A power system heterogeneous flexible resource aggregation method and device based on limit charging and discharging curve and a computer readable storage medium

Abstract

The application discloses a power system heterogeneous flexible resource aggregation method and device based on limit charging and discharging curves and a computer readable storage medium, relates to the technical field of power system dispatching operation, and comprises the following steps: constructing fine models of a battery energy storage system, an electric vehicle and temperature-controlled loads, wherein the battery energy storage system adopts a dynamic weight coefficient to distribute power, the electric vehicle adopts a Monte Carlo-particle swarm hybrid optimization model to generate charging demand, and the temperature-controlled loads adopt a high-order RC model and combine a temperature offset ratio value TOR to uniformly measure thermal comfort; the heterogeneous resources are unified into a virtual energy storage model; a mathematical modeling method of limit charging and discharging ECC / EDC curves is proposed, and a conservative internal approximation is realized based on the Lagrange mean value theorem; and finally, the slowest ECC / EDC curve is used to cover extreme working conditions, and an equivalent energy storage aggregation model is formed. The application solves the problems of low calculation efficiency and low precision of heterogeneous resources participating in dispatching, and provides an efficient and reliable tool for power system dispatching.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power system dispatching operation, and particularly relates to a power system heterogeneous flexible resource aggregation method based on limit charging and discharging curve, a device and a computer readable storage medium. BACKGROUND

[0002] Under the background of global energy structure accelerating transformation to low carbonization, high proportion of renewable energy grid connection has become an inevitable trend of power system development. However, the large-scale access of fluctuating power sources such as wind power and photovoltaic power significantly aggravates the uncertainty of power supply and demand on both sides, and traditional centralized regulation resources (such as thermal power and pumped storage) gradually fail to meet the needs of new power systems in response speed, regulation accuracy and environmental impact. Under this background, distributed flexible resources, including distributed energy storage systems, electric vehicles, temperature-controlled loads and demand-side adjustable loads, have become key elements to support the safe and stable operation of power systems due to their wide geographical distribution, flexible regulation response and environmental friendliness. However, distributed resources have the characteristics of large quantity, heterogeneous parameters and complex dynamic characteristics, and existing single modeling methods are difficult to meet the scheduling needs of large-scale clusters. SUMMARY

[0003] The present application aims to provide a power system heterogeneous flexible resource aggregation method based on limit charging and discharging curve, a device and a computer readable storage medium, which constructs a fine regulation capability model of multiple types of flexible resources (battery energy storage system, electric vehicle, temperature-controlled load), proposes a dynamic weight coefficient-based energy storage power distribution strategy for battery energy storage systems, establishes a Monte Carlo-particle swarm hybrid optimization model for electric vehicles, establishes a high-order RC equivalent model for temperature-controlled loads, and proposes a unified modeling of virtual energy storage for various resources. A mathematical modeling method of limit charging and discharging curve (ECC / EDC) is proposed, and the conservativeness of ECC / EDC is approximated based on Lagrange mean value theorem. An equivalent energy storage aggregation model considering limit charging and discharging is proposed, which constructs the most relaxed ECC / EDC curve to cover sudden power shortage and resource heterogeneity and other extreme conditions, to solve the problems proposed in the above background technology.

[0004] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0005] A power system heterogeneous flexible resource aggregation method based on limit charging and discharging curve, comprising the following steps:

[0006] (1) Constructing a fine regulation capability model of multiple types of flexible resources of battery energy storage system, electric vehicle and temperature-controlled load, and proposing a unified modeling of virtual energy storage for various resources;

[0007] (2) a mathematical modeling method of limit charging and discharging ECC / EDC curve is proposed, and the conservativeness of the ECC / EDC is approximated based on Lagrange mean value theorem;

[0008] (3) an equivalent energy storage aggregation model considering limit charging and discharging conditions is proposed, and the most relaxed ECC / EDC curve is used to construct a theoretical coverage of extreme working conditions.

