A multi-parameter programming-based distribution-microgrid gateway node scheduling domain characterization method

Abstract

The application discloses a kind of based on the scheduling domain delineation method of micro-grid gateway node of micro-grid planning, steps include: obtaining the operating parameter of micro-grid and grid topology structure parameter, establishes the micro-grid operation model with the minimum operating cost as target;For the energy storage constraint of micro-grid operation model, time is decoupled;Virtual queue of energy storage device is constructed, and energy storage constraint after decoupling is relaxed as stability problem of virtual queue and is solved using Lyapunov function, and the solution result is added to objective function to obtain decoupled micro-grid operation model and be expressed as compact form multi-parameter programming model with gateway node power as parameter, and the critical domain of gateway node and the segmented analytical function of scheduling cost are obtained by using multi-parameter programming method to solve the multi-parameter programming model;According to segmented analytical function, the visualized graphics of micro-grid gateway node scheduling domain is drawn in parameter feasible region.The application can efficiently and accurately delineate the feasible operation range and economic characteristics of micro-grid.

Description

Technical Field

[0001] This invention relates to the field of microgrid and distribution network coordinated operation optimization technology, specifically to a method for characterizing the scheduling domain of distribution-microgrid gateway interface nodes based on multi-parameter planning. Background Technology

[0002] In the traditional operation mode of distribution network and microgrid interaction, the microgrid is required to strictly track the power plans of the distribution network's designated nodes. This rigid source-load matching mode not only limits the flexible adjustment potential of the abundant distributed resources within the microgrid, but also makes it difficult for the distribution network dispatching layer to accurately and quickly assess the power adjustment range and cost characteristics that the microgrid can provide. As a result, system operation tends to be conservative, overall operating economy is poor, and the ability to absorb the volatility and uncertainty of a high proportion of renewable energy is limited.

[0003] In recent years, with the development of integrated power generation, grid, load, and storage systems, microgrids, as a system capable of internal multi-energy complementarity and autonomous operation, have attracted widespread attention for their dynamic interaction capabilities with distribution networks. Accurately characterizing the feasible power exchange range and corresponding costs of microgrid gateway nodes to form a visualized scheduling domain helps distribution network dispatch centers optimize resource allocation on a larger spatiotemporal scale, achieving economical and safe operation of distribution-microgrid coordination. Currently, research on characterizing such scheduling domains mainly focuses on model simplification, ignoring network constraints, or using computationally intensive methods such as enumeration and simulation. These methods suffer from poor analytical performance and low computational efficiency, making them unsuitable for real-time or forward-looking scheduling needs. Especially in complex scenarios considering energy storage temporal coupling, AC power flow constraints, and multi-type resource coordination, how to efficiently and accurately obtain gateway node scheduling domains with explicit analytical expressions remains a key technical problem that urgently needs to be solved in the field of distribution-microgrid coordinated scheduling. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method for characterizing the scheduling domain of the distribution-microgrid gateway node based on multi-parameter planning, which can efficiently and accurately characterize the feasible operating range and economic characteristics of the microgrid.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for characterizing the scheduling domain of a distribution-microgrid gateway interface node based on multi-parameter planning includes the following steps: The operating parameters of photovoltaic, wind power, energy storage and gas turbine in the microgrid and the grid topology parameters are obtained. A microgrid operation model is established based on the operating parameters and grid topology parameters. The microgrid operation model aims to minimize the operating cost and includes microgrid power flow constraints, flexibility resource constraints, power balance constraints and power exchange constraints between the common coupling point and the upper-level distribution network. The energy storage constraint in the flexibility resource constraint of the microgrid operation model is decoupled in time; a virtual queue of energy storage devices is constructed, and the decoupled energy storage constraint is relaxed into the stability problem of the virtual queue. The stability optimization problem of the virtual queue is solved using the Lyapunov function, and the solution is added to the objective function of the microgrid operation model to obtain the decoupled microgrid operation model. The decoupled microgrid operation model is expressed as a compact multi-parameter programming model with the power of the gateway node as a parameter. The multi-parameter programming method is used to solve the multi-parameter programming model to obtain the critical domain of the gateway node and the piecewise analytical function of the scheduling cost. Based on the piecewise analytical function, a visualization of the scheduling domain of the microgrid gateway node is drawn within the parameter feasible domain. The visualization shows the joint feasible domain boundary of the active and reactive power of the gateway node, and the corresponding operating cost of each point in the feasible domain is represented by the color gradient, providing decision support for the distribution network dispatch center to optimize the distribution-microgrid coordinated dispatch strategy.

[0006] Furthermore, the operating parameters include the maximum charge / discharge power, charge / discharge efficiency, upper and lower limits of state of charge, initial charge, and final state of charge of the energy storage device; the flexibility resource constraints include distributed energy storage constraints, which include: The energy storage discharge power constraint is mathematically expressed as follows:

[0007] In the formula: For microgrid nodes i Place t Energy storage and discharge power at all times; For nodes i Maximum energy storage charging and discharging power; The energy storage charging power constraint is expressed mathematically as follows:

[0008] In the formula: For microgrid nodes i Place t Real-time energy storage and charging power; Energy storage operation constraints, mathematically expressed as follows:

[0009] In the formula: For nodes i Energy storage t Constant energy storage state of charge; For nodes i Energy storage t+ State of charge of energy storage at moment 1; For nodes i Energy storage charging and discharging efficiency; For time intervals; The energy storage capacity constraint is expressed mathematically as follows:

[0010]

[0011] In the formula: and They are nodes i The maximum and minimum values ​​of the energy storage state of charge; and They are nodes i The initial and final states of energy storage.

