Source load cluster dynamic operation envelope distribution method and device considering network security and flexibility mining

Abstract

The invention provides a source load cluster dynamic operation envelope distribution method considering network security and flexibility mining and a device thereof. The method comprises the following steps that: each source load cluster reports a maximum theoretical reachable adjustable range of the source load cluster; the medium-voltage feeder layer calculates a simplified topological structure and parameters, namely, a power grid topological structure mastered by the medium-voltage feeder layer is considered, and a simplified topological model based on a two-port model and a cluster division result is constructed; the medium-voltage feeder layer establishes a dynamic operation envelope distribution model, and establishes a dynamic operation envelope distribution model considering the network security and the flexibility fulfillment level of each source-load cluster; iteratively solving a dynamic operation envelope distribution result of each source load cluster, namely providing a dynamic operation envelope iterative optimization distribution method of each source load cluster based on a model solving method of additional linear term equivalent quadratic transformation; and outputting a recommended operation point and a dynamic operation envelope result. And effective balance of medium-voltage feeder level scheduling comprehensive benefits and flexible adjustment level mining multiple targets is realized.

Description

Technical Field

[0001] The present invention belongs to the field of power distribution system optimization and scheduling, and specifically relates to a source-load cluster dynamic operation envelope allocation method and device taking network security and scheduling fairness into consideration. Background Art

[0002] As the global greenhouse effect continues to intensify, building a new green energy structure has become a global consensus. As the primary form of end-user energy consumption, developing a highly flexible and controllable power system dominated by renewable energy is highly beneficial to achieving carbon emission reduction targets. In this context, fully tapping the potential of user-side resource adjustments and enhancing the flexible regulation capabilities of the distribution network have become the development trends of the distribution system. However, this high flexibility also leads to a large-scale increase in the number of decision variables required for optimal scheduling of the distribution system. The traditional model, which relies on centralized management at the dispatching center, is no longer suitable for this new reality. Furthermore, distributed renewable energy generators are characterized by strong intermittent and fluctuating power. Therefore, ensuring that the scheduling plan can meet the power requirements of the upper power grid for the distribution network has become a core issue in optimizing the distribution network.

[0003] To address these issues, distributed optimal dispatching has become a viable solution for the future development of distribution networks. Existing literature often addresses this problem through methods such as the target cascade method and the alternating direction multiplier method. Following this approach, some experts have addressed the scheduling problem under the integration of multiple microgrids. By introducing multi-level agents to assist the distribution network-microgrid in scheduling decisions, they have employed improved target cascade methods and alternating direction multiplier algorithms, respectively. These methods, to a certain extent, address the difficulties of multi-level coordination and the diverse interests of the distribution system when multiple microgrids are integrated. However, these methods suffer from the unclear meaning of potential dual variables and are more suitable for profit-driven, multi-agent decentralized scheduling and outcome-oriented problems such as optimal power flow. However, they struggle to directly capture analyzable inter-regional interaction information. Therefore, it is necessary to further refine the "layered and clustered" approach to distribution systems and explore "centralized coordination-decentralized autonomy" scheduling methods tailored to the wide-area, distributed, and multi-resource characteristics of distribution systems, in order to achieve the scheduling goal of optimally supporting the needs of the upper-level power grid.

[0004] In this context, the dynamic operating envelope, a mathematical form used to characterize the power limits of nodes injected into the distribution network, can be applied to optimizing the external characteristic interval of a cluster. Therefore, characterizing the dynamic operating envelope of a cluster while balancing network security and the potential for flexibility of each source-load cluster under limited information is of great research value. Summary of the Invention

[0005] In order to solve the shortcomings and deficiencies in the existing technology, the present invention proposes a dynamic operation envelope allocation method for source-load clusters that takes into account network security and flexibility mining, establishes a dynamic operation envelope characterization model for source-load clusters at the medium-voltage feeder layer, and proposes a solution method based on equivalent quadratic changes.

