Microgrid operation domain calculation method and system considering uncertainty self-balancing capability

Abstract

The invention discloses a micro-grid operation domain calculation method and system considering uncertainty self-balancing capability, and the method comprises the following steps: constructing a micro-grid operation model, and carrying out the simplification processing; based on the simplified micro-grid operation model, revealing the physical meaning and mathematical expression of the micro-grid operation domain considering the uncertainty self-balancing capability, and establishing a max-max-min description model; the max-max-min description model is converted and solved, and a mixed integer linear programming model is obtained; and based on the feasible cut definition and a tie line operation domain multi-cell body form, providing a tie line operation domain solving method based on an improved Ad-CG algorithm, and solving the mixed integer linear programming model to obtain a micro-grid operation domain. According to the method, when photovoltaic and load prediction has uncertainty, the micro-grid operation domain considering the uncertainty self-balancing capability is calculated, so that the operation condition of the micro-grid is theoretically analyzed, the operation privacy of the micro-grid is improved, and a theoretical support is provided for optimized operation of the micro-grid.

Description

Technical Field

[0001] The present invention belongs to the field of power grids and relates to microgrid operation and optimization technology, and specifically to a microgrid operation domain calculation method and system considering uncertainty self-balancing capability. Background Art

[0002] Microgrids, as small power networks that integrate distributed energy resources, energy storage systems, and controllable loads, have attracted widespread attention for their ability to effectively utilize distributed energy. Distributed energy sources, such as photovoltaics, are significantly affected by natural conditions, exhibiting intermittent and fluctuating output. Load demand within microgrids also fluctuates due to a variety of factors. These uncertainties pose challenges to the stable operation of microgrids. Tie lines, as the channels for power exchange between microgrids and external grids, have a direct impact on their coordinated operation with the external grid, as well as the privacy and stability of the microgrid itself.

[0003] The existing operation domain calculation methods usually only consider the operation domain of the interconnection line under deterministic conditions, without considering the ability of the system to cope with uncertainty, and do not provide the physical meaning, mathematical expression and calculation method of the operation domain of the interconnection line when uncertainty exists.

[0004] Therefore, a new technical solution is needed to solve these problems. Summary of the Invention

[0005] Purpose of the invention: In order to overcome the deficiencies in the prior art, a method and system for calculating the operating domain of a microgrid taking into account the uncertainty self-balancing capability is provided. When there is uncertainty in photovoltaic and load forecasts, the operating domain of the microgrid taking into account the uncertainty self-balancing capability can be calculated, thereby theoretically analyzing the interconnection line status, improving the stability of the microgrid system operation, and providing theoretical support for the operation and optimization of the microgrid.

[0006] Technical solution: To achieve the above-mentioned purpose, the present invention provides a method for calculating the operating domain of a microgrid considering the uncertainty self-balancing capability, comprising the following steps:

[0007] S1: Build a microgrid operation model and simplify it;

[0008] S2: Based on the simplified microgrid operation model, the physical meaning and mathematical expression of the microgrid operation domain considering the uncertainty self-balancing capability are revealed, and a max-max-min characterization model is established;

[0009] S3: Transform and solve the max-max-min characterization model to obtain a mixed integer linear programming model;

[0010] S4: Based on the definition of feasible cut and the polyhedral form of the tie-line operating domain, a method for solving the tie-line operating domain based on the improved Ad-CG algorithm is proposed. The mixed integer linear programming model is solved to obtain the microgrid operating domain.

[0011] Furthermore, the construction of the microgrid operation model in step S1 includes:

[0012] Output constraints of micro gas turbine units:

[0013] (1)

[0014] in, is the active power output of micro gas turbine unit k at time t; and are the upper and lower limits of the active power output of micro gas turbine unit k respectively;

[0015] Micro gas turbine unit climbing constraints:

[0016] (2)

[0017] (3)

[0018] in, is the time interval; and are the maximum up and down ramp rates of micro gas turbine unit k respectively;

[0019] Energy storage charging and discharging power constraints:

[0020] (4)

[0021] (5)

[0022] in, and are the charging and discharging power of energy storage e at time t respectively; and are the upper limits of charge and discharge power of energy storage e respectively;

[0023] Energy storage charging and discharging logic constraints:

[0024] (6)

[0025] Energy storage SOC constraints:

[0026] (7)

[0027] (8)

[0028] (9)

[0029] in, The energy stored in the energy storage e at the initial moment; and are the charging and discharging efficiencies of the energy storage e, respectively; and are the upper and lower limits of energy storage e, respectively; is the total number of time periods;

[0030] Relaxation of energy storage charging and discharging complementarity constraints:

