Virtual power plant optimization operation method and system considering resource dynamic aggregation

Abstract

The invention belongs to the technical field of virtual power plant resource scheduling, and provides a virtual power plant optimization operation method and system considering resource dynamic aggregation, and the method comprises the steps: obtaining a multi-energy resource model of a virtual power plant; constructing a multi-energy resource dynamic aggregation model based on the obtained multi-energy resource model; in combination with the constructed multi-energy resource dynamic aggregation model, by taking the minimum net operation cost of the virtual power plant as a target, constructing a target function of optimal operation of the virtual power plant; and solving the constructed objective function to obtain an optimal scheduling scheme of the virtual power plant, and completing virtual power plant optimization operation considering resource dynamic aggregation.

Description

Technical Field

[0001] This invention belongs to the field of virtual power plant resource scheduling technology, specifically relating to a method and system for optimizing the operation of a virtual power plant that considers dynamic resource aggregation. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] With the global energy transition, the installed capacity of renewable energy sources, represented by wind power and photovoltaics, continues to grow, and their penetration rate in the power system is increasing year by year. However, renewable energy is characterized by intermittency, volatility, and uncertainty, and large-scale grid connection poses a severe challenge to the safe and stable operation of the power system. Virtual Power Plants (VPPs), as a new energy management technology, use advanced information and communication technologies and intelligent control methods to aggregate and coordinate the dispatch of various flexible resources such as distributed energy resources (DERs), energy storage systems, and flexible loads, forming "virtual controllable units" that can participate in electricity market transactions, thereby improving the system's flexibility, reliability, and renewable energy absorption capacity.

[0004] Currently, research on virtual power plant (VPP) dispatch optimization mainly focuses on the static aggregation and dispatch of single-type resources (such as wind power, photovoltaics, and energy storage), lacking systematic modeling of flexible resources with multiple types, multi-energy complementarity, and dynamic coupling. Especially under the "dual carbon" objective, power system operation must consider not only economic efficiency but also carbon emission constraints and environmental benefits. Traditional VPP dispatch models often neglect the coupling effects of new flexible resources such as carbon capture systems (CCS), flue gas treatment, and waste incineration, resulting in the failure to fully realize dispatch potential and making it difficult to adapt to the system operation requirements under high-proportion renewable energy integration.

[0005] In terms of resource aggregation, most methods use fixed capacity or typical daily curves for simplification, failing to fully consider the transferability and interruptibility of loads, the dynamic energy evolution process of energy storage systems, and the real-time uncertainty of wind and solar power output. This leads to deviations between the aggregation model and the actual operating capacity, affecting the feasibility and economy of the scheduling results. In particular, in terms of multi-timescale coordination, multi-objective optimization, and carbon emission constraints, existing methods have not yet formed a complete theoretical system and technical framework. At the same time, existing research focuses on the static aggregation of single-type resources, lacking a comprehensive consideration of multi-energy complementarity, carbon emission constraints, and source-grid-load-storage coordination. Summary of the Invention

[0006] To address the aforementioned issues, this invention proposes a method and system for optimizing the operation of virtual power plants that considers dynamic resource aggregation. By constructing a dynamic aggregation model of "source-storage-load" and a multi-objective optimization function, the operating status of virtual power plants is optimized and scheduled to achieve multi-energy complementarity and peak shaving and valley filling, thereby improving the renewable energy absorption capacity and system operation flexibility.

[0007] According to some embodiments, the first aspect of the present invention provides a method for optimizing the operation of a virtual power plant that considers dynamic resource aggregation, employing the following technical solution: A method for optimizing the operation of a virtual power plant considering dynamic resource aggregation includes: Obtain the multi-energy resource model of the virtual power plant; Based on the acquired multi-energy resource model, a dynamic aggregation model of multi-energy resources is constructed. Based on the constructed multi-energy resource dynamic aggregation model, an objective function for optimizing the operation of the virtual power plant is constructed with the goal of minimizing the net operating cost of the virtual power plant. Solving the constructed objective function yields the optimal scheduling scheme for the virtual power plant, thus completing the optimized operation of the virtual power plant considering dynamic resource aggregation.

