Power system look-ahead scheduling decision-making method and device based on two-stage robust optimization

Abstract

The invention discloses an electric power system look-ahead scheduling decision-making method and device based on two-stage robust optimization. The method comprises the following steps: constructing a robust optimization model according to each regulation and control resource characteristic; decomposing the robust optimization model into a main problem and a sub-problem in a mixed integer form through a CCG algorithm and a KKT condition, and solving the main problem and the sub-problem to obtain a look-ahead scheduling plan in a future period; and scheduling based on the look-ahead scheduling plan. Two-stage robust optimization is adopted, a decision maker is required to realize scheme optimization in a worst scene of uncertain variables according to the requirement of robust optimization, and when the scheme optimization is applied to a power system optimization scheduling model containing an uncertain power supply, a scheduling scheme meeting an optimization target is required to be searched in a scene with a maximum prediction error. Compared with random optimization in the prior art, the robust optimization obviously meets the requirements of actual intra-day scheduling.

Description

Technical Field

[0001] The present application relates to the technical field of power system dispatching, and in particular to a method and device for forward-looking dispatching decision-making of a power system based on two-stage robust optimization. Background Art

[0002] With the continuous advancement of the "dual carbon" goals, it is expected that by 2030, my country's total installed capacity of new energy will exceed 1.2 billion kW, and the power system will show the characteristic of high new energy penetration. In this context, since new energy power generation, mainly wind power and photovoltaic power, has the characteristics of randomness and volatility, as the proportion of new energy continues to increase, the uncertainty on the source side of the power grid will become more serious, and the optimal dispatch of the power system will face uncertainties on both the source and load sides at the same time.

[0003] Power system forward-looking dispatch, that is, forward-looking intraday rolling dispatch of power system, is one of the branches of intraday dispatch plan in the current power system adaptive cruise architecture. Ultra-short-term prediction can obtain more accurate wind, solar and load forecast data than the day-ahead forecast by using the real-time updated information during the day. According to the national standard, the wind power intraday forecast data must still meet 90% accuracy in the fourth hour after the current period. Forward-looking dispatch uses intraday ultra-short-term prediction, comprehensively considers several time periods in the forward-looking window, and according to the characteristics of ultra-short-term prediction accuracy, the intraday dispatch plan is revised in a rolling manner, effectively ensuring the safety and economy of the dispatch plan, while balancing the deviation between the dispatch plan and the expected plan caused by the prediction error in the intraday dispatch.

[0004] The optimization goal of the forward dispatch of the power system needs to maximize the absorption rate of renewable energy while ensuring the safe operation of the power system, while taking into account the economic efficiency of the system operation. The classic day-ahead wind and solar power forecast error is 15%~20%, while the classic day-ahead load forecast error is only 1%~3%. It can be seen that the main challenge facing the forward dispatch is the uncertainty generated by renewable energy in the system. For such problems, random optimization methods such as scenario analysis and chance constraints are often used to describe the uncertainty of renewable energy. The main idea of ​​the scenario analysis method is to generate multiple deterministic scenarios based on the probability distribution information of the uncertain variables to replace the original random variables for optimization. The core idea of ​​the chance constraint is not to require that the constraints containing random variables must be established, but to require that the probability of their establishment meet certain confidence level requirements. However, the scenario method needs to generate a large number of scenarios, which will significantly increase the solution time of the optimization model. The chance constraint has a confidence interval, which will ignore the possible small probability scenarios and reduce the safety of the optimization scheme. At the same time, both rely on the probability distribution information of the uncertain variables for modeling, which will reduce the accuracy of the description of the uncertain variables, so that the obtained model is out of touch with the actual scenario. Summary of the invention

[0005] In view of this, the present application provides a method and device for forward-looking scheduling decision-making of an electric power system based on two-stage robust optimization, which can better meet the needs of actual intraday scheduling.

[0006] In a first aspect, the present application provides a forward-looking dispatch decision method for a power system based on two-stage robust optimization, comprising: Constructing a robust optimization model, wherein the robust optimization model includes an objective function constructed according to load shedding information, power abandonment information, and power regulation cost information of each control resource in a preset control time period, and a constraint condition constructed according to power information and SOC information of each control resource; The robust optimization model is decomposed into a main problem and sub-problems in mixed integer form by using CCG algorithm and KKT condition to solve them, and a forward-looking scheduling plan for the future period is obtained; Scheduling is performed based on the forward-looking scheduling plan.

