WO2024060413A1 - 虚拟电厂可调容量构建方法、装置、电子设备、存储介质、程序、及程序产品 - Google Patents

虚拟电厂可调容量构建方法、装置、电子设备、存储介质、程序、及程序产品 Download PDF

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WO2024060413A1
WO2024060413A1 PCT/CN2022/137591 CN2022137591W WO2024060413A1 WO 2024060413 A1 WO2024060413 A1 WO 2024060413A1 CN 2022137591 W CN2022137591 W CN 2022137591W WO 2024060413 A1 WO2024060413 A1 WO 2024060413A1
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adjustable
resources
resource
uncertain
output value
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French (fr)
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王文悦
季宇
刘海涛
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国网上海能源互联网研究院有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06313—Resource planning in a project environment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply

Definitions

  • the present application relates to the technical field of distribution network regulation, and more specifically, to a virtual power plant adjustable capacity construction method, device, electronic equipment, storage medium, program, and program product.
  • virtual power plants can flexibly participate in the power market and provide auxiliary services for the power system. They can effectively improve energy efficiency, reduce energy costs, promote the consumption of new energy, and promote the construction and development of my country's new power system. .
  • virtual power plant operators participate in the ancillary service market, they need to report information such as quotation curves and load curves to the power grid in advance.
  • Virtual power plant operators need to consider the regulatory capabilities of all resources that may be aggregated the next day during the day-ahead application process.
  • how to evaluate the adjustable capabilities of virtual power plants based on their different aggregation situations and formulate dispatching strategies for virtual power plant day-ahead bidding ? Currently, Still to be resolved.
  • the existing virtual power plant regulation capacity assessment method based on the aggregation of multiple resources evaluates the regulation capacity and response capacity of various resources by establishing a physical analytical model and selecting evaluation indicators.
  • an uncertainty model considering the energy storage to smooth out new energy is established to evaluate the operating risks of virtual power plants in various scenarios, so as to evaluate the regulation capacity of various resources.
  • This method adopts the traditional physical modeling method, which only models photovoltaic, energy storage and temperature control loads. There are fewer types of resources inside the virtual power plant, and the model establishment is particular and the generalization ability is poor. It cannot generate universal value for the diverse dynamic aggregation of virtual power plants.
  • This method only analyzes the uncertainty of energy storage, and the application scope of the uncertainty model is small.
  • This method mainly revolves around the regulation capacity of energy storage, does not have the function of estimating the dynamic regulation of virtual power plants under diverse aggregation, and has poor versatility.
  • embodiments of the present application provide a virtual power plant adjustable capacity construction method, device, electronic equipment, storage medium, and program , and program products.
  • a method for constructing adjustable capacity of a virtual power plant comprising:
  • the internal resources of the virtual power plant are divided into uncertain resources and adjustable resources;
  • the output value set of the uncertain resource the operating baseline of the adjustable resource, the output value of the adjustable resource, and the output constraint set of the adjustable resource, Determine the adjustable capacity of the virtual power plant.
  • a virtual power plant adjustable capacity construction device including:
  • a resource division module configured to divide the internal resources of the virtual power plant into uncertain resources and adjustable resources according to the operating characteristics of the internal resources of the virtual power plant;
  • a first determination module configured to determine the operating baseline of the uncertain resources and the set of output values of the uncertain resources
  • a second determination module configured to determine the operating baseline of the adjustable resource, the output value of the adjustable resource, and the output constraint set of the adjustable resource
  • the third determination module is configured to determine based on the operating baseline of the uncertain resources, the output value set of the uncertain resources, the operating baseline of the adjustable resources, the output values of the adjustable resources and the adjustable resources. Adjust the output constraint set of resources to determine the adjustable capacity of the virtual power plant.
  • a computer-readable storage medium stores a computer program, and the computer program is used to execute the method described in any of the above aspects of the embodiments of the present application.
  • an electronic device includes: a processor; a memory for storing instructions executable by the processor; and the processor is configured to obtain instructions from the memory.
  • the executable instructions are read in and executed to implement the method described in any of the above aspects of the embodiments of the present application.
  • a computer program product including computer program instructions for executing the method described in any aspect of the above rights.
  • a computer program is provided, the computer program being used to execute the method described in any aspect of the above claims.
  • Figure 1 is a schematic flowchart of an exemplary virtual power plant adjustable capacity construction method provided by an embodiment of the present application
  • FIG2 is a basic structural diagram of an exemplary virtual power plant provided in an embodiment of the present application.
  • Figure 3 is a schematic diagram of the adjustable capacity of an exemplary virtual power plant provided by the embodiment of the present application.
  • Figure 4 is an exemplary operational framework diagram for virtual power plant operators to participate in the ancillary service market provided by the embodiment of this application;
  • Figure 5 is a schematic structural diagram of an exemplary virtual power plant adjustable capacity construction device provided by an embodiment of the present application.
  • FIG. 6 is a structure of an exemplary electronic device provided in an embodiment of the present application.
  • multiple may refer to two or more than two, and “at least one” may refer to one, two, or more than two.
  • the term "and/or" in the embodiment of this application is only an association relationship describing associated objects, indicating that there can be three relationships.
  • a and/or B can mean: A exists alone and A exists simultaneously. and B, there are three cases of B alone.
  • the character "/" in the embodiment of the present application generally indicates that the related objects are in an "or" relationship.
  • Embodiments of the present application may be applied to electronic devices such as terminal devices, computer systems, servers, etc., which may operate with numerous other general or special computing system environments or configurations.
  • Examples of well-known terminal devices, computing systems, environments and/or configurations suitable for use with terminal devices, computer systems, servers and other electronic devices include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients Computers, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, networked personal computers, small computer systems, mainframe computer systems and distributed cloud computing technology environments including any of the above systems, etc.
  • Electronic devices such as terminal devices, computer systems, servers, etc. may be described in the general context of computer system executable instructions (such as program modules) being executed by the computer system.
  • program modules may include routines, programs, object programs, components, logic, data structures, etc., that perform specific tasks or implement specific abstract data types.
  • the computer system/server may be implemented in a distributed cloud computing environment where tasks are performed by remote processing devices linked through a communications network.
  • program modules may be located on local or remote computing system storage media including storage devices.
  • FIG. 1 is a schematic flowchart of an exemplary virtual power plant adjustable capacity construction method provided by an embodiment of the present application. This embodiment can be applied to electronic equipment. As shown in Figure 1, the virtual power plant adjustable capacity construction method 100 includes the following steps:
  • Step 101 According to the operating characteristics of the internal resources of the virtual power plant, the internal resources of the virtual power plant are divided into uncertain resources and adjustable resources.
  • Figure 2 shows a typical virtual power plant structure.
  • the virtual power plant operator optimizes scheduling by aggregating internal resources and purchases and sells electricity with the power grid.
  • the internal resources of the virtual power plant can include, but are not limited to: wind power, photovoltaics, conventional loads, flexible loads, energy storage, gas turbines, electric vehicles, etc.
  • these internal resources can be divided into uncertain resources and adjustable resources.
  • uncertain resources include wind power, photovoltaic and other renewable distributed power sources and conventional loads.
  • Adjustable resources include flexible loads, energy storage, and controllable distributed power sources such as gas turbines and electric vehicles.
  • the virtual power plant operator analyzes the response mechanism to obtain data on the main energy market and ancillary service market of the power grid.
  • the virtual power plant operator establishes a unified model through a physical analysis model based on the aggregation of multiple resources to evaluate the adjustment capabilities of various resources.
  • Step 102 determine the operating baseline of the uncertain resource and the output value set of the uncertain resource.
  • Uncertain resources mainly include renewable distributed power sources such as wind power and photovoltaics and conventional loads.
  • output prediction values of uncertain resources are needed.
  • determining the operating baseline of the uncertain resource includes: determining the operating baseline P base,j (t) of the uncertain resource by the following formula:
  • N j is the number of uncertain resources within the virtual power plant.
  • a preset uncertainty analysis method can be used to model the predicted values and fluctuation intervals of uncertain resources, and initially determine the output value set of uncertain resources.
  • the preset uncertainty analysis method may be but is not limited to: uncertainty analysis method under dynamic robust constraints, data collection and data-driven analysis method. That is, the uncertainty analysis process for wind power, photovoltaic and load output is not limited to uncertainty analysis under dynamic robust constraints.
  • data collection and data-driven methods can be considered to analyze output uncertainty.
  • the robust optimization method can further construct the "worst scenario" of uncertain situations by modeling the predicted values and fluctuation intervals of uncertain resources, ensuring the feasibility of scheduling results under extreme circumstances, but its decision-making results are conservative.
  • dynamic robust optimization can adjust the degree of conservatism of the solution and balance the safety and economy of virtual power plants participating in dispatch decisions.
  • P Load (t), P PV (t), and P WT (t) are the actual output values of conventional load, wind power, and photovoltaic power inside the virtual power plant at time t respectively;
  • P j (t) is the actual output value inside the virtual power plant at time t.
  • the actual output value of uncertain resources is the predicted output value of uncertain resource j at time t; It is the maximum fluctuation deviation of the forecast output of uncertain resource j at time t; is a variable of 0-1.
  • ⁇ S and ⁇ T are the uncertainty budget values, that is, within a scheduling cycle The maximum space and maximum number of moments where the output value fluctuates against the predicted value, ⁇ S is the fluctuation parameter of the demand response output uncertainty set on the spatial scale, ⁇ T is the fluctuation parameter of the demand response output uncertainty set on the time scale, that is The maximum value of the deviation between the predicted value and the actual value within a scheduling period.
  • the load is divided into a regular load PL and a translatable load P DR based on the load's own properties and response capabilities.
  • the output and price of conventional loads in the day-ahead dispatching process are closely related.
  • the shiftable load has a corresponding expected power consumption plan, and the electric energy is shifted and adjusted in time according to the operator's schedule.
  • the conventional load response prediction problem can be regarded as a load prediction problem in different electricity price environments. Therefore, the uncertainty of the load output in the uncertainty set is modeled as a conventional load, and the conventional load in the virtual power plant's uncertainty resource set is added to the electricity price impact factor.