[0012] Furthermore, when decoupling the energy storage constraint within the flexibility resource constraints of the microgrid operation model over time, the energy storage operation constraint is summed over a specified time interval and substituted into the energy storage capacity constraint. The constraints are then used to obtain the decoupled energy storage constraints, the mathematical expression of which is as follows:

[0013] In the formula: T -1 represents the end time of the specified time interval.

[0014] Furthermore, the mathematical expression for constructing the virtual queue of energy storage devices is as follows:

[0015] In the formula: Represents a node i A virtual queue for energy storage devices; Let represent the auxiliary variables used to construct the virtual queue, and the auxiliary variables satisfy . .

[0016] Furthermore, the mathematical expression of the objective function of the decoupled microgrid operation model is as follows:

[0017] In the formula: This represents the set of nodes containing gas turbines; This represents the set of nodes equipped with photovoltaic devices. This indicates a collection of wind power equipment; This represents the set of nodes with energy storage. This represents the total cost of the microgrid; This represents the cost per unit of active power of a gas turbine. This represents the unit reactive power cost of a gas turbine. Cost per unit of active power for photovoltaic equipment; Cost per unit of reactive power for photovoltaic equipment; Cost per unit of active power of wind power equipment; Unit reactive power cost of wind power equipment; Cost of charging energy storage; Cost of energy storage and discharge; The objective for optimizing the stability of the virtual queue is obtained by using the Lyapunov function to solve the stability optimization problem of the virtual queue.

[0018] Furthermore, when expressing the decoupled microgrid operation model as a compact multi-parameter programming model with gateway node power as a parameter, it includes: The objective function is rewritten, and its mathematical expression is as follows:

[0019]

[0020] In the formula: For microgrids in t The cost of time; Based on the rewritten objective function, a compact multi-parameter programming model is established, with the following mathematical expression:

[0021]

[0022]

[0023]

[0024] In the formula: superscript T Indicates transpose; , , , This is a coefficient matrix related to microgrid parameters; A coefficient vector related to microgrid parameters; For cost vectors; For decision variables; These are random variables representing the power parameters of the gateway nodes; This represents the active power vector of the gas turbine during the scheduling cycle; This represents the active power vector of photovoltaic power during the dispatch cycle. This represents the active power vector of wind power during the dispatch cycle; This represents the active power vector of the line during the scheduling cycle. This represents the charging power vector of energy storage during the scheduling cycle. This represents the discharge power vector of the energy storage during the scheduling cycle. This represents the reactive power vector of the gas turbine during the scheduling cycle. This represents the reactive power vector of photovoltaic power during the dispatch cycle. This represents the reactive power vector of wind power during the dispatch cycle. This represents the reactive power vector of the line during the scheduling cycle. This represents the node voltage magnitude vector; The node voltage phase angle vector; For energy storage horizontal vector; For the coupled node External active power vector; For the coupled node External reactive power vector; As a cost vector, .

[0025] Furthermore, when using a multi-parameter programming method to solve the multi-parameter programming model to obtain the critical domain of the gateway node and the piecewise analytical function of the scheduling cost, the specific steps include: Construct the Lagrangian function corresponding to the compact form multi-parameter programming model, and calculate the first-order KKT conditions of the compact form multi-parameter programming model based on the Lagrangian function; According to the first-order KKT conditions, when the random variable of the power parameter at the gate node... When variations occur within the feasible region, a corresponding set of constraints is determined, which includes active constraints and inactive constraints. The optimal solution expression is calculated based on the active constraints of the constraint set. The optimal solution expression is then substituted into the corresponding inactive constraints to calculate the corresponding critical domain. Finally, the optimal solution expression is substituted into the objective function of the compact multi-parameter programming model to calculate the piecewise analytical function of the scheduling cost with respect to the power of the gateway node.

[0026] Furthermore, the optimal solution is expressed as follows:

[0027] In the formula: , Indicates the first n Group constraints X , Z The values ​​that a variable can take; Represents the coefficient matrix; This represents a constant vector.

[0028] Furthermore, when calculating the corresponding critical region, the mathematical expression is as follows:

[0029] In the formula: Indicates the first n One critical region; Represents the coefficient matrix; This represents a constant vector.

[0030] Furthermore, the mathematical expression of the piecewise analytic function is as follows:

[0031] In the formula: Indicates the first t Time period, the n Cost function within a critical region; , For coefficients; This represents a constant related to the microgrid.

[0032] Compared with the prior art, the advantages of the present invention are as follows: 1. This invention decouples the energy storage constraints of a microgrid operation model in time, constructs a virtual queue of energy storage devices, relaxes the decoupled energy storage constraints into the stability problem of the virtual queue, and uses the Lyapunov function to solve the stability optimization problem of the virtual queue. This can effectively decompose the time-domain coupling constraints of energy storage devices, transform the complex dynamic optimization problem into the stability problem of the virtual queue, significantly improve the model solution efficiency and computational real-time performance, and provide support for the rapid scheduling of microgrids with a high proportion of energy storage.