[0006] Specifically, the method for allocating dynamic operation envelopes of a source-load cluster considering network security and flexibility mining proposed in this application includes:

[0007] (1) Each source load cluster reports its own theoretical maximum adjustable range;

[0008] (2) The medium-voltage feeder layer calculates the simplified topology and parameters, that is, considering the grid topology structure mastered by the medium-voltage feeder layer, a simplified topology model based on the two-port model and cluster division results is constructed;

[0009] (3) Establish a dynamic operation envelope allocation model at the medium voltage feeder layer, taking into account network security and the flexibility realization level of each source and load cluster;

[0010] (4) Iteratively solve the dynamic operation envelope allocation results of each source-load cluster, that is, based on the model solution method of the equivalent quadratic transformation of the additional linear term, an iterative optimization allocation method for the dynamic operation envelope of each source-load cluster is proposed;

[0011] (5) Output the recommended operating point and dynamic operating envelope results.

[0012] The step (2) considers the limited grid topology structure known at the medium voltage feeder layer and constructs a simplified topology model based on the two-port model and the cluster division result, specifically including:

[0013] In practice, it is impossible to obtain the complete topology of the medium-voltage feeder in each cluster. Therefore, this model regards the entire topology as a two-port element and uses the equal incremental method and the LinDistflow power flow model to obtain simplified topology parameters. The simplified topology parameters can be calculated as follows:

[0014]

[0015] The step (3) establishes a dynamic operation envelope allocation model that takes into account network security and the flexibility realization level of each cluster, specifically including:

[0016] 1) Objective function

[0017] The objective function of this model contains two sub-functions. One is the economic objective. Considering that the system can obtain the global optimal economic result according to a deterministic scheduling curve, the dynamic operation envelope should ensure that the economic performance obtained by any scheduling strategy within this range is not much different from the global optimal economic performance. The mathematical expression of this function is as follows:

[0018]

[0019] In the formula, the numerator is the expected scheduling cost in the dynamic operation envelope, and the denominator is the global optimal scheduling cost result. Dynamic operation envelope for each cluster, P loss is the system network loss cost, c sw n sw is the cost of section tie switch operation.

[0020] The second sub-objective function is the flexibility realization level of each cluster. The mathematical expression of this function is as follows:

[0021]

[0022] Where, The theoretical maximum adjustable range for internal evaluation of each cluster, and are the upper and lower boundaries of the maximum adjustable range, and The upper and lower boundaries of the dynamic operation envelope.

[0023] 2) Constraint function

[0024] The first constraint is the dispatch demand constraint of the upper-level power grid and the net load constraint of the cluster, which can be expressed as follows:

[0025]

[0026] The second constraint is the line power flow constraint considering network reconstruction, which can be expressed as follows:

[0027]

[0028] The third constraint is the grid connectivity and radial constraint, which can be expressed as follows:

[0029] W vi,vj,t +W vj,vi,t =δ vi,vj,t

[0030]

[0031] The fourth constraint is the network security constraint, which envelops the node voltage deviation and line thermal stability constraint of the simplified topology and can be expressed as follows:

[0032]

[0033] The fifth constraint is the polygonal approximation transformation constraint, which can be expressed as follows:

[0034]

[0035] In step (4), based on the model solving method of the equivalent quadratic transformation of the additional linear term, a dynamic operation envelope iterative optimization allocation method for each cluster is proposed, which is as follows:

[0036] A multi-step collaborative dynamic operation envelope allocation process is proposed, which includes the following steps:

[0037] Step 1: Update the simplified topology and related impedance information based on the source-load cluster distribution and complete topology information;

[0038] Step 2: Calculate the spatial integral of the theoretically achievable maximum adjustable range of each cluster based on the theoretically achievable maximum adjustable range reported by the cluster

[0039] Step 3: Solve the theoretical optimal dispatch point corresponding to the distribution system, that is, solve the model corresponding to the following equation and update the result to f 1,MFL ;

[0040]

[0041] stThe above constraints meet the requirements

[0042] Step 4: Solve the above dynamic operation envelope allocation model considering network security and the flexibility realization level of each cluster.

