Power distribution network security defense improvement method and system considering distributed energy storage regulation effect under new energy access

Abstract

The invention relates to a power distribution network security defense improvement method and system considering a distributed energy storage regulation effect under new energy access. The method comprises the following steps: establishing a power distribution network security defense improvement model objective function considering the distributed energy storage regulation effect under new energy access; establishing constraint conditions when the power distribution network security defense promotion model operates normally, namely in an accident state; constructing a box type set of the uncertainty parameters, and performing preliminary transformation on uncertainty constraints; the problem is reprocessed into a standard robust equivalence form and is a deterministic double-layer optimization problem; converting the double-layer optimization problem into a single-layer optimization problem in a peer-to-peer manner; the power distribution network security defense promotion model is integrated into the following compact form; sealing a power grid security defense improvement model; and based on the quantified scheduling characteristics, converting the power distribution network security defense promotion model and solving based on a commercial solver.

Description

Technical Field

[0001] This invention belongs to the field of power distribution network optimization and operation technology, specifically relating to a method and system for improving power distribution network security defense under the influence of new energy access, taking into account the role of distributed energy storage regulation. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] With the rapid development of various new energy sources, the power system is gradually transforming into a new type of multi-energy complementary power system dominated by green electricity. However, renewable energy differs from traditional thermal power generation; its anti-interference capability, observability, controllability, and measurability are weaker, and it is also more volatile with low inertia. An excessively high proportion of renewable energy connected to the grid will crowd out the regulation space of stable and reliable backup power sources in the system, leading to a reduction in peak-shaving and frequency regulation capabilities, which is detrimental to supporting power balance. Therefore, to address the dilemma of high-proportion renewable energy grid connection in future new power systems, the regulatory effectiveness of flexible resources urgently needs to be deeply explored and rapidly developed.

[0004] Virtual power plant (VPS) technology, by tapping into the potential of massive distributed dispatchable resources, expands system flexibility and security capabilities at low marginal cost, and is expected to become one of the feasible ways to alleviate the future energy structure dilemma. At the same time, VPS participation in the energy and reserve markets also contributes to increasing the efficiency of grid operation. Integrating distributed power sources within a regional distribution network using VPS technology is of great significance for supporting the improvement of distribution network security.

[0005] Current research focuses on detailed internal modeling of virtual power plants, which can effectively solve the problem of coordinating dispersed and heterogeneous resources within virtual power plants. However, during the dispatching process, detailed physical models need to be reported to the dispatching center, increasing the difficulty of global grid optimization and hindering the protection of local information of virtual power plants and further game theory in various transactions under market conditions. Distributed dispatching frameworks based on mathematical decomposition algorithms are widely used. Some studies, based on grid partitioning, use the alternating direction multiplier method to construct a multi-regional collaborative model of wind power grid-connected systems containing virtual power plants, achieving optimal dispatching results while ensuring regional independence and privacy. However, although distributed dispatching can protect the privacy of virtual power plants, it relies on frequent interaction of boundary information between different levels of dispatching agencies, resulting in high communication pressure and low timeliness, and cannot support large-scale, multi-entity grid market clearing. Aggregating the internal resources of virtual power plants into an equivalent model for dispatching can effectively make up for the shortcomings of the above-mentioned research. Virtual power plant aggregation was achieved using statistical information from historical distributed energy data, including first-order and second-order moments. A distributed robust model was established to evaluate the maximum capacity and ramp-up capability of the virtual power plants. However, due to the high complexity of the model, its extension to large-scale power grid analysis is difficult, and it still has shortcomings in directly measuring the costs generated by different energy sources and reserve transactions. Therefore, further research is needed to explore the potential of distributed energy storage regulation and control in the form of virtual power plants to enhance the security and defense capabilities of distribution networks. Summary of the Invention

[0006] To address the aforementioned problems, this invention proposes a method and system for enhancing the security defense of distribution networks by considering the role of distributed energy storage in regulating new energy access. This invention considers the impact of new energy access on the security of the power grid, leverages virtual power plant technology to explore the coordinating role of distributed energy storage, and improves the security defense capabilities of distribution network operation.

[0007] This invention provides a method and system for improving the security defense of distribution networks under the influence of new energy access, considering the role of distributed energy storage regulation. First, a model for improving the security defense of distribution networks under the influence of new energy access, considering the role of distributed energy storage regulation, is established. Second, a box set of uncertain parameters is constructed, and the uncertain constraints are initially transformed, reprocessed into a standard robust equivalent form, and transformed into a single-layer optimization problem based on duality theory. Finally, based on multi-parameter linear programming theory, a method for encapsulating the model is given by solving the feasible region of the programming parameters. Based on the quantized scheduling characteristics, the original model is transformed to facilitate solving using commercial solvers.

