Virtual power plant-oriented multi-resource aggregation participation power grid regulation potential evaluation method

Abstract

The invention discloses a virtual power plant-oriented multi-resource aggregation participation power grid regulation potential evaluation method, and relates to the technical field of virtual power plant management, and the method comprises the steps: carrying out the resource classification of a virtual power plant, and analyzing the resource characteristics; performing fine evaluation on the resource scheduling potential of the virtual power plant; and calculating a virtual power plant resource scheduling potential value, and issuing a regulation and control instruction. According to the virtual power plant-oriented multi-resource aggregation participation power grid regulation potential evaluation method provided by the invention, aggregation and classification management of distributed photovoltaic, energy storage systems, controllable loads, electric vehicles and other dispersed resources and characteristic analysis of flexible loads, distributed power generation resources and source loads are carried out; the virtual power plant can provide more flexible power grid regulation services, such as frequency regulation and peak reduction, and can optimize resource configuration, more reasonably dispatch the resources and improve the use efficiency of the resources at the same time.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plant management, and in particular to a method and system for evaluating the potential of multi-resource aggregation participating in power grid regulation for virtual power plants. Background Art

[0002] With the growth of energy demand and the transformation of energy structure, the penetration rate of new energy continues to increase, and my country's photovoltaic and wind power generation has ranked among the top in the world. In the process of building new power systems, a large number of clean energy sources are connected, and the uncertainties in the operation of power grids have increased. How to improve the efficiency and stability of distributed energy has become a common problem that the power grids of new power systems need to deal with. Against this background, virtual power plants came into being. Virtual power plants use advanced communication technology to freely aggregate multiple types of distributed energy, effectively realizing the complementary and coordinated scheduling between traditional energy and renewable energy and the optimized operation of power grids, so as to maximize the strong random volatility of new energy power and achieve the optimal utilization of multiple energy sources, improve energy utilization efficiency, and meet the power balance needs of power systems.

[0003] However, in the diversified power market environment, the operation and control of virtual power plants face difficulties such as large frequency fluctuations, increased daily load density, and gradually increasing peak-to-valley differences. At present, there is no universal virtual power plant operation and control method that can effectively control the various resources it aggregates. Therefore, this requires a comprehensive and refined assessment of the dispatching potential of virtual power plant resources and effective aggregation of resources, in order to provide auxiliary frequency regulation, peak-shaving services, assist virtual power plants in peak shaving and valley filling, and ensure the optimal allocation and efficient utilization of resources and lay a solid foundation.

[0004] In response to the above problems, the present invention proposes a method for evaluating the potential of multi-resource aggregation participating in grid regulation for virtual power plants. By aggregating dispersed resources such as distributed photovoltaic, wind power, energy storage systems, flexible loads, electric vehicles, etc., resources are classified and managed. Based on resource classification and resource characteristic analysis, the potential of various resources in grid regulation is comprehensively evaluated from indicators such as consistency of use, frequency of use, responsiveness, and response speed. The adjustable potential of various resources is accurately calculated, the grid's adaptability to fluctuations in renewable energy is improved, the grid's operating status is optimized, and grid fluctuations and failures are reduced. Summary of the invention

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

[0006] Therefore, the technical problem solved by the present invention is: the existing operation control method of virtual power plants has problems such as large frequency fluctuations, increased daily load density and gradually increasing peak-to-valley differences faced by the power grid, as well as how to conduct a comprehensive and detailed assessment of the scheduling potential of virtual power plant resources and effectively aggregate the resources.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: a method and system for evaluating the potential of multi-resource aggregation participating in power grid regulation for virtual power plants, including classifying resources of virtual power plants and analyzing resource characteristics. Performing a refined evaluation of the resource scheduling potential of virtual power plants. Calculating the resource scheduling potential value of virtual power plants and issuing regulation instructions.

[0008] As an optimal solution of the method and system for evaluating the potential of multi-resource aggregation participating in grid regulation for virtual power plants described in the present invention, the resource classification of virtual power plants includes dividing the resources of virtual power plants into flexible loads, distributed power generation resources and source loads.

