Virtual power plant flexibility evaluation method and system

Abstract

The invention discloses a virtual power plant flexibility evaluation method and system, and the method comprises the steps: defining a reference state of a virtual power plant, and constructing a distributed photovoltaic operation optimization model according to a predicted generated output constraint and a power grid bearing capability constraint; evaluating a virtual power plant cost constraint through a distributed photovoltaic operation optimization model; and based on the virtual power plant cost constraint, constructing a virtual power plant flexibility evaluation optimization model by taking maximization of the flexibility of the virtual power plant as an optimization target, and solving a flexibility evaluation index. The invention belongs to the technical field of power dispatching, and is characterized in that a power generation output adjustment economy constraint evaluation method is provided, and economic constraint requirements of power generation output increase and decrease are evaluated from historical data; and considering the constraint, constructing an evaluation model based on the maximum flexibility of the virtual power plant as an optimization target, so as to fully consider the internal coupling relationship of generation output increase and reduction.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power dispatching, and in particular to a method and system for evaluating the flexibility of a virtual power plant. Background Art

[0002] In recent years, my country has accelerated the development of new energy sources such as wind power and photovoltaics to promote the construction of a new power system. In this context, virtual power plants, as an emerging flexible adjustment resource for power grids, have become increasingly prominent in promoting the consumption of new energy and have become a hot issue in the construction of new power systems.

[0003] Virtual power plants are essentially distributed energy resource clusters. Relying on advanced information communication and intelligent control technologies, they can flexibly call on distributed energy resources and present similar power generation output adjustment characteristics to traditional power plants. Virtual power plants are built on the basis of existing distributed energy resources. Compared with the construction of traditional power plants, the construction cost of virtual power plants is greatly reduced. At the same time, they can more effectively call on distributed energy resources such as distributed photovoltaics, distributed wind power, and energy storage devices, and have greater economic benefit potential.

[0004] In this context, how to effectively evaluate the flexibility of virtual power plants has become a key issue in this field. The so-called virtual power plant flexibility refers to the extent to which the power generation output of virtual power plants can be flexibly adjusted. This indicator can be used to evaluate short-term power supply and demand in the field of dispatching and operation, and analyze the operating benefits of virtual power plants participating in power dispatching; it can also be used in the field of power planning to evaluate the impact of virtual power plants on power supply and demand; in traditional methods, the flexibility of virtual power plants is defined as the difference between the maximum increase and the maximum decrease in power output, where the maximum increase and the maximum decrease are obtained by respectively constructing optimization models with the maximum increase and the maximum decrease in power output as the goals. Summary of the invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a virtual power plant flexibility evaluation method and system to solve the problem of effectively evaluating the flexibility of virtual power plants.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a method for evaluating the flexibility of a virtual power plant, comprising: defining a baseline state of a virtual power plant, constructing a distributed photovoltaic operation optimization model according to predicted power generation output constraints and grid carrying capacity constraints; evaluating the cost constraints of the virtual power plant through the distributed photovoltaic operation optimization model; based on the virtual power plant cost constraints, constructing a virtual power plant flexibility evaluation optimization model with maximizing the flexibility of the virtual power plant as the optimization goal, and solving the flexibility evaluation index.

[0009] As a preferred solution of the virtual power plant flexibility evaluation method described in the present invention, the construction of a distributed photovoltaic operation optimization model includes:

[0010] When the distributed energy resources in the virtual power plant are not included in the unified call of the virtual power plant, the overall power generation output plan of the virtual power plant is defined as the benchmark state. Under the benchmark state, an operation optimization model is constructed with the goal of maximizing the expected benefits, and a power generation output plan is formulated, wherein the operation optimization model is expressed as:

[0011]

[0012] in, represents the operating benefit function of distributed energy resource d, represents the power generation output of distributed energy resources in time period t, They represent the inequality constraint term and the equality constraint term in the operation constraint of the distributed energy resource d respectively;

[0013] Based on the requirement of the operation optimization model that the predicted power generation output constraint and the grid carrying capacity constraint do not exceed the predicted value of distributed photovoltaic power generation output and the grid operation limit, the distributed photovoltaic operation optimization model is expressed as:

[0014]

[0015] in, They represent the predicted value of distributed photovoltaic power generation output and the grid operation limit respectively. It indicates the electricity price specified in the power purchase and sales agreement of distributed photovoltaic power.