[0043] The present invention proposes a method for solving the equivalent quadratic transformation fractional programming based on additional linear terms for the nonlinear terms in the overall objective function. The method mainly includes the following steps:

[0044] Step 1: Using Δt as the time interval, the integral result in two-dimensional space can be simplified to And take the opposite of the objective function to convert it into a max problem. At this time, the objective function is converted into a function of the independent variable and A linear function of .

[0045] Step 2: Set the constant The objective function is converted into the following form using quadratic transformation.

[0046]

[0047] Step 3: Initialize the optimization result of the recommended scheduling strategy to and n sw , and select the corresponding Initialize the decision variable set and set the number of iterations k = 1.

[0048] Step 4: Based on the decision variable set and to update.

[0049] Step 5: Fix Solve the overall optimization problem with the formula in step 2 as the goal, and use the decision variable set at this time as the initial value of the k+1th iteration.

[0050] Step 6: Repeat steps 4 and 5 until

[0051] Step 7: Output the recommended scheduling strategy. and The constructed space is the dynamic operation envelope of cluster i.

[0052] The present invention also provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to call and run the computer program stored in the memory to execute the method steps as claimed in any one of claims 1 to 6.

[0053] The present invention also provides a computer-readable storage medium, characterized in that: the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

[0054] The beneficial effects and advantages of the present invention are:

[0055] 1. The present invention takes into account the control range of the source-load cluster and the information and energy interaction between the distribution network and the cluster, and develops a multi-level cluster scheduling framework for the distribution system with limited information transmission and interaction. By considering the simplification requirements of the distribution network topology structure, a dynamic operation envelope optimization allocation model for the source-load cluster considering network security and scheduling fairness is constructed based on the simplified topology. For the fractional nonlinear terms and the variable parameter expected objective function in the model, an equivalent quadratic change solution method for the additional linear term is proposed. Through multiple iterations, a dynamic operation envelope allocation result that ensures operation safety and a balanced degree of realization of the cluster's adjustable potential can be obtained, which can be used to guide the multi-level decentralized scheduling and operation of the distribution system.

[0056] 2. Aiming at the new problem of optimizing the decomposition of the dynamic operation envelope of the source-load cluster in the distribution system, the present invention proposes a method for decomposing the dynamic operation envelope of the source-load cluster that takes into account both network security and fair scheduling under limited information conditions. It innovatively adopts a multi-fractional programming solution method with additional linear terms based on quadratic transformation to solve the optimization problem, and achieves an effective balance among multiple objectives of comprehensive scheduling benefits and flexibility adjustment level mining at the medium-voltage feeder level. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is the system architecture diagram under layering and grouping;

[0058] Figure 2 This is a solution framework diagram for the source-load cluster dynamic operation envelope allocation method considering network security and flexibility mining in the present invention;

[0059] Figure 3 This is the basic structure diagram of a typical example;

[0060] Figure 4 Dynamic envelope allocation results for one of the source-load clusters;

[0061] Figure 5 This is a comparison chart of the dynamic operation envelope allocation results under different weights of the same source-load cluster. DETAILED DESCRIPTION

[0062] In order to more clearly illustrate the technical solution of the present invention, the technical solution of the present invention will be described in detail below with reference to the accompanying drawings and embodiments. For those skilled in the art, other drawings and embodiments can be obtained based on these drawings without creative work.

[0063] See Figure 2 The method for allocating dynamic operation envelopes of a source-load cluster considering network security and flexibility mining proposed in this application includes:

[0064] (1) Each source load cluster reports its own theoretical maximum adjustable range;

[0065] (2) The medium-voltage feeder layer calculates the simplified topology and parameters, that is, considering the grid topology structure mastered by the medium-voltage feeder layer, a simplified topology model based on the two-port model and cluster division results is constructed;

[0066] (3) Establish a dynamic operation envelope allocation model at the medium voltage feeder layer, taking into account network security and the flexibility realization level of each source and load cluster;

[0067] (4) Iteratively solve the dynamic operation envelope allocation results of each source-load cluster, that is, based on the model solution method of the equivalent quadratic transformation of the additional linear term, an iterative optimization allocation method for the dynamic operation envelope of each source-load cluster is proposed;

[0068] (5) Output the recommended operating point and dynamic operating envelope results.

