Microgrid self-balancing capability evaluation method and system based on basic polyhedron scaling

Abstract

The present invention discloses a method and system for evaluating the self-balancing capability of a microgrid based on basic polyhedron scaling. The method comprises: establishing a microgrid operation model that takes into account intraday interconnection line power deviations; establishing a general evaluation model for the uncertainty self-balancing capability of a microgrid based on the microgrid operation model; establishing a basic polyhedron model that takes into account time correlation based on the general evaluation model for the uncertainty self-balancing capability of a microgrid; converting the general evaluation model for the uncertainty self-balancing capability of a microgrid based on scaling of the basic polyhedron model into a scaled evaluation model; and solving the scaled evaluation model to obtain an evaluation result. The present invention establishes a basic polyhedron model with time coupling characteristics and utilizes basic polyhedron scaling to quantitatively evaluate the uncertainty self-balancing capability of a microgrid, providing a theoretical basis for improving the regulation capability and operational resilience of local power grids in extreme operating scenarios.

Description

Technical Field

[0001] The present invention belongs to the field of power systems and relates to microgrid evaluation technology, and in particular to a microgrid self-balancing capability evaluation method and system based on basic polyhedron scaling. Background Art

[0002] As renewable energy penetration continues to rise, power systems are facing challenges of high uncertainty and severe disturbances. Traditional approaches that rely on centralized regulation of large power grids to address uncertainty are gradually exposing limitations such as delayed response and low regulation efficiency. Against this backdrop, the responsibility for balancing uncertainty is gradually shifting to local power grids. Microgrids, in particular, are key components of local power grids. Their ability to locally self-balance uncertainty is becoming a core issue in research and engineering practice.

[0003] Existing research on power system uncertainty control can be categorized into two main categories: active management and passive management. In the active management field, some studies have constructed regional models of transmission system uncertainty adjustment, while others have introduced active management strategies within local power grids to improve economic efficiency. In the passive management field, uncertainty is typically modeled and analyzed across a large number of scenarios, resulting in computationally complex and dependent on higher-level systems to mitigate uncertainty. However, systematic research focusing on the uncertainty self-balancing capabilities of microgrids remains relatively scarce, lacking a unified modeling framework and quantitative evaluation methods.

[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 evaluating the uncertainty self-balancing capability of a microgrid based on basic polyhedral scaling is provided, which incorporates the day-ahead dispatch plan of the microgrid into the optimization decision variables, and optimizes the scaling factor by introducing a volume measurement index to achieve a quantitative evaluation of the microgrid's ability to cope with uncertainty during the intraday operation phase. This can provide a theoretical basis for improving the regulation capability and operational resilience of local power grids in extreme operating scenarios, and provide a theoretical basis and methodological support for uncertainty management in the planning, design, and economic operation of microgrids.

[0006] Technical solution: To achieve the above objectives, the present invention provides a microgrid self-balancing capability evaluation method based on basic polytope scaling, comprising the following steps:

[0007] S1: Establish a microgrid operation model considering intraday tie line power deviation;

[0008] S2: Based on the microgrid operation model in step S1, a general evaluation model for the uncertainty self-balancing capability of the microgrid is established;

[0009] S3: Based on the universal evaluation model of microgrid uncertainty self-balancing capability, a basic polyhedral model considering time correlation is established;

[0010] S4: Based on the scaling of the basic polyhedral model, the general evaluation model of microgrid uncertainty self-balancing capability is transformed into a scaled evaluation model;

[0011] S5: Solve the scaling evaluation model and obtain the evaluation results.