[0016] As a preferred solution of the virtual power plant flexibility evaluation method of the present invention, it also includes:

[0017] The power generation output of the virtual power plant under the baseline state is the sum of the power generation outputs of the distributed energy resources included in its unified dispatch and optimized by the optimization model when not included in the unified dispatch, expressed as:

[0018]

[0019] in, It represents the power generation output of the virtual power plant in the benchmark state during period v and t, and d∈v represents the distributed energy resources included in the unified dispatch of the virtual power plant.

[0020] As a preferred solution of the virtual power plant flexibility evaluation method of the present invention, wherein: the evaluation of the virtual power plant cost constraint includes:

[0021] According to the historical operation data of the power market and the organizational model of the power market, the price changes of the power grid calling on its power generation output regulation resources under the increase and decrease of the power generation output of the virtual power plant are counted. The limit of the compensation price declared in the peak market is the limit of the compensation price obtained by the virtual power plant for increasing the power generation output, that is, The limit of the compensation price declared in the peak load market is the limit of the compensation price obtained by the virtual power plant for reducing its power output, that is, in, represents the compensation price limit for the virtual power plant v to increase its power output, p US Indicates the clear limit of the declared compensation price in the peak market, represents the limit of compensation price obtained by virtual power plant v for reducing power output, p DS Indicates the clear limit of the declared compensation price in the peak-shaving market;

[0022] The cost constraint of the virtual power plant is constructed according to the limit of the compensation price obtained by the virtual power plant for increasing the power generation output and the limit of the compensation price obtained by the virtual power plant for reducing the power generation output, that is, the operating cost generated by the virtual power plant adjusting the power generation output does not exceed the operating benefit brought by the regulation service provided by the virtual power plant adjusting the power generation output. The formula is expressed as follows:

[0023]

[0024] Where, ΔC v represents the increase in operating costs caused by the virtual power plant v adjusting its power output, t∈tU and t∈tD represent the peak market trading period during the operating day when the power output needs to be increased and the peak market trading period during the operating day when the power output needs to be reduced, respectively. They respectively represent the increase and decrease in the power generation output of the virtual power plant in time period v and t.

[0025] As a preferred solution of the virtual power plant flexibility evaluation method of the present invention, the objective function of the virtual power plant flexibility evaluation optimization model includes:

[0026] Based on the cost constraint of the virtual power plant, a virtual power plant flexibility evaluation optimization model is constructed with the maximization of virtual power plant flexibility as the optimization goal. The objective function of the virtual power plant flexibility evaluation optimization model is expressed as:

[0027]

[0028] in, They respectively represent the increase in power generation output and the reduction in power generation output of the virtual power plant v during the peak market trading period when the power generation output needs to be increased and the peak market trading period when the power generation output needs to be reduced.

[0029] As a preferred scheme of the virtual power plant flexibility evaluation method described in the present invention, wherein: the constraints of the virtual power plant flexibility evaluation optimization model include inequality constraints, equality constraints and the virtual power plant cost constraints in the operation constraints of the distributed energy resources d in the operation optimization model.

[0030] As a preferred solution of the virtual power plant flexibility evaluation method of the present invention, the flexibility evaluation index to be solved includes:

[0031] A linear programming algorithm is used to solve the virtual power plant flexibility evaluation optimization model, and a set of feasible solutions that maximize the objective function are found as virtual power plant flexibility evaluation indicators. The virtual power plant flexibility evaluation indicators reflect the difference between the virtual power plant's ability to increase power generation output during peak market trading periods and its ability to reduce power generation output during peak market trading periods, and are used to evaluate the overall flexibility level of the virtual power plant.