[0007] Optionally, the objective function is expressed in the following formula: (1); in, Considering the total number of time periods for optimization and including a plurality of the preset scheduling time periods; A penalty function for the power regulation cost of each regulated resource within a preset scheduling time period; is the penalty function for load shedding of each control resource within the preset scheduling time period; It is the power abandonment penalty function of each control resource within the preset scheduling time period.

[0008] Optionally, the penalty function of the power regulation cost of each regulation resource in a preset scheduling time period is It is expressed in the following formula: (2); Among them, G, H, L, and E are the optimized conventional thermal power units, hydropower units, interconnection lines, and energy storage sets, respectively. , , are the adjustment cost coefficients of the corresponding controllable resources, , , It is the output of each adjustable resource within the preset scheduling time period; Adjust the cost coefficient for the energy storage action, and It is a 01 state variable and is an action identifier of energy storage within a preset scheduling time period; And / or, the power abandonment penalty function of each control resource within the preset scheduling time period It is expressed in the following formula: (3); Among them, W and V are the optimized wind power and photovoltaic collections, respectively; is the penalty factor for abandoning wind, is the penalty factor for abandoning light; is the actual power of wind power in the preset scheduling period, The actual power of the photovoltaic power plant during the preset scheduling period; is the maximum output of wind power in the preset dispatching time period, The maximum output of photovoltaic power in the preset scheduling time period; and / or, a penalty function for load shedding of each control resource within a preset scheduling period It is expressed in the following formula: (4); Where D is the optimized controllable load set; is the load shedding penalty factor; is the actual load power within the preset scheduling time period; It is the total load power within the preset scheduling time period.

[0009] Optionally, the constraint conditions include: The thermal power-climbing constraint is expressed in the following formula: (5); in, is the active power output of unit g in period t; is the minimum active output limit of unit g, is the maximum active output limit of unit g, is the maximum upward climbing rate of unit g, is the maximum downward climbing rate of unit g; The hydropower power-electricity constraint is expressed as follows, (6); in, is the active power output of unit h in period t; is the minimum active output limit of unit h, The maximum active output limit of unit h, is the power limit of unit h in the total optimization period T; The tie line power constraint is expressed as follows: (7); in, For contact line l Active power output in time period t; For contact line lThe minimum power limit, For contact line l Maximum power limit; The active power-SOC constraint is expressed as follows, (8); in, is the SOC of energy storage e in period t, is the minimum SOC limit of energy storage e, The maximum SOC limit of energy storage e, is the first and last SOC limits of the energy storage e during the optimization process; is the charging power of the energy storage e during the preset scheduling time period, is the discharge power of energy storage e in the preset scheduling time period, is the minimum charging power limit of energy storage e, is the maximum charging power limit of energy storage e, is the minimum discharge power limit of energy storage e, is the maximum discharge power limit of energy storage e; The charge and discharge constraints are expressed as follows: (9); in, For energy storage charging and discharging efficiency, is the energy storage rated power; As shown in the following formula, the wind and solar curtailment and load shedding constraints are: (10); in, is the maximum output of wind power in the preset dispatching time period, is the maximum output of photovoltaic power in the preset scheduling time period, is the total load power within the preset scheduling time period; The active power balance and line flow constraints are expressed in the following formula:

[0010] (11); Where B is the admittance matrix of the system; is the admittance between nodes i and j, is the reactance between node i and node j; is the voltage phase angle at node j, is the voltage phase angle at node i; is the maximum transmission power of line s.

[0011] Optionally, the main problem is expressed in the following formula: (13); Among them, η is the introduced auxiliary decision variable, y is the first-stage decision variable matrix and includes fire, water, energy storage, interconnection line and flow calculation auxiliary decision variables; z is the uncertainty variable matrix; x is the second-stage decision variable matrix, h represents the constant column vector corresponding to the second-stage decision variable, and d represents the constant column vector corresponding to the first-stage decision variable.

[0012] Optionally, the sub-problem is expressed in the following formula: (14);.

