Virtual power plant multi-type resource comprehensive operation evaluation method and system

Abstract

The invention discloses a virtual power plant multi-type resource comprehensive operation evaluation method which comprises the following steps: taking adjustable resources of a virtual power plant as a research object, considering source, load and storage operation of different resources and external characteristic differences of adjustment modes, and constructing a resource basic evaluation model; and integrating the resource response degree and the resource adjustment contribution degree to obtain an operation evaluation system of different types of resources of the virtual power plant. The method can more objectively reflect the actual operation and adjustment participation capability of different types of resources of power generation and power utilization of the virtual power plant in different time and different scenes, and can guide a scheduling department to reasonably arrange the calling demand of the virtual power plant.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plants, and in particular to a method and system for comprehensive operation evaluation of multiple types of resources in a virtual power plant. Background Art

[0002] Virtual power plants can aggregate various types of dispersed source, load and storage resources to form resource clusters for unified dispatch, thereby effectively leveraging the combined power of dispersed resources such as distributed power sources and energy storage systems, and improving the flexibility, safety and economy of power grid regulation. At present, the relevant evaluation technologies lack differentiated analysis of the individual capacity and comprehensive operating effects of different types of resource compositions in virtual power plants, and also lack analysis of the weight of aggregate response capability indicators.

[0003] In existing technologies, a unified standard is often used to evaluate all types of resources, ignoring the differences in the characteristics of different resources in terms of source, load, storage operation and regulation. At the same time, there is a lack of differentiated considerations: the differences in the external characteristics of different resources are not fully considered, resulting in the evaluation results may not accurately reflect the actual operating efficiency and contribution of the resources.

[0004] Moreover, time changes and different adjustment scenarios are not taken into account as influencing factors, which limits the dynamic adaptability of the evaluation system to the resource operation status.

[0005] Therefore, the lack of a system that can comprehensively consider multiple factors and conduct a comprehensive evaluation of different types of resources in virtual power plants is not conducive to the optimal allocation of resources. Summary of the invention

[0006] In order to overcome the deficiencies in the prior art, the present invention provides a method for comprehensive operation evaluation of multiple types of resources in a virtual power plant.

[0007] The technical solution of the present invention is: a method for comprehensive operation evaluation of multiple types of resources in a virtual power plant, comprising the following steps:

[0008] Step 1: Obtain virtual power plant resource data;

[0009] Step 2: Set evaluation dimensions based on resource data and build a basic evaluation model;

[0010] Step 3: Calculate resource response degree based on the basic evaluation model;

[0011] Step 4: Calculate resource adjustment contribution based on the basic evaluation model;

[0012] Step 5: Based on steps 2-4, calculate and display the comprehensive operation evaluation results.

[0013] Step 1 includes:

[0014] Step 1.1: Data collection and classification: Collect all resource data in the virtual power plant and classify them according to distributed photovoltaics, energy storage equipment, electric vehicles, smart buildings, and industrial and commercial loads;

[0015] Step 1.2: Data statistics: organize the collected data of various resources, count the quantity, power, and location distribution of various types of resources within the virtual power plant, and calculate the composition ratio of each type of resource.

[0016] Step 2 includes:

[0017] Step 2.1: Define the three-dimensional indicators of the basic evaluation model: daily data maintenance completeness, adjustment instruction execution accuracy and resource adjustment contribution;

[0018] Step 2.2: Use modeling tools to build a basic evaluation model.

[0019] The basic evaluation model is:

[0020]

[0021] In the formula, k is the evaluation result of the basic evaluation model, m is the number of indicators, ω i is the weight of the ith indicator, x i is the original value of the ith indicator, f(x i ) functions for normalizing and transforming metrics.

[0022] Step 3 includes:

[0023] Step 3.1: Parameter input and data processing: The issuance time, duration, adjustment target amount, and target adjustment resource of the adjustment instruction are used as input parameters and input into the basic evaluation model to obtain the corresponding calculation results and extract the response data of various resources after receiving the adjustment instruction;

[0024] Step 3.2: Calculate the response degree: Calculate the response degree based on the difference between the actual response data of the resource and the expected target; the response degree includes response speed, response amplitude and response stability.