[0009] As a preferred scheme of the present application, the method for constructing the fine regulation capability model of the battery energy storage system in step (1) is as follows:

[0010] The initial energy storage of the energy storage is ; wherein, is the initial state of charge, is the maximum energy upper limit. The integral model of the dynamic energy storage capacity changing with time is ; wherein, is the charging and discharging power. The total energy storage capacity expression can be calculated as .

[0011] Wherein, the charging and discharging power and energy need to satisfy the constraint ; the relationship expression of the total charging and discharging power is , wherein is the maximum discharging power, is the maximum charging power, is the energy lower limit.

[0012] As a preferred scheme of the present application, the method for constructing the fine regulation capability model of the electric vehicle in step (1) is as follows:

[0013] Charging behavior modeling: the formula of the charging duration is as follows. Wherein, is the charging duration, is the initial state of charge, is the electric capacity, is the charging power, is the charging efficiency;

[0014]

[0015] The formula of the charging mode selection probability is as follows. Wherein, is the charging mode, is the charging start time.

[0016]

[0017] As a preferred scheme of the present application, the method for constructing the fine regulation capability model of the temperature control load in step (1) is as follows:

[0018] For temperature-controlled load, a high-order RC equivalent model is established;

[0019] A uniform temperature offset ratio value TOR is defined to ensure that all participating buildings contribute to load regulation at the same level of thermal comfort compromise, and the uniform definition of the temperature offset ratio value TOR is as follows. is the total load regulation target, is the building sensitivity coefficient, is the maximum allowed temperature offset.

[0020]

[0021] As a preferred scheme of the present application, the unified modeling method of virtual energy storage in step (1) is as follows:

[0022] The regulation characteristics of battery energy storage systems, electric vehicles, and temperature-controlled loads are integrated, and three-dimensional equivalent parameters of virtual energy storage are defined, including power dimension, energy dimension, and time dimension. Among them, , and represent the aggregated resource instantaneous upper limit of adjustable power, equivalent adjustable energy capacity, and continuous regulation time window, is the adjustable power of a single resource, is the resource saturation time.

[0023]

[0024] As a preferred scheme of the present application, the mathematical modeling method of the limit charge-discharge ECC / EDC curve in step (2) is as follows:

[0025] For a single flexible resource, the mathematical expression of the limit charge-discharge ECC / EDC curve is derived based on the power-energy-time relationship as follows:

[0026]

[0027] Among them, is the upper limit of energy, is the initial state of charge, is the maximum charging power, is the maximum discharging power of the energy storage system, is the absolute value of the maximum discharging power, is the limit charging time curve, is the limit discharging time curve, is the time interval.

[0028] The limit charge-discharge ECC / EDC curve can intuitively reflect the power constraint and energy constraint of the energy storage, and provide a simplified characterization for aggregated modeling.

[0029] For a cluster containing N flexible resources, its overall characteristic curve is generated by superimposing the limit charge-discharge ECC / EDC curves of each unit, and the slope of the overall characteristic curve of the aggregated charge-discharge power satisfies the following relationship.

[0030]

[0031] wherein, is the maximum charge power of the aggregated resources, is the maximum discharge power of the aggregated resources, is the slope of the kth segment of the overall ECC, is the slope of the kth segment of the overall EDC, M is the number of segments;

[0032] As a preferred scheme of the present application, the method for realizing the conservative inner approximation of ECC / EDC based on the Lagrange mean value theorem in step (2) is as follows:

[0033] First, the ECC / EDC curve is divided into two segments from t=0 to t=T, and the midpoint t* is determined by using the Lagrange mean value theorem. This midpoint divides the ECC / EDC curve into two segments with different slopes, and then the mean value theorem is repeatedly applied to each segment of the sub-curve to further subdivide to a preset number of segments, and finally an equivalent ECC / EDC curve composed of multiple linear broken lines is obtained, and the ECC / EDC curve is finally obtained:

[0034]

[0035] wherein, is the derivative of the energy with respect to time at this moment, is the energy at this moment, is the energy at the initial moment. As a preferred scheme of the present application, the following steps are included:

[0036] First, based on the optimized allocation strategy of the initial SoC, the most conservative ECC / EDC curve covering extreme working conditions is generated, and then the Lagrange mean value theorem is used to recursively divide the most conservative ECC / EDC curve to construct a conservative inner approximation boundary, and finally the calculation complexity and accuracy are balanced by dynamically adjusting the segment density and error threshold.