  • the output value set of uncertain resources is obtained as:
  • P L (t), P PV (t), and P WT (t) are the actual output values of the conventional load, wind power, and photovoltaic power inside the virtual power plant at time t respectively; is the predicted output value of uncertain resource j at time t; It is the maximum fluctuation deviation of the forecast output of uncertain resource j at time t; A variable of 0-1. When the value is 1, it indicates that the uncertainty resource output reaches the upper/lower bound of the prediction interval, otherwise it is the expected value.
  • ⁇ (t) is the load self-elasticity coefficient at time t
  • ⁇ ⁇ c (t) is the electricity price change rate at time t
  • Step 103 Determine the operating baseline of the adjustable resource, the output value of the adjustable resource, and the output constraint set of the adjustable resource.
  • adjustable resources may include, but are not limited to, flexible loads, energy storage, and controllable distributed power sources such as gas turbines and electric vehicles.
  • controllable distributed power sources such as gas turbines and electric vehicles.
  • determining the operating baseline of the adjustable resource includes: determining the operating baseline P base,i (t) of the adjustable resource through the following formula:
  • P i e (t) is the predicted output value of adjustable resource i at time t
  • N i is the number of adjustable resources inside the virtual power plant.
  • determining the output value of the adjustable resource includes: determining the increased/reduced output value after the adjustable resource operation is scheduled as the output value of the adjustable resource; wherein, the adjustable resource The increased/reduced output value after resource operation scheduling is the difference between the actual output value of the adjustable resource after scheduling and the predicted output value of the adjustable resource.
  • determining the output constraint set of the adjustable resource includes: determining the output boundary constraint of the adjustable resource; determining the power generation and consumption constraint of the adjustable resource; determining where the adjustable resource is The power recovery constraint after the scheduling period ends; determine the output constraint set of the adjustable resource according to the output boundary constraint, the power generation and consumption constraint, and the power recovery constraint.
  • the main information that the virtual power plant operator needs to make decisions is:
  • adjustable resources have clear parameters such as capacity and output range.
  • Virtual power plant operators can determine their adjustable capabilities based on the aggregated resource parameters, which can be expressed as increasing/reducing the output value after the adjustable resource operation is scheduled. It can be expressed as the difference between the actual output value of the adjustable resource after dispatching and the expected power output of the shiftable load or the conventional output of the controllable distributed power supply (equal to the predicted output value of the adjustable resource).
  • the actual output value of the adjustable resources of the virtual power plant is modeled.
  • different adjustable resources operate in different ways, the output range, adjustable capacity and output expectations can be formed based on different data.
  • the output constraint set of any adjustable resource aggregated within the virtual power plant is as follows:
  • P i min (t) and P i max (t) are the minimum adjustable power and the maximum adjustable power respectively;
  • P i (t) is the actual output value of the adjustable resource after scheduling;
  • d 0 is the scheduling Initial power at the moment;
  • D max (t) is the maximum remaining power or the maximum power demand;
  • D min (t) is the minimum remaining power or the minimum power demand;
  • D 0 (t) is the value obtained through the uncertainty resource prediction curve The expected value of output and power consumption;
  • P i e (t) is the predicted output value of adjustable resource i at time t.
  • the formula (6) with concentrated output constraints represents the boundary constraint of the adjustable resource output; the formula (7) with concentrated output constraints represents that the adjustable resource generation and power consumption need to be within the adjustable capacity range in any period; the formula with concentrated output constraints (8) After the optimization scheduling cycle ends, the adjustable resources restore the power to the initial level (energy storage resources) or reach the target power (load resources).
  • Step 104 According to the operating baseline of the uncertain resource, the output value set of the uncertain resource, the operating baseline of the adjustable resource, the output value of the adjustable resource, and the output of the adjustable resource A constraint set determines the adjustable capacity of the virtual power plant.
  • the output value set of the uncertain resource, the operating baseline of the adjustable resource, the output value of the adjustable resource and the adjustable Determining the adjustable capacity of the virtual power plant based on the resource output constraint set includes: determining the operating baseline of the internal resources of the virtual power plant based on the operating baseline of the uncertain resources and the operating baseline of the adjustable resources; The output value set of the uncertain resources, the output value of the adjustable resources and the output constraint set of the adjustable resources determine the actual operating characteristics of the virtual power plant after dispatching; obtain the virtual power plant after dispatching The difference between the actual operating characteristics and the operating baseline of the internal resources of the virtual power plant is the adjustable capacity of the virtual power plant.
  • Figure 3 shows a schematic diagram of the adjustable capacity of the virtual power plant.
  • Figure 4 shows the application scenario of the adjustable capacity of the virtual power plant.
  • the virtual power plant participates in the day-ahead power market, it reports the next day's operating baseline based on the relevant electricity price information issued by the main energy market on the grid side.
  • the peak shaving instructions issued by the grid-side auxiliary service market it Optimize scheduling based on its own adjustable capacity, and finally report its own operating data and participate in peak-shaving decisions.
  • the adjustable capacity output value ⁇ P(t) of the virtual power plant is expressed as:
  • N i is the number of adjustable resources inside the virtual power plant
  • N j is the number of uncertain resources inside the virtual power plant
  • ⁇ P i (t) is the increase/reduction output value after the adjustable resource operation is scheduled
  • ⁇ P j (t) is Uncertain resource operating output fluctuation value
  • P i (t) is the actual output value of the adjustable resource after dispatch
  • P i e (t) is the expected power output of the shiftable load or the conventional output of the controllable distributed power supply
  • P j (t) is the actual output value of uncertainty resource j inside the virtual power plant at time t
  • the adjustable capacity of the virtual power plant can be expressed as the difference between the actual operating characteristics P vpp (t) after the virtual power plant is optimized and dispatched and the operating baseline P base (t) reported a few days ago.
  • P vpp (t) is the actual operating characteristics of the virtual power plant after dispatching;
  • P base (t) is the operating baseline of the internal resources of the virtual power plant;
  • N i is the number of adjustable resources within the virtual power plant;
  • N j is the number of uncertain resources within the virtual power plant;
  • P i (t) is the actual output value of the adjustable resources after dispatch;
  • W 0 is the output value set of the uncertain resources;
  • P base,i ( t) is the operating baseline of the adjustable resource;
  • P base,j (t) is the operating baseline of the uncertain resource;
  • P i e (t) is the predicted output value of the adjustable resource i at time t;
  • P j (t) is the actual output value of uncertainty resource j inside the virtual power plant at time t; is the predicted output value of uncertain resource j at time t.
  • the modeling and calculation can be carried out according to the following steps: First, the internal resources of the virtual power plant are classified into two types: adjustable resources and uncertain resources. Class, and solve its predicted value to obtain the next day's operation baseline of the virtual power plant. Secondly, dynamic robust constraints are used to model and calculate the actual output of uncertain resources. Extract the internal adjustable resource characteristic parameters and further express them in the form of the above constraints (6), (7), (8), where P i min (t), P i max (t), d 0 , D min (t), D max (t), and D 0 (t) are superimposed respectively, and the output constraint set of all adjustable resources within the virtual power plant can be obtained. Finally, the adjustable capacity of the virtual power plant is solved according to equations (10), (11), and (12) to provide guidance and optimization for the virtual power plant to participate in day-ahead peak shaving auxiliary services.
  • the advantage of this model is that it takes into account the uncertainty of virtual power plant aggregation resources and is not limited to model establishment under a specific aggregation method, making it easier for the virtual power plant to participate in the study of optimal dispatch bidding strategies as a whole.
  • virtual power plant operators aggregate new resources (such as comprehensive energy, gas or thermal energy, etc.), they only need to classify them according to their operating characteristics.
  • a virtual power plant operator manages multiple virtual power plants with different aggregation forms in subsequent research, these virtual power plant parameters can be directly superimposed to obtain the adjustable capacity of the virtual power plant operator without modifying the entire model. It can be found that this constraint set is easy to embed in some optimization models, making it convenient for virtual power plant operators to participate in different markets and conduct overall subsequent calculations.
  • the method for constructing the adjustable capacity of a virtual power plant proposed in the embodiment of this application can be oriented to the peak-shaving auxiliary service market, and its beneficial effects include:
  • the method for constructing the adjustable capacity of a virtual power plant proposed in the embodiment of the present application first conducts uncertainty analysis on resources with different operating characteristics, which can be based on robust uncertainty analysis and analysis of adjustable resource operating characteristics. Secondly, a unified model is performed for each uncertain resource to provide a universal model for virtual power plants to participate in optimal scheduling under dynamic aggregation, and the adjustable capacity of the virtual power plant is evaluated through a unified model, and finally the adjustable capacity of the virtual power plant is obtained, which effectively solves the difficulties caused by different aggregation methods of virtual power plants for subsequent optimal scheduling.
  • the embodiments of this application take into account the uncertainty of virtual power plant aggregation resources, are not limited to model establishment under a specific aggregation method, and facilitate the virtual power plant as a whole to participate in the research on optimal dispatch bidding strategies.
  • virtual power plant operators aggregate new resources (such as comprehensive energy, gas or thermal energy, etc.), they only need to classify them according to their operating characteristics.
  • these virtual power plant parameters can be directly superimposed to obtain the adjustable capacity of the virtual power plant operator without the need to remodel the whole.
  • the virtual power plant in the embodiment of the present application participates in the power market, it can fully evaluate the adjustability of the virtual power plant, fully consider the operating characteristics and output of multiple resources, fully schedule various resources, and balance the operation of the virtual power plant. Reliability and economy.
  • FIG. 5 is a schematic structural diagram of an exemplary virtual power plant adjustable capacity construction device provided by an embodiment of the present application. As shown in Figure 5, device 500 includes:
  • the resource division module 510 is configured to divide the internal resources of the virtual power plant into uncertain resources and adjustable resources according to the operating characteristics of the internal resources of the virtual power plant;
  • the first determination module 520 is configured to determine the operating baseline of the uncertain resources and the set of output values of the uncertain resources;
  • the second determination module 530 is configured to determine the operating baseline of the adjustable resource, the output value of the adjustable resource, and the output constraint set of the adjustable resource;
  • the third determination module 540 is configured to determine the adjustable capacity of the virtual power plant based on the operating baseline of the uncertain resources, the output value set of the uncertain resources, the operating baseline of the adjustable resources, the output value of the adjustable resources and the output constraint set of the adjustable resources.
