Main and distribution collaborative optimization scheduling method and system based on static security boundary of power distribution network

Abstract

The invention discloses a main and distribution collaborative optimization scheduling method and system based on a static safety boundary of a power distribution network, and relates to the field of power system optimization scheduling, and the method comprises the steps: converting a voltage / current safety boundary of the power distribution network into the static safety boundary of the power distribution network through hyperplane linearization; establishing global safety margin indexes of the node voltage and the line current of the power distribution network, and introducing a safety margin weight coefficient to balance the priorities of the node voltage margin and the line current margin; determining margin income based on the global safety margin index and the safety margin weight coefficient, and establishing a dynamic weight adjustment mechanism to update the safety margin weight coefficient; constructing a target function based on the power generation cost of the main power grid, the new energy power abandoning penalty of the power distribution network and the margin income; and obtaining power grid data, and solving the main-distribution collaborative optimization model by using a quadratic programming method and the power grid data to obtain a main-distribution collaborative optimization scheduling scheme. According to the invention, bidirectional power interaction and collaborative optimization of the main power grid and the power distribution network are realized.

Description

Technical Field

[0001] This application relates to the field of power system optimization scheduling technology, and in particular to a primary and secondary coordinated optimization scheduling method and system based on the static security boundary of the distribution network. Background Technology

[0002] With the large-scale integration of distributed renewable energy and flexible loads, traditional main-distribution coordinated scheduling methods face the dual challenges of low efficiency in safety boundary modeling and insufficient dynamics in coordinated optimization. This leads to increased risks of voltage exceeding limits and line overloads in the distribution network, as well as limited renewable energy absorption. Therefore, improving the safety and dynamism of main-distribution coordinated scheduling is urgently needed.

[0003] Currently, research on primary and secondary coordinated scheduling has the following limitations:

[0004] (1) Insufficient efficiency of online modeling of safety boundary: Over-reliance on nonlinear safety boundary models, lack of deep coupling with online scheduling, or dimensionality curse caused by high-dimensional nonlinear constraints, resulting in optimization problem solving efficiency that cannot meet the needs of online scheduling.

[0005] (2) Lack of dynamic coordination mechanism for main and distribution power interaction: The main strategy is to adopt a hierarchical optimization strategy for main and distribution. The safety margin and economic target have a static weight relationship, and there is no dynamic adjustment mechanism for main and distribution power interaction, which cannot adapt to the real-time coordination needs under the fluctuation of new energy. Summary of the Invention

[0006] The purpose of this application is to provide a main grid and distribution network coordinated optimization scheduling method and system based on the static safety boundary of the distribution network. By linearizing the safety boundary and constructing a margin-driven dynamic weight adjustment mechanism, bidirectional power interaction and coordinated optimization between the main grid and the distribution network are realized.

[0007] To achieve the above objectives, this application provides the following solution:

[0008] In a first aspect, this application provides a primary-distribution coordinated optimization scheduling method based on the static security boundary of a distribution network, the primary-distribution coordinated optimization scheduling method based on the static security boundary of a distribution network includes:

[0009] Hyperplane linearization is used to transform the voltage / current safety boundary of the distribution network into the static safety boundary of the distribution network; the static safety boundary of the distribution network includes the node voltage safety boundary and the line thermal stability boundary.

[0010] Based on the node voltage safety boundary and the line thermal stability boundary, the distribution network safety constraints are determined;

[0011] Establish global safety margin indicators for distribution network node voltage and line current, and introduce safety margin weighting coefficients to balance the priority of node voltage margin and line current margin.

[0012] The margin benefit is determined based on the global safety margin index and the safety margin weight coefficient, and a dynamic weight adjustment mechanism is established to update the safety margin weight coefficient.

[0013] An objective function is constructed based on the main grid generation cost, the distribution network renewable energy curtailment penalty, and the margin benefit, and the main grid and distribution network bidirectional power interaction constraints and basic constraints are determined.

[0014] Obtain power grid data, and use quadratic programming and the power grid data to solve the main distribution coordinated optimization model to obtain the main distribution coordinated optimization scheduling scheme; the main distribution coordinated optimization model is a model aimed at minimizing the objective function under the power grid security constraints, the main distribution bidirectional power interaction constraints and the basic constraints.