[0037] In the discharge scenario, the flexible resources with short discharge time are preferentially allocated with low SoC, which is mathematically expressed as:

[0038]

[0039] ​​​​

[0040] For a cluster consisting of N flexible resource units, the sufficient and necessary condition for the overall ECC curve slope attenuation rate to be the slowest is that each unit charging time reaches a maximum value, wherein, is a function expression about , is a function expression about , is the energy in the current state, is the maximum power of a single resource, is the maximum energy of a single resource, .

[0041] Further, the application also provides a power system heterogeneous flexible resource aggregation device based on limit charging and discharging curve, the device comprises:

[0042] a data acquisition and processing module for acquiring real-time operation parameters and state information of each flexible resource;

[0043] a resource modeling module for performing the step (1) of constructing a unified model of fine adjustment capability model and virtual energy storage form;

[0044] a curve calculation module for performing the step (2) of limit charging and discharging curve mathematical modeling and conservative internal approximation;

[0045] an aggregation optimization module for performing the step (3) of constructing an equivalent energy storage aggregation model;

[0046] a communication and control module for interacting with the power grid dispatching center to aggregate capability information, and issuing control instructions to the subordinate flexible resources.

[0047] Further, the application also provides a computer readable storage medium having a computer program stored thereon, the program being executed by a processor to implement a power system heterogeneous flexible resource aggregation method based on limit charging and discharging curve as described above.

[0048] The application has the advantages that the application solves the problems of low calculation efficiency and low precision of heterogeneous resources participating in dispatching, and provides an efficient and reliable tool for power system dispatching, and through a virtual energy storage unified modeling method, heterogeneous resources such as battery energy storage, electric vehicles and temperature-controlled loads are converted into standardized dispatching objects, which greatly simplifies the calculation complexity, and based on the mathematical modeling of limit charging and discharging curve (ECC / EDC) combined with Lagrange mean value theorem approximation algorithm, the calculation resource demand is greatly reduced while ensuring the accuracy, and the problem that the existing single modeling method is difficult to meet the dispatching demand of large-scale cluster is solved. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 Flow chart of the method of the present application. DETAILED DESCRIPTION

[0050] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0051] Please refer to Figure 1 In the embodiments of the present application, a power system heterogeneous flexible resource aggregation method and device based on limit charging and discharging curves are provided, which specifically include the following steps:

[0052] (1) A fine adjustment capability model of multiple types of flexible resources (battery energy storage system, electric vehicle, temperature-controlled load) is constructed, and a unified modeling of virtual energy storage form is proposed for various resources.

[0053] (1-1) For the battery energy storage system, an energy storage power distribution strategy based on a dynamic weight coefficient is proposed.

[0054] The initial energy storage of the energy storage is ; wherein, is the initial state of charge, is the maximum energy upper limit. The integral model of the dynamic energy storage capacity changing with time is ; wherein, is the charging and discharging power. The total energy storage capacity expression can be calculated as .

[0055] Wherein, the charging and discharging power and energy need to meet the constraint ; is the maximum discharging power, is the energy lower limit.

[0056] (1-2) For the electric vehicle, a Monte Carlo-particle swarm hybrid optimization model is established.

[0057] Charging behavior modeling: the formula of the charging duration is as follows. Wherein, is the charging duration, is the initial state of charge, is the electric capacity, is the charging power, is the charging efficiency;

[0058]

[0059] The formula of the charging mode selection probability is as follows. Wherein, is the charging mode, is the charging start time.

[0060]

[0061] (1-3) For temperature-controlled loads, a high-order RC equivalent model is established.