  • the first determination module 520 is configured to determine the operating baseline P base, j (t) of the uncertain resource through the following formula:
  • N j is the number of uncertain resources within the virtual power plant.
  • the first determination module 520 is configured as:
  • the initially determined output value set is optimized to obtain the output value set of the uncertain resources.
  • the first determination module 520 is also configured to:
  • the dynamic robust constraint method is used to model the predicted values and fluctuation intervals of the uncertain resources, and the output value set of the uncertain resources is initially determined to be:
  • P Load (t), P PV (t), and P WT (t) are the actual output values of conventional load, wind power, and photovoltaic power inside the virtual power plant at time t respectively;
  • P j (t) is the actual output value inside the virtual power plant at time t.
  • the actual output value of uncertain resources is the predicted output value of uncertain resource j at time t; It is the maximum fluctuation deviation of the forecast output of uncertain resource j at time t; is a variable of 0-1.
  • ⁇ S and ⁇ T are the uncertainty budget values, that is, within a scheduling cycle The maximum space and maximum number of moments where the output value fluctuates against the predicted value, ⁇ S is the fluctuation parameter of the demand response output uncertainty set on the spatial scale, ⁇ T is the fluctuation parameter of the demand response output uncertainty set on the time scale, that is The maximum value of the deviation between the predicted value and the actual value within a scheduling period;
  • the initially determined output value set is optimized, and the output value set of the uncertain resources is obtained as:
  • P L (t), P PV (t), and P WT (t) are the actual output values of the conventional load, wind power, and photovoltaic power inside the virtual power plant at time t respectively; is the predicted output value of uncertain resource j at time t; It is the maximum fluctuation deviation of the forecast output of uncertain resource j at time t; A variable of 0-1. When the value is 1, it indicates that the uncertainty resource output reaches the upper/lower bound of the prediction interval, otherwise it is the expected value.
  • the second determination module 530 is configured to determine the operating baseline P base,i (t) of the adjustable resource through the following formula:
  • P i e (t) is the predicted output value of adjustable resource i at time t
  • N i is the number of adjustable resources inside the virtual power plant.
  • the second determination module 530 is also configured to:
  • the output value increased/decreased after the adjustable resource operation is scheduled is the difference between the actual output value of the adjustable resource after scheduling and the predicted output value of the adjustable resource.
  • the second determination module 530 is also configured to:
  • the output constraint set of the adjustable resource is determined according to the output boundary constraint, the power generation and consumption constraint, and the power recovery constraint.
  • the output constraint set of the adjustable resources is:
  • P i min (t) and P i max (t) are the minimum adjustable power and the maximum adjustable power respectively;
  • P i (t) is the actual output value of the adjustable resource after scheduling;
  • d 0 is the scheduling Initial power at the moment;
  • D max (t) is the maximum remaining power or the maximum power demand;
  • D min (t) is the minimum remaining power or the minimum power demand;
  • D 0 (t) is the value obtained through the uncertainty resource prediction curve The expected value of output and power consumption;
  • P i e (t) is the predicted output value of adjustable resource i at time t.
  • the third determination module 540 is configured to:
  • the difference between the actual operating characteristics of the virtual power plant after dispatching and the operating baseline of the internal resources of the virtual power plant is obtained to obtain the adjustable capacity of the virtual power plant.
  • the calculation formula of the adjustable capacity ⁇ P(t) of the virtual power plant is:
  • P vpp (t) is the actual operating characteristics of the virtual power plant after dispatching;
  • P base (t) is the operating baseline of the internal resources of the virtual power plant;
  • N i is the number of adjustable resources within the virtual power plant;
  • N j is the number of uncertain resources within the virtual power plant;
  • P i (t) is the actual output value of the adjustable resources after dispatch;
  • W 0 is the output value set of the uncertain resources;
  • P base,i ( t) is the operating baseline of the adjustable resource;
  • P base,j (t) is the operating baseline of the uncertain resource;
  • P i e (t) is the predicted output value of the adjustable resource i at time t;
  • P j (t) is the actual output value of uncertainty resource j inside the virtual power plant at time t; is the predicted output value of uncertain resource j at time t.
  • the virtual power plant adjustable capacity construction device 500 of the embodiment of the present application corresponds to the virtual power plant adjustable capacity construction method 100 of another embodiment of the present application, which will not be repeated here.
  • Figure 6 is a structure of an exemplary electronic device provided by an embodiment of the present application.
  • the electronic device may be either or both of the first device and the second device, or a stand-alone device independent of them.
  • the stand-alone device may communicate with the first device and the second device to receive the collected information from them. input signal.
  • Figure 6 illustrates a block diagram of an electronic device according to an embodiment of the present application. As shown in FIG. 6 , electronic device 60 includes one or more processors 61 and memory 62 .
  • the processor 61 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.
  • CPU central processing unit
  • the processor 61 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.
  • Memory 62 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory.
  • the volatile memory may include, for example, random access memory (RAM) and/or cache memory (cache).
  • the non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.
  • One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 61 may execute the program instructions to implement the historical change recording of the software programs of various embodiments of the present application as described above. Methods for information mining and/or other desired functionality.
  • the electronic device may further include an input device 63 and an output device 64, and these components are interconnected through a bus system and/or other forms of connection mechanisms (not shown).
  • the input device 63 may also include, for example, a keyboard, a mouse, and the like.
  • the output device 64 can output various information to the outside, and can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto.
  • the electronic device may include any other suitable components depending on the specific application.
  • embodiments of the present application may also be a computer program product, which includes computer program instructions.
  • the computer program instructions When the computer program instructions are run by a processor, the computer program instructions cause the processor to execute the above-mentioned "example method" part of this specification. The steps in the method for information mining of historical change records according to various embodiments of the present application are described in .
  • the computer program product can be used to write program codes for performing the operations of the embodiments of the present application in any combination of one or more programming languages, including object-oriented programming languages, such as Java, C++, etc. , also includes conventional procedural programming languages, such as the "C" language or similar programming languages.
  • the program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server execute on.
  • embodiments of the present application may also be a computer-readable storage medium having computer program instructions stored thereon.
  • the computer program instructions When the computer program instructions are run by a processor, the computer program instructions cause the processor to execute the above-mentioned "example method" part of this specification. The steps in the method for information mining of historical change records according to various embodiments of the present application are described in .
  • the computer-readable storage medium may be any combination of one or more readable media.
  • the readable medium may be a readable signal medium or a readable storage medium.
  • Readable storage media may include, for example, but are not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination thereof. Examples of readable storage media (a non-exhaustive list) include: electrical connection with one or more conductors, portable disk, hard disk, random access memory (RAM), read only memory (ROM), erasable programmable Read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.
  • the methods and systems of embodiments of the present application may be implemented in many ways.
  • the methods and systems of the embodiments of the present application can be implemented through software, hardware, firmware, or any combination of software, hardware, and firmware.
  • the above-mentioned order for the steps of the method is for illustration only, and the steps of the methods of the embodiments of the present application are not limited to the above-described order unless otherwise specifically stated.
  • the present application can also be implemented as programs recorded in recording media, and these programs include machine-readable instructions for implementing methods according to the present application. Therefore, the present application also covers recording media storing programs for executing methods according to embodiments of the present application.
  • each component or each step can be decomposed and/or recombined.
  • These decompositions and/or recombinations should be regarded as equivalent solutions to the embodiments of the present application.
  • the above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the embodiments of the application.
  • Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of embodiments of the application.
  • the present embodiments are not intended to be limited to the aspects shown herein but are to be accorded the widest scope consistent with the principles and novel features disclosed herein.