[0033] 2. This invention uses multi-parameter programming theory to express the microgrid operation model in a compact form with the power of the gateway nodes as parameters. Based on this, the piecewise analytical function and critical domain of the scheduling cost are obtained, realizing an explicit and structured description of the feasible operating area and economy of the microgrid, providing clear and reliable boundary information for distribution network scheduling.

[0034] 3. The scheduling domain obtained by this invention has a clear analytical expression and geometric visualization features, which facilitates dispatchers' intuitive understanding of the operating costs and constraint boundaries of the microgrid at different power exchange points, and supports the allocation of power across the grid. Rapid decision-making and robust scheduling in microgrid collaborative optimization.

[0035] 4. In the process of constructing the scheduling domain, this invention only needs to perform planning and information interaction based on the boundary parameter of the switching power of the gateway nodes. It does not require uploading sensitive data such as the detailed topology, equipment parameters and real-time operating status of the microgrid, which effectively protects the data privacy and information security of the microgrid operator and meets the development needs of autonomous management of distributed energy. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0037] Figure 2 This is a schematic diagram of a 9-node microgrid.

[0038] Figure 3 This refers to the active and reactive power operation domain of the micro-gateway interface node.

[0039] Figure 4 This is the scheduling domain for the micro-gateway interface node. Detailed Implementation

[0040] The present invention will be further described below with reference to the accompanying drawings and specific preferred embodiments, but this does not limit the scope of protection of the present invention.

[0041] This embodiment proposes a method for characterizing the scheduling domain of distribution-microgrid gateway nodes based on multi-parameter programming. It analytically represents the complex operational constraints within the microgrid and the power exchange capability of the gateway nodes, enabling rapid and accurate characterization of the microgrid's dispatchable range and its economic viability, thereby improving the distribution-microgrid scheduling efficiency. The flexibility and economy of microgrid collaborative optimization scheduling promote the efficient consumption of renewable energy and reduce the overall operating cost of the system.

[0042] like Figure 1 As shown, the method includes the following steps: S1. To minimize the operating cost of the microgrid, a microgrid operation model is established, which includes power flow constraints, flexibility resource constraints, power balance constraints, and exchange power constraints. Specifically, this is achieved by obtaining the operating parameters of photovoltaic, wind power, energy storage, and gas turbines in the microgrid, as well as the grid topology parameters. Based on these parameters, a microgrid operation model is established, aiming to minimize operating costs and including power flow constraints, flexibility resource constraints, power balance constraints, and exchange power constraints between the common coupling point and the upper-level distribution network. S2. To address the runtime coupling problem of energy storage devices, a decoupled energy storage stability model based on Lyapunov functions and virtual queues is constructed, transforming energy storage constraints into a virtual queue stability optimization problem. Specifically, time decoupling is performed on the energy storage constraints within the flexibility resource constraints of the microgrid operation model. Then, a virtual queue of energy storage devices is constructed, relaxing the decoupled energy storage constraints into a virtual queue stability problem. The stability optimization problem of the virtual queue is solved using Lyapunov functions, and the solution is added to the objective function of the microgrid operation model to obtain the decoupled microgrid operation model. S3. The decoupled microgrid operation model is expressed as a compact multi-parameter programming model with the power of the gateway nodes as the parameter; S4. Use the multi-parameter programming method to solve the compact multi-parameter programming model to obtain the critical domain of the gate node and the piecewise analytical function of the scheduling cost. S5. Based on the piecewise analytical function, the scheduling domain of the microgrid gateway node is characterized within the parameter feasible domain and visualized using MATLAB. Specifically, according to the piecewise analytical function, a visualization of the scheduling domain of the microgrid gateway node is drawn within the parameter feasible domain. The visualization shows the joint feasible domain boundary of the active and reactive power of the gateway node, and the corresponding operating cost of each point within the feasible domain is represented by a color gradient, providing decision support for the distribution network dispatch center to optimize the distribution-microgrid coordinated dispatch strategy.

[0043] The following provides a detailed explanation of each step.

[0044] In step S1 of this embodiment, the operating parameters of photovoltaic, wind power, energy storage, and gas turbine in the microgrid specifically include: the rated capacity, maximum power factor angle, and maximum active power output at time t of the photovoltaic equipment; the rated capacity, maximum power factor angle, and maximum predicted output at time t of the wind power equipment; the maximum charging and discharging power, charging and discharging efficiency, upper and lower limits of state of charge, and initial / final state of charge of the energy storage equipment; and the upper and lower limits of active power output and reactive power output of the gas turbine. The obtained power grid topology parameters include: the conductance, susceptance, and capacity of each branch.

