Aggregated scheduling method and system based on flexible resources of power distribution network operation security domain

Abstract

The application discloses an aggregated scheduling method and system based on flexible resources of power distribution network operation security domain, and belongs to the technical field of power system energy management. The method comprises the following steps: clustering transferable load SL and delayable load DL to form an equivalent flexible resource aggregation model; adopting a security domain analysis method, constructing a voltage stability security domain through the relationship between node voltage and root node voltage, constructing a thermal stability security domain based on branch current upper limit, and obtaining a power distribution network operation security domain model describing the power distribution network under the condition of not violating thermal and voltage constraints; establishing a flexible resource aggregation objective function, and fusing the power distribution network operation security domain model and mixed integer nonlinear optimization to construct a flexible resource operation scheduling model; solving the flexible resource operation scheduling model, and performing flexible resource aggregation scheduling based on the solving result. The method realizes the aggregation and scheduling of flexible resources based on the security domain, and realizes the effective scheduling of numerous small-scale distributed resource clusters in the power distribution network.

Description

Technical Field

[0001] This application relates to a method and system for aggregated scheduling based on flexible resources in the safe operating domain of a distribution network, belonging to the field of power system energy management technology. Background Technology

[0002] With the widespread integration of renewable energy sources such as wind and solar power, power systems face new challenges. Traditional power generation requires rapid output adjustments during peak demand periods, leading to a significant increase in costs. Demand-side management (DSM) serves as a solution, adjusting the demand curve to match supply and demand fluctuations. Studies show that reducing load by 5% during just 1% of peak hours could save approximately $3 billion in DSM-related costs annually. While industrial load is considered a significant source of demand-side flexibility, the consumption potential of the residential and small service sectors remains largely untapped. Residential load accounts for a high proportion in most countries, offering advantages such as high cost efficiency and low investment requirements.

[0003] However, when residential loads (typically smaller in capacity) actively participate in system dispatch, the impact of individual loads is often limited, resulting in a smaller impact on individual interests. To effectively utilize the dispatch potential of these loads, load aggregation becomes a key strategy. Load aggregators, acting as a bridge between distribution network operators and small consumers, encourage consumer participation in demand response (DR) programs through incentives, thereby achieving effective management and utilization of these small-scale resources. Summary of the Invention

[0004] The purpose of this application is to address the shortcomings of the aforementioned background technology by proposing an aggregation scheduling method and system based on flexible resources in the distribution network operation safety domain. This method realizes the aggregation scheduling of flexible resources based on the safety domain, thereby achieving effective scheduling of numerous small-scale distributed resource clusters in the distribution network.

[0005] To achieve the above objectives, the technical solution adopted in this application is as follows:

[0006] Firstly, this application provides an aggregation scheduling method based on flexible resources in the distribution network operation safety domain, including:

[0007] By collaborating with the home energy management system (HEMS) through the load aggregator (LA), transferable loads (SL) and deferred loads (DL) are clustered to form an equivalent flexible resource aggregation model.

[0008] In the equivalent flexible resource aggregation model, for SL, a load model is constructed and cluster parameters are extracted based on available time grouping modeling; for DL, an interruptible discrete model is established based on the maximum available time constraint.

[0009] The security domain analysis method is adopted to construct the voltage stability security domain by the relationship between node voltage and root node voltage, and the thermal stability security domain is constructed based on the upper limit of branch current, so as to obtain the security domain model of distribution network operation under the condition of not violating thermal and voltage constraints.

[0010] A flexible resource aggregation objective function is established, and a flexible resource operation scheduling model is constructed by integrating the distribution network operation safety domain model and mixed integer nonlinear optimization. Based on the load model, cluster parameters and interruptible discrete model, the flexible resource operation scheduling model is solved, and flexible resource aggregation scheduling is performed based on the solution results.

[0011] As a further improvement to this application, the transferable load includes dishwashers, microwave ovens, vacuum cleaners, dryers, sensors, and irons;

[0012] Delayable loads include electric vehicles in grid-to-vehicle mode with discrete consumption levels.

[0013] As a further improvement to this application, the transferable load (SL) and the deferred load (DL) are clustered to form an equivalent flexible resource aggregation model, including:

[0014] Based on the k-means clustering algorithm, the LA performs aggregation on the information submitted by consumers and shares the aggregated demand data with the DSO.

[0015] As a further improvement to this application, the transferable load (SL) and the deferred load (DL) are clustered to form an equivalent flexible resource aggregation model, specifically including:

[0016] Load is partitioned based on similar available time.

[0017] After dividing the load, a profile is constructed based on the power demand of the load during the corresponding time intervals of its operating time. X i The profile has the following attributes: available time, power requirements, operating time requirements, and start-stop time;

[0018] The k-means clustering algorithm is used to aggregate the profiles to obtain a clustering result set C, such that | C |<| I |, where I is the total number of transferable loads, where the k-means clustering algorithm starts with an arbitrary set of centroids, selects a seed using k-means++, and clusters around the seed; at any given time, let D ( X i ) indicates from X i To the nearest centroid that has already been selected X c The shortest distance.

[0019] As a further improvement to this application, the load model for the transferable load SL is as follows:

[0020]

[0021]

[0022]

[0023] In the formula: For the first i A transferable load in time t Rated power; For the first i The start and stop times of each transferable load; u i,t Indicates the first i A transferable load in time t The state; u i,t-1 Indicates the first i A transferable load in time t The state of -1; Indicates the first i DR quantity of each transferable load For working hours, These are the behavioral attributes corresponding to availability. i ∈1,2,...,I; It is a universal quantifier symbol in mathematics, meaning it applies to any one of all;

[0024] For the set of numbers of SL The aggregate power of each load is equal to the sum of the power vectors of all loads in the set. ;

[0025]

[0026]

[0027]

[0028] In the formula: For aggregated power vectors, It is a power vector; Let c be the set of power characteristics of the c-th mobile cluster. Let be the aggregate power of the c-th cluster at time t. Let be the power of the i-th load at time t. The total operating time constraint for the i-th load is... The first cThe start and stop times of each transferable load; Let be the total energy requirement of the c-th cluster during its working hours; For the first c Energy requirements of a mobile cluster; Let represent the set of power models for the c-th mobile load cluster, which is affected by individual load constraints and aggregation parameters, and By a separate load vector composition, It is the independent variable of a function or a general representation; the superscript conforms to... This indicates the improved model, compared to the unimproved model. The parameters correspond to each other in meaning; parameters The total energy is limited by the lower and upper limits of power. The updated connection time is Improved model The power is limited to / Within, and obtain updated working hours, making , It is considered to be power-limited;

[0029] The load model for deferred load (DL) is as follows:

[0030]

[0031]

[0032] For a given time range t DR amount is Each load j and arrival time -Departure time ;

[0033] In the formula, Indicates deferred load j At any moment t power, Indicates deferred load j The rated power, i.e., the load j The maximum power that can be achieved Indicates deferred load j Total energy demand; number of load aggregates j ∈1,2,..., J , J A set of deferred loads, j ∈ J Indicates load j It is an element in the set of deferred loads.j The set of feasible regions is denoted as , Indicates load j The set of feasible regions, Is with load j Related vector forms of power and other related quantities, for a set n Aggregate power equals the set Power of all individual loads The sum of, and = ;

[0034]

[0035]

[0036] In the formula: Indicates the first d A deferred cluster at time t power, This represents the power in aggregate vector form corresponding to the set Ω. Indicates the first d The arrival time of the cluster can be delayed. Indicates the first d The departure time of the cluster can be postponed. D This represents a set of clusters that can be deferred. d ∈ D Represents a cluster d It is an element in the set of deferable clusters. For the first d The energy requirements of a cluster can be postponed; To improve the polymerization power after parameter adjustments; For clusters d Maximum power requirement during cluster arrival and departure times Indicates the improved version of the first d Each cluster at time t The polymerization power can only be 0 or j Multiple individual loads j At any moment t power, Let represent the set of power models for the d-th mobile load cluster, which is affected by individual load constraints and aggregation parameters. Show the set of aggregate power models after the d-th improved parameter.

