Unit combination optimization processing method and device

Abstract

The present application relates to a method, apparatus, computer equipment, storage medium and computer program product for optimizing unit combination. The method comprises: obtaining the operating cost data of the power system; the operating cost data comprises unit cost, standby cost and energy storage equipment regeneration cost; determining the SCUC model according to the operating cost data; the SCUC model takes minimizing the total operating cost as the objective function; based on the Benders decomposition model, decomposing the SCUC model into main sub-problems, and solving the main sub-problems to obtain the optimal solution for the joint operation of the units; wherein the main sub-problems comprise the UC main problem, the basic case sub-problem, the fault condition sub-problem and the scenario case sub-problem. The use of this method can effectively improve the optimization efficiency of the safety-constrained unit combination problem.

Description

Technical Field

[0001] The present application relates to the field of power system control technology, and in particular to a method and device for optimizing unit combination. Background Art

[0002] Cost and safety are primary concerns in the energy market. The intermittency of renewable energy sources (RESs) and unscheduled outage of the components (UOCs) pose a threat to energy market security and create numerous challenges for optimal operation. Therefore, effective methods are needed to address the security-constrained unit commitment (SCUC) problem, taking into account wind power instability.

[0003] At present, the operation problems under unstable conditions have been widely studied. Under large-scale unstable conditions of wind power, the SCUC problem becomes more complicated and suffers from the problem of low optimization efficiency. Summary of the Invention

[0004] Based on this, it is necessary to provide a unit combination optimization processing method, device, computer equipment, computer-readable storage medium and computer program product that can improve the optimization efficiency in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for optimizing unit commitment. The method comprises:

[0006] Obtaining operating cost data for the power system; the operating cost data includes unit costs, standby costs, and energy storage device regeneration costs;

[0007] Determine a SCUC model based on the operating cost data; the SCUC model takes minimizing the total operating cost as an objective function;

[0008] Based on the Benders decomposition model, the SCUC model is decomposed into main and subproblems, and the main and subproblems are solved to obtain the optimal solution for the joint operation of the units; wherein the main and subproblems include the UC main problem, the basic case subproblem, the fault condition subproblem and the scenario case subproblem.

[0009] In one embodiment, the operating cost data further includes lost opportunity cost and expected value of power generation; the unit cost includes unit startup cost, unit shutdown cost and unit no-load cost;

[0010] In the step of decomposing the SCUC model into main and sub-problems and solving the main and sub-problems to obtain the optimal solution for the joint operation of the units:

[0011] Determining the objective function of the SCUC model as the objective function of the UC main problem;

[0012] Acquiring operating data of generators, wind turbines, and energy storage devices of the power system;

[0013] Determining power generation constraints based on the generator operating data, determining wind power constraints based on the wind turbine operating data, and determining energy storage constraints based on the energy storage device operating data;

[0014] Based on the power generation constraint condition, the wind power constraint condition and the energy storage constraint condition, the objective function of the UC main problem is solved to obtain a main solution.

[0015] In one embodiment, it further includes:

[0016] Obtaining slack variables for load reduction under a base case, and minimizing the slack variables under the base case as an objective function of the base case subproblem;

[0017] Acquiring line flow data of the power system;

[0018] Determining constraints of a base case subproblem according to the line power flow data, and solving an objective function of the base case subproblem based on the constraints of the base case subproblem;

[0019] If the solution to the base case subproblem is positive, Benders decomposition is performed to generate a first safety constraint, and the main solution is optimized according to the first safety constraint; otherwise, the fault case subproblem is solved.

[0020] In one embodiment, the step of solving the fault condition sub-problem includes:

[0021] Obtaining slack variables of load reduction under different fault events, and minimizing the slack variables under the different fault events as the objective function of the fault condition subproblem;

[0022] Acquiring operating data of a standby unit of the power system;

[0023] Determining a fault condition sub-problem constraint condition according to the wind turbine operating data and the standby unit operating data, and solving an objective function of the fault condition sub-problem based on the fault condition sub-problem constraint condition;

[0024] If the solution to the fault case subproblem is positive, Benders decomposition is performed to generate a second safety constraint, and the main solution is optimized based on the second safety constraint. Otherwise, the scenario case subproblem is solved.

[0025] In one embodiment, the step of solving the scenario case sub-problem includes:

[0026] Obtaining slack variables for load reduction under a scenario case, and minimizing the slack variables under the scenario case as an objective function of the scenario case subproblem;

[0027] Obtain the dispatch power under the scenario case;

[0028] Determining a scenario case subproblem constraint condition according to the line flow data and the dispatch power, and solving an objective function of the scenario case subproblem based on the scenario case subproblem constraint condition;

[0029] Determine whether to output the optimal plan for the joint operation of the units based on the solution results of the scenario case sub-problems.

[0030] In one embodiment, the step of determining whether to output the optimal plan for joint operation of the units based on the solution results of the scenario case sub-problem includes:

[0031] If the solution result of the scenario case sub-problem is positive, Benders decomposition is performed to generate a third safety constraint condition, and the main solution is optimized according to the third safety constraint condition. Otherwise, the optimal solution for the joint operation of the units is output.