[0025] Step 4 includes:

[0026] Step 4.1: Adjustment weight setting: According to the proportion and distribution of resources aggregated by the virtual power plant, different adjustment weights are set for each type of resource;

[0027] Step 4.2: Contribution calculation: During each adjustment process, the basic evaluation model is used in combination with the response data of various resources to calculate the contribution of various resources to the overall adjustment effect.

[0028] Step 5 includes:

[0029] Step 5.1: Input the evaluation results of the basic evaluation model, the calculation results of the resource response degree and the contribution calculation results into the comprehensive evaluation model to obtain the overall evaluation results of the virtual power plant;

[0030] Step 5.2: Multi-dimensional comprehensive scoring: Design a weighted scoring model to calculate the comprehensive evaluation score of the virtual power plant based on the contribution of various resources;

[0031] Step 5.3: Result presentation and analysis: Use visualization tools to display the comprehensive operation evaluation results of the virtual power plant and generate a detailed analysis report.

[0032] The comprehensive evaluation model is:

[0033] k 虚拟电厂 =α1k 分布式光伏 +α2k 储能 +α3k 电动汽车 +α4k 智能楼宇 +α5k 工商业负荷

[0034] In the formula, k 虚拟电厂 is the overall evaluation result of the virtual power plant, k 分布式光伏 , k 储能 , k 电动汽车 , k 智能楼宇 , k 工商业负荷 are the evaluation results of different types of resources, and α1, α2, α3, α4, and α5 are the weights of the resource adjustment contribution corresponding to the above resources.

[0035] The weighted scoring model is:

[0036] R 虚拟电厂 =r 偏差率 δ 虚拟电厂 +r 完整性 η 虚拟电厂 +r 贡献度 k 虚拟电厂

[0037] In the formula, R 虚拟电厂 is the comprehensive evaluation score of the virtual power plant, r 偏差率 、r 完整性 、r 贡献度 are the weights of virtual power plant regulation deviation rate, daily data integrity and overall contribution, η 虚拟电厂 is the overall integrity index of the virtual power plant, δ 虚拟电厂 Execute accuracy indicators for virtual power plant regulation instructions.

[0038] The overall integrity indicators of the virtual power plant are:

[0039] η 虚拟电厂 =β1nrt / 96+β2n pl / 96+β3n ad / 96

[0040] Where n rt is the number of real-time power transmission points, n pl is the number of points sent to the planned power curve, n ad is the number of points uploaded to the adjustable power curve, 96 points are the total number of power curve points, β1, β2, and β3 are the weights of the real-time power upload completeness rate, the planned power curve upload completeness rate, and the adjustable power curve upload completeness rate, respectively. After comprehensive calculation, the calculation result of daily data maintenance integrity is obtained.

[0041] The execution accuracy index of the virtual power plant regulation instruction is:

[0042] δ 虚拟电厂 =(S 有效调节容量 -S 设定调节容量 ) / S 设定调节容量

[0043] In the formula, S 有效调节容量 To effectively adjust the capacity, S 设定调节容量 To set the adjustment capacity.

[0044] A virtual power plant multi-type resource comprehensive operation evaluation system, including:

[0045] Data module, used to obtain virtual power plant resource data;

[0046] Building a module to set evaluation dimensions based on resource data and build a basic evaluation model;

[0047] The response module is used to calculate the resource response degree based on the basic evaluation model;

[0048] Contribution module, used to calculate resource adjustment contribution based on the basic evaluation model;

[0049] The comprehensive module is used to calculate and display the comprehensive operation evaluation results.

[0050] In the work, the present invention can more accurately identify and evaluate the unique performance and regulation characteristics of each resource through differentiated single resource evaluation, thereby avoiding the evaluation bias that may be caused by the traditional unified evaluation standard; the resource response degree and resource regulation contribution are integrated to obtain the operation evaluation system of different types of resources in the virtual power plant. The constructed multi-dimensional comprehensive evaluation model not only focuses on the immediate performance of resources, but also comprehensively considers the long-term contribution and regulation potential, providing a more comprehensive perspective and helping to optimize resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0052] The present invention will be further described below in conjunction with specific embodiments.