[0012] Furthermore, the microgrid operation model considering the intraday tie line power deviation in step S1 is expressed as follows:

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[0033] in, Represents day-ahead / intraday variables; all day-ahead variables satisfy , T represents the day-ahead analysis period; all intraday variables satisfy Indicates the intraday analysis period; 、 and They represent the active output of renewable energy, active power of microgrid tie line and active load respectively; 、 Respectively represent the total active output of the controllable units and the total active charging / discharging output of the energy storage units; and They represent the intraday renewable energy fluctuation power, the intraday tie line power allowable deviation power and the intraday load fluctuation power respectively; Represents the active charge / discharge power boundary value of energy storage unit i; 、 They represent the upper and lower limits of the ramp power of energy storage unit i respectively; represents the energy dissipation rate of energy storage unit i; represents the charging / discharging efficiency of energy storage unit i; Indicates the state of charge of energy storage unit i; and They represent the upper and lower limits of the state of charge of the energy storage unit i respectively; 、 They represent the upper and lower limits of the active output of controllable unit i respectively; , They represent the upper and lower limits of the ramp power of controllable unit i respectively; 、 Respectively represent the upper and lower limits of the tie line power; Indicates the tie line power deviation factor; and Respectively represent the upper and lower limits of load fluctuation; 、 Respectively represent the end time of the day-ahead and intraday analysis periods; 、 Respectively represent the start time of the day-ahead and intraday analysis periods; Indicates the time resolution.

[0034] Furthermore, the establishment of a universal evaluation model for the uncertainty self-balancing capability of a microgrid in step S2 includes:

[0035] Based on equations (1)-(20), the day-ahead operation model of the microgrid is rewritten into the following compact form:

[0036]

[0037] in, , x represents the day-ahead dispatch plan of the microgrid; H represents the coefficient matrix of the day-ahead dispatch plan, and h represents the boundary information parameter matrix of the variables related to the day-ahead dispatch plan;

[0038] The microgrid daily operation model is simplified into the following compact form:

[0039]

[0040] Among them, y , y represents the intraday adjustment variable of the microgrid; represents the intraday fluctuation of renewable energy in the microgrid; A represents the coefficient matrix of the day-ahead dispatch plan during the intraday analysis period; B represents the coefficient matrix of the intraday adjustment variables of the microgrid during the intraday analysis period; C represents the coefficient matrix of the uncertainty fluctuation during the intraday analysis period; b represents the parameter matrix corresponding to the boundary information of the relevant variables in the intraday dispatch plan;

[0041] Based on formula (22), the uncertainty self-balancing capability of the microgrid is defined as:

[0042]

[0043] in, is the uncertainty self-balancing capability under a certain day-ahead scheduling plan x, i.e., the uncertainty fluctuation range that can be smoothed out;

[0044] Different day-ahead dispatch plans correspond to different uncertainty self-balancing capabilities within a day. The day-ahead dispatch plan x is regarded as a decision variable, forming a general evaluation model for the uncertainty self-balancing capability of microgrids.

[0045] Furthermore, the general evaluation model of the microgrid uncertainty self-balancing capability in step S2 is expressed as:

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[0047] in, Represents the ability to self-balance against uncertainty measure, Represents the feasible space of intraday uncertainty associated with the day-ahead scheduling plan.

[0048] Furthermore, the establishment of the basic polyhedral model in step S3 includes:

[0049] Construct the optimization problem shown in formula (25):

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[0051] in, is the given basic polyhedral boundary information; i is The row index of the matrix;

[0052] make (i) is the optimal solution of formula (25), and the following inequality holds:

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[0054] Traversal All rows in the matrix, generating boundary vectors , then the basic polyhedron Expressed as:

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[0056] Furthermore, the step S4 includes:

[0057] Transform Equation (24) into:

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[0059] Where β is the scaling factor; is a translation variable; since it is not possible to traverse For all points in Relaxing the constraints, we get:

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[0061] in, Represents a given basic polyhedron a subset of vertices, Represents a basic polyhedron The vertex of .

