A virtual power plant large-scale resource dynamic aggregation method, device and medium

Abstract

The application discloses a kind of virtual power plant scale resource dynamic aggregation method, device and medium, belong to virtual power plant technical field, method includes: according to the power information of distributed energy field station, constructs dynamic aggregation optimization model;With the maximum range of the equivalent parameter of virtual power plant as constraint condition solving optimization problem, obtain the initial parameter of dynamic aggregation optimization model;To dynamic aggregation optimization model is carried out day before rolling correction and day-to-day dynamic adjustment, obtain the corrected dynamic aggregation optimization model, and calculate new economic dispatch plan;New economic dispatch plan is sent to cloud server, so that cloud server generates and returns corresponding dispatching instruction;Decomposition is carried out to dispatching instruction, and the dispatching instruction after decomposition is issued to corresponding control edge end execution, to realize in guaranteeing system reliable operation while maximum degree utilization virtual power plant dynamic aggregation regulation capacity, improve the flexible dispatching capability of various distributed resources.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plants, and in particular to a method, device and storage medium for dynamically aggregating large-scale resources of a virtual power plant. Background Art

[0002] Currently, the inherent uncertainty of distributed resource output poses a significant challenge to the safe and stable operation of the power grid. While distributed resources generate revenue, the power grid also faces certain risks and losses. To address this issue, one of the key measures for the governance of distributed resources is to flexibly establish virtual power plants through dynamic aggregation, consider the uncertainty of source and load in virtual power plants, fully tap and utilize the flexible adjustment capabilities of various distributed energy sources, effectively avoid these risks and losses, thereby maximizing the benefits of virtual power plants and improving the safety level of power grid operation.

[0003] In existing technologies for dynamically aggregating large-scale resources in virtual power plants, the response of multi-energy distributed resources, primarily distributed on the user side, is highly uncertain. Distributed resources such as wind power and photovoltaics also exhibit significant randomness and volatility. This presents significant challenges to the aggregation and control of existing virtual power plants at varying energy, spatial, and temporal scales. Existing technologies struggle to fully account for the characteristics, speed, range, and duration of different types of distributed resources. This makes it difficult to maximize the dynamic aggregation and regulation capabilities of virtual power plants while ensuring reliable system operation. This leads to "secondary scheduling" on the grid side and limited flexible scheduling capabilities for various distributed resources. Summary of the Invention

[0004] The present invention provides a method, device and storage medium for dynamically aggregating large-scale resources of a virtual power plant, so as to maximize the regulation capability of dynamic aggregation of the virtual power plant while ensuring the reliable operation of the system, and improve the flexible scheduling capability of various distributed resources.

[0005] The present invention provides a method for dynamically aggregating large-scale resources of a virtual power plant, which is applied to a management system. The management system is connected to a cloud server and several control edges of a distributed energy station; the cloud server is connected to an electricity trading market;

[0006] The method comprises:

[0007] Constructing a dynamic aggregation optimization model based on the power information of distributed energy sites; the dynamic aggregation optimization model includes an equivalent model and a power baseline model;

[0008] According to the power baseline model, the maximum range of equivalent parameters of the virtual power plant is obtained;

[0009] Taking the maximum range of equivalent parameters of the virtual power plant as a constraint condition, solving several preset optimization problems respectively to obtain initial parameters of the dynamic aggregation optimization model;

[0010] The parameters in the equivalent value model are modified based on the day-ahead optimized dispatch data. The adjustable resource parameters of the dynamic aggregation optimization model are modified on a rolling basis based on the power information of the distributed energy devices within the day. The equivalent value model is optimized based on the cost function to obtain the modified dynamic aggregation optimization model.

[0011] Calculating a new economic dispatch plan using the modified dynamic aggregation optimization model; sending the new economic dispatch plan to a cloud server so that the cloud server generates and returns corresponding dispatch instructions;

[0012] The scheduling instruction is decomposed and sent to the corresponding control edge for execution.

[0013] Furthermore, before constructing the dynamic aggregation optimization model based on the power information of the distributed energy site, the method further includes:

[0014] Based on the power transaction information of the power trading market, the information parameters of the distributed energy equipment in the distributed energy station, and the distributed energy prediction curve data in the distributed energy station, a standardized modeling method is used to model different types of distributed resources in the distributed energy station, and several distributed resource models are obtained;

[0015] According to the plurality of distributed resource models, power information of the distributed energy site is obtained.

[0016] Furthermore, the dynamic aggregation optimization model is constructed based on the power information of the distributed energy sites, specifically:

[0017] According to the range of tie-line power, the range of current accumulated power of the virtual power plant and the range of change of the tie-line power of the virtual power plant, an equivalent model is constructed;

[0018] Taking minimizing the generation cost of the virtual power plant as the objective function, the maximum range power balance constraint of the virtual power plant equivalent parameters, the active power constraint of the distributed resource generation equipment, the power feasible region constraint of non-energy storage distributed resources, and the charging and discharging power constraints of the energy storage equipment are used as constraints of the objective function to construct a power baseline model;

[0019] The equivalent value model and the power baseline model are used as a dynamic aggregation optimization model.

[0020] Furthermore, the equivalent model is constructed according to the range of tie-line power, the range of current accumulated power of the virtual power plant, and the range of change in the tie-line power of the virtual power plant, specifically:

[0021] The expression of the equivalent model is:

[0022]

[0023]

[0024]

[0025] in, is the tie line power at time t, t∈Ω T is the set of all adjustable operating time periods, P tie,min and P tie,max are the minimum and maximum values ​​of the tie line power respectively; E tie,min and E tie,max are the minimum and maximum values ​​of the current accumulated electricity of the virtual power plant respectively; and are the minimum and maximum downward and upward changes in the power of the virtual power plant tie line at time t, respectively.