[0062] Due to the differences in thermal characteristics (such as wall insulation performance, air conditioning power) of different buildings and the tolerance range of users to temperature, a unified temperature offset ratio (TOR) value is defined to ensure that all participating buildings contribute to load regulation under the same thermal comfort compromise, avoiding the deterioration of user experience due to excessive regulation. The unified definition of TOR is as follows. Wherein, is the total load regulation target, is the building sensitivity coefficient, is the maximum allowed temperature offset.

[0063]

[0064] (1-4) Unified modeling of virtual energy storage. Combining the regulation characteristics of battery energy storage systems, electric vehicles, and temperature-controlled loads of multiple types of flexible resources, three-dimensional equivalent parameters are defined for virtual energy storage, including power dimension, energy dimension, and time dimension. Wherein, , and represent the adjustable power instantaneous upper limit, the equivalent adjustable energy capacity, and the continuous regulation time window, is the resource saturation time.

[0065]

[0066] (2) A mathematical modeling method of the limit charging and discharging curve (ECC / EDC) is proposed, and the conservativeness of the inner approximation of ECC / EDC is realized based on the Lagrange mean value theorem.

[0067] (2-1) ECC / EDC model of single flexible resource. The extreme charge curve (ECC) and the extreme discharge curve (EDC) are the core characteristic curves describing the charge-discharge behavior of a distributed energy storage system (DES). The ECC is the energy accumulation curve when the energy storage system starts charging from the initial state of charge (SoC0) with the maximum charging power until reaching the upper limit of energy (SoC0=1); the EDC is the energy release curve when the energy storage system starts discharging from the initial SoC0 with the maximum discharging power until reaching the lower limit of energy (SoC0=0). The multi-state refers to the heterogeneous distribution of the initial state of charge of each unit in the energy storage system, and the dynamic charge-discharge behavior difference caused thereby.

[0068] For a single flexible resource, the mathematical expression of its extreme charge-discharge ECC / EDC curve is derived based on the power-energy-time relationship as shown below:

[0069]

[0070] wherein, is the upper limit of energy, is the initial state of charge, is the maximum charging power of the energy storage system, is the maximum discharging power of the energy storage system, is the absolute value of the maximum discharging power, , is the time interval;

[0071] The power constraint (curve slope) and energy constraint (curve inflection point) of the energy storage can be intuitively reflected through the ECC / EDC curve, providing a simplified characterization for aggregation modeling.

[0072] (2-2) Overall characteristic modeling of multiple flexible resources. For a cluster containing N flexible resources, the overall characteristic curve is generated by superimposing the ECC / EDC of each unit. The slopes of the overall characteristic curves of the system charging power aggregation and discharging power aggregation satisfy the following relationship.

[0073]

[0074] wherein, is the maximum charging power of the aggregated resource, is the maximum discharging power of the aggregated resource, is the slope of the kth segment of the overall ECC, is the slope of the kth segment of the overall EDC, M is the number of segments;

[0075] (2-3) ECC / EDC approximation method based on Lagrange mean value theorem. First, the ECC curve is divided into n segments, and the Lagrange mean value theorem is used to approximate the slope of each segment. The time corresponding to SoC=1 is divided into two segments, and the midpoint is determined by using the Lagrange mean value theorem The midpoint divides the ECC curve into two segments with different slopes. Then, the mean value theorem is repeatedly applied to each sub-curve to further subdivide to a predetermined number of segments (such as five segments). Finally, an equivalent ECC curve composed of multiple linear broken lines is obtained, and the EDC curve is segmented in the same way, and finally the ECC / EDC curve is obtained:

[0076] .

[0077] (3) An equivalent energy storage aggregation model considering the limit charging and discharging conditions is proposed, and the optimal equivalent curve is constructed based on the slowest ECC / EDC curve to cover the sudden power shortage and resource heterogeneity.

[0078] (3-1) Optimal equivalent curve construction framework based on slowest ECC / EDC curve. In the aggregation modeling of distributed flexible resource clusters, the construction of the equivalent curve needs to meet two core requirements of conservativeness and flexibility: conservativeness means that the equivalent curve must be strictly within the actual cluster charging and discharging capability boundary to avoid the scheduling instruction exceeding the physical constraints. And flexibility means that under the constraint of conservativeness, the equivalent curve should be as close to the actual capability boundary as possible to maximize the utilization of the scheduled resources.