[0032] In a second aspect, the present application also provides a unit combination optimization processing device. The device includes:

[0033] A data acquisition module is used to acquire operating cost data of the power system; the operating cost data includes unit cost, standby cost and energy storage equipment regeneration cost;

[0034] A SCUC model determination module, configured to determine a SCUC model based on the operating cost data; the SCUC model takes minimizing the total operating cost as an objective function;

[0035] The model solving module is used to decompose the SCUC model into main and subproblems based on the Benders decomposition model, and solve the main and subproblems to obtain the optimal solution for the joint operation of the units; wherein the main and subproblems include the UC main problem, the basic case subproblem, the fault condition subproblem and the scenario case subproblem.

[0036] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are performed:

[0037] Obtaining operating cost data for the power system; the operating cost data includes unit costs, standby costs, and energy storage device regeneration costs;

[0038] Determine a SCUC model based on the operating cost data; the SCUC model takes minimizing the total operating cost as an objective function;

[0039] Based on the Benders decomposition model, the SCUC model is decomposed into main and subproblems, and the main and subproblems are solved to obtain the optimal solution for the joint operation of the units; wherein the main and subproblems include the UC main problem, the basic case subproblem, the fault condition subproblem and the scenario case subproblem.

[0040] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the following steps:

[0041] Obtaining operating cost data for the power system; the operating cost data includes unit costs, standby costs, and energy storage device regeneration costs;

[0042] Determine a SCUC model based on the operating cost data; the SCUC model takes minimizing the total operating cost as an objective function;

[0043] Based on the Benders decomposition model, the SCUC model is decomposed into main and subproblems, and the main and subproblems are solved to obtain the optimal solution for the joint operation of the units; wherein the main and subproblems include the UC main problem, the basic case subproblem, the fault condition subproblem and the scenario case subproblem.

[0044] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the following steps:

[0045] Obtaining operating cost data for the power system; the operating cost data includes unit costs, standby costs, and energy storage device regeneration costs;

[0046] Determine a SCUC model based on the operating cost data; the SCUC model takes minimizing the total operating cost as an objective function;

[0047] Based on the Benders decomposition model, the SCUC model is decomposed into main and subproblems, and the main and subproblems are solved to obtain the optimal solution for the joint operation of the units; wherein the main and subproblems include the UC main problem, the basic case subproblem, the fault condition subproblem and the scenario case subproblem.

[0048] The above-mentioned unit combination optimization processing method, device, computer equipment, storage medium and computer program product determine the SCUC model based on the operating cost data obtained from the power system, and then decompose the SCUC model into main and sub-problems based on the Benders decomposition model, so as to solve the main and sub-problems to obtain the optimal solution for the joint operation of the units, which can effectively improve the optimization efficiency of the safety-constrained unit combination problem. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A schematic flow chart of a method for optimizing unit commitment in one embodiment;

[0050] Figure 2 A schematic diagram of a process for solving the SCUC model in one embodiment;

[0051] Figure 3 Schematic diagram of the structure of a six-busbar test system in one embodiment;

[0052] Figure 4 Schematic diagram of the structure of a unit combination optimization processing device in one embodiment. DETAILED DESCRIPTION

[0053] To facilitate understanding of the present application, the present application will be described more fully below with reference to the accompanying drawings. The accompanying drawings provide embodiments of the present application. However, the present application may be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to make the disclosure of the present application more thorough and comprehensive.

[0054] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application pertains. The terms used herein in the specification of this application are for the purpose of describing specific embodiments only and are not intended to limit this application.

[0055] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0056] In one embodiment, Figure 1As shown, a unit commitment optimization processing method is provided. This embodiment uses the method applied to a terminal as an example for illustration. The method includes the following steps:

[0057] Step S110, obtaining the operating cost data of the power system; the operating cost data includes unit cost, standby cost and energy storage device regeneration cost;

[0058] Specifically, in some examples, the operating cost data also includes lost opportunity cost and expected power generation cost, where the lost opportunity cost (LOC) is the compensation for the contraction energy that is not utilized during operation; the unit cost includes the unit startup cost, the unit shutdown cost (or the unit shutdown cost) and the unit no-load cost. In some examples, the unit startup cost, the unit shutdown cost and the unit no-load cost refer to the thermal power unit startup cost, the thermal power unit shutdown cost and the thermal power unit no-load cost, respectively; the standby cost includes the unit upward standby cost and the unit downward standby cost; the energy storage equipment regeneration cost includes the energy storage equipment regeneration cost.

[0059] Step S120, determining the SCUC model based on the operating cost data; the SCUC model takes minimizing the total operating cost as the objective function;

[0060] Specifically, the objective function of the SCUC model is to minimize the total operating cost. In some examples, SC t represents the startup cost of thermal power units, DC g represents the shutdown cost of thermal power units, NC g represents the no-load cost of thermal power units, represents the unit's upward standby cost, represents the unit's downward standby cost, represents the price of energy storage equipment regeneration, Indicates the hourly discharge capacity of the energy storage device, Indicates the price of energy storage realization in the clearing market, Indicates the amount of energy storage realized in each scenario, represents the hourly electricity price of the generator, Taking the hourly power generation of the generator as an example, the objective function of the SCUC model is as follows:

[0061]

[0062] Among them, st g,t Indicates unit startup variables, sd g,t Indicates unit shutdown variable, I g,t Indicates the unit no-load variable, represents the maximum upward backup cost, represents the maximum downward backup cost, ζt Indicates the value of the reciprocal of the hourly load normalized to the reciprocal of the peak load, Ω s Represents the probability of each scene, ∑ t ∑ g (SC t st g,t +DC g sd g,t +NC g I g,t ) represents the unit cost, The term represents the standby cost, The term represents the regeneration cost of energy storage equipment, The term represents the lost opportunity cost in a certain scenario, The term represents the expected cost of electricity generation under a certain scenario.