[0053] The present invention Figure 1 As shown, a method for comprehensive operation evaluation of multi-type resources of a virtual power plant is provided, comprising the following steps:

[0054] Step 1: Obtain virtual power plant resource data;

[0055] Step 2: Set evaluation dimensions based on resource data and build a basic evaluation model;

[0056] Step 3: Calculate resource response degree based on the basic evaluation model;

[0057] Step 4: Calculate resource adjustment contribution based on the basic evaluation model;

[0058] Step 5: Based on steps 2-4, calculate and display the comprehensive operation evaluation results.

[0059] This invention constructs a basic evaluation model that takes into account the unique external characteristics of different types of adjustable resources to ensure more accurate and personalized evaluation. It innovatively introduces time changes and different adjustment scenarios as influencing factors, constructs a multi-dimensional comprehensive operation evaluation model, and improves the adaptability and dynamism of the evaluation system.

[0060] Taking into account the impact of different times and adjustment scenarios, it can dynamically adapt to changes in the operating status of resources, provide more flexible and real-time evaluation results, and better reflect the actual performance of resources under different conditions.

[0061] In specific applications, step 1: virtual power plant resource classification and composition survey. Specifically include: (1) Data collection and classification: Collect all resource data in the virtual power plant, including distributed photovoltaic, energy storage equipment, electric vehicles, smart buildings and industrial and commercial load related information. Through IoT sensors, smart meters and other equipment, obtain real-time data of these resources, including power generation, energy storage status, electric vehicle charging status, building power consumption and real-time demand of industrial and commercial loads; (2) Data statistics and analysis: Sort and classify the collected data of various resources, and count the quantity, power, location distribution and other conditions of various types of resources in the virtual power plant. Use data analysis tools (such as Python's Pandas or R language) to calculate the composition ratio of various resources and analyze their adjustable capabilities at different times (such as maximum output power, minimum load demand, etc.).

[0062] Step 1 can achieve: (1) a comprehensive understanding of the resource composition of the virtual power plant, providing data support for subsequent resource allocation and optimization; (2) determining the upper and lower limits of the adjustable capacity of each type of resource at different times, that is, the adjustable range within which the five types of resources can increase or decrease power at different times, laying the foundation for the formulation of adjustment strategies; (3) providing accurate resource composition data for the comprehensive evaluation model.

[0063] Step 2: Build a basic evaluation model; specifically include:

[0064] (1) Model dimension definition: Define the three core dimension indicators of the basic evaluation model: daily data maintenance integrity, adjustment instruction execution accuracy, and resource adjustment contribution. Each dimension is further subdivided into several specific indicators. For example, data maintenance integrity can include data update frequency, data accuracy, etc.; adjustment instruction execution accuracy can include response time, execution deviation, etc.; resource adjustment contribution can include adjustment effect, contribution ratio, etc.;

[0065] (2) Indicator quantification: The indicators of each dimension are further divided into statistical indicators and evaluation indicators according to different calculation methods. Statistical indicators are obtained by statistical processing of actual data, and evaluation indicators need to be calculated based on specific calculation formulas. The specific methods for defining the quantification of each type of indicator are shown in Table 1:

[0066]

[0067] Table 1

[0068] (3) Model implementation and testing: Use data analysis and modeling tools (such as Python's scikit-learn or TensorFlow) to build a basic evaluation model, and conduct preliminary testing and verification to ensure that the model can accurately reflect the operating conditions of various resources.

[0069] The calculation formula of the basic evaluation model is:

[0070]

[0071] Where k is the evaluation result of the basic evaluation model, m is the number of indicators, ω i is the weight of the i-th indicator, reflecting the importance of the indicator, x i is the original value of the ith indicator (can be a statistical indicator or an evaluation indicator), f(x i ) Functions used to standardize and transform indicators, converting raw data into the same dimension to ensure that different indicators are comparable (such as through normalization or standardization).

[0072] Step 2 can achieve: (1) establish a systematic basic evaluation framework that can comprehensively evaluate the operating quality of various resources in the virtual power plant; (2) provide a scientific basis for subsequent resource optimization and scheduling decisions; and (3) provide quantitative indicators for daily operation and management to help identify problem areas.

