Uncertainty set characterization method and system supporting power system operational flexibility assessment

Abstract

The present application relates to the technical field of integrated energy systems, and discloses a method and system for characterizing uncertainty sets that support the evaluation of power system operation flexibility, wherein the method includes: constructing an objective function with the minimum power system operation cost and the maximum uncertainty set envelope; constructing constraint conditions, including basic constraints that meet normal operation requirements under basic scenarios, and uncertain constraints that meet normal operation requirements under uncertain scenarios; constructing a power system operation flexibility evaluation model based on the objective function and the constraint conditions; processing the power system operation flexibility evaluation model to obtain a maximum uncertainty set, wherein the maximum uncertainty set is used to support the evaluation of power system operation flexibility, solves the technical problem of economically optimal uncertainty set characterization in power systems under basic scenarios and uncertain scenarios, and can be used to support the evaluation of power system operation flexibility.

Description

Technical Field

[0001] The present application relates to the technical field of integrated energy systems, and in particular to an uncertainty set characterization method and system for supporting the evaluation of power system operational flexibility. Background Art

[0002] As the penetration of renewable energy sources in the power system continues to increase, power system operations are exhibiting greater randomness and uncertainty. Uncertainty in operational scenarios is a key consideration in power system optimization and dispatch. Accurately understanding the uncertainty set within the power system is a prerequisite for effective dispatch. Characterizing the economically optimal uncertainty set in the power system has become a metric for assessing power system operational flexibility. Summary of the Invention

[0003] This application provides an uncertainty set characterization method and system for supporting the evaluation of power system operation flexibility, which solves the technical problem of economically optimal uncertainty set characterization in power systems under basic scenarios and uncertain scenarios, and can be used to support the evaluation of power system operation flexibility.

[0004] In order to achieve the above objectives, the main technical solutions adopted in this application include:

[0005] In a first aspect, an embodiment of the present application provides an uncertainty set characterization method for supporting the evaluation of power system operation flexibility, the method comprising: constructing an objective function with the minimum power system operation cost and the maximum uncertainty set envelope; constructing constraint conditions, including basic constraints that meet normal operation requirements under basic scenarios, and uncertain constraints that meet normal operation requirements under uncertain scenarios; constructing a power system operation flexibility evaluation model based on the objective function and the constraint conditions; processing the power system operation flexibility evaluation model to obtain a maximum uncertainty set, wherein the maximum uncertainty set is used to support the evaluation of power system operation flexibility.

[0006] Optionally, the objective function can be expressed as:

[0007] (1)

[0008] In the above formula, and Wind farms In time The upper and lower bounds of the uncertainty power output, and are the upper and lower bound vectors of the uncertain power output, is the uncertain power output vector, is the feasible space of uncertain power output; and The preset wind farms In time The upper and lower limits of power output; and They are respectively the basic scenario and the uncertain scenario Power generation unit at the moment The active power, is the active power vector under uncertain scenarios, is the active power vector in the basic scenario; 、 They are the power generation units in the basic scenario binary variables representing startup and shutdown; 、 They are the power generation units under uncertain scenarios. binary variables representing startup and shutdown; 、 are binary variable vectors under the basic scenario and uncertain scenario respectively; 、 They are energy storage units in basic scenarios. exist Charging and discharging power at all times; 、 Energy storage units under uncertain scenarios exist Charging and discharging power at all times; represents the search space, and are the collections of fast-start units and non-fast-start units respectively; 、 、 、 、 、 are the weight coefficients required to construct the objective function.

[0009] Optionally, the basic constraints that meet normal operation requirements in the basic scenario include minimum startup time constraints, minimum shutdown constraints, startup state constraints, shutdown state constraints, power generation limit constraints, ramp-up and ramp-down constraints, energy storage charging and discharging limits, energy storage state constraints, initial and final charge state constraints, transmission line capacity constraints, and power conservation constraints, specifically including:

[0010] (2)

[0011] (3)

[0012] (4)

[0013] (5)

[0014] (6)

[0015] (7)

[0016] (8)

[0017] (9)

[0018] (10)

[0019] (11)

[0020] (12)

[0021] (13)

[0022] (14)

[0023] Among the above constraints, (2) is the minimum startup time constraint, is the minimum boot time, Power generation unit in basic scenario binary variables; (3) is the minimum stop constraint, is the minimum downtime; (4) is the startup state constraint, It is the power generation unit in the basic scenario The binary variable representing the power-on state; (5) is the shutdown state constraint, It is the power generation unit in the basic scenario The binary variable representing the shutdown; (6) is the power generation limit constraint, It is a basic scenario Power generation unit at the moment The active power, It is a power generation unit The minimum value of active power, It is a power generation unit The maximum value of active power; (7) and (8) are the up-climbing and down-climbing constraints, respectively. and are the up and down ramp capacities respectively; (9) and (10) are the energy storage charge and discharge limits respectively, where is the energy storage unit n in A binary variable representing the discharge state at each moment in the basic scenario, 、 They are energy storage units in basic scenarios. exist Charging and discharging power at all times; 、 Energy storage units The maximum value of charge and discharge power; (11) is the energy storage state constraint, It is an energy storage unit exist The state of charge in basic scenarios at all times, and are the charge and discharge efficiency, and are the upper and lower limits of the state of charge respectively; (12) are the constraints of the initial and final states of charge, where is the initial state of charge, is the final state of charge; (13) is the capacity constraint of the transmission line, where 、 、 、 They are the line transfer factors of generators, energy storage, wind farms, and loads. is the load, is the expected output value, is the maximum transmission power of the line; (14) is the power conservation constraint, is the total load.