[0026] Furthermore, the objective function is to minimize the power generation cost of the virtual power plant, and the power balance constraint, the active power constraint of the distributed resource power generation equipment, the power feasible region constraint of the non-energy storage distributed resource, and the charging and discharging power constraint of the energy storage equipment are used as constraints of the objective function to construct a power baseline model, specifically:

[0027] The expression of the objective function is:

[0028]

[0029] Among them, Ω G It is the collection of all distributed resource power generation equipment. is the tie line power at time t, Ω T For, c i for, for, for;

[0030] The expression of the power balance constraint is:

[0031]

[0032] Among them, Ω G is the collection of all distributed resource power generation equipment, Ω H is the set of all non-energy storage distributed resources, Ω S It is the collection of all energy storage resources; is the target active power of distributed resource generation equipment i at time t, is the active power of distributed resource device i at time t, is the active power of energy storage device i at time t; Injecting active power into grid nodes for distributed resource generation equipment; is the active power injected into the grid node by non-storage distributed resources at time t; is the total load power at time t;

[0033] The expression of the active power constraint of the distributed resource power generation equipment at time t is:

[0034]

[0035] The expression of the power feasible region constraint of the non-energy storage distributed resource at time t is:

[0036]

[0037] The expression of the charging and discharging power constraint of the energy storage device at time t is:

[0038]

[0039] in, is the target active power column vector of distributed resource generation equipment i at time t, is the active power column vector of distributed resource device i at time t, is the active power column vector of energy storage device i at time t; Φ G,i represents the feasible domain of the active output variable of distributed resource generation equipment i at each moment; Φ H,i represents the feasible domain of the active output variable of non-energy storage distributed resource i at each moment; Φ S,i Represents the feasible region of the power output variable of energy storage resource i at each moment.

[0040] Furthermore, the maximum range of equivalent parameters of the virtual power plant is obtained based on the power baseline model, specifically:

[0041] The power balance constraint, the active power constraint of distributed resource generation equipment, the power feasible domain constraint of non-energy storage distributed resources, and the charging and discharging power constraints of energy storage equipment are taken as the maximum range of the equivalent parameters of the virtual power plant.

[0042] Furthermore, the maximum range of equivalent parameters of the virtual power plant is used as a constraint condition to solve several preset optimization problems respectively to obtain the initial parameters of the dynamic aggregation optimization model;

[0043] Among them, several preset optimization problems are: minimizing the interconnection line power at time t, maximizing the interconnection line power at time t, minimizing the sum of the interconnection line power of all adjustable operating periods, maximizing the sum of the interconnection line power of all adjustable operating periods, minimizing the change in the virtual power plant interconnection line power at time t, and maximizing the change in the virtual power plant interconnection line power at time t.

[0044] Furthermore, the parameters in the equivalent model are corrected according to the day-ahead optimized scheduling data, the adjustable resource parameters of the dynamic aggregation optimization model are revised on a rolling basis according to the power information of the distributed energy equipment within the day, and the equivalent model is optimized according to the cost function to obtain a corrected dynamic aggregation optimization model, specifically:

[0045] By optimizing and solving the dynamic aggregation optimization model, the economic dispatch plan data with the largest deviation is obtained;

[0046] Correcting parameters in the equivalent model according to the benchmark curve and the scheduling plan data with the largest deviation;

[0047] The benchmark curve is obtained based on the day-ahead optimized scheduling data of the power baseline model;

[0048] Based on the power information of distributed energy devices in the virtual power plant, the adjustable capacity range at the next scheduling moment is calculated, the information is updated to the power grid, and the adjustable resource parameters of the dynamic aggregation optimization model are adjusted on a daily rolling basis;

[0049] The power information includes: operating status information and energy storage charge status information;

[0050] An upper bound estimate of the virtual power plant cost function is obtained, and several calculation points are selected between the upper and lower power limits of the equivalent model. A single-period optimization problem with a penalty is solved for each of the calculation points, and the equivalent model is optimized to obtain a revised dynamic aggregation optimization model.

[0051] Furthermore, the economic dispatch plan data with the largest deviation is obtained by optimizing and solving the dynamic aggregation optimization model, specifically:

[0052] The expression for solving the economic dispatch plan data with the largest deviation is:

[0053]

[0054]

[0055] Among them, p tie is the column vector of the tie-line power planned by the virtual power plant at each moment; p is the column vector of the power of each device at each scheduling moment, and They correspond to the upward and downward deviations between the tie line power and the dispatch plan at time t, respectively.

[0056] Furthermore, the single-period optimization problem with penalty is solved for each of the calculation points, specifically:

[0057] The expression for solving the single-period optimization problem with penalty for each calculation point is:

[0058]

[0059]

[0060] in, and They correspond to the upward and downward deviations between the tie line power and the dispatch plan at time t, is the reference power of energy storage device i at time t, where the constraint Indicates that the energy storage charge and discharge plan is the same as the base state, is the power generation cost of distributed resources at time t, is the electricity price cost coefficient of purchasing electricity through the interconnection line at time t, and M is the deviation parameter.

[0061] As an optimal solution, the present invention adopts a cloud-edge collaborative architecture, takes into account load forecasting, power forecasting and electricity price forecasting, sends the information collected on the edge side to the control master station cloud, and dynamically aggregates the existing edge-side distributed resources of the virtual power plant through a dynamic aggregation method, with the goal of minimizing the total electricity purchase cost, and calculates the optimal output of distributed resources with the goal of minimizing the total electricity purchase cost considering multiple time scales; in addition, the present invention also takes into account the response characteristics and spatial distribution of various flexible resources, and the real-time collection of various distributed resource telesignaling and telemetry data, and adopts dynamic layering and partitioning measures for large-scale flexible resources in different business scenarios. On the one hand, it improves the collaborative control capability of distributed resources and increases the flexibility of power grid control; on the other hand, it fully considers the autonomous response rights of the aggregators of distributed resources, realizes timely and flexible dynamic aggregation and calling of virtual power plants, ensures the reliable operation of the system while making maximum use of the dynamic aggregation adjustment capability of virtual power plants, and improves the flexible scheduling capability of various distributed resources.

[0062] Accordingly, the present invention also provides a device for dynamically aggregating large-scale resources of a virtual power plant, which is applied to a management system. The management system is connected to a cloud server and several control edges of a distributed energy station; the cloud server is connected to an electricity trading market;

[0063] The device comprises: a model construction module, a model correction module and a scheduling control module;

[0064] The model construction module is used to construct a dynamic aggregation optimization model based on the power information of the distributed energy site; the dynamic aggregation optimization model includes an equivalent model and a power baseline model;

[0065] The model correction module is used to obtain the maximum range of equivalent parameters of the virtual power plant according to the power baseline model;

[0066] Taking the maximum range of equivalent parameters of the virtual power plant as a constraint condition, solving several preset optimization problems respectively to obtain initial parameters of the dynamic aggregation optimization model;

[0067] The parameters in the equivalent value model are modified based on the day-ahead optimized dispatch data. The adjustable resource parameters of the dynamic aggregation optimization model are modified on a rolling basis based on the power information of the distributed energy devices within the day. The equivalent value model is optimized based on the cost function to obtain the modified dynamic aggregation optimization model.

[0068] The scheduling control module is used to calculate a new economic scheduling plan using the modified dynamic aggregation optimization model; send the new economic scheduling plan to the cloud server so that the cloud server generates and returns corresponding scheduling instructions;

[0069] The scheduling instruction is decomposed and sent to the corresponding control edge for execution.