[0032] In a second aspect, the present invention provides a virtual power plant flexibility evaluation system, comprising:

[0033] The state definition unit is used to define the baseline state of the virtual power plant and build a distributed photovoltaic operation optimization model based on the predicted power output constraints and grid carrying capacity constraints;

[0034] A constraint evaluation unit, used for evaluating the virtual power plant cost constraint through the distributed photovoltaic operation optimization model;

[0035] An evaluation index solving unit is used to construct a virtual power plant flexibility evaluation optimization model based on the virtual power plant cost constraint and take maximizing the virtual power plant flexibility as the optimization goal to solve the flexibility evaluation index.

[0036] In a third aspect, the present invention provides a computing device, comprising:

[0037] Memory and processor;

[0038] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method are implemented.

[0039] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which implement the steps of the method when executed by a processor.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] ① By defining the benchmark state of the virtual power plant, the present invention can better understand the operating characteristics of the virtual power plant under different working conditions, and construct a distributed photovoltaic operation optimization model that takes into account the power generation output prediction of distributed photovoltaics and the grid carrying capacity, which helps to reasonably dispatch resources and avoid overload or waste;

[0042] ② By constructing a virtual power plant flexibility evaluation optimization model, the flexibility of virtual power plants in different market trading periods can be quantified, so as to better respond to market demand. The goal of maximizing the flexibility of virtual power plants enables virtual power plants to increase power generation output during peak market trading periods and reduce power generation output during peak market trading periods, so as to better adapt to the fluctuations of the power grid;

[0043] ③ Through the distributed photovoltaic operation optimization model, the power generation output of distributed photovoltaics can be predicted and scheduled more accurately, the utilization rate of renewable energy can be improved, and the enhanced flexibility of the virtual power plant is conducive to the intelligent management and scheduling of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0045] Figure 1 This is an overall flow chart of a method for evaluating the flexibility of a virtual power plant according to an embodiment of the present invention;

[0046] Figure 2 This is a flowchart for implementing a method for evaluating the flexibility of a virtual power plant according to an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0048] Example 1

[0049] Reference Figure 1-2As an embodiment of the present invention, a method for evaluating the flexibility of a virtual power plant is provided, which is characterized in that a method for evaluating the economic constraints of power generation output adjustment is proposed, and the economic constraint requirements for increasing and reducing power generation output are evaluated from historical data. Considering this constraint, an evaluation model based on the maximum flexibility of the virtual power plant as the optimization target is constructed to fully consider the inherent coupling relationship between the increase and reduction of power generation output. Specifically, the following steps are included:

[0050] S100: Define the baseline state of the virtual power plant and build a distributed photovoltaic operation optimization model based on the predicted power output constraints and grid carrying capacity constraints;

[0051] In the embodiment of the present application, the purpose of implementing this step is to define the overall power generation output plan of the virtual power plant when the distributed energy resources in the virtual power plant are not included in the unified call of the virtual power plant, that is, its baseline state. During the power dispatch operation, the virtual power plant increases or decreases the power generation output according to the requirements of the market operation agency, which is achieved by adjusting the power generation output operation state of its distributed energy resources on the basis of the baseline state;

[0052] In a feasible implementation, when the distributed energy resources in the virtual power plant are not included in the unified call of the virtual power plant, the overall power generation output plan of the virtual power plant is defined as the baseline state, and an operation optimization model is constructed under the baseline state with the goal of maximizing the expected profit to formulate a power generation output plan, wherein the operation optimization model is expressed as:

[0053]

[0054] in, represents the operating benefit function of distributed energy resource d, represents the power generation output of distributed energy resources in time period t, They represent the inequality constraint term and the equality constraint term in the operation constraint of the distributed energy resource d respectively;

[0055] Furthermore, based on the operation optimization model, the predicted power generation output constraint and the grid carrying capacity constraint are required not to exceed the predicted value of distributed photovoltaic power generation output and the grid operation limit respectively. The distributed photovoltaic operation optimization model is expressed as:

[0056]

[0057] in, They represent the predicted value of distributed photovoltaic power generation output and the grid operation limit respectively. Indicates the electricity price specified in the distributed photovoltaic power purchase and sales agreement;

[0058] It should be noted that the types of distributed energy resources in the current virtual power plant are constantly enriched, and different types of distributed energy resources are not included in the unified dispatch of the virtual power plant. Different operation optimization models are different. In this embodiment, only the operation optimization model of distributed photovoltaics is introduced, and other types of distributed energy resources should be constructed in combination with their own actual conditions. Distributed photovoltaics often adopt a pricing transaction model, that is, the price of power generation output throughout the day is independent of the time period and is a given value. Its operating efficiency is the product of power generation output and the price specified in the agreement; the operating constraints that need to be met include predicted power generation output constraints and grid carrying capacity constraints, which require that the power generation output does not exceed the predicted value of distributed photovoltaic power generation output and the grid operation limit, respectively.

[0059] In a feasible implementation, the power generation output of the virtual power plant under the baseline state is the sum of the power generation outputs of the distributed energy resources included in its unified dispatch and optimized by the optimization model when not included in the unified dispatch, expressed as:

[0060]

[0061] in, It represents the power generation output of the virtual power plant in the benchmark state during period v and t, and d∈v represents the distributed energy resources included in the unified dispatch of the virtual power plant.

[0062] S200: Evaluating virtual power plant cost constraints through distributed photovoltaic operation optimization model;

[0063] In the embodiment of the present application, the purpose of implementing this step is to calculate the price change or compensation cost of the power grid calling its power generation output regulation resources under the increase and decrease of the power generation output of the virtual power plant according to the historical operation data of the power market and the power market organization model. The expected profit change of the virtual power plant caused by the change in electricity price or the direct compensation cost is the economic cost constraint proposed by the present invention;

[0064] It should be noted that the compensation methods given by the power grid are different when virtual power plants adjust their power output under different power market structures. In the power spot market model, virtual power plants will participate in power market transactions as a whole, and the time-of-use electricity price in the power spot market will be used as the analysis basis for the economic benefits created by the adjustment of their power output. In the non-market model, virtual power plants are often included in the power dispatching in the agreement model, that is, the dispatching agency adjusts its power output according to the adjustment capacity reported by the virtual power plant, and the dispatching agency provides a fixed amount of compensation for the power output adjustment service provided by the virtual power plant in accordance with the agreement. As a transitional stage between the non-market model and the power spot market, my country currently often adopts the peak market and peak-shaving market mechanism, that is, the peak market is carried out during the period when market members need to increase their power output, and market members declare their increased power output capacity and quotations, and clear them according to price ranking; and the peak-shaving market is carried out during the period when power output needs to be reduced, and market members declare their reduced power output capacity and quotations, and clear them according to price ranking. Considering that my country is in the transition stage from a non-market model to a spot electricity market, most provincial power grids use peak-shaving markets and peak markets to organize market members. This invention focuses on the economic cost constraint evaluation method under this model. The spot electricity market model and the non-market model can refer to the approach of this invention to evaluate their expected economic benefits and thus construct their economic cost constraints.

[0065] In the embodiment of the present application, according to the historical operation data of the power market and the organizational model of the power market, the price changes of the power grid calling its power generation output regulation resources under the increase and decrease of the power generation output of the virtual power plant are counted, and the limit of the compensation price declared in the peak market is the limit of the compensation price obtained by the virtual power plant for increasing the power generation output, that is, The limit of the compensation price declared in the peak load market is the limit of the compensation price obtained by the virtual power plant for reducing its power output, that is, in, represents the compensation price limit for the virtual power plant v to increase its power output, p US Indicates the clear limit of the declared compensation price in the peak market, represents the limit of compensation price obtained by virtual power plant v for reducing power output, p DS Indicates the clear limit of the declared compensation price in the peak-shaving market;