[0037] As a further improvement to this application, the voltage stability safe domain is:

[0038]

[0039]

[0040] In the formula, The voltage magnitude at the root node; node s For nodes j With nodes k The node number of the first intersection of the upstream branch; , The equivalent impedance parameter represents the impedance from the root node to the node. i and nodes h The equivalent impedance on the common path; , The standardized sensitivity parameter for the upper boundary of voltage safety. For corresponding active power P Sensitivity, Corresponding active power Q Sensitivity; , The standardized sensitivity parameter for the upper boundary of voltage safety. For corresponding active power P Sensitivity, For corresponding active power Q Sensitivity; U M , U m These are the upper and lower limits of the voltage. P h For nodes h active power, Q h For nodes h reactive power, n The count related to the number of nodes participating in the voltage stability security domain calculation represents the number of nodes involved in the calculation. h From 1 to n To perform a summation operation, it involves n One related node;

[0041] Thermal stability safety domain is

[0042]

[0043]

[0044] In the formula, P k For nodes k active power, Q k For nodes k reactive power, U 0 represents the voltage amplitude at the root node. For a set of nodes, For thermal stability current limits; α k and β k These are power-current mapping coefficients, binary variables, used to determine nodes. k Does the load flow through the branch? ij .

[0045] As a further improvement to this application, a flexible resource aggregation objective function is established, and a flexible resource operation scheduling model is constructed by integrating the distribution network operation security domain model and mixed-integer nonlinear optimization, including:

[0046] Considering grid operation constraints and line limitations, a flexible resource aggregation objective function is constructed with the goal of minimizing total operating costs. The flexible resource aggregation objective function includes the energy production costs of all distributed generator units and the sum of costs associated with wind and solar power curtailment when maximum power generation is available.

[0047] The objective function for flexible resource aggregation includes a first constraint, which includes active and reactive power flow balance constraints, wind power reduction constraints, minimum-maximum available wind power constraints, hourly solar power reduction constraints, and available solar power generation constraints.

[0048] The specific objective function for flexible resource aggregation is as follows:

[0049]

[0050]

[0051]

[0052]

[0053]

[0054]

[0055] In the formula: t is the current scheduling time, for t Time of the first n Wind power wastage on the busbar for t Time of the first n The amount of solar power wasted by the busbar; for t The cost of responding to demand at any given moment; In order to be in t Time connection to the first n Active power generation of wind turbines via bus; In order to be in t Time connection to the firstn Active power generation of the bus-mounted solar generator; In order to be in t Always connected to the bus n Inelastic active power; In order to be in t Always connected to the bus n Inelastic reactive load; for t Time bus n and m The positive trend between them; for t Time bus n and m The reactive current between them; exist t Bus at time n The availability of wind energy at the location; exist t Bus at time n The availability of solar energy at the location; busbar n Total wind power generation capacity; busbar n Total solar power generation capacity at point b; b is the first A conventional distributed generator; For the first The unit power generation cost of a conventional distributed generator; For the first The active power generation of a conventional distributed generator; for Time of the first The unit cost of wind power curtailment at each busbar; for Time of the first The unit cost of solar power curtailment at each busbar; For demand response costs, for Time of the first The original fixed active power demand of the type of transferable load; for Time of the first Adjusted active power demand for shiftable loads; for Time of the first The original fixed active power demand that can reduce the load; : Time of the first Adjusted active power demand for shiftable loads; for The unit cost of load adjustment at any given time; T is the time set, C is the set of load types that can be shifted, and D is the set of load types that can be reduced; : Time of the first Adjusted active power demand for shiftable loads; : time t The reactive power generation of a conventional distributed generator; G is the set of conventional distributed generator types.

[0056] As a further improvement to this application, the flexible resource aggregation objective function also includes a second constraint, which includes: line flow rate, line limit, voltage amplitude and angle limit, and the operating range and ramp of the DG, specifically:

[0057]

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065] In the formula: No. c Each cluster t The start indicator of the interruptible period of time; For the first c Each cluster t The stop indicator for the interruptible period of time; The duration of the time interval; For the first Each cluster Interruptible status indicator at any given time; For the first Classes can reduce load in The time-reduction status indicator; For the first The baseline active power of each cluster; : No. The maximum number of interruptible events threshold for a single cluster; Let c be the set of available time for the c-th interruptible load cluster; Let be the interruptibility status indicator for the c-th cluster at time t-1; h is the time variable for summation. This is the status indicator of the c-th interruptible load cluster at time h; Let be the upper limit of the interruptible duration for the c-th cluster at time t. This is the state indicator for the c-th cluster at time t-1; The threshold for the maximum number of interruptible cycles for the c-th interruptible load cluster; This is the stop indicator for the interruptible period of the c-th interruptible load cluster at time h; The duration of the time interval. This represents the upper limit of the total energy reduction for the d-th type of load that can be reduced. For the set of available adjustment start times for the d-th type of load that can be reduced; This represents the set of available adjustment end times for the d-th type of load that can be reduced; This represents the maximum power reduction per time period for the load that can be reduced in category d.

[0066] As a further improvement to this application, flexible resource aggregation scheduling based on the solution results includes:

[0067] The scheduling is performed by the DSO to minimize the system operating cost considering network topology, LA, and distributed energy resource operation constraints; the scheduling result includes a decision vector, which is a reference schedule for optimal energy allocation to DER and LA.

[0068] After scheduling, the LA calculates the maximum absolute deviation from the reference plan established by the DSO.

[0069] Secondly, this application provides an aggregation scheduling method based on flexible resources in the distribution network operation safety domain, including:

[0070] The collaborative clustering module is used to cluster transferable loads (SL) and deferred loads (DL) in collaboration with the home energy management system (HEMS) through the load aggregator (LA).

[0071] The flexible resource aggregation module is used to build a load model and extract cluster parameters for SL based on available time grouping in an equivalent flexible resource aggregation model; and to build an interruptible discrete model for DL ​​based on maximum available time constraints.

[0072] The security domain establishment module is used to construct a voltage stability security domain by using the relationship between node voltage and root node voltage through the security domain analysis method, and to construct a thermal stability security domain based on the upper limit of branch current, so as to obtain a security domain model describing the operation of the distribution network without violating thermal and voltage constraints.

[0073] The flexible resource aggregation and scheduling module is used to establish the flexible resource aggregation objective function and integrate the distribution network operation safety domain model with mixed integer nonlinear optimization to construct a flexible resource operation and scheduling model. Based on the load model, cluster parameters and interruptible discrete model, the flexible resource operation and scheduling model is solved, and flexible resource aggregation and scheduling is performed based on the solution results.

[0074] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements an aggregation scheduling method based on flexible resources in the power distribution network operation safety domain.

[0075] Fourthly, this application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements an aggregation scheduling method based on flexible resources in the power distribution network operation safety domain.

[0076] Fifthly, this application provides a computer program product, which includes computer instructions that instruct a computer to execute an aggregated scheduling method based on flexible resources in the power distribution network operation security domain.

[0077] The beneficial effects of the technical solution proposed in this application are:

[0078] This method establishes a flexible resource potential model considering distribution network operational constraints by aggregating transferable and deferred loads. By constructing voltage and thermal stability safety domains, it ensures a safe and feasible power injection space for the distribution network without violating thermal and voltage constraints. The method utilizes clustering to group loads, forming an equivalent aggregated load model, simplifying the scheduling problem while preserving the main technical attributes and consumer preferences of the original loads. Through a decomposition step, the scheduling plan for aggregated loads is broken down into scheduling plans for individual loads, ensuring that the scheduling plan for each flexible load meets consumer preferences and constraints, while minimizing scheduling deviations and interruptions. Compared to traditional methods, this application can more accurately assess the network's operational status, ensuring the network operates within safety boundaries, improving computational efficiency and the accuracy of calculating safety domain boundaries in high-dimensional space. This method effectively solves the concurrent scheduling problem of large-scale flexible energy devices and small distributed resource clusters, improving scheduling flexibility and efficiency. Attached Figure Description

[0079] Figure 1 A schematic diagram of the aggregation scheduling framework based on flexible resources in the distribution network operation safety domain provided in this application;

[0080] Figure 2 This is a schematic diagram of the aggregation dispatch system based on the flexible resources of the distribution network operation security domain provided in this application;

[0081] Figure 3 A schematic diagram of an electronic device provided in this application. Detailed Implementation

[0082] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application. The step numbers in the following embodiments are set only for ease of explanation, and there is no limitation on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0083] In the description of this application, unless otherwise expressly defined, terms such as "setup," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this application in conjunction with the specific content of the technical solution.