[0062] Furthermore, in step S5, the scaling evaluation model is solved using a heuristic algorithm based on basic polyhedral scaling, specifically including:

[0063] Solve equation (29) to get the current optimal solution , verified by formula (30):

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[0065] in, The optimal day-ahead scheduling plan obtained by equation (29) is expressed as follows; s represents the slack variable; I represents the identity matrix; represents the optimal scaling factor; represents the optimal translation vector; represents the transpose of a column vector of all 1s;

[0066] However, Equation (30) cannot be solved directly. The inner min problem of Equation (30) is transformed using the duality theory to form Equation (31):

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[0068] in, and They are inequality groups 、 The corresponding dual variable; M represents the set large constant; α represents the binary variable; express ; represents a binary variable; express The transposed matrix of represents the transposed matrix of B; express The transposed matrix of express The transposed matrix of represents the transposed matrix of C;

[0069] Given a small positive constant ε, according to formula (29) And solve equation (31), if This means that the current solution is the optimal solution of formula (28); otherwise, the most severe point in formula (31) Add the vertex subset of formula (29) Solve equation (29) again until we get .

[0070] The present invention also provides a microgrid self-balancing capability evaluation system based on basic polytope scaling, comprising:

[0071] A microgrid operation model establishment module is used to establish a microgrid operation model that takes into account the intraday tie line power deviation;

[0072] A general evaluation model building module is used to establish a general evaluation model for the uncertainty self-balancing capability of microgrids;

[0073] Basic polyhedral model building module, used to build a basic polyhedral model considering time correlation;

[0074] A model conversion module is used to convert the general evaluation model of microgrid uncertainty self-balancing capability into a scaling evaluation model;

[0075] The model solving module is used to solve the scaling evaluation model and obtain the evaluation results.

[0076] Beneficial effects: Compared with the existing technology, the present invention takes into account the differences in microgrid day-ahead dispatch plans, establishes a general evaluation model for the uncertainty self-balancing capability of microgrids, considers the time correlation of power, and evaluates the uncertainty self-balancing capability of microgrids based on basic polyhedron scaling. Specifically, the day-ahead dispatch plan of the microgrid is incorporated into the optimization decision variable, and the scaling factor is optimized by introducing volume measurement indicators to achieve a quantitative evaluation of the microgrid's ability to cope with uncertainty during the intraday operation stage. It can provide a theoretical basis for improving the regulation and operational resilience of local power grids in extreme operating scenarios, and has important reference value for the planning, design and economic operation of local power grids. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 Schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0078] 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.

[0079] Example 1:

[0080] like Figure 1 As shown, this embodiment provides a microgrid self-balancing capability evaluation method based on basic polytope scaling, comprising the following steps:

[0081] S1: Establish a microgrid operation model considering intraday tie line power deviation:

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[0102] in, Represents day-ahead / intraday variables; all day-ahead variables satisfy , T represents the day-ahead analysis period; all intraday variables satisfy Indicates the intraday analysis period; 、 and They represent the active output of renewable energy, active power of microgrid tie line and active load respectively; 、 Respectively represent the total active output of the controllable units and the total active charging / discharging output of the energy storage units; and They represent the intraday renewable energy fluctuation power, the intraday tie line power allowable deviation power and the intraday load fluctuation power respectively; Represents the active charge / discharge power boundary value of energy storage unit i; 、 They represent the upper and lower limits of the ramp power of energy storage unit i respectively; represents the energy dissipation rate of energy storage unit i; represents the charging / discharging efficiency of energy storage unit i; Indicates the state of charge of energy storage unit i; and They represent the upper and lower limits of the state of charge of the energy storage unit i respectively; 、 They represent the upper and lower limits of the active output of controllable unit i respectively; , They represent the upper and lower limits of the ramp power of controllable unit i respectively; 、 Respectively represent the upper and lower limits of the tie line power; Indicates the tie line power deviation factor; and Respectively represent the upper and lower limits of load fluctuation; 、 Respectively represent the end time of the day-ahead and intraday analysis periods; 、 Respectively represent the start time of the day-ahead and intraday analysis periods; Indicates the time resolution.