[0024] Optionally, the uncertain constraints that meet normal operation requirements in the uncertain scenario include: upper and lower bound constraints on the power generation capacity of the non-fast start unit in the uncertain scenario, up and down ramp constraints of the non-fast start unit in the uncertain scenario, power generation capacity constraints of the fast start unit in the uncertain scenario, up and down ramp constraints of the fast start unit in the uncertain scenario, on and off state constraints of the fast start unit in the uncertain scenario, charging and discharging power constraints of the energy storage unit in the uncertain scenario, state of charge constraints of the energy storage unit in the uncertain scenario, node power balance and line capacity constraints in the uncertain scenario, specifically including:

[0025] (15)

[0026] (16)

[0027] (17)

[0028] (18)

[0029] (19)

[0030] (20)

[0031] (twenty one)

[0032] (twenty two)

[0033] (twenty three)

[0034] (twenty four)

[0035] (25)

[0036] (26)

[0037] , (27)

[0038] Among the above constraints, (15) is the upper and lower bound constraints of the power generation capacity of the non-fast start unit under uncertain scenarios, It is a power generation unit in the basic scenario The minimum value of active power, It is a power generation unit in the basic scenario The maximum value of active power, In uncertain scenarios Power generation unit at the moment The active power, Power generation unit in basic scenario Binary variables; (16) and (17) are the up and down climbing limits of non-fast start units under uncertain scenarios, It is a basic scenario Power generation unit at the moment The active power, In uncertain scenarios Power generation unit at the moment The active power, and Power generation units Up and down climbing capacity, is the set of non-fast start units; (18) is the power generation capacity constraint of the fast start unit under uncertain scenarios, It is a power generation unit The minimum value of active power, It is a power generation unit The maximum value of active power, For power generation units in uncertain scenarios Binary variables; (19) and (20) are the up and down climbing limits of the fast start unit under uncertain scenarios, It is a basic scenario Power generation unit at the moment The active power, In uncertain scenarios Power generation unit at the moment The active power, It is a power generation unit The maximum value of active power, is the fast start capacity of the fast start unit i; (21) and (22) are the fast start unit start and stop state constraints under uncertain scenarios, respectively. It is a power generation unit under uncertain scenarios A binary variable representing the startup, It is a power generation unit under uncertain scenarios Shutdown characterization binary variable; (23) and (24) are the charging and discharging power limits of the energy storage unit under uncertain scenarios, 、 Energy storage units under uncertain scenarios exist The charging and discharging power at each moment, is the energy storage unit n in A binary variable representing the discharge state at any moment in an uncertain scenario, 、 Energy storage units The maximum value of charging and discharging power; (25) is the state of charge constraint of the energy storage unit under uncertain scenarios, and are the upper and lower limits of the state of charge, It is an energy storage unit exist The state of charge in basic scenarios at all times, and are the charging and discharging efficiencies respectively; (26) and (27) are the node power balance and line capacity constraints under uncertain scenarios respectively. Output variable, and is the lower and upper output limits, is the total load; is the maximum transmission power of the line, 、 、 、 are the line transfer factors of generators, energy storage, wind farms, and loads; For load.

[0039] Optionally, the processing of the power system operation flexibility evaluation model to obtain the maximum uncertainty set specifically includes: converting the power system operation flexibility evaluation model into a compact matrix form; using the GUROBI solver to process the power system operation flexibility evaluation model to obtain the optimal solution of the basic scenario; based on the optimal solution of the basic scenario, using the duality and big M method to process to obtain the maximum uncertainty set.

[0040] Optionally, converting the power system operation flexibility assessment model into a compact matrix form specifically includes:

[0041] (28)

[0042] (29)

[0043] (30)

[0044] (31)

[0045] (32)

[0046] (33)

[0047] (34)

[0048] In the above formula, represents the Hadamard product of matrices, and are the upper and lower boundary conditions of the uncertainty set, is the binary variable vector in the basic scenario, is the active power vector in the basic scenario; 、 、 and g are the corresponding limits; and They are the upper and lower limits of power respectively; is a vector of all 1s; is a set of non-negative slack variables; represents an uncertain set; 、 、 、 is the coefficient matrix of the objective function, is the coefficient matrix of the constraints; (28) is the compact matrix form of (1); (29) is the compact matrix form of (2) to (14); (33) is the compact matrix form of (15) to (27); (30) to (31) are the upper and lower bound constraints of the uncertainty set; (32) and (34) are the representations of the safety check subproblem and the uncertainty set, respectively.