[0084] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application. The step numbers in the following embodiments are set only for ease of explanation, and there is no limitation on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0085] In the description of this application, unless otherwise expressly defined, terms such as "setup," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this application in conjunction with the specific content of the technical solution.

[0086] Terminology Explanation:

[0087] LA: Load Aggregator; a core entity that acts as a bridge between distribution network operators (DSOs) and small consumers, clustering and aggregating transferable and deferred loads in collaboration with home energy management systems (HEMS), participating in demand response (DR) planning, and sharing demand data with DSOs.

[0088] HEMS: Home Energy Management System; a management system used to acquire information submitted by flexible load consumers, monitor the consumption of individual appliances, enable information flow interaction between consumers and load aggregators (LAs), and control home loads based on reference scheduling allocated by LAs.

[0089] SL: Shiftable Load; refers to a load that is independent of continuous tasks, uninterrupted, and has a continuous consumption level, and can be shifted during working hours within a certain available time range, such as dishwashers, microwave ovens, vacuum cleaners, etc.

[0090] DL: Deferable Load; refers to a load that can be interrupted and transferred in time, is constrained by the maximum available time, and has discrete consumption levels, mainly referring to electric vehicles (EVs) in grid-to-vehicle (G2V) mode.

[0091] DSO: Distribution System Operator; the main body responsible for implementing flexible resource optimal scheduling with the goal of minimizing system operating costs, taking into account network topology, load aggregator (LA) and distributed energy resources (DER) operational constraints, and formulating reference scheduling plans.

[0092] DSM: Demand-Side Management; a management strategy that matches electricity supply and demand fluctuations by adjusting the demand curve. By aggregating small-scale flexible resources through load aggregators to participate in this management, the cost of power generation during peak hours can be reduced.

[0093] DR: Demand Response; refers to the mechanism by which consumers change their electricity consumption behavior (such as shifting or delaying load) in response to grid demand based on electricity market signals or incentives. It is one of the core objectives of flexible resource aggregation and dispatch.

[0094] DER: Distributed Energy Resources; refers to small energy production or storage devices distributed in the power distribution network, including conventional distributed generators, wind turbines, solar generators, etc., and is an important scheduling object in the scheduling model.

[0095] G2V: Grid to Vehicle; refers to the mode of power supply from the power grid to electric vehicles (EVs), in which EVs are the main type of deferred load (DL), with discrete consumption levels and interruptible characteristics.

[0096] EV: Electric Vehicle; as a typical example of a deferred load (DL), it has discrete consumption levels in the grid-to-vehicle (G2V) mode, and its charging process can be interrupted and delayed, making it an important component of flexible resources.

[0097] FL: Flexible Load; refers to a general term for loads with adjustable characteristics (transferable, delayable, interruptible, etc.), including transferable load (SL) and delayable load (DL), which is the core object of this patent's aggregation scheduling.

[0098] DG: Distributed Generator; refers to small power generation equipment distributed in the power distribution network, including conventional distributed generators, wind turbines, solar generators, etc., whose power generation cost and output are important optimization variables in the scheduling model.

[0099] ACOPF: Alternating Current Optimal Power Flow; an optimal power flow calculation method that considers the physical constraints of AC power systems, used to analyze the impact of aggregated demand response (DR) on the system level and ensure that scheduling plans meet network constraints.

[0100] The first objective of this application is to provide an aggregation scheduling method based on flexible resources in the distribution network operation safety domain, including:

[0101] S1, through the collaboration of load aggregator LA and home energy management system HEMS, clusters transferable loads SL and deferred loads DL to form an equivalent flexible resource aggregation model;

[0102] S2, in the equivalent flexible resource aggregation model, for SL, a load model is constructed and cluster parameters are extracted based on available time grouping modeling; for DL, an interruptible discrete model is established based on the maximum available time constraint.

[0103] S3. Using the security domain analysis method, a voltage stability security domain is constructed by the relationship between node voltage and root node voltage, and a thermal stability security domain is constructed based on the upper limit of branch current, thus obtaining a security domain model describing the operation of the distribution network without violating thermal and voltage constraints.

[0104] S4. Establish the objective function for flexible resource aggregation, and integrate the distribution network operation safety domain model with mixed integer nonlinear optimization to construct a flexible resource operation scheduling model. Based on the load model, cluster parameters and interruptible discrete model, solve the flexible resource operation scheduling model, and perform flexible resource aggregation scheduling based on the solution results.

[0105] The main steps of the above scheme are as follows: (1) Flexible load aggregation framework: Through the collaboration of load aggregator LA and home energy management system HEMS, transferable load SL and deferred load DL are integrated, and the k-means++ clustering algorithm is used to realize the equivalent modeling of load flexibility and reduce the scheduling dimension. (2) Flexible resource aggregation model: For SL, the load model is constructed and the cluster parameters are extracted based on the available time grouping model; for DL, the interruptible discrete model is established based on the maximum available time constraint to support dynamic power adjustment. (3) Distribution network security domain model: Combining the voltage stability domain (node ​​voltage amplitude constraint) and the thermal stability domain (branch current amplitude limit), the power space for safe and feasible injection of power grid is defined to ensure that the scheduling scheme does not exceed the limit. (4) Flexible resource operation scheduling model: With the goal of minimizing the total system cost, the security domain constraint and mixed integer nonlinear optimization are integrated, and the efficient concurrent scheduling of distributed resources is realized through the "aggregation-optimization-decomposition" process. The transferable load SL and deferred load DL are clustered by the clustering algorithm to form an equivalent aggregated load model. Loads with similar characteristics (SL and DL) have different operating characteristics and consumer preferences. A clustering algorithm is used to group loads with similar characteristics, simplifying the scheduling problem while preserving the main technical attributes and consumer preferences of the original loads. Voltage stability and thermal stability security domains are constructed to describe the safe and feasible power injection space of the distribution network under conditions that do not violate thermal and voltage constraints. The voltage stability security domain is defined by the relationship between node voltage and root node voltage, while the thermal stability security domain is based on the upper limit of branch current to ensure the thermal stability of the system. With the goal of minimizing system operating costs, the network topology, the operating constraints of distributed energy resources (DERs), and the scheduling requirements of flexible loads are considered. The scheduling model is based on AC optimal power flow (ACOPF), analyzing the impact of aggregated demand response (DR) on the system level and ensuring that the scheduling plan meets network constraints.

[0106] Furthermore, the load aggregator (LA) interacts with the home energy management system (HEMS): the LA acts as an intermediary between flexible loads (FL) and distribution network operators (DSOs) through the HEMS. Consumers can interact with the HEMS through a dedicated application / tool. Two sources of demand flexibility are considered: transferable loads (SL) and deferred loads (DL). SLs are considered to be independent of continuous tasks, uninterrupted, and have continuous consumption levels, such as dishwashers, microwave ovens, vacuum cleaners, dryers, sensors, irons, etc. DLs are electric vehicles (EVs) in a grid-to-vehicle (G2V) mode with discrete consumption levels.

[0107] Furthermore, LA performs aggregation based on information submitted by consumers to reduce the dimensionality of the problem while maintaining overall flexibility. A centroid-based k-means clustering algorithm is used to group the loads, forming an equivalent aggregated load model. This approach reduces the constraints and the size of control variables in the scheduling problem, making it more manageable, while preserving the main technical attributes of the original load and consumer preferences.

[0108] The proposed framework in this application is based on a cooperative model, with the LA acting as the entity collaborating with the DSO. The distribution network (DN) can purchase energy from the upstream grid to meet demand, and it is assumed that the DN possesses some traditional distributed generation facilities. Through problem formalization, concrete calculations can be implemented, comprising three steps: load aggregation, optimization, and decomposition. The aggregation step involves grouping, aggregating, and formalizing the equivalent aggregated load model for SLs and DLs. SLs are grouped according to availability, while DLs are not grouped because they are inserted while parked, with availability time only between the arrival and departure of EVs. After grouping, representative parameters are extracted from each cluster, which form the basis of the equivalent model.