[0063] In step S130, based on the Benders decomposition model, the SCUC model is decomposed into main and sub-problems, and the main and sub-problems are solved to obtain the optimal solution for the joint operation of the units; wherein the main and sub-problems include the UC main problem, the basic case sub-problem, the fault condition sub-problem and the scenario case sub-problem.

[0064] Specifically, the Benders decomposition model can refer to the Benders decomposition algorithm. Using the Benders decomposition algorithm, the objective function of the above-mentioned SCUC model can be decomposed into main subproblems for solution. In some examples, a relaxed UC main problem can be first constructed for preliminary solution, and then the basic case subproblem, fault condition subproblem and scenario case subproblem are solved in turn. By continuously adding one of the constraints to the main problem, that is, adding a valid cut plane, until the optimal solution is generated, the optimal solution for the joint operation of the units can be obtained.

[0065] In a specific example, the solution process of the SCUC model is as follows: Figure 2As shown in the figure, i represents the number of iterations, t is the time variable, NT is the time length, CI is the fault impact variable, e is the fault variable, NE is the number of faults, s is the scenario variable, and NS is the number of scenarios. The solution process is to first solve the UC main problem to obtain the main solution for hourly scheduling, and then use the network constraints of each t, ​​e and s to check the main solution. Specifically, the basic case sub-problem is first solved, with time t=1:NT as the variable, solving the basic case from 1 to NT. If the solution result is a positive value, Benders decomposition is performed to generate constraint conditions to constrain the objective function of the main problem safely. Otherwise, the intelligent fault ranking method (Intelligent Contingency Ranking) is used. Analysis, referred to as ICRA), obtains the parameters required for the fault situation sub-problem, that is, uses the dispatch value obtained by the above iteration to calculate the index "CI", and classifies the units according to "CI" and selects high-impact interruptions, specifically considering the shutdown of the generator as a fault. Since the SCUC model evaluates the impact of the dispatch generator failure on the line congestion and establishes a priority list, the parameter of all generators can be calculated first. This parameter represents the change in line current caused by a 1MW change in the unit's power generation. Then, this parameter is multiplied by the normalized line flow and the unit output power to obtain the impact of the power outage on the line load. Indicates the parameters of the generator, Indicates the line flow, Taking the output power of the unit as an example, the impact of power outage on line load is CI g,t Calculated by the following formula:

[0066]

[0067] in, Indicates the upper limit of the line flow, is the normalized line flow.

[0068] The impact of generator outage on line load is analyzed by CI g,t For sorting, the outage scheme with the highest ranking in the intelligent fault sorting can be selected to ensure that the selected high-ranking fault and the final scheme match, because the algorithm will terminate only when the scheme is safe under the calculated fault conditions;

[0069] Furthermore, after obtaining the parameters required for the fault situation subproblem, the fault situation subproblem can be solved. With time t=1:NT and e=1:NE as variables, the fault cases of t from 1 to NT and e from 1 to NE are solved. If the solution result is a positive value, Benders decomposition is performed to generate constraints to perform safety constraints on the objective function of the main problem. Otherwise, the scenario case subproblem is continued to be solved. With time t=1:NT and s=1:NS as variables, the scenario cases of t from 1 to NT and s from 1 to NS are solved. If the solution result is a positive value, Benders decomposition is performed to generate constraints to perform safety constraints on the objective function of the main problem. This is done until no Benders decomposition is generated, and the optimal solution is output.

[0070] The above-mentioned unit combination optimization processing method proposes an exhaustive model that takes into account the instability and unplanned failures of renewable energy sources. At the same time, the tailor-made framework ensures the accuracy of the solution and keeps the solution time within a reasonable range. In addition, the complexity of the model is reduced through the intelligent fault sorting method. By discovering high-impact faults in practice during the solution process, the proposed framework enhances the robustness of the solution. At the same time, energy storage equipment is used to improve the solutions under fault and normal conditions, effectively improving the optimization efficiency of the safety-constrained unit combination problem.

[0071] In one embodiment, the operating cost data further includes lost opportunity cost and expected value of power generation; the unit cost includes unit startup cost, unit shutdown cost and unit no-load cost;

[0072] Decompose the SCUC model into main and sub-problems, and solve the main and sub-problems to obtain the optimal solution for the joint operation of the units:

[0073] The objective function of the SCUC model is determined as the objective function of the UC main problem;

[0074] Obtaining the operating data of generators, wind turbines and energy storage devices in the power system;

[0075] Determining power generation constraints based on generator operating data, determining wind power constraints based on wind turbine operating data, and determining energy storage constraints based on energy storage device operating data;

[0076] Based on the power generation constraints, wind power constraints and energy storage constraints, the objective function of the UC main problem is solved to obtain the main solution.