[0031] (10)

[0032] Demand response load demand power consumption constraint:

[0033] (11)

[0034] (12)

[0035] in, is the active power of the demand response load at time t; is the total electricity demand of the demand response load during the entire period; and are the upper and lower limits of electricity demand for demand response load respectively;

[0036] Photovoltaic unit output:

[0037] (13)

[0038] (14)

[0039] in, is the actual output of photovoltaic unit v at time t; is the predicted output of photovoltaic unit v at time t; is the uncertainty of the predicted output of photovoltaic unit v at time t; and are the upper and lower limits of the uncertainty of the predicted output of photovoltaic unit v;

[0040] Normal load active power:

[0041] (15)

[0042] (16)

[0043] in, is the actual active power consumed by the load at time t; is the predicted active power consumed by the load at time t; is the uncertainty of the predicted active power consumption of the load at time t; and They are the upper and lower limits of the uncertainty of the predicted active power consumption of the load;

[0044] Transmission capacity constraints of microgrid tie lines:

[0045] (17)

[0046] in, is the active power obtained by the microgrid tie line from the external grid at time t; and are the upper and lower limit constraints of the transmission capacity of the microgrid tie line respectively;

[0047] Microgrid power balance constraints:

[0048] (18)

[0049] Furthermore, in step S1, the microgrid operation model is simplified into a matrix form with the tie line power as the aggregate variable, the photovoltaic and load forecast errors as the uncertain variables, and the output of other units as the adjustment variables. The simplified microgrid operation model is:

[0050] (19)

[0051] in, A high-dimensional operating domain consisting of all constraints within the microgrid; Aggregation variable, which describes the physical quantity obtained by aggregating microgrids, usually the tie line power ; As a regulating variable, the operating point under a certain uncertainty can be adjusted in the entire constraint set Position in It is an uncertain variable, which is caused by the prediction error caused by many random factors faced in the actual operation of the microgrid, and affects the range of the aggregated variable; 、 and Variables 、 and The coefficient matrix of is a constant vector in the system.

[0052] Furthermore, the physical meaning and mathematical expression of the microgrid operation domain in step S2 include:

[0053] Adjustment variable set:

[0054] (20)

[0055] in, For all moderating variables The physical meaning is: to deal with uncertain variables in the microgrid and tie line power All adjustment methods;

[0056] (twenty one)

[0057] (twenty two)

[0058] in, For variables only and The sub-high-dimensional operation domain is composed of the high-dimensional operation domain Directed variables and Directional projection, the physical meaning is: for the second highest dimensional operation domain Any uncertain variable within and any tie line power , all adjustable; and Variables projected into the second-higher-dimensional space and The coefficient matrix of is a constant vector projected into the next-highest-dimensional space; Formula (22) represents the next-highest-dimensional operating domain The projected form of linear inequalities;

[0059] (twenty three)

[0060] (twenty four)

[0061] (25)

[0062] in, For the uncertainty situation, For the Tie-line power under uncertainty The operation domain is composed of the second high-dimensional operation domain Towards Directional projection; The tie line power The operating domain is Operational domain The physical meaning of the intersection is: for the operating domain Tie line power in , we can find a regulating variable to deal with any uncertainty situation; The tie line power The coefficient matrix of is a constant vector; Formula (25) represents the tie line power Operational Domain The linear inequality form of .

[0063] Furthermore, the establishment of the max-max-min characterization model in step S2 includes:

[0064] Design formula (26) to determine a certain operating point Is it located in the next highest dimensional operating domain? Inside:

[0065] (26)

[0066] in, is the relaxation variable, which means that the operating point Ability to adjust and adjust the original constraints; when the optimization results When , it indicates that there is a moderating variable Belongs to a non-empty set , so that the operating point Satisfy the original constraints, proving that the operating point is in the second highest dimensional operating domain Inside;

[0067] Drawing on the robust optimization idea, we design the max-min optimization problem (27) to determine a certain operating point In the most severe uncertainty situation, whether it is located in the operating area of ​​the tie line Inside:

[0068] (27)

[0069] (28)

[0070] in, Uncertain quantity The coefficient matrix of Uncertain quantity is a constant vector; is the uncertainty variation space, which is expressed as the linear inequality form of formula (28); formula (27) means: when the optimization result When , it indicates that there is a moderating variable Belongs to a non-empty set , so that the operating point It can cope with any uncertain situation and satisfy the original constraints, proving that the operating point is in the operating domain of the tie line. At this time, the tie line power It has the ability to self-balance the uncertainty within the microgrid;

[0071] Design a max-max-min optimization problem (29) to determine the space of a certain connection line Is the worst point in the most severe uncertainty situation located in the operating domain of the tie line? Inside:

[0072] (29)

[0073] (30)

[0074] in, The tie line power The coefficient matrix of The tie line power A constant vector of ; is the tie line space, expressed as the linear inequality form of formula (30); formula (29) means: when the optimization result When the contact line space All operating points in All are located in the tie line operation area Inside, that is, the contact line space It is the operating area of ​​the tie line A subset of , this space is the operating area of ​​the tie line , and all operating points in the operating domain have the ability to self-balance the uncertainties within the microgrid.