[0008] According to some embodiments, the present invention adopts the following technical solution:

[0009] A method for enhancing the security defense of distribution networks under the integration of new energy sources, considering the role of distributed energy storage in regulation, includes:

[0010] The objective function of the distribution network security defense enhancement model considering the role of distributed energy storage regulation under the access of new energy sources is to minimize the comprehensive cost f, which includes the adjustable resource operation cost f1, the power trading cost f2 and the standby trading cost.

[0011] Establish the constraints for the normal operation of the aforementioned distribution network security defense enhancement model, including threshold power constraints, line power flow constraints, power balance constraints, generator capacity constraints, demand response constraints, and distributed power output constraints.

[0012] Establish the constraints for calling up the standby mode under the fault state in the aforementioned distribution network security defense enhancement model, including threshold power capacity constraints, power balance constraints, and line power flow constraints.

[0013] Construct a box set of uncertainty parameters and perform preliminary transformation of uncertainty constraints;

[0014] The problem is reprocessed into a standard robust equivalent form, which is a deterministic bilevel optimization problem;

[0015] Based on duality theory, the bi-level optimization problem is equivalently transformed into a single-level optimization problem;

[0016] The power distribution network security defense enhancement model is integrated into the following compact form ROP(t);

[0017] Based on multi-parameter linear programming theory, the power distribution network security defense enhancement model is encapsulated by solving the feasible region of the planning parameter w.

[0018] Based on the quantified scheduling characteristics, the power distribution network security defense enhancement model is transformed and solved using a commercial solver.

[0019] According to a preferred embodiment of the present invention, the objective function of the distribution network security defense enhancement model considering the role of distributed energy storage regulation under new energy access is to minimize the overall cost f, including:

[0020] The total cost f is expressed as: f = f1 + f2 + f3;

[0021] The formula for calculating the adjustable resource operating cost f1 is as follows: Where t is the scheduling period index; Φ G For the set of unit indexes; Φ DR For the set of all participating demand response nodes; Φ S For the set of interrupt level indexes; C G,i,t P is the unit cost coefficient; G,i,t s represents the generator output power; s represents the interruption level. The interrupt load cost coefficient for the s-th level interruption. The interrupt load is at the s-th level.

[0022] The formula for calculating f2 is expressed as follows: Where, Φ V For VPP gateway node index set; and These are the electricity purchase cost and the electricity sales revenue coefficient, respectively. and These are the power purchased and the power sold, respectively.

[0023] The formula for calculating f3 is expressed as follows: Among them, P GR,i,t As backup power; These are the positive and negative reserve benefit coefficients for VPP, respectively. These are the positive and negative standby power of VPP, respectively.

[0024] According to a preferred embodiment of the present invention, the constraints for establishing the power distribution network security defense enhancement model during normal operation include:

[0025] The gate power constraint is: In the formula: P V,i,t VPP gate power, P V and These are the lower and upper limits of the VPP gate power, respectively; and These are the power purchased and the power sold, respectively.

[0026] The power flow constraints of the line are: In the formula: P In,i,t Inject power into the node; P DG,i,t Power output for distributed generation; P L,i,t For load power; Φ N A set of node indexes; In the formula: P flow and The lower and upper limits of the power flow for each line; G l-i Φ is the power transfer factor from node i to line l; Lines A set of line indexes;

[0027] The power balance constraint is: In the formula: Let j be the set of lines starting from node j. Let j be the set of lines that terminate at node j.

[0028] The generator capacity constraint is: In the formula: U G,i,t Indicates the start / stop status of the unit, given by the unit combination; where U G,i,t =0 indicates that the unit is started, UG,i,t =1 indicates the unit is shut down; P G,i,t and The upper and lower limits of the unit's output;

[0029] The demand response constraints are: In the formula: λ s The interruption level coefficient is {i}; {i} represents a random variable; P DR,i,t This represents the total interruption load of node i; The interrupt load is at the s-th level.