[0070] Accordingly, the present invention also provides a computer-readable storage medium, which includes a stored computer program; wherein, when the computer program is running, it controls the device where the computer-readable storage medium is located to execute a dynamic aggregation method of virtual power plant scale resources as described in the content of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 This is a flow chart of an embodiment of a method for dynamically aggregating large-scale resources of a virtual power plant provided by the present invention;

[0072] Figure 2 It is a structural diagram of a framework for dynamic aggregation and control of a virtual power plant according to an embodiment of a method for dynamic aggregation of large-scale resources of a virtual power plant provided by the present invention;

[0073] Figure 3 This is a structural diagram of an embodiment of a device for dynamically aggregating large-scale resources of a virtual power plant provided by the present invention;

[0074] Figure 4 This is a structural diagram of a basic platform of a general-purpose industrial control hardware device based on an embedded system, which is an embodiment of a dynamic aggregation device for large-scale resources of a virtual power plant provided by the present invention. DETAILED DESCRIPTION

[0075] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0076] Example 1

[0077] Please refer to Figure 1 , a method for dynamically aggregating large-scale resources of a virtual power plant provided by an embodiment of the present invention, is applied to a management system, wherein the management system is connected to a cloud server and several control edges of a distributed energy site; the cloud server is connected to an electricity trading market;

[0078] The method comprises steps S101-S106:

[0079] Step S101: constructing a dynamic aggregation optimization model based on the power information of the distributed energy site; the dynamic aggregation optimization model includes an equivalent model and a power baseline model;

[0080] Step S102: obtaining the maximum range of equivalent parameters of the virtual power plant according to the power baseline model;

[0081] Step S103: using the maximum range of equivalent parameters of the virtual power plant as a constraint condition, solving several preset optimization problems respectively to obtain initial parameters of the dynamic aggregation optimization model;

[0082] Step S104: Modify the parameters in the equivalent model based on the day-ahead optimized scheduling data, perform intraday rolling corrections on the adjustable resource parameters of the dynamic aggregation optimization model based on the power information of the distributed energy devices within the day, and optimize the equivalent model based on the cost function to obtain a modified dynamic aggregation optimization model;

[0083] Step S105: Calculate a new economic dispatch plan using the modified dynamic aggregation optimization model; send the new economic dispatch plan to the cloud server, so that the cloud server generates and returns corresponding dispatch instructions;

[0084] Step S106: Decompose the scheduling instruction and send the decomposed scheduling instruction to the corresponding control edge for execution.

[0085] Please refer to Figure 2 ,The framework of dynamic aggregation and regulation of virtual power plants includes: market layer, supervision layer, dynamic aggregation layer and station control layer;

[0086] The electricity trading market at the market level is responsible for formulating a market interaction mechanism that conforms to the dynamic aggregation of large-scale resources of virtual power plants, and a trading mechanism that meets the layered zoning and dynamic resource aggregation requirements of the operation and management of virtual power plant aggregators.

[0087] The distributed energy site control edge terminals located at the station control layer are responsible for establishing the regulation capability model of various distributed resources, and establishing the collection data and sensor data of real-time equipment operation, including resource parameters, topology structure, measured data, power forecast, etc., and conducting real-time data interaction with the virtual power plant aggregator operation and management system located at the dynamic aggregation layer, and automatically receiving the decomposition control instructions of the resource cluster to which it belongs.

[0088] The cloud server of the energy management system located at the regulatory layer obtains the peak-shaving demand information and price signals issued by the power trading market at the market layer, obtains the quotation information of the virtual power plant aggregation model reported by the management system, generates the corresponding aggregation scheduling instructions and returns them to the virtual power plant aggregator operation management system (management system).

[0089] The virtual power plant aggregator operation and management system located in the dynamic aggregation layer constructs a dynamic aggregation optimization model based on the power information of distributed energy sites; the dynamic aggregation optimization model includes an equivalent model and a power baseline model;

[0090] According to the power baseline model, the maximum range of equivalent parameters of the virtual power plant is obtained;

[0091] Taking the maximum range of equivalent parameters of the virtual power plant as a constraint condition, solving several preset optimization problems respectively to obtain initial parameters of the dynamic aggregation optimization model;

[0092] The parameters in the equivalent value model are modified based on the day-ahead optimized dispatch data. The adjustable resource parameters of the dynamic aggregation optimization model are modified on a rolling basis based on the power information of the distributed energy devices within the day. The equivalent value model is optimized based on the cost function to obtain the modified dynamic aggregation optimization model.

[0093] Calculating a new economic dispatch plan using the modified dynamic aggregation optimization model; sending the new economic dispatch plan to a cloud server so that the cloud server generates and returns corresponding dispatch instructions;

[0094] The scheduling instruction is decomposed and sent to the corresponding control edge for execution.

[0095] The management system dynamically aggregates the scaled resources within the cluster, realizes data storage and management of the entire life cycle of distributed resources through the data management layer, and uses sensors to collect data on the internal resources of the distributed energy control terminal equipment of the virtual power plant, including site parameters, measured data and power forecasts.

[0096] The management system reports the obtained aggregated data to the energy management system cloud server (cloud server) located at the supervisory layer, and receives the aggregation scheduling instructions issued by the cloud server.

[0097] This embodiment considers the response characteristics and spatial distribution of various flexible resources, along with real-time telesignaling and telemetry data collected from various distributed resources, to implement dynamic hierarchical partitioning for large-scale flexible resources in different business scenarios. Furthermore, considering the combinatorial optimization of massive flexible resources in a high-dimensional space for multiple business scenarios, data processing can be performed using a distributed real-time database, a wide-area message queue, and a task scheduler to meet the data processing requirements in complex scenarios. Furthermore, the hierarchical partitioning results are presented, combining the distribution network topology and the resource's scenario adaptability.

[0098] Furthermore, before constructing the dynamic aggregation optimization model based on the power information of the distributed energy site, the method further includes:

[0099] Based on the power transaction information of the power trading market, the information parameters of the distributed energy equipment in the distributed energy station, and the distributed energy prediction curve data in the distributed energy station, a standardized modeling method is used to model different types of distributed resources in the distributed energy station, and several distributed resource models are obtained;

[0100] According to the plurality of distributed resource models, power information of the distributed energy site is obtained.

