Microgrid self-balancing capability evaluation method and system based on two-stage robust optimization

Abstract

The present invention discloses a method and system for evaluating the self-balancing capability of a microgrid based on two-stage robust optimization. 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; explicitly transforming the general evaluation model based on robust optimization to establish a microgrid uncertainty self-balancing capability evaluation model based on two-stage robust optimization; and solving the microgrid uncertainty self-balancing capability evaluation model based on two-stage robust optimization based on a C&CG algorithm to obtain an evaluation result. The present invention regards the microgrid's day-ahead scheduling plan as a decision variable, considers complex time-coupled variables such as the active output of energy storage units and controllable units, and evaluates the uncertainty self-balancing capability of the microgrid in the intraday stage under volume measurement, so as to improve the local grid's ability to cope with extreme scenarios and provide theoretical support for the planning, design, and economic operation of the local grid.

Description

Technical Field

[0001] The present invention belongs to the field of power systems and relates to microgrid evaluation technology, and specifically to a microgrid self-balancing capability evaluation method and system based on two-stage robust optimization. Background Art

[0002] With the continuous increase in the penetration rate of new energy, the power system is facing the challenges of high uncertainty and strong disturbances. The traditional model of relying solely on large power grids to balance uncertainty has gradually revealed its drawbacks. The responsibility for balancing uncertainty has gradually shifted to local power grids. As an important part of local power grids, how to evaluate the uncertainty self-balancing capability of microgrids has become a major challenge at present.

[0003] Existing research on system uncertainty mitigation is mainly divided into active management and passive management. In terms of active management, some studies have characterized the uncertainty mitigation range of the transmission system, and some studies have achieved certain economic benefits by actively managing uncertainty in local power grids. In terms of passive management, most existing studies passively deal with uncertainty, and uncertainty appears in the form of scenarios. Existing uncertainty mitigation research rarely focuses on microgrid scenarios, and lacks a systematic method to characterize the self-balancing ability of microgrids' intraday uncertainties.

[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 two-stage robust optimization are provided. The method regards the day-ahead dispatch plan of the microgrid as a decision variable, considers complex time-coupled variables such as the active output of energy storage units and controllable units, and evaluates the uncertainty self-balancing capability of the microgrid in the intraday stage under volume measurement, so as to enhance the ability of the local power grid to cope with extreme scenarios and provide theoretical support for the planning, design and economic operation of the local power grid.

[0006] Technical solution: To achieve the above objectives, the present invention provides a microgrid self-balancing capability evaluation method based on two-stage robust optimization, 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 robust optimization, the general evaluation model established in step S2 is explicitly transformed to establish a microgrid uncertainty self-balancing capability evaluation model based on two-stage robust optimization;

[0010] S4: Solve the microgrid uncertainty self-balancing capability evaluation model based on two-stage robust optimization based on the C&CG algorithm and obtain the evaluation results.

[0011] Furthermore, the microgrid operation model considering the intraday tie line power deviation in step S1 is expressed as follows:

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[0032] in, Represents day-ahead / intraday variables; all day-ahead variables satisfy T, 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.

[0033] Furthermore, the establishment of a universal evaluation model for the uncertainty self-balancing capability of a microgrid in step S2 includes:

[0034] Based on - , rewrite the microgrid day-ahead operation model into the following compact form:

[0035]

[0036] 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;

[0037] The microgrid daily operation model is simplified into the following compact form:

[0038]

[0039] 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;

[0040] Based on ,The uncertainty self-balancing capability of the microgrid is defined as:

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[0042] 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;

[0043] 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.

[0044] Furthermore, the general evaluation model of the microgrid uncertainty self-balancing capability in step S2 is expressed as:

[0045]

[0046] in, Represents the ability to self-balance against uncertainty measure, Represents the feasible space of intraday uncertainty associated with the day-ahead scheduling plan.

[0047] Furthermore, step S3 includes:

[0048] The uncertainty self-balancing ability is defined as a box-shaped polyhedron that satisfies:

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[0050] in, 、 They represent the upper and lower limit variables of uncertainty fluctuation at time t respectively; 、 The uncertainty fluctuations in the period The upper and lower bound variable sets of ; is the optimization variable of the box-type polyhedron;

[0051] Define the uncertainty set U to satisfy:

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[0053] in, is an uncertain variable; represents the Hamada multiplier; Representing uncertainty variables The tth element in ;

[0054] A microgrid uncertainty self-balancing capability evaluation model based on two-stage robust optimization is established.

[0055] Furthermore, the microgrid uncertainty self-balancing capability evaluation model based on two-stage robust optimization in step S3 is expressed as:

[0056] (27)

[0057] Among them, 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; and b represents the parameter matrix corresponding to the boundary information of the relevant variables in the intraday dispatch plan.

