grid connection point voltage constraints, synchronous machine capacity constraints, wind turbine capacity limitations and reactive current coefficient range, dynamic models, voltage safety constraints in transient processes, and wind turbine capacity limitations in transient processes, one or more combinations thereof.

4. The unit commitment optimization method with transient voltage safety constraints according to claim 3 is characterized in that: The expected fault machine in the dynamic model is a three-phase ground short circuit fault occurring at the beginning or end of the AC system line.

5. The unit commitment optimization method with transient voltage safety constraints according to claim 1, characterized in that: The embedded differential-algebraic equations in the unit combination model are simplified by using the trajectory sensitivity algorithm to obtain the optimization model of transient voltage under multiple time scales, including: Corresponding constraints are established according to the voltage amplitude at the time of fault in the transient process, and the sensitivity of the voltage amplitude at the time of fault to the controllable quantity is obtained through vector calculation composed of the controllable quantity of the fault-related dynamic reactive source; An optimization model for transient voltage at multiple time scales is obtained according to the distance between the power grid and the safety and stability boundary and the sensitivity.

6. The unit commitment optimization method with transient voltage safety constraints according to claim 1, characterized in that: Mixed integer linear programming problems that do not include AC power flow calculations include: The main problem model is generated by optimizing the start and stop of units and active output at different time periods to minimize the operating cost; The main problem model is obtained by using active power balance constraints and feasible cut constraints to obtain a mixed integer linear programming problem that does not include AC power flow calculation.

7. The unit commitment optimization method with transient voltage safety constraints according to claim 1, characterized in that: The nonlinear programming problem that satisfies transient voltage constraints includes: The results of fixing the start and stop of the units and the active output are obtained based on the optimization results of the mixed integer linear programming problem that does not include AC power flow calculations; According to the results of unit start-up and shutdown and active output, the sub-problem model is obtained by optimizing the unit reactive output and fan control parameters in each period to meet the transient voltage safety constraint; A nonlinear programming problem satisfying transient voltage constraints is obtained through the sub-problem model of coupling constraints, voltage safety constraints at the moment of fault occurrence, voltage safety constraints in a steady state during a fault, and voltage safety constraints at the moment of fault clearing.

8. The unit commitment optimization method with transient voltage safety constraints according to claim 1, characterized in that: Solving the mixed integer linear programming problem and the nonlinear programming problem by a solver to obtain the optimal solution of the unit commitment model comprises: Solving the mixed integer linear programming problem by a solver for solving mixed integer linear programming to obtain a first optimal solution; Solving the nonlinear programming problem by using a solver constructed by an interior point method to obtain a second optimal solution to the subproblem; An optimal solution of the unit combination model is obtained according to the first optimal solution and the second optimal solution.

9. A unit commitment optimization system with transient voltage safety constraints, characterized in that: The system comprises a building module, a simplifying module, a decomposing module and a solving module; The construction module is used to construct a unit combination model according to transient voltage safety constraints; The simplification module is used to simplify the embedded differential-algebraic equation group constraints in the unit combination model through a trajectory sensitivity algorithm to obtain an optimization model under transient voltage at multiple time scales; The decomposition module is used to decompose the optimization model according to the generalized Benders decomposition algorithm to obtain the iterative mixed integer linear programming problem that does not include AC power flow calculation and the nonlinear programming problem that meets the transient voltage constraint; The solution module is used to respectively solve the mixed integer linear programming problem and the nonlinear programming problem through a solver to obtain an optimal solution to the unit commitment model.

10. 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 method according to any one of claims 1 to 8 is implemented.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

12. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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