[0013] Among them, y is the first-stage decision variable matrix and includes fire, water, energy storage, interconnection line and flow calculation auxiliary decision variables; z is the uncertainty variable matrix; x is the second-stage decision variable matrix Optionally, scheduling based on the forward scheduling plan includes: A. Execute the scheduling plan for the first preset scheduling period in the forward scheduling plan and use it as the benchmark value for optimizing the next preset scheduling period; B. Repeat operation A according to the number of preset scheduling time periods to form a forward-looking rolling optimization process.

[0014] In a second aspect, the present application provides a power system forward-looking dispatch decision-making device based on two-stage robust optimization, comprising: A comparison analysis module is used to compare and analyze the wind power representative curve with the representative curves of each grid-connected energy source to obtain a correlation analysis curve; A first determination module, used to determine each continuous curve segment from the correlation analysis curve according to a preset curve analysis value, and fit each continuous curve segment to form a continuous curve function; A second determination module is used to obtain an associated evaluation value model by taking the time period corresponding to each continuous curve segment as an integral interval and the continuous curve function as an integral function, and calculate the associated evaluation value according to the associated evaluation value model, and determine the associated power source from each grid-connected energy source based on the associated evaluation value; The control module is used to obtain a joint dispatching mode according to the associated collected data of the associated power sources, so as to perform grid connection control through the joint dispatching mode.

[0015] In a third aspect, the present application provides a computer-readable storage medium, including a program, which, when executed on a computer, enables the computer to execute the method as described above. In a fourth aspect, the present application provides an execution device, comprising a processor and a memory, wherein the processor is coupled to the memory; The memory is used to store programs; The processor is used to execute the program in the memory, so that the execution device executes the above method.

[0016] In the method disclosed in this application, two-stage robust optimization is adopted. The core idea of ​​robust optimization is to require decision makers to achieve optimal solutions under the worst scenario of uncertain variables. When applied to the power system optimization dispatch model containing uncertain wind and solar power sources, it requires finding a dispatch solution that meets the optimization goal under the scenario with the largest prediction error. Compared with random optimization in related technologies, robust optimization obviously better meets the needs of actual intraday dispatch. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The technical solution and other beneficial effects of the present application will be made apparent by describing in detail the specific implementation methods of the present application in conjunction with the accompanying drawings.

[0018] Figure 1 An operation flow chart of a power system prospective dispatch decision method based on two-stage robust optimization provided by an exemplary embodiment is shown; Figure 2 A schematic diagram of a forward-looking rolling window provided by an exemplary embodiment is shown; Figure 3 A CCG algorithm calculation flow chart provided by an exemplary embodiment is shown; Figure 4 A block diagram of a power system forward-looking dispatch decision-making device based on two-stage robust optimization provided by an exemplary embodiment is shown; Figure 5 A block diagram of the execution device provided by an exemplary embodiment is shown. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present application, so the present application is not limited by the specific embodiments disclosed below. The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or modules is not necessarily limited to those steps or modules clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices. The naming or numbering of steps in this application does not mean that the steps in the method flow must be executed in the time / logical sequence indicated by the naming or numbering. The process steps that have been named or numbered can change the execution order according to the technical purpose to be achieved, as long as the same or similar technical effects can be achieved. The division of units in this application is a logical division. There may be other division methods when it is implemented in actual applications. For example, multiple units can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection between units can be electrical or other similar forms, which are not limited in this application. In addition, the units or sub-units described as separate components may or may not be physically separated, may or may not be physical units, or may be distributed in multiple circuit units, and some or all of the units may be selected according to actual needs to achieve the purpose of the present application.

[0020] Please refer to Figure 1 , Figure 1 A flow chart of a wind power grid control method provided by an exemplary embodiment is shown. The method is implemented through steps 102-106.

[0021] In step 102, a robust optimization model is constructed, which includes an objective function constructed according to the load shedding information, power abandonment information and power regulation cost information of each control resource in a preset control time period, and a constraint condition constructed according to the power information and SOC information of each control resource.

[0022] Here, the term "regulated resources" refers to various energy sources connected to the power grid, including but not limited to wind power, hydropower, thermal power, photovoltaic power, etc.

