Virtual power plant flexible resource aggregation control layered connection management method and device

Abstract

The invention provides a virtual power plant flexible resource aggregation control layered connection management method and device, and belongs to the field of virtual power plant control. The method comprises the following steps: dynamically partitioning a virtual power plant, and obtaining resource data of multi-type flexible resources of each partition; determining a peak time period and a valley time period of daily electricity consumption of each partition according to the resource data of each partition, and determining adjustable data of each partition corresponding to different time periods according to an adjustment model; wherein the adjustable data comprises load capacity and energy storage capacity; the adjustment model takes resource scheduling cost minimization as a target and takes flexible resource adjustment capability as a constraint; generating a scheduling instruction for controlling a scheduling device and an energy storage control instruction for controlling an energy storage device according to the adjustable data of each partition; and obtaining scheduling response information fed back by the scheduling equipment and energy storage response information fed back by the energy storage equipment, and generating visual control data according to the scheduling response information and the energy storage response information. According to the invention, the scheduling accuracy of each region can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of virtual power plant control, and particularly to a hierarchical through management method and device for flexible resource aggregation control of a virtual power plant. Background Art

[0002] A virtual power plant is a power coordination management system that realizes the aggregation and coordinated optimization of distributed energy resources (DER) such as distributed generators (DG), energy storage systems, controllable loads, and electric vehicles through advanced information and communication technologies and software systems, and participates in the power market and grid operation as a special power plant. The core of the virtual power plant concept can be summarized as "communication" and "aggregation". The key technologies of the virtual power plant mainly include coordinated control technology, intelligent metering technology, and information and communication technology. The most attractive function of the virtual power plant is its ability to aggregate DER to participate in the operation of the power market and ancillary service market, and provide management and ancillary services for the distribution network and transmission network. The solution idea of the virtual power plant has great market potential in China.

[0003] A virtual power plant is a system that integrates multiple types of power sources to provide a reliable overall power supply. The sources usually consist of a cluster of different types of dispatchable and non-dispatchable, controllable or flexible load (CL or FL) distributed generation (DG) systems, which are controlled by a central agency and can include micro-CHP, natural gas reciprocating engines, small wind power plants (WPP), photovoltaic (PV), river hydropower plants, small hydropower, biomass, backup generators, and energy storage systems (ESS). The advantages of this system include the ability to provide peak load power or follow load generation in a short period of time. Such a VPP can replace traditional power plants while providing higher efficiency and greater flexibility. Greater flexibility enables the system to respond better to fluctuations, but its complexity requires complex optimization, control, and secure communication.

[0004] The patent with the publication number CN107506851A discloses a multi-region virtual power plant integrated energy coordinated scheduling optimization model, which considers the cold, heat, and power interaction between different regions within the virtual power plant and the complementary problems of electricity, heat, and cold within the region, and realizes the coordinated optimization scheduling of cold, heat, and power for different regions and different types of aggregation units within the virtual power plant. In the process of implementing the embodiments of the present application, it is found that the scheduling accuracy for each region in the related technologies needs to be further improved. Summary of the Invention

[0005] The embodiments of the present invention provide a hierarchical through management method and device for flexible resource aggregation control of a virtual power plant to solve the problem that the scheduling accuracy for each region needs to be further improved.

[0006] In a first aspect, an embodiment of the present invention provides a hierarchical and integrated management method for flexible resource aggregation control in a virtual power plant, including:

[0007] Dynamically partition the virtual power plant to obtain resource data of various types of flexible resources in each partition; wherein, the resource data includes: electricity load data, energy storage data, current data, voltage data, and power data;

[0008] Determine the peak period and valley period of daily electricity consumption in each partition according to the resource data of each partition, and determine the adjustable data in each partition corresponding to different periods according to the adjustment model; wherein, the adjustable data includes the load amount and the energy storage amount; the adjustment model aims to minimize the resource scheduling cost and is constrained by the flexible resource adjustment ability;

[0009] Generate a scheduling instruction for controlling the scheduling device and an energy storage control instruction for controlling the energy storage device according to the adjustable data in each partition;

[0010] Obtain the scheduling response information fed back by the scheduling device and the energy storage response information fed back by the energy storage device, and generate visual control data according to the scheduling response information and the energy storage response information.

[0011] In a second aspect, an embodiment of the present invention provides a hierarchical and integrated management device for flexible resource aggregation control in a virtual power plant, including:

[0012] An acquisition module for dynamically partitioning the virtual power plant to obtain resource data of various types of flexible resources in each partition; wherein, the resource data includes: electricity load data, energy storage data, current data, voltage data, and power data;

[0013] An adjustable data determination module for determining the peak period and valley period of daily electricity consumption in each partition according to the resource data of each partition, and determining the adjustable data in each partition corresponding to different periods according to the adjustment model; wherein, the adjustable data includes the load amount and the energy storage amount; the adjustment model aims to minimize the resource scheduling cost and is constrained by the flexible resource adjustment ability;

[0014] A control module for generating a scheduling instruction for controlling the scheduling device and an energy storage control instruction for controlling the energy storage device according to the adjustable data in each partition, and obtaining the scheduling response information fed back by the scheduling device and the energy storage response information fed back by the energy storage device, and generating visual control data according to the scheduling response information and the energy storage response information.