[0069] The step (2) considers the limited grid topology structure known at the medium voltage feeder layer and constructs a simplified topology model based on the two-port model and the cluster division result, specifically including:

[0070] In practice, it is impossible to obtain the complete topology of the medium-voltage feeder in each cluster. Therefore, this model regards the entire topology as a two-port element and uses the equal incremental method and the LinDistflow power flow model to obtain simplified topology parameters. The simplified topology parameters can be calculated as follows:

[0071]

[0072] The step (3) establishes a dynamic operation envelope allocation model that takes into account network security and the flexibility realization level of each cluster, specifically including:

[0073] 1) Objective function

[0074] The objective function of this model contains two sub-functions. One is the economic objective. Considering that the system can obtain the global optimal economic result according to a deterministic scheduling curve, the dynamic operation envelope should ensure that the economic performance obtained by any scheduling strategy within this range is not much different from the global optimal economic performance. The mathematical expression of this function is as follows:

[0075]

[0076] In the formula, the numerator is the expected scheduling cost in the dynamic operation envelope, and the denominator is the global optimal scheduling cost result. Dynamic operation envelope for each cluster, P loss is the system network loss cost, c sw n sw is the cost of section tie switch operation.

[0077] The second sub-objective function is the flexibility realization level of each cluster. The mathematical expression of this function is as follows:

[0078]

[0079] Where, The theoretical maximum adjustable range for internal evaluation of each cluster, and are the upper and lower boundaries of the maximum adjustable range, and The upper and lower boundaries of the dynamic operation envelope.

[0080] 2) Constraint function

[0081] The first constraint is the dispatch demand constraint of the upper-level power grid and the net load constraint of the cluster, which can be expressed as follows:

[0082]

[0083] The second constraint is the line power flow constraint considering network reconstruction, which can be expressed as follows:

[0084]

[0085] The third constraint is the grid connectivity and radial constraint, which can be expressed as follows:

[0086] W vi,vj,t +W vj,vi,t =δ vi,vj,t

[0087]

[0088] The fourth constraint is the network security constraint, which envelops the node voltage deviation and line thermal stability constraint of the simplified topology and can be expressed as follows:

[0089]

[0090] The fifth constraint is the polygonal approximation transformation constraint, which can be expressed as follows:

[0091]

[0092] In step (4), based on the model solving method of the equivalent quadratic transformation of the additional linear term, a dynamic operation envelope iterative optimization allocation method for each cluster is proposed, which is as follows:

[0093] A multi-step collaborative dynamic operation envelope allocation process is proposed, which includes the following steps:

[0094] Step 1: Update the simplified topology and related impedance information based on the source-load cluster distribution and complete topology information;

[0095] Step 2: Calculate the spatial integral of the theoretically achievable maximum adjustable range of each cluster based on the theoretically achievable maximum adjustable range reported by the cluster

[0096] Step 3: Solve the theoretical optimal dispatch point corresponding to the distribution system, that is, solve the model corresponding to the following equation and update the result to f 1,MFL ;

[0097]

[0098] stThe above constraints meet the requirements

[0099] Step 4: Solve the above dynamic operation envelope allocation model considering network security and the flexibility realization level of each cluster.

[0100] The present invention proposes a method for solving the equivalent quadratic transformation fractional programming based on additional linear terms for the nonlinear terms in the overall objective function. The method mainly includes the following steps:

[0101] Step 1: Using Δt as the time interval, the integral result in two-dimensional space can be simplified to And take the opposite of the objective function to convert it into a max problem. At this time, the objective function is converted into a function of the independent variable and A linear function of .