[0015] In a second aspect, this application also provides a computer system, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the main distribution coordinated optimization scheduling method based on the static security boundary of the distribution network as described in the first aspect.

[0016] According to the specific embodiments provided in this application, the following technical effects are disclosed:

[0017] This application first utilizes hyperplane linearization technology to compress the voltage / current safety boundary of the distribution network into a linear hyperplane constraint in the nodal power injection space, overcoming the dimensionality catastrophe limitation of traditional nonlinear models. Second, this application defines global safety margin indices for distribution network nodal voltages and line currents, and constructs a margin-driven dynamic weight adjustment mechanism. This mechanism dynamically updates the safety margin weight coefficients, achieving bidirectional power interaction and online collaborative optimization of safety and economy between the main grid and the distribution network. From a global perspective, this application completes bidirectional power interaction and collaborative optimization between the main grid and the distribution network, improving the safety and economy of the power grid. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of the main distribution coordinated optimization scheduling method based on the static security boundary of the distribution network in the embodiments of this application;

[0020] Figure 2This is a diagram showing the internal structure of the computer system in an embodiment of this application. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] Given the complex security boundaries resulting from the high penetration of distributed renewable energy into power distribution systems, a collaborative optimization method that balances computational efficiency and dynamic coordination between the main grid and distribution network is urgently needed. Therefore, this application proposes an integrated framework of "static security domain modeling - main grid-distribution network collaborative optimization - margin-driven decision-making." Based on hyperplane linearization technology, this application compresses the voltage / current security boundaries of the distribution network into linear hyperplane constraints within the node power injection space, overcoming the dimensionality curse limitation of traditional nonlinear models. It defines global security margin indices for distribution network node voltages and line currents, and constructs a margin-driven dynamic weight adjustment mechanism, achieving online collaborative optimization of bidirectional power interaction and safety / economic efficiency between the main grid and distribution network.

[0023] The purpose of this application is to provide a main grid and distribution network coordinated optimization scheduling method and system based on the static safety boundary of the distribution network. By linearizing the safety boundary and constructing a margin-driven dynamic weight adjustment mechanism, bidirectional power interaction and coordinated optimization between the main grid and the distribution network are realized.

[0024] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0025] In one exemplary embodiment, such as Figure 1 As shown, a primary-distribution coordinated optimization scheduling method based on the static security boundary of the distribution network is provided. This method includes:

[0026] Step S1: Transform the distribution network voltage / current safety boundary into the distribution network static safety boundary using hyperplane linearization; the distribution network static safety boundary includes the node voltage safety boundary and the line thermal stability (current) boundary.

[0027] In this embodiment, step S1 specifically includes the following process:

[0028] The first step is to linearize the node voltage safety boundary.

[0029] (1) Establish the voltage amplitude relationship between nodes.

[0030] Assuming the two endpoints of line ij are i and j respectively, then:

[0031]

[0032] In the formula, and V represents the voltage vectors at nodes i and j, respectively; i and V j Let R represent the voltage magnitudes at nodes i and j, respectively; and let R represent the impedance of line ij. ij +jX ij R ij Let X be the resistance value of line ij. ij P represents the reactance value of line ij. ij and Q ij Let i and j represent the active power and reactive power flowing from node i to node j via line ij, respectively; i,j∈N dist N dist Let L be the set of all nodes in the distribution network; ij∈L dist L dist This is a set of branches within the distribution network (excluding main and distribution interconnection lines), with each branch connecting node i→j.

[0033] Considering that the voltage angle between any two nodes is not significantly different, the voltage magnitude relationship between node i and node j can be approximately described as follows:

[0034]

[0035] In the formula, ΔV j Let be the voltage drop between nodes i and j on line ij.

[0036] Considering the radial topology of the distribution network, establish the voltage magnitude relationship between node j and node 0 (i.e., the root node in the distribution network):

[0037]

[0038] In the formula, L j Let be the set of all lines from node j to node 0; gh is the line whose first and last nodes are g and h respectively; V0 is the voltage magnitude of the root node; ΔV h This represents the voltage drop between nodes g and h on line gh.

