A method and device for evaluating regulation capability of a distributed optical storage aggregation unit

Abstract

The present application relates to the technical field of distributed new energy operation, and specifically provides a kind of distributed light storage aggregation unit regulating capacity evaluation method and device, comprising: clustering aggregation is carried out to distributed photovoltaic resource and energy storage resource, and the distributed light storage aggregation unit is obtained;The regulating capacity index of the distributed light storage aggregation unit is obtained, and the regulating capacity of the distributed light storage aggregation unit is evaluated based on the regulating capacity index.The technical scheme provided by the present application realizes the accurate, dynamic quantitative evaluation of the multi-time scale regulation potential of the aggregation unit, and provides core data support for market participation and power grid dispatching.

Description

Technical Field

[0001] This invention relates to the field of distributed renewable energy operation technology, specifically to a method and apparatus for evaluating the regulation capability of a distributed photovoltaic-storage aggregation unit. Background Technology

[0002] With the rapid development of distributed new energy sources such as wind power and photovoltaics, the new power system is facing the challenge of increasing uncertainty on both the source and load sides. Against this backdrop, how to effectively aggregate and dispatch massive, decentralized distributed resources to participate in grid peak shaving, frequency regulation, and other ancillary services through market mechanisms has become a key issue in building the new power system.

[0003] Currently, ancillary services such as peak shaving and frequency regulation still mainly rely on large-capacity centralized power sources. Fully exploring the regulation potential of distributed photovoltaic and energy storage, and efficiently aggregating and regulating them through market mechanisms, is both a key focus and a challenge in building a new power dispatching system. The core issue is that the grid side needs to possess the ability to accurately assess and utilize the regulation capacity of massive distributed resource aggregations, while users and operators need to optimize the economic benefits of their regulation activities while meeting grid demands and market rules.

[0004] However, through a comprehensive analysis of existing research, applications, and the needs of various stakeholders, the following prominent bottlenecks still exist at the technical level, hindering the efficient transformation of distributed resource regulation potential into system schedulable capabilities: First, the flexibility and responsiveness of resource aggregation are insufficient. Existing aggregation methods are mostly based on fixed topology or static clustering, which makes it difficult to adapt to the physical characteristics of distributed resources that are "numerous, widespread, and dynamically changing." They also cannot quickly respond to real-time dynamic needs such as power grid peak shaving and frequency regulation, resulting in aggregates that are "adjustable but not fast, and aggregateable but not optimal."

[0005] Second, there is a lack of refined and quantitative assessment of regulation capabilities. Faced with diverse and heterogeneous resources such as distributed photovoltaics and energy storage, there is a lack of unified and accurate quantitative modeling methods for their regulation capabilities across multiple time scales (day-ahead, intraday, real-time). The inability to clearly see and accurately calculate regulation potential has become a core obstacle to their participation in refined market and precise regulation. Summary of the Invention

[0006] To overcome the above-mentioned shortcomings, this invention proposes a method and apparatus for evaluating the regulation capability of a distributed photovoltaic-storage aggregation unit.

[0007] Firstly, a method for evaluating the regulation capability of a distributed optical-storage aggregation unit is provided, the method comprising: Distributed photovoltaic resources and energy storage resources are clustered and aggregated to obtain distributed photovoltaic-storage aggregation units; Obtain the regulation capability index of the distributed optical energy storage aggregation unit, and evaluate the regulation capability of the distributed optical energy storage aggregation unit based on the regulation capability index.

[0008] Preferably, the clustering and aggregation of distributed photovoltaic resources and energy storage resources includes: Based on the clustering characteristics of distributed photovoltaic resources and energy storage resources, the K-means clustering algorithm is used to cluster distributed photovoltaic resources and energy storage resources to obtain clustering results; The centroids of each cluster in the clustering results are used as a resource to construct a similarity matrix among the resources; Obtain the standard Laplacian matrix corresponding to the similarity matrix, and extract the first preset number of non-zero elements from each row of the standard Laplacian matrix to construct a resource feature matrix; Using each row vector in the resource feature matrix as the basis for clustering the corresponding resource, the K-means clustering algorithm is used to cluster each resource to obtain the clustering unit; The clusters corresponding to the resources contained in the aggregation unit are merged into a distributed optical storage aggregation unit.