[0075] Furthermore, the specific process of step S3 is as follows:

[0076] Based on the dual transformation method, the inner min problem in formula (29) is transformed into a max problem and merged with the two outer max problems:

[0077] (31)

[0078] in, is the dual variable; is the dual variable The transpose of is the transpose of the coefficient matrix B;

[0079] Decouple the variables in the objective function of formula (31):

[0080] (32)

[0081] in, For the contact line space The dual variable of For uncertain space The dual variable of

[0082] According to the strong duality principle, the KKT conditions for obtaining the optimal solution of the two internal max problems are:

[0083] (33)

[0084] (34)

[0085] The big M method is introduced to transform the nonlinear constraints in formulas (33) and (34) into linear constraints:

[0086] (35)

[0087] (36)

[0088] Where M is a sufficiently large constant. and are the first and second variables respectively;

[0089] After multiple dual transformations and decoupling operations, formula (29) is transformed into a mixed integer linear programming problem that can be processed by commercial solvers:

[0090] (37)

[0091] Furthermore, the feasible cut definition and the contact line operation domain polyhedron form in step S4 are:

[0092] According to the weak duality theorem, the transformed formula (31) should be smaller than formula (29). If the current connection line space For the tie line operation area , there must be:

[0093] (38)

[0094] When the operating point In the tie line operation area When the value of and In uncertain space and dual space Take the extreme value, that is, the vertex and , each set of vertex combinations will generate a feasible cut constraint:

[0095] (39)

[0096] Substituting all vertex combinations into formula (39), we can obtain the operating domain of the tie line. Polyhedral form:

[0097] (40)

[0098] in, is a set of vertices in the uncertain space, is the set of vertices in the dual space.

[0099] Furthermore, in step S4, an outer cutting approximation method based on an improved Ad-CG algorithm is proposed to adaptively generate vertices. and , and continuously obtain the feasible cut constraints on the original connection line space Cut and finally get the operating area of ​​the tie line .

[0100] Furthermore, in step S4, the tie line operation domain The acquisition process includes:

[0101] 1) Input specific parameters according to the microgrid operation domain;

[0102] 2) Based on the transmission capacity constraints of the microgrid tie line and the upper and lower limits of the forecast uncertainty of the photovoltaic units and conventional loads, the tie line space under the initial situation is generated respectively. and uncertain space ;

[0103] 3) In the contact line space Solving formula (37) below, we get five optimal solutions: 、 、 、 and , only extracting and , these two variables are the uncertainty space that maximizes the objective function and dual space The vertices in

[0104] 4) According to formula (39), substitute and Vertex data, adaptively generate an external feasible cut constraint ;

[0105] 5) Add feasible cut constraints to the tie line space Update the contact line space Boundary parameters and ;

[0106] 6) Repeat the above steps 3) to 5) until the objective function of formula (37) is less than the tolerance , output the current space, which is the operating domain of the tie line .

[0107] The present invention also provides a microgrid operation domain computing system considering uncertainty self-balancing capability, comprising:

[0108] A microgrid operation model construction model is used to construct a microgrid operation model and perform simplified processing;

[0109] The max-max-min characterization model is constructed to reveal the physical meaning and mathematical expression of the microgrid operation domain considering the uncertainty self-balancing capability and to establish the max-max-min characterization model;

[0110] The conversion module is used to convert and solve the max-max-min characterization model to obtain a mixed integer linear programming model;

[0111] The solution module is used to solve the mixed integer linear programming model to obtain the microgrid operation domain.