[0009] As a preferred solution of the method and system for evaluating the potential of multi-resource aggregation participating in grid regulation for virtual power plants described in the present invention, the analysis of resource characteristics includes flexible load characteristics analysis, introducing incentive-based demand response IBDR, using interruptible load as response resource, and the cost of demand response is the difference between VPP power sales revenue before and after load response. Before and after load response, the power sales revenue of the virtual power plant is expressed as:

[0010]

[0011] in, are the system benefits before and after the implementation of interruptible load, are the internal load output, is the electricity selling price for internal load, is the corresponding output of the interruptible load, a and b are the quadratic and linear coefficients of the compensation function respectively, and the cost of demand response is expressed as:

[0012]

[0013] in, is the demand response cost.

[0014] As a preferred solution of the method and system for evaluating the potential of multi-resource aggregation participating in grid regulation for virtual power plants described in the present invention, the analysis of resource characteristics also includes analysis of distributed power generation resource characteristics and source load characteristics, and the analysis of distributed power generation resource characteristics includes analysis of power generation characteristics of distributed photovoltaics, distributed wind power and gas turbines.

[0015] As a preferred scheme of the method and system for evaluating the potential of multi-resource aggregation participating in grid regulation for virtual power plants described in the present invention, the refined evaluation of the resource scheduling potential of virtual power plants includes, on the basis of analyzing the resource characteristics of virtual power plants, conducting a comprehensive and refined evaluation of the potential of resources in grid regulation from the indicators of consistency of use, frequency of use, response capability and response speed.

[0016] As a preferred solution of the method and system for evaluating the potential of multi-resource aggregation participating in grid regulation for virtual power plants described in the present invention, the calculation of the virtual power plant resource scheduling potential value includes calculating the comprehensive demand response potential value using standardized consistency, usage frequency, response capability, and response speed potential scores.

[0017] As a preferred solution of the method and system for evaluating the potential of multi-resource aggregation participating in grid regulation for virtual power plants described in the present invention, the issuing of regulation instructions includes the virtual power plant intelligently issuing regulation instructions according to the comprehensive scheduling potential score to perform peak-shaving and frequency regulation auxiliary services. After the instructions are issued, the distributed photovoltaic, wind power and energy storage systems execute regulation measures according to the instructions.

[0018] Another object of the present invention is to provide a method and system for evaluating the potential of multi-resource aggregation to participate in grid regulation for virtual power plants. By providing a method for evaluating the potential of multi-resource aggregation to participate in grid regulation for virtual power plants, the present invention solves the problems that the current operation control technology of virtual power plants contains large grid frequency fluctuations, increased daily load density and gradually increasing peak-to-valley differences, and cannot effectively control the various types of resources aggregated by the grid.

[0019] As a preferred solution of the method and system for evaluating the potential of multi-resource aggregation participating in power grid regulation for virtual power plants described in the present invention, it includes: a resource classification module, a characteristic analysis module, a potential evaluation module, and a potential value calculation module. The resource analysis module is used to classify resources of virtual power plants and analyze resource characteristics. The potential evaluation module is used to perform a refined evaluation of the resource scheduling potential of virtual power plants. The potential value calculation module is used to calculate the resource scheduling potential value of virtual power plants and issue control instructions. A computer device includes a memory and a processor, the memory stores a computer program,

[0020] The processor executing the computer program is a step of implementing a method for evaluating the potential of multi-resource aggregation participating in grid regulation for virtual power plants.

[0021] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for evaluating the potential of multi-resource aggregation participating in grid regulation for virtual power plants.