[0009] Furthermore, the standard Laplacian matrix corresponding to the similarity matrix is ​​as follows: L sym = D 1 / 2 LD 1 / 2 In the above formula, L sym This is the standard Laplacian matrix corresponding to the similarity matrix. D For degree matrix, L This is the Laplacian matrix corresponding to the similarity matrix. L = D S , S This is a similarity matrix.

[0010] Preferably, the regulation capability indicators of the distributed optical-storage aggregation unit include: capacity indicators and performance indicators.

[0011] Furthermore, the capacity-related indicators include at least one of the following: power generation capacity, real-time upward adjustment capacity, day-ahead-intraday upward adjustment capacity, real-time downward adjustment capacity, day-ahead-intraday downward adjustment capacity, and adjustment capacity.

[0012] Furthermore, the real-time upward adjustment capability is as follows:

[0013] In the above formula, They are respectively in t Upward adjustment capability of distributed photovoltaic-storage aggregation units and distributed photovoltaic power stations i Theoretical power generation and actual power generation, energy storage j Discharge capability, n, m These refer to the number of distributed photovoltaic power stations and energy storage power stations in the distributed photovoltaic-energy storage aggregation unit, respectively; the day-to-day upward adjustment capability is the sum of the energy storage discharge capacity; the adjustment capacity is [downward adjustment capability, upward adjustment capability].

[0014] Furthermore, the real-time downward adjustment capability is as follows:

[0015] The downward adjustment capability for the day-to-day period is as follows:

[0016] In the above formula, They are respectively in t Downward adjustment capability and energy storage of distributed photovoltaic-storage aggregation units j Rechargeable capability, In order to be in t Photovoltaic power station i The predicted power generation capacity.

[0017] Furthermore, the performance indicators include at least one of the following: adjustment rate, response time, adjustment accuracy, and adjustment success rate.

[0018] Furthermore, the adjustment rate is as follows:

[0019] The response time is as follows:

[0020] The adjustment precision is as follows: or

[0021] The adjustment success rate is as follows:

[0022] In the above formula, These are the moments when the actual output first reaches 90% of the commanded value and the moment the command is issued, respectively. X To adjust the accuracy, K For the total number of samples, P Pk , P MkThey are aggregation units k The actual output power value and command value corresponding to each sample.

[0023] Preferably, the step of evaluating the regulation capability of the distributed photovoltaic-storage aggregation unit based on the regulation capability index includes: evaluating the regulation capability of the distributed photovoltaic-storage aggregation unit using the analytic hierarchy process (AHP) based on the regulation capability index.

[0024] Secondly, a distributed optical-storage aggregation unit adjustment capability evaluation device is provided, the distributed optical-storage aggregation unit adjustment capability evaluation device comprising: The aggregation module is used to cluster and aggregate distributed photovoltaic resources and energy storage resources to obtain distributed photovoltaic-storage aggregation units. The evaluation module is used to obtain the regulation capability index of the distributed optical-storage aggregation unit and evaluate the regulation capability of the distributed optical-storage aggregation unit based on the regulation capability index.

[0025] Thirdly, a computer device is provided, comprising: one or more processors; The processor is used to store one or more programs; When the one or more programs are executed by the one or more processors, the method for evaluating the regulation capability of the distributed optical storage aggregation unit is implemented.

[0026] Fourthly, a computer-readable storage medium is provided, on which a computer program is stored, wherein when the computer program is executed, the method for evaluating the adjustment capability of the distributed optical storage aggregation unit is implemented.