[0023] It should be noted that, here, power information refers to various types of power or parameters regarded as power, including but not limited to thermal power, hydropower power, interconnection line power, active output, etc.

[0024] Here, the preset time period can be any value, such as 15 minutes.

[0025] As an example, the objective function is expressed in the following formula: (1); Among them, among them, Considering the total number of time periods for optimization and including a plurality of the preset scheduling time periods; A penalty function for the power regulation cost of each regulated resource within a preset scheduling time period; is the penalty function for load shedding of each control resource within the preset scheduling time period; It is the power abandonment penalty function of each control resource within the preset scheduling time period.

[0026] Here, the penalty function of the power regulation cost of each regulation resource in the preset scheduling time period is It is expressed in the following formula: (2); Among them, G, H, L, and E are the optimized conventional thermal power units, hydropower units, interconnection lines, and energy storage sets, respectively. , , are the adjustment cost coefficients of the corresponding controllable resources, , , It is the output of each adjustable resource within the preset scheduling time period; Adjust the cost coefficient for the energy storage action, and It is a 01 state variable and is an action identifier of energy storage within a preset scheduling time period; And / or, the power abandonment penalty function of each control resource within the preset scheduling time period It is expressed in the following formula: (3); Among them, W and V are the optimized wind power and photovoltaic collections, respectively; is the penalty factor for abandoning wind, is the penalty factor for abandoning light; is the actual power of wind power in the preset scheduling period, The actual power of the photovoltaic power plant during the preset scheduling period; is the maximum output of wind power in the preset dispatching time period, The maximum output of photovoltaic power in the preset scheduling time period; Penalty function for load shedding of each control resource within the preset scheduling time period It is expressed in the following formula: (4); Where D is the optimized controllable load set; is the load shedding penalty factor; is the actual load power within the preset scheduling time period; It is the total load power within the preset scheduling time period.

[0027] As an exemplary embodiment, the constraints include: The thermal power-climbing constraint is expressed in the following formula: (5); in, is the active power output of unit g in period t; is the minimum active output limit of unit g, is the maximum active output limit of unit g, is the maximum upward climbing rate of unit g, is the maximum downward climbing rate of unit g.

[0028] The hydropower power-electricity constraint is expressed as follows, (6); in, is the active power output of unit h in period t; is the minimum active output limit of unit h, The maximum active output limit of unit h, is the power limit of unit h in the total optimization period T.

[0029] The tie line power constraint is expressed as follows: (7); in, For contact line l Active power output in time period t; For contact line l The minimum power limit, For contact line l The maximum power limit.

[0030] The active power-SOC constraint is expressed as follows, (8); in, is the SOC of energy storage e in period t, is the minimum SOC limit of energy storage e, The maximum SOC limit of energy storage e, is the first and last SOC limits of the energy storage e during the optimization process; is the charging power of the energy storage e during the preset scheduling time period, is the discharge power of energy storage e in the preset scheduling time period, is the minimum charging power limit of energy storage e, is the maximum charging power limit of energy storage e, is the minimum discharge power limit of energy storage e, is the maximum discharge power limit of energy storage e.

[0031] The charge and discharge constraints are expressed as follows: (9); in, For energy storage charging and discharging efficiency, is the energy storage rated power.

[0032] As shown in the following formula, the wind and solar curtailment and load shedding constraints are: (10); in, is the maximum output of wind power in the preset dispatching time period, is the maximum output of photovoltaic power in the preset scheduling time period, It is the total load power within the preset scheduling time period.

[0033] The active power balance and line flow constraints are expressed in the following formula:

[0034] (11); Where B is the admittance matrix of the system; is the admittance between nodes i and j, is the reactance between node i and node j; is the voltage phase angle at node j, is the voltage phase angle at node i; is the maximum transmission power of line s.

[0035] In step 104, the robust optimization model is decomposed into a main problem and sub-problems in mixed integer form by using the CCG algorithm and the KKT condition to solve them, and a forward-looking scheduling plan for the future period is obtained.

[0036] Here, the future period can be any set time, such as 4 hours.

[0037] It should be understood that the text herein uses the term "CCG algorithm" which refers to the column constraint generation algorithm.

[0038] As used herein, the term "KKT conditions" refers to the Carlos-Kuhn-Tucker (KKT) conditions.