[0015] An embodiment of the present invention provides a hierarchical and integrated management method and device for flexible resource aggregation control in a virtual power plant. By implementing a dynamic zoning strategy for the virtual power plant, data of various types of flexible resources in each zone are accurately collected, including electricity load data, energy storage data, current data, voltage data, and power data. Based on these data, the peak and trough periods of the daily power demand in each zone are accurately defined, and an adjustment model is used to determine the adjustable parameters in each region during different time periods, including: load quantity and energy storage capacity. The core of this adjustment model lies in optimizing the resource scheduling cost, while considering the adjustment ability of flexible resources as a constraint to ensure that the adjustment ability of flexible resources is fully released. Based on the adjustable data (load quantity and energy storage quantity) within the zone, precise scheduling instructions are generated, which directly act on the control scheduling equipment and energy storage equipment to achieve refined and efficient resource scheduling. In addition, the system also needs to collect response information from the scheduling equipment and energy storage equipment, and generate visual control data based on this for easy monitoring and analysis. This implementation plan starts from the most basic resource level, evaluates the adjustability of each resource, thereby more accurately grasping the regulation range of the underlying resources, and enhancing the scalability and flexibility of resource aggregation. By determining the peak and trough periods of electricity consumption in each region and formulating corresponding adjustable parameters accordingly, and finally generating control instructions, the effective integration and hierarchical management of a large number of flexible resources in the virtual power plant are realized, ensuring the safety, stability, and economic benefits of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0017] Figure 1 is a flowchart of the implementation of the hierarchical and integrated management method for flexible resource aggregation control in a virtual power plant provided by an embodiment of the present invention;

[0018] Figure 2 is a schematic diagram of the types of flexible resources provided by an embodiment of the present invention;

[0019] Figure 3 is a schematic diagram of the data processing architecture provided by an embodiment of the present invention;

[0020] Figure 4 is a schematic diagram of the system structure of the hierarchical and integrated management method for flexible resource aggregation control in a virtual power plant provided by an embodiment of the present invention;

[0021] Figure 5It is a schematic structural diagram of a hierarchical and integrated management device for flexible resource aggregation control of a virtual power plant provided by an embodiment of the present invention. Detailed implementation manners

[0022] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present invention. However, those skilled in the art should clearly understand that the present invention can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present invention.

[0023] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will be described through specific embodiments in conjunction with the accompanying drawings.

[0024] Figure 1 It is an application scenario diagram of the hierarchical and integrated management method for flexible resource aggregation control of a virtual power plant provided by an embodiment of the present invention. As Figure 1 shown, it includes the following steps:

[0025] S101, dynamically partition the virtual power plant to obtain resource data of various types of flexible resources in each partition; wherein, the resource data includes: electricity load data, energy storage data, current data, voltage data, and power data.

[0026] The execution subject of the hierarchical and integrated management method for flexible resource aggregation control of a virtual power plant provided by the embodiments of this application is the central control system or the data center. This execution subject is communicatively connected to the power consumption information acquisition system within the target resource management scope. Optionally, the central control system or the data center can set up a management system that can be deployed in a computer to facilitate more intuitive and convenient visualization management.

[0027] Among them, the power consumption information acquisition system uses sensors and intelligent meters and other facilities to monitor the voltage, current, and power in the power system in real time, and then accurately converts these analog signals into digital signals for efficient sampling and quantization processing. The data collected by the power consumption information acquisition system is transmitted to the central control system or the data center by means of communication technology. The central control system or the data center undertakes the tasks of receiving, parsing, and storing the collected power consumption data, and can perform calibration, sorting, and analysis processing on the data.

[0028] In the specific implementation process, in view of the large number of intelligent electronic devices and sensors in the power grid, the data generated by these devices presents the characteristics of multi-source heterogeneity. Therefore, this article constructs a data acquisition layer to support distributed acquisition and fusion to ensure the comprehensiveness, accuracy, and real-time nature of the data.

[0029] In a possible implementation, the dynamic partitioning of the virtual power plant includes: dynamic partitioning based on users and / or parks.

[0030] Among them, for the dynamic partitioning of the virtual power plant, on the one hand, the partitioning method based on users and / or parks aims to achieve accurate output partitioning of the control resources of the virtual power plant; on the other hand, by dynamically executing the partitioning mechanism, the virtual power plant can flexibly adjust and optimize resource allocation according to the real-time operating state.

[0031] In addition, the partitioning method based on users and / or parks can divide the vast power grid area into several partitions with similar characteristics according to various factors such as geographical location, resource characteristics, and electricity demand. For example: one partition may mainly cover the coastal areas rich in wind resources, and another may be composed of solar power plants and industrial parks. The dynamic partitioning of the virtual power plant lays a foundation for improving the accuracy of subsequent resource scheduling.

[0032] Optionally, flexible resources include wind turbines, solar generators, industrial parks, public buildings, commercial buildings, energy storage devices, and electric vehicles.

[0033] Such as Figure 2 Exemplarily shows the types of flexible resources. For the specific partitioning results of different users or parks, the types of flexible resources equipped in each area may vary. To enhance the accuracy of control, effective integration is carried out for each partition, and various flexible resources within the partition are uniformly allocated, so as to achieve the efficient utilization of renewable energy.

[0034] S102. Determine the peak period and valley period of daily electricity consumption in each partition according to the resource data of each partition, and determine the adjustable data of each partition corresponding to different periods according to the adjustment model; among them, the adjustable data includes the load amount and the energy storage amount; the adjustment model aims to minimize the resource scheduling cost and is constrained by the flexible resource adjustment ability.

[0035] Among them, determining the peak period and valley period of daily electricity consumption in each partition depends on the statistical analysis of historical data and plays an important role in the stable operation of the power system. Specifically:

[0036] During the peak period, the power demand surges, and the virtual power plant needs to quickly mobilize various flexible resources, such as starting the energy storage device to discharge, adjusting the electricity consumption plans of industrial parks and commercial buildings, and guiding electric vehicles to charge during non-peak hours, etc., to relieve the power grid pressure and ensure the stable and reliable power supply. During the valley period, the power demand is relatively low, and the virtual power plant can use this opportunity to charge the energy storage device, optimize the operation of wind and solar generators, etc., to improve the energy utilization efficiency and reduce the operation cost.