[0027] The above-described technical solutions of the present invention have at least one or more of the following beneficial effects: This invention provides a method and apparatus for evaluating the regulation capacity of distributed photovoltaic (PV) and energy storage (ESS) aggregation units. The method includes: clustering and aggregating distributed PV resources and energy storage resources to obtain distributed PV-ESS aggregation units; obtaining regulation capacity indicators for the distributed PV-ESS aggregation units; and evaluating the regulation capacity of the aggregation units based on these indicators. The technical solution provided by this invention achieves accurate and dynamic quantitative evaluation of the regulation potential of aggregation units across multiple time scales. By accurately evaluating and aggregating the regulation potential of distributed resources, it can effectively guide and incentivize massive distributed PV, energy storage, and their operators to participate in grid peak shaving and other ancillary services, thereby enhancing the demand-side response capability of the power system. This helps to achieve peak shaving and valley filling of loads, alleviate grid congestion, and improve the grid's balance capacity and operational resilience under high-proportion renewable energy access. It is an important technical support for promoting renewable energy consumption and energy structure transformation. Attached Figure Description

[0028] Figure 1This is a schematic diagram of the main steps of the distributed photovoltaic-storage aggregation unit regulation capability evaluation method according to an embodiment of the present invention. Detailed Implementation

[0029] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] Example 1 See appendix Figure 1 , Figure 1 This is a schematic flowchart illustrating the main steps of a distributed photovoltaic-storage aggregation unit regulation capability evaluation method according to an embodiment of the present invention. Figure 1 As shown, the method for evaluating the adjustment capability of a distributed optical-storage aggregation unit in this embodiment of the invention mainly includes the following steps: Step S101: Cluster and aggregate distributed photovoltaic resources and energy storage resources to obtain distributed photovoltaic-storage aggregation units; Step S102: Obtain the regulation capability index of the distributed optical energy storage aggregation unit, and evaluate the regulation capability of the distributed optical energy storage aggregation unit based on the regulation capability index.

[0032] In this embodiment, the clustering and aggregation of distributed photovoltaic resources and energy storage resources includes: Based on the clustering characteristics of distributed photovoltaic resources and energy storage resources, the K-means clustering algorithm is used to cluster distributed photovoltaic resources and energy storage resources to obtain clustering results; The centroids of each cluster in the clustering results are used as a resource to construct a similarity matrix among the resources; Obtain the standard Laplacian matrix corresponding to the similarity matrix, and extract the first preset number of non-zero elements from each row of the standard Laplacian matrix to construct a resource feature matrix; Using each row vector in the resource feature matrix as the basis for clustering the corresponding resource, the K-means clustering algorithm is used to cluster each resource to obtain the clustering unit; The clusters corresponding to the resources contained in the aggregation unit are merged into a distributed optical storage aggregation unit.

[0033] Clustering features may include: resource type identifiers, normalized historical average output curves, regulation capacity indicators, geographic location codes (or regional electricity price tags), etc. To eliminate the influence of different dimensions (such as power and light intensity), methods such as Z-score normalization (suitable for feature distributions that approximate a Gaussian distribution) or Min-Max normalization (suitable for scenarios requiring a defined value range) are used to normalize the data.

[0034] In one implementation, the standard Laplacian matrix corresponding to the similarity matrix is ​​as follows: L sym = D 1 / 2 LD 1 / 2 In the above formula, L sym This is the standard Laplacian matrix corresponding to the similarity matrix. D For degree matrix, L This is the Laplacian matrix corresponding to the similarity matrix. L = D S , S This is a similarity matrix.

[0035] In this embodiment, the adjustment capability indicators of the distributed optical storage aggregation unit include: capacity indicators and performance indicators.

[0036] In one embodiment, the capacity indicators include at least one of the following: power generation capacity, real-time upward adjustment capacity, day-ahead-intra-day upward adjustment capacity, real-time downward adjustment capacity, day-ahead-intra-day downward adjustment capacity, and adjustment capacity.