[0077] Specifically, the objective function of the UC main problem is also to minimize the total operating cost, so the objective function of the above SCUC model is used as the objective function of the UC main problem;

[0078] Generator operating data includes the total power generation of the generator, the startup ramp amount of the generator, the shutdown ramp amount, the unit output power, the unit maximum output power, the unit minimum output power and the maximum power generation power of the unit in each scenario of each line. In some examples, Indicates the total power generation of the generator and SRU in each scenario g Indicates the startup ramp amount of the generator, SRD g Indicates the shutdown ramp of the generator, P g,t Indicates the power output of unit g at time t, Indicates the maximum output power of the unit, Indicates the minimum output power of the unit, Taking the maximum power generation of each unit under each scenario of each line as an example, the power generation constraints are as follows (3)-(12):

[0079] st g,t -sd g,t =I g,t -I g,(t-1) (3)

[0080]

[0081]

[0082]

[0083]

[0084]

[0085]

[0086]

[0087] P g,t -P g,(t-1) ≤RU g I g,t +SRU g st g,t (11)

[0088] P g,(t-1) -P g,t ≤RD g I g,t +SRD g sd g,t (12)

[0089] in, Indicates the minimum online time, represents the minimum offline time, and constraints (3)-(5) are the constraints between the unit startup variables, unit shutdown variables, and unit no-load variables; RU g Indicates the unit's upward reserve capacity, RD g represents the unit's downward reserve capacity, constraints (7)-(10) are the generator power constraints, and constraints (11)-(12) are the generator ramp rate constraints.

[0090] by Indicates the unit's upward reserve power. Indicates the unit's reserve power. Indicates the maximum upward reserve capacity of the unit, Taking the maximum downward reserve capacity of the unit as an example, the sufficient storage capacity for wind power fluctuations is calculated using the constraints of equations (13)-(17):

[0091]

[0092]

[0093]

[0094]

[0095]

[0096] The SCUC model takes into account the generation of wind speed scenarios. Specifically, in some examples, the wind speed meteorological forecast is used as the average value, and the standard deviation is increased from 1% to 20% during the operation. 1,000 samples with the above conditions are generated, and a scenario reduction method based on probability distance is used. After this stage, the output power of the wind turbine is calculated through the power curve, and then multiplied by the number of wind turbines to calculate the wind farm power generation.

[0097] This application uses the random behavior of wind power generation as a source of instability to evaluate the performance of the SCUC model in dealing with faults and variable energy sources. In addition, other types of renewable energy generation can also be incorporated in the same way.

[0098] Wind turbine operating data includes wind turbine power generation, wind turbine available capacity and wind turbine curtailable capacity, etc. represents the power generation of wind turbines under scenario s, Indicates the available capacity of wind turbines under scenario s, CW ω,t Indicates that the wind turbine can reduce capacity, ψ s Taking the number of wind farms connected to the busbar in scenario s as an example, the wind power constraints of wind power generation subject to available wind energy are as shown in the following equations (18) and (19):

[0099]

[0100]

[0101] The total power generation constraint and power consumption balance constraint under the basic situation and scenario are shown in equations (20) and (21) respectively:

[0102]

[0103]

[0104] Among them, P c,t Indicates the power generation of energy storage equipment, Indicates the load of each bus.

[0105] The CAES (Compressed Air Energy Storage) unit is regarded as an energy storage device. The working data of the energy storage device includes the compressed power of the energy storage device, the discharged power of the energy storage device, the compression efficiency of the energy storage device, the discharge efficiency of the energy storage device, the stored energy of the energy storage device, the maximum discharge capacity and the minimum discharge capacity of the energy storage device, etc. Represents the compressed state variable of the energy storage device, Represents the discharge state variable of the energy storage device, Indicates the compressed power of the energy storage device, Indicates the discharge capacity of the energy storage device, Indicates the compression efficiency of energy storage equipment, Indicates the discharge efficiency of energy storage equipment, SE c,t Energy storage equipment stores energy, Indicates the maximum discharge capacity of the energy storage device, Indicates the minimum discharge capacity of the energy storage device, Indicates the maximum compression of the energy storage device, Taking the minimum compression of the energy storage device as an example, the energy storage constraints are as follows (22)-(28):

[0106]

[0107]

[0108]

[0109]

[0110]

[0111]

[0112]

[0113] Among them, during the charge and discharge process shown in formula (22), the efficiency of CAES can reach 95%.

[0114] Based on the above power generation constraints, wind power constraints and energy storage constraints, the objective function of the UC main problem is solved to obtain the main solution.

[0115] In one embodiment, it further includes:

[0116] Obtain the slack variables of the load reduction under the base case, and determine the minimum slack variables under the base case as the objective function of the base case subproblem;

[0117] Obtain line flow data of the power system;

[0118] Determine the constraints of the base case subproblem based on the line flow data, and solve the objective function of the base case subproblem based on the constraints of the base case subproblem;

[0119] If the solution to the base case subproblem is positive, Benders decomposition is performed to generate the first safety constraint, and the main solution is optimized according to the first safety constraint. Otherwise, the fault case subproblem is solved.