[0008] As a further technical limitation, the multi-energy resource model of the virtual power plant obtained includes at least the energy balance model of carbon capture power plant and wind and solar resources, the source-storage-load joint adjustable power boundary model, the energy storage system dynamic model, the transferable load model, and the interruptible load model.

[0009] As a further technical limitation, the objective function for the optimized operation of the constructed virtual power plant is: ;in, The objective function is... Fuel costs for thermal power plants; For carbon trading market revenue; To maintain costs; Cost compensation for interruptible loads; Cost of purchasing electricity from the grid; The cost of punitive measures for environmental pollution.

[0010] As a further technical limitation, the constraints of the objective function for the optimized operation of the virtual power plant include at least power balance constraints and upper and lower limits constraints for carbon capture power consumption.

[0011] As a further technical limitation, a virtual power plant optimization operation method that considers dynamic resource aggregation also includes multi-timescale collaborative optimization of the virtual power plant, wherein the multi-timescale collaborative optimization includes day-ahead scheduling, intraday rolling correction and real-time power balance correction.

[0012] As a further technical limitation, mixed-integer linear programming is used to solve the constructed objective function.

[0013] According to some embodiments, the second aspect of the present invention provides a virtual power plant optimized operation system considering dynamic resource aggregation, employing the following technical solution: A virtual power plant optimization operation system considering dynamic resource aggregation includes: The acquisition module is configured to acquire the multi-energy resource model of the virtual power plant; The module is configured to construct a dynamic aggregation model of multi-energy resources based on the acquired multi-energy resource model; and, in conjunction with the constructed dynamic aggregation model of multi-energy resources, to construct an objective function for optimizing the operation of the virtual power plant with the goal of minimizing the net operating cost of the virtual power plant. The optimization module is configured to solve the constructed objective function to obtain the optimal scheduling scheme of the virtual power plant, thus completing the optimized operation of the virtual power plant considering dynamic resource aggregation.

[0014] According to some embodiments, a third aspect of the present invention provides a computer-readable storage medium, employing the following technical solution: A computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps of the virtual power plant optimization operation method considering dynamic resource aggregation as described in the first aspect of the present invention.

[0015] According to some embodiments, the fourth aspect of the present invention provides an electronic device, which adopts the following technical solution: An electronic device includes a memory, a processor, and a program stored in the memory and running on the processor, wherein the processor executes the program to implement the steps in the virtual power plant optimization operation method considering dynamic resource aggregation as described in the first aspect of the present invention.

[0016] According to some embodiments, the fifth aspect of the present invention provides a computer program product, which adopts the following technical solution: A computer program product includes software code, wherein the program in the software code performs the steps of the virtual power plant optimization operation method considering dynamic resource aggregation as described in the first aspect of the present invention.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention optimizes the operation of virtual power plants by constructing a dynamic aggregation model of "source-storage-load" and a multi-objective optimization function, thereby achieving multi-energy complementarity and peak shaving and valley filling, and improving the renewable energy absorption capacity and system operation flexibility. Attached Figure Description

[0018] The accompanying drawings, which form part of this embodiment, are used to provide a further understanding of this embodiment. The illustrative embodiments and their descriptions are used to explain this embodiment and do not constitute an improper limitation of this embodiment.

[0019] Figure 1 This is a flowchart of the virtual power plant optimization operation method considering dynamic resource aggregation in Embodiment 1 of the present invention; Figure 2 This is a structural block diagram of the virtual power plant optimized operation system considering dynamic resource aggregation in Embodiment 2 of the present invention. Detailed Implementation

[0020] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0021] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0022] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0023] In this invention, terms such as "upper," "lower," "left," "right," "front," "back," "vertical," "horizontal," "side," and "bottom" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used only to facilitate the description of the structural relationships of the various components or elements of this invention and do not specifically refer to any component or element in this invention. They should not be construed as limiting the invention.

[0024] In this invention, terms such as "fixed connection," "connected," and "linked" should be interpreted broadly, indicating a fixed connection, an integral connection, or a detachable connection; a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can determine the specific meaning of these terms in this invention based on the specific circumstances, and they should not be construed as limitations on the invention.