[0037] In addition, for different partitions, the peak and valley periods of daily electricity consumption may also vary. For example, by analyzing and comparing the daily electricity consumption data collected in Area A, it is found that the peak period of daily electricity consumption in Area A is from 17:00 to 20:00, and the valley period is from 1:00 to 7:00; by analyzing and comparing the daily electricity consumption data collected in Area B, it is found that the peak period of daily electricity consumption in Area A is from 18:00 to 22:00, and the valley period is from 3:00 to 8:30. In other possible implementation manners, optionally, the partition analysis result includes the electricity consumption and electricity price during this period.

[0038] When determining the adjustable data for each partition corresponding to different time periods, the core lies in the adjustment model. The virtual power plant determines the optimal adjustment strategy for each partition at different time periods by comprehensively considering the power demand, resource characteristics, and economic costs at different time periods, so as to maximize the economic and social benefits while meeting the power demand.

[0039] Optionally, the resource operates with the goal of minimizing the deviation from the planned operation curve, enabling the virtual power plant to flexibly adjust and optimize the resource allocation according to the real-time operation status.

[0040] In a specific embodiment, the adjustment model aims to minimize the resource scheduling cost. This minimization of the resource scheduling cost is analyzed based on the aggregated energy to ensure the overall economic benefits of the system. Constrained by the flexible resource adjustment ability, this constraint constructs the constraint conditions based on each flexible resource within the partition. The adjustable data for each partition at different time periods includes the adjustable amount of each flexible resource within the partition. In this embodiment, starting from the underlying flexible resources, the adjustable ability of each resource is evaluated to more precisely control the feasible region regulation range of each underlying resource, improving the scalability and flexibility of the aggregation model.

[0041] S103. Generate a scheduling instruction for controlling the scheduling device and a storage control instruction for controlling the energy storage device according to the adjustable data of each partition.

[0042] Among them, the generated scheduling instruction and energy storage instruction are transmitted to the corresponding scheduling device and energy storage device through a communication network. Optionally, the communication network is a wired communication technology or a wireless communication technology, such as optical fiber communication, wireless communication, etc.

[0043] S104. Obtain the scheduling response information fed back by the scheduling device and the energy storage response information fed back by the energy storage device, and generate visual control data according to the scheduling response information and the energy storage response information.

[0044] In this embodiment, by implementing a dynamic partitioning strategy for the virtual power plant, data of various types of flexible resources in each partition are accurately collected, including electricity load data, energy storage data, current data, voltage data, and power data. Based on these data, the peak and valley periods of the daily power demand in each partition are accurately defined, and an adjustment model is used to determine the adjustable parameters in each region during different time periods, including: the load amount and the energy storage capacity. The core of this adjustment model lies in optimizing the resource scheduling cost, while considering the adjustment ability of flexible resources as a constraint to ensure that the adjustment ability of flexible resources is fully released. Based on the adjustable data (load amount and energy storage amount) within the partition, accurate scheduling instructions are generated, which directly act on the control scheduling equipment and energy storage equipment to achieve refined and efficient resource scheduling. In addition, the system also needs to collect the response information from the scheduling equipment and energy storage equipment, and generate visual control data based on this for easy monitoring and analysis. This implementation plan starts from the most basic resource level, evaluates the adjustability of each resource, thereby more accurately grasping the regulation range of the underlying resources, and enhancing the scalability and flexibility of resource aggregation. By determining the peak and valley periods of electricity consumption in each region and formulating corresponding adjustable parameters accordingly, and finally generating control instructions, the effective integration and hierarchical management of a large number of flexible resources in the virtual power plant are realized, ensuring the safety, stability, and economic benefits of the power system.

[0045] Based on the previous embodiment, when establishing the adjustment model, the energy-related elements that affect cost calculation are defined as cost constraints, and the conditions that are not directly related to cost, such as power limits and ramp rates, are classified as non-cost constraints.

[0046] In a possible implementation manner, the constraint conditions of the adjustment model include: energy storage device constraints and controllable load device constraints;

[0047] Among them, energy storage devices can store / release energy. Taking the charge / discharge power and state of charge as boundaries, the constraints of energy storage devices generally include power, ramp, energy, etc. The energy storage device constraints include:

[0048] 0≤p ES,in (t)≤p ES,in,max (t)

[0049] 0≤p ES,out (t)≤p -ES,out,max (t)

[0050] -r ES,max ≤[p ES,in (t - 1)-p ES,in (t - 1),p ES,out (t - 1)-p ES,out (t)≤p -ES,in (t)≤r ES,max

[0051]

[0052] e ES，min ≤e ES (t) ≤ e ES，max

[0053] Wherein, p ES,in (t), p ES,out (t) and e ES (t) are respectively the charging power, discharging power and remaining energy of the energy storage device at time t; θ ES is the energy dissipation rate; p ES,in,max (t), p ES,out,max (t) and -r ES,max are respectively the maximum charging power, maximum discharging power and ramp power limit of the ES; e ES,max and e ES,min are respectively the maximum and minimum acceptable remaining energies constrained by the state of charge boundary; n ES,in and n ES,out are respectively the charging power efficiency and discharging power efficiency of the energy storage device;

[0054] The controllable load device constraints include:

[0055] p CL,t,min ≤ p CL,t ≤ p CL,t,max

[0056] -r CL,lim ≤ p CL,t-1 ≤ r CL,lim

[0057] e CL,t = θ CL e CL,t-1 η CL p CL,t +(1 - θ CL ) ω CL,t

[0058] e CL,t,min ≤ e CL,t ≤ e CL,t,max

[0059] Wherein, p CL,t and e CL,t are respectively the power consumption of the controllable load and the indoor temperature; p CL,t,max , p CL,t,min and r CL,lim are respectively the maximum, minimum power consumption and ramp limit of the controllable load; e CL,t,min and e CL,t,max are respectively the upper and lower bounds of the desired indoor temperature; ω CL,t is the ambient temperature; θCL is the dissipation rate of the controllable load; η CL is the conversion coefficient of active power to temperature.