[0022] Beneficial effects of the present invention: The method for evaluating the potential of multi-resource aggregation to participate in grid regulation for virtual power plants provided by the present invention can provide more flexible grid regulation services such as frequency regulation and peak reduction by aggregating and classifying decentralized resources such as distributed photovoltaics, energy storage systems, controllable loads, electric vehicles, and analyzing the characteristics of flexible loads, distributed power generation resources, and source loads. At the same time, it can optimize resource allocation, more reasonably dispatch these resources, and improve their utilization efficiency. A comprehensive and refined evaluation of the resource dispatching potential of virtual power plants can determine the maximum regulation capacity that each resource can provide under different circumstances, and fully tap the value that the freely aggregated multi-type distributed resources bring to the grid and users, thereby improving the economy and reliability of the system. The present invention has achieved better results in comprehensively and finely evaluating the dispatching potential of virtual power plant resources and promoting the efficient optimization and integration of various energy sources. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] 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.

[0024] Figure 1 An overall flow chart of a method for evaluating the potential of multi-resource aggregation participating in grid regulation for a virtual power plant provided in the first embodiment of the present invention.

[0025] Figure 2 A virtual power plant resource composition diagram for a method for evaluating the potential of multi-resource aggregation participating in grid regulation in a virtual power plant provided in the first embodiment of the present invention. DETAILED DESCRIPTION

[0026] 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.

[0027] Example 1, reference Figure 1-Figure 2 , which is an embodiment of the present invention, provides a method for evaluating the potential of multi-resource aggregation participating in power grid regulation for virtual power plants, including:

[0028] S1: Classify the resources of virtual power plants and analyze resource characteristics.

[0029] Further, refer to Figure 2 For horizontal expansion, virtual power plant resources are divided into three categories based on comprehensive consideration of resource functional characteristics, namely flexible loads, distributed generation resources and source loads. Among them, flexible loads are mainly loads that can participate in demand response, distributed generation resources include renewable energy (distributed photovoltaic, distributed wind power) and gas turbines, and source loads include general distributed energy storage and electric vehicles.

[0030] S2: Conduct a detailed assessment of the resource scheduling potential of virtual power plants.

[0031] Furthermore, according to the analysis type, characteristic analysis is performed.

[0032] (1) Analysis of flexible load characteristics

[0033] Flexible loads are mainly loads that can participate in demand response. Demand response can guide the autonomous changes of loads by adjusting the electricity consumption behavior on the load side, thereby having a positive impact on the generation and dispatch of virtual power plants. Incentive-based demand response (IBDR) is introduced, with interruptible loads as response resources. The cost of demand response is the difference between the VPP's electricity sales revenue before and after the load response. Before and after the load response, the electricity sales revenue of the virtual power plant is:

[0034]

[0035] in, are the system benefits before and after the implementation of interruptible load, Output for internal load, is the electricity selling price for internal load, is the corresponding output of the interruptible load, a and b are the quadratic and linear coefficients of the compensation function respectively. The cost of demand response is:

[0036]

[0037] in, is the demand response cost.

[0038] (2) Analysis of characteristics of distributed generation resources

[0039] The distributed power generation resource characteristic analysis of the present invention mainly involves the power generation characteristic analysis of distributed photovoltaic, distributed wind power and gas turbine.

[0040] The output power of photovoltaic power generation is related to the intensity of sunlight, the temperature of the environment, the angle of sunlight exposure, the state of the solar cell and its photoelectric conversion coefficient. Therefore, photovoltaic power generation has the characteristics of randomness and volatility. It is generally believed that the output power varies with the change of sunlight radiation intensity, and the sunlight radiation intensity conforms to the Beta distribution.

[0041]

[0042] Where f(r) is the probability density function, a and β are the shape parameters of the Beta distribution, r and r max They represent the actual value and maximum value of solar radiation intensity during the study period respectively, and τ represents the gamma function.

[0043] The output power of photovoltaic power generation varies with the intensity of solar radiation, as shown below.

[0044]

[0045] Among them, r rated Indicates the rated solar radiation intensity, P PV (r) represents the rated output power of photovoltaic power generation.

[0046] The output power of wind power generation mainly changes with the wind speed at the location of the wind farm. The wind speed is affected by factors such as the humidity and weather of the environment and the terrain where the wind farm is located, and is uncontrollable. Therefore, wind power generation also has certain volatility and rapid changes. However, over a longer time scale, the wind speed in most areas obeys the Weibull distribution, as shown below.