[0109] The final optimal scheduling is performed by the DSO to minimize system operating costs that take into account network, LA, and distributed energy resource (DER) operational constraints. The scheduling result includes a decision vector, i.e., a reference schedule with optimal energy allocation to DER and LA. After scheduling, the LA calculates the maximum absolute deviation from the reference plan to alleviate the computational burden on the DSO.

[0110] As a preferred approach, the transferable load (SL) and the deferred load (DL) are grouped using the k-means clustering algorithm to form an equivalent aggregated load model. The loads are aggregated using k-means clustering. k-means++ is used to select the seed to eliminate the limitation of k-means clustering relying on centroid initialization.

[0111] In the SL load model: SL is independent of continuous tasks, uninterrupted, and has a continuous consumption level. The model ensures that the load power equals the rated power required for operation, the operating time is sufficient, and the minimum runtime requirement is met. The aggregation model consists of the algebraic summation of SL vectors, and is improved into an equivalent model to support optimal scheduling and decomposition.

[0112] The DL model allows for interruptions and transfers over time, but is limited by maximum available time. The model ensures that the energy storage charging and discharging amounts are consistent within a single cycle.

[0113] As a sustainable solution, considering a distribution system that ignores phase angle, the voltage difference between any two nodes is determined by the node voltage amplitude, line transmission power, and line resistance and reactance. Line transmission power can be approximated and simplified. Based on the relationship between node voltage and root node voltage, a voltage stability safety region is constructed to ensure that all node voltages remain within their upper and lower limits. Based on Ohm's law, the branch current amplitude is determined by the node injected power and line parameters. A thermal stability safety region is constructed to ensure that branch currents do not exceed their upper limits, maintaining system thermal stability.

[0114] This model aims to minimize total operating cost, comprehensively considering generation costs as well as the costs of wind and solar power reduction. Demand response costs are incorporated to incentivize flexible user-side resource allocation. Regarding constraints, the model ensures a balance between active and reactive power, preventing grid instability caused by power imbalances. Simultaneously, wind and solar power reductions are limited to reduce renewable energy waste. Line flow limits are strictly enforced to ensure safe grid operation. Furthermore, the model maintains node voltage and angle within safe ranges to prevent voltage collapse and other issues. Finally, the operating range and slope limitations of distributed generation units are considered to ensure stable operation of these units.

[0115] The following is in conjunction with the appendix Figure 1 This application will be described in further detail. This embodiment provides a method for aggregated scheduling based on flexible resources in the operational safety domain of a distribution network, the specific implementation of which is described below.

[0116] (1) The principle of flexible load aggregation for optimized operation of distribution network is explained in detail below:

[0117] Load aggregators (LAs) act as intermediaries between flexible loads (FLs) and distribution network operators (DSOs) through home energy management systems (HEMS), leveraging the ability of small consumers in a geographic area to adjust their consumption at certain times of the day in response to their willingness to receive monetary rewards. Consumers can interact with HEMS through a dedicated app / tool.

[0118] For day-ahead scheduling, HEMS: (i) acquires information submitted by FL consumers; (ii) monitors the consumption of individual appliances; (iii) supports information flow between consumers and LAs; and (iv) controls the load based on reference scheduling allocated by the LA. This framework considers two sources of demand flexibility: SL and DL.

[0119] In this application, the transferable load SL is considered independent of continuous tasks, uninterrupted, and has a continuous consumption level. SL consists of loads such as dishwashers, microwave ovens, vacuum cleaners, dryers, sensors, and irons. Electric vehicles are considered as deferred loads DL in a grid-to-vehicle (G2V) mode with discrete consumption levels. FLs have different slack levels depending on consumer comfort. To mitigate the complexity caused by the participation of different loads, LA attempts to reduce the dimensionality of the problem while maintaining overall flexibility. To this end, LA performs aggregation based on information submitted by consumers and shares the aggregated demand data with the DSO. In this application, a centroid-based k-means clustering algorithm is used to represent load flexibility because it is suitable for comprehensive analysis and effective on large-scale datasets. Cluster-based aggregation reduces the constraints and size of control variables in the scheduling problem, making the problem more tractable. Furthermore, it preserves the original description of FLs and their main technical properties and preferences, directly aggregating and approximating the model.

[0120] The proposed framework is based on a cooperative model, where LA is assumed to be an entity cooperating with DSO. The distribution network DN can purchase energy from the upstream grid to meet its demand. For simplicity, it is assumed that the DN has some traditional distributed generation facilities.

[0121] A detailed overview of the problem-based approach includes three steps: load aggregation, optimization, and decomposition. The aggregation step involves grouping, aggregating, and formalizing the equivalent aggregated load model of SLs and DLs. Typically, SLs are not always inserted in residential environments, and the availability of SLs used to perform tasks may vary. Therefore, SLs are grouped according to availability to achieve effective management of these loads and reflect actual behavior. DLs are not grouped because they are assumed to be inserted once parked; therefore, the corresponding availability time is only between the arrival and departure of EVs. However, without loss of generality, the grouping step can be performed on both categories of load. After grouping, representative parameters are extracted from each resulting cluster. These parameters form the basis of the equivalent model. Optimal scheduling is performed by the DSO with (OF1) as the objective to minimize the system operating cost considering operational constraints of network topology, LA, and DER. The result of the DSO scheduling involves a decision vector, the optimal energy allocation to the DER, and the reference schedule LA. The decomposition of the comprehensive decision, i.e., the reference schedule, is obtained after scheduling. The maximum absolute deviation from the reference schedule is calculated by the LA to alleviate the computational burden on the DSO.

[0122] (2) Establish a flexible resource aggregation model that combines the operational constraints of the distribution network, as detailed below:

[0123] This application presents the mathematical formula for the load model to aggregate FL, and then proposes an equivalent model using the obtained parameters.

[0124] SL aggregation is performed in two steps. In the first step, loads are grouped based on their similar availability. After grouping, profiles are constructed based on the power requirements of the loads during corresponding time intervals of their operating time. This profile has the following attributes: availability, power requirements, operating time requirements, and start-stop time. The profiles are aggregated using the k-means clustering algorithm to obtain cluster C, such that | C |<| I The k-means clustering algorithm starts with an arbitrary set of centroids, so k-means++ is used to select a seed around which clusters are formed. It eliminates the drawback of k-means relying on centroid initialization. At any given time, let... D ( X i ) indicates from the brief file X i To the nearest centroid that has already been selected X c The shortest distance, where I is the total number.

[0125] A profile is a structured data record or descriptive model of the power demand variation pattern of a divided load within a specific time interval of its operating time.

[0126] In the above scheme, the step-by-step process of using k-means++ seed selection to obtain cluster representativeness and membership weights is as follows:

[0127] Step 1. From the consumption vector X i The initial centroid is selected uniformly and randomly in the (i.e., the summary document). X c ;

[0128] Step 2. Select Next X c ,choose X c = ∈ χ probability ;

[0129] Step 3. Repeat step 2 until a selection is made. k The center of mass;

[0130] Step 4. Based on Euclidean distance, divide each X i Assigned to the nearest X c To obtain k One cluster;

[0131] Step 5. Calculate the new Xc As all that is assigned to it X i The average value;

[0132] Step 6. Repeat steps 5 and 6 until distance minimization no longer improves, or until the fixed number of iterations of ITMAX is completed;

[0133] Step 7. For each X i :turn up X c And X i Assign to this cluster;

[0134] Step 8. For each cluster c = 1...k, after update X c Equal to all those assigned to this cluster X i The average value;

[0135] Step 9. Output has X c The final clustering is based on membership degree.

[0136] The cluster FL obtained from the above steps has approximate available time, operating time, and power distribution represented by the centroid. These parameters are crucial for optimizing cluster scheduling. They can be used to represent the flexibility of load shifting, while the profile represents the demand that has a significant impact on the scheduling process. To achieve optimal cluster scheduling, the operating time and demand distribution of the cluster obtained above need to be optimized.