[0079] The method of the application combines the slowest ECC / EDC curve with the approximation of the Lagrange mean value theorem to construct an equivalent model with safety and economy. First, based on the optimal allocation strategy of the initial SoC, the slowest ECC / EDC curve covering the extreme working condition is generated, and then the slowest ECC / EDC curve is recursively divided by using the Lagrange mean value theorem to construct a conservative inner approximation boundary. Finally, by dynamically adjusting the segmentation density and error threshold, the calculation complexity and accuracy are balanced.

[0080] (3-2) Optimal equivalent curve construction method based on conservativeness constraint. Since the SoC allocation of high-power flexible resources has a significant impact on the curve shape, the SoC allocation of low-power flexible resources has a weak impact, so in the charging scenario, high SoC is allocated to flexible resources with short charging time to make them quickly saturated and exit charging; in the discharging scenario, low SoC is allocated to flexible resources with short discharging time, mathematically expressed as:

[0081]

[0082] For a cluster consisting of N flexible resource units, the necessary and sufficient condition for the total ECC curve slope decay rate of each unit to be the slowest is that the charging time of each unit reaches the maximum value, where is a function expression about , and is a function expression about a function expression of the state, is the energy in the current state, is the maximum power of the single resource, is the maximum energy of the single resource, is the time interval.

[0083] It will be apparent to those skilled in the art that the application is not limited to the details of the above-exemplified embodiments and that the application can be implemented in other particular forms without departing from the spirit or essential characteristics of the application. The presently disclosed embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. No reference signs in the claims shall be construed as limiting the scope of the claims to the features

[0084] Furthermore, it is to be understood that the application is not limited to the details of the embodiments described herein, but can be implemented with other embodiments that are within the scope of the present application. Indeed, various modifications can be made to the embodiments described herein without departing from the scope or spirit of the application described herein. Accordingly, it is not intended that the application be limited, except as by the appended claims.

Claims

1. A method for aggregating heterogeneous flexible resources in power systems based on limit charge-discharge curves, characterized by, The method comprises the following steps: (1) constructing a fine adjustment capability model of a battery energy storage system, an electric vehicle, and a temperature-controlled load multi-type flexible resource, and proposing a unified modeling of a virtual energy storage form for the multi-type flexible resource; (2) proposing a mathematical modeling method of an extreme charging and discharging ECC / EDC curve, and realizing a conservative internal approximation of the extreme charging and discharging ECC / EDC curve based on a Lagrange mean value theorem; (3) constructing a theoretical coverage extreme working condition through a minimum ECC / EDC curve, and forming an equivalent energy storage aggregation model; The mathematical modeling method of the extreme charging and discharging ECC / EDC curve in the step (2) is specifically as follows: For a single flexible resource, a mathematical expression of an extreme charging and discharging ECC / EDC curve thereof is derived based on a power-energy-time relationship and is as follows: wherein, is an upper energy limit, is an initial state of charge, is a maximum charging power, is a maximum discharging power of the energy storage system, is an absolute value of the maximum discharging power, is a limit charging time curve, is a limit discharging time curve, is a time interval; For a cluster containing N flexible resources, a total characteristic curve thereof is generated by superimposing the extreme charging and discharging ECC / EDC curves of each unit, and a slope of the total characteristic curve of the charging power aggregation and the discharging power aggregation satisfies the following relationship with an energy limit: wherein, is the maximum charging power of the aggregated resource, is the maximum discharging power of the aggregated resource, is the slope of the kth segment of the overall ECC, is the slope of the kth segment of the overall EDC, M is the number of segments.