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Abstract

一种虚拟电厂可调容量构建方法、装置、电子设备、存储介质、程序、及程序产品。所述方法包括：根据虚拟电厂内部资源的运行特性，将所述虚拟电厂的内部资源分为不确定性资源和可调节资源；确定所述不确定性资源的运行基线和所述不确定性资源的出力值集合；确定所述可调节资源的运行基线、所述可调节资源的出力值以及所述可调节资源的出力约束集；根据所述不确定性资源的运行基线、所述不确定性资源的出力值集合、所述可调节资源的运行基线、所述可调节资源的出力值以及所述可调节资源的出力约束集，确定所述虚拟电厂的可调容量。

Description

虚拟电厂可调容量构建方法、装置、电子设备、存储介质、程序、及程序产品
相关申请的交叉引用
本申请基于申请号为202211140316.1、申请日为2022年9月20日、发明名称为“虚拟电厂可调容量构建方法、装置、存储介质及电子设备”的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此引入本申请作为参考。
技术领域
本申请涉及配电网调控技术领域，并且更具体地，涉及一种虚拟电厂可调容量构建方法、装置、电子设备、存储介质、程序、及程序产品。
背景技术
虚拟电厂作为能源供给与能源消费的融合点，可以灵活参与电力市场，为电力系统提供辅助服务，能够有效提高能源效率，降低能源成本，促进新能源消纳，推动我国新型电力系统的建设与发展。虚拟电厂运营商参与辅助服务市场时，需要在日前向电网申报报价曲线以及负荷曲线等信息。虚拟电厂运营商在日前申报环节需要考虑次日可能聚合的所有资源的调控能力，但如何根据虚拟电厂不同的聚合情况，对其可调能力进行评估，并为虚拟电厂日前投标制定调度策略，目前仍有待解决。
现有的基于多种资源聚合的虚拟电厂调节能力评估方法通过建立物理解析模型，并选择评估指标，对各类资源的调节能力及响应能力进行评估。同时，建立考虑储能平抑新能源所带来的不确定性模型，评估各个场景下的虚拟电厂运行风险情况，从而能对各类资源的调节能力进行评估。该方法采用的是传统的物理建模方法，仅对光伏、储能以及温控负荷进行建模，虚拟电厂内部资源类型较少，且模型建立具有特殊性，泛化能力较差，无法对于虚拟电厂多样动态聚合产生普适性价值。该方法仅针对储能的不确定性展开分析，不确定性模型应用范围较小。该方法主要围绕储能调节能力进行展开，不具备估计多样聚合下虚拟电厂动态调节的功能，通用性较差。
因此，现有的虚拟电厂在参与电力市场时无法充分对各类资源进行调度。
发明内容
针对现有技术中存在的虚拟电厂在参与电力市场时无法充分对各类资源进行调度的技术问题，本申请实施例提供一种虚拟电厂可调容量构建方法、装置、电子设备、存储介质、程序、及程序产品。
根据本申请实施例的一个方面，提供了一种虚拟电厂可调容量构建方法，包括：
根据虚拟电厂内部资源的运行特性，将所述虚拟电厂的内部资源分为不确定性资源和可调节资源；
确定所述不确定性资源的运行基线和所述不确定性资源的出力值集合；
确定所述可调节资源的运行基线、所述可调节资源的出力值以及所述可调节资源的出力约束集；
根据所述不确定性资源的运行基线、所述不确定性资源的出力值集合、所述可调节资源的运行基线、所述可调节资源的出力值以及所述可调节资源的出力约束集，确定所述虚拟电 厂的可调容量。
根据本申请实施例的另一个方面，提供了一种虚拟电厂可调容量构建装置，包括：
资源划分模块，配置为根据虚拟电厂内部资源的运行特性，将所述虚拟电厂的内部资源分为不确定性资源和可调节资源；
第一确定模块，配置为确定所述不确定性资源的运行基线和所述不确定性资源的出力值集合；
第二确定模块，配置为确定所述可调节资源的运行基线、所述可调节资源的出力值以及所述可调节资源的出力约束集；
第三确定模块，配置为根据所述不确定性资源的运行基线、所述不确定性资源的出力值集合、所述可调节资源的运行基线、所述可调节资源的出力值以及所述可调节资源的出力约束集，确定所述虚拟电厂的可调容量。
根据本申请实施例的又一个方面，提供了一种计算机可读存储介质，所述存储介质存储有计算机程序，所述计算机程序用于执行本申请实施例上述任一方面所述的方法。
根据本申请实施例的又一个方面，提供了一种电子设备，所述电子设备包括：处理器；用于存储所述处理器可执行指令的存储器；所述处理器，用于从所述存储器中读取所述可执行指令，并执行所述指令以实现本申请实施例上述任一方面所述的方法。
根据本申请实施例的又一个方面，提供了一种计算机程序产品，包括计算机程序指令，该计算机程序指令用于执行上述权利任一方面所述的方法。
根据本申请实施例的又一个方面，提供了一种计算机程序，所述计算机程序用于执行上述权利要求任一方面所述的方法。
附图说明
通过参考下面的附图，可以更为完整地理解本申请的示例性实施方式：
图1是本申请实施例提供的一种示例性的虚拟电厂可调容量构建方法的流程示意图；
图2是本申请实施例提供的一种示例性的虚拟电厂的基本组成结构图；
图3是本申请实施例提供的一种示例性的虚拟电厂的可调容量的示意图；
图4是本申请实施例提供的一种示例性的虚拟电厂运营商参与辅助服务市场的运行框架图；
图5是本申请实施例提供的一种示例性的虚拟电厂可调容量构建装置的结构示意图；
图6是本申请实施例提供的一种示例性的电子设备的结构。
此处的附图被并入说明书中并构成本说明书的一部分，这些附图示出了符合本申请的实施例，并与说明书一起用于说明本申请的技术方案。
具体实施方式
下面，将参考附图详细地描述根据本申请的示例实施例。显然，所描述的实施例仅仅是本申请的一部分实施例，而不是本申请的全部实施例，应理解，本申请不受这里描述的示例实施例的限制。
应注意到：除非另外具体说明，否则在这些实施例中阐述的部件和步骤的相对布置、数字表达式和数值不限制本申请实施例的范围。
本领域技术人员可以理解，本申请实施例中的“第一”、“第二”等术语仅用于区别不同步骤、设备或模块等，既不代表任何特定技术含义，也不表示它们之间的必然逻辑顺序。
还应理解，在本申请实施例中，“多个”可以指两个或两个以上，“至少一个”可以指一个、两个或两个以上。
还应理解，对于本申请实施例中提及的任一部件、数据或结构，在没有明确限定或者在前后文给出相反启示的情况下，一般可以理解为一个或多个。
另外，本申请实施例中术语“和/或”，仅仅是一种描述关联对象的关联关系，表示可以存在三种关系，例如，A和/或B，可以表示：单独存在A，同时存在A和B，单独存在B这三种情况。另外，本申请实施例中字符“/”，一般表示前后关联对象是一种“或”的关系。
还应理解，本申请对各个实施例的描述着重强调各个实施例之间的不同之处，其相同或相似之处可以相互参考，为了简洁，不再一一赘述。
同时，应当明白，为了便于描述，附图中所示出的各个部分的尺寸并不是按照实际的比例关系绘制的。
以下对至少一个示例性实施例的描述实际上仅仅是说明性的，决不作为对本申请及其应用或使用的任何限制。
对于相关领域普通技术人员已知的技术、方法和设备可能不作详细讨论，但在适当情况下，技术、方法和设备应当被视为说明书的一部分。
应注意到：相似的标号和字母在下面的附图中表示类似项，因此，一旦某一项在一个附图中被定义，则在随后的附图中不需要对其进行进一步讨论。
本申请实施例可以应用于终端设备、计算机系统、服务器等电子设备，其可与众多其它通用或专用计算系统环境或配置一起操作。适于与终端设备、计算机系统、服务器等电子设备一起使用的众所周知的终端设备、计算系统、环境和/或配置的例子包括但不限于：个人计算机系统、服务器计算机系统、瘦客户机、厚客户机、手持或膝上设备、基于微处理器的系统、机顶盒、可编程消费电子产品、网络个人电脑、小型计算机系统、大型计算机系统和包括上述任何系统的分布式云计算技术环境，等等。
终端设备、计算机系统、服务器等电子设备可以在由计算机系统执行的计算机系统可执行指令(诸如程序模块)的一般语境下描述。通常，程序模块可以包括例程、程序、目标程序、组件、逻辑、数据结构等等，它们执行特定的任务或者实现特定的抽象数据类型。计算机系统/服务器可以在分布式云计算环境中实施，分布式云计算环境中，任务是由通过通信网络链接的远程处理设备执行的。在分布式云计算环境中，程序模块可以位于包括存储设备的本地或远程计算系统存储介质上。
示例性方法
图1是本申请实施例提供的一种示例性的虚拟电厂可调容量构建方法的流程示意图。本实施例可应用在电子设备上，如图1所示，虚拟电厂可调容量构建方法100包括以下步骤：
步骤101，根据虚拟电厂内部资源的运行特性，将所述虚拟电厂的内部资源分为不确定性资源和可调节资源。
在本申请实施例中，图2所示为典型的虚拟电厂结构，虚拟电厂运营商通过聚合内部资源进行优化调度，与电网进行购电、售电。如图2所示，虚拟电厂内部资源可以是但不限于包括：风电、光伏、常规负荷、柔性负荷、储能、燃气轮机以及电动汽车等。根据虚拟电厂内部资源的运行特性，可以将这些内部资源分为不确定性资源和可调节资源。其中，不确定性资源包括风电、光伏等可再生分布式电源和常规负荷等。可调节资源包括柔性负荷、储能以及燃气轮机和电动汽车等可控分布式电源。虚拟电厂运营商响应机制分析获取电网主能量市场与辅助服务市场数据，虚拟电厂运营商基于多种资源聚合通过物理解析模型统一模型建立，对各类资源的调节能力进行评估。
步骤102，确定所述不确定性资源的运行基线和所述不确定性资源的出力值集合。
在本申请实施例中，根据运行特性的不同，将虚拟电厂内部资源分为不确定性资源和可调节资源之后，需要对不确定性资源和可调节资源分别进行建模，然后建立虚拟电厂统一的可调容量模型。
不确定性资源主要包括风电、光伏等可再生分布式电源和常规负荷等。在虚拟电厂运营商决策过程中，需要对不确定性资源的出力预测值
Figure PCTCN2022137591-appb-000001
进行统计，并上报次日虚拟电厂某不确定性资源的运行基线P base，j(t)。
在一些实施例中，确定所述不确定性资源的运行基线，包括：通过以下公式确定所述不确定性资源的运行基线P base，j(t)：
Figure PCTCN2022137591-appb-000002
式中，
Figure PCTCN2022137591-appb-000003
为不确定性资源j在t时刻的出力预测值，N j为所述虚拟电厂内部不确定性资源数目。