[0039] Since line losses are negligible compared to the load, they can be ignored when calculating voltage amplitude. Furthermore, in power flow calculations, the voltage amplitude at each node is assumed to be approximately equal to the voltage amplitude V0 at the root node; therefore, ΔV h It can be approximated as:

[0040]

[0041] In the formula, and These represent the net injected active power and net injected reactive power at node h, respectively; the impedance of line gh is R. gh +jX gh R gh X is the resistance value of line gh. gh Let gh be the reactance value of the line.

[0042] (2) Linearization is achieved by introducing equivalent resistance and equivalent reactance.

[0043] By aggregating path impedance, an explicit expression for the node voltage is obtained:

[0044]

[0045] In the formula, and These are the equivalent resistance and equivalent reactance between node j and node k, respectively, which are related to the node voltage safety boundary. and These represent the net injected active power and net injected reactive power at node k, respectively.

[0046] The equivalent resistance and equivalent reactance between node j and node k, which are related to the node voltage safety boundary, are expressed as follows:

[0047]

[0048] In the formula, L k Let L be the set of all paths from node k to the root node; j C is the set of all paths from node j to the root node; j Let j be the set of downstream nodes of node j.

[0049] (3) Derive the linearized expression for the node voltage safety boundary.

[0050] For the upper voltage limit of node j, when Then:

[0051]

[0052] When V0 is given It is the voltage constant. Therefore, the above equation is a set of linear expressions composed of nodal power injection, which, after normalization, yields:

[0053]

[0054] Similarly, for the lower voltage limit at node j, when When this is done, the following relational expression can be derived:

[0055]

[0056] In the formula, and These are the upper and lower voltage limits for node j, respectively; and These are the normalization coefficients corresponding to the active power injection and reactive power injection at node k in the upper limit constraint of the node voltage safety boundary; and These are the normalization coefficients corresponding to the active power injection and reactive power injection at node k in the lower limit constraint of the node voltage safety boundary.

[0057] The second step is to linearize the thermal stability boundary of the line.

[0058] (1) Establish the relationship between line current and node voltage.

[0059] Line current vector The relationship between voltage variables:

[0060]

[0061] The current value I of line ij can be obtained. ij The relationship between voltage amplitude and voltage amplitude:

[0062]

[0063] In the formula, θ ij Let be the voltage phase angle difference between node i and node j.

[0064] Considering that the voltage phase angle difference between any two nodes is not significantly different, then:

[0065]

[0066] (2) Linearization is achieved by introducing equivalent resistance and equivalent reactance.

[0067] Will In the middle, and by introducing equivalent resistance and equivalent reactance, we can obtain:

[0068]

[0069] In the formula, D ij Let be the set of downstream nodes of line ij; and These are the equivalent resistance and equivalent reactance between line ij and node k, respectively, which are related to the thermal stability boundary of the line.

[0070] The equivalent resistance and equivalent reactance between line ij and node k, which are related to the thermal stability boundary of the line, are expressed as follows:

[0071]

[0072] (3) Derive the linearized expression for the thermal stability boundary of the circuit.

[0073] For the upper limit of the current in line ij, when Then:

[0074]

[0075] When V0 is given This is the current constant. Therefore, the above equation is a linear combination expression, which, after normalization, yields:

[0076]

[0077] In the formula, is the upper limit of the current in line ij; and These are the normalization coefficients corresponding to the active power injection and reactive power injection at node k in the thermal stability boundary constraints of the line.

[0078] Given that the upper and lower limits of the line current ij have equal magnitudes but opposite signs (directions), by rearranging the above equation, we can obtain the linearized expressions for the line thermal stability boundaries corresponding to the upper and lower limits of the line current ij:

[0079]

[0080] The third step is to establish an online update mechanism for the security domain (including the impact of distributed new energy / energy storage system output).

[0081] Considering the impact of power output fluctuations from distributed renewable energy (DG) and energy storage systems (ESS) on the safety boundary, the equivalent impedance parameters are dynamically corrected through sensitivity analysis. A sensitivity matrix of node voltage / line current on power injection is established. When the predicted output deviation of DG or the change in ESS charging and discharging power exceeds the historical average by ±15% (threshold ε = 15% empirical value), the boundary coefficient update process is triggered.

[0082] Sensitivity matrix of node voltage / line current to power injection:

[0083]

[0084] In the formula, Node voltage vector Injecting active power into nodes The Jacobian matrix reflects the voltage offset caused by a unit power change; Nodal current vector Injecting active power into nodes The Jacobian matrix reflects the current offset caused by a unit power change.