[0049] Optionally, the power system operation flexibility assessment model is processed using a GUROBI solver to obtain an optimal solution for a basic scenario, specifically comprising: processing compact matrices (28) to (31) using a GUROBI solver to obtain an optimal solution for the basic scenario, including an optimal binary variable vector under the basic scenario , optimal active power vector in basic scenarios , optimal uncertainty power output lower bound vector and the optimal uncertainty power output upper bound vector .

[0050] Optionally, the optimal solution based on the basic scenario is processed using the duality and big M method to obtain the maximum uncertainty set, and further includes: setting the initial value of the upper bound of the algorithm to positive infinity and the initial value of the lower bound of the algorithm to negative infinity; using the duality and big M method, converting the compact matrices (32) to (34) into the following integer problem:

[0051] (35)

[0052] (36)

[0053] (37)

[0054] (38)

[0055] (39)

[0056] (40)

[0057] In the above formula, is the optimal dual objective value, Optimal dual objective function; 、 are the Lagrange multipliers respectively; is a large M value; 、 、 、 、 、 are the coefficient matrices of the constraints respectively; is the corresponding limit value; and are the upper and lower boundary conditions of the uncertainty set respectively; is the optimal binary variable vector in the basic scenario; is the optimal active power vector in the basic scenario; Wind farms under uncertain scenarios The initial value of the associated optimal binary variable vector; Indicates the position of the center of the uncertainty set; z is the number of inner iterations of the algorithm; is the upper bound of the algorithm; is the lower bound of the algorithm;

[0058] According to integer problems (35) to (40), an uncertainty set is obtained; according to the uncertainty set, an algorithm upper bound and an algorithm lower bound are iterated; according to the difference between the algorithm upper bound and the algorithm lower bound, it is determined whether to output a maximum uncertainty set.

[0059] Optionally, according to the uncertainty set, the iterative algorithm upper bound and the algorithm lower bound, and according to the difference between the algorithm upper bound and the algorithm lower bound, it is judged whether to output the maximum uncertainty set, specifically including: according to the uncertainty set, the iterative algorithm upper bound and the algorithm lower bound, it is judged whether the difference between the algorithm upper bound and the algorithm lower bound is less than a set threshold, and if so, outputting the uncertainty set as the maximum uncertainty set.

[0060] The uncertainty set characterization method for supporting the evaluation of power system operation flexibility provided in this embodiment is achieved by constructing an objective function with the minimum power system operation cost and the maximum uncertainty set envelope; constructing constraints, including basic constraints that meet normal operation requirements under basic scenarios, and uncertain constraints that meet normal operation requirements under uncertain scenarios; constructing a power system operation flexibility evaluation model based on the objective function and the constraints; processing the power system operation flexibility evaluation model to obtain a maximum uncertainty set, wherein the maximum uncertainty set is used to support the evaluation of power system operation flexibility, solving the technical problem of economically optimal uncertainty set characterization in power systems under basic scenarios and uncertain scenarios, and can be used to support the evaluation of power system operation flexibility.

[0061] In the second aspect, an embodiment of the present application provides an uncertainty set characterization system that supports the evaluation of power system operation flexibility, and the system includes: a first construction module, used to construct an objective function with the minimum power system operation cost and the maximum uncertainty set envelope; used to construct constraint conditions, including basic constraints that meet normal operation requirements under basic scenarios, and uncertain constraints that meet normal operation requirements under uncertain scenarios; a second construction module, used to construct a power system operation flexibility evaluation model based on the objective function, the basic constraints and the uncertain constraints; a processing module, used to process the power system operation flexibility evaluation model to obtain the maximum uncertainty set, wherein the maximum uncertainty set is used to support the evaluation of power system operation flexibility.

[0062] In a third aspect, an embodiment of the present application provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, computer instructions are stored in the memory, and the processor executes the above method by executing the computer instructions.

[0063] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to enable a computer to execute the above method.

[0064] In a fifth aspect, an embodiment of the present application provides a computer program product, comprising computer instructions, which are used to enable a computer to execute the above method. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the specific implementation methods or the description of the prior art. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0066] Figure 1 A flowchart of a method for characterizing uncertainty sets supporting power system operational flexibility assessment provided in an embodiment of the present application;

[0067] Figure 2 An uncertainty set characterization system supporting power system operational flexibility assessment provided in an embodiment of the present application;

[0068] Figure 3 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0069] To make the purpose, technical solutions, and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of this application.

[0070] As the penetration of renewable energy sources in the power system continues to increase, power system operations are exhibiting increased randomness and uncertainty. Accurately understanding the uncertainty set in a variety of possible operational scenarios is a prerequisite for effective power system dispatch optimization. Characterizing the economically optimal uncertainty set in a power system has become a metric for assessing power system operational flexibility.