[0037] For distributed photovoltaic (PV) systems, power generation capacity refers to the amount of electricity that can be generated at a given moment, primarily corresponding to theoretical power output, which is the power output that can be generated when all inverters in the distributed PV station are operating normally under current solar resource conditions. There are two main calculation methods: First, using K-means clustering, selecting the average power output of distributed PV power generation under similar weather conditions over the past 30 days that was not subject to control and regulation; second, using a sample inverter method, which establishes a mapping model between the output of a selected sample inverter and the total output of the entire station to obtain the theoretical power output of the entire station. For aggregated units composed of distributed PV and energy storage, power generation capacity refers to the sum of the theoretical power output of the distributed PV and the maximum discharge power of the energy storage.

[0038] Upward adjustment capability refers to the maximum power that an aggregation unit can generate beyond its current output at the assessment time. It corresponds to the "positive adjustment capacity" in the electricity market's "reported capacity." The calculation method is the difference between the upper limit of the aggregation unit's total adjustment potential and its current actual total output. In one implementation, the real-time upward adjustment capability is as follows:

[0039] In the above formula, They are respectively in t Upward adjustment capability of distributed photovoltaic-storage aggregation units and distributed photovoltaic power stations i Theoretical power generation and actual power generation, energy storage j Discharge capability, n, m These refer to the number of distributed photovoltaic power stations and energy storage power stations in the distributed photovoltaic-energy storage aggregation unit, respectively; the day-ahead-intraday upward regulation capability is the sum of the energy storage discharge capacity; regulation capacity is a general term for upward and downward regulation capabilities, usually referring to the range of regulation power that the aggregation unit promises to provide to the grid within a specific time period (such as 5 minutes, 15 minutes, 1 hour, etc.), usually expressed in the form of [downward regulation capability, upward regulation capability] interval.

[0040] Downward adjustment capability refers to the maximum power reduction that an aggregation unit can generate based on its existing output at the assessment time. It corresponds to the "negative adjustment capacity" in the electricity market's "reported output." The calculation method is the difference between the current actual total output and the lower limit of the total adjustment potential. In one implementation, the real-time downward adjustment capability is as follows:

[0041] The downward adjustment capability for the day-to-day period is as follows:

[0042] In the above formula, They are respectively in t Downward adjustment capability and energy storage of distributed photovoltaic-storage aggregation units j Rechargeable capability, In order to be in t Photovoltaic power station i The predicted power generation capacity.

[0043] For power generation forecasting, this invention provides high-precision short-term and ultra-short-term total output forecasts and generates an executable power generation plan based on the adjustable capabilities of energy storage. The core idea is to utilize the spatial correlation between meteorological conditions and power output within the aggregation unit, selecting a small number of representative photovoltaic power plants as "baseline stations." By establishing a mapping model between "measured output of the baseline station and meteorological conditions," and combining customized regional numerical weather prediction, the total output of the entire aggregation unit is extrapolated and predicted. The prediction results, after being corrected by the energy storage adjustment capabilities, form the power generation plan for the aggregation unit and are then submitted. Specifically: (1) Basic data collection and aggregation unit confirmation Collect ledger information (installed capacity, geographical location, grid connection level, etc.), historical power generation / consumption data, and real-time operating status and power data for all distributed photovoltaic and energy storage sites within the region. Confirm and apply the results of the N aggregation units defined in Step 1 as the physical boundaries predicted in this step.

[0044] (2) Selection of reference stations within the aggregation unit For each aggregation unit, one or more representative distributed photovoltaic power stations will be selected as benchmark stations. The selection criteria include: High data quality: It has complete and reliable real-time power monitoring and uploading capabilities.

[0045] Scale representativeness: Prioritize power plants with larger installed capacity and stable operation.

[0046] System compatibility: It is best if the power plant itself has an output prediction system.

[0047] In principle, each aggregation unit should select at least one reference station. If there is no suitable power station in the region, the selection scope can be expanded to adjacent areas with similar meteorological conditions. For aggregation units with a large geographical area and dense photovoltaic installations, the number of reference stations can be appropriately increased to improve representativeness.