[0066] Furthermore, the cost constraint of the virtual power plant is constructed according to the limit of the compensation price obtained by the virtual power plant for increasing the power generation output and the limit of the compensation price obtained by the virtual power plant for reducing the power generation output, that is, the operating cost of the virtual power plant for adjusting the power generation output does not exceed the operating benefit brought by the regulation service provided by the virtual power plant for adjusting the power generation output. The formula is expressed as:

[0067]

[0068] Where, ΔCv represents the increase in operating costs caused by the virtual power plant v adjusting its power output, t∈tU and t∈tD represent the peak market trading period during the operating day when the power output needs to be increased and the peak market trading period during the operating day when the power output needs to be reduced, respectively. They respectively represent the increase and decrease in the power generation output of the virtual power plant in time period v and t.

[0069] S300: Based on the cost constraints of the virtual power plant, a virtual power plant flexibility evaluation optimization model is constructed with the maximization of virtual power plant flexibility as the optimization goal, and the flexibility evaluation index is solved;

[0070] In the embodiment of the present application, the purpose of implementing this step is to maximize the flexibility of the virtual power plant as the optimization goal, build an operation optimization model, and solve its flexibility evaluation index. Compared with the traditional evaluation method, the main differences are: (1) Introducing the coupling constraints of power generation increase and reduction, and taking flexibility maximization as the optimization goal, instead of solving the sub-items of power generation increase and reduction separately, to solve the problem that the evaluation results of the traditional evaluation method do not conform to the actual operation of the virtual power plant; (2) Introducing economic cost constraints to control the flexibility corresponding to the power generation output adjustment range to meet the economic operation requirements of the virtual power plant, to solve the problem that the traditional evaluation method does not fully consider the economic cost constraints.

[0071] In a feasible implementation, based on the virtual power plant cost constraint, a virtual power plant flexibility evaluation optimization model is constructed with the virtual power plant flexibility maximization as the optimization goal. The objective function of the virtual power plant flexibility evaluation optimization model is expressed as:

[0072]

[0073] in, They respectively represent the increase in power generation output and the reduction in power generation output of the virtual power plant v during the peak market trading period when the power generation output needs to be increased and the peak market trading period when the power generation output needs to be reduced.

[0074] In a feasible implementation, the constraints of the virtual power plant flexibility evaluation optimization model include inequality constraints, equality constraints and virtual power plant cost constraints in the operation constraints of the distributed energy resources d in the operation optimization model.

[0075] Furthermore, solving the flexibility evaluation index includes: using a linear programming algorithm to solve the virtual power plant flexibility evaluation optimization model, and finding a set of feasible solutions that maximize the objective function as the virtual power plant flexibility evaluation index. The virtual power plant flexibility evaluation index reflects the difference between the virtual power plant's ability to increase power generation output during peak market trading periods and its ability to reduce power generation output during peak market trading periods, and is used to evaluate the overall flexibility level of the virtual power plant.

[0076] From the above, we can see that the technical problems of the traditional evaluation method are: first, it does not take into account the economic constraints. The cost of the maximum increase and maximum decrease in power output exceeds the power grid's call price tolerance, and the evaluation results lack practical reference. Second, it does not take into account the inherent coupling characteristics of power output adjustment. The maximum increase and maximum decrease in power output of the traditional evaluation method are obtained by constructing optimization models with the maximum increase and decrease in power output as the goals, respectively. It does not fully consider the inherent coupling of the increase and decrease in power output in actual calls, which will amplify the flexibility evaluation results. The evaluation method provided by the present invention: ① By defining the baseline state of the virtual power plant, the operating characteristics of the virtual power plant under different working conditions can be better understood, and the construction of a distributed photovoltaic operation optimization model takes into account the prediction of distributed photovoltaic power generation output and the grid carrying capacity, which is conducive to the reasonable scheduling of resources to avoid overload or waste; ② By constructing a virtual power plant flexibility evaluation optimization model, the flexibility of the virtual power plant in different market trading periods can be quantified, so as to better respond to market demand, and the goal of maximizing the flexibility of the virtual power plant enables the virtual power plant to increase power generation output during peak market trading periods and reduce power generation output during peak market trading periods, so as to better adapt to grid fluctuations; ③ Through the distributed photovoltaic operation optimization model, the power generation output of distributed photovoltaics can be more accurately predicted and scheduled, and the utilization rate of renewable energy can be improved. The enhanced flexibility of the virtual power plant is conducive to the realization of intelligent management and scheduling of the power system.