[0120] Specifically, the base case includes a forecast scenario for wind power, and the base case subproblems are used to check the main solution if any violation of the network constraints in the base case occurs. Taking the slack variable representing the load reduction under the base case as an example, the objective function of the base case subproblem is as follows:

[0121]

[0122] Line flow data includes line flow quantity and line flow upper limit. Indicates the line flow, Taking the upper limit of the line flow as an example, the constraints of the basic case sub-problem are as follows (30)-(33):

[0123]

[0124]

[0125]

[0126]

[0127] Among them, K represents the line reference power, X lIndicates line reactance, Indicates the phase angle of the power plant bus input voltage, Indicates the phase angle of the power plant bus output voltage, Indicates the voltage phase angle of all buses, represents the phase angle of the balanced node voltage, Indicates the power generation of the standby units on the line, Indicates the power generation of energy storage equipment, Represents the power generation of the wind farm under scenario constraints.

[0128] Based on the constraints of the above base case subproblem, the objective function of the base case subproblem shown in Equation (29) is solved. If the solution of the base case subproblem is positive, Benders decomposition is performed to generate the first safety constraint, as shown in Equation (34):

[0129]

[0130] in, and are the dual variables of Equations (32) and (33) respectively.

[0131] Based on the first safety constraint, the main solution is optimized. If the solution to the base case subproblem is not positive, the fault case subproblem is solved.

[0132] In one embodiment, the step of solving the fault condition sub-problem includes:

[0133] Obtain the slack variables of load reduction under different fault events, and determine the minimum slack variables under different fault events as the objective function of the fault condition sub-problem;

[0134] Obtaining the working data of the backup units of the power system;

[0135] Determine the constraints of the fault situation sub-problem according to the working data of the wind turbine unit and the working data of the standby unit, and solve the objective function of the fault situation sub-problem based on the constraints of the fault situation sub-problem;

[0136] If the solution to the fault case subproblem is positive, Benders decomposition is performed to generate the second safety constraint, and the main solution is optimized based on the second safety constraint. Otherwise, the scenario case subproblem is solved.

[0137] Specifically, this fault case subproblem (also called the emergency subproblem) uses a priority list based on the base case power flow calculation to examine the main solutions to determine the emergency shutdown solution with the highest priority.

[0138] In some examples, represents the slack variable for load shedding under fault event 1, represents the slack variable for load shedding under fault event 2, represents the slack variable for load shedding under fault event 3, Taking the sum of the slack variables representing the load reduction under the three fault events as an example, the minimization of the sum of the slack variables representing the load reduction under the three fault events is used as the objective function of the fault condition subproblem, as shown in the following formula (35):

[0139]

[0140] The wind turbine operating data also includes the wind farm power generation under scenario constraints and the available capacity of the wind farm under various scenarios. The standby unit operating data includes the standby unit power generation on the line in the event of an emergency failure. In some examples, represents the wind farm power generation under scenario constraints, Indicates the available capacity of wind turbines under scenario s, Taking the power generation capacity of the standby unit on the line under emergency fault conditions as an example, the constraints of the fault condition sub-problem are as follows (36)-(45):

[0141]

[0142]

[0143]

[0144]

[0145]

[0146]

[0147]

[0148]

[0149]

[0150]

[0151] in, Indicates the phase angle of the power plant bus input voltage under fault conditions, Indicates the phase angle of the power plant bus output voltage under fault conditions, Indicates the voltage phase angle of all buses under fault conditions, represents the voltage phase angle of the balanced node under fault conditions, represents the dispatch power under fault events, Indicates the status of the unit in a fault event.

[0152] In each failure event, the model will correspond to the generator The binary multiplier of is set to zero. Based on the above fault condition subproblem constraint, the objective function of the fault condition subproblem shown in Equation (35) is solved. If the solution of the fault condition subproblem is positive, Benders decomposition is performed to generate the second safety constraint, as shown in Equation (46):

[0153]

[0154] in, are the dual variables of Equations (38)-(44) respectively.

[0155] Based on the second safety constraint, the main solution is optimized. If the solution to the fault condition subproblem is not positive, the scenario case subproblem is solved.

[0156] In one embodiment, the steps of solving the scenario case sub-problem include:

[0157] Obtaining the slack variables of the load reduction under the scenario case, and minimizing the slack variables under the scenario case as the objective function of the scenario case subproblem;

[0158] Obtain the dispatch power under the scenario case;

[0159] Determine the constraints of the scenario case sub-problem based on the line flow data and dispatch power, and solve the objective function of the scenario case sub-problem based on the constraints of the scenario case sub-problem;

[0160] Determine whether to output the optimal plan for joint operation of the units based on the solution results of the scenario case sub-problems.