[0071] In the field of power system optimization and dispatch, related technologies can generally be divided into fully knowledge-driven, data-driven optimization knowledge-driven, and fully data-driven. Fully knowledge-driven relies on the dispatch planner's expert understanding of the grid's historical operation, making it difficult to adapt to rapidly changing grid environments. Data-driven optimization knowledge-driven involves long-term simulations supported by powerful machines, placing high demands on computing resources. Fully data-driven approaches suffer from the problem that the depiction of scenario boundaries does not meet the actual conditions for safe and stable grid operation.

[0072] According to an embodiment of the present application, an embodiment of an uncertainty set characterization method for supporting the evaluation of power system operation flexibility is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0073] Figure 1 : is a flowchart of a method for characterizing an uncertainty set supporting power system operational flexibility assessment according to an embodiment of the present application, the process comprising the following steps:

[0074] Step S1: construct an objective function with the minimum power system operation cost and the maximum uncertainty set envelope.

[0075] The minimum power system operating cost includes the minimum system operating cost under the basic scenario and the minimum system operating cost under the uncertain scenario. The maximum uncertainty set envelope refers to the maximum uncertainty set under the constraints of the security and stability conditions of the envelope power grid.

[0076] The objective function can be expressed as:

[0077] (1)

[0078] In the above formula, and Wind farms In time The upper and lower bounds of the uncertainty power output, and are the upper and lower bound vectors of the uncertain power output, is the uncertain power output vector, is the feasible space of uncertain power output; and The preset wind farms In time The upper and lower limits of power output; and They are respectively the basic scenario and the uncertain scenario Power generation unit at the moment The active power, is the active power vector under uncertain scenarios, is the active power vector in the basic scenario; 、 They are the power generation units in the basic scenario binary variables representing startup and shutdown; 、 They are the power generation units under uncertain scenarios. binary variables representing startup and shutdown; 、 are binary variable vectors under the basic scenario and uncertain scenario respectively; 、 They are energy storage units in basic scenarios. exist Charging and discharging power at all times; 、 Energy storage units under uncertain scenarios exist Charging and discharging power at all times; represents the search space, and are the collections of fast-start units and non-fast-start units respectively; 、 、 、 、 、 are the weight coefficients required to construct the objective function.

[0079] Step S3: constructing constraint conditions, including basic constraints that meet normal operation requirements in basic scenarios and uncertain constraints that meet normal operation requirements in uncertain scenarios.

[0080] Among them, the basic constraints include minimum startup time constraint, minimum shutdown constraint, startup state constraint, shutdown state constraint, power generation limit constraint, up-ramp and down-ramp constraints, energy storage charging and discharging limit, energy storage state constraint, initial and final charge state constraints, transmission line capacity constraint, and power conservation constraint, specifically including:

[0081] (2)

[0082] (3)

[0083] (4)

[0084] (5)

[0085] (6)

[0086] (7)

[0087] (8)

[0088] (9)

[0089] (10)

[0090] (11)

[0091] (12)

[0092] (13)

[0093] (14)

[0094] Among the above constraints, (2) is the minimum startup time constraint, is the minimum boot time, Power generation unit in basic scenario binary variables; (3) is the minimum stop constraint, is the minimum downtime; (4) is the startup state constraint, It is the power generation unit in the basic scenario The binary variable representing the power-on state; (5) is the shutdown state constraint, It is the power generation unit in the basic scenario The binary variable representing the shutdown; (6) is the power generation limit constraint, It is a basic scenario Power generation unit at the moment The active power, It is a power generation unit The minimum value of active power, It is a power generation unit The maximum value of active power; (7) and (8) are the up-climbing and down-climbing constraints, respectively. and are the up and down ramp capacities respectively; (9) and (10) are the energy storage charge and discharge limits respectively, where is the energy storage unit n in A binary variable representing the discharge state at each moment in the basic scenario, 、 They are energy storage units in basic scenarios. exist Charging and discharging power at all times; 、 Energy storage units The maximum value of charge and discharge power; (11) is the energy storage state constraint, It is an energy storage unit exist The state of charge in basic scenarios at all times, and are the charge and discharge efficiency, and are the upper and lower limits of the state of charge respectively; (12) are the constraints of the initial and final states of charge, where is the initial state of charge, is the final state of charge; (13) is the capacity constraint of the transmission line, where 、 、 、 They are the line transfer factors of generators, energy storage, wind farms, and loads. is the load, is the expected output value, is the maximum transmission power of the line; (14) is the power conservation constraint, is the total load.

[0095] The uncertain constraints that meet the normal operation requirements in the uncertain scenario include: upper and lower bound constraints on the power generation capacity of non-fast start units in the uncertain scenario, up and down ramp constraints of non-fast start units in the uncertain scenario, power generation capacity constraints of fast start units in the uncertain scenario, up and down ramp constraints of fast start units in the uncertain scenario, on and off state constraints of fast start units in the uncertain scenario, charging and discharging power constraints of energy storage units in the uncertain scenario, state of charge constraints of energy storage units in the uncertain scenario, node power balance and line capacity constraints in the uncertain scenario, specifically including:

[0096] (15)

[0097] (16)

[0098] (17)

[0099] (18)

[0100] (19)

[0101] (20)

[0102] (twenty one)

[0103] (twenty two)

[0104] (twenty three)