[0101] In this embodiment, sensing technology is used to obtain static information about the configuration of various edge-side distributed resources, including basic information about distributed resources, resource capacity, power regulation response characteristics, upper and lower limits of ramp rates, and other related distributed resource parameters;

[0102] Conduct data modeling based on key information such as the type, age, capacity, and response times of distributed resources, and establish an open capacity assessment model based on security conditions;

[0103] In this embodiment, common resources in a dynamically aggregated virtual power plant, such as distributed energy storage, photovoltaic power generation, wind turbines, electric heat pump systems, thermal cogeneration equipment, thermal storage boilers, gas boilers, electric vehicle charging stations, electrochemical energy storage, and cold storage air conditioners, are flexibly classified. A standardized modeling method is used to model different types of distributed resources. These resources can be categorized into the following five types:

[0104] 1) Interruptible load resources: refers to load resources that can withdraw a certain amount of load within a certain period of time during system operation.

[0105] 2) Peak load shaving resources: refers to the load resources that can be used to adjust the operating time period by connecting a portion of the load in a staggered manner during system operation.

[0106] 3) Continuously adjustable resources: refers to resources that can adjust the power emitted or absorbed within a certain range during system operation.

[0107] 4) Tiered adjustment resources: refers to the resources that can be adjusted at different upper gears during system operation.

[0108] 5) Energy storage resources: refers to the resources that receive power absorption instructions within an adjustable time interval to store energy during system operation, and adjust power output within other planned time intervals to compensate for the power injected into the grid and provide power support.

[0109] In this embodiment, the information interaction method of the power communication protocol is adopted to collect online telemetry data such as the operating status and power measurement of edge resources;

[0110] Adopting the information interaction mode of server-client mode, a time series information list of distributed resources such as load forecast and power forecast is established.

[0111] Based on the edge-side resources and their power adjustment response speed, the user-side resources are divided into fast-response adjustable resources and slow-response adjustable resources.

[0112] Furthermore, the dynamic aggregation optimization model is constructed based on the power information of the distributed energy sites, specifically:

[0113] According to the range of tie-line power, the range of current accumulated power of the virtual power plant and the range of change of the tie-line power of the virtual power plant, an equivalent model is constructed;

[0114] Taking minimizing the generation cost of the virtual power plant as the objective function, the maximum range power balance constraint of the virtual power plant equivalent parameters, the active power constraint of the distributed resource generation equipment, the power feasible region constraint of non-energy storage distributed resources, and the charging and discharging power constraints of the energy storage equipment are used as constraints of the objective function to construct a power baseline model;

[0115] The equivalent value model and the power baseline model are used as a dynamic aggregation optimization model.

[0116] Furthermore, the equivalent model is constructed according to the range of tie-line power, the range of current accumulated power of the virtual power plant, and the range of change in the tie-line power of the virtual power plant, specifically:

[0117] The expression of the equivalent model is:

[0118]

[0119]

[0120]

[0121] in, is the tie line power at time t, t∈Ω T is the set of all adjustable operating time periods, P tie,min and P tie,max are the minimum and maximum values ​​of the tie line power respectively; E tie,min and E tie,max are the minimum and maximum values ​​of the current accumulated electricity of the virtual power plant respectively; and are the minimum and maximum downward and upward changes in the power of the virtual power plant tie line at time t, respectively.

[0122] Furthermore, the objective function is to minimize the power generation cost of the virtual power plant, and the power balance constraint, the active power constraint of the distributed resource power generation equipment, the power feasible region constraint of the non-energy storage distributed resource, and the charging and discharging power constraint of the energy storage equipment are used as constraints of the objective function to construct a power baseline model, specifically:

[0123] The expression of the objective function is:

[0124]

[0125] Among them, Ω G It is the collection of all distributed resource power generation equipment. is the tie line power at time t, Ω T For, c i for, for, for;

[0126] The expression of the power balance constraint is:

[0127]

[0128] Among them, Ω G is the collection of all distributed resource power generation equipment, Ω H is the set of all non-energy storage distributed resources, Ω S It is the collection of all energy storage resources; is the target active power of distributed resource generation equipment i at time t, is the active power of distributed resource device i at time t, is the active power of energy storage device i at time t; Injecting active power into grid nodes for distributed resource generation equipment; is the active power injected into the grid node by non-storage distributed resources at time t; is the total load power at time t;

[0129] The expression of the active power constraint of the distributed resource power generation equipment at time t is:

[0130]

[0131] The expression of the power feasible region constraint of the non-energy storage distributed resource at time t is:

[0132]

[0133] The expression of the charging and discharging power constraint of the energy storage device at time t is:

[0134]

[0135] in, is the target active power column vector of distributed resource generation equipment i at time t, is the active power column vector of distributed resource device i at time t, is the active power column vector of energy storage device i at time t; Φ G,i represents the feasible domain of the active output variable of distributed resource generation equipment i at each moment; Φ H,i represents the feasible domain of the active output variable of non-energy storage distributed resource i at each moment; Φ S,i Represents the feasible region of the power output variable of energy storage resource i at each moment.

[0136] The dynamic aggregation optimization model constructed in this invention combines load forecasting, power forecasting, and electricity price forecasting, taking into account power balance constraints, interconnection line power constraints, equipment operation constraints, and energy storage system charging and discharging power constraints. It dynamically aggregates existing edge-side distributed resources at the control master station and calculates the optimal output of distributed resources with the goal of minimizing the total electricity purchase cost, taking into account multiple time scales.

[0137] The dynamic aggregation method provided by the present invention is divided into two stages: a day-ahead rolling optimization stage and a dynamic adjustment stage. In the day-ahead rolling optimization stage, the load forecast model parameters are updated according to the latest weather information, light intensity, wind speed and intraday load forecast information of the day, and the fitting model is adjusted to the optimal prediction parameters for the load forecast of the day, thereby improving the accuracy of the prediction results within the day.

[0138] According to the power baseline model, the maximum range of the equivalent parameters of the virtual power plant is obtained and used as the constraint condition of the optimization problem. The economic dispatch plan with the largest deviation is solved through optimization, and the equivalent parameters are cyclically corrected according to the economic dispatch plan with the largest deviation and the benchmark curve. The cycle is repeated until the maximum deviation is less than or equal to the convergence accuracy, ensuring that all operating variables of the equivalent model are within the feasible domain. The dynamic aggregation optimization model is corrected in the rolling optimization link of the day before, and an aggregation model that is robust in both feasibility and economy is obtained.

[0139] Furthermore, the maximum range of equivalent parameters of the virtual power plant is used as a constraint condition to solve several preset optimization problems respectively to obtain the initial parameters of the dynamic aggregation optimization model;

[0140] Among them, several preset optimization problems are: minimizing the interconnection line power at time t, maximizing the interconnection line power at time t, minimizing the sum of the interconnection line power of all adjustable operating periods, maximizing the sum of the interconnection line power of all adjustable operating periods, minimizing the change in the virtual power plant interconnection line power at time t, and maximizing the change in the virtual power plant interconnection line power at time t.