[0161] Specifically, the scenario case sub-questions are used to evaluate whether the main solution has any violations in the network bus of each scenario. In some examples, Taking the slack variable representing the load reduction under the scenario case as an example, the objective function of the scenario case subproblem is as follows (47):

[0162]

[0163] The dispatch power under the scenario case refers to the dispatch power under different scenarios, that is, the total power generation of the generator under the aforementioned scenarios. In some examples, Taking the dispatch power under different scenarios as an example, the scenario case sub-problem constraints are as follows (48)-(51):

[0164]

[0165]

[0166]

[0167]

[0168] in, They represent the power plant bus input voltage phase angle and output voltage phase angle under scenario constraints, Indicates the voltage phase angle of all buses under scenario constraints, represents the voltage phase angle of the equilibrium node under scenario constraints, Indicates the line power flow in different scenarios.

[0169] It should be noted that in order to obtain a feasible C for energy storage, the power supply There is no scene index. This example is used to check different scenes. The power of the generator is based on the constraints of the above scenario case sub-problem, and the objective function of the scenario case sub-problem shown in Equation (47) is solved to determine whether to output the optimal solution for the joint operation of the units.

[0170] In one embodiment, the step of determining whether to output the optimal plan for joint operation of the units based on the solution results of the scenario case sub-problem includes:

[0171] If the solution to the scenario case sub-problem is positive, Benders decomposition is performed to generate the third safety constraint, and the main solution is optimized based on the third safety constraint. Otherwise, the optimal solution for joint operation of the units is output.

[0172] Specifically, if the solution to the scenario case subproblem is positive, Benders decomposition is performed to generate the third safety constraint, as shown in Equation (52):

[0173]

[0174] in, are the dual variables of Equations (50) and (51) respectively.

[0175] According to the third safety constraint mentioned above, the main solution is optimized; if the solution result of the scenario case sub-problem is not positive, it means that no Benders decomposition has been generated, and the optimal solution for the joint operation of the units is output.

[0176] In order to better illustrate the technical solution of the present application, a specific embodiment is described below.

[0177] A six-bus test system is used to examine the proposed SCUC model, which accounts for wind speed instability and unexpected faults. The proposed solution is analyzed with respect to fault safety, and the impact of wind turbines and energy storage on total system cost and peak splitting is analyzed. In the six-bus test system, the duality gap of the solver for the master problem is set to zero.

[0178] Figure 3 The six-bus test system shown here is used to analyze the proposed solution. G1, G2, and G3 represent three thermal power units, and W1 is a wind turbine. The system includes three generators, a wind farm, and a CAES unit, both of which are connected to bus 4. The design performance indicators of the energy storage systems (ESSs) in the six-bus test system are shown in the following table:

[0179]

[0180] The design assumes that the maximum permissible resetting degree of the generator is equal to its slope limit and uses the power curve of the Lagerwey 750 kW wind turbine generator. In this case, 134 wind turbines of this type are considered for the wind farm.

[0181] The power generation scheduling in different scenarios under high load is shown in the following table:

[0182]

[0183] The table above shows the costs of unit rescheduling, generation, LOC, and various scenarios at hour 15. The revised dispatch is provided by G1. The combined generation of generators and wind farms in each scenario is a fixed value of 289.1 MW. The energy storage value remains at 9.5 MW, as it does not contribute to the power supply in this scenario. The total generation is 298.6 MW, equivalent to the load value at hour 15.

[0184] The analysis under different load levels and unit combinations is shown below:

[0185]

[0186] The table above reflects the objective function values ​​for different combinations of wind farms and CAES units added to the basic SCUC model, demonstrating the impact of ICRA and varying load levels on total operating costs. Under low load conditions, ICRA has no impact on operating costs under different scenarios, as the optimal solution in the base case satisfies the fault constraints. Under medium load conditions without ESSs, ICRA is not feasible. In this case, adding wind farms merely reduces total costs, while adding energy storage makes ICRA feasible. Although CAES units do not fail, the corresponding additional capacity enhances preventive action capabilities, thereby changing the base case dispatch. Under high load conditions, the additional cost of ICRA is 2,805 yuan, approximately 2% of the cost of the unprotected scenario.

[0187] In this test system, ICRA considered the worst-case scenario (NE = 1). To ensure a fixed arrangement of UOCs and energy storage equipment, it was assumed that only wind turbines and conventional generators participated in the recovery operation. The hourly emergency shutdowns and re-dispatch of each generator at high load are shown in the following table:

[0188]

[0189] The results show that the worst failure is G2 at hour 1 and hour 15, and G3 at the rest of the time.

[0190] A sensitivity analysis was conducted for different locations of CAES units in different cases, as shown in the following table:

[0191]

[0192] Among them, B1, B2…B6 represent the buses in the six-bus system. As can be seen from the above table, the SCUC model is only feasible at high loads B4 and B5, and the optimal location of CAES can also be found at locations other than the B4 wind farm.

[0193] This unit combination optimization method utilizes wind farms and energy storage to reduce the system's total operating cost. It simultaneously clears and stores energy, solving the problem of wind power grid integration in combined energy and reserve markets. It uses an intelligent fault prioritization method to address computational burdens and locates actual critical faults based on the final solution. To consider the most effective outage solution under wind power instability, energy storage systems are used to mitigate the negative impact of random faults, while an accelerated Benders decomposition model is used to reduce computational complexity. Numerical results on a six-busbar speed measurement system demonstrate the effectiveness of this method.