[0030] The output constraints of distributed power sources are:

[0031] In the formula: P DG,i,k,t Let i be the output of the k-th distributed power source on node i; Φ is the set of distributed power source indices on node i; DG A set of indexes for distributed power supply connection nodes; Predict the output value of distributed generation;

[0032]

[0033] U ci,t +U di,t =1

[0034] 0≤P di (t)≤P dimax U di (t)

[0035] -P cimax U ci,t ≤P ci,t ≤0

[0036] In the formula: C i,t (t), C imim C imax Let C be the energy stored in the energy storage system, the minimum energy stored in the energy storage system, and the maximum energy stored in the energy storage system; iInitial α is the initial energy; i β i U is the energy conversion coefficient. ci,t U di,t These are binary variables, representing the energy storage charging and discharging states respectively; P di,k P ci,k P represents the discharge and charging power during the k-th time period, respectively. di,t P ci,t P represents the discharge and charge power during time period t. cimax This represents the maximum energy storage capacity.

[0037] According to a preferred embodiment of the present invention, the constraints for activating the standby mode under an accident state in the power distribution network security defense enhancement model include:

[0038] The power capacity constraint at the cutoff point is as follows:

[0039]

[0040]

[0041] Power balance constraints are as follows:

[0042]

[0043]

[0044] The line power flow constraints are as follows:

[0045]

[0046] In the formula: P VR,i,t To utilize the gate power during standby; These refer to the changes in power consumption when positive / negative standby time groups are activated, distributed power sources, and demand response power. Power is injected into nodes to activate positive / negative standby.

[0047] According to a preferred embodiment of the present invention, a box-shaped set of uncertainty parameters is constructed, and the uncertainty constraints are initially transformed; including:

[0048] Construct a box set of uncertainty parameters as follows:

[0049]

[0050] In the formula: Errors in predicting power output for new energy sources;

[0051] Introducing the auxiliary variable χ DG,i,j,t The uncertainty constraints are initially transformed into actual constraints, as shown below:

[0052]

[0053] According to a preferred embodiment of the present invention, the problem is reprocessed into a standard robust equivalent form, which is a deterministic bilevel optimization problem; including:

[0054] The deterministic bilevel optimization problem is shown below:

[0055]

[0056] In the formula: Γ iJ is the conservative budget parameter. i It is a set of uncertain parameters.

[0057] According to a preferred embodiment of the present invention, based on duality theory, the bi-level optimization problem is equivalently transformed into a single-level optimization problem; including:

[0058] The single-layer optimization problem is shown below:

[0060]

[0061] In the formula: z i As an auxiliary variable; Γ i Conservatism adjustment factor; y j It is an auxiliary variable.

[0062] According to a preferred embodiment of the present invention, the power distribution network security defense enhancement model is integrated into the following compact form ROP(t); as shown below:

[0063]

[0064] In the formula: A and B are parameter matrices; κ and d are parameter vectors, both of which are column vectors; w is the planning parameter.

[0065] According to a preferred embodiment of the present invention, based on multi-parameter linear programming theory, the power distribution network security defense enhancement model is encapsulated by solving the feasible region of the planning parameter w; including:

[0066] Construct the Lagrangian function L of ROP(t), then the optimality condition of ROP(t) is:

[0067] L = κ T x+u T (A(Γ)x+Bw+d)

[0068]

[0069]

[0070] u≥0

[0071] In the formula: L is the Lagrangian function; A and B are parameter matrices; κ and d are parameter vectors, both of which are column vectors; w is the planning parameter; Γ is the conservatism adjustment factor; u is the multiplier;

[0072] In the above formula, A, B, d, and u can be written in vector form:

[0073] d=(d i ) i∈τ ,u=(u i ) i∈τ

[0074] In the formula: τ is the set of indices for all constraints, if To effectively constrain the index set, This is the set of invalid constraint indices; a and b are parameter column vectors; d and u are constant parameters;

[0075] Combining the above equations, we get:

[0076]

[0077] The feasible region of the planning parameter w is the union of the critical regions formed by all effective and ineffective constraints:

[0078]

[0079] In the formula: CR is the critical region; FR is the feasible region.

[0080] According to a preferred embodiment of the present invention, based on the quantized scheduling characteristics, the power distribution network security defense enhancement model is transformed and solved using a commercial solver; including:

[0081] The transformed model is as follows:

[0082] MP:

[0083] Where: Φ VPP For the VPP index set; f1 Grid Main network operating costs; P V for The column vector formed, R V for and The column vector formed by Φ T Set of scheduling period indexes; A Grid Main network model coefficient matrix; d Grid This is the column vector of constant terms in the main network model.

[0084] The transformed model MP is a linear programming model with deterministic properties, aiming to minimize F, and corresponding constraints are established.