[0047]

[0048] Where f(v) is the probability density function of wind speed, v is the wind speed, k and c are the shape parameter and scale parameter of the Weibull distribution, respectively, and σ w , μ w are the standard deviation and mean of wind speed respectively.

[0049] Actual wind power output power P w It changes with the wind speed at the location of the wind turbine, and is specifically expressed by the following formula.

[0050]

[0051] Among them, P wr Indicates the rated power of the wind turbine, V c1 、V co 、V r They respectively represent the cut-in and cut-out of the wind turbine and the rated wind speed.

[0052] Gas turbines (MT) have the characteristics of short start-up and shutdown time and flexible power adjustment. During the operation of virtual power plants, they can adjust renewable power to achieve coordinated operation within the virtual power plant and provide auxiliary services to the electricity market to maximize their own economic benefits. The output model of gas turbines is as follows.

[0053]

[0054] in, and They represent the fuel cost of MT power generation and the start-up and shutdown cost of power generation, respectively. MC ,b MC and c MC is the MT power generation energy consumption coefficient, g MT,t is the power generation of MT, μ MT,t is the MT power generation state variable, which is a 0-1 variable, 1 means MT is in operation, 0 means MT is in shutdown, D MT,t It represents the MT power generation startup cost, which is related to the cold start and hot start status of the MT.

[0055] The MT operation constraints mainly include output constraints, climbing constraints and start-stop constraints, which are described as follows:

[0056] μ MT,t g MT,t min ≤g MT,t ≤μ MT,t g MT,t max

[0057]

[0058] Among them, g MT,t min and g MT,t max Indicates the upper and lower limits of MT output. and Indicates the upper and lower limits of MT climbing. Indicates the time MT has been running at time t, Indicates the shortest startup time of MT. Indicates the time that MT has been out of service at time t, Indicates the minimum downtime of MT.

[0059] (3) Source load characteristics analysis

[0060] A source load is a load with two-way energy interaction. It can be used as a power source to deliver active power to a virtual power plant, and can also be used as a load to absorb the output of renewable energy, mainly including energy storage and electric vehicles.

[0061] Energy storage system, the output of the energy storage system can be expressed as:

[0062]

[0063] Where SOC(t) represents the state of charge of the energy storage device at time t, η chr With η disc Respectively represent the charging and discharging efficiency of the energy storage system, Pchr (t) and P disc (t) represents the charging and discharging power of the energy storage system at time t, E n is the rated capacity of the energy storage system, and Δt is the time interval between time t-1 and time t.

[0064] During operation, the energy storage device must meet the following technical constraints:

[0065] SOC min ≤SOC(t)≤SOC max

[0066]

[0067] in, Indicates the charging and discharging state of the energy storage device, which is a 0-1 variable, that is, the charging and discharging states of the energy storage system cannot exist at the same time.

[0068] Electric vehicle (EV), the analysis of electric vehicle related characteristics is related to the starting state of charge SOC, starting charging time, charging power and final full charge requirements of electric vehicles. When not receiving the response signal from the power grid, if the electric vehicle SOC does not meet the power requirements, it will be in the charging state until the required power is reached. The load model of a single electric vehicle charging pile can be expressed as:

[0069] p EV,t =P EV S EV,t

[0070]

[0071] Among them, p EV,t P represents the charging power of the electric vehicle during period t. EV Indicates the rated charging power of the charging vehicle. EV,t Indicates the charging status of the electric vehicle during period t, and takes the value 0 or 1, indicating "not charging" or "charging" respectively. t+1 The remaining capacity of the electric vehicle in period t+1. SOC0 is the initial remaining capacity of the electric vehicle battery. SOC t represents the remaining capacity of the electric vehicle battery during period t. η is the charging efficiency. C total is the total battery capacity of the electric vehicle. Δt is the time interval, which is 1 min. S mileage E is the travel distance of electric vehicles. EV is the driving efficiency of electric vehicles, B EV Indicates the last time charging is completed, SOC max It is the maximum remaining capacity of the electric vehicle battery.