[0025] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0026] Example 1 Embodiment 1 of this invention introduces a method for optimizing the operation of a virtual power plant that considers dynamic resource aggregation.

[0027] like Figure 1 The virtual power plant optimization operation method shown includes considering dynamic resource aggregation, comprising: Obtain the multi-energy resource model of the virtual power plant; Based on the acquired multi-energy resource model, a dynamic aggregation model of multi-energy resources is constructed. Based on the constructed multi-energy resource dynamic aggregation model, an objective function for optimizing the operation of the virtual power plant is constructed with the goal of minimizing the net operating cost of the virtual power plant. Solving the constructed objective function yields the optimal scheduling scheme for the virtual power plant, thus completing the optimized operation of the virtual power plant considering dynamic resource aggregation.

[0028] To achieve optimized scheduling of virtual power plants, this embodiment dynamically aggregates multiple types of energy and flexible resources in the virtual power plant. By constructing a multi-energy resource dynamic aggregation model and a VPP scheduling optimization objective function, the optimized scheduling of the virtual power plant is completed.

[0029] To address the significant uncertainties and carbon reduction demands arising from a high proportion of renewable energy, this embodiment employs a multi-energy resource dynamic aggregation framework. This framework treats distributed generation units, energy storage systems, and load-side adjustable resources as a unified whole, quantifying their adjustable capabilities to form a dispatchable range that can be reported by the virtual power plant. The core principle is to macroscopically integrate five completely heterogeneous resource categories—wind, solar, thermal, energy storage, and flexible loads—through "adjustable capability range gradation."

[0030] The energy balance equation for carbon capture power plants and wind and solar resources indicates that the energy source for a carbon capture system can be thermal power, wind power, photovoltaic power, or waste incineration power plants, achieving multi-energy coordinated power supply. (1) in, This refers to the energy provided by the thermal power plant to the carbon capture system during time period t. This refers to the energy provided by wind power to the carbon capture system during time period t. This refers to the energy provided by photovoltaics to the carbon capture system during time period t. This refers to the energy provided by the waste-to-energy plant to the carbon capture system during time period t. It is the total energy consumption of the carbon capture system in time period t; It is the carbon capture energy consumption coefficient; It represents the amount of carbon captured in time period t; , , , It refers to the energy consumption used for flue gas treatment in photovoltaic, wind power, thermal power, and waste incineration power plants; This is the total energy consumption of the flue gas treatment system; , , , It refers to the net power generation (grid-connected electricity) from thermal power, waste incineration power plants, wind power, and photovoltaic power. , , , It is the total power generation from thermal power, waste incineration power plants, wind power, and photovoltaic power. It represents the net carbon emissions during time period t; It is the carbon emission intensity per unit output of thermal power plants.

[0031] The source-storage-load joint adjustable power boundary quantifies the flexible adjustment capability of the VPP, enabling dynamic adjustment of output power within a certain range to respond to grid dispatch demands. (2) in, , This represents the maximum / minimum adjustable power of the virtual power plant during time period t. , This is the minimum output of thermal power and combined heat and power; , This represents the maximum predicted output of wind and solar power. , It is the minimum predicted output of wind power and photovoltaic power.

[0032] A dynamic model of an energy storage system describes the energy state evolution process of the system and limits its charging and discharging power to ensure safe operation. (3) in, It is the energy storage system during the time period t The amount of electricity; , It is a time period t The charging and discharging power; , It is the maximum charging and discharging power.

[0033] The load transferable model allows loads to flexibly adjust their electricity consumption behavior over time, achieving peak shaving and valley filling, and improving system flexibility. (4) (5) (6) in, This indicates the load. i During the period t A 0-1 variable indicating whether or not it participates in the transition; It is the power transferred from the load; , These are the minimum and maximum transferable power; It is the minimum number of consecutive time periods that a single load transfer must last. It is the earliest and latest transfer time interval allowed by the user or process.

[0034] Interruptible load model, i.e. (7) in, It is the actual active power interrupted by load k during time period t; It is the maximum interruption ratio of load k; It is the original load power; This is the total interrupt power that VPP can report during the time period t.