[0105] (twenty four)

[0106] (25)

[0107] (26)

[0108] , (27)

[0109] Among the above constraints, (15) is the upper and lower bound constraints of the power generation capacity of the non-fast start unit under uncertain scenarios, It is a power generation unit in the basic scenario The minimum value of active power, It is a power generation unit in the basic scenario The maximum value of active power, In uncertain scenarios Power generation unit at the moment The active power, Power generation unit in basic scenario Binary variables; (16) and (17) are the up and down climbing limits of non-fast start units under uncertain scenarios, It is a basic scenario Power generation unit at the moment The active power, In uncertain scenarios Power generation unit at the moment The active power, and Power generation units Up and down climbing capacity, is the set of non-fast start units; (18) is the power generation capacity constraint of the fast start unit under uncertain scenarios, It is a power generation unit The minimum value of active power, It is a power generation unit The maximum value of active power, For power generation units in uncertain scenarios Binary variables; (19) and (20) are the up and down climbing limits of the fast start unit under uncertain scenarios, It is a basic scenario Power generation unit at the moment The active power, In uncertain scenarios Power generation unit at the moment The active power, It is a power generation unit The maximum value of active power, is the fast start capacity of the fast start unit i; (21) and (22) are the fast start unit start and stop state constraints under uncertain scenarios, respectively. It is a power generation unit under uncertain scenarios A binary variable representing the startup, It is a power generation unit under uncertain scenarios Shutdown characterization binary variable; (23) and (24) are the charging and discharging power limits of the energy storage unit under uncertain scenarios, 、 Energy storage units under uncertain scenarios exist The charging and discharging power at each moment, is the energy storage unit n in A binary variable representing the discharge state at any moment in an uncertain scenario, 、 Energy storage units The maximum value of charging and discharging power; (25) is the state of charge constraint of the energy storage unit under uncertain scenarios, and are the upper and lower limits of the state of charge, It is an energy storage unit exist The state of charge in basic scenarios at all times, and are the charging and discharging efficiencies respectively; (26) and (27) are the node power balance and line capacity constraints under uncertain scenarios respectively. Output variable, and is the lower and upper output limits, is the total load; is the maximum transmission power of the line, 、 、 、 are the line transfer factors of generators, energy storage, wind farms, and loads; For load.

[0110] Step S5: constructing a power system operation flexibility evaluation model based on the objective function and the constraint conditions.

[0111] Among them, the power system operation flexibility evaluation model is constructed based on a robust optimization framework, including objective functions and constraints, and is based on objective power data under basic grid scenarios and uncertain scenarios.

[0112] Step S7: Processing the power system operation flexibility assessment model to obtain a maximum uncertainty set, wherein the maximum uncertainty set is used to support the power system operation flexibility assessment, specifically including:

[0113] Step S71, converting the power system operation flexibility assessment model into a compact matrix form, specifically includes:

[0114] (28)

[0115] (29)

[0116] (30)

[0117] (31)

[0118] (32)

[0119] (33)

[0120] (34)

[0121] In the above formula, represents the Hadamard product of matrices, and are the upper and lower boundary conditions of the uncertainty set, is the binary variable vector in the basic scenario, is the active power vector in the basic scenario; 、 、 and g are the corresponding limits; and They are the upper and lower limits of power respectively; is a vector of all 1s; is a set of non-negative slack variables; represents an uncertain set; 、 、 、 is the coefficient matrix of the objective function, is the coefficient matrix of the constraints; (28) is the compact matrix form of (1); (29) is the compact matrix form of (2) to (14); (33) is the compact matrix form of (15) to (27); (30) to (31) are the upper and lower bound constraints of the uncertainty set; (32) and (34) are the representations of the safety check subproblem and the uncertainty set, respectively.

[0122] Step 72, using the GUROBI solver to process the power system operational flexibility assessment model to obtain the optimal solution for the basic scenario, specifically including:

[0123] The GUROBI solver is used to process the compact matrices (28) to (31) to obtain the optimal solution for the basic scenario, including the optimal binary variable vector under the basic scenario. , optimal active power vector in basic scenarios , optimal uncertainty power output lower bound vector and the optimal uncertainty power output upper bound vector , set the number of outer iterations of the algorithm .

[0124] Step 73, based on the optimal solution of the basic scenario, the duality and big M methods are used to process and obtain the maximum uncertainty set, which specifically includes:

[0125] Step 731, set the initial value of the algorithm upper bound to positive infinity, and the initial value of the algorithm lower bound to negative infinity; set the wind farm under uncertain scenario The associated optimal start or stop binary variable vector Initial value, set the number of inner iterations of the algorithm ; Set the number of outer iterations of the algorithm ;

[0126] Step 733: Using the duality and big M method, convert the compact matrices (32) to (34) into the following integer problem:

[0127] (35)

[0128] (36)

[0129] (37)

[0130] (38)

[0131] (39)

[0132] (40)