[0102] Step 2: Set the constant The objective function is converted into the following form using quadratic transformation.

[0103]

[0104] Step 3: Initialize the optimization result of the recommended scheduling strategy to and n sw , and select the corresponding Initialize the decision variable set and set the number of iterations k = 1.

[0105] Step 4: Based on the decision variable set and to update.

[0106] Step 5: Fix Solve the overall optimization problem with the formula in step 2 as the goal, and use the decision variable set at this time as the initial value of the k+1th iteration.

[0107] Step 6: Repeat steps 4 and 5 until

[0108] Step 7: Output the recommended scheduling strategy. and The constructed space is the dynamic operation envelope of cluster i.

[0109] Combined with the above-mentioned source-load cluster dynamic operation envelope allocation method considering network security and flexibility mining, it is applied to the hierarchical cluster scheduling of multi-level distribution systems. The modified TPC-94 node system is selected as a typical case for specific analysis. The system structure diagram is shown in the attached figure. Figure 1As shown in the figure, based on the location of the sectionalized tie switches, the system is divided into 27 clusters. Each cluster contains a varying number of low-voltage substations, which in turn contain distributed photovoltaics, energy storage, and various types of loads that may have demand response regulation capabilities, such as administrative, residential, commercial, and industrial loads. The overall solution process of the power supply unit optimization division method considering the demand response master-slave game is as follows:

[0110] Step 1: Enter the case information. The case content can be used as a reference. Figure 3 The content shown;

[0111] Step 2: Based on the low-voltage areas and resources contained in each cluster, each cluster reports its own theoretical maximum adjustable range;

[0112] Step 3: Based on the scheduling requirements, set the weights of the two sub-objective functions: scheduling cost and flexibility realization degree;

[0113] Step 4: Based on the reported information of each cluster and the simplified topology of the system, the dynamic operation envelope of each cluster is calculated according to the source-load cluster dynamic operation envelope allocation model and its solution algorithm considering network security and flexibility mining proposed in the present invention.

[0114] Among them, under the weight ratio of 50%-50%, the dynamic operation envelope of cluster 3 is obtained in step 4 as shown in the attached figure. Figure 4 As shown in the figure, under the weight ratio of 20%-80% and 80%-20%, the dynamic operation envelope of cluster 3 is as shown in the figure. Figure 5 As shown in the figure, the proposed method can fully consider network security and the realization level of the flexibility adjustment potential of each source-load cluster under limited information conditions, overcoming the problem that the net load is difficult to follow the deterministic scheduling curve in real time under distributed cluster scheduling.

[0115] The respective sequence numbers in the above-described embodiments are merely for description and do not represent the assembly or sequential order of the various components during use. Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the application can adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0116] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0117] The above descriptions are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for allocating dynamic operation envelopes of a source-load cluster considering network security and flexibility mining, characterized by: Applied to electronic equipment, including the following steps: (1) Each source load cluster reports its own theoretical maximum adjustable range; (2) The medium-voltage feeder layer calculates the simplified topology and parameters, that is, considering the grid topology structure mastered by the medium-voltage feeder layer, a simplified topology model based on the two-port model and cluster division results is constructed; (3) Establish a dynamic operation envelope allocation model at the medium voltage feeder layer, taking into account network security and the flexibility realization level of each source and load cluster; (4) Iteratively solve the dynamic operation envelope allocation results of each source-load cluster, that is, based on the model solution method of the equivalent quadratic transformation of the additional linear term, an iterative optimization allocation method for the dynamic operation envelope of each source-load cluster is proposed; (5) Output the recommended operating point and dynamic operating envelope results.