[0085] This leads to the update equations for the node voltage safety boundary parameters:

[0086]

[0087] Update equations for the thermal stability boundary parameters of the line:

[0088]

[0089] In the formula, t is the (scheduling) time period, t∈{1,2,...,T}, and T is the total length of the scheduling time period; and These are the equivalent resistance coefficient and equivalent reactance coefficient between node j and node k, respectively, which are related to the node voltage safety boundary during time period t. and These are the equivalent resistivity and equivalent reactance coefficients between line ij and node k, respectively, which are related to the thermal stability boundary of the line during time period t. and These are the equivalent resistance coefficient and equivalent reactance coefficient between node j and node k under the reference operating condition, respectively. and These are the equivalent resistance coefficient and equivalent reactance coefficient between line ij and node k under the reference operating condition, respectively; D dist S is the set of nodes in a distribution network that are connected to distributed renewable energy sources; dist S is the set of nodes in the distribution network connected to energy storage devices; V,jm Let be the voltage change at node j when the injected power at node m changes by 1MW, i.e., the voltage sensitivity of node j to m. S V,ij,m Let be the change in current of line ij when the injected power at node m changes by 1MW, i.e., the current sensitivity of line ij to m. The change in DG / energy storage active power at node m during time period t; Let DG / energy storage reactive power change at node m during time period t.

[0090] Then update the static safety boundary coefficient of the distribution network:

[0091]

[0092] That is to Update.

[0093] Step S2: Determine the distribution network security constraints based on the node voltage security boundary and the line thermal stability boundary; the distribution network security constraints include the node voltage security boundary constraints and the line thermal stability boundary constraints.

[0094] When considering security constraints based on the hyperplane expression of the static security boundary of the distribution network, the node voltage security boundary constraints and the line thermal stability boundary constraints in the distribution network can be linearly expressed in the node power injection space as follows:

[0095]

[0096] Step S3: Establish global safety margin indices for distribution network node voltage and line current, and introduce safety margin weighting coefficients to balance the priority of node voltage margin and line current margin.

[0097] To quantify the safety status of the weakest link in the power system, dispatching schemes are required to retain a certain margin. Therefore, a global safety margin index for distribution network node voltage and line current is established, and a safety margin weighting coefficient is introduced to balance the priority of node voltage margin and line current margin.

[0098]

[0099] In the formula, η t This represents the global safety margin indicator for time period t. The node voltage margin during time period t; ω represents the line current margin during time period t; V and ω I These are the safety weighting factors for node voltage margin and line current margin, respectively; V i t Let be the voltage amplitude at node i during time period t; L represents the current value of line ij during time period t; dist This refers to the collection of internal branches within the distribution network, excluding main-distribution interconnection lines.

[0100] Step S4: Determine the margin benefit based on the global safety margin index and the safety margin weight coefficient, and establish a dynamic weight adjustment mechanism to update the safety margin weight coefficient.

[0101] To achieve a dynamic balance between safety and economy, a dynamic weight adjustment mechanism based on an exponential function is designed. When the global safety margin index η of the power system... t When the safety margin is reduced, the safety margin weight coefficient μ(t) is automatically increased, forcing the optimization model to prioritize increasing the safety margin; conversely, μ(t) is reduced to focus on economic objectives, i.e., satisfying:

[0102]

[0103] In the formula, μ(t) is the safety margin weighting coefficient for time period t; μ min γ is the minimum guaranteed return value; μ0 is the benchmark margin return weighting coefficient; γ is the decay coefficient.

[0104] Step S5: Construct an objective function based on the main grid generation cost, the distribution network renewable energy curtailment penalty, and margin benefits, and determine the main grid and distribution network bidirectional power interaction constraints and basic constraints.

[0105] The main grid-distribution coordinated optimization scheduling model based on the static safety boundary of the distribution network aims to minimize the generation cost of the main grid and the penalty for curtailment of renewable energy in the distribution network. The negative term μ(t)η t The safety margin is transformed into a benefit term, driving the optimization process to proactively improve the system's safety level. The objective function is as follows:

[0106]

[0107] In the formula, G main The set of dispatchable generating units within the main power grid; a g and b g These are the cost coefficients for unit g, respectively; λ represents the active power output of unit g during time period t; λ is the power curtailment penalty coefficient. To predict the active power output of distributed renewable energy j during time period t; For the active power output of distributed new energy j during time period t.