[0073] The calculation process of the evaluation results in step 2 involves different indicators and parameters, including the integrity of daily data maintenance, accuracy of adjustment instruction execution, contribution of resource adjustment, etc. The corresponding key general parameter table is shown in Table 2:

[0074]

[0075] Table 2

[0076] Among them, the calculation of the indicators associated with the calculation of the accuracy results of the adjustment instructions is the most complicated, and in order to calculate the accuracy result, different types of indicators need to be calculated. The indicators are divided into two categories, as shown in Table 1. One category is the instruction statistical indicators, that is, the processing statistics of the instruction information itself, which provides convenience for in-depth calculations by improving the instruction information table; the other category is the instruction evaluation indicators, that is, the comprehensive statistics of the instruction completion status, which is used to evaluate the actual completion status and provide a basis for the calculation of subsequent comprehensive evaluation results.

[0077] Specifically, the actual start time is calculated as follows: starting from the "instruction setting start time", the time point when the "actual power" first reaches the following conditions within the "setting duration".

[0078] (Set adjustment amount × effective upper limit coefficient) ≥ (actual power - baseline power) and (actual power - baseline power) ≥ (set adjustment amount × effective lower limit coefficient)

[0079] If there is still no time point that meets the above conditions from the time the command is issued to (the time the command is issued + the set duration), it is an "invalid response" command, the actual start time of the command is empty, and the command status is set to "invalid response".

[0080] The actual duration is calculated as follows: if the instruction start time is empty, the actual duration of the instruction is 0.

[0081] From the "actual start time" to the "set end time" of the instruction, the time of "actual power" in the interval of [set adjustment amount × effective lower limit coefficient, set adjustment amount × effective upper limit coefficient] is accumulated. If the instruction start time is not empty, the actual duration of the instruction is: starting from the actual start time of the instruction, calculate the (actual power-baseline power) value of each sampling point. If (set adjustment amount MW × effective upper limit coefficient) ≥ (actual power-baseline power) and (actual power-baseline power) ≥ (set adjustment amount MW × effective lower limit coefficient), the duration is increased by 5 minutes on the original basis, that is, t 持续时间 =n×Δt, Δt is the 5-minute collection interval, and n is the number of time points that meet the above conditions from the actual start time to the set end time.

[0082] The actual method of adjusting capacity is as follows: from the actual start time of the instruction, within the actual duration of the instruction,

[0083] The calculation method of effective regulation capacity is: from the command "actual start time" to the "command set end time", the cumulative sum of [min{actual power, baseline power + set regulation amount × effective upper limit coefficient}-min(actual power, baseline power + set regulation amount × effective lower limit coefficient)]. At time t, the minimum value of the actual power value and the baseline power × the effective power upper limit value, and the minimum value of the actual power value and the baseline power × the effective power lower limit value are taken.

[0084] The calculation method for setting the regulating capacity is: The setting regulating capacity, in MWh, is: p 设定调节量 ×t 设定持续

[0085] The calculation method of adjustment deviation is: S 有效调节容量 -S 设定调节容量

[0086] The calculation method of adjustment deviation rate is: (S 有效调节容量 -S 设定调节容量 ) / S 设定调节容量

[0087] The maximum increase power calculation method is:

[0088] For a single instruction, if the "Set Adjustment Amount" is a positive value, there is a "Maximum Increased Power", which is the maximum value of ("Actual Power" - "Planned Power") within the range of the "Actual Start Time" to the "Set End Time" of the instruction. If the "Set Adjustment Amount" is a negative value, the "Maximum Increased Power" is 0.

[0089] Maximum down-regulation power:

[0090] For a single instruction, if the "Set Adjustment Amount" is a negative value, there is a "Maximum Power Down Adjustment", which is the minimum value of ("Actual Power" - "Planned Power") within the range of the "Actual Start Time" to the "Set End Time" of the instruction. If the "Set Adjustment Amount" is a positive value, the "Maximum Power Down Adjustment" is 0.

[0091] Step 3: Calculation of resource response degree. Specifically, it includes: (1) Parameter input and data processing: input the information such as the issuance time, duration, adjustment target, target adjustment resource, etc. of the adjustment instruction as input parameters into the basic evaluation model, obtain the corresponding calculation results and extract the response data of various resources after receiving the adjustment instruction, such as the increase or decrease of photovoltaic power generation, the charging and discharging of energy storage equipment, the charging change of electric vehicles, the load adjustment of intelligent buildings, etc.; (2) Response degree calculation: design a calculation formula or algorithm to calculate the response degree based on the difference between the actual response of the resource and the expected target. For example, use the time series analysis method to evaluate the response speed (response time), response amplitude (the difference between the actual adjustment amount and the target adjustment amount), and response stability (fluctuation after adjustment) of the resource; (3) Data feedback and optimization: feed back the calculation results to the basic evaluation model, adjust the weights and parameters of various resources, and optimize the accuracy and practicality of the model. Among them, the response degree includes response speed, response amplitude and response stability.