[0048] (3) Customized meteorological data acquisition Customized numerical weather forecasts: For the geographic range of each aggregated unit, a high-resolution meteorological grid is constructed, and numerical weather forecast products for the grid area are customized from professional meteorological service agencies, focusing on obtaining gridded forecast data of key meteorological elements such as future irradiance and temperature.

[0049] Meteorological data collection: Within the aggregation unit, locate stations with deployed meteorological monitoring (such as those with built-in monitoring in some high-voltage distributed photovoltaic power stations) and collect real-time measured data on meteorological elements such as irradiance. For areas lacking monitoring points, data from adjacent areas can be used as substitutes, or a small number of additional monitoring points can be considered. The measured data is used for: 1) as auxiliary input to the prediction model; 2) post-event verification and evaluation of numerical weather prediction errors, and optimization of the prediction model through a feedback mechanism.

[0050] (4) Modeling of aggregated unit output prediction based on reference station Model input: The core inputs are the real-time / historical measured power output data of the reference station of each aggregation unit, the numerical weather forecast data of the corresponding location, and the meteorological measured data.

[0051] Modeling approach: A hybrid modeling approach combining physical-driven (e.g., photovoltaic module power model) and data-driven (e.g., machine learning regression algorithm) methods is adopted to establish a high-precision mapping relationship from meteorological conditions to the power output of the base station.

[0052] Spatial extrapolation: Based on the assumption that "meteorological conditions and power generation characteristics are highly correlated within the same aggregation unit", the trained base station prediction model is combined with customized numerical weather forecasts covering the entire aggregation unit to extrapolate and calculate the total power output prediction value of all distributed photovoltaics within the aggregation unit.

[0053] Predicted output: The model outputs two types of results: Short-term forecast: Output forecast curves for the next 24 hours, broken down into 15-minute or hourly increments, used to formulate day-ahead power generation plans.

[0054] Ultra-short-term forecasting: High-frequency power output forecast curves for the next 0-4 hours, spaced 5-15 minutes apart, are used to formulate rolling daily power generation plans, with higher accuracy.

[0055] (5) Generation and revision of power generation plans considering energy storage Initial plan: The above power output forecast results will be directly used as the initial power generation plan for the aggregation unit.

[0056] Energy Storage Smoothing and Plan Tracking: Considering the biases and inherent volatility of photovoltaic forecasts, when the aggregation unit includes energy storage or adjustable loads, the charging and discharging plan of the energy storage is dynamically optimized based on grid dispatch requirements (such as peak-shaving instructions) and the initial power generation plan. Through the real-time charging and discharging actions of the energy storage, random fluctuations in the actual net output of the aggregation unit are smoothed, ensuring that its actual operating curve tracks the reported power generation plan as closely as possible. This effectively improves the forecast accuracy and dispatchability for the dispatching side. It should be noted that this step optimizes the "operation strategy" of the distributed photovoltaic-energy storage aggregation unit, rather than directly modifying the forecast model.

[0057] (6) Reporting of forecast and planning results Taking each aggregation unit as a unified object, the total net output forecast curve and corresponding power generation plan, including distributed photovoltaic and energy storage, are summarized and uploaded to the superior power dispatching agency and power trading agency in accordance with the standard format and communication protocol, as the basis for dispatching operation and market trading.

[0058] In one embodiment, the performance metrics include at least one of the following: adjustment rate, response time, adjustment accuracy, and adjustment success rate.