[0108] Furthermore, the main and auxiliary bidirectional power interaction constraints are based on η t Dynamic adjustment: when η t When the voltage is low, the upper limit of the support power from the main grid to the distribution network is relaxed, while the reverse power from the distribution network to the main grid is tightened to reduce risk; when η t When the power is relatively high, the reverse power limit is relaxed to promote the consumption of new energy sources, i.e., the following conditions are met:

[0109]

[0110] In the formula, and These represent the interactive active power and interactive reactive power of the interconnection lines between the main power grid and the distribution network during time period t; and These are the lower and upper limits of the interactive active power of the interconnection lines between the main power grid and the distribution network, respectively. and These represent the lower and upper limits of the reactive power exchanged between the main power grid and the distribution network, respectively; P base The baseline interactive power limit; k p ΔP is the gain coefficient for margin power adjustment. rateThe baseline gradeability limit; This is the limit for the dynamic gradient rate.

[0111] In addition to the aforementioned distribution network security constraints and main-distribution bidirectional power interaction constraints, this embodiment also involves another set of fundamental constraints, which mainly include: main grid power balance constraints, main grid generating unit constraints, distributed renewable energy output constraints, and energy storage system security constraints. Among these, the main grid power balance constraints are:

[0112]

[0113] In the formula, N main Main grid node set; L inter This is the set of interconnecting lines between the main power grid and the distribution network, with each branch connecting node i→j; and These represent the total active load and total reactive load of all dispatchable generating units within the main power grid during time period t. and These represent the total active load and total reactive load of node j in the main power grid during time period t, respectively. and These represent the total active power injected from the main power grid into the distribution network and the total reactive power (outflow from the main power grid is positive).

[0114] The main grid unit constraints are:

[0115]

[0116] In the formula, and These are the lower and upper limits of the active power output of unit g, respectively; This represents the maximum change in the active power output of unit g.

[0117] The output constraints of distributed renewable energy sources are:

[0118]

[0119] In the formula, and These represent the active and reactive power outputs of distributed renewable energy source j during time period t. and These are the predicted active power output and predicted reactive power output of distributed new energy source j during time period t, respectively. This represents the maximum change in the active power output of distributed renewable energy sources.

[0120] The safety constraints for energy storage systems are:

[0121]

[0122] In the formula, Let j be the capacity of energy storage device j during time period t; and η represents the charging power and discharging power of energy storage device j during time period t, respectively; ch and η dis Δt represents the charging power and discharging efficiency of energy storage device j, respectively; Δt is the change over time. S represents the rated capacity of energy storage device j; dist The set of nodes connected to energy storage devices in the power distribution network; and These represent the lower and upper limits of the capacity of energy storage device j during time period t, respectively. and These represent the upper limits of charging power and discharging power for energy storage device j during time period t, respectively.

[0123] Step S6: Obtain power grid data, use quadratic programming and power grid data to solve the main and distribution coordinated optimization model, and obtain the main and distribution coordinated optimization scheduling scheme.

[0124] In this embodiment, the main-distribution coordinated optimization model includes the above-mentioned objective function, as well as distribution network security constraints, main-distribution bidirectional power interaction constraints, main grid power balance constraints, main grid unit constraints, distributed new energy output constraints, and energy storage system security constraints. It is an optimization model aimed at minimizing the objective function under all the above constraints.

[0125] Before solving the main-distribution coordinated optimization model, it is necessary to first determine the input conditions of the model (i.e., power grid data):

[0126] (1) Network parameters: Distribution network topology, line impedance parameters (R ij +jX ij ).

[0127] (2) Main power grid parameters: Main power grid unit operating parameters ( wait).

[0128] (3) Forecast data: Forecast values ​​of main power grid load Predicted active / reactive power output of distributed renewable energy sources (such as photovoltaic and wind power)

[0129] (4) Distributed resource parameters: D dist , S dist , η ch η dis , wait.