[0085] A power distribution network security enhancement system considering the role of distributed energy storage in regulating new energy access includes:

[0086] The objective function establishment module is configured to: establish the objective function of the distribution network security defense improvement model under the new energy access and considering the role of distributed energy storage regulation, which is to minimize the comprehensive cost f. The comprehensive cost f includes the adjustable resource operation cost f1, the power trading cost f2 and the standby trading cost.

[0087] The constraint establishment module is configured to: establish constraint conditions for the normal operation of the distribution network security defense enhancement model, including threshold power constraints, line power flow constraints, power balance constraints, generator capacity constraints, demand response constraints, and distributed power output constraints; and establish constraint conditions for the distribution network security defense enhancement model when calling up backup under accident conditions, including threshold power capacity constraints, power balance constraints, and line power flow constraints.

[0088] The problem transformation and solution module is configured to: construct a box set of uncertain parameters and perform preliminary transformation of uncertain constraints; reprocess the problem into a standard robust equivalent form, which is a deterministic bi-level optimization problem; based on duality theory, transform the bi-level optimization problem into a single-level optimization problem; integrate the distribution network security defense improvement model into the following compact form ROP(t); based on multi-parameter linear programming theory, encapsulate the distribution network security defense improvement model by solving the feasible region of the planning parameters w; based on the quantized scheduling characteristics, transform the distribution network security defense improvement model and solve it based on a commercial solver.

[0089] The beneficial effects of this invention are as follows:

[0090] 1. This invention, based on a typical multi-parameter planning method, aggregates distributed energy storage into a virtual power plant for scheduling. This effectively improves the model's solution efficiency, deeply explores the control potential of distributed resources, expands system flexibility at low marginal cost, and enhances the security and defense capabilities of the distribution network. Simultaneously, the aggregation into a virtual power plant does not rely on frequent boundary information exchanges between different levels of scheduling agencies, and protects the privacy of information within the virtual power plant.

[0091] 2. This invention transforms the original uncertain model into a deterministic model through robust optimization. While reducing the complexity of the model, it helps to mitigate the impact of new energy uncertainty on the distribution network and enhances the adaptability of the distribution network to the worst fluctuation scenarios of new energy. Attached Figure Description

[0092] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0093] Figure 1 This is a schematic diagram of the power distribution network of the present invention;

[0094] Figure 2 This is a flowchart illustrating a distribution network security defense enhancement method that considers the role of distributed energy storage regulation under new energy access, according to the present invention. Detailed Implementation

[0095] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0096] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0097] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0098] Example 1

[0099] A method for enhancing the security defense of distribution networks under the integration of new energy sources, considering the role of distributed energy storage in regulation, such as... Figure 2 As shown, it includes:

[0100] The objective function of a distribution network security defense enhancement model considering the role of distributed energy storage regulation under new energy access is to minimize the comprehensive cost f, which includes the adjustable resource operation cost f1, the power transaction cost f2, and the reserve transaction cost. The distribution network is as follows: Figure 1 As shown;

[0101] Establish constraints for the normal operation of the distribution network security defense enhancement model, including threshold power constraints, line power flow constraints, power balance constraints, generator capacity constraints, demand response constraints, and distributed generation output constraints.

[0102] Establish constraints for the use of standby power in a power distribution network security defense enhancement model under accident conditions, including threshold power capacity constraints, power balance constraints, and line power flow constraints.

[0103] Construct a box set of uncertainty parameters and perform preliminary transformation of uncertainty constraints;

[0104] The problem is reprocessed into a standard robust equivalent form, which is a deterministic bilevel optimization problem;

[0105] Considering that bi-level optimization cannot be solved directly, the bi-level optimization problem is transformed into a single-level optimization problem based on duality theory.

[0106] After robust transformation, all the original uncertainties are now expressed as linear programming problems. Therefore, the power distribution network security defense enhancement model is integrated into the following compact form, ROP(t), to facilitate further processing.

[0107] Based on multi-parameter linear programming theory, a power distribution network security defense enhancement model is encapsulated by solving the feasible region of the planning parameter w.

[0108] Based on the quantitative scheduling characteristics, the power distribution network security defense improvement model is transformed and solved using a commercial solver.

[0109] Example 2

[0110] The difference between the distribution network security defense enhancement method considering the distributed energy storage regulation effect under new energy access as described in Example 1 and the method described in Example 1 is as follows:

[0111] The objective function of a distribution network security defense enhancement model considering the role of distributed energy storage regulation under new energy access is established to minimize the overall cost f, including:

[0112] The total cost f is expressed as: f = f1 + f2 + f3;

[0113] The formula for calculating the adjustable resource operating cost f1 is as follows: Where t is the scheduling period index; Φ G For the set of unit indexes; Φ DR For the set of all participating demand response nodes; Φ S For the set of interrupt level indexes; C G,i,t P is the unit cost coefficient; G,i,t s represents the generator output power; s represents the interruption level. The interrupt load cost coefficient for the s-th level interruption. The interrupt load is at the s-th level.