[0137] 1) SL load model

[0138] The considered load factor (SL) is independent of continuous tasks and cannot be interrupted or adjusted before the operation ends. However, these loads can be shifted within a certain available timeframe. If Indicates the first i The DR capacity (kW) of the transferable load, and the operating time is The behavioral attribute corresponding to availability is The single load model of SL can be represented as follows:

[0139] (1)

[0140] (2)

[0141] (3)

[0142] In the formula: For the first iA transferable load in time t Rated power (kW); For the first i Start and stop times (h) of each transferable load; u i,t Indicates the first i A transferable load in time t The state; u i,t-1 Indicates the first i A transferable load in time t The state of -1; Indicates the first i DR quantity of each transferable load For working hours, These are the behavioral attributes corresponding to availability. i ∈1,2,...,I; It is a universal quantifier symbol in mathematics, meaning it applies to any one of all; Indicates the first i A transferable load in time t The state is 1 if scheduled, and 0 otherwise.

[0143] Equation (1) ensures the first i The power of each transferable load should be equal to the rated power required for operation, and equation (2) ensures sufficient operating time. For a load to complete its operation, the minimum operating time requirement is satisfied by equation (3). Here, for continuous independent loads participating in DR, is a positive integer.

[0144] For the set of numbers of SL , i ∈ (1, 2, ..., I), the aggregate power of each load is equal to the sum of the power of all loads in the set. ;

[0145] (4)

[0146] (5)

[0147] (6)

[0148] In the formula: For aggregated power vectors, It is a power vector; Let c be the set of power characteristics of the c-th mobile cluster. Let be the aggregate power of the c-th cluster at time t. Let be the power of the i-th load at time t. The total operating time constraint for the i-th load is... The first c The start and stop times of each transferable load; Let be the total energy requirement of the c-th cluster during its working hours; For the first c Energy requirements of a mobile cluster; Let represent the set of power models for the c-th mobile load cluster, which is affected by individual load constraints and aggregation parameters, and By a separate load vector composition, It is the independent variable of a function or a general representation; the superscript conforms to... This indicates the improved model, compared to the unimproved model. The parameters correspond to each other in meaning; parameters The total energy is limited by the lower and upper limits of power. The updated connection time is Improved model The power is limited to / Within, and obtain updated working hours, making , It is considered to be power-limited;

[0149] further, For the first c Energy requirements (kWh) for a mobile cluster; Affected by individual load constraints and aggregation parameters, and By a separate load vector Composition. Corresponding to equation (4), in equation (5) it represents the use of having i Clustering of SL c The clustering model is obtained by clustering. The clustering model consists of the algebraic summation of SL vectors, which is improved into an equivalent model to support optimal scheduling and decomposition, and presents as a continuous power range as shown in Equation (6). Improved parameters Limited by the lower and upper limits of power, and within the available time. Internal binary variables Control. Total energy is limited by The updated connection time is This equivalent model is considered for a given cluster. c Scheduling of all loads under [the specified load].

[0150] Improved model in equation (6) The power is limited to / Within, and obtain updated working hours, making .here, To assist in selection in a way that meets energy requirements at an appropriate number of intervals. At the same time, the on-time requirements of each load must be met during the decomposition process. Otherwise, if all loads are connected simultaneously, If a load is considered power-limited, then in this case, power might be dispatched within a single interval to save energy. For loads with time requirements exceeding one interval, this dispatching would be contradictory, as the power in the next dispatch interval might be less than the minimum power requirement. Therefore, for effective decomposition, the reference schedule should meet the minimum working time requirement of the load, which can be achieved by using... , and restrictions This also proves the model equivalence of each SL cluster and can be obtained by decomposing the reference timeline ( To verify.

[0151] 2) DL model

[0152] These loads can be interrupted and transferred over time, but are limited by maximum available time; for example, EV charging loads can be interrupted and serviced before the last trigger. For a given time range... t The DR capacity (kW) is Each load j and arrival-departure time ( , The individual load model of DL is represented by equation (7):

[0153] (7)

[0154] (8)

[0155] For a given time range t DR amount is Each load j and arrival time -Departure time ;

[0156] In the formula, Indicates deferred load j At any moment t power, Indicates deferred load j The rated power, i.e., the load j The maximum power that can be achieved Indicates deferred load j Total energy demand; number of load aggregates j∈1,2,..., J , J A set of deferred loads, j ∈ J Indicates load j It is an element in the set of deferred loads. j The set of feasible regions is denoted as , Indicates load j The set of feasible regions, Is with load j Related vector forms of power and other related quantities, for a set n Aggregate power equals the set Power of all individual loads The sum of, and = ;

[0157] For the first j The energy demand (kWh) of a deferable cluster.

[0158] In equation (7) j The set of feasible regions can be represented as For a set n Load aggregation quantity j ∈(1,2,..., J The aggregate power should be equal to the set Power of all individual loads The sum of, and = .

[0159] (9)

[0160] (10)

[0161] In the formula: Indicates the first d A deferred cluster at time t power, This represents the power in aggregate vector form corresponding to the set Ω. Indicates the first d The arrival time of the cluster can be delayed. Indicates the first d The departure time of the cluster can be postponed. D This represents a set of clusters that can be deferred. d ∈ D Represents a cluster d It is an element in the set of deferable clusters. For the first dThe energy requirements of a cluster can be postponed; To improve the polymerization power after parameter adjustments; For clusters d Maximum power requirement during cluster arrival and departure times Indicates the improved version of the first d Each cluster at time t The polymerization power can only be 0 or j Multiple individual loads j At any moment t power, Let represent the set of power models for the d-th mobile load cluster, which is affected by individual load constraints and aggregation parameters. Show the set of aggregate power models after the d-th improved parameter.

[0162] For any DL cluster d The aggregated load model can be represented by equation (9). The total load model in (9) is improved to the equivalent load model with discrete power range proposed in equation (10) for load scheduling. The improved model describes how, before the latest departure time, DL cluster scheduling can be adjusted by discontinuous power range to meet load demand. Improved parameters Limited by cluster d Maximum power demand during cluster arrival and departure times In the formula, The lower bound is zero because DL demand is interruptible and can be served in the next interval. Total energy demand must be met during arrival and departure times.

[0163] (3) Construction of the security domain model for distribution network operation, detailed as follows:

[0164] The security domain used in this application consists of voltage stability and thermal stability security domains, which are used to describe the safe and feasible power injection space for all nodes.

[0165] 1) Voltage stability safety domain

[0166] This application considers a power distribution system where phase angle is negligible. Therefore, the voltage difference between any two nodes can be described as:

[0167] (11)

[0168] In the formula: U i and U j Representing nodes respectively i With nodes j The voltage amplitude; P and Q They represent the lines respectively. ijThe line transmission power; R and X They represent the lines respectively. ij The line resistance and reactance.

[0169] Meanwhile, since line losses are relatively small compared to the load, the line transmission power can be approximated as:

[0170] (12)

[0171] (13)

[0172] In the formula: express j The set of downstream branch nodes of a node.

[0173] Substituting equations (12) and (13) into equation (11), we get:

[0174] (14)

[0175] Applying (14) to all branches, while considering that the node voltage of each node is close to the root node voltage, the relationship between the voltage of each node and the root node can be given as follows:

[0176] (15)

[0177] (16)

[0178] In the formula, The voltage magnitude at the root node; node s For nodes j With nodes k The node number of the first intersection of the upstream branch; , The equivalent impedance parameter represents the impedance from the root node to the node. i and nodes h The equivalent impedance on the common path.