[0130] (5) Others: V0,

[0131] The solver is used to solve the primary-secondary collaborative optimization model, and the output includes, but is not limited to, the following scheduling information:

[0132] (1) Main power grid plan: Active / reactive power output plan of each unit in the main power grid during time period t.

[0133] (2) Distribution network dispatching scheme: The actual dispatching output of distributed new energy j during time period t and abandoned electricity State of charge / discharge power of energy storage device j during time period t and capacity

[0134] (3) Interaction and security information: bidirectional interaction power of the main and distribution network tie lines during time period t. Global safety margin index η t Including local margins, voltage amplitude at each node of the distribution network, and current amplitude of each line.

[0135] In summary, this embodiment utilizes hyperplane linearization technology to transform the voltage and thermal stability constraints of distribution network nodes into linear inequalities in the node power injection space, constructs a margin-driven dynamic weight adjustment mechanism, and establishes bidirectional power interaction constraints between the main grid and distribution network. Through a main grid-distribution collaborative optimization model aimed at minimizing the main grid generation cost, minimizing the distribution network's renewable energy curtailment penalty, and maximizing the safety margin benefit, the voltage exceedance probability is significantly reduced by 6.5%, the main grid operating cost by 22.5%, and the renewable energy curtailment rate is significantly decreased. The solution time of the linearized model based on quadratic programming (i.e., the main grid-distribution collaborative optimization model) is less than 2 minutes (compared to more than 15 minutes for traditional nonlinear models), meeting the real-time scheduling requirements. This effectively improves the safety, economy, and real-time performance of grid operation in high-penetration renewable energy scenarios.

[0136] In another exemplary embodiment, a computer system is provided, which may be a server or a terminal, and its internal structure diagram may be as follows: Figure 2As shown, the computer system includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores power grid data. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements the aforementioned primary-distribution coordinated optimization scheduling method based on the static safety boundary of the distribution network.

[0137] Those skilled in the art will understand that Figure 2 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer system to which the present application is applied. A specific computer system may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0138] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0139] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0140] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0141] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A primary-distribution coordinated optimization scheduling method based on the static security boundary of a distribution network, characterized in that, The primary and secondary coordinated optimization scheduling method based on the static security boundary of the distribution network includes: Hyperplane linearization is used to transform the voltage / current safety boundary of the distribution network into the static safety boundary of the distribution network; the static safety boundary of the distribution network includes the node voltage safety boundary and the line thermal stability boundary. Based on the node voltage safety boundary and the line thermal stability boundary, the distribution network safety constraints are determined; Establish global safety margin indicators for distribution network node voltage and line current, and introduce safety margin weighting coefficients to balance the priority of node voltage margin and line current margin. The margin benefit is determined based on the global safety margin index and the safety margin weight coefficient, and a dynamic weight adjustment mechanism is established to update the safety margin weight coefficient. An objective function is constructed based on the main grid generation cost, the distribution network renewable energy curtailment penalty, and the margin benefit, and the main grid and distribution network bidirectional power interaction constraints and basic constraints are determined. Obtain power grid data, and use quadratic programming and the power grid data to solve the main distribution coordinated optimization model to obtain the main distribution coordinated optimization scheduling scheme; the main distribution coordinated optimization model is a model aimed at minimizing the objective function under the power grid security constraints, the main distribution bidirectional power interaction constraints and the basic constraints.

2. The primary and secondary coordinated optimization scheduling method based on the static security boundary of the distribution network according to claim 1, characterized in that, The distribution network security constraints include node voltage security boundary constraints and line thermal stability boundary constraints; the expression for the node voltage security boundary constraints is: In the formula, and These are the normalization coefficients corresponding to the active power injection and reactive power injection at node k in the upper limit constraint of the node voltage safety boundary; and These are the normalization coefficients corresponding to the active power injection and reactive power injection at node k in the lower limit constraint of the node voltage safety boundary; and These represent the net injected active power and net injected reactive power at node k, respectively; N dist For the set of all nodes in the distribution network; The expression for the thermal stability boundary constraint of the line is: In the formula, and These are the normalization coefficients corresponding to the active power injection and reactive power injection at node k in the thermal stability boundary constraints of the line.

3. The primary and secondary coordinated optimization scheduling method based on the static security boundary of the distribution network according to claim 2, characterized in that, The primary and secondary coordinated optimization scheduling method based on the static security boundary of the distribution network also includes: Introduce an online security domain update mechanism; The static security boundary coefficient of the distribution network is updated using the aforementioned online security domain update mechanism.