[0103] S2: Based on the microgrid operation model in step S1, a general evaluation model for the uncertainty self-balancing capability of the microgrid is established;

[0104] Based on equations (1)-(20), the day-ahead operation model of the microgrid is rewritten into the following compact form:

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[0106] in, , x represents the day-ahead dispatch plan of the microgrid; H represents the coefficient matrix of the day-ahead dispatch plan, and h represents the boundary information parameter matrix of the variables related to the day-ahead dispatch plan;

[0107] The microgrid daily operation model is simplified into the following compact form:

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[0109] Among them, y , y represents the microgrid’s intraday adjustment variables; Δw represents the microgrid’s intraday renewable energy fluctuation; A represents the coefficient matrix of the day-ahead dispatch plan during the intraday analysis period; B represents the coefficient matrix of the microgrid’s intraday adjustment variables during the intraday analysis period; C represents the coefficient matrix of the uncertainty fluctuation during the intraday analysis period; b represents the parameter matrix corresponding to the boundary information of the relevant variables in the intraday dispatch plan;

[0110] Based on formula (22), the uncertainty self-balancing capability of the microgrid is defined as:

[0111]

[0112] in, is the uncertainty self-balancing capability under a certain day-ahead scheduling plan x, i.e., the uncertainty fluctuation range that can be smoothed out;

[0113] Different day-ahead dispatch plans correspond to different uncertainty self-balancing capabilities within a day. Taking the day-ahead dispatch plan x as the decision variable, a general evaluation model for the uncertainty self-balancing capability of microgrids is formed.

[0114] The general evaluation model of microgrid uncertainty self-balancing capability is expressed as:

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[0116] in, Represents the ability to self-balance against uncertainty measure, Represents the feasible space of intraday uncertainty associated with the day-ahead scheduling plan.

[0117] S3: Based on the universal evaluation model of microgrid uncertainty self-balancing capability, a basic polyhedral model considering time correlation is established;

[0118] The establishment of basic polytope models includes:

[0119] Construct the optimization problem shown in formula (25):

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[0121] in, is the given basic polyhedral boundary information; i is The row index of the matrix;

[0122] make (i) is the optimal solution of formula (25), and the following inequality holds:

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[0124] Traversal All rows in the matrix, generating boundary vectors , then the basic polyhedron Expressed as:

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[0126] S4: Based on the scaling of the basic polyhedral model, the general evaluation model of the microgrid's uncertainty self-balancing capability is transformed into a scaled evaluation model:

[0127] Transform Equation (24) into:

[0128]

[0129] Where β is the scaling factor; is a translation variable; since it is not possible to traverse For all points in the equation (28), relax the constraints and get:

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[0131] in, Represents a given basic polyhedron a subset of vertices, Represents a basic polyhedron The vertex of .

[0132] S5: Solve the scaling evaluation model using a heuristic algorithm based on basic polytope scaling:

[0133] Solve equation (29) to get the current optimal solution , verified by formula (30):

[0134]

[0135] in, The optimal day-ahead scheduling plan obtained by equation (29) is expressed as follows; s represents the slack variable; I represents the identity matrix; represents the optimal scaling factor; represents the optimal translation vector; represents the transpose of a column vector of all 1s;

[0136] However, Equation (30) cannot be solved directly. The inner min problem of Equation (30) is transformed using the duality theory to form Equation (31):

[0137]

[0138] in, and They are inequality groups 、 The corresponding dual variable; M represents the set large constant; α represents the binary variable; express ; represents a binary variable; express The transposed matrix of represents the transposed matrix of B; express The transposed matrix of express The transposed matrix of represents the transposed matrix of C;

[0139] Given a small positive constant ε, according to formula (29) And solve equation (31), if This means that the current solution is the optimal solution of formula (28); otherwise, the most severe point in formula (31) Add the vertex subset of formula (29) Solve equation (29) again until we get ;

[0140] Finally, the evaluation results are obtained.

[0141] Example 2:

[0142] Based on the method of Example 1, this embodiment provides a microgrid self-balancing capability evaluation system based on basic polytope scaling, including:

[0143] A microgrid operation model establishment module is used to establish a microgrid operation model that takes into account the intraday tie line power deviation;

[0144] A general evaluation model building module is used to establish a general evaluation model for the uncertainty self-balancing capability of microgrids;

[0145] Basic polyhedral model building module, used to build a basic polyhedral model considering time correlation;

[0146] A model conversion module is used to convert the general evaluation model of microgrid uncertainty self-balancing capability into a scaling evaluation model;

[0147] The model solving module is used to solve the scaling evaluation model and obtain the evaluation results.