[0072] The electric vehicle cluster aggregate load model can be expressed as:

[0073]

[0074] in, is the total power load during this time period. The charging status of the j-th electric car in the t-th period. is the charging power of the j-th electric car in the t-th time period, and the total number of electric cars is M.

[0075] S3: Conduct a detailed assessment of the resource scheduling potential of virtual power plants.

[0076] Furthermore, based on the analysis of the resource characteristics of virtual power plants, a comprehensive and detailed assessment of the potential of various types of resources in grid regulation is conducted from indicators such as consistency of use, frequency of use, responsiveness, and response speed.

[0077] (1) Consistency of use

[0078] This indicator is used to measure whether the operation of the user equipment is deterministic or random, reflecting the possibility of the equipment following the expected pattern. The higher the consistency of the equipment, the higher the score, that is, the greater the scheduling potential. The potential score of the usage consistency is quantified in the [0,1] interval, as follows:

[0079]

[0080] in, represents the standardized usage consistency potential score, Represents the standardized root mean square error. RSME xy Represents the unstandardized root mean square error, which is used to measure the deviation between the observed value and the predicted value. The smaller the root mean square error, the higher the consistency of the equipment use and the higher the potential score. This patent adopts the maximum-minimum value standardization, K represents the number of historical days of the test, is the minimum unstandardized root mean square error among all users, is the maximum unstandardized root mean square error among all users, represents the standardized observation value of user x and device y on the kth day in time period t, which is specifically expressed as follows.

[0081]

[0082] in, It represents the normalized predicted value of user x and device y in time period t, which is expressed as follows:

[0083]

[0084] (2) Frequency of use

[0085] The frequency of use is expressed as the probability of use, which indicates the proportion of the number of days the device is used to the total number of days tested. The more frequently used the device is, the higher the potential score is and the greater the adjustable potential is. The potential score of the frequency of use is quantified in the interval [0,1], as shown below:

[0086]

[0087] in, represents the frequency of use potential score, ε y Indicates the minimum power at which device y operates, which is used to eliminate the influence of noise and avoid errors during detection.

[0088] (3) Responsiveness

[0089] The response capacity describes the amount of load reduction or transfer. The higher the response capacity, the greater the allowable change in the user's electricity consumption behavior in response to the incentive mechanism, and the higher the potential score. Assuming that the electricity consumption period is [t1, t2], the response capacity is expressed as follows.

[0090]

[0091] Among them, PS xy The potential score of responsiveness is expressed as:

[0092]

[0093] in, is the minimum potential score of responsiveness among all users. The maximum potential score response among all users.

[0094] (4) Response speed

[0095] Analogous to the concept of generator ramp rate, the greater the response speed under the demand response incentive mechanism, the higher the potential score. The response speed is defined as: the average of the rate of increase and decrease of load during peak hours.

[0096]

[0097] Among them, SS xy Indicates the potential score for response speed, It means that during the peak period of electricity consumption, the power consumption of user x and device y on the kth day reaches the maximum value at time, It represents the power consumption at the time Δt1 before the peak of power consumption. It represents the power consumption at Δt2 after the peak period, Δt1 and Δt2 represent the time intervals of rise and fall respectively. The response speed potential score is standardized, and the standardized response speed is expressed as

[0098]

[0099] in, The lowest response speed potential score among all users, The highest responsiveness potential score among all users.

[0100] S4: Calculate the resource dispatch potential value of the virtual power plant and issue control instructions.

[0101] Furthermore, using the above four standardized indicator potential scores Calculate the comprehensive demand response potential value.

[0102]

[0103] Among them, S xy Indicates the comprehensive potential value of demand response. If the score of any indicator is low, the overall potential score will be reduced. The overall potential score is normalized to obtain the standardized comprehensive potential score.

[0104]

[0105] According to the standardized comprehensive potential score Get the comprehensive potential score of the user and the device.