在本申请实施例中，可以运用预设的不确定性分析方法，对不确定性资源的预测值和波动区间进行建模，初步确定不确定性资源的出力值集合。其中，预设的不确定性分析方法可以是但不限于为：动态鲁棒约束下的不确定性分析方法、采集数据并运用数据驱动的分析方法。即，在针对风电、光伏以及负荷出力不确定分析过程中，不仅局限于动态鲁棒约束下的不确定性分析。在某些实施虚拟电厂优化调度过程中，可以考虑采集数据并运用数据驱动等方式对出力不确定性进行分析。
考虑到不确定性资源出力具有随机性，采用鲁棒优化法对其进行建模。鲁棒优化法可以通过对不确定性资源的预测值和波动区间进行建模，进一步构建不确定情况的“最恶劣场景”，保证极端情况下调度结果可行性，但其决策结果具有保守性。而，动态鲁棒优化可以调节求解的保守程度，平衡虚拟电厂参与调度决策安全性和经济性。
因此，提出了动态鲁棒约束下虚拟电厂可调能力不确定性集合统一刻画模型。首先，在日前优化调度过程中，虚拟电厂不确定性资源出力取值集合W 0表示为：
Figure PCTCN2022137591-appb-000004
式中，P Load(t)、P PV(t)、P WT(t)分别为t时刻虚拟电厂内部常规负荷、风电、光伏的实际出力值；P j(t)为t时刻虚拟电厂内部的不确定性资源的实际出力值；
Figure PCTCN2022137591-appb-000005
为不确定性资源j在t时刻的出力预测值；
Figure PCTCN2022137591-appb-000006
为不确定性资源j在t时刻的预测出力最大波动偏差；
Figure PCTCN2022137591-appb-000007
Figure PCTCN2022137591-appb-000008
为0-1的变量，取值为1时，分别表示不确定性资源出力达到预测区间上界/下界，否则为期望值；Γ S、Γ T为不确定性的预算值，即一个调度周期内出力值对预测值存在波动的最大空间、最大时刻数目，Γ S为需求响应出力不确定集合在空间尺度上的波动参数，Γ T为需求响应出力不确定集合在时间尺度上的波动参数，即一个调度周期内预测值与实际值偏差的最大数值。
在一些实施例中，由于虚拟电厂参与电力市场交易，负荷出力受市场电价影响，应进一步对虚拟电厂内部常规负荷出力进行精细化建模。本申请实施例基于负荷自身性质和响应能力，将负荷分为常规负荷P L和可平移负荷P DR。常规负荷在日前调度过程中出力与价格息息相关。可平移负荷有相应的期望用电计划，并根据运营商调度在时间上进行电能量的平移和调节。在分时电价环境下，常规负荷响应量预测问题可看作在不同电价环境下的负荷预测问题。因此将不确定集合中负荷出力为常规负荷的不确定性建模，虚拟电厂不确定性资源集合中常规负荷加入电价影响因子。
因此，需要考虑电价影响因子，对初步确定的出力值集合进行优化，得到不确定性资源的出力值集合为：
Figure PCTCN2022137591-appb-000009
式中，P L(t)、P PV(t)、P WT(t)分别为t时刻虚拟电厂内部常规负荷、风电、光伏的实际出力值；
Figure PCTCN2022137591-appb-000010
为不确定性资源j在t时刻的出力预测值；
Figure PCTCN2022137591-appb-000011
为不确定性资源j在t时刻的预测出力最大波动偏差；
Figure PCTCN2022137591-appb-000012
为0-1的变量，取值为1时，分别表示不确定性资源出力达到预测区间上界/下界，否则为期望值。
其中，当不确定性资源j为常规负荷P L时，常规负荷P L在t时刻的出力预测值
Figure PCTCN2022137591-appb-000013
表示为：
Figure PCTCN2022137591-appb-000014
Figure PCTCN2022137591-appb-000015
式中，ε(t)为t时刻负荷自弹性系数；λ Δc(t)为t时刻电价变化率；
Figure PCTCN2022137591-appb-000016
为虚拟电厂内部常规负荷预测值；
Figure PCTCN2022137591-appb-000017
为虚拟电厂常规负荷预测值。
步骤103，确定所述可调节资源的运行基线、所述可调节资源的出力值以及所述可调节资源的出力约束集。
在本申请实施例中，可调节资源可以是但不限于包括柔性负荷、储能以及燃气轮机和电动汽车等可控分布式电源。在虚拟电厂运营商决策过程中，首先需要对可调节资源的出力预测值进行统计，并上报次日虚拟电厂可调节资源的运行基线P base，i(t)。
在一些实施例中，确定所述可调节资源的运行基线，包括：通过以下公式确定所述可调节资源的运行基线P base，i(t)：
Figure PCTCN2022137591-appb-000018
式中，P i e(t)为可调节资源i在t时刻的出力预测值，N i为所述虚拟电厂内部可调节资源数目。
在一些实施例中，确定所述可调节资源的出力值，包括：将所述可调节资源运行调度后增加/削减的出力值确定为所述可调节资源的出力值；其中，所述可调节资源运行调度后增加/削减的出力值为调度后所述可调节资源的实际出力值与所述可调节资源的出力预测值的差值。
在一些实施例中，确定所述可调节资源的出力约束集，包括：确定所述可调节资源的出力边界约束；确定所述可调节资源的发用电量约束；确定所述可调节资源在调度周期结束后的电量恢复约束；根据所述出力边界约束、所述发用电量约束以及所述电量恢复约束，确定所述可调节资源的出力约束集。
在本申请实施例中，在虚拟电厂运营商需要决策的主要信息有：
1)可平移负荷的期望用电计划，各时段可调度范围以及单位调度成本；
2)可控分布式电源的常规出力、可调度功率最大/最小值；
3)储能单元的容量、最大/最小充放电量以及调度周期所需初始容量。
可以看出，此类可调节资源具有明确的容量、出力范围等参数，虚拟电厂运营商可根据聚合资源参数确定其可调能力，可以表现为可调节资源运行调度后增加/削减出力值。可表示为调度后可调节资源实际出力值与可平移负荷的期望用电出力或可控分布式电源的常规出力(等同于可调节资源的出力预测值)的差值。
基于以上分析，针对虚拟电厂可调节资源的实际出力值进行建模。虽然不同的可调节资源运行方式不同，但根据不同的数据都可形成出力范围、可调容量以及出力期望值。虚拟电厂内部聚合任一可调节资源的出力约束集如下：
P i min(t)≤P i(t)≤P i max(t)    (6)
Figure PCTCN2022137591-appb-000019
Figure PCTCN2022137591-appb-000020
式中，P i min(t)、P i max(t)分别为最小可调功率和最大可调功率；P i(t)为调度后所述可调节资源的实际出力值；d 0为调度时刻初始电量；D max(t)为最大可剩余电量或最大用电需求；D min(t)为最小可剩余电量或最小用电需求；D 0(t)为通过不确定性资源预测曲线得出的期望发、用电量期望值；P i e(t)为可调节资源i在t时刻的出力预测值。
其中，出力约束集中的式(6)表示可调节资源出力边界约束；出力约束集中的式(7)表示任何时段可调节资源发、用电量需要在可调容量范围内；出力约束集中的式(8)在优化调度周期结束后，可调节资源将电量恢复到初始水平(储能类资源)或达到目标电量(负荷类资源)。
步骤104，根据所述不确定性资源的运行基线、所述不确定性资源的出力值集合、所述可调节资源的运行基线、所述可调节资源的出力值以及所述可调节资源的出力约束集，确定所述虚拟电厂的可调容量。
在一些实施例中，根据所述不确定性资源的运行基线、所述不确定性资源的出力值集合、所述可调节资源的运行基线、所述可调节资源的出力值以及所述可调节资源的出力约束集，确定所述虚拟电厂的可调容量，包括：根据所述不确定性资源的运行基线以及所述可调节资源的运行基线，确定所述虚拟电厂内部资源的运行基线；根据所述不确定性资源的出力值集合、所述可调节资源的出力值以及所述可调节资源的出力约束集，确定所述虚拟电厂调度后的实际运行特性；求取所述虚拟电厂调度后的实际运行特性与所述虚拟电厂内部资源的运行基线的差值，得到所述虚拟电厂的可调容量。
在本申请实施例中，图3给出了虚拟电厂可调容量的示意图。图4为虚拟电厂可调容量的应用场景。如图4所示，虚拟电厂在参与日前电力市场过程中，根据电网侧主能量市场下发的相关电价信息，上报次日运行基线，同时根据电网侧辅助服务市场下发的调峰指令，对自身可调容量进行优化调度，最终上报自身运行数据以及参与调峰的决策。如图3和图4所示，其中虚拟电厂可调容量出力值ΔP(t)表示为：
Figure PCTCN2022137591-appb-000021
式中，N i为虚拟电厂内部可调节资源数目；N j为虚拟电厂内部不确定性资源数目；ΔP i(t)为可调资源运行调度后增加/削减出力值；ΔP j(t)为不确定性资源运行出力波动值；P i(t)为调度后可调节资源实际出力值；P i e(t)为可平移负荷的期望用电出力或可控分布式电源的常规出力；P j(t)为t时刻虚拟电厂内部的不确定性资源j的实际出力值；
Figure PCTCN2022137591-appb-000022
为不确定性资源j在t时刻的出力预测值。
由式(9)，虚拟电厂可调容量可表示为虚拟电厂优化调度后实际运行特性P vpp(t)与日前上报的运行基线P base(t)的差值。
因此，虚拟电厂的可调容量ΔP(t)的计算公式为：
ΔP(t)＝P vpp(t)-P base(t)     (10)
Figure PCTCN2022137591-appb-000023
Figure PCTCN2022137591-appb-000024
式中，P vpp(t)为所述虚拟电厂调度后的实际运行特性；P base(t)为所述虚拟电厂内部资源的运行基线；N i为所述虚拟电厂内部可调节资源数目；N j为所述虚拟电厂内部不确定性资源数目；P i(t)为调度后所述可调节资源的实际出力值；W 0为所述不确定性资源的出力值集合；P base,i(t)为所述可调节资源的运行基线；P base,j(t)为所述不确定性资源的运行基线；P i e(t)为可调节资源i在t时刻的出力预测值；P j(t)为t时刻虚拟电厂内部的不确定性资源j的实际出力值；
Figure PCTCN2022137591-appb-000025
为不确定性资源j在t时刻的出力预测值。