[0077] The above is a schematic scheme of a virtual power plant flexibility evaluation method of this embodiment. It should be noted that the technical scheme of the virtual power plant flexibility evaluation system and the technical scheme of the above-mentioned virtual power plant flexibility evaluation method belong to the same concept. For details not described in detail in the technical scheme of a virtual power plant flexibility evaluation system in this embodiment, please refer to the description of the technical scheme of the above-mentioned virtual power plant flexibility evaluation method.

[0078] This embodiment also provides a virtual power plant flexibility evaluation system, including:

[0079] The state definition unit is used to define the baseline state of the virtual power plant and build a distributed photovoltaic operation optimization model based on the predicted power output constraints and grid carrying capacity constraints;

[0080] A constraint evaluation unit, used to evaluate the virtual power plant cost constraints through a distributed photovoltaic operation optimization model;

[0081] The evaluation index solving unit is used to construct a virtual power plant flexibility evaluation optimization model based on the virtual power plant cost constraint and taking the virtual power plant flexibility maximization as the optimization goal, and solve the flexibility evaluation index.

[0082] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned modules.

[0083] This embodiment also provides a computing device, which includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for evaluating the flexibility of a virtual power plant is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0084] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method proposed in the above embodiment is implemented.

[0085] The storage medium proposed in this embodiment and the method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0086] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0087] Example 2

[0088] Based on the previous embodiment, this embodiment provides an application example of a virtual power plant flexibility evaluation method and system to verify and illustrate the technical effects adopted in this method.

[0089] In order to verify the effectiveness of the method proposed in the present invention, this embodiment constructs a virtual power plant containing 3 distributed wind power, and compares the difference in evaluation results between the traditional method and the method proposed in the present invention. As shown in Table 1, in a given evaluation period, the three distributed power sources under the jurisdiction of the virtual power plant have a power output of 20MW, and the price of green certificates is 500 yuan / MWh, while the power generation prices are different. The power generation prices of the three distributed power sources are 400 yuan / MWh, 500 yuan / MWh, and 600 yuan / MWh respectively. The same green certificate price is because green certificate transactions generally rely on centralized transactions in trading centers, while the power generation price is affected by factors such as the location of the distributed power source and the administrative approval price.

[0090] Table 1: Basic information of distributed power generation in virtual power plants.

[0091]

[0092]

[0093] According to the predicted power generation output and grid carrying capacity, a distributed photovoltaic operation optimization model is constructed. In this embodiment, we focus on wind energy resources, so it is necessary to collect the predicted power generation output data of wind energy resources and the grid carrying capacity limit. Then, an optimization model is established based on these data, aiming to maximize the flexibility of the virtual power plant while taking into account cost constraints. Evaluate the cost constraints of the virtual power plant: For traditional methods, only the changes in power generation prices are considered, while the carbon emission costs are ignored. However, the method proposed in the present invention carefully compares the changes in operating costs and carbon emission costs during the period when each distributed energy source provides peak-shaving services. In this case, the power generation price of distributed energy 1 is relatively low. If peak-shaving services are provided, the carbon emission benefits it creates exceed the power generation benefits, and it can be included in the peak-shaving service category; the power generation benefits of distributed energy 2 are the same as the carbon emission benefits, and it can also be considered for inclusion; while the power generation benefits of distributed energy 3 exceed the carbon emission benefits, and it should not be included.