[0179] Substituting the upper / lower limit of the node voltage into the node voltage equation (15), we can obtain the voltage stability safe region as follows:

[0180] (17)

[0181] (18)

[0182] In the formula, The voltage magnitude at the root node; node s For nodes j With nodes k The node number of the first intersection of the upstream branch; , The equivalent impedance parameter represents the impedance from the root node to the node. i and nodes h The equivalent impedance on the common path; , The standardized sensitivity parameter for the upper boundary of voltage safety. For corresponding active power P Sensitivity, Corresponding active power Q Sensitivity; , The standardized sensitivity parameter for the upper boundary of voltage safety. For corresponding active power P Sensitivity, For corresponding active power Q Sensitivity; U M , U m These are the upper and lower limits of the voltage. P h For nodes h active power, Q h For nodes h reactive power, n The count related to the number of nodes participating in the voltage stability security domain calculation represents the number of nodes involved in the calculation. h From 1 to n To perform a summation operation, it involves n One related node;

[0183] 2) Thermal stability safety domain

[0184] Based on Ohm's law, branches in a radial power grid ij Current amplitude I It can be represented as:

[0185] (19)

[0186] Substituting equations (12) and (13) into equation (19), we get:

[0187] (20)

[0188] Since the node voltage of each node is close to the root node voltage, the upper limit of the branch current is set accordingly. Substituting into equation (20), the thermal stability safety region can be obtained as follows:

[0189] (twenty one)

[0190] (twenty two)

[0191] In the formula, P k For nodes k active power, Q k For nodes k reactive power, U 0 represents the voltage amplitude at the root node. For a set of nodes, For thermal stability current limits; α k and β k These are power-current mapping coefficients, binary variables, used to determine nodes. k Does the load flow through the branch? ij .

[0192] (4) Establish a flexible resource operation scheduling model that combines the operational constraints of the distribution network, as detailed below:

[0193] Considering grid operation constraints and line limitations, an optimal scheduling problem is constructed with the goal of minimizing total operating cost. The objective function (23) minimizes the total cost of power generation, which is the sum of the cost of energy production from all distributed generator (DG) units, and the costs associated with wind (VWcurt) and solar curtailment (VScurt) when maximum power generation is available. The cost of load transfer is derived from equation (24).

[0194] (twenty three)

[0195] (twenty four)

[0196] (25)

[0197] (26)

[0198] (27)

[0199] (28)

[0200] In the formula: t is the current scheduling time, for t Time of the first n Wind power wastage on the busbar for t Time of the first n The amount of solar power wasted by the busbar; for t The cost of responding to demand at any given moment; In order to be in tTime connection to the first n Active power generation of wind turbines via bus; In order to be in t Time connection to the first n Active power generation of the bus-mounted solar generator; In order to be in t Always connected to the bus n Inelastic active power; In order to be in t Always connected to the bus n Inelastic reactive load; for t Time bus n and m The positive trend between them; for t Time bus n and m The reactive current between them; exist t Bus at time n The availability of wind energy at the location; exist t Bus at time n The availability of solar energy at the location; busbar n Total wind power generation capacity; busbar n Total solar power generation capacity at point b; b is the first A conventional distributed generator; For the first The unit power generation cost of a conventional distributed generator; For the first The active power generation of a conventional distributed generator; for Time of the first The unit cost of wind power curtailment at each busbar; for Time of the first The unit cost of solar power curtailment at each busbar; For demand response costs, for Time of the first The original fixed active power demand of the type of transferable load; for Time of the first Adjusted active power demand for shiftable loads; for Time of the first The original fixed active power demand that can reduce the load; : Time of the first Adjusted active power demand for shiftable loads; for The unit cost of load adjustment at any given time; T is the time set, C is the set of load types that can be shifted, and D is the set of load types that can be reduced; : Time of the first Adjusted active power demand for shiftable loads; : time t The reactive power generation of a conventional distributed generator; G is the set of conventional distributed generator types.

[0201] Specifically, / for t Time of the first n Wind / solar power curtailment (kW) per busbar; for t Demand response cost per second ($ / kWh); In order to be in t Time connection to the first n Active power generation (kW) of the wind / solar generator on the bus; / In order to be in t Always connected to the bus n Inelastic active / reactive load (kW / kVAr); / for t Time bus n and m Active / reactive power flow (kW) between them; / exist t Bus at time n The availability of wind / solar energy at the location; / busbar n Total wind / solar power generation capacity (kW).

[0202] The constraints listed in Equations (25)-(28) correspond to active and reactive power flow balance, wind power reduction, minimum-maximum available wind power, hourly solar power reduction, and available solar power generation, respectively. Since load remodeling at the LA location also affects network operation, other relevant constraints are considered, including: line flow, line limits, voltage amplitude and angle limits, DG operating range, and ramp.

[0203] (29)

[0204] (30)

[0205] (31)

[0206] (32)

[0207] (33)

[0208] (34)

[0209] (35)

[0210] (36)

[0211] In the formula: No. c Each cluster t The start indicator of the interruptible period of time; For the first c Each cluster t The stop indicator for the interruptible period of time; The duration of the time interval; For the first Each cluster Interruptible status indicator at any given time; For the first Classes can reduce load in The time-reduction status indicator; For the first The baseline active power of each cluster; : No. The maximum number of interruptible events threshold for a single cluster; Let c be the set of available time for the c-th interruptible load cluster; Let be the interruptibility status indicator for the c-th cluster at time t-1; h is the time variable for summation. This is the status indicator of the c-th interruptible load cluster at time h; Let be the upper limit of the interruptible duration for the c-th cluster at time t. This is the state indicator for the c-th cluster at time t-1; The threshold for the maximum number of interruptible cycles for the c-th interruptible load cluster; This is the stop indicator for the interruptible period of the c-th interruptible load cluster at time h; The duration of the time interval. This represents the upper limit of the total energy reduction for the d-th type of load that can be reduced. For the set of available adjustment start times for the d-th type of load that can be reduced; This represents the set of available adjustment end times for the d-th type of load that can be reduced; This represents the maximum power reduction per time period for the load that can be reduced in category d.

[0212] The number of DRs provided by SL is limited by equation (29). This is the reference plan for DSO. The start-stop decision for cluster scheduling is determined by binary variables. Give, and It is an on / off state indicator. When equation (30) represents the correlation of binary variables; when When changing from 0 to 1, It becomes 1; when When it changes from 1 to 0, It becomes 1. Equation (31) gives the way to avoid start and stop indicators at any interval. t The binary relations overlap. When equation (32) is constrained... Set the work time to 1 to update the work time ( ), making ( ≤ ), as shown in the equivalent model (6). For daily DR events (N DR The maximum number of ) should be such that the start indicator is higher, as shown in equation (33). Stop indicator ( The update time for the cluster, as shown in equation (34), will become 1. For equations (29)-(34), the time ( t ) is limited to available time ( To maintain consumer preferences. Constraints related to DL clusters are given by equations (35)-(36). The total load after planning is determined by... This corresponds to equation (23). The energy constraint is represented by equation (35). Binary variables. It is an on / off status indicator, and It is limited by the maximum power of the DL cluster.

[0213] Considering the constraints (25)-(36) above along with the flexible load modeling constraints (1)-(10), solve the objective (OF) of the operation scheduling model. This model is a mixed-integer nonlinear problem that can be solved using the MATLAB solver.

[0214] The method proposed in this application can tap the adjustment potential of demand-side prosumers and consumers, more accurately describe the operating state range of the distribution network under the condition of not violating the safety domain constraints, and help solve the concurrent scheduling problem of a large number of small-scale distributed resource clusters in the distribution network.

[0215] like Figure 2As shown, the second objective of this application is to provide an aggregation scheduling method based on flexible resources in the distribution network operation safety domain. Based on the aforementioned aggregation scheduling method based on flexible resources in the distribution network operation safety domain, the method includes:

[0216] The collaborative clustering module is used to cluster transferable loads (SL) and deferred loads (DL) in collaboration with the home energy management system (HEMS) through the load aggregator (LA).

[0217] The flexible resource aggregation module is used to build a load model and extract cluster parameters for SL based on available time grouping in an equivalent flexible resource aggregation model; and to build an interruptible discrete model for DL ​​based on maximum available time constraints.

[0218] The security domain establishment module is used to construct a voltage stability security domain by using the relationship between node voltage and root node voltage through the security domain analysis method, and to construct a thermal stability security domain based on the upper limit of branch current, so as to obtain a security domain model describing the operation of the distribution network without violating thermal and voltage constraints.

[0219] The flexible resource aggregation and scheduling module is used to establish the flexible resource aggregation objective function and integrate the distribution network operation safety domain model with mixed integer nonlinear optimization to construct a flexible resource operation and scheduling model. Based on the load model, cluster parameters and interruptible discrete model, the flexible resource operation and scheduling model is solved, and flexible resource aggregation and scheduling is performed based on the solution results.