[0059] The adjustment rate refers to the ability of an aggregation unit to increase or decrease power per unit time, reflecting its adjustment agility. It is generally divided into upward adjustment rate (climb rate) and downward adjustment rate (slippage rate). In one embodiment, the adjustment rate is as follows:

[0060] Response time refers to the time required from when the aggregation unit receives an instruction from the superior mechanism until the total output of the aggregation unit reaches a certain percentage (e.g., 90%) required by the instruction. The response time is as follows:

[0061] Regulation accuracy refers to the deviation between the actual output power of the aggregation unit and the power required by the upper-level control command during regulation or after steady state. Higher accuracy indicates stronger regulation capability of the aggregation unit. Common calculation methods mainly include the root mean square error or mean absolute error between the actual value and the commanded value within the regulation time range. The regulation accuracy is as follows: or

[0062] The adjustment success rate is the percentage of times the aggregation unit successfully completes a scheduling instruction within a specified time limit and accuracy requirement, out of the total number of scheduling instructions. The adjustment success rate is as follows:

[0063] In the above formula, These are the moments when the actual output first reaches 90% of the commanded value and the moment the command is issued, respectively. X To adjust the accuracy, K For the total number of samples, P Pk , P Mk They are aggregation units k The actual output power value and command value corresponding to each sample.

[0064] In this embodiment, the evaluation of the regulation capability of the distributed photovoltaic-storage aggregation unit based on the regulation capability index includes: evaluating the regulation capability of the distributed photovoltaic-storage aggregation unit using the analytic hierarchy process (AHP) based on the regulation capability index.

[0065] By implementing the above methods, this invention can efficiently aggregate massive distributed photovoltaic, energy storage, and other decentralized resources, enabling them to participate in power market peak shaving in a large-scale and dispatchable manner. In practical applications, control strategies can be flexibly formulated and adjusted according to the power grid peak shaving needs, new energy resource distribution, and user characteristics in different regions. This fully taps the potential of distributed resources while providing a wide-area and flexible regulation capability for the safe and stable operation of the power grid.

[0066] For example, taking a typical application scenario in a certain region as an example, the specific application of the present invention is illustrated: Resource aggregation: The distributed photovoltaic, energy storage and other resources in the region are feature extracted and dynamically aggregated according to the method provided in this invention.

[0067] Market participation: Connect the aggregated distributed optical storage units to the local ancillary services market.

[0068] Transaction clearing: Aggregation units participate in day-ahead and intraday market clearing for peak-shaving ancillary services through a “volume-based bidding” method, based on their accurately assessed regulation capabilities and power generation forecasts.

[0069] Command Execution: After the market clearing results form the control command, it is issued to the aggregation unit. The aggregation unit, based on its internal optimization algorithm, decomposes the total power command and issues it to each specific distributed photovoltaic and energy storage device for execution.

[0070] Closed loop and benefits: While achieving peak shaving and valley filling in the power system and improving the efficiency of power grid operation, this process also enables aggregators to obtain market revenue by providing regulation services, achieving a win-win situation of system optimization and commercial benefits.

[0071] Example 2 Based on the same inventive concept, the present invention also provides a distributed photovoltaic-storage aggregation unit adjustment capability evaluation device, the distributed photovoltaic-storage aggregation unit adjustment capability evaluation device comprising: The aggregation module is used to cluster and aggregate distributed photovoltaic resources and energy storage resources to obtain distributed photovoltaic-storage aggregation units. The evaluation module is used to obtain the regulation capability index of the distributed optical-storage aggregation unit and evaluate the regulation capability of the distributed optical-storage aggregation unit based on the regulation capability index.

[0072] Preferably, the clustering and aggregation of distributed photovoltaic resources and energy storage resources includes: Based on the clustering characteristics of distributed photovoltaic resources and energy storage resources, the K-means clustering algorithm is used to cluster distributed photovoltaic resources and energy storage resources to obtain clustering results; The centroids of each cluster in the clustering results are used as a resource to construct a similarity matrix among the resources; Obtain the standard Laplacian matrix corresponding to the similarity matrix, and extract the first preset number of non-zero elements from each row of the standard Laplacian matrix to construct a resource feature matrix; Using each row vector in the resource feature matrix as the basis for clustering the corresponding resource, the K-means clustering algorithm is used to cluster each resource to obtain the clustering unit; The clusters corresponding to the resources contained in the aggregation unit are merged into a distributed optical storage aggregation unit.