[0094] Based on the above cost constraints, an optimization model is constructed with the goal of maximizing the flexibility of the virtual power plant. By solving this model, the flexibility evaluation index of the virtual power plant can be obtained.

[0095] Comparative analysis: By comparing the traditional method with the evaluation method proposed in this invention, it is found that the main differences between the two are: ① The peak-shaving capacity evaluation results are different, and distributed energy with better power generation efficiency is eliminated to avoid reducing the overall efficiency of the virtual power plant; ② The peak-shaving cost evaluation results are different, and the carbon emission benefits and power generation benefits are more carefully coordinated.

[0096] It should be noted that the traditional evaluation method has the following technical problems: First, it does not take into account economic constraints. The cost of the maximum increase and maximum decrease in power output exceeds the grid's ability to bear the price of the call, and the evaluation results lack practical reference. Second, it does not take into account the inherent coupling characteristics of power output adjustment. The traditional evaluation method evaluates the maximum increase and maximum decrease in power output by respectively building an optimization model with the goal of maximum increase and maximum decrease in power output. It does not fully consider the inherent coupling of the increase and decrease in power output in actual calls, which will amplify the flexibility evaluation results. The evaluation method provided by the present invention: ① By defining the baseline state of the virtual power plant, the operating characteristics of the virtual power plant under different working conditions can be better understood, and the construction of a distributed photovoltaic operation optimization model takes into account the prediction of distributed photovoltaic power generation output and the grid carrying capacity, which is conducive to the reasonable scheduling of resources to avoid overload or waste; ② By constructing a virtual power plant flexibility evaluation optimization model, the flexibility of the virtual power plant in different market trading periods can be quantified, so as to better respond to market demand, and the goal of maximizing the flexibility of the virtual power plant enables the virtual power plant to increase power generation output during peak market trading periods and reduce power generation output during peak market trading periods, so as to better adapt to grid fluctuations; ③ Through the distributed photovoltaic operation optimization model, the power generation output of distributed photovoltaics can be more accurately predicted and scheduled, and the utilization rate of renewable energy can be improved. The enhanced flexibility of the virtual power plant is conducive to the realization of intelligent management and scheduling of the power system.

[0097] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0098] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt 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.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0099] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0100] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0101] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0102] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0103] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for evaluating the flexibility of a virtual power plant, characterized in that: include: Define the baseline state of the virtual power plant and build a distributed photovoltaic operation optimization model based on the predicted power generation output constraints and grid carrying capacity constraints; Evaluating virtual power plant cost constraints through the distributed photovoltaic operation optimization model; Based on the cost constraints of the virtual power plant, a virtual power plant flexibility evaluation optimization model is constructed with maximizing the flexibility of the virtual power plant as the optimization goal, and the flexibility evaluation index is solved.

2. The virtual power plant flexibility evaluation method according to claim 1, characterized in that: The construction of the distributed photovoltaic operation optimization model includes: When the distributed energy resources in the virtual power plant are not included in the unified call of the virtual power plant, the overall power generation output plan of the virtual power plant is defined as the benchmark state. Under the benchmark state, an operation optimization model is constructed with the goal of maximizing the expected benefits, and a power generation output plan is formulated, wherein the operation optimization model is expressed as: in, represents the operating benefit function of distributed energy resource d, represents the power generation output of distributed energy resources in time period t, They represent the inequality constraint term and the equality constraint term in the operation constraint of the distributed energy resource d respectively; Based on the requirement of the operation optimization model that the predicted power generation output constraint and the grid carrying capacity constraint do not exceed the predicted value of distributed photovoltaic power generation output and the grid operation limit, the distributed photovoltaic operation optimization model is expressed as: in, They represent the predicted value of distributed photovoltaic power generation output and the grid operation limit respectively. It indicates the electricity price specified in the power purchase and sales agreement of distributed photovoltaic power.