[0060] In a possible implementation, the constraint conditions for adjusting the model further include: energy storage cost constraint:

[0061]

[0062] In the formula, T represents the interval corresponding to the capacity constraint of the continuous operation period of the energy storage; α i represents the cost constraint coefficient, P i represents the power of the energy storage, and β is the cost parameter.

[0063] Optionally, in step S102, the Chino polyhedron technology is adopted to construct the aggregated energy analysis model. The determination process of the resource scheduling cost follows the following logic:

[0064] Based on the resource cost aggregation strategy, the aggregator can effectively integrate the underlying flexible resources and define the boundary of its operable range. This process not only helps optimize the scheduling of resources but also promotes the refinement of management. At the same time, through this strategy, the adjustable load amount and energy storage capacity can be obtained.

[0065] As a bridge between power grid dispatching and demand-side resources, the virtual power plant needs to aggregate the response characteristics of demand-side resources and then participate in power grid dispatching. The key lies in the final power consumption curve generated by power users, which results from the final power consumption pattern achieved by users through power consumption strategies such as regulating energy storage devices. Based on the above requirements, the flexible adjustment potential range of demand-side resources is clarified, that is, its flexibility feasible space or feasible region is defined. However, since this aggregation problem involves complex calculations and a high degree of coupling in the time dimension, directly solving the exact feasible region often faces challenges, so a strategy of first approximately estimating the feasible region of a single resource is often adopted. As an effective inner approximation technology, the Chino polyhedron provides an inner approximation framework for the flexibility feasible region of flexible resources (such as energy storage devices) by constructing a fully symmetric polytope model. This method is known for its diverse morphological adaptability and high aggregation performance, and can more accurately characterize the flexibility characteristics of distributed resources, thereby optimizing resource scheduling and utilization.

[0066] For the aggregation of cost constraints, a scenario including multiple energy storage resources is set, where each resource i is associated with a series of unit regulation power costs (c i,j ∈s i , where j is a positive integer). At the same time, each cost item is accompanied by a maximum radius data set r i, which characterizes the effective scope of action or influence boundary corresponding to the cost. When these energy storage resources are integrated into an aggregate, the unit regulation power cost is no longer a simple summation of individual resources, but forms a new cost set S, which contains K cost items (C k ∈S, where k is a positive integer), and these cost items are the result of the interaction and optimization among resources under the aggregation effect. Correspondingly, the maximum influence radius of the aggregate cost also constitutes a new set R, where R k represents the maximum scope of action of the aggregated cost function under specific conditions.

[0067] In one possible implementation, the method further includes:

[0068] Constructing an aggregated cost function according to the energy storage cost constraint and energy storage resources;

[0069] Among them, the cost function is:

[0070] C = c k , r ∈ [R k-1 , R k ), k = 1, 2,..., K

[0071] Among them, R k is obtained by superimposing energy storage with a cost value of c k , c i,j is the unit regulation power cost; i is the i-th resource; j is a positive integer.

[0072] In one possible implementation, before determining the adjustable data for each partition corresponding to different time periods according to the adjustment model, it further includes:

[0073] Constructing an objective function according to the resource scheduling cost:

[0074] min f(x, y)

[0075] In the formula, f represents the resource scheduling cost, and x and y respectively represent the power variable and other variables on the aggregation.

[0076] Based on the power generation power, energy storage charge and discharge power, and load regulation amount, the adjustable load and energy storage capacity are obtained. These results will show at each time point which loads are adjusted (increased or decreased) and how the energy storage system charges and discharges to balance supply and demand. Among them, the feasible region of the resource scheduling cost is:

[0077] Z = {x ∈ R N : x = c z,cost + G z,cost β z,cost}

[0078] In the formula, c z,costis the center point of the aggregator cost constraint feasible region, G z,cost is the generator matrix of the aggregator cost constraint feasible region, β z,cost is the scaling coefficient matrix of the aggregator cost constraint feasible region.

[0079] Among them, based on the feasible region aggregation method of the Chino polyhedron, considering the economic optimal dispatching problem of the distribution network where the aggregator actively participates in demand response, the above objective function is constructed.

[0080] In this embodiment, the energy-related elements that affect cost calculation are defined as cost constraints, and the conditions that are not directly related to cost, such as power limits and ramp rates, are classified as non-cost constraints, which enriches the level of energy storage system management, deepens the optimization of energy storage system dispatching and management, and initially establishes a mathematical model framework based on the cost characteristics of the feasible region, laying a solid theoretical foundation for subsequent cost-benefit evaluation. In view of the complexity of high-dimensional feasible regions in visualization and volume calculation, the inner approximation method of the Chino polyhedron is adopted to characterize its volume ratio with the maximum approximation degree. This method realizes the rapidity and efficiency of the aggregation process while reducing the loss of resource flexibility, providing strong technical support for resource optimization allocation.

[0081] Based on the foregoing embodiments, during the peak period of daily electricity consumption in each zone, there may be a situation where new energy storage devices such as wind and solar power generation cannot meet the electricity demand of the load. Therefore, to ensure the stable operation of the system, controllable distributed power sources are needed to supplement the power supply.

[0082] In a possible implementation manner, the method further includes:

[0083] When it is determined according to the adjustable data of each zone that the energy storage capacity does not meet the electricity demand of the load, a power control instruction for the controllable distributed power source is generated to ensure power balance; among them, the controllable distributed power source is a gas turbine generator, a diesel generator or a gasoline generator.