[0035] This embodiment constructs a dynamic aggregation model of multiple types of flexible resources, providing basic data and adjustable capability boundaries for subsequent optimized scheduling. It not only covers traditional power sources, energy storage, and loads, but also introduces new flexible resources such as carbon capture, flue gas treatment, and waste incineration, greatly expanding the dispatchable range of VPPs and providing support for the efficient consumption of renewable energy.

[0036] To achieve economical operation and low carbon emissions of the virtual power plant, this embodiment constructs a multi-objective optimization objective function. The core objective is to minimize the net operating cost of the VPP, comprehensively considering fuel costs, carbon trading costs, energy storage costs, load interruption compensation, electricity purchase costs, and environmental penalty costs. This reflects a balance between economic efficiency and environmental protection. (8) in, It is the objective function; It is the fuel cost of thermal power plants; It is the revenue from the carbon trading market; It is the system maintenance cost; This is the cost of interruptible load compensation; It is the cost of purchasing electricity from the power grid; It is the cost of penalties for environmental pollution.

[0037] (9) in, , , , , It is the fuel cost coefficient; It is the minimum technical output of thermal power.

[0038] Carbon trading revenue model, i.e. (10) in, It is the carbon trading price; It refers to carbon emission allowances.

[0039] Maintenance costs, i.e. (11) in, It is the unit effort maintenance rate; It is the output of various devices in VPP.

[0040] Interruptible load compensation cost, i.e. (12) in, It's a compensation electricity price.

[0041] Purchased electricity cost (13) in, It is the market electricity price; The power purchased from the grid during time period t.

[0042] Environmental penalty costs, i.e. (14) in, It is the number of pollutant types; It is the emission intensity of pollutant z corresponding to electricity purchase; It is the environmental value coefficient of pollutant z; It is the penalty coefficient for pollutant z.

[0043] In this embodiment, the constraints of the objective function for optimizing the operation of the virtual power plant include: Power balance constraint, i.e. (15) (16) in, It is the output of the combined heat and power unit; It refers to the new power load demand after a transfer or interruption.

[0044] The upper and lower limits of carbon capture power consumption, i.e. (17) (18) in, This is the minimum operating power consumption of the carbon capture system; It is the maximum allowable carbon capture power consumption during time period t; It is the upper limit coefficient for carbon capture power consumption.

[0045] This embodiment uses resource aggregation to dynamically model all elements of source-storage-load, unifying and quantifying diverse and heterogeneous units such as wind and solar power, thermal power, P2G-CCS, energy storage, and interruptible loads into an adjustable range. It retains the minimum output of thermal power for maximum downward adjustment and superimposes the maximum predicted output of wind and solar power and the discharge of energy storage to form an ultra-high flexibility zone. The objective is to construct an economic-low carbon function that includes carbon trading, environmental penalties, and compensation electricity fees, transforming emission reduction responsibilities into market benefits and achieving the simultaneous optimization of minimum cost and green benefits.

[0046] Example 2 Embodiment 2 of the present invention introduces a virtual power plant optimization operation system that considers dynamic resource aggregation.

[0047] like Figure 2 The virtual power plant optimization operation system shown includes: The acquisition module is configured to acquire the multi-energy resource model of the virtual power plant; The module is configured to construct a dynamic aggregation model of multi-energy resources based on the acquired multi-energy resource model; and, in conjunction with the constructed dynamic aggregation model of multi-energy resources, to construct an objective function for optimizing the operation of the virtual power plant with the goal of minimizing the net operating cost of the virtual power plant. The optimization module is configured to solve the constructed objective function to obtain the optimal scheduling scheme of the virtual power plant, thus completing the optimized operation of the virtual power plant considering dynamic resource aggregation.

[0048] The detailed steps are the same as those of the virtual power plant optimization operation method considering dynamic resource aggregation provided in Example 1, and will not be repeated here.

[0049] Example 3 Embodiment 3 of the present invention provides a computer-readable storage medium.

[0050] A computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps of the virtual power plant optimization operation method considering dynamic resource aggregation as described in Embodiment 1 of the present invention.

[0051] The detailed steps are the same as those of the virtual power plant optimization operation method considering dynamic resource aggregation provided in Example 1, and will not be repeated here.