[0112] Beneficial effects: Compared with the existing technology, the present invention can reveal the physical meaning and mathematical expression of the microgrid operating domain considering the self-balancing capability of uncertainties, and clearly analyze the boundaries and characteristics of the microgrid operating domain. At the same time, the nonlinear optimization problem of the microgrid operating domain calculation is converted into a mixed integer linear programming model, so that it can be solved using mature commercial solvers, solving the problem of high computational complexity and difficulty in solving in the existing technology; and a microgrid operating domain solution method and framework based on the improved Ad-CG algorithm are proposed, which solves the problem of the existing technology that it is difficult to accurately characterize the microgrid operating domain considering the self-balancing capability of uncertainties, thereby improving the privacy and reliability of microgrid operation, which is of great significance to the dispatching operation and optimization management of microgrids. BRIEF DESCRIPTION OF THE DRAWINGS

[0113] Figure 1 It is a flowchart of the method of the present invention;

[0114] Figure 2 This is a microgrid structure diagram used in the example of the present invention;

[0115] Figure 3 This is a flow chart of the overall framework of the outer cutting approximation method of the improved Ad-CG algorithm designed in the example of the present invention;

[0116] Figure 4 It is a microgrid operation domain constructed by an example of the present invention with three time periods and considering the uncertainty self-balancing capability. DETAILED DESCRIPTION

[0117] The present invention is further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, modifications of various equivalent forms of the present invention made by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0118] Example 1:

[0119] like Figure 1 As shown, this embodiment provides a method for calculating the operating domain of a microgrid considering the uncertainty self-balancing capability, including the following steps:

[0120] S1: Construct a microgrid operation model and simplify it into a matrix form with the tie line power as the aggregate variable, the photovoltaic and load forecast errors as the uncertain variables, and the output of other units as the regulating variables.

[0121] The specific process is:

[0122] Output constraints of micro gas turbine units:

[0123] (1)

[0124] in, is the active power output of micro gas turbine unit k at time t; and are the upper and lower limits of the active power output of micro gas turbine unit k respectively;

[0125] Micro gas turbine unit climbing constraints:

[0126] (2)

[0127] (3)

[0128] in, is the time interval; and are the maximum up and down ramp rates of micro gas turbine unit k respectively;

[0129] Energy storage charging and discharging power constraints:

[0130] (4)

[0131] (5)

[0132] in, and are the charging and discharging power of energy storage e at time t respectively; and are the upper limits of charge and discharge power of energy storage e respectively;

[0133] Energy storage charging and discharging logic constraints:

[0134] (6)

[0135] Energy storage SOC constraints:

[0136] (7)

[0137] (8)

[0138] (9)

[0139] in, The energy stored in the energy storage e at the initial moment; and are the charging and discharging efficiencies of the energy storage e, respectively; and are the upper and lower limits of energy storage e, respectively; is the total number of time periods;

[0140] Relaxation of energy storage charging and discharging complementarity constraints:

[0141] (10)

[0142] Demand response load demand power consumption constraint:

[0143] (11)

[0144] (12)

[0145] in, is the active power of the demand response load at time t; is the total electricity demand of the demand response load during the entire period; and are the upper and lower limits of electricity demand for demand response load respectively;

[0146] Photovoltaic unit output:

[0147] (13)

[0148] (14)

[0149] in, is the actual output of photovoltaic unit v at time t; is the predicted output of photovoltaic unit v at time t; is the uncertainty of the predicted output of photovoltaic unit v at time t; and are the upper and lower limits of the uncertainty of the predicted output of photovoltaic unit v;

[0150] Normal load active power:

[0151] (15)

[0152] (16)

[0153] in, is the actual active power consumed by the load at time t; is the predicted active power consumed by the load at time t; is the uncertainty of the predicted active power consumption of the load at time t; and They are the upper and lower limits of the uncertainty of the predicted active power consumption of the load;

[0154] Transmission capacity constraints of microgrid tie lines:

[0155] (17)

[0156] in, is the active power obtained by the microgrid tie line from the external grid at time t; and are the upper and lower limit constraints of the transmission capacity of the microgrid tie line respectively;

[0157] Microgrid power balance constraints:

[0158] (18)

[0159] Simplified model of microgrid system operation:

[0160] (19)

[0161] in, A high-dimensional operating domain consisting of all constraints within the microgrid; Aggregation variable, which describes the physical quantity obtained by aggregating microgrids, usually the tie line power ; As a regulating variable, the operating point under a certain uncertainty can be adjusted in the entire constraint set Position in It is an uncertain variable, which is caused by the prediction error caused by many random factors faced in the actual operation of the microgrid, and affects the range of the aggregated variable; 、 and Variables 、 and The coefficient matrix of is a constant vector in the system.

[0162] S2: Reveal the physical meaning and mathematical expression of the microgrid operation domain considering the uncertainty self-balancing capability, and establish a max-max-min characterization model.