[0114] The formula for calculating f2 is expressed as follows: Where, Φ V For VPP gateway node index set; and These are the electricity purchase cost and the electricity sales revenue coefficient, respectively. and These are the power purchased and the power sold, respectively; to prevent VPP arbitrage, the power purchase cost coefficient is greater than the power sales revenue coefficient.

[0115] The formula for calculating f3 is expressed as follows: Among them, P GR,i,t As backup power; These are the positive and negative reserve benefit coefficients for VPP, respectively. These are the positive and negative standby power of VPP, respectively.

[0116] Establish constraints for the normal operation of the distribution network security defense enhancement model, including threshold power constraints, line power flow constraints, power balance constraints, generator capacity constraints, demand response constraints, and distributed generation output constraints; including:

[0117] The gate power constraint is: In the formula: P V,i,t For VPP gate power, P V and These are the lower and upper limits of the VPP gate power, respectively; and These are the power purchased and the power sold, respectively.

[0118] It is worth noting that, due to the difference in electricity purchase and sales coefficients, and There must be at least one of them that is 0; otherwise, f2 is not the optimal solution.

[0119] The power flow constraints of the line are: In the formula: P In,i,t Inject power into the node; P DG,i,t Power output for distributed generation; P L,i,t For load power; Φ N A set of node indexes; In the formula: P flow and The lower and upper limits of the power flow for each line; G l-i Φ is the power transfer factor from node i to line l; Lines A set of line indexes;

[0120] The power balance constraint is: In the formula: Let j be the set of lines starting from node j. Let j be the set of lines that terminate at node j.

[0121] The generator capacity constraint is: In the formula: U G,i,t Indicates the start / stop status of the unit, given by the unit combination; where U G,i,t =0 indicates that the unit is started, U G,i,t =1 indicates the unit is shut down; P G,i,t and The upper and lower limits of the unit's output;

[0122] The demand response constraints are: In the formula: λ s The interruption level coefficient is {i}; {i} represents a random variable; P DR,i,t This represents the total interruption load of node i; The interrupt load is at the s-th level.

[0123] The output constraints of distributed power sources are:

[0124] In the formula: PDG,i,k,t Let i be the output of the k-th distributed power source on node i; Φ is the set of distributed power source indices on node i; DG A set of indexes for distributed power supply connection nodes; Predict the output value of distributed generation;

[0125]

[0126] U ci,t +U di,t =1

[0127] 0≤P di (t)≤P dimax U di (t)

[0128] -P cimax U ci,t ≤P ci,t ≤0

[0129] In the formula: C i,t (t), C imin C imax Let C be the energy stored in the energy storage system, the minimum energy stored in the energy storage system, and the maximum energy stored in the energy storage system; iInitial α is the initial energy; i β i U is the energy conversion coefficient. ci,t U di,t These are binary variables, representing the energy storage charging and discharging states respectively; P di,k P ci,k P represents the discharge and charging power during the k-th time period, respectively. di,t P ci,t P represents the discharge and charge power during time period t. cimax This represents the maximum energy storage capacity.

[0130] Establish constraints for calling up backup power under accident conditions in the power distribution network security defense enhancement model, including:

[0131] The power capacity constraint at the cutoff point is as follows:

[0132]

[0133]

[0134] Power balance constraints are as follows:

[0135]

[0136]

[0137] The line power flow constraints are as follows:

[0138]

[0139] In the formula: P VR,i,t To utilize the gate power during standby; These refer to the changes in power consumption when positive / negative standby time groups are activated, distributed power sources, and demand response power. Power is injected into nodes to activate positive / negative standby.

[0140] Construct a box set of uncertainty parameters and perform preliminary transformation of uncertainty constraints; including:

[0141] Construct a box set of uncertainty parameters as follows:

[0142]

[0143] In the formula: Errors in predicting power output for new energy sources;

[0144] Introducing the auxiliary variable χ DG,i,j,t The uncertainty constraints are initially transformed into actual constraints, as shown below:

[0145]

[0146] The problem is reprocessed into a standard robust equivalent form, which is a deterministic bilevel optimization problem; including:

[0147] The deterministic bilevel optimization problem is shown below:

[0148]

[0149] In the formula: Γ i J is the conservative budget parameter. i It is a set of uncertain parameters.