[0133] In the above formula, is the optimal dual objective value, Optimal dual objective function; 、 are the Lagrange multipliers respectively; is a large M value; 、 、 、 、 、 are the coefficient matrices of the constraints respectively; is the corresponding limit value; and are the upper and lower boundary conditions of the uncertainty set respectively; is the optimal binary variable vector in the basic scenario; is the optimal active power vector in the basic scenario; Wind farms under uncertain scenarios The initial value of the associated optimal binary variable vector; Indicates the position of the center of the uncertainty set; z is the number of inner iterations of the algorithm; is the upper bound of the algorithm; is the lower bound of the algorithm;

[0134] Step 735, according to integer problems (35) to (40), the uncertainty set is obtained According to the uncertainty set , the upper bound and lower bound of the iterative algorithm include: , solve from (34) and , that is, the upper and lower boundary conditions of the uncertainty set, and the upper bound of the update algorithm ;

[0135] Solve (41), with the constraint (33), and update the lower bound of the algorithm , where the expression of (41) is:

[0136] (41)

[0137] In the above formula, is the binary variable vector under uncertain scenarios; is a set of non-negative slack variables; is the active power vector in the uncertain scenario.

[0138] Step 737: Determine whether the difference between the algorithm upper bound and the algorithm lower bound is less than the set threshold. , if so, then we get the optimal uncertainty set If not, update , return to step 733;

[0139] Step 739, determine Is it true? If so, it means that the security check has not been violated, and the optimal uncertainty set is output. is the maximum uncertainty set. If not, return to step 731 and update .

[0140] The uncertainty set characterization method for supporting the evaluation of power system operation flexibility provided in this embodiment is achieved by constructing an objective function with the minimum power system operation cost and the maximum uncertainty set envelope; constructing constraints, including basic constraints that meet normal operation requirements under basic scenarios, and uncertain constraints that meet normal operation requirements under uncertain scenarios; constructing a power system operation flexibility evaluation model based on the objective function and the constraints; processing the power system operation flexibility evaluation model to obtain a maximum uncertainty set, wherein the maximum uncertainty set is used to support the evaluation of power system operation flexibility, solving the technical problem of economically optimal uncertainty set characterization in power systems under basic scenarios and uncertain scenarios, and can be used to support the evaluation of power system operation flexibility.

[0141] Accordingly, please refer to Figure 2 , an embodiment of the present application provides an uncertainty set characterization system that supports the evaluation of power system operation flexibility, the system including: a first construction module, used to construct an objective function with the minimum power system operation cost and the maximum uncertainty set envelope; used to construct constraint conditions, including basic constraints that meet normal operation requirements under basic scenarios, and uncertain constraints that meet normal operation requirements under uncertain scenarios; a second construction module, used to construct a power system operation flexibility evaluation model based on the objective function, the basic constraints and the uncertain constraints; a processing module, used to process the power system operation flexibility evaluation model to obtain the maximum uncertainty set, wherein the maximum uncertainty set is used to support the evaluation of power system operation flexibility.

[0142] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0143] The uncertainty set characterization system supporting the evaluation of power system operation flexibility in this embodiment is presented in the form of functional units, where the units refer to ASIC (Application Specific Integrated Circuit) circuits, processors and memories that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0144] See also Figure 3 , Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present application. Figure 3 As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system).

[0145] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0146] The memory 20 stores instructions that can be executed by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0147] The memory 20 may include a program storage area and a data storage area. The program storage area may store an operating system and application programs required for at least one function; the data storage area may store data generated based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state memory device. In some optional embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and such remote memory may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof. The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, a hard disk, or a solid-state drive; or a combination of the aforementioned types of memory. The computer device also includes a communication interface 30 for communicating with other devices or a communication network.

[0148] The embodiments of the present application also provide a computer-readable storage medium. The above-mentioned method according to the embodiment of the present application can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0149] An embodiment of the present application provides a computer program product, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform a method according to any embodiment of the present application.

[0150] Although the embodiments of the present application have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations shall fall within the scope defined by the appended claims.

[0151] The systems or modules described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A basic implementation device is a computer. Specifically, the computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0152] For the convenience of description, the above devices are described as being divided into various units according to their functions. Of course, when implementing this application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0153] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0154] The present application is described with reference to the flowcharts and / or block diagrams of the methods, systems, and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0155] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0156] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0157] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0158] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

[0159] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

[0160] Although the embodiments of the present application have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations shall fall within the scope defined by the appended claims.