[0163] The specific process is:

[0164] Adjustment variable set:

[0165] (20)

[0166] in, For all moderating variables The physical meaning is: to deal with uncertain variables in the microgrid and tie line power All adjustment methods;

[0167] (twenty one)

[0168] (twenty two)

[0169] in, For variables only and The sub-high-dimensional operation domain is composed of the high-dimensional operation domain Directed variables and Directional projection, the physical meaning is: for the second highest dimensional operation domain Any uncertain variable within and any tie line power , all adjustable; and Variables projected into the second-higher-dimensional space and The coefficient matrix of is a constant vector projected into the next-highest-dimensional space; Formula (22) represents the next-highest-dimensional operating domain The projected form of linear inequalities;

[0170] (twenty three)

[0171] (twenty four)

[0172] (25)

[0173] in, For the uncertainty situation, For the Tie-line power under uncertainty The operation domain is composed of the second high-dimensional operation domain Towards Directional projection; The tie line power The operating domain is Operational domain The physical meaning of the intersection is: for the operating domain Tie line power in , we can find a regulating variable to deal with any uncertainty situation; The tie line power The coefficient matrix of is a constant vector; Formula (25) represents the tie line power Operational Domain The linear inequality form of and The boundary information of the operating domain that needs to be solved;

[0174] The establishment of the max-max-min characterization model includes:

[0175] Design formula (26) to determine a certain operating point Is it located in the next highest dimensional operating domain? Inside:

[0176] (26)

[0177] in, is the relaxation variable, which means that the operating point Ability to adjust and adjust the original constraints; when the optimization results When , it indicates that there is a moderating variable Belongs to a non-empty set , so that the operating point Satisfy the original constraints, proving that the operating point is in the second highest dimensional operating domain Inside;

[0178] Drawing on the robust optimization idea, we design the max-min optimization problem (27) to determine a certain operating point In the most severe uncertainty situation, whether it is located in the operating area of ​​the tie line Inside:

[0179] (27)

[0180] (28)

[0181] in, Uncertain quantity The coefficient matrix of Uncertain quantity is a constant vector; is the uncertainty variation space, which is expressed as the linear inequality form of formula (28); formula (27) means: when the optimization result When , it indicates that there is a moderating variable Belongs to a non-empty set , so that the operating point It can cope with any uncertain situation and satisfy the original constraints, proving that the operating point is in the operating domain of the tie line. At this time, the tie line power It has the ability to self-balance the uncertainty within the microgrid;

[0182] Design a max-max-min optimization problem (29) to determine the space of a certain connection line Is the worst point in the most severe uncertainty situation located in the operating domain of the tie line? Inside:

[0183] (29)

[0184] (30)

[0185] in, The tie line power The coefficient matrix of The tie line power A constant vector of ; is the tie line space, expressed as the linear inequality form of formula (30); formula (29) means: when the optimization result When the contact line space All operating points in All are located in the tie line operation area Inside, that is, the contact line space It is the operating area of ​​the tie line A subset of , this space is the operating area of ​​the tie line , and all operating points in the operating domain have the ability to self-balance the uncertainties within the microgrid.

[0186] S3: Transform and solve the max-max-min characterization model to obtain a mixed integer linear programming model.

[0187] The specific process is:

[0188] Based on the dual transformation method, the inner min problem in formula (29) is transformed into a max problem and merged with the two outer max problems:

[0189] (31)

[0190] in, is the dual variable; is the dual variable The transpose of is the transpose of the coefficient matrix B;

[0191] Decouple the variables in the objective function of formula (31):

[0192] (32)

[0193] in, For the contact line space The dual variable of For uncertain space The dual variable of

[0194] According to the strong duality principle, the KKT conditions for obtaining the optimal solution of the two internal max problems are:

[0195] (33)

[0196] (34)

[0197] The big M method is introduced to transform the nonlinear constraints in formulas (33) and (34) into linear constraints:

[0198] (35)

[0199] (36)

[0200] Where M is a sufficiently large constant. and are the first and second variables, respectively, both of which are 0-1 variables;

[0201] After multiple dual transformations and decoupling operations, Equation (29) is transformed into a mixed integer linear programming (MILP) problem that can be processed by commercial solvers:

[0202] (37)

[0203] S4: Based on the definition of feasible cut and the polyhedral form of the tie line operation domain, a method for solving the tie line operation domain based on the improved Ad-CG algorithm is proposed.