[0194] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0195] Based on the same inventive concept, an embodiment of the present application further provides a unit commitment optimization processing device for implementing the above-mentioned unit commitment optimization processing method. The implementation solution provided by this device is similar to the implementation solution described in the above-mentioned method. Therefore, the specific limitations of the unit commitment optimization processing device embodiment provided below can be referred to the limitations of the unit commitment optimization processing method above and will not be repeated here.

[0196] In one embodiment, Figure 4 As shown, a unit commitment optimization processing device is provided, comprising: a data acquisition module 410, a SCUC model determination module 420 and a model solution module 430, wherein:

[0197] Data acquisition module 410, for acquiring operating cost data of the power system; the operating cost data includes unit cost, standby cost and energy storage device regeneration cost;

[0198] SCUC model determination module 420, for determining the SCUC model based on the operating cost data; the SCUC model takes minimizing the total operating cost as the objective function;

[0199] The model solving module 430 is used to decompose the SCUC model into main and subproblems based on the Benders decomposition model, and solve the main and subproblems to obtain the optimal solution for the joint operation of the units; wherein the main and subproblems include the UC main problem, the basic case subproblem, the fault condition subproblem and the scenario case subproblem.

[0200] In one embodiment, the operating cost data further includes lost opportunity cost and expected value of power generation; the unit cost includes unit startup cost, unit shutdown cost and unit no-load cost;

[0201] The model solving module 430 is further configured to:

[0202] The objective function of the SCUC model is determined as the objective function of the UC main problem;

[0203] Obtaining the operating data of generators, wind turbines and energy storage devices in the power system;

[0204] Determining power generation constraints based on generator operating data, determining wind power constraints based on wind turbine operating data, and determining energy storage constraints based on energy storage device operating data;

[0205] Based on the power generation constraints, wind power constraints and energy storage constraints, the objective function of the UC main problem is solved to obtain the main solution.

[0206] In one embodiment, the model solving module 430 is further configured to:

[0207] Obtain the slack variables of the load reduction under the base case, and determine the minimum slack variables under the base case as the objective function of the base case subproblem;

[0208] Obtain line flow data of the power system;

[0209] Determine the constraints of the base case subproblem based on the line flow data, and solve the objective function of the base case subproblem based on the constraints of the base case subproblem;

[0210] If the solution to the base case subproblem is positive, Benders decomposition is performed to generate the first safety constraint, and the main solution is optimized according to the first safety constraint. Otherwise, the fault case subproblem is solved.

[0211] In one embodiment, the model solving module 430 is further configured to:

[0212] Obtain the slack variables of load reduction under different fault events, and determine the minimum slack variables under different fault events as the objective function of the fault condition sub-problem;

[0213] Obtaining the working data of the backup units of the power system;

[0214] Determine the constraints of the fault situation sub-problem according to the working data of the wind turbine unit and the working data of the standby unit, and solve the objective function of the fault situation sub-problem based on the constraints of the fault situation sub-problem;

[0215] If the solution to the fault case subproblem is positive, Benders decomposition is performed to generate the second safety constraint, and the main solution is optimized based on the second safety constraint. Otherwise, the scenario case subproblem is solved.

[0216] In one embodiment, the model solving module 430 is further configured to:

[0217] Obtaining the slack variables of the load reduction under the scenario case, and minimizing the slack variables under the scenario case as the objective function of the scenario case subproblem;

[0218] Obtain the dispatch power under the scenario case;

[0219] Determine the constraints of the scenario case sub-problem based on the line flow data and dispatch power, and solve the objective function of the scenario case sub-problem based on the constraints of the scenario case sub-problem;

[0220] Determine whether to output the optimal plan for joint operation of the units based on the solution results of the scenario case sub-problems.

[0221] In one embodiment, the model solving module 430 is further configured to:

[0222] If the solution to the scenario case sub-problem is positive, Benders decomposition is performed to generate the third safety constraint, and the main solution is optimized based on the third safety constraint. Otherwise, the optimal solution for joint operation of the units is output.

[0223] Each module in the above-mentioned unit combination optimization processing device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0224] In one embodiment, a computer device is provided, which may be a terminal. The computer device includes a processor, memory, a communication interface, a display screen, and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal via wired or wireless communication, where the wireless communication may be achieved via Wi-Fi, a mobile cellular network, NFC (near-field communication), or other technologies. When executed by the processor, the computer program implements a unit combination optimization processing method. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen. The input device of the computer device may be a touch screen covering the display screen, or may be buttons, a trackball, or a touchpad provided on the computer device housing, or may be an external keyboard, touchpad, or mouse.