[0141] Furthermore, the maximum range of equivalent parameters of the virtual power plant is obtained based on the power baseline model, specifically:

[0142] The power balance constraint, the active power constraint of distributed resource generation equipment, the power feasible domain constraint of non-energy storage distributed resources, and the charging and discharging power constraints of energy storage equipment are taken as the maximum range of the equivalent parameters of the virtual power plant.

[0143] Furthermore, the parameters in the equivalent model are corrected according to the day-ahead optimized scheduling data, the adjustable resource parameters of the dynamic aggregation optimization model are revised on a rolling basis according to the power information of the distributed energy equipment within the day, and the equivalent model is optimized according to the cost function to obtain a corrected dynamic aggregation optimization model, specifically:

[0144] By optimizing and solving the dynamic aggregation optimization model, the economic dispatch plan data with the largest deviation is obtained;

[0145] Correcting parameters in the equivalent model according to the benchmark curve and the scheduling plan data with the largest deviation;

[0146] The benchmark curve is obtained based on the day-ahead optimized scheduling data of the power baseline model;

[0147] Based on the power information of distributed energy devices in the virtual power plant, the adjustable capacity range at the next scheduling moment is calculated, the information is updated to the power grid, and the adjustable resource parameters of the dynamic aggregation optimization model are adjusted on a daily rolling basis;

[0148] The power information includes: operating status information and energy storage charge status information;

[0149] An upper bound estimate of the virtual power plant cost function is obtained, and several calculation points are selected between the upper and lower power limits of the equivalent model. A single-period optimization problem with a penalty is solved for each of the calculation points, and the equivalent model is optimized to obtain a revised dynamic aggregation optimization model.

[0150] In this embodiment, the solution of the economic dispatch plan includes the equivalent model and the power baseline model. load and renewable energy power generation Therefore, the equivalent model parameters and cost functions of the virtual power plant vary with the scheduling time and need to be calculated separately for each scheduling time.

[0151] The benchmark curve is the power curve received or injected into the power grid by the virtual power plant through the tie line. It is the day-ahead optimization scheduling result, that is, the calculation result of the power baseline model.

[0152] Furthermore, the economic dispatch plan data with the largest deviation is obtained by optimizing and solving the dynamic aggregation optimization model, specifically:

[0153] The expression for solving the economic dispatch plan data with the largest deviation is:

[0154]

[0155]

[0156] Among them, p tie is the column vector of the tie-line power planned by the virtual power plant at each moment; p is the column vector of the power of each device at each scheduling moment, and They correspond to the upward and downward deviations between the tie line power and the dispatch plan at time t, respectively.

[0157] Furthermore, the single-period optimization problem with penalty is solved for each of the calculation points, specifically:

[0158] The expression for solving the single-period optimization problem with penalty for each calculation point is:

[0159]

[0160]

[0161] in, and They correspond to the upward and downward deviations between the tie line power and the dispatch plan at time t, is the reference power of energy storage device i at time t, where the constraint Indicates that the energy storage charging and discharging plan is the same as the base state, is the power generation cost of distributed resources at time t, is the electricity price cost coefficient of purchasing electricity through the interconnection line at time t, and M is the deviation parameter.

[0162] In the dynamic adjustment phase, for the current time t, the optimization problem is solved with power balance constraints, active power constraints of distributed resource generation equipment, power feasible region constraints of non-energy storage distributed resources, and charging and discharging power constraints of energy storage equipment as constraints. and Solving the deterministic economic dispatch plan at time t requires intraday rolling corrections to adjustable resource parameters, i.e., to the adjustable capacity. Solving the adjustable range of the virtual power plant now becomes a deterministic optimization problem, reducing the need for rolling corrections and even eliminating the conservatism introduced by uncertainty in the upper-level grid's dispatch plan.

[0163] Uncertainty in the upper-level grid's dispatch plan affects the operational schedules of time-coupled components like energy storage, thus impacting costs. Therefore, an upper bound estimate for the virtual power plant's cost function is obtained. The cost function is described by a piecewise linear function. Several calculation points are taken between the upper and lower power limits of the equivalent model. A single-period optimization problem with a penalty term is solved for each calculation point. Rolling corrections are made to the upper and lower power limits within the day to reduce the impact of uncertainty, resulting in a more accurate dynamic aggregation optimization model.

[0164] The implementation of the present invention has the following effects:

[0165] The present invention adopts a cloud-edge collaborative architecture, takes into account load forecasting, power forecasting and electricity price forecasting, sends the information collected on the edge side to the control master station cloud, and dynamically aggregates the existing edge-side distributed resources of the virtual power plant through a dynamic aggregation method, with the goal of minimizing the total electricity purchase cost, and calculates the optimal output of distributed resources with the goal of minimizing the total electricity purchase cost considering multiple time scales; in addition, the present invention also takes into account the response characteristics and spatial distribution of various flexible resources, and the real-time collection of various distributed resource telesignaling and telemetry data, and adopts dynamic layering and partitioning measures for large-scale flexible resources in different business scenarios. On the one hand, it improves the collaborative control capability of distributed resources and increases the flexibility of power grid control; on the other hand, it fully considers the autonomous response rights of the aggregators of distributed resources, realizes timely and flexible dynamic aggregation and calling of virtual power plants, ensures the reliable operation of the system, and maximizes the adjustment capability of the dynamic aggregation of virtual power plants, and improves the flexible scheduling capability of various distributed resources.

[0166] Example 2

[0167] An embodiment of the present invention provides a device for dynamically aggregating large-scale resources of a virtual power plant, which is applied to a management system. The management system is connected to a cloud server and several control edges of a distributed energy station; the cloud server is connected to an electricity trading market;

[0168] Please refer to Figure 3 , a dynamic aggregation device for scaled resources of a virtual power plant provided by an embodiment of the present invention includes: a model construction module 201, a model correction module 202 and a dispatch control module 203;

[0169] The model construction module is used to construct a dynamic aggregation optimization model based on the power information of the distributed energy site; the dynamic aggregation optimization model includes an equivalent model and a power baseline model;

[0170] The model correction module is used to obtain the maximum range of equivalent parameters of the virtual power plant according to the power baseline model;

[0171] Taking the maximum range of equivalent parameters of the virtual power plant as a constraint condition, solving several preset optimization problems respectively to obtain initial parameters of the dynamic aggregation optimization model;

[0172] The parameters in the equivalent value model are modified based on the day-ahead optimized dispatch data. The adjustable resource parameters of the dynamic aggregation optimization model are modified on a rolling basis based on the power information of the distributed energy devices within the day. The equivalent value model is optimized based on the cost function to obtain the modified dynamic aggregation optimization model.