[0150] Based on duality theory, the bi-level optimization problem is equivalently transformed into a single-level optimization problem; including:

[0151] The single-layer optimization problem is shown below:

[0152]

[0154] In the formula: z i As an auxiliary variable; Γ i Conservatism adjustment factor; y j It is an auxiliary variable.

[0155] The power distribution network security defense enhancement model is integrated into the following compact form ROP(t) for easier subsequent processing. See below:

[0156]

[0157] In the formula: A and B are parameter matrices; κ and d are parameter vectors, both of which are column vectors; w is the planning parameter.

[0158] Based on multi-parameter linear programming theory, a power distribution network security defense enhancement model is encapsulated by solving the feasible region of the planning parameter w; including:

[0159] Construct the Lagrangian function L of ROP(t), then the optimality condition of ROP(t) is:

[0160] L = κ T x+u T (A(Γ)x+Bw+d)

[0161]

[0162]

[0163] u≥0

[0164] In the formula: L is the Lagrangian function; A and B are parameter matrices; κ and d are parameter vectors, both of which are column vectors; w is the planning parameter; Γ is the conservatism adjustment factor; u is the multiplier;

[0165] In the above formula, A, B, d, and u can be written in vector form:

[0166] d=(d i ) i∈τ ,u=(u i ) i∈τ

[0167] In the formula: τ is the set of indices for all constraints, if To effectively constrain the index set, This is the set of invalid constraint indices; a and b are parameter column vectors; d and u are constant parameters;

[0168] Combining the above equations, we get:

[0169]

[0170] The feasible region of the planning parameter w is the union of the critical regions formed by all effective and ineffective constraints:

[0171]

[0172] In the formula: CR is the critical region; FR is the feasible region.

[0173] Based on the quantified scheduling characteristics, the power distribution network security defense improvement model is transformed and solved using a commercial solver; including:

[0174] The transformed model is as follows:

[0175] MP:

[0176] Where: Φ VPP For the VPP index set; f1 Grid Main network operating costs; P V for The column vector formed, R V for and The column vector formed by Φ T Set of scheduling period indexes; A Grid Main network model coefficient matrix; d Grid This is the column vector of constant terms in the main network model.

[0177] The transformed model MP is a linear programming model with deterministic properties, aiming to minimize F, and with corresponding constraints established. It can then be solved using solvers such as Cplex.

[0178] Example 3

[0179] A power distribution network security enhancement system considering the role of distributed energy storage in regulating new energy access includes:

[0180] The objective function establishment module is configured to: establish the objective function of the distribution network security defense improvement model under the new energy access and considering the role of distributed energy storage regulation, which is to minimize the comprehensive cost f. The comprehensive cost f includes the adjustable resource operation cost f1, the power trading cost f2 and the standby trading cost.

[0181] The constraint establishment module is configured to: establish constraint conditions for the normal operation of the distribution network security defense enhancement model, including threshold power constraints, line power flow constraints, power balance constraints, generator capacity constraints, demand response constraints, and distributed power output constraints; and establish constraint conditions for the distribution network security defense enhancement model when calling up backup under accident conditions, including threshold power capacity constraints, power balance constraints, and line power flow constraints.

[0182] The problem transformation and solution module is configured to: construct a box set of uncertain parameters and perform preliminary transformation of uncertain constraints; reprocess the problem into a standard robust equivalent form, which is a deterministic bi-level optimization problem; based on duality theory, transform the bi-level optimization problem into a single-level optimization problem; integrate the distribution network security defense improvement model into the following compact form ROP(t); based on multi-parameter linear programming theory, encapsulate the distribution network security defense improvement model by solving the feasible region of the planning parameters w; based on the quantized scheduling characteristics, transform the distribution network security defense improvement model and solve it based on a commercial solver.

Claims

1. A method for enhancing the security defense of a distribution network considering the role of distributed energy storage regulation under the access of new energy sources, characterized in that, include: The objective function of the distribution network security defense enhancement model considering the role of distributed energy storage regulation under the access of new energy sources is to minimize the comprehensive cost f, which includes the adjustable resource operation cost f1, the power trading cost f2 and the standby trading cost. Establish the constraints for the normal operation of the aforementioned distribution network security defense enhancement model, including threshold power constraints, line power flow constraints, power balance constraints, generator capacity constraints, demand response constraints, and distributed power output constraints. Establish the constraints for calling up the standby mode under the fault state in the aforementioned distribution network security defense enhancement model, including threshold power capacity constraints, power balance constraints, and line power flow constraints. Construct a box set of uncertainty parameters and perform preliminary transformation of uncertainty constraints; The problem is reprocessed into a standard robust equivalent form, which is a deterministic bilevel optimization problem; Based on duality theory, the bi-level optimization problem is equivalently transformed into a single-level optimization problem; The power distribution network security defense enhancement model is integrated into the following compact form ROP(t); Based on multi-parameter linear programming theory, the power distribution network security defense enhancement model is encapsulated by solving the feasible region of the planning parameter w. Based on the quantified scheduling characteristics, the power distribution network security defense enhancement model is transformed and solved using a commercial solver.