2. The method for dynamic operation envelope allocation of source-load clusters considering network security and flexibility mining according to claim 1 is characterized by: In step (2), the simplified topology model based on the two-port model and the cluster partitioning result regards the entire topology structure as a two-port element, and uses the equal incremental method and the LinDistflow power flow model to obtain the simplified topology parameters. The simplified topology parameters are calculated by the following formula:

3. The method for dynamic operation envelope allocation of a source-load cluster considering network security and flexibility mining according to claim 1 is characterized by: In step (3), the objective function of the dynamic operation envelope allocation model considering network security and the flexibility realization level of each cluster involves the scheduling cost deviation target, which contains two sub-functions, one of which is the economic target. Considering that the system can obtain the global optimal economic result according to a deterministic scheduling curve, the dynamic operation envelope should ensure that the economic performance obtained by any scheduling strategy within this interval is not much different from the global optimal economic performance. The mathematical expression of this function is as follows: In the formula, the numerator is the expected scheduling cost in the dynamic operation envelope, and the denominator is the global optimal scheduling cost result. Dynamic operation envelope for each cluster, P loss is the system network loss cost, c sw n sw is the cost of section tie switch operation; The second sub-objective function is the flexibility realization level of each cluster. The mathematical expression of this function is as follows: Where, The theoretical maximum adjustable range for internal evaluation of each cluster, and are the upper and lower boundaries of the maximum adjustable range, and are the upper and lower boundaries of the dynamic operation envelope, and the overall objective function is shown in the form: α1+α2＝1 4. The method for dynamic operation envelope allocation of a source-load cluster considering network security and flexibility mining according to claim 1 is characterized by: In step (3), the related constraints of the simplified topology in the dynamic operation envelope allocation model considering network security and the flexibility realization level of each cluster include: The upper-level grid dispatch demand constraint and the cluster net load constraint are expressed as follows: The simplified topology virtual line power flow constraint considering network reconstruction is expressed as follows: The simplified virtual grid connectivity and radial constraints are expressed as follows: W vi,vj,t +W vj,vi,t =δ vi,vj,t Simplified network security constraints, including node voltage deviation and line thermal stability constraints of the simplified topology, are expressed as follows:

5. The method for dynamic operation envelope allocation of source-load clusters considering network security and flexibility mining according to claim 1 is characterized by: In step (4), the dynamic operation envelope iterative optimization allocation method includes a multi-step collaborative dynamic operation envelope allocation process, the steps of which are: Step 1: Update the simplified topology and related impedance information based on the source-load cluster distribution and complete topology information; Step 2: Calculate the spatial integral of the theoretical maximum adjustable range of each source-load cluster based on the theoretical maximum adjustable range reported by the source-load cluster. Step 3: Solve the theoretical optimal dispatch point corresponding to the distribution system, that is, solve the model corresponding to the following equation and update the result to f 1,MFL ; stThe above constraints meet the requirements; Step 4: Apply the dynamic operation envelope iterative optimization allocation method to solve the dynamic operation envelope allocation model that takes into account network security and the flexibility realization level of each source and load cluster.

6. The method for dynamic operation envelope allocation of a source-load cluster considering network security and flexibility mining according to claim 5 is characterized by: The dynamic operation envelope iterative optimization allocation method described in step 4 includes an equivalent quadratic transformation fractional programming solution method based on additional linear terms for the nonlinear terms in the overall objective function, and the steps are as follows: a: Taking Δt as the time interval, the integral result in two-dimensional space can be simplified to And take the opposite of the objective function to convert it into a max problem. At this time, the objective function is converted into a function of the independent variable and A linear function of b: set constant Use the quadratic transformation to transform the objective function into the following form; c: Initialize the optimization result of the recommended scheduling strategy to and n sw , and select the corresponding Initialize the decision variable set and set the number of iterations k = 1; d: According to the decision variable set and Make updates; e: Fixed Solve the overall optimization problem with the equation in step 2 as the goal, and use the decision variable set at this time as the initial value of the k+1th iteration; f: Repeat steps d and e until g: Output recommended scheduling strategy, all and The constructed space is the dynamic operation envelope of cluster i.

7. An electronic device, characterized in that: The electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor is configured to call and run the computer program stored in the memory to perform the method steps according to any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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