[0039] After linearizing the absolute value and bilinear terms of the above model by introducing auxiliary variables, it can be transformed into a compact form as shown below: (12); Among them, y is the first-stage decision variable matrix, which includes fire, water, energy storage, tie line and flow calculation auxiliary decision variables, that is, , , , , , , and ; z is the uncertainty variable matrix, i.e. the maximum output of wind and solar , ; x is the second-stage decision variable matrix, including wind power, photovoltaic and load decision variables, that is, , , .

[0040] The above two-stage model is decomposed into a main problem and a sub-problem. The main problem is the regulation cost optimization problem, and the sub-problem is the new energy consumption and load supply problem, as follows: Main question: (13); Among them, η is the introduced auxiliary decision variable, y is the first-stage decision variable matrix and includes fire, water, energy storage, interconnection line and flow calculation auxiliary decision variables; z is the uncertainty variable matrix; x is the second-stage decision variable matrix, h represents the constant column vector corresponding to the second-stage decision variable, and d represents the constant column vector corresponding to the first-stage decision variable.

[0041] The sub-problem is expressed in the following formula: (14); Among them, y is the first-stage decision variable matrix and includes fire, water, energy storage, interconnection lines, and flow calculation auxiliary decision variables; z is the uncertainty variable matrix; and x is the second-stage decision variable matrix.

[0042] The KKT condition transforms the problem into a single-layer problem for solution, namely: (15); Among them, a, b, and c are introduced auxiliary decision variables.

[0043] Please refer to Figure 3, the CCG algorithm is used to solve the main problem and sub-problems obtained by decomposition. The solution process is as follows: 1) Given a set of uncertain variables z as the initial extreme scenario, set the lower bound LB=-∞ and the upper bound UB=+∞ of the objective function; 2) Solve the main problem according to the extreme scenario z, obtain the optimal solution y of the main problem, and use the objective function value of the main problem as the new lower bound LB; 3) Substitute the solution y obtained from solving the main problem into the sub-problem to obtain the extreme scenario value of the uncertainty variable z, and update the upper bound UB according to the objective function value of the sub-problem; 4) Given the convergence threshold ε of the algorithm, if UB-LB < ε, stop the iteration and return the optimal solution and extreme scenario; otherwise, add the variable x to the main problem l And the following constraints: (16); Continue step 2) until the algorithm converges and obtain the forward-looking scheduling plan for the future period.

[0044] In step 106, scheduling is performed based on the forward scheduling plan.

[0045] Please refer to Figure 2 , as a reference, scheduling is performed based on the forward scheduling plan, including: A. Execute the scheduling plan for the first preset scheduling period in the forward scheduling plan and use it as the benchmark value for optimizing the next preset scheduling period; B. Repeat operation A according to the number of preset scheduling time periods to form a forward-looking rolling optimization process.

[0046] Please refer to Figure 4 , which shows a block diagram of the structure of the power system forward-looking scheduling decision-making device based on two-stage robust optimization provided by the present application. The device 200 includes: A construction module 202 is used to construct a robust optimization model, wherein the robust optimization model includes an objective function constructed according to load shedding information, power abandonment information, and power regulation cost information of each control resource in a preset control time period, and a constraint condition constructed according to power information and SOC information of each control resource; A decomposition module 204 is used to decompose the robust optimization model into a main problem and sub-problems in mixed integer form by using CCG algorithm and KKT conditions to solve them and obtain a forward-looking scheduling plan for a future period; The scheduling module 206 is used to perform scheduling based on the forward scheduling plan.

[0047] Since the above methods have been discussed in detail, the specific implementation of the above modules will not be repeated here.

[0048] Next, an execution device provided by an embodiment of the present application is introduced. Figure 5 , Figure 5 This is a schematic diagram of a structure of an execution device provided in an embodiment of the present application. The execution device 300 can be specifically manifested as an autonomous driving vehicle, a mobile phone, a tablet, a laptop computer, a desktop computer, a monitoring data processing device, etc., which is not limited here. Figure 1 The functions of the execution device in the corresponding embodiment. Specifically, the execution device 300 includes: a receiver 301, a transmitter 302, a processor 303 and a memory 304 (wherein the number of the processor 303 in the execution device 300 can be one or more, Figure 5 In the example of FIG. 3 , the processor 303 may include an application processor 3031 and a communication processor 3032. In some embodiments of the present application, the receiver 301, the transmitter 302, the processor 303 and the memory 304 may be connected via a bus or other means.