Claims

1. An uncertainty set characterization method supporting power system operational flexibility assessment is characterized by: The method comprises: The objective function is constructed with the minimum power system operation cost and the maximum uncertainty set envelope. The minimum power system operation cost includes the minimum cost under the basic scenario and the minimum cost under the uncertain scenario. The maximum uncertainty set envelope is used to represent the maximum uncertainty set under the power grid security constraints. The objective function can be expressed as: (1) In the above formula, and Wind farms In time The upper and lower bounds of the uncertainty power output, and are the upper and lower bound vectors of the uncertain power output, is the uncertain power output vector, is the feasible space of uncertain power output; and The preset wind farms In time The upper and lower limits of power output; and They are the basic scenario and the uncertain scenario respectively. Power generation unit at the moment The active power, is the active power vector under uncertain scenarios, is the active power vector in the basic scenario; 、 They are the power generation units in the basic scenario binary variables representing startup and shutdown; 、 They are power generation units under uncertain scenarios binary variables representing startup and shutdown; 、 are binary variable vectors under the basic scenario and uncertain scenario respectively; 、 They are energy storage units in basic scenarios exist Charging and discharging power at all times; 、 Energy storage units under uncertain scenarios exist Charging and discharging power at all times; represents the search space, and are the collections of fast-start units and non-fast-start units respectively; 、 、 、 、 、 are the weight coefficients required to construct the objective function; Constructing constraints, including basic constraints that meet normal operating requirements under basic scenarios, and uncertain constraints that meet normal operating requirements under uncertain scenarios, wherein the basic constraints include minimum startup time constraints, minimum shutdown constraints, startup state constraints, shutdown state constraints, power generation limit constraints, up-climbing and down-climbing constraints, energy storage charging and discharging limits, energy storage state constraints, initial and final charge state constraints, transmission line capacity constraints, and power conservation constraints; the uncertain constraints include: upper and lower bound constraints on power generation capacity of non-fast startup units under uncertain scenarios, up-climbing and down-climbing constraints of non-fast startup units under uncertain scenarios, power generation capacity constraints of fast startup units under uncertain scenarios, up-climbing and down-climbing constraints of fast startup units under uncertain scenarios, startup and shutdown state constraints of fast startup units under uncertain scenarios, charging and discharging power constraints of energy storage units under uncertain scenarios, state of charge constraints of energy storage units under uncertain scenarios, node power balance under uncertain scenarios, and line capacity constraints; Constructing a power system operation flexibility evaluation model based on the objective function and the constraint conditions; The power system operation flexibility assessment model is processed to obtain a maximum uncertainty set, wherein the maximum uncertainty set is used to support the power system operation flexibility assessment.

2. The method according to claim 1, characterized in that The basic constraints specifically include: (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) Among the above constraints, (2) is the minimum startup time constraint, is the minimum boot time, Power generation unit in basic scenario binary variables; (3) is the minimum stop constraint, is the minimum downtime; (4) is the startup state constraint, It is the power generation unit in the basic scenario The binary variable representing the power-on state; (5) is the shutdown state constraint, It is the power generation unit in the basic scenario The binary variable representing the shutdown; (6) is the power generation limit constraint, It is a basic scenario Power generation unit at the moment The active power, It is a power generation unit The minimum value of active power, It is a power generation unit The maximum value of active power; (7) and (8) are the up-climbing and down-climbing constraints, respectively. and are the up and down ramp capacities respectively; (9) and (10) are the energy storage charge and discharge limits respectively, where is the energy storage unit n in A binary variable representing the discharge state at each moment in the basic scenario, 、 They are energy storage units in basic scenarios exist Charging and discharging power at all times; 、 Energy storage units The maximum value of charge and discharge power; (11) is the energy storage state constraint, It is an energy storage unit exist The state of charge in basic scenarios at all times, and are the charge and discharge efficiency, and are the upper and lower limits of the state of charge respectively; (12) are the constraints of the initial and final states of charge, where is the initial state of charge, is the final state of charge; (13) is the capacity constraint of the transmission line, where 、 、 、 They are the line transfer factors of generators, energy storage, wind farms, and loads. is the load, is the expected output value, is the maximum transmission power of the line; (14) is the power conservation constraint, is the total load.

3. The method according to claim 1, characterized in that The uncertain constraints specifically include: (15) (16) (17) (18) (19) (20) (21) (22) (23) (24) (25) (26) , (27) Among the above constraints, (15) is the upper and lower bound constraints of the power generation capacity of the non-fast start unit under uncertain scenarios, It is a power generation unit in the basic scenario The minimum value of active power, It is a power generation unit in the basic scenario The maximum value of active power, In uncertain scenarios Power generation unit at the moment The active power, Power generation unit in basic scenario Binary variables; (16) and (17) are the up and down climbing limits of non-fast start units under uncertain scenarios, It is a basic scenario Power generation unit at the moment The active power, In uncertain scenarios Power generation unit at the moment The active power, and Power generation units Up and down climbing capacity, is the set of non-fast start units; (18) is the power generation capacity constraint of the fast start unit under uncertain scenarios, It is a power generation unit The minimum value of active power, It is a power generation unit The maximum value of active power, For power generation units in uncertain scenarios Binary variables; (19) and (20) are the up and down climbing limits of the fast start unit under uncertain scenarios, It is a basic scenario Power generation unit at the moment The active power, In uncertain scenarios Power generation unit at the moment The active power, It is a power generation unit The maximum value of active power, Quick Start Unit The fast start capacity; (21) and (22) are the fast start unit start and stop state constraints under uncertain scenarios, It is a power generation unit under uncertain scenarios A binary variable representing the startup, It is a power generation unit under uncertain scenarios Shutdown characterization binary variable; (23) and (24) are the charging and discharging power limits of the energy storage unit under uncertain scenarios, 、 Energy storage units under uncertain scenarios exist The charging and discharging power at each moment, is the energy storage unit n in A binary variable representing the discharge state at any moment in an uncertain scenario, 、 Energy storage units The maximum value of charging and discharging power; (25) is the state of charge constraint of the energy storage unit under uncertain scenarios, and are the upper and lower limits of the state of charge, It is an energy storage unit exist The state of charge in basic scenarios at all times, and are the charging and discharging efficiencies respectively; (26) and (27) are the node power balance and line capacity constraints under uncertain scenarios respectively. Output variable, and is the lower and upper output limits, is the total load; is the maximum transmission power of the line, 、 、 、 are the line transfer factors of generators, energy storage, wind farms, and loads; For load.