[0204] The specific process is:

[0205] According to the weak duality theorem, the transformed formula (31) should be smaller than formula (29). If the current connection line space For the tie line operation area , there must be:

[0206] (38)

[0207] When the operating point In the tie line operation area When the value of and In uncertain space and dual space Take the extreme value, that is, the vertex and , each set of vertex combinations will generate a feasible cut constraint:

[0208] (39)

[0209] Substituting all vertex combinations into formula (39), we can obtain the operating domain of the tie line. Polyhedral form:

[0210] (40)

[0211] in, is a set of vertices in the uncertain space, is the set of vertices in the dual space;

[0212] Due to the uncertainty of space and dual space The number of vertices in the image is too large to be fully enumerated. Therefore, an outer cutting approximation method based on the improved Ad-CG (Adaptive Constraint Generation) algorithm is proposed to adaptively generate vertices. and , and continuously obtain the feasible cut constraints on the original connection line space Cut and finally get the operating area of ​​the tie line , the specific solution process is as follows:

[0213] 1) According to the microgrid operation status, input specific parameters, such as 、 、 、 、 ;

[0214] 2) Based on the transmission capacity constraints of the microgrid tie line and the upper and lower limits of the forecast uncertainty of the photovoltaic units and conventional loads, the tie line space under the initial situation is generated respectively. and uncertain space ;

[0215] 3) In the tie line space Solving formula (37) below, we get five optimal solutions: 、 、 、 and , only extracting and , these two variables are the uncertainty space that maximizes the objective function and dual space The vertices in

[0216] 4) According to formula (39), substitute and Vertex data, adaptively generate an external feasible cut constraint ;

[0217] 5) Add the feasible cut constraint to the tie line space Update the contact line space Boundary parameters and ;

[0218] 6) Repeat the above steps 3) to 5) until the objective function of formula (37) is less than the tolerance , output the current space, which is the operating domain of the tie line .

[0219] Example 2:

[0220] Based on the method of Example 1, this embodiment provides a microgrid operation domain computing system that considers uncertainty self-balancing capability, including:

[0221] A microgrid operation model construction model is used to construct a microgrid operation model and perform simplified processing;

[0222] The max-max-min characterization model is constructed to reveal the physical meaning and mathematical expression of the microgrid operation domain considering the uncertainty self-balancing capability and to establish the max-max-min characterization model;

[0223] The conversion module is used to convert and solve the max-max-min characterization model to obtain a mixed integer linear programming model;

[0224] The solution module is used to solve the mixed integer linear programming model to obtain the microgrid operation domain.

[0225] Example 3:

[0226] In order to verify the effectiveness and effect of the solution of the present invention, this embodiment applies and analyzes the method of the present invention as follows:

[0227] In this embodiment, select Figure 2 The microgrid shown is the research object, which considers the operation domain of the microgrid with uncertainty self-balancing capability in three consecutive time periods and displays it visually.

[0228] The basic operating parameters of the microgrid are set as follows: the total number of time periods T is set to 3, the time interval Set to 1 hour, starting time is 14:00, tolerance Set to 10 -4 , M in the large M method is set to 2000. The operating parameters of the microgrid equipment are shown in Table 1.

[0229] Table 1 Microgrid equipment operating parameters

[0230]

[0231] Considering that the microgrid tie line power has upper and lower power limits due to the transformer capacity limitation, the initial tie line space is directly given according to this constraint. , the Ad-CG algorithm is used to iteratively calculate and cut the initial tie line space; for the uncertain variables in the photovoltaic units and conventional loads, the uncertainty space is directly given by constructing a box-type uncertainty set .

[0232] Based on the microgrid parameters, the input matrix parameters in formula (37) can be obtained, as follows: 、 、 、 etc., combined with the outer cutting approximation method of the improved Ad-CG algorithm, and Figure 3 The solution framework shown in the figure can be used to obtain the microgrid operation domain with three-period uncertainty self-balancing capability, as shown in the figure. Figure 4 The entire calculation process took 7.514 seconds, generated 22 feasible cut constraints, and finally obtained the microgrid operation domain image considering the uncertainty self-balancing capability in three periods.

[0233] According to the results obtained in this embodiment, when uncertainty occurs in a microgrid, the microgrid operation domain that takes into account the uncertainty self-balancing capability can be quickly and accurately characterized, which is of great significance for the operation and optimization of the microgrid.

Claims

1. A method for calculating the operating domain of a microgrid considering the uncertainty self-balancing capability, characterized in that: The steps include: S1: Build a microgrid operation model and simplify it; S2: Based on the simplified microgrid operation model, the physical meaning and mathematical expression of the microgrid operation domain considering the uncertainty self-balancing capability are revealed, and a max-max-min characterization model is established; S3: Transform and solve the max-max-min characterization model to obtain a mixed integer linear programming model; S4: Based on the definition of feasible cut and the polyhedral form of the tie-line operating domain, a method for solving the tie-line operating domain based on the improved Ad-CG algorithm is proposed. The mixed integer linear programming model is solved to obtain the microgrid operating domain.