[0225] In one embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0226] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0227] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0228] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0229] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0230] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A unit commitment optimization processing method, characterized in that: The method comprises: Obtaining operating cost data for the power system; the operating cost data includes unit costs, standby costs, and energy storage device regeneration costs; Determine a SCUC model based on the operating cost data; the SCUC model takes minimizing the total operating cost as an objective function; Based on the Benders decomposition model, the SCUC model is decomposed into main and subproblems, and the main and subproblems are solved to obtain the optimal solution for the joint operation of the units; wherein the main and subproblems include the UC main problem, the base case subproblem, the fault condition subproblem, and the scenario case subproblem; The operating cost data also includes the lost opportunity cost and the expected value of power generation; the unit cost includes the unit startup cost, unit shutdown cost and unit no-load cost; In the step of decomposing the SCUC model into main and sub-problems and solving the main and sub-problems to obtain the optimal solution for the joint operation of the units: Determining the objective function of the SCUC model as the objective function of the UC main problem; Acquiring operating data of generators, wind turbines, and energy storage devices of the power system; Determining power generation constraints based on the generator operating data, determining wind power constraints based on the wind turbine operating data, and determining energy storage constraints based on the energy storage device operating data; Based on the power generation constraint, the wind power constraint, and the energy storage constraint, solving the objective function of the UC main problem to obtain a main solution; The method further comprises: Obtaining slack variables for load reduction under a base case, and minimizing the slack variables under the base case as an objective function of the base case subproblem; Acquiring line flow data of the power system; Determining constraints of a base case subproblem according to the line power flow data, and solving an objective function of the base case subproblem based on the constraints of the base case subproblem; If the solution to the base case subproblem is positive, perform Benders decomposition to generate a first safety constraint, and optimize the main solution based on the first safety constraint; otherwise, solve the fault case subproblem; The step of solving the fault condition sub-problem includes: Obtaining slack variables of load reduction under different fault events, and minimizing the slack variables under the different fault events as the objective function of the fault condition subproblem; Acquiring operating data of a standby unit of the power system; Determining a fault condition sub-problem constraint condition according to the wind turbine operating data and the standby unit operating data, and solving an objective function of the fault condition sub-problem based on the fault condition sub-problem constraint condition; If the solution to the fault case subproblem is positive, Benders decomposition is performed to generate a second safety constraint, and the main solution is optimized based on the second safety constraint. Otherwise, the scenario case subproblem is solved.

2. The method according to claim 1, characterized in that The step of solving the scenario case sub-problem includes: Obtaining slack variables for load reduction under a scenario case, and minimizing the slack variables under the scenario case as an objective function of the scenario case subproblem; Obtain the dispatch power under the scenario case; Determining a scenario case subproblem constraint condition according to the line flow data and the dispatch power, and solving an objective function of the scenario case subproblem based on the scenario case subproblem constraint condition; Determine whether to output the optimal plan for the joint operation of the units based on the solution results of the scenario case sub-problems.

3. The method according to claim 2, characterized in that The step of determining whether to output the optimal plan for joint operation of the units based on the solution results of the scenario case sub-problem includes: If the solution result of the scenario case sub-problem is positive, Benders decomposition is performed to generate a third safety constraint condition, and the main solution is optimized according to the third safety constraint condition. Otherwise, the optimal solution for the joint operation of the units is output.

4. A unit combination optimization processing device, characterized in that: The device comprises: A data acquisition module is used to acquire operating cost data of the power system; the operating cost data includes unit cost, standby cost and energy storage equipment regeneration cost; A SCUC model determination module, configured to determine a SCUC model based on the operating cost data; the SCUC model takes minimizing the total operating cost as an objective function; A model solving module is used to decompose the SCUC model into main and subproblems based on the Benders decomposition model, and solve the main and subproblems to obtain the optimal solution for the joint operation of the units; wherein the main and subproblems include the UC main problem, the base case subproblem, the fault condition subproblem, and the scenario case subproblem; The operating cost data also includes the lost opportunity cost and the expected value of power generation; the unit cost includes the unit startup cost, unit shutdown cost and unit no-load cost; Decomposing the SCUC model into main and sub-problems, and solving the main and sub-problems to obtain the optimal solution for the joint operation of the units includes: Determining the objective function of the SCUC model as the objective function of the UC main problem; Acquiring generator operating data, wind turbine operating data, and energy storage device operating data of the power system; Determining power generation constraints based on the generator operating data, determining wind power constraints based on the wind turbine operating data, and determining energy storage constraints based on the energy storage device operating data; Based on the power generation constraint, the wind power constraint, and the energy storage constraint, solving the objective function of the UC main problem to obtain a main solution; The model solving module is further configured to obtain slack variables for load reduction under a base case, and to minimize the slack variables under the base case as the objective function of the base case subproblem; obtain line flow data of the power system; determine constraints of the base case subproblem based on the line flow data, and solve the objective function of the base case subproblem based on the constraints of the base case subproblem; if the solution to the base case subproblem is positive, perform Benders decomposition to generate a first safety constraint, and optimize the main solution based on the first safety constraint; otherwise, solve the fault condition subproblem; Wherein, solving the fault condition sub-problem includes: Obtaining slack variables of load reduction under different fault events, and minimizing the slack variables under the different fault events as the objective function of the fault condition subproblem; Acquiring operating data of a standby unit of the power system; Determining a fault condition sub-problem constraint condition according to the wind turbine operating data and the standby unit operating data, and solving an objective function of the fault condition sub-problem based on the fault condition sub-problem constraint condition; If the solution to the fault case subproblem is positive, Benders decomposition is performed to generate a second safety constraint, and the main solution is optimized based on the second safety constraint. Otherwise, the scenario case subproblem is solved.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 3 are implemented.

6. 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 3 are implemented.

7. A computer program product comprising a computer program, 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 3 are implemented.

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