[0173] The scheduling control module is used to calculate a new economic scheduling plan using the modified dynamic aggregation optimization model; send the new economic scheduling plan to the cloud server so that the cloud server generates and returns corresponding scheduling instructions;

[0174] The scheduling instruction is decomposed and sent to the corresponding control edge for execution.

[0175] Furthermore, before constructing the dynamic aggregation optimization model based on the power information of the distributed energy site, the method further includes:

[0176] Based on the power transaction information of the power trading market, the information parameters of the distributed energy equipment in the distributed energy station, and the distributed energy prediction curve data in the distributed energy station, a standardized modeling method is used to model different types of distributed resources in the distributed energy station, and several distributed resource models are obtained;

[0177] According to the plurality of distributed resource models, power information of the distributed energy site is obtained.

[0178] Furthermore, the dynamic aggregation optimization model is constructed based on the power information of the distributed energy sites, specifically:

[0179] According to the range of tie-line power, the range of current accumulated power of the virtual power plant and the range of change of the tie-line power of the virtual power plant, an equivalent model is constructed;

[0180] Taking minimizing the generation cost of the virtual power plant as the objective function, the maximum range power balance constraint of the virtual power plant equivalent parameters, the active power constraint of the distributed resource generation equipment, the power feasible region constraint of non-energy storage distributed resources, and the charging and discharging power constraints of the energy storage equipment are used as constraints of the objective function to construct a power baseline model;

[0181] The equivalent value model and the power baseline model are used as a dynamic aggregation optimization model.

[0182] Furthermore, the equivalent model is constructed according to the range of tie-line power, the range of current accumulated power of the virtual power plant, and the range of change in the tie-line power of the virtual power plant, specifically:

[0183] The expression of the equivalent model is:

[0184]

[0185]

[0186]

[0187] in, is the tie line power at time t, t∈Ω T is the set of all adjustable operating time periods, P tie,min and P tie,max are the minimum and maximum values ​​of the tie line power respectively; E tie,min and E tie,max are the minimum and maximum values ​​of the current accumulated electricity of the virtual power plant respectively; and are the minimum and maximum downward and upward changes in the power of the virtual power plant tie line at time t, respectively.

[0188] Furthermore, the objective function is to minimize the power generation cost of the virtual power plant, and the power balance constraint, the active power constraint of the distributed resource power generation equipment, the power feasible region constraint of the non-energy storage distributed resource, and the charging and discharging power constraint of the energy storage equipment are used as constraints of the objective function to construct a power baseline model, specifically:

[0189] The expression of the objective function is:

[0190]

[0191] Among them, Ω G It is the collection of all distributed resource power generation equipment. is the tie line power at time t, Ω T For, c i for, for, for;

[0192] The expression of the power balance constraint is:

[0193]

[0194] Among them, Ω G is the collection of all distributed resource power generation equipment, Ω H is the set of all non-energy storage distributed resources, Ω S It is the collection of all energy storage resources; is the target active power of distributed resource generation equipment i at time t, is the active power of distributed resource device i at time t, is the active power of energy storage device i at time t; Injecting active power into grid nodes for distributed resource generation equipment; is the active power injected into the grid node by non-storage distributed resources at time t; is the total load power at time t;

[0195] The expression of the active power constraint of the distributed resource power generation equipment at time t is:

[0196]

[0197] The expression of the power feasible region constraint of the non-energy storage distributed resource at time t is:

[0198]

[0199] The expression of the charging and discharging power constraint of the energy storage device at time t is:

[0200]

[0201] in, is the target active power column vector of distributed resource generation equipment i at time t, is the active power column vector of distributed resource device i at time t, is the active power column vector of energy storage device i at time t; Φ G,i represents the feasible domain of the active output variable of distributed resource generation equipment i at each moment; Φ H,irepresents the feasible domain of the active output variable of non-energy storage distributed resource i at each moment; Φ S,i Represents the feasible region of the power output variable of energy storage resource i at each moment.

[0202] Furthermore, the maximum range of equivalent parameters of the virtual power plant is obtained based on the power baseline model, specifically:

[0203] The power balance constraint, the active power constraint of distributed resource generation equipment, the power feasible domain constraint of non-energy storage distributed resources, and the charging and discharging power constraints of energy storage equipment are taken as the maximum range of the equivalent parameters of the virtual power plant.

[0204] Furthermore, the maximum range of equivalent parameters of the virtual power plant is used as a constraint condition to solve several preset optimization problems respectively to obtain the initial parameters of the dynamic aggregation optimization model;

[0205] Among them, several preset optimization problems are: minimizing the interconnection line power at time t, maximizing the interconnection line power at time t, minimizing the sum of the interconnection line power of all adjustable operating periods, maximizing the sum of the interconnection line power of all adjustable operating periods, minimizing the change in the virtual power plant interconnection line power at time t, and maximizing the change in the virtual power plant interconnection line power at time t.

[0206] Furthermore, the parameters in the equivalent model are corrected according to the day-ahead optimized scheduling data, the adjustable resource parameters of the dynamic aggregation optimization model are revised on a rolling basis according to the power information of the distributed energy equipment within the day, and the equivalent model is optimized according to the cost function to obtain a corrected dynamic aggregation optimization model, specifically:

[0207] By optimizing and solving the dynamic aggregation optimization model, the economic dispatch plan data with the largest deviation is obtained;

[0208] Correcting parameters in the equivalent model according to the benchmark curve and the scheduling plan data with the largest deviation;

[0209] The benchmark curve is obtained based on the day-ahead optimized scheduling data of the power baseline model;

[0210] Based on the power information of distributed energy devices in the virtual power plant, the adjustable capacity range at the next scheduling moment is calculated, the information is updated to the power grid, and the adjustable resource parameters of the dynamic aggregation optimization model are adjusted on a daily rolling basis;

[0211] The power information includes: operating status information and energy storage charge status information;

[0212] An upper bound estimate of the virtual power plant cost function is obtained, and several calculation points are selected between the upper and lower power limits of the equivalent model. A single-period optimization problem with a penalty is solved for each of the calculation points, and the equivalent model is optimized to obtain a revised dynamic aggregation optimization model.

[0213] Furthermore, the economic dispatch plan data with the largest deviation is obtained by optimizing and solving the dynamic aggregation optimization model, specifically:

[0214] The expression for solving the economic dispatch plan data with the largest deviation is:

[0215]

[0216]

[0217] Among them, p tie is the column vector of the tie-line power planned by the virtual power plant at each moment; p is the column vector of the power of each device at each scheduling moment, and They correspond to the upward and downward deviations between the tie line power and the dispatch plan at time t, respectively.