[0058] Furthermore, the step S4 includes:

[0059] Due to the Cannot be solved directly by current commercial software, Decomposed into main problem and sub-problems, the main problem is:

[0060]

[0061] in, is a given parameter; y k is the intraday scheduling plan variable under uncertainty scenario k, i.e., the adjustment variable; 、 、 They represent the intermediate variables of the upper bound of uncertainty fluctuation, the intermediate variables of the lower bound of uncertainty fluctuation, and the intermediate variables of the day-ahead scheduling plan respectively;

[0062] The sub-problems are:

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[0064] in, It is the optimal solution of the main problem in the current cycle. The sub-problem is to determine whether there is a tracing variable based on this optimal solution. Satisfy the constraints, given a small positive number ,like , the optimal solution of the subproblem is , then in the main problem New constraints added in:

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[0066] Cycle between the main problem and subproblems until Output the current optimal solution.

[0067] The present invention also provides a microgrid self-balancing capability evaluation system based on two-stage robust optimization, comprising:

[0068] A microgrid operation model establishment module is used to establish a microgrid operation model that takes into account the intraday tie line power deviation;

[0069] A general evaluation model building module is used to establish a general evaluation model for the uncertainty self-balancing capability of microgrids;

[0070] The model conversion module is used to explicitly convert the general evaluation model based on robust optimization and establish a microgrid uncertainty self-balancing capability evaluation model based on two-stage robust optimization;

[0071] The model solving module is used to solve the microgrid uncertainty self-balancing capability evaluation model based on two-stage robust optimization and obtain the evaluation results.

[0072] Beneficial effects: Compared with the existing technology, the present invention takes into account the differences in microgrid day-ahead dispatch plans, establishes a universal microgrid uncertainty self-balancing capability evaluation model, and evaluates the uncertainty self-balancing capability of the microgrid based on two-stage robust optimization; this capability reflects the uncertainty fluctuation size that the microgrid can smooth out in the intraday stage, explores the potential uncertainty management capability of the microgrid, and is of great value to the planning, design and economic operation of the local power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 Schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0074] 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.

[0075] Example 1:

[0076] like Figure 1 As shown, this embodiment provides a microgrid self-balancing capability evaluation method based on two-stage robust optimization, comprising the following steps:

[0077] S1: Establish a microgrid operation model considering intraday tie line power deviation:

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[0098] in, Represents day-ahead / intraday variables; all day-ahead variables satisfy T, 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.

[0099] 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;

[0100] Based on - , rewrite the microgrid day-ahead operation model into the following compact form:

[0101]

[0102] 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;

[0103] The microgrid daily operation model is simplified into the following compact form:

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[0105] 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;

[0106] Based on ,The uncertainty self-balancing capability of the microgrid is defined as:

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[0108] 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;

[0109] 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.

[0110] The general evaluation model of microgrid uncertainty self-balancing capability is expressed as:

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[0112] in, Represents the ability to self-balance against uncertainty measure, Represents the feasible space of intraday uncertainty associated with the day-ahead scheduling plan.

[0113] S3: Based on the robust optimization, the general evaluation model established in step S2 is explicitly transformed to establish a microgrid uncertainty self-balancing capability evaluation model based on two-stage robust optimization;

[0114] The uncertainty self-balancing ability is defined as a box-shaped polyhedron that satisfies:

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[0116] in, 、 They represent the upper and lower limit variables of uncertainty fluctuation at time t respectively; 、 The uncertainty fluctuations in the period The upper and lower bound variable sets of ; is the optimization variable of the box-type polyhedron;

[0117] Define the uncertainty set U to satisfy:

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[0119] in, is an uncertain variable; represents the Hamada multiplier; Representing uncertainty variables The tth element in ;

[0120] A microgrid uncertainty self-balancing capability evaluation model based on two-stage robust optimization is established, which is specifically expressed as follows:

[0121] (27)

[0122] Among them, 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; and b represents the parameter matrix corresponding to the boundary information of the relevant variables in the intraday dispatch plan.

[0123] S4: Solve the microgrid uncertainty self-balancing capability evaluation model based on two-stage robust optimization based on the C&CG algorithm and obtain the evaluation results;

[0124] Due to the Cannot be solved directly by current commercial software, Decomposed into main problem and sub-problems, the main problem is:

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[0126] in, is a given parameter; y k is the intraday scheduling plan variable under uncertainty scenario k, i.e., the adjustment variable; 、 、 They represent the intermediate variables of the upper bound of uncertainty fluctuation, the intermediate variables of the lower bound of uncertainty fluctuation, and the intermediate variables of the day-ahead scheduling plan respectively;

[0127] The sub-problems are:

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[0129] in, It is the optimal solution of the main problem in the current cycle. The sub-problem is to determine whether there is a tracing variable based on this optimal solution. Satisfy the constraints, given a small positive number ,like , the optimal solution of the subproblem is , then in the main problem New constraints added in:

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[0131] Cycle between the main problem and subproblems until Output the current optimal solution.