[0106]

[0107] Among them, S x S represents the comprehensive potential score of user x’s demand response, and Y represents the total number of devices of user x. y represents the comprehensive potential score of demand response of device y, and X represents the number of users who own device y. x and S y Perform standardization to obtain standardized demand response potential scores for users and devices and

[0108]

[0109] It should be noted that the virtual power plant intelligently issues control instructions based on the comprehensive dispatch potential score, which are used for auxiliary services such as peak load regulation and frequency regulation, and assist the virtual power plant in peak load shaving and valley filling. After the instructions are issued, each participating resource, such as distributed photovoltaic, wind power, energy storage system, etc., executes corresponding control measures according to the instructions.

[0110] Example 2, an embodiment of the present invention, provides a method and system for evaluating the potential of multi-resource aggregation participating in grid regulation for virtual power plants. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0111] First, in order to verify the effectiveness of the proposed method for fine-grained evaluation of virtual power plant resource scheduling potential, a virtual power plant actually operating in a certain region is selected as the test object. The virtual power plant contains various types of distributed energy and load resources, including flexible loads, distributed generation resources, and source loads.

[0112] Table 1 Experimental data comparison table

[0113]

[0114] It can be seen from the data in Table 1 that the method of the present invention conducts a refined assessment of the dispatching potential of various resources in the virtual power plant, and formulates targeted dispatching instructions based on the comprehensive potential scores.

[0115] First, the comprehensive potential score of flexible load 1 (air conditioning) is the highest, reaching 0.82. This is mainly due to its high usage consistency (0.85) and usage frequency (0.90), which shows that the operation mode of this load is stable and it is highly motivated to participate in demand response. Therefore, arranging it to participate in demand response can effectively adjust the load curve and smooth out the peaks and fill the valleys.

[0116] The energy storage system has a comprehensive potential score of 0.85, the highest among all resources. This is because the energy storage system has high responsiveness (0.85) and response speed (0.85), and can respond quickly to dispatch instructions. Arranging the energy storage system to discharge during peak hours and charge during off-peak hours not only improves the utilization efficiency of electricity, but also smoothes the load fluctuations of the power grid.

[0117] In contrast, the comprehensive potential score of the electric vehicle group is 0.69, slightly lower than other resources. This is mainly due to its low consistency of use (0.65) and response speed (0.65). The charging behavior of electric vehicles is affected by the travel needs of users and has a certain degree of uncertainty. Therefore, the dispatch strategy focuses on guiding electric vehicles to adjust the charging time and try to avoid the peak load period of the power grid.

[0118] The comprehensive potential score of gas turbines is 0.84, and the response capability (0.90) is outstanding, indicating that they have obvious advantages in providing peak load regulation services. Since gas turbines can be started and stopped flexibly and their output is controllable, arranging them to participate in grid peak load regulation can effectively compensate for the impact of fluctuations in renewable energy.

[0119] From the above analysis, it can be seen that the method of the present invention fully considers the characteristics and scheduling potential of various resources, and performs refined evaluation and optimized scheduling.

[0120] An embodiment of the present invention provides a method and system for evaluating the potential of multi-resource aggregation participating in grid regulation for virtual power plants, including a resource analysis module, a potential evaluation module, and a potential value calculation module.

[0121] The resource analysis module is used to classify the resources of the virtual power plant and analyze the resource characteristics. The potential assessment module is used to conduct a refined assessment of the resource scheduling potential of the virtual power plant. The potential value calculation module is used to calculate the resource scheduling potential value of the virtual power plant and issue control instructions.

[0122] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it is stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or partly in other words, the part that contributes to the prior art or part of the technical solution is embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for a computer device (personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0123] The logic and / or steps represented in the flowchart or otherwise described herein, for example, are considered to be an ordered list of executable instructions for implementing logical functions, embodied in any computer-readable medium for use by an instruction execution system, apparatus or device (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, apparatus or device and execute instructions), or in conjunction with such instruction execution system, apparatus or device. For the purposes of this specification, "computer-readable medium" can be any device that contains, stores, communicates, propagates or transmits a program for use by an instruction execution system, apparatus or device, or in conjunction with such instruction execution system, apparatus or device.