[0045] Based on the above parameters, the power flow constraints, flexibility resource constraints, power balance constraints, and switching power constraints, along with their objective functions, in the microgrid operation model are expressed as follows: (1) Power flow constraints of microgrids Microgrids typically operate in a radial or weak loop configuration, and their network structure is similar to that of medium-voltage distribution networks. Therefore, the power flow balance constraints they follow are mathematically consistent with those of distribution networks. The specific model is as follows: 1. Meritorious trend: (1) In the formula: For microgrids in t Time Branch ij The positive trend between them; branch road ij The electrical conductance values ​​between; branch road ij The susceptance values ​​between; and They are respectively t Time Node i Voltage amplitude and phase angle at the point; and They are respectively t Time Nodej The voltage amplitude and phase angle at that point.

[0046] 2. Reactive current flow: (2) In the formula: Indicates the microgrid at time t branch road ij The reactive current between them.

[0047] 3. Branch flow constraints (3) In the formula: for t Time Branch ij The capacity of the function; cos represents the cosine function; sin represents the sine function; Pi is a constant. G The number of segments for piecewise linearization; g This is a segmented index.

[0048] 4. Node voltage amplitude constraints: (4) In the formula: and These are the maximum and minimum values ​​of the voltage amplitude, respectively. 5. Node voltage phase constraint: (5) In the formula: and These are the maximum and minimum phase angles, respectively.

[0049] (2) Resource constraints on microgrid flexibility Microgrids integrate various flexible resources such as photovoltaics, wind power, energy storage, and gas turbines. Through the coordinated operation of various devices, they can significantly improve the local absorption capacity of distributed energy resources. Internally, they mainly include four resource types: distributed energy storage, gas turbines, photovoltaic power generation, and wind power generation. Their models are described below: 1. Constraints of Distributed Energy Storage: Energy storage systems are important flexible regulation units in microgrids, possessing the ability for bidirectional power flow and energy time shifting. Their operation must meet multiple physical constraints, including charging and discharging power and operational requirements.

[0050] Energy storage discharge power constraint: The charging and discharging power of an energy storage system is limited by the capacity of its converter. The discharge power should not exceed the maximum permissible discharge power, as stated below: (6) In the formula: For microgrid nodes i Placet Energy storage and discharge power at all times; For nodes i The maximum charging and discharging power of the energy storage.

[0051] Energy storage charging power constraints: The charging power should also not exceed the maximum allowable charging power, as stated below: (7) In the formula: For microgrid nodes i Place t Energy storage and charging power at all times.

[0052] Energy storage operation constraints: These describe the continuity of the energy storage state of charge over time, and its variation depends on the charging and discharging power and efficiency, as stated below: (8) In the formula: For nodes i Energy storage t Constant energy storage state of charge; For nodes i Energy storage t+ State of charge of energy storage at moment 1; For nodes i Energy storage charging and discharging efficiency; For time intervals.

[0053] Energy storage capacity constraints: These limit the upper and lower limits of the energy storage state of charge, preventing overcharging or over-discharging and ensuring equipment lifespan and safety.

[0054] (9) (10) In the formula: and They are nodes i The maximum and minimum values ​​of the energy storage state of charge; and They are nodes i The initial and final states of energy storage.

[0055] 2. Gas Turbine Constraints: As a stable and controllable power generation unit in a microgrid, the gas turbine plays a crucial role in maintaining system power balance and providing rapid backup capacity. Its model is described below: (11) (12) In the formula: For nodes i Place t The active power of the gas turbine at any given time; and They are nodes i Place t The upper and lower limits of gas turbine output at all times; For nodes i Place t The gas turbine's reactive power output is constantly being reduced. and They are nodes i Place t The upper and lower limits of reactive power output of the gas turbine at all times.

[0056] 3. Constraints of Photovoltaic Power Generation: Photovoltaic power generation units are typical intermittent renewable energy sources in microgrids, and their output is directly affected by solar irradiance. Modeling needs to consider their maximum available power limit and reactive power regulation capability, and the model is expressed as follows: (13) (14) In the formula: , For nodes i Place t The active power output and reactive power compensation of photovoltaic power generation equipment at all times; For nodes i The maximum power factor angle of the PV equipment; For nodes i Place t The maximum active power output of the PV device at any given time.

[0057] 4. Wind Power Constraints: Wind power is also an important fluctuating renewable energy source. Its output depends on wind speed and exhibits significant temporal volatility and uncertainty. Therefore, the range of its active and reactive power output needs to be considered. The model is expressed as follows: (15) (16) In the formula: , For nodes i The active power output and reactive power compensation of the wind power equipment; For nodes i The maximum power factor angle of the wind turbine; For nodes i Place t The maximum predicted output of wind power equipment at any given time.

[0058] (3) Power balance constraint In the dynamic operation and collaborative optimization scheduling of microgrids, power balance constraints are the most fundamental physical constraints. They are a prerequisite for ensuring the safe, reliable, and economical operation of the system. These constraints are as follows: (17) (18) In the formula: For nodes i Adjacent node index; For nodes i Place t Active load is always available; For nodes i Place t Constant reactive load.

[0059] (4) Switching power constraint As an important component of the power distribution system, microgrids achieve bidirectional power interaction with the upper-level distribution network through a point of common coupling (PCC). This interaction process can be described by the following mathematical model: (19) (20) In the formula: for t The active power of the common coupling current at all times; for t The active power of the common coupling current at all times; , For the branch connected to PCC t The constant ebb and flow of merit and demerit; This is the set of indices of nodes connected to the PCC node.