综上，若对一个聚合多种资源的虚拟电厂可调容量进行建模，可按以下步骤进行建模并计算：首先将虚拟电厂内部资源进行分类，分为可调节资源和不确定性资源两类，并对其预测值进行求解，获取虚拟电厂次日运行基线。其次，运用动态鲁棒约束对不确定性资源实际出力情况进行建模并计算。对内部的可调节资源特征参数进行提取，进一步表示为上述约束式(6)、(7)、(8)的形式，将式中P i min(t)、P i max(t)、d 0、D min(t)、D max(t)、D 0(t)分别叠加，即可得到虚拟电厂内部全部可调节资源的出力约束集。最后，根据式(10)、(11)、(12)求解虚拟电厂的可调容量，为虚拟电厂参与日前调峰辅助服务进行指导和优化。
此模型的优点在于考虑了虚拟电厂聚合资源的不确定性，不限于某一特定聚合方式下的模型建立，便于虚拟电厂整体参与优化调度投标策略的研究。同时，若虚拟电厂运营商聚合新的资源(如综合能源，燃气或热能等)，只需按其运行特性进行归类。进一步，若后续研究中一个虚拟电厂运营商管理多个不同聚合形式的虚拟电厂，这些虚拟电厂参数可直接叠加，得到虚拟电厂运营商可调容量，而无需对模型整体进行修改。可以发现此约束集易于嵌入一些优化模型中，方便虚拟电厂运营商参与不同市场进整体行后续运算。
本申请实施例提出的一种虚拟电厂可调容量构建方法，能够面向调峰辅助服务市场，其有益效果包括：
(1)考虑了虚拟电厂聚合资源的不确定性，不限于某一特定聚合方式下的模型建立，便于虚拟电厂整体参与优化调度投标策略的研究。同时，若虚拟电厂运营商聚合新的资源(如综合能源，燃气或热能等)，只需按其运行特性进行归类。进一步，若后续研究中一个虚拟电厂运营商管理多个不同聚合形式的虚拟电厂，这些虚拟电厂参数可直接叠加，得到虚拟电厂运营商可调容量，而无需对整体进行重新建模。
(2)虚拟电厂参与日前辅助服务优化调度过程中，可以充分对虚拟电厂可调能力进行评估，可以充分考虑多种资源的运行特性以及出力情况，平衡虚拟电厂运行的可靠性与经济性。
针对虚拟电厂内部聚合资源多样，不同聚合资源下的虚拟电厂调节能力不同，且其内部资源出力具有不确定性，应对其动态聚合下调节能力进行分析。本申请实施例提出的虚拟电厂可调容量构建方法，首先对于不同运行特性的资源进行不确定性分析，可以是基于鲁棒不确定性分析以及可调资源运行特性分析。其次，对于各不确定性资源进行统一建模，为动态聚合下的虚拟电厂参与优化调度提供普适性模型，并通过统一模型对虚拟电厂可调能力进行评估，最终得到虚拟电厂的可调容量，有效解决了虚拟电厂不同的聚合方式为后续优化调度带来的困难。
从而，本申请实施例考虑了虚拟电厂聚合资源的不确定性，不限于某一特定聚合方式下 的模型建立，便于虚拟电厂整体参与优化调度投标策略的研究。同时，若虚拟电厂运营商聚合新的资源(如综合能源、燃气或热能等)，只需按其运行特性进行归类。进一步，若后续研究中一个虚拟电厂运营商管理多个不同聚合形式的虚拟电厂，这些虚拟电厂参数可直接叠加，得到虚拟电厂运营商可调容量，无需对整体进行重新建模。本申请实施例的虚拟电厂在参与电力市场时，可以充分对虚拟电厂可调能力进行评估，可以充分考虑多种资源的运行特性以及出力情况，充分对各类资源进行调度，平衡虚拟电厂运行的可靠性与经济性。
示例性装置
图5是本申请实施例提供的一种示例性的虚拟电厂可调容量构建装置的结构示意图。如图5所示，装置500包括：
资源划分模块510，配置为于根据虚拟电厂内部资源的运行特性，将所述虚拟电厂的内部资源分为不确定性资源和可调节资源；
第一确定模块520，配置为确定所述不确定性资源的运行基线和所述不确定性资源的出力值集合；
第二确定模块530，配置为确定所述可调节资源的运行基线、所述可调节资源的出力值以及所述可调节资源的出力约束集；
第三确定模块540，配置为根据所述不确定性资源的运行基线、所述不确定性资源的出力值集合、所述可调节资源的运行基线、所述可调节资源的出力值以及所述可调节资源的出力约束集，确定所述虚拟电厂的可调容量。
在一些实施例中，第一确定模块520，配置为：通过以下公式确定所述不确定性资源的运行基线P base，j(t)：
Figure PCTCN2022137591-appb-000026
式中，
Figure PCTCN2022137591-appb-000027
为不确定性资源j在t时刻的出力预测值，N j为所述虚拟电厂内部不确定性资源数目。
在一些实施例中，第一确定模块520，配置为：
运用预设的不确定性分析方法，对所述不确定性资源的预测值和波动区间进行建模，初步确定所述不确定性资源的出力值集合；
考虑电价影响因子，对初步确定的出力值集合进行优化，得到所述不确定性资源的出力值集合。
在一些实施例中，第一确定模块520，还配置为：
运用动态鲁棒约束方法，对所述不确定性资源的预测值和波动区间进行建模，初步确定所述不确定性资源的出力值集合为：
Figure PCTCN2022137591-appb-000028
式中，P Load(t)、P PV(t)、P WT(t)分别为t时刻虚拟电厂内部常规负荷、风电、光伏的实际出力值；P j(t)为t时刻虚拟电厂内部的不确定性资源的实际出力值；
Figure PCTCN2022137591-appb-000029
为不确定性资源j在t时刻的出力预测值；
Figure PCTCN2022137591-appb-000030
为不确定性资源j在t时刻的预测出力最大波动偏差；
Figure PCTCN2022137591-appb-000031
Figure PCTCN2022137591-appb-000032
为0-1的变量，取值为1时，分别表示不确定性资源出力达到预测区间上界/下界，否 则为期望值；Γ S、Γ T为不确定性的预算值，即一个调度周期内出力值对预测值存在波动的最大空间、最大时刻数目，Γ S为需求响应出力不确定集合在空间尺度上的波动参数，Γ T为需求响应出力不确定集合在时间尺度上的波动参数，即一个调度周期内预测值与实际值偏差的最大数值；
并且，考虑电价影响因子，对初步确定的出力值集合进行优化，得到所述不确定性资源的出力值集合为：
Figure PCTCN2022137591-appb-000033
式中，P L(t)、P PV(t)、P WT(t)分别为t时刻虚拟电厂内部常规负荷、风电、光伏的实际出力值；
Figure PCTCN2022137591-appb-000034
为不确定性资源j在t时刻的出力预测值；
Figure PCTCN2022137591-appb-000035
为不确定性资源j在t时刻的预测出力最大波动偏差；
Figure PCTCN2022137591-appb-000036
为0-1的变量，取值为1时，分别表示不确定性资源出力达到预测区间上界/下界，否则为期望值。
在一些实施例中，第二确定模块530，配置为：通过以下公式确定所述可调节资源的运行基线P base，i(t)：
Figure PCTCN2022137591-appb-000037
式中，P i e(t)为可调节资源i在t时刻的出力预测值，N i为所述虚拟电厂内部可调节资源数目。
在一些实施例中，第二确定模块530，还配置为：
将所述可调节资源运行调度后增加/削减的出力值确定为所述可调节资源的出力值；
其中，所述可调节资源运行调度后增加/削减的出力值为调度后所述可调节资源的实际出力值与所述可调节资源的出力预测值的差值。
在一些实施例中，第二确定模块530，还配置为：
确定所述可调节资源的出力边界约束；
确定所述可调节资源的发用电量约束；
确定所述可调节资源在调度周期结束后的电量恢复约束；
根据所述出力边界约束、所述发用电量约束以及所述电量恢复约束，确定所述可调节资源的出力约束集。
在一些实施例中，所述可调节资源的出力约束集为：
P i min(t)≤P i(t)≤P i max(t)；
Figure PCTCN2022137591-appb-000038
Figure PCTCN2022137591-appb-000039
式中，P i min(t)、P i max(t)分别为最小可调功率和最大可调功率；P i(t)为调度后所述可调节资源的实际出力值；d 0为调度时刻初始电量；D max(t)为最大可剩余电量或最大用电需求；D min(t)为最小可剩余电量或最小用电需求；D 0(t)为通过不确定性资源预测曲线得出的期望发、用电量期望值；P i e(t)为可调节资源i在t时刻的出力预测值。
在一些实施例中，第三确定模块540，配置为：
根据所述不确定性资源的运行基线以及所述可调节资源的运行基线，确定所述虚拟电厂内部资源的运行基线；
根据所述不确定性资源的出力值集合、所述可调节资源的出力值以及所述可调节资源的出力约束集，确定所述虚拟电厂调度后的实际运行特性；
求取所述虚拟电厂调度后的实际运行特性与所述虚拟电厂内部资源的运行基线的差值，得到所述虚拟电厂的可调容量。
在一些实施例中，所述虚拟电厂的可调容量ΔP(t)的计算公式为：
ΔP(t)＝P vpp(t)-P base(t)；
Figure PCTCN2022137591-appb-000040
Figure PCTCN2022137591-appb-000041
式中，P vpp(t)为所述虚拟电厂调度后的实际运行特性；P base(t)为所述虚拟电厂内部资源的运行基线；N i为所述虚拟电厂内部可调节资源数目；N j为所述虚拟电厂内部不确定性资源数目；P i(t)为调度后所述可调节资源的实际出力值；W 0为所述不确定性资源的出力值集合；P base,i(t)为所述可调节资源的运行基线；P base,j(t)为所述不确定性资源的运行基线；P i e(t)为可调节资源i在t时刻的出力预测值；P j(t)为t时刻虚拟电厂内部的不确定性资源j的实际出力值；
Figure PCTCN2022137591-appb-000042
为不确定性资源j在t时刻的出力预测值。
本申请实施例的虚拟电厂可调容量构建装置500与本申请另一个实施例的虚拟电厂可调容量构建方法100相对应，在此不再赘述。
示例性电子设备
图6是本申请实施例提供的一种示例性的电子设备的结构。该电子设备可以是第一设备和第二设备中的任一个或两者、或与它们独立的单机设备，该单机设备可以与第一设备和第二设备进行通信，以从它们接收所采集到的输入信号。图6图示了根据本申请实施例的电子设备的框图。如图6所示，电子设备60包括一个或多个处理器61和存储器62。
处理器61可以是中央处理单元(CPU)或者具有数据处理能力和/或指令执行能力的其他形式的处理单元，并且可以控制电子设备中的其他组件以执行期望的功能。