[0049] The memory 304 may include a read-only memory and a random access memory, and provides instructions and data to the processor 303. A portion of the memory 304 may also include a non-volatile random access memory (NVRAM). The memory 304 stores processor and operation instructions, executable modules or data structures, or subsets thereof, or extended sets thereof, wherein the operation instructions may include various operation instructions for implementing various operations.

[0050] The processor 303 controls the operation of the execution device. In a specific application, the various components of the execution device are coupled together through a bus system, wherein the bus system includes not only a data bus but also a power bus, a control bus, and a status signal bus, etc. However, for the sake of clarity, various buses are referred to as bus systems in the figure.

[0051] The method disclosed in the above embodiment of the present application can be applied to the processor 303, or implemented by the processor 303. The processor 303 can be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 303. The above processor 303 can be a general-purpose processor, a digital signal processor (digital signal processing, DSP), a microprocessor or a microcontroller, and can further include an application specific integrated circuit (application specific integrated circuit, ASIC), a field programmable gate array (field-programmable gate array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The processor 303 can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present application can be directly embodied as a hardware decoding processor to be executed, or the hardware and software modules in the decoding processor can be combined and executed. The software module may be located in a storage medium mature in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 304, and the processor 303 reads the information in the memory 304 and completes the steps of the above method in combination with its hardware.

[0052] The receiver 301 can be used to receive inputted digital or symbol information, and generate signal input related to the relevant settings and function control of the execution device. The transmitter 302 can be used to output the digital or symbol information through the first interface; the transmitter 302 can also be used to send instructions to the disk group through the first interface to modify the data in the disk group; the transmitter 302 can also include a display device such as a display screen.

[0053] In the embodiment of the present application, the processor 303 is used to execute Figure 1 The specific manner in which the application processor 3031 in the processor 303 performs the above steps is the same as that in the present application. Figure 1 The corresponding method embodiments are based on the same concept, and the technical effects they bring are the same as those in this application. Figure 1 The corresponding method embodiments are the same. For specific contents, please refer to the description in the method embodiments shown above in this application, which will not be repeated here.

[0054] The above is only a preferred specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed in the present application should be covered within the protection scope of the present application.

Claims

1. A two-stage robust optimization-based forward-looking dispatch decision method for power systems, characterized in that: include: Constructing a robust optimization model, wherein the robust optimization model includes an objective function constructed according to load shedding information, power abandonment information, and power regulation cost information of each control resource in a preset control time period, and a constraint condition constructed according to power information and SOC information of each control resource; The robust optimization model is decomposed into a main problem and sub-problems in mixed integer form by using CCG algorithm and KKT condition to solve them, and a forward-looking scheduling plan for the future period is obtained; Scheduling is performed based on the forward-looking scheduling plan.

2. The method according to claim 1, characterized in that The objective function is expressed in the following formula: (1)； in, Considering the total number of time periods for optimization and including a plurality of the preset scheduling time periods; is a penalty function of the power regulation cost of each regulated resource within a preset scheduling time period; is the penalty function for load shedding of each control resource within the preset scheduling time period; It is the power abandonment penalty function of each control resource within the preset scheduling time period.

3. The method according to claim 2, characterized in that The penalty function of the power regulation cost of each regulation resource in the preset scheduling time period It is expressed in the following formula: (2)； Among them, G, H, L, and E are the optimized conventional thermal power units, hydropower units, interconnection lines, and energy storage sets, respectively. , , are the adjustment cost coefficients of the corresponding controllable resources, , , It is the output of each adjustable resource within the preset scheduling time period; Adjust the cost coefficient for the energy storage action, and It is a 01 state variable and is an action identifier of energy storage within a preset scheduling time period; And / or, the power abandonment penalty function of each control resource within the preset scheduling time period It is expressed in the following formula: (3)； Among them, W and V are the optimized wind power and photovoltaic collections, respectively; is the penalty factor for abandoning wind, is the penalty factor for abandoning light; is the actual power of wind power in the preset scheduling period, The actual power of the photovoltaic power plant during the preset scheduling period; is the maximum output of wind power in the preset dispatching time period, The maximum output of photovoltaic power in the preset scheduling time period; and / or, a penalty function for load shedding of each control resource within a preset scheduling period It is expressed in the following formula: (4)； Where D is the optimized controllable load set; is the load shedding penalty factor; is the actual load power within the preset scheduling time period; It is the total load power within the preset scheduling time period.