[0218] Furthermore, the single-period optimization problem with penalty is solved for each of the calculation points, specifically:

[0219] The expression for solving the single-period optimization problem with penalty for each calculation point is:

[0220]

[0221]

[0222] in, and They correspond to the upward and downward deviations between the tie line power and the dispatch plan at time t, is the reference power of energy storage device i at time t, where the constraint Indicates that the energy storage charging and discharging plan is the same as the base state, is the power generation cost of distributed resources at time t, is the electricity price cost coefficient of purchasing electricity through the interconnection line at time t, and M is the deviation parameter.

[0223] The above-mentioned dynamic aggregation device for virtual power plant scale resources can implement the dynamic aggregation method for virtual power plant scale resources of the above-mentioned method embodiment. The optional options in the above-mentioned method embodiment are also applicable to this embodiment and are not described in detail here. The remaining contents of the embodiment of this application can refer to the contents of the above-mentioned method embodiment and are not repeated in this embodiment.

[0224] In this embodiment, since the virtual power plant involves many forms of user-side energy resources, please refer to Figure 4 The present invention proposes a general-purpose industrial control hardware device basic platform based on an embedded system. Through cloud-edge collaborative technology, it reforms and innovates the monitoring architecture system of traditional distributed energy stations, establishes a new data flow interaction architecture for energy production and consumption, supports the economical and efficient use of computing resources, emphasizes the scalability and on-demand service of resources, and comprehensively integrates resource combination, monitoring, economic and environmental benefits in the process of distributed autonomous operation to obtain the optimal solution.

[0225] This platform connects to the virtual power plant aggregator operations and management system at the upper-level grid control center, aggregating a large number of adjustable resources southward. After the control center sends a request to the virtual power plant aggregator operations and management system, the general-purpose chemical control hardware infrastructure platform dynamically decomposes the power or load of the upper-level demand response based on different characteristics. By solving the dynamic aggregation optimization model for the virtual power plant's scaled resources, it effectively decomposes the adjustable load to meet demand response requirements.

[0226] The software modular design of the basic platform of general-purpose industrial control hardware devices based on embedded systems includes: system layer, application layer, driver layer and hardware board;

[0227] The system layer and application layer set up basic functions such as synchronous sampling, frequency tracking and FFT calculation to support the functions of the virtual power plant aggregator operation and management system. The driver layer connects the system layer, application layer and hardware boards respectively, and configures the corresponding drivers.

[0228] The basic platform for general-purpose industrial control hardware devices based on embedded systems integrates network, computing, storage, and application functions to realize functions such as collection, storage, and forwarding. It has flexible communication ports, sends the target value of the optimized distributed resource output to the edge end, changes the output through its power controller, and returns the actual power measurement value.

[0229] Example 3

[0230] Accordingly, the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the dynamic aggregation method of virtual power plant scale resources as described in any one of the above embodiments.

[0231] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device.

[0232] The terminal device may be a computing device such as a desktop computer, a notebook computer, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0233] The processor may be a central processing unit (CPU), or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and connects various parts of the entire terminal device using various interfaces and lines.

[0234] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the terminal device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile terminal, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Med ia Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0235] Wherein, if the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0236] The specific embodiments described above further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for dynamically aggregating large-scale resources of a virtual power plant, characterized in that: Applied to a management system, the management system is connected to a cloud server and several control edges of a distributed energy site; the cloud server is connected to the power trading market; The method comprises: Constructing a dynamic aggregation optimization model based on the power information of distributed energy sites; the dynamic aggregation optimization model includes an equivalent model and a power baseline model; According to the power baseline model, the maximum range of equivalent parameters of the virtual power plant is obtained; Taking the maximum range of equivalent parameters of the virtual power plant as a constraint condition, solving several preset optimization problems respectively to obtain initial parameters of the dynamic aggregation optimization model; The parameters in the equivalent value model are modified based on the day-ahead optimized dispatch data. The adjustable resource parameters of the dynamic aggregation optimization model are modified on a rolling basis based on the power information of the distributed energy devices within the day. The equivalent value model is optimized based on the cost function to obtain the modified dynamic aggregation optimization model. Calculating a new economic dispatch plan using the modified dynamic aggregation optimization model; sending the new economic dispatch plan to a cloud server so that the cloud server generates and returns corresponding dispatch instructions; The scheduling instruction is decomposed and sent to the corresponding control edge for execution.

2. A method for dynamically aggregating large-scale resources of a virtual power plant according to claim 1, characterized in that: Before constructing the dynamic aggregation optimization model based on the power information of the distributed energy site, the method further includes: Based on the power transaction information of the power trading market, the information parameters of the distributed energy equipment in the distributed energy station, and the distributed energy prediction curve data in the distributed energy station, a standardized modeling method is used to model different types of distributed resources in the distributed energy station, and several distributed resource models are obtained; According to the plurality of distributed resource models, power information of the distributed energy site is obtained.

3. The method for dynamically aggregating large-scale resources of a virtual power plant according to claim 1, characterized in that: The dynamic aggregation optimization model is constructed based on the power information of the distributed energy site, specifically: According to the range of tie-line power, the range of current accumulated power of the virtual power plant and the range of change of the tie-line power of the virtual power plant, an equivalent model is constructed; Taking minimizing the generation cost of the virtual power plant as the objective function, the maximum range power balance constraint of the virtual power plant equivalent parameters, the active power constraint of the distributed resource generation equipment, the power feasible region constraint of non-energy storage distributed resources, and the charging and discharging power constraints of the energy storage equipment are used as constraints of the objective function to construct a power baseline model; The equivalent value model and the power baseline model are used as a dynamic aggregation optimization model.

4. A method for dynamically aggregating large-scale resources of a virtual power plant according to claim 3, characterized in that: The equivalent model is constructed according to the range of the tie line power, the range of the current accumulated power of the virtual power plant, and the range of the change in the tie line power of the virtual power plant, specifically: The expression of the equivalent model is: in, is the tie line power at time t, t∈Ω T is the set of all adjustable operating time periods, P tie,min and P tie,max are the minimum and maximum values ​​of the tie line power respectively; E tie,min and E tie,max are the minimum and maximum values ​​of the current accumulated electricity of the virtual power plant respectively; and are the minimum and maximum downward and upward changes in the virtual power plant tie line power at time t, respectively.