4. The method according to any one of claims 1 to 3, characterized in that Processing the power system operation flexibility assessment model to obtain a maximum uncertainty set specifically includes: Converting the power system operation flexibility assessment model into a compact matrix form; The power system operation flexibility assessment model is processed using the GUROBI solver to obtain the optimal solution for the basic scenario; Based on the optimal solution of the basic scenario, the duality and big M methods are used to process and obtain the maximum uncertainty set.

5. The method according to claim 4, characterized in that Converting the power system operation flexibility assessment model into a compact matrix form specifically includes: (28) (29) (30) (31) (32) (33) (34) In the above formula, represents the Hadamard product of matrices, and are the upper and lower boundary conditions of the uncertainty set, is the binary variable vector in the basic scenario, is the active power vector in the basic scenario; 、 、 and g are the corresponding limits; and They are the upper and lower limits of power respectively; is a vector of all 1s; is a set of non-negative slack variables; represents an uncertain set; 、 、 、 is the coefficient matrix of the objective function, is the coefficient matrix of the constraints; (28) is the compact matrix form of (1); (29) is the compact matrix form of (2) to (14); (33) is the compact matrix form of (15) to (27); (30) to (31) are the upper and lower bound constraints of the uncertainty set; (32) and (34) are the representations of the safety check subproblem and the uncertainty set, respectively.

6. The method according to claim 5, characterized in that The GUROBI solver is used to process the power system operation flexibility assessment model to obtain the optimal solution for the basic scenario, specifically including: The GUROBI solver is used to process the compact matrices (28) to (31) to obtain the optimal solution for the basic scenario, including the optimal binary variable vector under the basic scenario. , optimal active power vector , optimal uncertainty power output lower bound vector and the optimal uncertainty power output upper bound vector .

7. The method according to claim 5, characterized in that The optimal solution based on the basic scenario is processed using the duality and big M methods to obtain the maximum uncertainty set, and further includes: Set the initial value of the algorithm's upper bound to positive infinity and the initial value of the algorithm's lower bound to negative infinity; Using the duality and big M method, the compact matrices (32)~(34) are transformed into the following integer problem: (35) (36) (37) (38) (39) (40) In the above formula, is the optimal dual objective value, Optimal dual objective function; 、 are the Lagrange multipliers respectively; is a large M value; 、 、 、 、 、 are the coefficient matrices of the constraints respectively; is the corresponding limit value; and are the upper and lower boundary conditions of the uncertainty set respectively; is the optimal binary variable vector in the basic scenario; is the optimal active power vector in the basic scenario; Wind farms under uncertain scenarios The initial value of the associated optimal binary variable vector; Indicates the position of the center of the uncertainty set; z is the number of inner iterations of the algorithm; is the upper bound of the algorithm; is the lower bound of the algorithm; According to integer problems (35) to (40), the uncertain set is obtained; According to the uncertainty set, the upper bound and the lower bound of the iterative algorithm are obtained; According to the difference between the algorithm upper bound and the algorithm lower bound, determine whether to output the maximum uncertainty set.

8. The method according to claim 7, characterized in that The iterating algorithm upper bound and algorithm lower bound according to the uncertainty set, and determining whether to output the maximum uncertainty set according to the difference between the algorithm upper bound and the algorithm lower bound, specifically includes: According to the uncertainty set, the algorithm upper bound and the algorithm lower bound are iterated to determine whether the difference between the algorithm upper bound and the algorithm lower bound is less than a set threshold. If so, the uncertainty set is output as the maximum uncertainty set.

9. An uncertainty set characterization system supporting power system operation flexibility assessment, comprising: an uncertainty set characterization method supporting power system operation flexibility assessment according to any one of claims 1 to 8, characterized in that: The system comprises: The first building block is used to construct an objective function with the goal of minimizing the power system operating cost and maximizing the uncertainty set envelope; it is used to construct constraint conditions, including basic constraints that meet normal operating requirements under basic scenarios, and uncertain constraints that meet normal operating requirements under uncertain scenarios; A second building module is configured to build a power system operation flexibility evaluation model based on the objective function, the basic constraints, and the uncertain constraints; A processing module is used to process the power system operation flexibility evaluation model to obtain a maximum uncertainty set, wherein the maximum uncertainty set is used to support the power system operation flexibility evaluation.

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