[0084] Among them, the fast response ability of the controllable distributed power source is particularly suitable for providing stable energy support in the case of a sudden increase in power demand, so as to ensure the balance of the power system under non-ideal conditions.

[0085] In an alternative embodiment, the controllable distributed power source is a micro gas turbine generator. The application of the micro gas turbine generator in the embodiments of the present application has the following advantages:

[0086] (1). Micro gas turbine generators are a newly developed type of small thermal engines with a single-unit power range of 25 - 300 kW. Their basic technical features include the use of radial flow turbomachinery (centripetal turbines and centrifugal compressors) and regenerative cycles. The adjustable and modular characteristics of micro gas turbines enable them to be more flexible and diverse in virtual power plants, allowing them to adjust their operating strategies in response to real-time electricity prices and load demands, making virtual power plants highly competitive in electricity market transactions and maximizing the economic benefits of virtual power plants.

[0087] (2). Micro gas turbines can adjust their output power within a short time to respond to the real-time changes in the frequency of the power system, thereby maintaining the frequency stability of the virtual power plant and ensuring the balance between power supply and demand. When the load of the virtual power plant increases sharply or the renewable energy generation suddenly decreases, causing the grid frequency to drop, micro gas turbines can quickly start and increase their power generation to counter the trend of frequency decline. Conversely, when the virtual power plant is overloaded or the renewable energy generation is excessive, causing the frequency to rise, they can quickly reduce their output to help the virtual power plant restore frequency balance. In the electricity market, micro gas turbines can participate in services such as frequency regulation, voltage support, and emergency backup, and respond to the immediate demands of the power system by adjusting their operating status in real time.

[0088] (3). During the normal operation of a virtual power plant, various unpredictable problems are faced. Micro gas turbines can quickly start in emergencies such as the intermittency of renewable energy, sudden peaks in power demand, and equipment failures, providing immediate power support, making up for power supply shortages, and ensuring the continuous and stable operation of the power system. The short start-up time of micro gas turbines makes this characteristic play a crucial role when rapid load supplementation or replacement of faulty power sources is required. In the electricity market, this rapid response ability of micro gas turbines makes them an ideal choice for providing backup services. When situations such as frequency drop or voltage instability occur, they can quickly intervene, stabilize the situation, prevent the spread of faults, and reduce the impact on users. In addition, in emergency situations such as extreme weather and large-scale power outages, micro gas turbines can not only provide power output but also, through their flexible adjustment ability, help restore the normal operation of the power system, ensure the power supply to critical infrastructure and services, and reduce economic losses and social impacts.

[0089] (4) The fast start-up and flexible adjustment capabilities of micro gas turbines enable demand response strategies to quickly provide or cut electricity in regulating the supply-demand relationship, allowing virtual power plants to effectively respond to load fluctuations. During high-load or high electricity price periods, micro gas turbines can quickly start up to provide additional power support, alleviating the supply-demand relationship in virtual power plants and avoiding increased operating costs due to purchasing high-priced electricity. During low-load periods, micro gas turbines can quickly stop operating to avoid unnecessary power production, promoting the rational allocation of power resources and enhancing the utilization rate of renewable energy. In addition to coping with short-term source-load fluctuations, micro gas turbines can also participate in long-term demand response. Through planned pre-scheduling, micro gas turbines can provide stable and predictable power support for virtual power plants, enabling operators to more effectively plan power resources and prevent power shortages or surpluses.

[0090] In the specific implementation process, energy storage devices include battery energy storage systems, pumped storage, wind energy storage, photovoltaic energy storage, etc.

[0091] (1) Energy storage systems can store excess renewable energy electricity during low-demand periods and release the stored electrical energy during high-demand periods to meet the instantaneous demand of the power grid, reducing the peak shaving pressure of power grid operation and enhancing the market competitiveness of distributed energy.

[0092] (2) In the electricity market, the supply-demand relationship of electricity is usually reflected in the fluctuation of electricity prices. Especially, the price difference between peak and valley electricity prices is relatively significant. Energy storage systems can charge during periods of low electricity prices and release energy during periods of high electricity prices, realizing the transfer of energy in terms of time value, thereby optimizing the cost of purchasing electricity and increasing the competitiveness of virtual power plants in the electricity market.

[0093] In the contemporary power architecture, energy storage devices play an important role. These devices cover diverse technical paths, such as battery energy storage systems, pumped storage, etc. The core value of energy storage devices lies in their ability to effectively reconcile the mismatch between power supply and demand, becoming a solid backing and reliable partner for the stable operation of the power grid. In key areas such as facing the challenge of large-scale access of renewable energy, alleviating the peak-valley contradiction of power supply and demand, and providing emergency backup power, energy storage devices have demonstrated important functions. In addition, with the continuous advancement of technological innovation and the significant improvement of cost-effectiveness, the application prospect of energy storage technology is becoming increasingly clear, indicating that it will play a more core role in shaping the future power system, accelerating the transformation of the energy structure, and promoting the evolution of smart grids.

[0094] Start the energy storage device during the valley period through the energy storage instruction. During the valley period, by reducing or stopping the operation of the micro gas turbine and instead purchasing low-cost electricity from the power grid to increase the charging of the energy storage device for energy storage of electric energy; start the dispatching device during the peak period through the dispatching instruction, and the dispatching device dispatches the electric energy stored in the energy storage device during the peak period to meet the power demand of the virtual power plant; quickly put the micro gas turbine into use during the peak electricity price period to meet the power demand of the virtual power plant or directly sell electricity to the market, so as to maximize the economic benefits of the entire virtual power plant.

[0095] In a possible implementation manner, after generating the dispatching instruction for controlling the dispatching device and the energy storage control instruction for controlling the energy storage device according to the adjustable data of each partition, it further includes:

[0096] During the valley period of daily electricity consumption, send the first energy storage instruction to the energy storage device and send the first dispatching instruction to the dispatching device; wherein, the first energy storage instruction is used to instruct the energy storage device to start charging and energy storage; the first dispatching instruction is used to instruct to turn off the controllable distributed power source.