[0092] Step 3 can achieve the following: (1) accurately evaluate the response capabilities of various types of resources under different regulation instructions and identify resource types with faster or slower responses; (2) provide the dispatch center with actual operation data reference to help optimize the regulation strategy; (3) provide targeted improvement suggestions to improve the overall response efficiency of the virtual power plant.

[0093] Step 4: Calculation of resource regulation contribution. Specifically including: (1) Regulation weight setting: different regulation weights are set for each type of resource according to the proportion and distribution of resources aggregated by different virtual power plants. For example, for virtual power plants with a high proportion of photovoltaic power generation, the regulation weight of photovoltaic resources may be higher; and for power plants with a high proportion of energy storage, the regulation weight of energy storage equipment may be greater; (2) Contribution calculation: In each regulation process, the basic evaluation model is used, combined with the response data of various resources, to calculate the contribution of various resources to the overall regulation effect. For example, the proportion of the actual regulation amount achieved by photovoltaics, energy storage, electric vehicles, etc. in the regulation to the total regulation target; (3) Result analysis and optimization: The contribution of each type of resource is normalized, its actual role in the virtual power plant is analyzed, and the regulation strategy and resource allocation plan are adjusted to ensure that the regulation capacity of the virtual power plant is maximized.

[0094] Step 4 can achieve the following: (1) quantify the actual contribution of various resources in the regulation process and identify the main and secondary regulating forces; (2) optimize resource allocation and regulation strategies to make the overall regulation capability of the virtual power plant more efficient and flexible; (3) provide data support for resource allocation and scheduling decisions to improve the operation quality of the virtual power plant.

[0095] Step 5: Calculate and display the comprehensive operation evaluation results. Specifically include: (1) Comprehensive evaluation model input: take the evaluation results, resource response degree calculation results and contribution calculation results in the basic evaluation model as input parameters, comprehensively consider the performance of various types of resources in daily operation and regulation, consider the differences of various types of resources, and obtain the overall evaluation results of the virtual power plant; specifically, the weighted calculation value of each type of indicator; (2) Multi-dimensional comprehensive scoring: design a weighted scoring model, and calculate the comprehensive operation score of the virtual power plant according to the contribution of various resources. For example, the integrity of daily data maintenance may account for 20%, the accuracy of regulation instruction execution accounts for 30%, and the resource regulation contribution accounts for 50%; (3) Result presentation and analysis: use visualization tools to display the comprehensive operation evaluation results of the virtual power plant, including the overall evaluation results of the virtual power plant and the comprehensive evaluation score of the virtual power plant, generate a detailed analysis report, and put forward targeted optimization suggestions.

[0096] Step 5 achieves: (1) forming a comprehensive virtual power plant operation evaluation result that can reflect the comprehensive performance of daily operation quality and regulation capacity; (2) providing a scientific basis for the long-term optimization and development of virtual power plants, identifying potential problems and proposing improvement measures; (3) providing data support to managers and operators to help improve the overall efficiency and stability of virtual power plants.

[0097] The comprehensive evaluation model is:

[0098] k 虚拟电厂 =α1k 分布式光伏 +α2k 储能 +α3k 电动汽车 +α4k 智能楼宇 +α5k 工商业负荷

[0099] In the formula, k 虚拟电厂 is the overall evaluation result of the virtual power plant, k 分布式光伏 , k 储能 , k 电动汽车 , k 智能楼宇 , k 工商业负荷 are the evaluation results of different types of resources, and α1, α2, α3, α4, and α5 are the weights of the resource adjustment contribution corresponding to the above resources.

[0100] In order to intuitively express the comprehensive evaluation results in digital form and form a comparison ranking of multiple virtual power plants, the comprehensive evaluation results are mapped to obtain the corresponding comprehensive scores;

[0101] The weighted scoring model is:

[0102] R 虚拟电厂 =r 偏差率 δ 虚拟电厂 +r 完整性 η 虚拟电厂 +r 贡献度 k 虚拟电厂

[0103] In the formula, R 虚拟电厂 is the comprehensive evaluation score of the virtual power plant, r 偏差率 、r 完整性 、r 贡献度 are the weights of virtual power plant regulation deviation rate, daily data integrity and overall contribution, η 虚拟电厂 is the overall integrity index of the virtual power plant, δ 虚拟电厂 Execute accuracy indicators for virtual power plant regulation instructions.