[0073] Furthermore, the standard Laplacian matrix corresponding to the similarity matrix is ​​as follows: L sym = D 1 / 2 LD 1 / 2 In the above formula, L sym This is the standard Laplacian matrix corresponding to the similarity matrix. D For degree matrix, L This is the Laplacian matrix corresponding to the similarity matrix. L = D S , S This is a similarity matrix.

[0074] Preferably, the regulation capability indicators of the distributed optical-storage aggregation unit include: capacity indicators and performance indicators.

[0075] Furthermore, the capacity-related indicators include at least one of the following: power generation capacity, real-time upward adjustment capacity, day-ahead-intraday upward adjustment capacity, real-time downward adjustment capacity, day-ahead-intraday downward adjustment capacity, and adjustment capacity.

[0076] Furthermore, the real-time upward adjustment capability is as follows:

[0077] In the above formula, They are respectively in t Upward adjustment capability of distributed photovoltaic-storage aggregation units and distributed photovoltaic power stations i Theoretical power generation and actual power generation, energy storage j Discharge capability, n, m These refer to the number of distributed photovoltaic power stations and energy storage power stations in the distributed photovoltaic-energy storage aggregation unit, respectively; the day-to-day upward adjustment capability is the sum of the energy storage discharge capacity; the adjustment capacity is [downward adjustment capability, upward adjustment capability].

[0078] Furthermore, the real-time downward adjustment capability is as follows:

[0079] The downward adjustment capability for the day-to-day period is as follows:

[0080] In the above formula, They are respectively in t Downward adjustment capability and energy storage of distributed photovoltaic-storage aggregation units j Rechargeable capability, In order to be in t Photovoltaic power station i The predicted power generation capacity.

[0081] Furthermore, the performance indicators include at least one of the following: adjustment rate, response time, adjustment accuracy, and adjustment success rate.

[0082] Furthermore, the adjustment rate is as follows:

[0083] The response time is as follows:

[0084] The adjustment precision is as follows: or

[0085] The adjustment success rate is as follows:

[0086] In the above formula, These are the moments when the actual output first reaches 90% of the commanded value and the moment the command is issued, respectively. X To adjust the accuracy, K For the total number of samples, P Pk , P Mk They are aggregation units k The actual output power value and command value corresponding to each sample.

[0087] Preferably, the step of evaluating the regulation capability of the distributed photovoltaic-storage aggregation unit based on the regulation capability index includes: evaluating the regulation capability of the distributed photovoltaic-storage aggregation unit using the analytic hierarchy process (AHP) based on the regulation capability index.

[0088] Example 3 Based on the same inventive concept, this invention also provides a computer device, which includes a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement corresponding method flows or corresponding functions, thereby realizing the steps of the distributed optical storage aggregation unit adjustment capability evaluation method in the above embodiments.

[0089] Example 4 Based on the same inventive concept, this invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the distributed optical storage aggregation unit adjustment capability evaluation method in the above embodiments.

[0090] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0091] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0092] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0093] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0094] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for distributed optical storage aggregation unit regulation capability assessment, characterized in that, The method includes: Distributed photovoltaic resources and energy storage resources are clustered and aggregated to obtain distributed photovoltaic-storage aggregation units; Obtain the regulation capability index of the distributed optical energy storage aggregation unit, and evaluate the regulation capability of the distributed optical energy storage aggregation unit based on the regulation capability index.

2. The method as described in claim 1, characterized in that, The clustering and aggregation of distributed photovoltaic resources and energy storage resources includes: Based on the clustering characteristics of distributed photovoltaic resources and energy storage resources, the K-means clustering algorithm is used to cluster distributed photovoltaic resources and energy storage resources to obtain clustering results; The centroids of each cluster in the clustering results are used as a resource to construct a similarity matrix among the resources; Obtain the standard Laplacian matrix corresponding to the similarity matrix, and extract the first preset number of non-zero elements from each row of the standard Laplacian matrix to construct a resource feature matrix; Using each row vector in the resource feature matrix as the basis for clustering the corresponding resource, the K-means clustering algorithm is used to cluster each resource to obtain the clustering unit; The clusters corresponding to the resources contained in the aggregation unit are merged into a distributed optical storage aggregation unit.