[0097] During the peak period of daily electricity consumption in the partition, send the second energy storage instruction to the energy storage device and send the second dispatching instruction to the dispatching device; wherein, the second energy storage instruction is used to instruct the energy storage device to stop charging and energy storage and start discharging; the second dispatching instruction is used to instruct to start the controllable distributed power source.

[0098] Among them, start the dispatching device during the peak period through the dispatching instruction, and the dispatching device dispatches the electric energy stored in the energy storage device during the peak period to meet the power demand of the virtual power plant, effectively relieve the power grid pressure, ensure the stability and reliability of power supply, and at the same time improve the response speed and flexibility of the virtual power plant. At the same time, control the micro gas turbine to quickly put into use during the peak electricity price period through the dispatching device, so as to meet the power demand of the virtual power plant or directly sell electricity to the market, and maximize the economic benefits of the entire virtual power plant.

[0099] In this embodiment, through the hierarchical and integrated management of the aggregation control of the power resources of the virtual power plant by the distributed power source, the energy storage device and the dispatching device, the virtual power plant can meet the power consumption demand of the virtual power plant through the power in the micro gas turbine and the dispatching energy storage device during the peak period, and during the valley period, it can increase the energy storage of the energy storage device through low-cost electricity by purchasing, so as to ensure the power demand of the virtual power plant during the peak period. And when the virtual power plant meets its own power demand during the peak period, the remaining power can be sold to the market, so as to maximize the economic benefits of the entire virtual power plant.

[0100] Based on the foregoing embodiments, to further improve the control accuracy, it is necessary to preprocess the collected data to obtain high-quality power consumption data and ensure data standardization.

[0101] In a possible implementation manner, after obtaining the resource data of various types of flexible resources in each partition, it further includes: preprocessing the resource data;

[0102] Among them, preprocessing the resource data includes:

[0103] Performing horizontal processing on the resource data to remove noise data;

[0104] Filtering the resource data according to the screening conditions to eliminate abnormal information.

[0105] In an alternative implementation manner, horizontal processing is performed on the data to remove noise data. Taking the power load data as an example, the calculation formula is as follows:

[0106] max[|Y(d,t)-Y(d,t-1)|,|Y(d,t)-Y(d,t+1)|]>ε(t)

[0107]

[0108] In the formula, d is the load of the power system on a certain day; t represents the power load at a certain moment; Y(d,t) refers to the load value at the t-th hour on the d-th day; ε(t) refers to the load threshold. When the difference between Y(d,t), Y(d,t + 1), and Y(d,t - 1) exceeds the threshold, the power load value at time t is taken as the average of t + 1 and t - 1.

[0109] Filtering the resource data according to the screening conditions aims to improve the flexibility and availability of the data. For the distorted data information that appears during the data collection process, multi-level processing and screening are carried out. After eliminating the abnormal information, the normal information is integrated, summarized, and stored.

[0110] By setting various screening conditions to filter the data, the flexibility and availability of the data are improved. For the distorted data information that appears during the data collection process, multi-level processing and screening are carried out. After eliminating the abnormal information, the normal information is integrated, summarized, and stored. In the process of optimizing the information collection process, for the intelligent processing of user historical data, especially for target data with characteristic differences (such as power data), a method of automatically screening distorted data is adopted. Based on the operating capacity of the user equipment, it is used as an important reference basis for subsequent classification and screening. The fluctuation range of the abnormal coefficient is usually defined as between 3.2 and 4.1 as the judgment standard to accurately evaluate and determine the current amount of distorted data, aiming to efficiently and accurately eliminate the distorted part from the massive data and ensure the data quality and the reliability of the analysis results.

[0111] In other possible implementation manners, before horizontally processing the resource data, according to the data cleaning rules, for data missing and data redundancy, effective cleaning and disposal are performed by means of deletion, replacement, transformation, etc.

[0112] In a possible implementation manner, the method further includes constructing a data processing architecture to improve the data processing efficiency and ensure the accuracy and availability of the data.

[0113] As Figure 3 shown, the data processing architecture includes: a data acquisition layer, a data cleaning layer, a data processing layer, and a data storage layer;

[0114] Among them, the data acquisition layer is used to execute the operation of the acquisition system to acquire data. Specifically, it performs the operation of acquiring the resource data of various types of flexible resources in each partition in step S101 of the foregoing embodiment;

[0115] The data cleaning layer is used to execute the cleaning operation of the acquired data.

[0116] The data processing layer is used to execute the operation of removing noise data and data screening.

[0117] The data storage layer is used to execute the operation of data integration and storage.

[0118] Among them, the data acquisition layer supports distributed acquisition and fusion. The acquisition layer integrates the data stream by adopting the publish-subscribe design pattern, and the acquisition frequency is realized by dynamically adjusting the sampling period. The acquisition content includes voltage, current, power, frequency, etc. The data cleaning layer, according to the data cleaning rules, for data missing and data redundancy, performs effective cleaning and disposal by means of deletion, replacement, transformation, etc. At the same time, the data processing layer horizontally processes the data to remove noise data.

[0119] In the specific implementation process, the hierarchical through management method for flexible resource aggregation control of the virtual power plant provided by the embodiment of the present application can be summarized as including an upper-layer entity and a lower-layer entity for different entities. For the convenience of understanding, Figure 4 shows the system architecture diagram of the hierarchical through management method for flexible resource aggregation control of the virtual power plant.