[0104] The overall integrity indicators of the virtual power plant are:

[0105] η 虚拟电厂 =β1n rt / 96+β2n pl / 96+β3n ad / 96

[0106] Where n rt is the number of real-time power transmission points, n pl is the number of points sent to the planned power curve, n ad is the number of points uploaded to the adjustable power curve, 96 points are the total number of power curve points, β1, β2, and β3 are the weights of the real-time power upload completeness rate, the planned power curve upload completeness rate, and the adjustable power curve upload completeness rate, respectively. After comprehensive calculation, the calculation result of daily data maintenance integrity is obtained.

[0107] The execution accuracy index of the virtual power plant regulation instruction is:

[0108] δ 虚拟电厂 =(S 有效调节容量 -S 设定调节容量 ) / S 设定调节容量

[0109] In the formula, S 有效调节容量 To effectively adjust the capacity, S 设定调节容量 To set the adjustment capacity.

[0110] A virtual power plant multi-type resource comprehensive operation evaluation system, including:

[0111] Data module, used to obtain virtual power plant resource data;

[0112] Building a module to set evaluation dimensions based on resource data and build a basic evaluation model;

[0113] The response module is used to calculate the resource response degree based on the basic evaluation model;

[0114] Contribution module, used to calculate resource adjustment contribution based on the basic evaluation model;

[0115] The comprehensive module is used to calculate and display the comprehensive operation evaluation results.

[0116] The present invention mainly includes three aspects:

[0117] First, the resources are classified considering the differences between them. The differences are mainly reflected in the different power generation and consumption characteristic curves of different resources (differences in peak, flat and valley loads in different time periods) and different adjustable directions (only upward adjustment, only downward adjustment, both upward and downward adjustment). At the same time, taking into account the commercial attributes of the virtual power plant operator for the aggregated resources (own resources, agency resources), the virtual power plant resources are divided into five typical categories, namely: distributed photovoltaics, energy storage, electric vehicles, smart buildings, and industrial and commercial loads.

[0118] Combined with the difference classification standards, these five types of loads have the following specific differences: Distributed photovoltaics can only generate electricity and only support downward adjustment under full power generation conditions, and the power generation characteristics are related to light conditions. Power generation is concentrated during the day, and the power generation power is zero at night;

[0119] Energy storage can generate electricity and use electricity. In terms of regulation capability, it supports both upward and downward regulation. The power generation characteristics are closely related to the regulation behavior of the virtual power plant. However, considering the regulation habits of users, energy storage is usually discharged during peak hours and charged during valley hours, and a single charge and discharge action usually lasts 1 to 2 hours.

[0120] Without considering V2G, electric vehicles mainly adjust downward, that is, the charging pile adjusts the power consumption curve downward by reducing the charging power. When considering V2G, electric vehicles can both charge and discharge, and the adjustment ability can be adjusted upward or downward. The charging behavior is relatively random and closely related to the installation location of the charging pile, the capacity of the power station, and the vehicle usage in the load area. For online ride-hailing vehicles, the charging behavior is concentrated at night and at noon when the vehicle usage is low, while for daily commuting electric vehicles, the charging behavior is concentrated at night or during work.

[0121] For smart buildings, electricity consumption is the main factor. Small rooftop photovoltaics or distributed energy storage and other related distributed power sources may be integrated inside the building, but overall, self-generation and self-use are the main methods. The power shortage is still mainly purchased from the power grid. Smart buildings can adjust the power consumption both upward and downward. Through their own energy management system, they can achieve power generation regulation of distributed power sources and energy-saving power consumption of energy-consuming equipment. The power consumption curve is concentrated in the working hours, and the night is the low point of power consumption. At the same time, the air conditioning load will affect the peak power consumption due to the influence of seasons.