2. The method for improving the security defense of a distribution network considering the regulation role of distributed energy storage under new energy access as described in claim 1, characterized in that, The objective function of a distribution network security defense enhancement model considering the role of distributed energy storage regulation under new energy access is established to minimize the overall cost f, including: The total cost f is expressed as: f = f1 + f2 + f3; The formula for calculating the adjustable resource operating cost f1 is as follows: Where t is the scheduling period index; Φ G For the set of unit indexes; Φ DR For the set of all participating demand response nodes; Φ S For the set of interrupt level indexes; C G,i,t P is the unit cost coefficient; G,i,t s represents the generator output power; s represents the interruption level. The interrupt load cost coefficient for the s-th level interruption. The interrupt load is at the s-th level. The formula for calculating f2 is expressed as follows: Where, Φ V For VPP gateway node index set; and These are the electricity purchase cost and the electricity sales revenue coefficient, respectively. and These are the power purchased and the power sold, respectively. The formula for calculating f3 is expressed as follows: Among them, P GR,i,t As backup power; These are the positive and negative reserve benefit coefficients for VPP, respectively. These are the positive and negative standby power of VPP, respectively.

3. The method for improving the security defense of a distribution network considering the regulation role of distributed energy storage under the access of new energy sources, as described in claim 1, is characterized in that... Establish the constraints for the normal operation of the aforementioned power distribution network security defense enhancement model, including: The gate power constraint is: In the formula: P V,i,t VPP gate power, P V and These are the lower and upper limits of the VPP gate power, respectively; and These are the power purchased and the power sold, respectively. The power flow constraints of the line are: In the formula: P In,i,t Inject power into the node; P DG,i,t Power output for distributed generation; P L,i,t For load power; Φ N A set of node indexes; In the formula: P flow and The lower and upper limits of the power flow for each line; G l-i Φ is the power transfer factor from node i to line l; Lines A set of line indexes; The power balance constraint is: In the formula: Let j be the set of lines starting from node j. Let j be the set of lines that terminate at node j. The generator capacity constraint is: In the formula: U G,i,t Indicates the start / stop status of the unit, given by the unit combination; where U G,i,t =0 indicates that the unit is started, U G,i,t =1 indicates that the unit is shut down; P G,i,t and The upper and lower limits of the unit's output; The demand response constraints are: In the formula: λ s The interruption level coefficient is {i}; {i} represents a random variable; P DR,i,t This represents the total interruption load of node i; The interrupt load is at the s-th level. The output constraints of distributed power sources are: In the formula: P DG,i,k,t Let i be the output of the k-th distributed power source on node i; Φ is the set of distributed power source indices on node i; DG A set of indexes for distributed power supply connection nodes; Predict the output value of distributed generation; IN ci,t +U di,t =1 0≤P di (t)≤P dimax U di (t) -P cimax IN ci,t ≤P ci,t ≤0 In the formula: C i,t (t), C imin C imax Let C be the energy stored in the energy storage system, the minimum energy stored in the energy storage system, and the maximum energy stored in the energy storage system; iInitial α is the initial energy; i β i U is the energy conversion coefficient. ci,t U di,t These are binary variables, representing the energy storage charging and discharging states respectively; P di,k P ci,k P represents the discharge and charging power during the k-th time period, respectively. di,t P ci,t P represents the discharge and charge power during time period t. cimax This represents the maximum energy storage capacity. Further preferably, the constraints for calling up the standby mode under accident conditions in the power distribution network security defense enhancement model include: The power capacity constraint at the cutoff point is as follows: Power balance constraints are as follows: The line power flow constraints are as follows: In the formula: P VR,i,t To utilize the gate power during standby; These refer to the changes in power consumption when positive / negative standby time groups are activated, distributed power sources, and demand response power. Power is injected into nodes to activate positive / negative standby.