4. The method according to claim 1, characterized in that: The constraints include: The thermal power-climbing constraint is expressed in the following formula: (5)； in, is the active power output of unit g in period t; is the minimum active output limit of unit g, is the maximum active output limit of unit g, is the maximum upward climbing rate of unit g, is the maximum downward climbing rate of unit g; The hydropower power-electricity constraint is expressed as follows, (6)； in, is the active power output of unit h in period t; is the minimum active output limit of unit h, The maximum active output limit of unit h, is the power limit of unit h in the total optimization period T; The tie line power constraint is expressed as follows: (7)； in, For contact line l Active power output in time period t; For contact line l The minimum power limit, For contact line l Maximum power limit; The active power-SOC constraint is expressed as follows: (8)； in, is the SOC of energy storage e in period t, is the minimum SOC limit of energy storage e, The maximum SOC limit of energy storage e, is the first and last SOC limits of the energy storage e during the optimization process; is the charging power of the energy storage e during the preset scheduling time period, is the discharge power of energy storage e in the preset scheduling time period, is the minimum charging power limit of energy storage e, is the maximum charging power limit of energy storage e, is the minimum discharge power limit of energy storage e, is the maximum discharge power limit of energy storage e; The charge and discharge constraints are expressed as follows: (9)； in, For energy storage charging and discharging efficiency, is the energy storage rated power; As shown in the following formula, the wind and solar curtailment and load shedding constraints are: (10)； in, is the maximum output of wind power in the preset dispatching time period, is the maximum output of photovoltaic power in the preset scheduling period. is the total load power within the preset scheduling time period; The active power balance and line flow constraints are expressed in the following formula: (11)； Where B is the admittance matrix of the system; is the admittance between nodes i and j, is the reactance between node i and node j; is the voltage phase angle at node j, is the voltage phase angle at node i; is the maximum transmission power of line s.

5. The method according to claim 1, characterized in that The main problem is expressed in the following formula: (13)； Among them, η is the introduced auxiliary decision variable, y is the first-stage decision variable matrix and includes fire, water, energy storage, interconnection line and flow calculation auxiliary decision variables; z is the uncertainty variable matrix; x is the second-stage decision variable matrix, h represents the constant column vector corresponding to the second-stage decision variable, and d represents the constant column vector corresponding to the first-stage decision variable.

6. The method according to claim 1, characterized in that The sub-problem is expressed in the following formula: (14)； Among them, y is the first-stage decision variable matrix and includes fire, water, energy storage, interconnection lines, and flow calculation auxiliary decision variables; z is the uncertainty variable matrix; and x is the second-stage decision variable matrix.

7. The method according to claim 1, characterized in that Scheduling based on the forward-looking scheduling plan includes: A. Execute the scheduling plan for the first preset scheduling period in the forward scheduling plan and use it as the benchmark value for optimizing the next preset scheduling period; B. Repeat operation A according to the number of preset scheduling time periods to form a forward-looking rolling optimization process.

8. A power system forward-looking dispatch decision-making device based on two-stage robust optimization, characterized in that: include: A construction module is used to construct a robust optimization model, wherein the robust optimization model includes an objective function constructed according to load shedding information, power abandonment information and power regulation cost information of each control resource in a preset control time period, and a constraint condition constructed according to power information and SOC information of each control resource; A decomposition module, used to decompose the robust optimization model into a main problem and sub-problems in mixed integer form through CCG algorithm and KKT conditions for solving, and obtain a forward-looking scheduling plan for the future period; A scheduling module is used to perform scheduling based on the forward scheduling plan.

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

10. An execution device, characterized in that: comprising a processor and a memory, wherein the processor is coupled to the memory; The memory is used to store programs; The processor is configured to execute the program in the memory so that the execution device executes the method according to any one of claims 1 to 8.

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