[0120] Among them, the upper-layer power grid entity usually refers to the high-level responsible for overall scheduling, management, and control in the power system, including State Grid Corporation of China, provincial power grid companies, and relevant power dispatching agencies, etc. It is responsible for the planning, design, construction, and operation management of the power system to ensure the safe, stable, and economic operation of the power grid. The bottom-layer resource entity refers to various resources directly participating in power production and consumption in the power system, including distributed energy, energy storage devices, etc. It is a direct participant in power supply and demand in the power system, and its operating state and behavior have an important impact on the stability and economy of the power system.

[0121] The upper - layer power grid main body guides and manages the behaviors of the lower - layer resource main body by formulating and implementing dispatching plans, controlling the operation of the power grid, etc.; while the lower - layer resource main body responds to the dispatching and management requirements of the upper - layer power grid main body through power production, demand response, energy storage and regulation, etc., jointly ensuring the safe, stable and economic operation of the power system.

[0122] It should be understood that the magnitudes of the sequence numbers of the steps in the above - mentioned embodiments do not mean the sequence of execution. The execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0123] The following is the device embodiment of the present invention. For the details not described in detail, reference can be made to the corresponding method embodiments above.

[0124] Figure 5 The structure diagram of the hierarchical and integrated management device for flexible resource aggregation control of a virtual power plant provided by an embodiment of the present invention is shown. For the sake of convenience of description, only the parts related to the embodiments of the present invention are shown and are described in detail as follows:

[0125] As Figure 5 shown, the hierarchical and integrated management device 5 for flexible resource aggregation control of a virtual power plant includes:

[0126] An acquisition module 501, configured to dynamically partition the virtual power plant and acquire the resource data of various types of flexible resources in each partition; wherein, the resource data includes: electricity load data, energy storage data, current data, voltage data, and power data;

[0127] An adjustable data determination module 502, configured to determine the peak period and valley period of daily electricity consumption in each partition according to the resource data of each partition, and determine the adjustable data of each partition corresponding to different periods according to an adjustment model; wherein, the adjustable data includes the load amount and the energy storage amount; the adjustment model aims to minimize the resource dispatching cost and is constrained by the flexible resource adjustment ability;

[0128] A control module 503, configured to generate a dispatching instruction for controlling a dispatching device and an energy storage control instruction for controlling an energy storage device according to the adjustable data of each partition, and acquire the dispatching response information fed back by the dispatching device and the energy storage response information fed back by the energy storage device, and generate visual control data according to the dispatching response information and the energy storage response information.

[0129] In a possible implementation manner, it further includes: a model construction module, configured to construct an aggregated cost function according to the energy storage cost constraint and energy storage resources.

[0130] In a possible implementation manner, the model construction module is further configured to construct an objective function according to the resource scheduling cost before the adjustable data determination module 502 determines the adjustable data of each partition corresponding to different time periods according to the adjustment model.

[0131] In a possible implementation manner, the control module 503 is further configured to generate a power control instruction for the controllable distributed power source to ensure power balance when the energy storage capacity determined according to the adjustable data of each partition does not meet the electricity demand of the load; wherein, the controllable distributed power source is a gas turbine generator, a diesel generator or a gasoline generator.

[0132] In a possible implementation manner, the acquisition module 501 is specifically configured to perform dynamic partitioning based on users and / or the park.

[0133] In a possible implementation manner, the acquisition module 501 is further configured to preprocess the resource data.

[0134] Among them, the acquisition module 501 is specifically configured to:

[0135] Perform horizontal processing on the resource data to remove noise data;

[0136] Filter the resource data according to the screening conditions to eliminate abnormal information.

[0137] In this embodiment, by implementing a dynamic partitioning strategy for the virtual power plant, data of multiple types of flexible resources in each partition are accurately collected, including electricity load data, energy storage data, current data, voltage data, and power data. Based on these data, the peak and trough periods of the daily power demand of each partition are accurately defined, and an adjustment model is used to determine the adjustable parameters of each region at different time periods, including: load capacity and energy storage capacity. The core of this adjustment model is to optimize the resource scheduling cost, and at the same time consider the adjustment ability of flexible resources as a constraint to ensure that the adjustment ability of flexible resources is fully released. Based on the adjustable data (load capacity and energy storage capacity) within the partition, accurate scheduling instructions are generated, which directly act on the control scheduling equipment and energy storage equipment to achieve refined and efficient resource scheduling. In addition, the system also needs to collect response information from the scheduling equipment and energy storage equipment, and generate visual control data based on this for monitoring and analysis. This implementation plan starts from the most basic resource level, evaluates the adjustability of each resource, so as to more accurately grasp the regulation range of the underlying resources, and enhances the scalability and flexibility of resource aggregation. By determining the peak and trough periods of electricity consumption in each region, and formulating corresponding adjustable parameters accordingly, and finally generating control instructions, the effective integration and hierarchical management of a large number of flexible resources in the virtual power plant are realized, ensuring the safety, stability and economic benefits of the power system.

[0138] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not detailed or recorded in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0139] Those of ordinary skill in the art can realize that the templates, units, and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present invention.

[0140] If the said module / unit 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 such understanding, to implement all or part of the processes in the above embodiments of the method of the present invention, it can also be completed by a computer program instructing the relevant hardware. The said computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above embodiments of the method for hierarchical and seamless management of flexible resource aggregation control in a virtual power plant. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0141] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A virtual power plant flexible resource aggregation control hierarchical management method, characterized in that: include: Dynamically partition the virtual power plant and obtain resource data of multiple types of flexible resources in each partition; wherein the resource data includes: power load data, energy storage data, current data, voltage data and power data; Determine the peak and valley periods of daily electricity consumption of each partition based on the resource data of each partition, and determine the adjustable data of each partition corresponding to different periods based on the adjustment model; wherein the adjustable data includes load and storage capacity; the adjustment model takes minimization of resource scheduling cost as the goal and flexible resource adjustment capability as the constraint; Generate dispatching instructions for controlling the dispatching device and energy storage control instructions for controlling the energy storage device according to the adjustable data of each partition; obtain dispatching response information fed back by the dispatching device and energy storage response information fed back by the energy storage device, and generate visual control data according to the dispatching response information and the energy storage response information.