[0052] Example 4 Embodiment 4 of the present invention provides an electronic device.

[0053] An electronic device includes a memory, a processor, and a program stored in the memory and running on the processor. When the processor executes the program, it implements the steps in the virtual power plant optimization operation method considering dynamic resource aggregation as described in Embodiment 1 of the present invention.

[0054] The detailed steps are the same as those of the virtual power plant optimization operation method considering dynamic resource aggregation provided in Example 1, and will not be repeated here.

[0055] Example 5 Embodiment 5 of the present invention provides a computer program product.

[0056] A computer program product includes software code, wherein the program in the software code performs the steps of the virtual power plant optimization operation method considering dynamic resource aggregation as described in Embodiment 1 of the present invention.

[0057] The detailed steps are the same as those of the virtual power plant optimization operation method considering dynamic resource aggregation provided in Example 1, and will not be repeated here.

[0058] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0059] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0060] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0061] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0062] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0063] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0064] The above description is merely a preferred embodiment of this practice and is not intended to limit the scope of this practice. Various modifications and variations can be made to this practice by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this practice should be included within the protection scope of this practice.

Claims

1. A method for optimizing the operation of a virtual power plant considering dynamic resource aggregation, characterized in that, include: Obtain the multi-energy resource model of the virtual power plant; Based on the acquired multi-energy resource model, a dynamic aggregation model of multi-energy resources is constructed. Based on the constructed multi-energy resource dynamic aggregation model, an objective function for optimizing the operation of the virtual power plant is constructed with the goal of minimizing the net operating cost of the virtual power plant. Solving the constructed objective function yields the optimal scheduling scheme for the virtual power plant, thus completing the optimized operation of the virtual power plant considering dynamic resource aggregation.

2. The virtual power plant optimization operation method considering dynamic resource aggregation as described in claim 1, characterized in that, The obtained multi-energy resource model of the virtual power plant includes at least the energy balance model of carbon capture power plant and wind and solar resources, the source-storage-load joint adjustable power boundary model, the energy storage system dynamic model, the transferable load model and the interruptible load model.

3. The virtual power plant optimization operation method considering dynamic resource aggregation as described in claim 1, characterized in that, The objective function for optimizing the operation of the constructed virtual power plant is: ;in, The objective function is... Fuel costs for thermal power plants; For revenue from the carbon trading market; To maintain costs; To compensate for interruptible load costs; Cost of purchasing electricity from the grid; The cost of punitive measures for environmental pollution.

4. The virtual power plant optimization operation method considering dynamic resource aggregation as described in claim 1, characterized in that, The objective function for optimizing the operation of the virtual power plant includes at least the constraints of power balance constraints and upper and lower limits of carbon capture power consumption.

5. The virtual power plant optimization operation method considering dynamic resource aggregation as described in claim 1, characterized in that, It also includes multi-timescale collaborative optimization of virtual power plants, which includes day-ahead scheduling, intraday rolling correction, and real-time power balance correction.

6. The virtual power plant optimization operation method considering dynamic resource aggregation as described in claim 1, characterized in that, The constructed objective function is solved using mixed-integer linear programming.

7. A virtual power plant optimization operation system considering dynamic resource aggregation, characterized in that, include: The acquisition module is configured to acquire the multi-energy resource model of the virtual power plant; The module is configured to construct a dynamic aggregation model of multi-energy resources based on the acquired multi-energy resource model; and, in conjunction with the constructed dynamic aggregation model of multi-energy resources, to construct an objective function for optimizing the operation of the virtual power plant with the goal of minimizing the net operating cost of the virtual power plant. The optimization module is configured to solve the constructed objective function to obtain the optimal scheduling scheme of the virtual power plant, thus completing the optimized operation of the virtual power plant considering dynamic resource aggregation.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the virtual power plant optimization operation method considering dynamic resource aggregation as described in any one of claims 1-6.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the program, it implements the steps of the virtual power plant optimization operation method considering dynamic resource aggregation as described in any one of claims 1-6.

10. A computer program product, comprising software code, characterized in that, The program in the software code executes the steps of the virtual power plant optimization operation method considering dynamic resource aggregation as described in any one of claims 1-6.

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