2. The method of claim 1, wherein, The method for constructing the fine adjustment capability model of the battery energy storage system in the step (1) is as follows: The initial energy storage of the energy storage is ; wherein, is the initial state of charge, is the maximum energy upper limit; The integral model of dynamic energy storage capacity changing with time is ; wherein, is the charge and discharge power; The total energy storage expression is calculated as ; wherein the charge and discharge power and energy need to satisfy the constraints ; the relationship expression between the total charge and discharge power is , wherein is the maximum discharge power, is the maximum charge power, is the energy lower limit.

3. The method of claim 2, wherein, The method for constructing the fine adjustment capability model of the electric vehicle in the step (1) is as follows: Charging behavior modeling: The charging duration is formulated as follows; where, is the charging duration, is the initial state of charge, is the charge capacity, is the charging power, is the charging efficiency; ； The formula of the charging mode selection probability is shown as follows; where, is the charging mode, is the charging start time; 。 4. The method of claim 3, wherein, The method for constructing the fine adjustment capability model of the temperature-controlled load in the step (1) is as follows: For the temperature-controlled load, a high-order RC equivalent model is established; defining a uniform temperature offset ratio value TOR, ensuring that all buildings participating in the regulation contribute to the amount of load regulation at the same degree of thermal comfort compromise, the uniform definition of the temperature offset ratio value TOR is as follows; wherein, is the total load regulation target, is the building sensitivity coefficient, is the maximum allowed temperature offset; 。 5. The method of claim 4, wherein, The unified modeling method of the virtual energy storage in the step (1) is as follows: The regulating characteristics of the integrated battery energy storage system, electric vehicle, and temperature-controlled load multi-type flexible resources are comprehensively considered, three-dimensional equivalent parameters are defined for the virtual energy storage, which are power dimension, energy dimension, and time dimension, respectively, wherein, , and represent the aggregated resource adjustable power instantaneous upper limit, the equivalent adjustable energy capacity, and the continuous regulation time window, respectively, is the adjustable power of a single resource, is the resource saturation time; 。 6. The method of claim 5, wherein, The method for realizing the conservative internal approximation of the extreme charging and discharging ECC / EDC curve based on the Lagrange mean value theorem in the step (2) is as follows: First, the ECC / EDC curve is divided into two segments from t = 0 to t = t1 The midpoint is determined by the Lagrange mean value theorem The midpoint divides the ECC / EDC curve into two segments with different slopes, and then the mean value theorem is repeatedly applied to each segment to further subdivide to a preset number of segments, and finally an equivalent ECC / EDC curve composed of multiple linear broken lines is obtained, and the ECC / EDC curve is finally obtained: wherein, is the energy at the derivative with respect to time at this moment, is the energy at this moment, is the energy at the initial moment.

7. The method of claim 6, wherein, The method further comprises the following steps: First, a minimum ECC / EDC curve covering an extreme working condition is generated based on an initial SoC optimization distribution strategy, then a recursive segmentation of the minimum ECC / EDC curve is performed by using the Lagrange mean value theorem to construct a conservative internal approximation boundary, and finally a calculation complexity and an accuracy are balanced by dynamically adjusting a segmentation density and an error threshold; In a discharging scenario, a flexible resource with a short discharging time is preferentially allocated a low SoC, and a mathematical expression is as follows: A sufficient and necessary condition for the total ECC curve slope attenuation rate of a cluster consisting of N flexible resource units to be the slowest is that each unit has a charging time reaching a maximum value, wherein is a function expression about , is a function expression about , is the energy in the current state, is the maximum power of a single resource, is the maximum energy of a single resource, is a time interval.

8. An apparatus for implementing the method of any one of claims 1-7, characterized by The device comprises: a data acquisition and processing module configured to acquire real-time operation parameters and state information of each flexible resource; a resource modeling module configured to perform the construction of the fine adjustment capability model and the unified modeling of the virtual energy storage form in the step (1); a curve calculation module configured to perform the mathematical modeling of the extreme charging and discharging curve and the conservative internal approximation in the step (2); an aggregation optimization module configured to perform the construction of the equivalent energy storage aggregation model in the step (3); a communication and control module configured to interact with a power grid dispatching center to aggregate capability information, and to issue a control instruction to a lower flexible resource.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the method in any one of claims 1-7.

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