[0148] Example 3:

[0149] In order to verify the effectiveness of the solution of the present invention, this embodiment applies and analyzes the solution of the present invention as follows:

[0150] Intraday analysis period , day-ahead analysis period The current forecast of new energy output and load size is shown in Table 1.

[0151] Table 1 - Load and new energy output

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[0153] The parameters of the energy storage unit and the controllable unit are shown in Table 2 and Table 3 respectively:

[0154] Table 2-Energy storage unit parameters

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[0156] Table 3-Controllable unit parameters

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[0158] The basic polytope boundary information given in this embodiment , and by What you want

[0159] As shown in Table 4:

[0160] Table 4 - Basic polytope boundary information and

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[0162] Based on the basic polytopes obtained in Table 4, the combined formula Japanese style , when the per unit volume value is Under these conditions, the assessed microgrid's uncertainty self-balancing capability is 326.561. This provides a quantitative reference for improving the microgrid's regulation capability and operational resilience in extreme scenarios, and has important reference value for the planning, design, and economic operation of local power grids.

Claims

1. A microgrid self-balancing capability evaluation method based on basic polytope scaling, characterized in that: The steps include: S1: Establish a microgrid operation model considering intraday tie line power deviation; S2: Based on the microgrid operation model in step S1, a general evaluation model for the uncertainty self-balancing capability of the microgrid is established; S3: Based on the universal evaluation model of microgrid uncertainty self-balancing capability, a basic polyhedral model considering time correlation is established; S4: Based on the scaling of the basic polyhedral model, the general evaluation model of microgrid uncertainty self-balancing capability is transformed into a scaled evaluation model; S5: Solve the scaling evaluation model and obtain the evaluation results; The microgrid operation model considering the intraday tie line power deviation in step S1 is expressed as follows: in,* DA||ID represents day-ahead / intraday variables; all day-ahead variables satisfy t∈T, where T represents the day-ahead analysis period; all intraday variables satisfy t∈T', where T' represents the intraday analysis period; and They represent the active output of renewable energy, active power of microgrid tie line and active load respectively; Respectively represent the total active output of the controllable units and the total active charging / discharging output of the energy storage units; and They represent the intraday renewable energy fluctuation power, the intraday tie line power allowable deviation power and the intraday load fluctuation power respectively; Represents the active charge / discharge power boundary value of energy storage unit i; They represent the upper and lower limits of the ramp power of energy storage unit i respectively; represents the energy dissipation rate of energy storage unit i; represents the charging / discharging efficiency of energy storage unit i; Indicates the state of charge of energy storage unit i; and They represent the upper and lower limits of the state of charge of the energy storage unit i respectively; They represent the upper and lower limits of the active output of controllable unit i respectively; They represent the upper and lower limits of the ramp power of controllable unit i respectively; P line Respectively represent the upper and lower limits of the tie line power; λ MG Indicates the tie line power deviation factor; and Δ P load Respectively represent the upper and lower limits of load fluctuation; Respectively represent the end time of the day-ahead and intraday analysis periods; They represent the start time of the day-ahead and intraday analysis periods respectively; Δt represents the time resolution; The establishment of a general evaluation model for the uncertainty self-balancing capability of the microgrid in step S2 includes: Based on equations (1)-(20), the day-ahead operation model of the microgrid is rewritten into the following compact form: Hx≤h (21) in, x represents the day-ahead dispatch plan of the microgrid; H represents the coefficient matrix of the day-ahead dispatch plan; h represents the boundary information parameter matrix of the variables related to the day-ahead dispatch plan; The microgrid daily operation model is simplified into the following compact form: Ax+By+CΔw≤b(22) in, y represents the microgrid's intraday adjustment variables; Δw represents the microgrid's intraday renewable energy fluctuation; A represents the coefficient matrix of the day-ahead dispatch plan during the intraday analysis period; B represents the coefficient matrix of the microgrid's intraday adjustment variables during the intraday analysis period; C represents the coefficient matrix of the uncertainty fluctuation during the intraday analysis period; b represents the parameter matrix corresponding to the boundary information of the relevant variables in the intraday dispatch plan; Based on formula (22), the uncertainty self-balancing capability of the microgrid is defined as: Where Ω is the uncertainty self-balancing capability under a certain day-ahead scheduling plan x, that is, the uncertainty fluctuation range that can be smoothed out; Different day-ahead dispatch plans correspond to different uncertainty self-balancing capabilities within a day. Taking the day-ahead dispatch plan x as the decision variable, a general evaluation model for the uncertainty self-balancing capability of microgrids is formed. The general evaluation model of the microgrid uncertainty self-balancing capability in step S2 is expressed as: Among them, R(Ω) represents the Ω measure of the uncertainty self-balancing ability, Φ ID (x) represents the feasible space of intraday uncertainty associated with the day-ahead scheduling plan; The establishment of the basic polyhedral model in step S3 includes: Construct the optimization problem shown in formula (25): Among them, D ini is the given basic polytope boundary information; i is D ini The row index of the matrix; Let d ini (i) is the optimal solution of formula (25), and the following inequality holds: D ini (i)Δw≤d ini (i)(26) Traverse D ini All rows in the matrix generate the boundary vector d ini , then the basic polyhedron Expressed as: Step S4 includes: Transform formula (24) into: Where β is the scaling factor and v is the translation variable. Relaxing the constraints of Equation (28) yields: Where Γ represents a given subset of vertices of the basic polytope Ω0, Δw j Represents the vertices of the basic polyhedron Ω0.