[0220] like Figure 3 As shown, a third objective of this application embodiment is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the aforementioned aggregation scheduling method based on flexible resources in the power distribution network operation safety domain. It also includes a communication interface and a bus.

[0221] The aforementioned aggregation scheduling method based on flexible resources in the distribution network operation safety domain includes:

[0222] S1, through the collaboration of load aggregator LA and home energy management system HEMS, clusters transferable loads SL and deferred loads DL to form an equivalent flexible resource aggregation model;

[0223] S2, in the equivalent flexible resource aggregation model, for SL, a load model is constructed and cluster parameters are extracted based on available time grouping modeling; for DL, an interruptible discrete model is established based on the maximum available time constraint.

[0224] S3. Using the security domain analysis method, a voltage stability security domain is constructed by the relationship between node voltage and root node voltage, and a thermal stability security domain is constructed based on the upper limit of branch current, thus obtaining a security domain model describing the operation of the distribution network without violating thermal and voltage constraints.

[0225] S4. Establish the objective function for flexible resource aggregation, and integrate the distribution network operation safety domain model with mixed integer nonlinear optimization to construct a flexible resource operation scheduling model. Based on the load model, cluster parameters and interruptible discrete model, solve the flexible resource operation scheduling model, and perform flexible resource aggregation scheduling based on the solution results.

[0226] The fourth objective of this application is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described aggregation scheduling method based on flexible resources in the power distribution network operation safety domain.

[0227] The aforementioned aggregation scheduling method based on flexible resources in the distribution network operation safety domain includes:

[0228] S1, through the collaboration of load aggregator LA and home energy management system HEMS, clusters transferable loads SL and deferred loads DL to form an equivalent flexible resource aggregation model;

[0229] S2, in the equivalent flexible resource aggregation model, for SL, a load model is constructed and cluster parameters are extracted based on available time grouping modeling; for DL, an interruptible discrete model is established based on the maximum available time constraint.

[0230] S3. Using the security domain analysis method, a voltage stability security domain is constructed by the relationship between node voltage and root node voltage, and a thermal stability security domain is constructed based on the upper limit of branch current, thus obtaining a security domain model describing the operation of the distribution network without violating thermal and voltage constraints.

[0231] S4. Establish the objective function for flexible resource aggregation, and integrate the distribution network operation safety domain model with mixed integer nonlinear optimization to construct a flexible resource operation scheduling model. Based on the load model, cluster parameters and interruptible discrete model, solve the flexible resource operation scheduling model, and perform flexible resource aggregation scheduling based on the solution results.

[0232] A fifth objective of this application is to provide a computer program product comprising computer instructions that instruct a computer to execute the above-described aggregation scheduling method based on flexible resources in the power distribution network operation security domain.

[0233] The aforementioned aggregation scheduling method based on flexible resources in the distribution network operation safety domain includes:

[0234] S1, through the collaboration of load aggregator LA and home energy management system HEMS, clusters transferable loads SL and deferred loads DL to form an equivalent flexible resource aggregation model;

[0235] S2, in the equivalent flexible resource aggregation model, for SL, a load model is constructed and cluster parameters are extracted based on available time grouping modeling; for DL, an interruptible discrete model is established based on the maximum available time constraint.

[0236] S3. Using the security domain analysis method, a voltage stability security domain is constructed by the relationship between node voltage and root node voltage, and a thermal stability security domain is constructed based on the upper limit of branch current, thus obtaining a security domain model describing the operation of the distribution network without violating thermal and voltage constraints.

[0237] S4. Establish the objective function for flexible resource aggregation, and integrate the distribution network operation safety domain model with mixed integer nonlinear optimization to construct a flexible resource operation scheduling model. Based on the load model, cluster parameters and interruptible discrete model, solve the flexible resource operation scheduling model, and perform flexible resource aggregation scheduling based on the solution results.

[0238] These 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 function 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 function specified in one or more boxes.

[0239] These computer program instructions can also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment 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.

[0240] This application may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application may take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, readable storage media, optical storage, etc.) containing computer-usable program code.

[0241] 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, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0242] Obviously, the described embodiments are only some, not all, of the embodiments in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort should fall within the scope of protection of this application.

[0243] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and not to limit them. Although this application has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation methods of this application. Any modifications or equivalent substitutions that do not depart from the spirit and scope of this application should be covered within the protection scope of this application.

Claims

1. A method for aggregated scheduling based on flexible resources in the operational safety domain of a distribution network, characterized in that, include: By collaborating with the home energy management system (HEMS) through the load aggregator (LA), transferable loads (SL) and deferred loads (DL) are clustered to form an equivalent flexible resource aggregation model. In the equivalent flexible resource aggregation model, for SL, a load model is constructed and cluster parameters are extracted based on available time grouping modeling; for DL, an interruptible discrete model is established based on the maximum available time constraint. The security domain analysis method is adopted to construct the voltage stability security domain by the relationship between node voltage and root node voltage, and the thermal stability security domain is constructed based on the upper limit of branch current, so as to obtain the security domain model of distribution network operation under the condition of not violating thermal and voltage constraints. A flexible resource aggregation objective function is established, and the aforementioned distribution network operation security domain model and mixed integer nonlinear optimization are integrated to construct a flexible resource operation scheduling model. Based on the load model, cluster parameters, and interruptible discrete model, the flexible resource operation scheduling model is solved, and flexible resource aggregation scheduling is performed based on the solution results.

2. The aggregation scheduling method based on flexible resources in the distribution network operation safety domain according to claim 1, characterized in that, The transferable loads include dishwashers, microwave ovens, vacuum cleaners, dryers, sensors, and irons; The delayable load includes electric vehicles in grid-to-vehicle mode with discrete consumption levels.

3. The aggregation scheduling method based on flexible resources in the distribution network operation safety domain according to claim 1, characterized in that, The clustering of transferable loads (SL) and deferred loads (DL) to form an equivalent flexible resource aggregation model includes: Based on the k-means clustering algorithm, the LA performs aggregation on the information submitted by consumers and shares the aggregated demand data with the distribution network operator (DSO).

4. The aggregation scheduling method based on flexible resources in the distribution network operation safety domain according to claim 1, characterized in that, The clustering of transferable loads (SL) and deferred loads (DL) to form an equivalent flexible resource aggregation model specifically includes: Load is partitioned based on similar available time. After dividing the load, a profile is constructed based on the power demand of the load during the corresponding time intervals of its operating time. X i The profile has the following attributes: available time, power requirements, operating time requirements, and start-stop time; The k-means clustering algorithm is used to aggregate the profiles to obtain a clustering result set C, such that | C | < | I |, where I is the total number of transferable loads, where the k-means clustering algorithm starts with an arbitrary set of centroids, selects a seed using k-means++, and clusters around the seed; at any given time, let D ( X i ) indicates from X i To the nearest centroid that has already been selected X c The shortest distance.