[0060] (5) Objective function: The microgrid aims to minimize operating costs, and its model is expressed as follows: (twenty one) In the formula: This represents the set of nodes containing gas turbines; This represents the set of nodes equipped with photovoltaic devices. This indicates a collection of wind power equipment; This represents the set of nodes with energy storage. This represents the total cost of the microgrid; This represents the cost per unit of active power of a gas turbine. This represents the unit reactive power cost of a gas turbine. Cost per unit of active power for photovoltaic equipment; Cost per unit of reactive power for photovoltaic equipment; Cost per unit of active power of wind power equipment; Unit reactive power cost of wind power equipment; Cost of charging energy storage; Cost of energy storage and discharge.

[0061] In this embodiment, step S2 transforms the energy storage constraint into a virtual queue stability optimization problem. The specific process is as follows: S2.1. Decouple the energy storage constraint in the flexibility resource constraint of the microgrid operation model from time constraints.

[0062] Since the microgrid operation model contains energy storage devices, there is temporal coupling, so it is necessary to decouple the energy storage constraints in time. Specifically, the energy storage operation constraints in formula (8) are decoupled according to a specified time interval ( t From 0 to T -1) Summate the results and substitute them into the constraint formula (10) for energy storage capacity. The decoupled energy storage constraint is obtained, and its mathematical expression is as follows: (twenty two) In the formula: T -1 represents the end time of the specified time interval.

[0063] S2.2 Construct a virtual queue for energy storage devices. The mathematical expression is as follows: (twenty three) In the formula: Represents a node i A virtual queue for energy storage devices; This represents an auxiliary variable used to construct the virtual queue.

[0064] in: (twenty four) Assume the number of batteries is n Then all queues can be represented as: (25) In the formula: express t A virtual queue of all energy storage devices at any given time.

[0065] S2.3 Relax the decoupled energy storage constraints into a stability problem of a virtual queue, and use the Lyapunov function to solve the stability optimization problem of the virtual queue.

[0066] Based on the concept of virtual queues, formula (22) can be relaxed to a net flow value of 0 for the virtual queue during the scheduling period, which is equivalent to a virtual queue. The stability problem of the virtual queue is then addressed. The stability problem of the virtual queue is then solved using the Lyapunov function. The Lyapunov function is used to characterize the crowding level of the virtual queue, and then the stability model of the virtual queue is solved. Virtual queue Lyapunov functions for: (26) The difference form of formula (26), i.e., the Lyapunov-Drift function, is: This indicates the difference in congestion levels between adjacent time periods. To ensure the stability of the virtual queue, The size should be as small as possible; thus, the stability problem of the virtual queue is transformed into... The problem is to minimize this problem. Furthermore, according to Lyapunov optimization theory, we can obtain: (27) In formula (27), This is a fixed value related to the maximum charge and discharge power of the energy storage battery. Therefore, the stability problem of the virtual queue can be further transformed into the following minimization problem: (28) By adding the solution result of formula (28) to the objective function of the microgrid operation model, the decoupled microgrid operation model can be obtained. This can effectively decompose the time-domain coupling constraints of energy storage devices, transform the complex dynamic optimization problem into the stability problem of the virtual queue, significantly improve the model solution efficiency and computational real-time performance, and provide support for the rapid scheduling of microgrids with a high proportion of energy storage.

[0067] This embodiment expresses the microgrid operation model in a compact form with the power of the gateway nodes as a parameter through steps S3 and S4. Based on this, the piecewise analytical function and critical domain of the scheduling cost are obtained, realizing an explicit and structured description of the feasible operating area and economy of the microgrid, and providing clear and reliable boundary information for distribution network scheduling.

[0068] The specific process of step S3 in this embodiment is as follows: S3.1 According to formula (28), the mathematical expression of the objective function of the decoupled microgrid operation model is as follows: (29) In the formula: The objective for optimizing the stability of the virtual queue is obtained by using the Lyapunov function to solve the stability optimization problem of the virtual queue.

[0069] S3.2. Rewrite the objective function of formula (29). The mathematical expression of the rewritten objective function is as follows: (30) (31) In the formula: For microgrids in t The cost of time; S3.3. Based on the rewritten objective function, establish a compact multi-parameter programming model, the mathematical expression of which is as follows: (32) (33) (34) (35) in C , F They are respectively

[0070] matrix D , E They are respectively

[0071] In the above matrix, , , , , , , , , , They are represented as follows:

[0072]

[0073]

[0074]

[0075]

[0076]

[0077]

[0078]

[0079]

[0080]

[0081]