3. The method as described in claim 2, characterized in that, The standard Laplace matrix corresponding to the similarity matrix is ​​as follows: L sym = D 1 / 2 LD 1 / 2 In the above formula, L sym This is the standard Laplacian matrix corresponding to the similarity matrix. D For degree matrix, L This is the Laplacian matrix corresponding to the similarity matrix. L = D S , S This is a similarity matrix.

4. The method as described in claim 1, characterized in that, The regulation capability indicators of the distributed optical-storage aggregation unit include: capacity indicators and performance indicators.

5. The method as described in claim 4, characterized in that, The capacity-related indicators include at least one of the following: power generation capacity, real-time upward adjustment capacity, day-ahead-intraday upward adjustment capacity, real-time downward adjustment capacity, day-ahead-intraday downward adjustment capacity, and adjustment capacity.

6. The method as described in claim 5, characterized in that, The real-time upward adjustment capability is as follows: In the above formula, They are respectively in t Upward adjustment capability of distributed photovoltaic-storage aggregation units and distributed photovoltaic power stations i Theoretical power generation and actual power generation, energy storage j Discharge capability, n, m These refer to the number of distributed photovoltaic power stations and energy storage power stations in the distributed photovoltaic-energy storage aggregation unit, respectively; the day-to-day upward adjustment capability is the sum of the energy storage discharge capacity; the adjustment capacity is [downward adjustment capability, upward adjustment capability].

7. The method as described in claim 6, characterized in that, The real-time downward adjustment capability is as follows: The downward adjustment capability for the day-to-day period is as follows: In the above formula, They are respectively in t Downward adjustment capability and energy storage of distributed photovoltaic-storage aggregation units j Rechargeable capability, In order to be in t Photovoltaic power station i The predicted power generation capacity.

8. The method as described in claim 4, characterized in that, The performance metrics include at least one of the following: adjustment rate, response time, adjustment accuracy, and adjustment success rate.

9. The method as described in claim 8, characterized in that, The adjustment rate is as follows: The response time is as follows: The adjustment precision is as follows: or The adjustment success rate is as follows: In the above formula, These are the moments when the actual output first reaches 90% of the commanded value and the moment the command is issued, respectively. X To adjust the accuracy, K For the total number of samples, P Pk , P Mk They are aggregation units k The actual output power value and command value corresponding to each sample.

10. The method as described in claim 1, characterized in that, The assessment of the regulation capability of the distributed photovoltaic-storage aggregation unit based on the regulation capability index includes: assessing the regulation capability of the distributed photovoltaic-storage aggregation unit using the analytic hierarchy process (AHP) based on the regulation capability index.

11. An apparatus for evaluating the regulation capability of a distributed photovoltaic-storage aggregation unit based on any one of claims 1-10, characterized in that, The device includes: The aggregation module is used to cluster and aggregate distributed photovoltaic resources and energy storage resources to obtain distributed photovoltaic-storage aggregation units. The evaluation module is used to obtain the regulation capability index of the distributed optical-storage aggregation unit and evaluate the regulation capability of the distributed optical-storage aggregation unit based on the regulation capability index.

12. A computer device, characterized in that, include: One or more processors; The processor is used to execute one or more programs; When the one or more programs are executed by the one or more processors, the method for evaluating the regulation capability of a distributed optical storage aggregation unit as described in any one of claims 1 to 10 is implemented.

13. A computer-readable storage medium, characterized in that, It contains a computer program, which, when executed, implements the method for evaluating the regulation capability of a distributed optical storage aggregation unit as described in any one of claims 1 to 10.

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