5. The aggregation scheduling method based on flexible resources in the distribution network operation safety domain according to claim 4, characterized in that, The load model for the transferable load SL is as follows: In the formula: For the first i A transferable load in time t Rated power; For the first i The start and stop times of each transferable load; u i,t Indicates the first i A transferable load in time t The state; u i,t-1 Indicates the first i A transferable load in time t The state of -1; Indicates the first i Demand response DR for each transferable load For working hours, These are the behavioral attributes corresponding to availability. i ∈1,2,...,I; It is a universal quantifier symbol in mathematics, meaning it applies to any one of all; For the set of numbers of SL The aggregate power of each load is equal to the sum of the power vectors of all loads in the set. ; In the formula: For aggregated power vectors, It is a power vector; Let c be the set of power characteristics of the c-th mobile cluster. Let be the aggregate power of the c-th cluster at time t. Let be the power of the i-th load at time t. The total operating time constraint for the i-th load is... The first c The start and stop times of each transferable load; Let be the total energy requirement of the c-th cluster during its working hours; For the first c Energy requirements of a mobile cluster; Let represent the set of power models for the c-th mobile load cluster, which is affected by individual load constraints and aggregation parameters, and By a separate load vector composition, It is the independent variable of a function or a general representation; superscript symbol This indicates the improved model, compared to the unimproved model. The parameters correspond to each other in meaning; parameters The total energy is limited by the lower and upper limits of power. The updated connection time is Improved model The power is limited to / Within, and obtain updated working hours, making , It is considered to be power-limited; The load model for deferred load (DL) is as follows: For a given time range t DR amount is Each load j and arrival time -Departure time ; In the formula, Indicates deferred load j At any moment t power, Indicates deferred load j The rated power, i.e., the load j The maximum power that can be achieved Indicates deferred load j Total energy demand; number of load aggregates j ∈1,2,..., J , J A set of deferred loads, j ∈ J Indicates load j It is an element in the set of deferred loads. j The set of feasible regions is denoted as , Indicates load j The set of feasible regions, Is with load j The relevant power correlation quantities in vector form for a set n Aggregate power equals the set Power of all individual loads The sum of, and = ; In the formula: Indicates the first d A deferred cluster at time t power, This represents the power in aggregate vector form corresponding to the set Ω. Indicates the first d The arrival time of the cluster can be delayed. Indicates the first d The departure time of the cluster can be postponed. D This represents a set of clusters that can be deferred. d ∈ D Represents a cluster d It is an element in the set of deferable clusters. For the first d The energy requirements of a cluster can be postponed; To improve the polymerization power after parameter adjustments; For clusters d Maximum power requirement during cluster arrival and departure times Indicates the improved version of the first d Each cluster at time t The polymerization power can only be 0 or j Multiple individual loads j At any moment t power, Let represent the set of power models for the d-th mobile load cluster, which is affected by individual load constraints and aggregation parameters. Show the set of aggregate power models after the d-th improved parameter.

6. The aggregation scheduling method based on flexible resources in the distribution network operation safety domain according to claim 4, characterized in that, The voltage stability safe range is: In the formula, The voltage amplitude at the root node; , The equivalent impedance parameter represents the impedance from the root node to the node. i and nodes h The equivalent impedance on the common path; , The standardized sensitivity parameter for the upper boundary of voltage safety. For corresponding active power P Sensitivity, Corresponding active power Q Sensitivity; , The standardized sensitivity parameter for the upper boundary of voltage safety. For corresponding active power P Sensitivity, For corresponding active power Q Sensitivity; U M , U m These are the upper and lower limits of the voltage. P h For nodes h active power, Q h For nodes h reactive power, n The count related to the number of nodes participating in the voltage stability security domain calculation represents the number of nodes involved in the calculation. h From 1 to n To perform a summation operation, it involves n One related node; Thermal stability safety domain is In the formula, P k For nodes k active power, Q k For nodes k reactive power, U 0 represents the voltage magnitude at the root node. For a set of nodes, For thermal stability current limits; α k and β k These are power-current mapping coefficients, binary variables, used to determine nodes. k Does the load flow through the branch? ij .

7. The aggregation scheduling method based on flexible resources in the distribution network operation safety domain according to claim 1, characterized in that, The establishment of a flexible resource aggregation objective function, combined with the integration of a distribution network operation security domain model and mixed-integer nonlinear optimization, to construct a flexible resource operation scheduling model includes: Considering grid operation constraints and line limitations, a flexible resource aggregation objective function is constructed with the goal of minimizing total operating costs. The flexible resource aggregation objective function includes the energy production costs of all distributed generator units and the sum of costs associated with wind and solar power curtailment when maximum power generation is available. The objective function for flexible resource aggregation includes a first constraint, which includes active and reactive power flow balance constraints, wind power reduction constraints, minimum-maximum available wind power constraints, hourly solar power reduction constraints, and available solar power generation constraints. The specific objective function for flexible resource aggregation is as follows: In the formula: t is the current scheduling time, for t Time of the first n Wind power wastage on the busbar for t Time of the first n The amount of solar power wasted by the busbar; for t The cost of responding to demand at any given moment; In order to be in t Time connection to the first n Active power generation of wind turbines via bus; In order to be in t Time connection to the first n Active power generation of the bus-mounted solar generator; In order to be in t Always connected to the bus n Inelastic active power; In order to be in t Always connected to the bus n Inelastic reactive load; for t Time bus n and m The positive trend between them; for t Time bus n and m The reactive current between them; exist t Bus at time n The availability of wind energy at the location; exist t Bus at time n The availability of solar energy at the location; busbar n Total wind power generation capacity; busbar n Total solar power generation capacity at point b; b is the first A conventional distributed generator; For the first The unit power generation cost of a conventional distributed generator; For the first The active power generation of a conventional distributed generator; for Time of the first The unit cost of wind power curtailment at each busbar; for Time of the first The unit cost of solar power curtailment at each busbar; For demand response costs, for Time of the first The original fixed active power demand of the type of transferable load; for Time of the first Adjusted active power demand for shiftable loads; for Time of the first The original fixed active power demand that can reduce the load; for Time of the first Adjusted active power demand for shiftable loads; for The unit cost of load adjustment at any given time; T is the time set, C is the set of load types that can be shifted, and D is the set of load types that can be reduced; for Time of the first Adjusted active power demand for shiftable loads; : time t The reactive power generation of a conventional distributed generator; G is the set of conventional distributed generator types.

8. The aggregation scheduling method based on flexible resources in the distribution network operation safety domain according to claim 7, characterized in that, The flexible resource aggregation objective function also includes a second constraint, which includes: line flow, line limitations, voltage amplitude and angle limitations, and the operating range and ramp of the DG, specifically: In the formula: No. c Each cluster t The start indicator of the interruptible period of time; For the first c Each cluster t The stop indicator for the interruptible period of time; The duration of the time interval; For the first Each cluster Interruptible status indicator at any given time; For the first Classes can reduce load in The time-reduction status indicator; For the first The baseline active power of each cluster; : No. The maximum number of interruptible events threshold for a single cluster; Let c be the set of available time for the c-th interruptible load cluster; Let be the stop indicator for the c-th cluster at time t-1; h is the time variable for summation. This is the status indicator of the c-th interruptible load cluster at time h; Let be the upper limit of the interruptible duration for the c-th cluster at time t. This is the state indicator for the c-th cluster at time t-1; The threshold for the maximum number of interruptible cycles for the c-th interruptible load cluster; This is the stop indicator for the interruptible period of the c-th interruptible load cluster at time h; This represents the upper limit of the total energy reduction for the d-th type of load that can be reduced. For the set of available adjustment start times for the d-th type of load that can be reduced; This represents the set of available adjustment end times for the d-th type of load that can be reduced; For the first d A deferred cluster at time t The power.

9. The aggregation scheduling method based on flexible resources in the distribution network operation safety domain according to claim 1, characterized in that, The flexible resource aggregation scheduling based on the solution results includes: The scheduling is performed by the distribution network operator (DSO) to minimize system operating costs that take into account network topology, energy availability (LA), and distributed energy resource (DES) operational constraints. The scheduling results include a decision vector, which is a reference schedule for optimal energy allocation to DES and LA. After scheduling, the LA calculates the maximum absolute deviation from the reference plan established by the DSO.

10. A converged scheduling system based on flexible resources in the operational safety domain of a distribution network, characterized in that, include: The collaborative clustering module is used to cluster transferable loads (SL) and deferred loads (DL) in collaboration with the home energy management system (HEMS) through the load aggregator (LA). The flexible resource aggregation module is used to build a load model and extract cluster parameters for SL based on available time grouping in an equivalent flexible resource aggregation model; and to build an interruptible discrete model for DL ​​based on maximum available time constraints. The security domain establishment module is used to construct a voltage stability security domain by using the relationship between node voltage and root node voltage through the security domain analysis method, and to construct a thermal stability security domain based on the upper limit of branch current, so as to obtain a security domain model describing the operation of the distribution network without violating thermal and voltage constraints. The flexible resource aggregation and scheduling module is used to establish a flexible resource aggregation objective function and integrate the power distribution network operation security domain model with mixed integer nonlinear optimization to construct a flexible resource operation and scheduling model. Based on the load model, cluster parameters and interruptible discrete model, the flexible resource operation and scheduling model is solved, and flexible resource aggregation and scheduling is performed based on the solution results.

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