[0124] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program can be printed, such as by optically scanning the paper or other medium, then editing, interpreting or processing in other suitable ways as necessary to obtain the program electronically, and then storing it in a computer memory.

[0125] It should be understood that the various parts of the present invention can be implemented with hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented with software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented with hardware, as in another embodiment, any one of the following technologies known in the art or their combination can be used to implement: a discrete logic circuit with a logic gate circuit for implementing a logical function for a data signal, a dedicated integrated circuit with a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those of ordinary skill in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should all be included in the scope of the claims of the present invention.

[0126] 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.

Claims

1. A method for evaluating the potential of multi-resource aggregation participating in power grid regulation for virtual power plants, characterized in that: include: Classify resources of virtual power plants and analyze resource characteristics; Conduct detailed assessment of the potential for virtual power plant resource scheduling; Calculate the resource dispatch potential of the virtual power plant and issue control instructions.

2. The method for evaluating the potential of multi-resource aggregation participating in power grid regulation for virtual power plants according to claim 1, characterized in that: The resource classification of the virtual power plant includes dividing the virtual power plant resources into flexible loads, distributed power generation resources and source loads.

3. The method for evaluating the potential of multi-resource aggregation participating in power grid regulation for virtual power plants according to claim 2, characterized in that: The analysis of resource characteristics includes flexible load characteristics analysis, introducing incentive-based demand response IBDR, taking interruptible load as response resource, and the cost of demand response is the difference between VPP power sales revenue before and after load response. Before and after load response, the power sales revenue of virtual power plant is expressed as: in, are the system benefits before and after the implementation of interruptible load, are the internal load output, is the electricity selling price for internal load, is the corresponding output of the interruptible load, a and b are the quadratic and linear coefficients of the compensation function respectively, and the cost of demand response is expressed as: in, is the demand response cost.

4. The method for evaluating the potential of multi-resource aggregation participating in power grid regulation for virtual power plants according to claim 3, characterized in that: The analysis of resource characteristics also includes analysis of distributed power generation resource characteristics and source load characteristics, and the analysis of distributed power generation resource characteristics includes analysis of power generation characteristics of distributed photovoltaic, distributed wind power and gas turbines; Source load characteristics analysis includes source loads including energy storage and electric vehicles.

5. The method for evaluating the potential of multi-resource aggregation participating in power grid regulation for virtual power plants according to claim 4, characterized in that: The detailed evaluation of the virtual power plant resource scheduling potential includes, based on the analysis of the virtual power plant resource characteristics, conducting a comprehensive and detailed evaluation of the resource's potential in grid regulation from the indicators of usage consistency, usage frequency, responsiveness and response speed.

6. The method for evaluating the potential of multi-resource aggregation participating in power grid regulation for virtual power plants according to claim 5, characterized in that: The calculation of the virtual power plant resource scheduling potential value includes using standardized consistency, usage frequency, response capability, and response speed potential scores to calculate a comprehensive demand response potential value.

7. The method for evaluating the potential of multi-resource aggregation participating in power grid regulation for virtual power plants according to claim 6, characterized in that: The issuing of control instructions includes the virtual power plant intelligently issuing control instructions according to the comprehensive dispatching potential score value to provide peak-shaving and frequency regulation auxiliary services. After the instructions are issued, the distributed photovoltaic, wind power and energy storage systems execute control measures according to the instructions.

8. A system using the method for evaluating the potential of multi-resource aggregation participating in power grid regulation for virtual power plants as described in any one of claims 1 to 7, characterized in that: Including resource analysis module, potential assessment module, potential value calculation module; The resource analysis module is used to classify resources of the virtual power plant and analyze resource characteristics; The potential assessment module is used to perform a refined assessment of the resource scheduling potential of the virtual power plant; The potential value calculation module is used to calculate the potential value of virtual power plant resource scheduling and issue control instructions.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method and system for evaluating the potential of multi-resource aggregation participating in grid regulation for virtual power plants described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method and system for evaluating the potential of multi-resource aggregation participating in grid regulation for virtual power plants described in any one of claims 1 to 7 are implemented.

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