[0132] Example 2:

[0133] Based on the method of Example 1, this embodiment provides a microgrid self-balancing capability evaluation system based on two-stage robust optimization, including:

[0134] A microgrid operation model establishment module is used to establish a microgrid operation model that takes into account the intraday tie line power deviation;

[0135] A general evaluation model building module is used to establish a general evaluation model for the uncertainty self-balancing capability of microgrids;

[0136] The model conversion module is used to explicitly convert the general evaluation model based on robust optimization and establish a microgrid uncertainty self-balancing capability evaluation model based on two-stage robust optimization;

[0137] The model solving module is used to solve the microgrid uncertainty self-balancing capability evaluation model based on two-stage robust optimization and obtain the evaluation results.

[0138] Example 3:

[0139] 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:

[0140] Intraday analysis period , day-ahead analysis period The current forecast of new energy output and load size is shown in Table 1.

[0141] Table 1 - Load and renewable energy output

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[0143] The parameters of the energy storage unit and the controllable unit are shown in Table 2 and Table 3 respectively:

[0144] Table 2-Energy storage unit parameters

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[0146] Table 3-Controllable unit parameters

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[0148] Given the initial K=1, given According to the microgrid uncertainty self-balancing capability evaluation model provided by the present invention, the boundary value of the box-type polyhedron is obtained. 、 The values ​​are shown in Table 4:

[0149] Table 4 - Upper and lower limits of intraday uncertainty fluctuations

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[0151] From Table 4, we can see that when the volume per unit value is Under the condition of uncertainty, the estimated uncertainty self-balancing capability of the microgrid is 60.839. This provides a quantitative reference for exploring the potential uncertainty management capabilities of microgrids 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 two-stage robust optimization, 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 robust optimization, the general evaluation model established in step S2 is explicitly transformed to establish a microgrid uncertainty self-balancing capability evaluation model based on two-stage robust optimization; S4: Solve the microgrid uncertainty self-balancing capability evaluation model based on two-stage robust optimization based on the C&CG algorithm and obtain the evaluation results; The microgrid uncertainty self-balancing capability evaluation model based on two-stage robust optimization in step S3 is expressed as: Among them, 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; Step S4 includes: Decompose Equation (27) into the main problem and sub-problems. The main problem is: in, is a given parameter; y k is the intraday scheduling plan variable under uncertainty scenario k, i.e., the adjustment variable; Δ w ′ and x′ represent the intermediate variables of the upper bound of uncertainty fluctuation, the intermediate variables of the lower bound of uncertainty fluctuation, and the intermediate variables of the day-ahead scheduling plan, respectively; The sub-problems are: in, is the optimal solution of the main problem in the current cycle. The subproblem is to determine whether there is a tracing variable y(ξ) that satisfies the constraint conditions based on this optimal solution. Given a positive constant ε, if |f S |≥ε, the optimal solution of the subproblem is Then add a new constraint in the main problem (28); Cycle between the main problem and subproblems until |f S |<ε outputs the current optimal solution.

2. A microgrid self-balancing capability evaluation method based on two-stage robust optimization according to claim 1, characterized in that: The microgrid operation model considering the intraday tie line power deviation in step S1 is expressed as follows: Where *DA||ID represents the 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.

3. A microgrid self-balancing capability evaluation method based on two-stage robust optimization according to claim 2, characterized in that: The establishment of a universal evaluation model for the uncertainty self-balancing capability of a 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. 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.

4. A microgrid self-balancing capability evaluation method based on two-stage robust optimization according to claim 3, characterized in that: The general evaluation model of the uncertainty self-balancing capability of the microgrid 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.

5. A microgrid self-balancing capability evaluation method based on two-stage robust optimization according to claim 4, characterized in that: The step S3 comprises: The uncertainty self-balancing ability is defined as a box-shaped polyhedron that satisfies: in, Δ w (t) represent the upper and lower limit variables of uncertainty fluctuation at time t; Δ w are the upper and lower bound variable sets of uncertainty fluctuations in period T'; P0 is the optimization variable of the box polyhedron; Define the uncertainty set U to satisfy: Among them, ξ is the uncertainty variable; represents the Hamada multiplier; ξ t represents the tth element in the uncertainty variable ξ; A microgrid uncertainty self-balancing capability evaluation model based on two-stage robust optimization is established.

6. A microgrid self-balancing capability evaluation method based on two-stage robust optimization according to claim 5, characterized in that: In step S4, the new constraint added to the main problem (28) is:

7. A microgrid self-balancing capability evaluation system based on two-stage robust optimization, characterized in that: For implementing the method described in claim 1, the system includes: 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; The model conversion module is used to explicitly convert the general evaluation model based on robust optimization and establish a microgrid uncertainty self-balancing capability evaluation model based on two-stage robust optimization; The model solving module is used to solve the microgrid uncertainty self-balancing capability evaluation model based on two-stage robust optimization and obtain the evaluation results.

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