存储器62可以包括一个或多个计算机程序产品，所述计算机程序产品可以包括各种形式的计算机可读存储介质，例如易失性存储器和/或非易失性存储器。所述易失性存储器例如可以包括随机存取存储器(RAM)和/或高速缓冲存储器(cache)等。所述非易失性存储器例如可以包括只读存储器(ROM)、硬盘、闪存等。在所述计算机可读存储介质上可以存储一个或多个计算机程序指令，处理器61可以运行所述程序指令，以实现上文所述的本申请的各个实施例的软件程序的对历史变更记录进行信息挖掘的方法以及/或者其他期望的功能。在一个示例中，电子设备还可以包括：输入装置63和输出装置64，这些组件通过总线系统和/或其他形式的连接机构(未示出)互连。
此外，该输入装置63还可以包括例如键盘、鼠标等等。
该输出装置64可以向外部输出各种信息。该输出装置64可以包括例如显示器、扬声器、打印机、以及通信网络及其所连接的远程输出设备等等。
当然，为了简化，图6中仅示出了该电子设备中与本申请实施例有关的组件中的一些，省略了诸如总线、输入/输出接口等等的组件。除此之外，根据具体应用情况，电子设备还可以包括任何其他适当的组件。
示例性计算机程序产品和计算机可读存储介质
除了上述方法和设备以外，本申请实施例还可以是计算机程序产品，其包括计算机程序 指令，所述计算机程序指令在被处理器运行时使得所述处理器执行本说明书上述“示例性方法”部分中描述的根据本申请各种实施例的对历史变更记录进行信息挖掘的方法中的步骤。
所述计算机程序产品可以以一种或多种程序设计语言的任意组合来编写用于执行本申请实施例操作的程序代码，所述程序设计语言包括面向对象的程序设计语言，诸如Java、C++等，还包括常规的过程式程序设计语言，诸如“C”语言或类似的程序设计语言。程序代码可以完全地在用户计算设备上执行、部分地在用户设备上执行、作为一个独立的软件包执行、部分在用户计算设备上部分在远程计算设备上执行、或者完全在远程计算设备或服务器上执行。
此外，本申请的实施例还可以是计算机可读存储介质，其上存储有计算机程序指令，所述计算机程序指令在被处理器运行时使得所述处理器执行本说明书上述“示例性方法”部分中描述的根据本申请各种实施例的对历史变更记录进行信息挖掘的方法中的步骤。
所述计算机可读存储介质可以采用一个或多个可读介质的任意组合。可读介质可以是可读信号介质或者可读存储介质。可读存储介质例如可以包括但不限于电、磁、光、电磁、红外线、或半导体的系统、系统或器件，或者任意以上的组合。可读存储介质的例子(非穷举的列表)包括：具有一个或多个导线的电连接、便携式盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、光纤、便携式紧凑盘只读存储器(CD-ROM)、光存储器件、磁存储器件、或者上述的任意合适的组合。
以上结合实施例描述了本申请的基本原理，但是，需要指出的是，在本申请实施例中提及的优点、优势、效果等仅是示例而非限制，不能认为这些优点、优势、效果等是本申请实施例的各个实施例必须具备的。另外，上述公开的细节仅是为了示例的作用和便于理解的作用，而非限制，上述细节并不限制本申请实施例为必须采用上述的细节来实现。
本说明书中各个实施例均采用递进的方式描述，每个实施例重点说明的都是与其它实施例的不同之处，各个实施例之间相同或相似的部分相互参见即可。对于系统实施例而言，由于其与方法实施例基本对应，所以描述的比较简单，相关之处参见方法实施例的部分说明即可。
本申请实施例中涉及的器件、系统、设备、系统的方框图仅作为例示性的例子并且不意图要求或暗示必须按照方框图示出的方式进行连接、布置、配置。如本领域技术人员将认识到的，可以按任意方式连接、布置、配置这些器件、系统、设备、系统。诸如“包括”、“包含”、“具有”等等的词语是开放性词汇，指“包括但不限于”，且可与其互换使用。这里所使用的词汇“或”和“和”指词汇“和/或”，且可与其互换使用，除非上下文明确指示不是如此。这里所使用的词汇“诸如”指词组“诸如但不限于”，且可与其互换使用。
可能以许多方式来实现本申请实施例的方法和系统。例如，可通过软件、硬件、固件或者软件、硬件、固件的任何组合来实现本申请实施例的方法和系统。用于所述方法的步骤的上述顺序仅是为了进行说明，本申请实施例的方法的步骤不限于以上描述的顺序，除非以其它方式特别说明。此外，在一些实施例中，还可将本申请实施为记录在记录介质中的程序，这些程序包括用于实现根据本申请的方法的机器可读指令。因而，本申请还覆盖存储用于执行根据本申请实施例的方法的程序的记录介质。
还需要指出的是，在本申请实施例的系统、设备和方法中，各部件或各步骤是可以分解和/或重新组合的。这些分解和/或重新组合应视为本申请实施例的等效方案。提供所公开的方面的以上描述以使本领域的任何技术人员能够做出或者使用本申请实施例。对这些方面的各种修改对于本领域技术人员而言是非常显而易见的，并且在此定义的一般原理可以应用于其他方面而不脱离本申请实施例的范围。因此，本申请实施例不意图被限制到在此示出的方面，而是按照与在此公开的原理和新颖的特征一致的最宽范围。
为了例示和描述的目的已经给出了以上描述。此外，此描述不意图将本申请的实施例限制到在此公开的形式。尽管以上已经讨论了多个示例方面和实施例，但是本领域技术人员将认识到其某些变型、修改、改变、添加和子组合。

Claims (24)

  1. 一种虚拟电厂可调容量构建方法，所述方法由电子设备执行，所述方法包括：
    根据虚拟电厂内部资源的运行特性，将所述虚拟电厂的内部资源分为不确定性资源和可调节资源；
    确定所述不确定性资源的运行基线和所述不确定性资源的出力值集合；
    确定所述可调节资源的运行基线、所述可调节资源的出力值以及所述可调节资源的出力约束集；
    根据所述不确定性资源的运行基线、所述不确定性资源的出力值集合、所述可调节资源的运行基线、所述可调节资源的出力值以及所述可调节资源的出力约束集，确定所述虚拟电厂的可调容量。
  2. 根据权利要求1所述的方法，其中，所述确定所述不确定性资源的运行基线，包括：通过以下公式确定所述不确定性资源的运行基线P base，j(t)：
    Figure PCTCN2022137591-appb-100001
    式中，
    Figure PCTCN2022137591-appb-100002
    为不确定性资源j在t时刻的出力预测值，N j为所述虚拟电厂内部不确定性资源数目。
  3. 根据权利要求1或2所述的方法，其中，所述确定所述不确定性资源的出力值集合，包括：
    运用预设的不确定性分析方法，对所述不确定性资源的预测值和波动区间进行建模，初步确定所述不确定性资源的出力值集合；
    考虑电价影响因子，对初步确定的出力值集合进行优化，得到所述不确定性资源的出力值集合。
  4. 根据权利要求3所述的方法，其中，
    所述运用预设的不确定性分析方法，对所述不确定性资源的预测值和波动区间进行建模，初步确定所述不确定性资源的出力值集合，包括：
    运用动态鲁棒约束方法，对所述不确定性资源的预测值和波动区间进行建模，初步确定所述不确定性资源的出力值集合为：
    Figure PCTCN2022137591-appb-100003
    式中，P Load(t)、P PV(t)、P WT(t)分别为t时刻虚拟电厂内部常规负荷、风电、光伏的实际出力值；P j(t)为t时刻虚拟电厂内部的不确定性资源的实际出力值；
    Figure PCTCN2022137591-appb-100004
    为不确定性资源j在t时刻的出力预测值；
    Figure PCTCN2022137591-appb-100005
    为不确定性资源j在t时刻的预测出力最大波动偏差；
    Figure PCTCN2022137591-appb-100006
    Figure PCTCN2022137591-appb-100007
    为0-1的变量，取值为1时，分别表示不确定性资源出力达到预测区间上界/下界，否则为期望值；Γ S、Γ T为不确定性的预算值，即一个调度周期内出力值对预测值存在波动的最大空间、最大时刻数目，Γ S为需求响应出力不确定集合在空间尺度上的波动参数，Γ T为需求响应出力不确定集合在时间尺度上的波动参数，即一个调度周期内预测值与实际值偏差的最大数值；
    考虑电价影响因子，对初步确定的出力值集合进行优化，得到所述不确定性资源的出力值集合为：
    Figure PCTCN2022137591-appb-100008
    式中，P L(t)、P PV(t)、P WT(t)分别为t时刻虚拟电厂内部常规负荷、风电、光伏的实际出力值；
    Figure PCTCN2022137591-appb-100009
    为不确定性资源j在t时刻的出力预测值；
    Figure PCTCN2022137591-appb-100010
    为不确定性资源j在t时刻的预测出力最大波动偏差；
    Figure PCTCN2022137591-appb-100011
    为0-1的变量，取值为1时，分别表示不确定性资源出力达到预测区间上界/下界，否则为期望值。
  5. 根据权利要求1至4任一项所述的方法，其中，所述确定所述可调节资源的运行基线，包括：通过以下公式确定所述可调节资源的运行基线P base，i(t)：
    Figure PCTCN2022137591-appb-100012
    式中，P i e(t)为可调节资源i在t时刻的出力预测值，N i为所述虚拟电厂内部可调节资源数目。
  6. 根据权利要求1至5任一项所述的方法，其中，所述确定所述可调节资源的出力值，包括：
    将所述可调节资源运行调度后增加/削减的出力值确定为所述可调节资源的出力值；
    其中，所述可调节资源运行调度后增加/削减的出力值为调度后所述可调节资源的实际出力值与所述可调节资源的出力预测值的差值。
  7. 根据权利要求1至6任一项所述的方法，其中，所述确定所述可调节资源的出力约束集，包括：
    确定所述可调节资源的出力边界约束；
    确定所述可调节资源的发用电量约束；
    确定所述可调节资源在调度周期结束后的电量恢复约束；
    根据所述出力边界约束、所述发用电量约束以及所述电量恢复约束，确定所述可调节资源的出力约束集。
  8. 根据权利要求7所述的方法，其中，所述可调节资源的出力约束集为：
    P i min(t)≤P i(t)≤P i max(t)；
    Figure PCTCN2022137591-appb-100013
    Figure PCTCN2022137591-appb-100014