5. The method for dynamically aggregating large-scale resources of a virtual power plant according to claim 3, characterized in that: The objective function is to minimize the power generation cost of the virtual power plant, and the power balance constraint, the active power constraint of the distributed resource power generation equipment, the power feasible region constraint of the non-energy storage distributed resource, and the charging and discharging power constraint of the energy storage equipment are used as constraints of the objective function to construct a power baseline model. Specifically, The expression of the objective function is: Among them, Ω G It is the collection of all distributed resource power generation equipment. is the tie line power at time t, Ω T is the set of all adjustable operating time periods, c i is the generation cost function of distributed resources, is the electricity price cost coefficient of purchasing electricity through the tie line at time t, is the active power output of distributed resources at time t; The expression of the power balance constraint is: Among them, Ω G is the collection of all distributed resource power generation equipment, Ω H is the set of all non-energy storage distributed resources, Ω S It is the collection of all energy storage resources; is the target active power of distributed resource generation equipment i at time t, is the active power of distributed resource device i at time t, is the active power of energy storage device i at time t; Injecting active power into grid nodes for distributed resource generation equipment; is the active power injected into the grid node by non-storage distributed resources at time t; is the total load power at time t; The expression of the active power constraint of the distributed resource power generation equipment at time t is: The expression of the power feasible region constraint of the non-energy storage distributed resource at time t is: The expression of the charging and discharging power constraint of the energy storage device at time t is: in, is the target active power column vector of distributed resource generation equipment i at time t, is the active power column vector of distributed resource device i at time t, is the active power column vector of energy storage device i at time t; Φ G,i represents the feasible domain of the active output variable of distributed resource generation equipment i at each moment; Φ H,i represents the feasible domain of the active output variable of non-energy storage distributed resource i at each moment; Φ S,i Represents the feasible region of the power output variable of energy storage resource i at each moment.

6. A method for dynamically aggregating large-scale resources of a virtual power plant according to claim 5, characterized in that: The maximum range of equivalent parameters of the virtual power plant is obtained according to the power baseline model, specifically: The power balance constraint, the active power constraint of distributed resource generation equipment, the power feasible domain constraint of non-energy storage distributed resources, and the charging and discharging power constraints of energy storage equipment are taken as the maximum range of the equivalent parameters of the virtual power plant.

7. A method for dynamically aggregating large-scale resources of a virtual power plant according to claim 6, characterized in that: The method uses the maximum range of equivalent parameters of the virtual power plant as a constraint condition to solve several preset optimization problems respectively to obtain the initial parameters of the dynamic aggregation optimization model; Among them, several preset optimization problems are: minimizing the interconnection line power at time t, maximizing the interconnection line power at time t, minimizing the sum of the interconnection line power of all adjustable operating periods, maximizing the sum of the interconnection line power of all adjustable operating periods, minimizing the change in the virtual power plant interconnection line power at time t, and maximizing the change in the virtual power plant interconnection line power at time t.

8. A method for dynamically aggregating large-scale resources of a virtual power plant according to any one of claims 1 to 7, characterized in that: The parameters in the equivalent model are modified according to the day-ahead optimized scheduling data, the adjustable resource parameters of the dynamic aggregation optimization model are modified on a rolling basis according to the power information of the distributed energy equipment within the day, and the equivalent model is optimized according to the cost function to obtain the modified dynamic aggregation optimization model, which is specifically: By optimizing and solving the dynamic aggregation optimization model, the economic dispatch plan data with the largest deviation is obtained; Correcting parameters in the equivalent model according to the benchmark curve and the scheduling plan data with the largest deviation; The benchmark curve is obtained based on the day-ahead optimized scheduling data of the power baseline model; Based on the power information of distributed energy devices in the virtual power plant, the adjustable capacity range at the next scheduling moment is calculated, the information is updated to the power grid, and the adjustable resource parameters of the dynamic aggregation optimization model are adjusted on a daily rolling basis; The power information includes: operating status information and energy storage charge status information; An upper bound estimate of the virtual power plant cost function is obtained, and several calculation points are selected between the upper and lower power limits of the equivalent model. A single-period optimization problem with a penalty is solved for each of the calculation points, and the equivalent model is optimized to obtain a revised dynamic aggregation optimization model.

9. A method for dynamically aggregating large-scale resources of a virtual power plant according to claim 8, characterized in that: The economic dispatch plan data with the largest deviation is obtained by optimizing and solving the dynamic aggregation optimization model, specifically: The expression for solving the economic dispatch plan data with the largest deviation is: Among them, p tie is the column vector of the tie-line power planned by the virtual power plant at each moment; p is the column vector of the power of each device at each scheduling moment, and They correspond to the upward and downward deviations between the tie line power and the dispatch plan at time t, respectively.

10. A method for dynamically aggregating large-scale resources of a virtual power plant according to claim 8, characterized in that: The single-period optimization problem with penalty is solved for each calculation point, specifically: The expression for solving the single-period optimization problem with penalty for each calculation point is: in, and They correspond to the upward and downward deviations between the tie line power and the dispatch plan at time t, is the reference power of energy storage device i at time t, where the constraint Indicates that the energy storage charging and discharging plan is the same as the base state, is the power generation cost of distributed resources at time t, is the electricity price cost coefficient of purchasing electricity through the interconnection line at time t, and M is the deviation parameter.

11. A dynamic aggregation device for large-scale resources of a virtual power plant, characterized in that: Applied to a management system, the management system is connected to a cloud server and several control edges of a distributed energy site; the cloud server is connected to the power trading market; The device comprises: a model construction module, a model correction module and a scheduling control module; The model construction module is used to construct a dynamic aggregation optimization model based on the power information of the distributed energy site; the dynamic aggregation optimization model includes an equivalent model and a power baseline model; The model correction module is used to obtain the maximum range of equivalent parameters of the virtual power plant according to the power baseline model; Taking the maximum range of equivalent parameters of the virtual power plant as a constraint condition, solving several preset optimization problems respectively to obtain initial parameters of the dynamic aggregation optimization model; The parameters in the equivalent value model are modified based on the day-ahead optimized dispatch data. The adjustable resource parameters of the dynamic aggregation optimization model are modified on a rolling basis based on the power information of the distributed energy devices within the day. The equivalent value model is optimized based on the cost function to obtain the modified dynamic aggregation optimization model. The scheduling control module is used to calculate a new economic scheduling plan using the modified dynamic aggregation optimization model; send the new economic scheduling plan to the cloud server so that the cloud server generates and returns corresponding scheduling instructions; The scheduling instruction is decomposed and sent to the corresponding control edge for execution.

12. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program; wherein, when the computer program is running, it controls the device where the computer-readable storage medium is located to execute a method for dynamically aggregating large-scale resources of a virtual power plant as described in any one of claims 1 to 10.

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