[0082] In the formula: superscript T Indicates transpose; It is a 24-dimensional unit row vector; C , , This is a coefficient matrix related to microgrid parameters; A coefficient vector related to microgrid parameters; For cost vectors; For decision variables; Let be the random variable in the multi-parameter programming; where It is a 24-dimensional zero matrix; It is a 24-dimensional zero vector; It is a 24-dimensional identity diagonal matrix; It is a 24-dimensional unit vector; in the above matrix, ij This represents the line, and G represents the number of segments. i The symbols represent line nodes, Ess represents energy storage, GT represents gas turbine, PV represents photovoltaic, W represents wind power, and PCC represents coupling point. In this embodiment, the power parameters of the gateway node are random variables, including active power. and reactive power ; This represents the active power vector of the gas turbine during the scheduling cycle; This represents the active power vector of photovoltaic power during the dispatch cycle. This represents the active power vector of wind power during the dispatch cycle; This represents the active power vector of the line during the scheduling cycle. This represents the charging power vector of energy storage during the scheduling cycle. This represents the discharge power vector of the energy storage during the scheduling cycle. This represents the reactive power vector of the gas turbine during the scheduling cycle. This represents the reactive power vector of photovoltaic power during the dispatch cycle. This represents the reactive power vector of wind power during the dispatch cycle. This represents the reactive power vector of the line during the scheduling cycle. This represents the node voltage magnitude vector; The node voltage phase angle vector; For energy storage horizontal vector; For the coupled node External active power vector; For the coupled node External reactive power vector; As a cost vector, .

[0083] The specific process of step S4 in this embodiment is as follows: S4.1 Constructing the Lagrangian function corresponding to the compact form of the multi-parameter programming model of formula (32) l as follows: (36) In the formula: is a Lagrange multiplier vector.

[0084] S4.2 Based on the Lagrangian function mentioned above, the first-order Karush-Kuhn-Tucker (KKT) conditions for the compact multi-parameter programming model are calculated as follows: (37) S4.3 According to the first-order KKT conditions, when the random variable of the power parameter at the checkpoint node... When the parameters vary within the feasible region, the corresponding constraint set is determined. Specifically, when the parameters... When varying within the feasible region, there are a total of N Let the set of constraints be set 1. n The optimal solution corresponding to the set of constraints is From the original feasibility conditions, we can obtain the first... n The constraint set of the group is: (38) (39) In the formula: (38) is the active constraint; (39) is the inactive constraint; , , Represents the coefficient matrix of the effective constraints; Represents the constant vector of the effective constraints; , Indicates the first n Group constraints X , Z The values ​​that a variable can take; , , Represents the coefficient matrix that does not have any constraints; Represents a constant vector that has no binding constraints; S4.4, Solve for the optimal solution. n The process of finding the optimal solution under the set of constraints is as follows: First, calculate the corresponding optimal solution expression based on the effective constraints of the constraint set. Specifically, rewrite formula (38) as follows: (40) Solving the above equation yields: (41) After rearranging the above equation, we obtain the expression for the optimal solution: (42) In the formula: The coefficient matrix after rearranging expression (41); The constant vector after simplification of expression (41); Then, substituting the optimal solution expression into the corresponding ineffective constraints, the corresponding critical region is calculated. Specifically, substituting formula (42) into formula (39) yields: (43) The critical region can then be represented as (44) In the formula: Indicates the first n One critical region; This represents the coefficient matrix after simplification of formula (43); This represents the constant vector after simplification of formula (43).

[0085] Simultaneously, substituting the optimal solution expression into the objective function of the compact multi-parameter programming model, the piecewise analytical function of scheduling cost with respect to the power of the gateway nodes is calculated. The piecewise analytical mapping function of cost, obtained from formula (42), is expressed as: (45) In the formula: Indicates the first t Time period, the n Cost function within a critical region; , For coefficients; This represents a constant and is related to microgrids.

[0086] In step S5 of this embodiment, when drawing a visualization of the micro-gateway interface node scheduling domain within the parameter feasible domain according to the piecewise analytical function, specifically based on the critical domain in the piecewise analytical function... With system operating parameters Based on the relationship, within the parameter feasible domain, the joint feasible boundary of the active power and reactive power of the micro-gateway port node is determined by traversal calculation; based on the joint feasible boundary, the graphics drawing function is called in the MATLAB environment to generate and display the visualization graph of the scheduling domain of the micro-gateway port node.

[0087] In this embodiment, the visualization graphic presents the joint feasible domain boundary of the active and reactive power of the gateway node on the PQ plane, and the corresponding operating cost of each point within the feasible operating domain is represented by a color gradient, such as... Figure 4As shown, this results in a scheduling domain with explicit analytical expressions and geometric visualization features, facilitating dispatchers' intuitive understanding of the microgrid's operating costs and constraint boundaries at different power exchange points, and supporting the allocation of power across the grid. Rapid decision-making and robust scheduling in microgrid collaborative optimization.

[0088] Specifically, the distribution network dispatch center can identify the operating cost of the current gate power exchange point on the PQ plane based on the visual dispatch domain in the visualization graphic and the color gradient. When the distribution network needs the microgrid to provide upward power support, the dispatch center selects the power exchange point that meets the power demand of the distribution network and has the lowest operating cost (the lowest cost area in the color gradient) within the boundary of the feasible domain, generates a gate node power exchange plan, and realizes economical coordinated dispatch of distribution and microgrid.

[0089] The effects of the method in this embodiment will be illustrated below with specific examples.