2. A microgrid self-balancing capability evaluation method based on basic polytope scaling according to claim 1, characterized in that: In step S5, a heuristic algorithm based on basic polyhedral scaling is used to solve the scaling evaluation model, which specifically includes: Solve equation (29) to get the current optimal solution (β * ,v * ), verified by formula (30): s.t.By-Is≤b-Ax * -C(β * Δw+v * ) (30) Among them, x * The optimal day-ahead scheduling plan is obtained by equation (29); s represents the slack variable; I represents the identity matrix; β * represents the optimal scaling factor; v * represents the optimal translation vector; represents the transpose of a column vector of all 1s; The inner min problem of formula (30) is transformed using the duality theory to form formula (31): 0≤ζ≤Mα 0≤d ini -D ini Δw≤M(1-α) (31) Where z and ζ are the inequality groups By-Is≤b'-C(β * Δw+v * ), D ini Δw≤d ini The corresponding dual variable; M represents the set large constant; α represents the binary variable; b' represents b-Ax * ;α represents a binary variable; represents the transposed matrix of z, represents the transposed matrix of B; Indicates d ini The transposed matrix of Indicates D ini The transposed matrix of represents the transposed matrix of C; Given a small positive constant ε, according to formula (29) (β * ,v * ) and solve equation (31). If f'<ε, the current solution is the optimal solution of equation (28); otherwise, the most severe point Δw in equation (31) * Add the vertex subset Γ of Equation (29) and solve Equation (29) again until f' < ε is obtained.

3. A microgrid self-balancing capability evaluation system based on basic polyhedron scaling according to the method of claim 1, characterized in that: include: A microgrid operation model establishment module is used to establish a microgrid operation model that takes into account the intraday tie line power deviation; A general evaluation model building module is used to establish a general evaluation model for the uncertainty self-balancing capability of microgrids; Basic polyhedral model building module, used to build a basic polyhedral model considering time correlation; A model conversion module is used to convert the general evaluation model of microgrid uncertainty self-balancing capability into a scaling evaluation model; The model solving module is used to solve the scaling evaluation model and obtain the evaluation results.

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