2. The method for hierarchical management of flexible resource aggregation control of virtual power plants according to claim 1 is characterized in that: The constraint conditions of the adjustment model include: energy storage device constraint and controllable load device constraint; The energy storage device constraints include: 0≤p ES,in (t)≤p ES,in,max (t) 0≤p ES,out (t)≤p -ES,out,max (t) -r ES,max ≤[p ES,in (t-1)-p ES,in (t-1),p ES,out (t-1)-p ES,out (t)≤p -ES,in (t)≤r ES,max e ES，min ≤e ES (t)≤e ES，max In the formula, p ES,in (t), p ES,out (t) and e ES (t) are the charging power, discharging power and remaining energy of the energy storage device at time t; θ ES is the energy dissipation rate; p ES,in,max (t), p ES,out,max (t) and -r ES,max are the maximum charging power, maximum discharging power and climbing power limit of ES respectively; e ES,max and e ES,min are the maximum and minimum acceptable remaining energies subject to the state of charge boundary constraints; n ES,in and n ES,out are the charging power efficiency and discharging power efficiency of the energy storage device respectively; The controllable load equipment constraints include: p CL,t,min ≤p CL,t ≤p CL,t,max -r CL,lim ≤p CL,t-1 ≤r CL,lim e CL,t =θ CL e CL,t-1 or CL p CL,t +(1-θ CL )oh CL,t and CL,t,min ≤e CL,t ≤e CL,t,max In the formula, p CL,t and e CL,t are the power consumption of controllable load and indoor temperature respectively; p CL,t,max 、p CL,t,min and r CL,lim are the maximum and minimum power consumption and climbing limit of the controllable load respectively; e CL,t,min and e CL,t,max are the upper and lower bounds of the expected indoor temperature respectively; ω CL,t is the ambient temperature; θ CL is the dissipation rate of the controllable load; η CL It is the conversion coefficient of active power to temperature.

3. The method for hierarchical management of flexible resource aggregation control of virtual power plants according to claim 2 is characterized in that: The constraints of the adjustment model also include: energy storage cost constraints: Where T represents the interval of capacity constraint corresponding to the continuous operation period of energy storage; α i represents the cost constraint coefficient, P i represents the power of energy storage and β is the cost parameter.

4. The method for hierarchical management of flexible resource aggregation control of virtual power plants according to claim 3 is characterized in that: Also includes: Constructing an aggregated cost function based on the energy storage cost constraint and the energy storage resources; Wherein, the cost function is: C=c k ,r∈[R k-1 ,R k ),k=1,2,…,K Among them, R k By having cost value c k The energy storage is superimposed to obtain: c i,j is the unit adjustment electricity cost; i is the i-th resource; j is a positive integer.

5. The method for hierarchical and interconnected management of flexible resource aggregation control of virtual power plants according to claim 1, characterized in that: Before determining the adjustable data of each partition corresponding to different time periods according to the adjustment model, the method further includes: Construct the objective function based on resource scheduling cost: minf(x,y) In the formula, f represents the resource scheduling cost, x and y represent the power variables and other variables on the aggregation respectively; The feasible domain of the resource scheduling cost is: Z={x∈R N :x=c z,cost +G z,cost β z,cost } In the formula, c z,cost G is the center point of the feasible region of the aggregator cost constraint, z,cost is the generator matrix of the feasible domain of the aggregator cost constraint, β z,cost is the scaling factor matrix of the feasible domain of the aggregator cost constraints.

6. The method for hierarchical management of flexible resource aggregation control of virtual power plants according to claim 1, characterized in that: Also includes: When it is determined based on the adjustable data of each partition that the energy storage does not meet the power demand of the load, a power control instruction of the controllable distributed power source is generated to ensure power balance; wherein the controllable distributed power source is a gas turbine generator, a diesel generator or a gasoline generator.

7. The method for hierarchical management of flexible resource aggregation control of virtual power plants according to claim 1, characterized in that: The dynamic partitioning of the virtual power plant includes: dynamic partitioning based on users and / or parks.

8. The method for hierarchical management of flexible resource aggregation control of virtual power plants according to claim 1, characterized in that: The flexible resources include wind turbines, solar generators, industrial parks, public buildings, commercial buildings, energy storage equipment and electric vehicles.

9. The method for hierarchical management of flexible resource aggregation control of virtual power plants according to claim 1, characterized in that: After acquiring the resource data of the multi-type flexible resources of each partition, the method further includes: preprocessing the resource data; Wherein, the preprocessing of the resource data includes: horizontally processing the resource data to remove noise data; The resource data is filtered according to the screening conditions to remove abnormal information.

10. A virtual power plant flexible resource aggregation control hierarchical management device, characterized in that: include: An acquisition module is used to dynamically partition the virtual power plant and acquire resource data of multiple types of flexible resources in each partition; wherein the resource data includes: power load data, energy storage data, current data, voltage data and power data; An adjustable data determination module is used to determine the peak period and valley period of daily electricity consumption of each partition according to the resource data of each partition, and determine the adjustable data of each partition corresponding to different periods according to the adjustment model; wherein the adjustable data includes load and storage energy; the adjustment model takes minimization of resource scheduling cost as the goal and flexible resource adjustment capability as the constraint; A control module is used to generate dispatching instructions for controlling the dispatching device and energy storage control instructions for controlling the energy storage device according to the adjustable data of each partition, and to obtain dispatching response information fed back by the dispatching device and energy storage response information fed back by the energy storage device, and to generate visual control data according to the dispatching response information and the energy storage response information.

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