[0090] For example Figure 2 Taking the 9-node microgrid shown as an example, power is positive when the flow from the microgrid to the distribution network is positive. The microgrid consists of 2 energy storage devices, 3 gas turbines, 2 wind turbines, 2 photovoltaic power generation devices, and their loads, with node 1 connected to the distribution network. Relevant parameters are shown in Tables 1-3: Table 1 PV parameters

[0091] Table 2 Energy Storage Parameters

[0092] Table 3 Wind Power Parameters

[0093] Figure 3 This paper demonstrates the active and reactive power joint feasible operating domain of a microgrid gateway node generated using the method described in this embodiment. This feasible operating domain is presented as a convex polygon region on the PQ plane, its boundary determined by various physical constraints within the microgrid, including energy storage charging and discharging power limits, gas turbine and renewable energy output ranges, line power flow capacity, and node voltage safety constraints. This graphic visually defines the set of all possible points where the microgrid can provide power to the upper-level distribution network without compromising its own operational safety, providing a clear technically feasible boundary for system scheduling.

[0094] exist Figure 3 Based on the feasible domain, Figure 4An economic dimension was further introduced, forming a gate node scheduling domain. This diagram clearly represents the minimum operating cost corresponding to each point (P, Q) within the operating domain using color gradients. As can be seen in the diagram, in areas with abundant wind and solar resources (the blue area), the system operating cost is low; while in areas near the operating boundary or requiring the use of high-cost backup resources, such as gas turbines, the cost increases significantly. This scheduling domain achieves a unified visual representation of technical feasibility and economic operability.

[0095] In summary, this invention proposes a method for characterizing the scheduling domain of a distribution-microgrid gateway interface node based on multi-parameter programming. First, aiming to minimize microgrid operating costs, a microgrid operation model is established, incorporating power flow constraints, flexibility resource constraints, power balance constraints, and switching power constraints. Second, addressing the runtime coupling problem of energy storage devices, a decoupled energy storage stability model based on Lyapunov functions and virtual queues is constructed, transforming energy storage constraints into a virtual queue stability optimization problem. Then, the decoupled microgrid operation model is expressed as a compact multi-parameter programming model with interface node power as a parameter. The compact model is solved using multi-parameter programming to obtain the critical domain of the interface node and a piecewise analytical function of the scheduling cost. Finally, based on the piecewise analytical function, the microgrid gateway interface node scheduling domain is characterized within the parameter feasible region and visualized using MATLAB. This method efficiently and accurately transforms the complex microgrid operation optimization problem into a piecewise cost function and critical domain with a clear analytical form, using interface node power as a parameter, and ultimately visualizes it as the operating domain and scheduling domain. This provides clear and quantitative decision support for the distribution network dispatch center, enabling it to optimize the network-wide collaborative dispatch strategy based on cost information while taking into account the internal constraints and privacy of the microgrid, thereby improving the renewable energy absorption capacity and the overall economic efficiency of the system.

[0096] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0097] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for characterizing the scheduling domain of a distribution-microgrid gateway interface node based on multi-parameter programming, characterized in that, Includes the following steps: The operating parameters of photovoltaic, wind power, energy storage and gas turbine in the microgrid and the grid topology parameters are obtained. A microgrid operation model is established based on the operating parameters and grid topology parameters. The microgrid operation model aims to minimize the operating cost and includes microgrid power flow constraints, flexibility resource constraints, power balance constraints and power exchange constraints between the common coupling point and the upper-level distribution network. The energy storage constraint in the flexibility resource constraint of the microgrid operation model is decoupled in time; a virtual queue of energy storage devices is constructed, and the decoupled energy storage constraint is relaxed into the stability problem of the virtual queue. The stability optimization problem of the virtual queue is solved using the Lyapunov function, and the solution is added to the objective function of the microgrid operation model to obtain the decoupled microgrid operation model. The decoupled microgrid operation model is expressed as a compact multi-parameter programming model with the power of the gateway node as a parameter. The multi-parameter programming method is used to solve the multi-parameter programming model to obtain the critical domain of the gateway node and the piecewise analytical function of the scheduling cost. Based on the piecewise analytical function, a visualization of the scheduling domain of the microgrid gateway node is drawn within the parameter feasible domain. The visualization shows the joint feasible domain boundary of the active and reactive power of the gateway node, and the corresponding operating cost of each point in the feasible domain is represented by the color gradient, providing decision support for the distribution network dispatch center to optimize the distribution-microgrid coordinated dispatch strategy.

2. The method for characterizing the scheduling domain of a distribution-micro gateway interface node based on multi-parameter planning according to claim 1, characterized in that, The operating parameters include the maximum charge / discharge power, charge / discharge efficiency, upper and lower limits of state of charge, initial charge, and final state of charge of the energy storage device; the flexibility resource constraints include distributed energy storage constraints, which include: The energy storage discharge power constraint is mathematically expressed as follows: In the formula: For microgrid nodes i Place t Energy storage and discharge power at all times; For nodes i Maximum energy storage charging and discharging power; The energy storage charging power constraint is expressed mathematically as follows: In the formula: For microgrid nodes i Place t Real-time energy storage and charging power; Energy storage operation constraints, mathematically expressed as follows: In the formula: For nodes i Energy storage t Constant energy storage state of charge; For nodes i Energy storage t+ State of charge of energy storage at moment 1; For nodes i Energy storage charging and discharging efficiency; For time intervals; The energy storage capacity constraint is expressed mathematically as follows: In the formula: and They are nodes i The maximum and minimum values ​​of the energy storage state of charge; and They are nodes i The initial and final states of energy storage.