4. The primary and secondary coordinated optimization scheduling method based on the static security boundary of the distribution network according to claim 3, characterized in that, The formula for updating the static safety boundary coefficient of the distribution network is: In the formula, and These are the normalization coefficients corresponding to the active power injection and reactive power injection of node k in time period t, respectively, within the upper limit constraint of the node voltage safety boundary. and These are the normalization coefficients corresponding to the active power injection and reactive power injection at node k in time period t, respectively, within the lower limit constraint of the node voltage safety boundary. and These are the equivalent resistance coefficient and equivalent reactance coefficient between node j and node k, respectively, which are related to the node voltage safety boundary during time period t; V0 is the voltage amplitude of the root node in the distribution network. and These are the upper and lower voltage limits for node j, respectively; and These are the normalization coefficients corresponding to the active power injection and reactive power injection at node k in time period t, respectively, within the thermal stability boundary constraints of the line. and These are the equivalent resistivity and equivalent reactance coefficients between line ij and node k, respectively, which are related to the thermal stability boundary of the line during time period t. is the upper limit of the current in line ij; R ij and X ij These are the resistance and reactance values ​​of line ij, respectively.

5. The primary and secondary coordinated optimization scheduling method based on the static security boundary of the distribution network according to claim 1, characterized in that, The expression for the global security margin index is: ω V +ω I =1; In the formula, η t This represents the global safety margin indicator for time period t. The node voltage margin during time period t; ω represents the line current margin during time period t; V and ω I These are the safety weighting coefficients for node voltage margin and line current margin, respectively. V i t Let be the voltage amplitude at node i during time period t; and These are the upper and lower voltage limits for node j, respectively; N dist For the set of all nodes in the distribution network; is the upper limit of the current in line ij; L represents the current value of line ij during time period t; dist This refers to the collection of internal branches within the distribution network, excluding main-distribution interconnection lines.

6. The primary and secondary coordinated optimization scheduling method based on the static security boundary of the distribution network according to claim 1, characterized in that, The formula for updating the safety margin weighting coefficient is as follows: In the formula, μ(t) is the safety margin weighting coefficient for time period t; μ min Minimum guaranteed return value; μ0 is the benchmark margin return weighting coefficient; γ is the decay coefficient; η t This represents the global safety margin indicator for time period t.

7. The primary and secondary coordinated optimization scheduling method based on the static security boundary of the distribution network according to claim 1, characterized in that, The expression for the objective function is: In the formula, T is the total length of the scheduling period; G main The set of dispatchable generating units within the main power grid; a g and b g These are the cost coefficients for unit g, respectively; λ is the active power output of unit g during time period t; λ is the curtailment penalty coefficient; D dist The set of nodes in a distribution network that are connected to distributed renewable energy sources; To predict the active power output of distributed renewable energy j during time period t; Let μ(t) be the active power output of distributed renewable energy j during time period t; μ(t) is the safety margin weighting coefficient for time period t. η t This represents the global safety margin indicator for time period t.

8. The primary and secondary coordinated optimization scheduling method based on the static security boundary of the distribution network according to claim 1, characterized in that, The expression for the primary and secondary bidirectional power interaction constraint is: In the formula, and These represent the interactive active power and interactive reactive power of the interconnection lines between the main power grid and the distribution network during time period t; and These are the lower and upper limits of the interactive active power of the interconnection lines between the main power grid and the distribution network, respectively. and These represent the lower and upper limits of the reactive power exchanged between the main power grid and the distribution network, respectively; P base The baseline interactive power limit; k p η is the margin power adjustment gain coefficient; t ΔP is the global safety margin indicator for time period t. rate The baseline gradeability limit; This is the limit for the dynamic gradient rate.

9. The primary and secondary coordinated optimization scheduling method based on the static security boundary of the distribution network according to claim 1, characterized in that, The basic constraints include: power balance constraints of the main power grid, constraints of main power grid generating units, constraints of distributed renewable energy output, and safety constraints of energy storage systems.

10. A computer system, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the main-distribution coordinated optimization scheduling method based on the static security boundary of the distribution network as described in any one of claims 1-9.

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