3. The virtual power plant flexibility evaluation method according to claim 2, characterized in that: Also includes: The power generation output of the virtual power plant under the baseline state is the sum of the power generation outputs of the distributed energy resources included in its unified dispatch and optimized by the optimization model when not included in the unified dispatch, expressed as: in, It represents the power generation output of the virtual power plant in the benchmark state during period v and t, and d∈v represents the distributed energy resources included in the unified dispatch of the virtual power plant.

4. The virtual power plant flexibility evaluation method according to claim 3, characterized in that: The evaluation of virtual power plant cost constraints includes: According to the historical operation data of the power market and the organizational model of the power market, the price changes of the power grid calling on its power generation output regulation resources under the increase and decrease of the power generation output of the virtual power plant are counted. The limit of the compensation price declared in the peak market is the limit of the compensation price obtained by the virtual power plant for increasing the power generation output, that is, The limit of the compensation price declared in the peak load market is the limit of the compensation price obtained by the virtual power plant for reducing its power output, that is, in, represents the compensation price limit for the virtual power plant v to increase its power output, p US Indicates the clear limit of the declared compensation price in the peak market, represents the limit of compensation price obtained by virtual power plant v for reducing power output, p DS Indicates the clear limit of the declared compensation price in the peak-shaving market; The cost constraint of the virtual power plant is constructed according to the limit of the compensation price obtained by the virtual power plant for increasing the power generation output and the limit of the compensation price obtained by the virtual power plant for reducing the power generation output, that is, the operating cost generated by the virtual power plant adjusting the power generation output does not exceed the operating benefit brought by the regulation service provided by the virtual power plant adjusting the power generation output. The formula is expressed as follows: Where, ΔC v represents the increase in operating costs caused by the virtual power plant v adjusting its power output, t∈tU and t∈tD represent the peak market trading period during the operating day when the power output needs to be increased and the peak market trading period during the operating day when the power output needs to be reduced, respectively. They respectively represent the increase and decrease in the power generation output of the virtual power plant in time period v and t.

5. The virtual power plant flexibility evaluation method according to claim 4, characterized in that: The objective function of the virtual power plant flexibility evaluation optimization model includes: Based on the cost constraint of the virtual power plant, a virtual power plant flexibility evaluation optimization model is constructed with the maximization of virtual power plant flexibility as the optimization goal. The objective function of the virtual power plant flexibility evaluation optimization model is expressed as: in, They respectively represent the increase in power generation output and the reduction in power generation output of the virtual power plant v during the peak market trading period when the power generation output needs to be increased and the peak market trading period when the power generation output needs to be reduced.

6. The virtual power plant flexibility evaluation method according to claim 5, characterized in that: The constraints of the virtual power plant flexibility evaluation optimization model include inequality constraints, equality constraints in the operation constraints of the distributed energy resources d in the operation optimization model, and the virtual power plant cost constraints.

7. The virtual power plant flexibility evaluation method according to claim 6, characterized in that: The solution flexibility evaluation index includes: A linear programming algorithm is used to solve the virtual power plant flexibility evaluation optimization model, and a set of feasible solutions that maximize the objective function are found as virtual power plant flexibility evaluation indicators. The virtual power plant flexibility evaluation indicators reflect the difference between the virtual power plant's ability to increase power generation output during peak market trading periods and its ability to reduce power generation output during peak market trading periods, and are used to evaluate the overall flexibility level of the virtual power plant.

8. A system using the virtual power plant flexibility evaluation method according to any one of claims 1 to 7, characterized in that: include: The state definition unit is used to define the baseline state of the virtual power plant and build a distributed photovoltaic operation optimization model based on the predicted power output constraints and grid carrying capacity constraints; A constraint evaluation unit, used for evaluating the virtual power plant cost constraint through the distributed photovoltaic operation optimization model; An evaluation index solving unit is used to construct a virtual power plant flexibility evaluation optimization model based on the virtual power plant cost constraint and take maximizing the virtual power plant flexibility as the optimization goal to solve the flexibility evaluation index.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having computer program instructions stored thereon, wherein the computer-executable instructions, when executed by a processor, implement the steps of the method according to any one of claims 1 to 7.

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