3. The method for characterizing the scheduling domain of a distribution-micro gateway interface node based on multi-parameter planning according to claim 2, characterized in that, When decoupling the energy storage constraint within the flexibility resource constraints of the microgrid operation model over time, the energy storage operation constraint is summed over a specified time interval and substituted into the energy storage capacity constraint. The constraints are then used to obtain the decoupled energy storage constraints, the mathematical expression of which is as follows: In the formula: T -1 represents the end time of the specified time interval.

4. The method for characterizing the scheduling domain of a distribution-micro gateway interface node based on multi-parameter planning according to claim 3, characterized in that, The mathematical expression for constructing a virtual queue of energy storage devices is as follows: In the formula: Represents a node i A virtual queue for energy storage devices; Let represent the auxiliary variables used to construct the virtual queue, and the auxiliary variables satisfy . .

5. The method for characterizing the scheduling domain of a distribution-micro gateway interface node based on multi-parameter planning according to claim 4, characterized in that, The mathematical expression for the objective function of the decoupled microgrid operation model is as follows: In the formula: This represents the set of nodes containing gas turbines; This represents the set of nodes equipped with photovoltaic devices. This indicates a collection of wind power equipment; This represents the set of nodes with energy storage. This represents the total cost of the microgrid; This represents the cost per unit of active power of a gas turbine. This represents the unit reactive power cost of a gas turbine. Cost per unit of active power for photovoltaic equipment; Cost per unit of reactive power for photovoltaic equipment; Cost per unit of active power of wind power equipment; Unit reactive power cost of wind power equipment; Cost of charging energy storage; Cost of energy storage and discharge; The objective for optimizing the stability of the virtual queue is obtained by using the Lyapunov function to solve the stability optimization problem of the virtual queue.

6. The method for characterizing the scheduling domain of a distribution-micro gateway interface node based on multi-parameter planning according to claim 5, characterized in that, When expressing the decoupled microgrid operation model as a compact multi-parameter programming model with gateway node power as a parameter, it includes: The objective function is rewritten, and its mathematical expression is as follows: In the formula: For microgrids in t The cost of time; Based on the rewritten objective function, a compact multi-parameter programming model is established, with the following mathematical expression: In the formula: superscript T Indicates transpose; Unit row vector; C , , This is a coefficient matrix related to microgrid parameters; A coefficient vector related to microgrid parameters; For cost vectors; For decision variables; These are random variables representing the power parameters of the gateway nodes; This represents the active power vector of the gas turbine during the scheduling cycle; This represents the active power vector of photovoltaic power during the dispatch cycle. This represents the active power vector of wind power during the dispatch cycle; This represents the active power vector of the line during the scheduling cycle. This represents the charging power vector of energy storage during the scheduling cycle. This represents the discharge power vector of the energy storage during the scheduling cycle. This represents the reactive power vector of the gas turbine during the scheduling cycle. This represents the reactive power vector of photovoltaic power during the dispatch cycle. This represents the reactive power vector of wind power during the dispatch cycle. This represents the reactive power vector of the line during the scheduling cycle. This represents the node voltage magnitude vector; The node voltage phase angle vector; For energy storage horizontal vector; For the coupled node External active power vector; For the coupled node External reactive power vector; As a cost vector, .

7. The method for characterizing the scheduling domain of a distribution-micro gateway interface node based on multi-parameter planning according to claim 6, characterized in that, When using a multi-parameter programming method to solve the multi-parameter programming model to obtain the critical domain of the gateway node and the piecewise analytical function of the scheduling cost, the specific steps include: Construct the Lagrangian function corresponding to the compact form multi-parameter programming model, and calculate the first-order KKT conditions of the compact form multi-parameter programming model based on the Lagrangian function; According to the first-order KKT conditions, when the random variable of the power parameter at the gate node... When variations occur within the feasible region, a corresponding set of constraints is determined, which includes active constraints and inactive constraints. The optimal solution expression is calculated based on the active constraints of the constraint set. The optimal solution expression is then substituted into the corresponding inactive constraints to calculate the corresponding critical domain. Finally, the optimal solution expression is substituted into the objective function of the compact multi-parameter programming model to calculate the piecewise analytical function of the scheduling cost with respect to the power of the gateway node.

8. The method for characterizing the scheduling domain of a distribution-micro gateway interface node based on multi-parameter planning according to claim 7, characterized in that, The optimal solution expression is as follows: In the formula: , Indicates the first n Group constraints X , Z The values ​​that a variable can take; Represents the coefficient matrix; This represents a constant vector.

9. The method for characterizing the scheduling domain of a distribution-micro gateway interface node based on multi-parameter planning according to claim 8, characterized in that, When the corresponding critical region is calculated, the mathematical expression is as follows: In the formula: Indicates the first n One critical region; Represents the coefficient matrix; This represents a constant vector.

10. The method for characterizing the scheduling domain of a distribution-micro gateway interface node based on multi-parameter planning according to claim 9, characterized in that, The mathematical expression for the piecewise analytic function is as follows: In the formula: Indicates the first t Time period, the n Cost function within a critical region; , For coefficients; This represents a constant related to the microgrid.

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