2. A method for calculating the operating domain of a microgrid considering the uncertainty self-balancing capability according to claim 1, characterized in that: The construction of the microgrid operation model in step S1 includes: Output constraints of micro gas turbine units: （1）； in, is the active power output of micro gas turbine unit k at time t; and are the upper and lower limits of the active power output of micro gas turbine unit k respectively; Micro gas turbine unit climbing constraints: （2）； （3）； in, is the time interval; and are the maximum up and down ramp rates of micro gas turbine unit k, respectively; Energy storage charging and discharging power constraints: （4）； （5）； in, and are the charging and discharging power of energy storage e at time t respectively; and are the upper limits of charge and discharge power of energy storage e respectively; Energy storage charging and discharging logic constraints: （6）； Energy storage SOC constraints: （7）； （8）； （9）； in, The energy stored in the energy storage e at the initial moment; and are the charging and discharging efficiencies of the energy storage e, respectively; and are the upper and lower limits of energy storage e, respectively; is the total number of time periods; Relaxation of energy storage charging and discharging complementarity constraints: （10）； Demand response load demand power consumption constraint: （11）； （12）； in, is the active power of the demand response load at time t; is the total electricity demand of the demand response load during the entire period; and are the upper and lower limits of electricity demand for demand response load respectively; Photovoltaic unit output: （13）； （14）； in, is the actual output of photovoltaic unit v at time t; is the predicted output of photovoltaic unit v at time t; is the uncertainty of the predicted output of photovoltaic unit v at time t; and are the upper and lower limits of the uncertainty of the predicted output of photovoltaic unit v; Normal load active power: （15）； （16）； in, is the actual active power consumed by the load at time t; is the predicted active power consumed by the load at time t; is the uncertainty of the predicted active power consumption of the load at time t; and They are the upper and lower limits of the uncertainty of the predicted active power consumption of the load; Transmission capacity constraints of microgrid tie lines: （17）； in, is the active power obtained by the microgrid tie line from the external grid at time t; and are the upper and lower limit constraints of the transmission capacity of the microgrid tie line respectively; Microgrid power balance constraints: （18）。 3. A method for calculating the microgrid operating domain considering the uncertainty self-balancing capability according to claim 2, characterized in that: In step S1, the microgrid operation model is simplified into a matrix form with the tie line power as the aggregate variable, the photovoltaic and load forecast errors as the uncertain variables, and the output of other units as the adjustment variables. The simplified microgrid operation model is: （19）； in, A high-dimensional operating domain consisting of all constraints within the microgrid; is the aggregation variable, which describes the physical quantity obtained by aggregating the microgrids; is the moderating variable; is an uncertain variable; 、 and Variables 、 and The coefficient matrix of is a constant vector in the system.

4. A method for calculating the microgrid operating domain considering the uncertainty self-balancing capability according to claim 3, characterized in that: The physical meaning and mathematical expression of the microgrid operation domain in step S2 include: Adjustment variable set: （20）； in, For all moderating variables The physical meaning is: to deal with uncertain variables in the microgrid and tie line power All adjustment methods; （21）； （22）； in, For variables only and The sub-high-dimensional operation domain is composed of the high-dimensional operation domain Directed variables and Directional projection, the physical meaning is: for the second highest dimensional operation domain Any uncertain variable within and any tie line power , are all adjustable; and Variables projected into the second-higher-dimensional space and The coefficient matrix of is a constant vector projected into the next-highest-dimensional space; Formula (22) represents the next-highest-dimensional operating domain The projected form of linear inequalities; （23）； （24）； （25）； in, For the uncertainty situation, For the Tie-line power under uncertainty The operation domain is composed of the second high-dimensional operation domain Towards Directional projection; The tie line power The operating domain is Operational domain The physical meaning of the intersection is: for the operating domain Tie line power in , we can find a regulating variable to deal with any uncertainty situation; The tie line power The coefficient matrix of is a constant vector; Formula (25) represents the tie line power Operational Domain The linear inequality form of .