    式中，P i min(t)、P i max(t)分别为最小可调功率和最大可调功率；P i(t)为调度后所述可调节资源的实际出力值；d 0为调度时刻初始电量；D max(t)为最大可剩余电量或最大用电需求；D min(t)为最小可剩余电量或最小用电需求；D 0(t)为通过不确定性资源预测曲线得出的期望发、用电量期望值；P i e(t)为可调节资源i在t时刻的出力预测值。
  9. 根据权利要求1至8任一项所述的方法，其中，所述根据所述不确定性资源的运行基线、所述不确定性资源的出力值集合、所述可调节资源的运行基线、所述可调节资源的出力值以及所述可调节资源的出力约束集，确定所述虚拟电厂的可调容量，包括：
    根据所述不确定性资源的运行基线以及所述可调节资源的运行基线，确定所述虚拟电厂内部资源的运行基线；
    根据所述不确定性资源的出力值集合、所述可调节资源的出力值以及所述可调节资源的出力约束集，确定所述虚拟电厂调度后的实际运行特性；
    求取所述虚拟电厂调度后的实际运行特性与所述虚拟电厂内部资源的运行基线的差值，得到所述虚拟电厂的可调容量。
  10. 根据权利要求1至9任一项所述的方法，其中，所述虚拟电厂的可调容量ΔP(t)的计算公式为：
    ΔP(t)＝P vpp(t)-P base(t)；
    Figure PCTCN2022137591-appb-100015
    Figure PCTCN2022137591-appb-100016
    式中，P vpp(t)为所述虚拟电厂调度后的实际运行特性；P base(t)为所述虚拟电厂内部资源的运行基线；N i为所述虚拟电厂内部可调节资源数目；N j为所述虚拟电厂内部不确定性资源数目；P i(t)为调度后所述可调节资源的实际出力值；W 0为所述不确定性资源的出力值集合；P base,i(t)为所述可调节资源的运行基线；P base,j(t)为所述不确定性资源的运行基线；P i e(t)为可调节资源i在t时刻的出力预测值；P j(t)为t时刻虚拟电厂内部的不确定性资源j的实际出力值；
    Figure PCTCN2022137591-appb-100017
    为不确定性资源j在t时刻的出力预测值。
  11. 一种虚拟电厂可调容量构建装置，包括：
    资源划分模块，配置为根据虚拟电厂内部资源的运行特性，将所述虚拟电厂的内部资源分为不确定性资源和可调节资源；
    第一确定模块，配置为确定所述不确定性资源的运行基线和所述不确定性资源的出力值集合；
    第二确定模块，配置为确定所述可调节资源的运行基线、所述可调节资源的出力值以及所述可调节资源的出力约束集；
    第三确定模块，配置为根据所述不确定性资源的运行基线、所述不确定性资源的出力值集合、所述可调节资源的运行基线、所述可调节资源的出力值以及所述可调节资源的出力约束集，确定所述虚拟电厂的可调容量。
  12. 根据权利要求11所述的装置，其中，所述第一确定模块，配置为：通过以下公式确定所述不确定性资源的运行基线P base，j(t)：
    Figure PCTCN2022137591-appb-100018
    式中，
    Figure PCTCN2022137591-appb-100019
    为不确定性资源j在t时刻的出力预测值，N j为所述虚拟电厂内部不确定性资源数目。
  13. 根据权利要求11或12所述的装置，其中，所述第一确定模块，配置为：
    运用预设的不确定性分析方法，对所述不确定性资源的预测值和波动区间进行建模，初步确定所述不确定性资源的出力值集合；
    考虑电价影响因子，对初步确定的出力值集合进行优化，得到所述不确定性资源的出力值集合。
  14. 根据权利要求11至13任一项所述的装置，其中，
    所述第一确定模块，还配置为：
    运用动态鲁棒约束方法，对所述不确定性资源的预测值和波动区间进行建模，初步确定所述不确定性资源的出力值集合为：
    Figure PCTCN2022137591-appb-100020
    式中，P Load(t)、P PV(t)、P WT(t)分别为t时刻虚拟电厂内部常规负荷、风电、光伏的实际出力值；P j(t)为t时刻虚拟电厂内部的不确定性资源的实际出力值；
    Figure PCTCN2022137591-appb-100021
    为不确定性资源j在t时刻的出力预测值；
    Figure PCTCN2022137591-appb-100022
    为不确定性资源j在t时刻的预测出力最大波动偏差；
    Figure PCTCN2022137591-appb-100023
    Figure PCTCN2022137591-appb-100024
    为0-1的变量，取值为1时，分别表示不确定性资源出力达到预测区间上界/下界，否则为期望值；Γ S、Γ T为不确定性的预算值，即一个调度周期内出力值对预测值存在波动的最大空间、最大时刻数目，Γ S为需求响应出力不确定集合在空间尺度上的波动参数，Γ T为需求响应出力不确定集合在时间尺度上的波动参数，即一个调度周期内预测值与实际值偏差的最大数值；
    并且，考虑电价影响因子，对初步确定的出力值集合进行优化，得到所述不确定性资源的出力值集合为：
    Figure PCTCN2022137591-appb-100025
    式中，P L(t)、P PV(t)、P WT(t)分别为t时刻虚拟电厂内部常规负荷、风电、光伏的实际出力值；
    Figure PCTCN2022137591-appb-100026
    为不确定性资源j在t时刻的出力预测值；
    Figure PCTCN2022137591-appb-100027
    为不确定性资源j在t时刻的预测出力最大波动偏差；
    Figure PCTCN2022137591-appb-100028
    为0-1的变量，取值为1时，分别表示不确定性资源出力达到预测区间上界/下界，否则为期望值。
  15. 根据权利要求11至14任一项所述的装置，其中，所述第二确定模块，配置为：通过以下公式确定所述可调节资源的运行基线P base，i(t)：
    Figure PCTCN2022137591-appb-100029
    式中，P i e(t)为可调节资源i在t时刻的出力预测值，N i为所述虚拟电厂内部可调节资源数目。
  16. 根据权利要求11至15任一项所述的装置，其中，所述第二确定模块，还配置为：
    将所述可调节资源运行调度后增加/削减的出力值确定为所述可调节资源的出力值；
    其中，所述可调节资源运行调度后增加/削减的出力值为调度后所述可调节资源的实际出力值与所述可调节资源的出力预测值的差值。
  17. 根据权利要求11至16任一项所述的装置，其中，所述第二确定模块，还配置为：
    确定所述可调节资源的出力边界约束；
    确定所述可调节资源的发用电量约束；
    确定所述可调节资源在调度周期结束后的电量恢复约束；
    根据所述出力边界约束、所述发用电量约束以及所述电量恢复约束，确定所述可调节资源的出力约束集。
  18. 根据权利要求17所述的装置，其中，所述可调节资源的出力约束集为：
    P i min(t)≤P i(t)≤P i max(t)；
    Figure PCTCN2022137591-appb-100030
    Figure PCTCN2022137591-appb-100031
    式中，P i min(t)、P i max(t)分别为最小可调功率和最大可调功率；P i(t)为调度后所述可调节资源的实际出力值；d 0为调度时刻初始电量；D max(t)为最大可剩余电量或最大用电需求；D min(t)为最小可剩余电量或最小用电需求；D 0(t)为通过不确定性资源预测曲线得出的期望发、用电量期望值；P i e(t)为可调节资源i在t时刻的出力预测值。
  19. 根据权利要求11至18任一项所述的装置，其中，所述第三确定模块，配置为：
    根据所述不确定性资源的运行基线以及所述可调节资源的运行基线，确定所述虚拟电厂内部资源的运行基线；
    根据所述不确定性资源的出力值集合、所述可调节资源的出力值以及所述可调节资源的出力约束集，确定所述虚拟电厂调度后的实际运行特性；
    求取所述虚拟电厂调度后的实际运行特性与所述虚拟电厂内部资源的运行基线的差值，得到所述虚拟电厂的可调容量。
  20. 根据权利要求11至19任一项所述的装置，其中，所述虚拟电厂的可调容量ΔP(t)的计算公式为：
    ΔP(t)＝P vpp(t)-P base(t)；
    Figure PCTCN2022137591-appb-100032
    Figure PCTCN2022137591-appb-100033
    式中，P vpp(t)为所述虚拟电厂调度后的实际运行特性；P base(t)为所述虚拟电厂内部资源的运行基线；N i为所述虚拟电厂内部可调节资源数目；N j为所述虚拟电厂内部不确定性资源数目；P i(t)为调度后所述可调节资源的实际出力值；W 0为所述不确定性资源的出力值集合；P base,i(t)为所述可调节资源的运行基线；P base,j(t)为所述不确定性资源的运行基线；P i e(t)为可调节资源i在t时刻的出力预测值；P j(t)为t时刻虚拟电厂内部的不确定性资源j的实际出力值；
    Figure PCTCN2022137591-appb-100034
    为不确定性资源j在t时刻的出力预测值。
  21. 一种计算机可读存储介质，所述存储介质存储有计算机程序，所述计算机程序用于执行上述权利要求1-10任一所述的方法。
  22. 一种电子设备，所述电子设备包括：
    处理器；
    用于存储所述处理器可执行指令的存储器；
    所述处理器，用于从所述存储器中读取所述可执行指令，并执行所述指令以实现上述权利要求1-10任一所述的方法。
  23. 一种计算机程序产品，包括计算机程序指令，该计算机程序指令用于执行上述权利要求1-10任一所述的方法。
  24. 一种计算机程序，所述计算机程序用于执行上述权利要求1-10任一所述的方法。
PCT/CN2022/137591 2022-09-20 2022-12-08 虚拟电厂可调容量构建方法、装置、电子设备、存储介质、程序、及程序产品 WO2024060413A1 (zh)

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