4. The method for improving the security defense of a distribution network considering the regulation role of distributed energy storage under new energy access as described in claim 1, characterized in that, Construct a box set of uncertainty parameters and perform preliminary transformation of uncertainty constraints; including: Construct a box set of uncertainty parameters as follows: In the formula: Errors in predicting power output for new energy sources; Introducing the auxiliary variable χ DG,i,j,t The uncertainty constraints are initially transformed into actual constraints, as shown below:

5. The method for improving the security defense of a distribution network considering the regulation role of distributed energy storage under new energy access as described in claim 1, characterized in that, The problem is reprocessed into a standard robust equivalent form, which is a deterministic bilevel optimization problem; including: The deterministic bilevel optimization problem is shown below: In the formula: Γ i J is the conservative budget parameter. i It is a set of uncertain parameters.

6. The method for improving the security defense of a distribution network considering the regulation role of distributed energy storage under new energy access as described in claim 1, characterized in that, Based on duality theory, the bi-level optimization problem is equivalently transformed into a single-level optimization problem; including: The single-layer optimization problem is shown below: In the formula: z i As an auxiliary variable; Γ i Conservatism adjustment factor; y j It is an auxiliary variable.

7. The method for improving the security defense of a distribution network considering the regulation role of distributed energy storage under new energy access as described in claim 1, characterized in that, The power distribution network security defense enhancement model is integrated into the following compact form ROP(t); as shown below: ROP(t): In the formula: A and B are parameter matrices; κ and d are parameter vectors, both of which are column vectors; w is the planning parameter.

8. The method for improving the security defense of a distribution network considering the regulation role of distributed energy storage under new energy access as described in claim 1, characterized in that, Based on multi-parameter linear programming theory, the power distribution network security defense enhancement model is encapsulated by solving the feasible region of the planning parameter w; including: Construct the Lagrangian function L of ROP(t), then the optimality condition of ROP(t) is: L=k T x+u T (A(Γ)x+Bw+d) u≥0 In the formula: L is the Lagrangian function; A and B are parameter matrices; κ and d are parameter vectors, both of which are column vectors; w is the planning parameter; Γ is the conservatism adjustment factor; u is the multiplier; In the above formula, A, B, d, and u can be written in vector form: In the formula: τ is the set of indices for all constraints, if To effectively constrain the index set, This is the set of invalid constraint indices; a and b are parameter column vectors; d and u are constant parameters; Combining the above equations, we get: The feasible region of the planning parameter w is the union of the critical regions formed by all effective and ineffective constraints: In the formula: CR is the critical region; FR is the feasible region.

9. A method for enhancing the security defense of a distribution network considering the regulation role of distributed energy storage under new energy access, as described in any one of claims 1-8, characterized in that, Based on the quantified scheduling characteristics, the power distribution network security defense enhancement model is transformed and solved using a commercial solver; including: The transformed model is as follows: MP: Where: Φ VPP For the VPP index set; f1 Grid Main network operating costs; P V for The column vector formed, R V for and The column vector formed by Φ T Set of scheduling period indexes; A Grid Main network model coefficient matrix; d Grid The column vector of constant terms in the main network model; The transformed model MP is a linear programming model with deterministic properties, aiming to minimize F, and corresponding constraints are established.

10. A power distribution network security defense enhancement system considering the role of distributed energy storage regulation under new energy access, characterized in that, include: The objective function establishment module is configured to: establish the objective function of the distribution network security defense improvement model under the new energy access and considering the role of distributed energy storage regulation, which is to minimize the comprehensive cost f. The comprehensive cost f includes the adjustable resource operation cost f1, the power trading cost f2 and the standby trading cost. The constraint establishment module is configured to: establish constraint conditions for the normal operation of the distribution network security defense enhancement model, including threshold power constraints, line power flow constraints, power balance constraints, generator capacity constraints, demand response constraints, and distributed power output constraints; and establish constraint conditions for the distribution network security defense enhancement model when calling up backup under accident conditions, including threshold power capacity constraints, power balance constraints, and line power flow constraints. The problem transformation and solution module is configured to: construct a box set of uncertain parameters and perform preliminary transformation of uncertain constraints; reprocess the problem into a standard robust equivalent form, which is a deterministic bi-level optimization problem; based on duality theory, transform the bi-level optimization problem into a single-level optimization problem; and integrate the power distribution network security defense enhancement model into the following compact form ROP(t). Based on multi-parameter linear programming theory, the power distribution network security defense enhancement model is encapsulated by solving the feasible region of the planning parameter w. Based on the quantified scheduling characteristics, the power distribution network security defense enhancement model is transformed and solved using a commercial solver.

Images