[0122] Industrial and commercial loads are mainly composed of large industrial users and are mainly electricity users. Considering that the electricity consumption behavior of production lines is usually relatively fixed, the adjustment range of industrial and commercial loads is usually relatively limited. Downward adjustment can be made by shutting down some auxiliary loads, or upward adjustment can be made by shutting down the production line the day before and starting it up the next day.

[0123] In summary, the adjustable capacity and power generation and consumption behavior of each type of resource are not fixed. Classifying them according to their own attributes helps to evaluate the actual effect of their participation in regulation.

[0124] Second, based on the adjustable capabilities of each type of resource, the effectiveness of regulation is evaluated in a targeted manner in combination with the capabilities of a certain resource. Different from the previous methods of evaluating the regulation behavior of virtual power plants, the present invention requires virtual power plants to track the complete regulation process of a certain type of resource in the process of organizing various types of resources to carry out regulation, so as to achieve targeted rating of the specific regulation behavior of each type of resource. At the same time, it is necessary to formulate different scoring and evaluation standards in combination with the objective adjustable capabilities of different resources, and finally normalize them into a unified evaluation method.

[0125] Differentiated evaluation results are given according to the actual capabilities of resources. For a certain regulation behavior, it is not required that all resources be regulated according to a unified execution standard. The advantage of this evaluation is that it can more completely reflect the capabilities of the resources themselves, reflect the contribution of various types of resources in the overall regulation process of the virtual power plant, and refine the regulation attributes of resources, so that the virtual power plant can focus on the process of aggregating resources, and also facilitate the generation of target regulation values ​​in a targeted manner according to the characteristics of the resources themselves when generating regulation instructions.

[0126] Third, the dimension of comprehensive evaluation is not limited to the command regulation capability, but also takes into account the integrity of daily data maintenance of internal resources of the virtual power plant, the actual contribution of resources in a certain regulation process, and the deviation rate between the target regulation requirements and the actual regulation effect. The comprehensive evaluation is expanded to the scope of normalized operations, making the evaluation more three-dimensional and the evaluation statistics more instructive.

[0127] The present invention takes the adjustable resources of differentiated virtual power plants as the research object, takes into account the differences in the external characteristics of the source, load, storage operation and regulation mode of different resources, and constructs a basic model for single resource evaluation; further, taking different times and different regulation scenarios as influencing factors, a multi-dimensional comprehensive operation evaluation model is constructed to obtain an operation evaluation system for different types of resources in virtual power plants. The present invention can more objectively reflect the actual operation and participation in regulation capabilities of different types of resources for power generation and electricity consumption of virtual power plants at different times and in different scenarios, and can guide the dispatching department to reasonably arrange the call demand of virtual power plants.

[0128] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for comprehensive operation evaluation of multi-type resources of a virtual power plant, characterized in that: The following steps are involved: Step 1: Obtain virtual power plant resource data; Step 2: Set evaluation dimensions based on resource data and build a basic evaluation model; Step 3: Calculate resource response degree based on the basic evaluation model; Step 4: Calculate resource adjustment contribution based on the basic evaluation model; Step 5: Based on steps 2-4, calculate and display the comprehensive operation evaluation results.

2. According to claim 1, a virtual power plant multi-type resource comprehensive operation evaluation method is characterized by: Step 1 includes: Step 1.1: Data collection and classification: Collect all resource data in the virtual power plant and classify them according to distributed photovoltaics, energy storage equipment, electric vehicles, smart buildings, and industrial and commercial loads; Step 1.2: Data statistics: organize the collected data of various resources, count the quantity, power, and location distribution of various types of resources within the virtual power plant, and calculate the composition ratio of each type of resource.

3. A virtual power plant multi-type resource comprehensive operation evaluation method according to claim 1, characterized in that: Step 2 includes: Step 2.1: Define the three-dimensional indicators of the basic evaluation model: daily data maintenance completeness, adjustment instruction execution accuracy and resource adjustment contribution; Step 2.2: Use modeling tools to build a basic evaluation model.

4. A virtual power plant multi-type resource comprehensive operation evaluation method according to claim 3, characterized in that: The basic evaluation model is: In the formula, k is the evaluation result of the basic evaluation model, m is the number of indicators, ω i is the weight of the ith indicator, x i is the original value of the ith indicator, f(x i ) functions for normalizing and transforming metrics.