5. A method for calculating the microgrid operating domain considering the uncertainty self-balancing capability according to claim 4, characterized in that: The establishment of the max-max-min characterization model in step S2 includes: Design formula (26) to determine a certain operating point Is it located in the next highest dimensional operating domain? Inside: （26）； in, is the relaxation variable, which means that the operating point Ability to adjust and adjust the original constraints; when the optimization results When , it indicates that there is a moderating variable Belongs to a non-empty set , so that the operating point Satisfy the original constraints, proving that the operating point is in the second highest dimensional operating domain Inside; Drawing on the robust optimization idea, we design the max-min optimization problem (27) to determine a certain operating point In the most severe uncertainty situation, whether it is located in the operating area of ​​the tie line Inside: （27）； （28）； in, Uncertain quantity The coefficient matrix of Uncertain quantity is a constant vector; is the uncertainty variation space, which is expressed as the linear inequality form of formula (28); formula (27) means: when the optimization result When , it indicates that there is a moderating variable Belongs to a non-empty set , so that the operating point It can cope with any uncertain situation and satisfy the original constraints, proving that the operating point is in the operating domain of the tie line. At this time, the tie line power It has the ability to self-balance the uncertainty within the microgrid; Design a max-max-min optimization problem (29) to determine the space of a certain connection line Is the worst point in the most severe uncertainty situation located in the operating domain of the tie line? Inside: （29）； （30）； in, The tie line power The coefficient matrix of The tie line power A constant vector of ; is the tie line space, expressed as the linear inequality form of formula (30); formula (29) means: when the optimization result When the contact line space All operating points in All are located in the tie line operation area Inside, that is, the contact line space It is the operating area of ​​the tie line A subset of , this space is the operating area of ​​the tie line , and all operating points in the operating domain have the ability to self-balance the uncertainties within the microgrid.

6. A method for calculating the microgrid operating domain considering the uncertainty self-balancing capability according to claim 5, characterized in that: The specific process of step S3 is: Based on the dual transformation method, the inner min problem in formula (29) is transformed into a max problem and merged with the two outer max problems: （31）； in, is the dual variable; is the dual variable The transpose of is the transpose of the coefficient matrix B; Decouple the variables in the objective function of formula (31): （32）； in, For the contact line space The dual variable of For uncertain space The dual variable of According to the strong duality principle, the KKT conditions for obtaining the optimal solution of the two internal max problems are: （33）； （34）； The big M method is introduced to transform the nonlinear constraints in formulas (33) and (34) into linear constraints: （35）； （36）； Where M is a constant, and are the first and second variables respectively; After multiple dual transformations and decoupling operations, formula (29) is transformed into a mixed integer linear programming problem that can be processed by commercial solvers: （37）。 7. A method for calculating the microgrid operating domain considering the uncertainty self-balancing capability according to claim 6, characterized in that: The feasible cut definition and the polyhedral form of the tie line operation domain in step S4 are: According to the weak duality theorem, the transformed formula (31) should be smaller than formula (29). If the current connection line space For the tie line operation area , there must be: （38）； When the operating point In the tie line operation area When the value of and In uncertain space and dual space Take the extreme value, that is, the vertex and , each set of vertex combinations will generate a feasible cut constraint: （39）； Substituting all vertex combinations into formula (39), we can obtain the operating domain of the tie line. Polyhedral form: （40）； in, is a set of vertices in the uncertain space, is the set of vertices in the dual space.

8. A method for calculating the microgrid operating domain considering the uncertainty self-balancing capability according to claim 7, characterized in that: In step S4, an outer cutting approximation method based on an improved Ad-CG algorithm is proposed to adaptively generate vertices. and , and continuously obtain the feasible cut constraints on the original connection line space Cut and finally get the operating area of ​​the tie line .

9. A method for calculating the microgrid operating domain considering the uncertainty self-balancing capability according to claim 8, characterized in that: In step S4, the tie line operation domain The acquisition process includes: 1) Input specific parameters according to the microgrid operation domain; 2) Based on the transmission capacity constraints of the microgrid tie line and the upper and lower limits of the forecast uncertainty of the photovoltaic units and conventional loads, the tie line space under the initial situation is generated respectively. and uncertain space ; 3) In the contact line space Solving formula (37) below, we get five optimal solutions: 、 、 、 and , only extracting and , these two variables are the uncertainty space that maximizes the objective function and dual space The vertices in 4) According to formula (39), substitute and Vertex data, adaptively generate an external feasible cut constraint ; 5) Add feasible cut constraints to the tie line space Update the contact line space Boundary parameters and ; 6) Repeat the above steps 3) to 5) until the objective function of formula (37) is less than the tolerance , output the current space, which is the operating domain of the tie line .

10. A microgrid operation domain computing system considering uncertainty self-balancing capability, characterized in that: include: A microgrid operation model construction model is used to construct a microgrid operation model and perform simplified processing; The max-max-min characterization model is constructed to reveal the physical meaning and mathematical expression of the microgrid operation domain considering the uncertainty self-balancing capability and to establish the max-max-min characterization model; The conversion module is used to convert and solve the max-max-min characterization model to obtain a mixed integer linear programming model; The solution module is used to solve the mixed integer linear programming model to obtain the microgrid operation domain.

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