5. The method for comprehensive operation evaluation of multi-type resources of a virtual power plant according to claim 1 is characterized by: Step 3 includes: Step 3.1: Parameter input and data processing: The issuance time, duration, adjustment target amount, and target adjustment resource of the adjustment instruction are used as input parameters and input into the basic evaluation model to obtain the corresponding calculation results and extract the response data of various resources after receiving the adjustment instruction; Step 3.2: Calculate the response level: Calculate the response level based on the difference between the actual response data of the resource and the expected target.

6. A virtual power plant multi-type resource comprehensive operation evaluation method according to claim 1, characterized in that: Step 4 includes: Step 4.1: Adjustment weight setting: According to the proportion and distribution of resources aggregated by the virtual power plant, different adjustment weights are set for each type of resource; Step 4.2: Contribution calculation: During each adjustment process, the basic evaluation model is used in combination with the response data of various resources to calculate the contribution of various resources to the overall adjustment effect.

7. The method for comprehensive operation evaluation of multi-type resources of a virtual power plant according to claim 1 is characterized by: Step 5 includes: Step 5.1: Input the evaluation results of the basic evaluation model, the calculation results of the resource response degree and the contribution calculation results into the comprehensive evaluation model to obtain the overall evaluation results of the virtual power plant; Step 5.2: Multi-dimensional comprehensive scoring: Design a weighted scoring model to calculate the comprehensive evaluation score of the virtual power plant based on the contribution of various resources; Step 5.3: Result presentation and analysis: Use visualization tools to display the comprehensive operation evaluation results of the virtual power plant and generate a detailed analysis report.

8. The method for comprehensive operation evaluation of multi-type resources of a virtual power plant according to claim 1 is characterized by: The comprehensive evaluation model is: k 虚拟电厂 =α1k 分布式光伏 +α2k 储能 +α3k 电动汽车 +α4k 智能楼宇 +α5k 工商业负荷 In the formula, k 虚拟电厂 is the overall evaluation result of the virtual power plant, k 分布式光伏 , k 储能 , k 电动汽车 , k 智能楼宇 , k 工商业负荷 are the evaluation results of different types of resources, and α1, α2, α3, α4, and α5 are the weights of the resource adjustment contribution corresponding to the above resources.

9. A virtual power plant multi-type resource comprehensive operation evaluation method according to claim 8, characterized in that: The weighted scoring model is: R 虚拟电厂 =r 偏差率 d 虚拟电厂 +r 完整性 or 虚拟电厂 +r 贡献度 k 虚拟电厂 In the formula, R 虚拟电厂 is the comprehensive evaluation score of the virtual power plant, r 偏差率 、r 完整性 、r 贡献度 are the weights of virtual power plant regulation deviation rate, daily data integrity and overall contribution, η 虚拟电厂 is the overall integrity index of the virtual power plant, δ 虚拟电厂 Execute accuracy indicators for virtual power plant regulation instructions.

10. A virtual power plant multi-type resource comprehensive operation evaluation method according to claim 9, characterized in that: The overall integrity indicators of the virtual power plant are: or 虚拟电厂 =β1n rt / 96+β2n pl / 96+β3n ad / 96 Where n rt is the number of real-time power transmission points, n pl is the number of points sent to the planned power curve, n ad is the number of points uploaded to the adjustable power curve, 96 points are the total number of power curve points, β1, β2, and β3 are the weights of the real-time power upload completeness rate, the planned power curve upload completeness rate, and the adjustable power curve upload completeness rate, respectively. After comprehensive calculation, the calculation result of daily data maintenance integrity is obtained.

11. A virtual power plant multi-type resource comprehensive operation evaluation method according to claim 9, characterized in that: The execution accuracy index of the virtual power plant regulation instruction is: d 虚拟电厂 =(S 有效调节容量 -S 设定调节容量 ) / S 设定调节容量 In the formula, S 有效调节容量 To effectively adjust the capacity, S 设定调节容量 To set the adjustment capacity.

12. A virtual power plant multi-type resource comprehensive operation evaluation system, characterized in that: include: Data module, used to obtain virtual power plant resource data; Building a module to set evaluation dimensions based on resource data and build a basic evaluation model; The response module is used to calculate the resource response degree based on the basic evaluation model; Contribution module, used to calculate resource adjustment contribution based on the basic evaluation model; The comprehensive module is used to calculate and display the comprehensive operation evaluation results.

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