Electric power spot market environment analysis system for virtual power plant management

Abstract

The invention relates to the technical field of operation management, and discloses an electric power spot market environment analysis system for virtual power plant management. Comprising the steps of screening out effective participants from original participants, analyzing a real-time stability level of an electric power spot market, collecting environment summary data of the electric power spot market in an analysis interval, predicting a supply and demand state value of a next analysis interval, identifying management data from the environment summary data, and analyzing the management data. Providing management suggestions for effective participants in the virtual power plant; compared with the prior art, the method can improve the environment analysis threshold of the electric power spot market, can predict whether the future supply and demand state of the electric power spot market is balanced or not in advance, and provides management suggestions for the power supply party and the power utilization party in the virtual power plant according to the prediction result. Therefore, the negative phenomenon that the environment of the electric power spot market is unbalanced is avoided, and the hysteresis caused by the real-time analysis and judgment mode of the current electric power spot market is also solved.

Description

Technical Field

[0001] The present invention relates to the technical field of operation management, and more specifically, to a power spot market environment analysis system for virtual power plant management. Background Art

[0002] The power spot market refers to a market where eligible business entities conduct day-ahead, intra-day, and real-time electricity energy trading. By observing and understanding the relevant data that changes in the power spot market, the stable state of the trading environment in the power spot market can be analyzed and evaluated. Based on the analysis and evaluation results, timely and effective management can be carried out on the participants of the virtual power plant, and ultimately the efficiency of power resource trading can be improved.

[0003] The patent application with the publication number CN116720885A discloses a distributed virtual power plant control method and system in a power spot market environment, including: S1. Denote each power generation enterprise participating in the power resource trading market as each designated power generation enterprise, and according to the information of each designated power generation enterprise, compare and screen each designated power generation enterprise eligible to enter the market, and denote them as each permitted power generation enterprise; S2. Obtain the declaration data of each permitted power generation enterprise before the trading day and the demand data of each power user side before the trading day, analyze the day-ahead power resource supply-demand balance index in the power resource trading market, and then formulate the day-ahead unit electricity price in the power resource trading market; S3. Obtain the real-time supply data of each permitted power generation enterprise on the trading day and the real-time demand data of each power user side on the trading day, and then coordinate the power resources in the power resource trading market; S4. Obtain the integrated supply data of each permitted power generation enterprise on the trading day and the integrated demand data of each power user side on the trading day, and analyze the deviation assessment index of each permitted power generation enterprise and each power user side; S5. Perform corresponding processing on them according to the deviation assessment index of each permitted power generation enterprise and each power user side.

[0004] When analyzing the environment of the existing power spot market, by collecting and analyzing the real-time comprehensive trading data in the power spot market, the stability of the trading environment in the power spot market can be judged, and thus management suggestions can be provided for the virtual power plant. For example, in the above patent application, it coordinates the power resource trading market by obtaining the real-time supply data of each permitted power generation enterprise on the trading day and the real-time demand data of each power user side on the trading day. Although this method can accurately analyze the trading environment in the power spot market, it cannot analyze in advance the trading environment situation in the power spot market at future moments, so it cannot predict in advance the upcoming negative trading environment, and thus cannot coordinate and manage the participants of the virtual power plant in advance, resulting in the lag of the method for analyzing the power spot market environment and reducing the safety and rationality of power resource trading.

[0005] In view of this, the present invention proposes a power spot market environment analysis system for virtual power plant management to solve the above problems. Summary of the Invention

[0006] To overcome the above-mentioned defects of the prior art and to achieve the above object, the present invention provides the following technical solution: A power spot market environment analysis system for virtual power plant management, which is applied to a virtual management server and includes:

[0007] A participant screening module, which is used to identify the participation attributes of the original participants in the virtual power plant one by one, and based on the screening criteria, screen out the effective participants from the original participants. The effective participants include effective power generation enterprises and effective electricity users;

[0008] A real-time determination module, which is used to obtain the real-time environmental characteristics of the power spot market. The real-time environmental characteristics include the effective transaction rate and the effective online quantity, analyze the real-time stability level of the power spot market, and determine whether to enter the environment analysis mode;

[0009] A data collection module, which is used to divide the set evaluation and analysis period into continuous analysis intervals, and collect the environmental summary data of the power spot market in the analysis intervals. The environmental summary data includes the difference between power supply and demand, the electricity price fluctuation value, and the market liquidity rate;

[0010] A prediction and prompt module, which is used to input the environmental summary data into the trained supply and demand state prediction model, predict the supply and demand state value of the next analysis interval. The supply and demand state value includes a balanced state and an unbalanced state, and determine whether to issue an environmental warning prompt;

[0011] A management suggestion module, which is used to identify the management data from the environmental summary data, and provide management suggestions to the effective participants in the virtual power plant according to the management data.

[0012] Furthermore, the participation attributes include long-term attributes, stage attributes, and temporary attributes. The identification methods for the long-term attributes, stage attributes, and temporary attributes include:

[0013] Mark all the original participants in the virtual power plant one by one, and query the attribute comment boxes of the original participants one by one through the power database;

[0014] Identify the comment values in the attribute comment boxes through natural language processing technology, and split down the numerical parts in the comment values;

[0015] Record the attribute comment boxes with the numerical part being 11 as long-term boxes, and record the participation attributes of the original participants corresponding to the long-term boxes as long-term attributes;

[0016] Denote the attribute note box with the digital part being 10 as the phase box, and denote the participation attribute of the original participant corresponding to the phase box as the phase attribute;

[0017] Denote the attribute note box with the digital part being 00 as the temporary box, and denote the participation attribute of the original participant corresponding to the temporary box as the temporary attribute.

[0018] Furthermore, the screening criterion is: take the original participants within the effective screening period as the screening scope;

[0019] The screening methods for effective power generation enterprises and effective electricity users include:

[0020] Query all analysis events participating in the electricity spot market environment analysis in the past time period one by one through the power database, and query the duration values of all analysis events one by one through the time stamp. After accumulating all the duration values and taking the average, obtain the effective duration;

[0021] Taking the current moment as the starting moment, push back a duration corresponding to the effective duration forward to obtain the ending moment, and denote the time period between the starting moment and the ending moment as the effective screening period;

[0022] During the effective screening period, mark the original participants with participation attributes of long-term attribute and phase attribute one by one, and query the identity types of the original participants;

[0023] Denote the original participants with identity types of power generation and electricity consumption as effective power generation enterprises and effective electricity users respectively, and obtain A effective power generation enterprises and B effective electricity users.

[0024] Furthermore, the method for obtaining the effective transaction rate includes:

[0025] Query the electricity quantities sent by A effective power generation enterprises and put into the electricity spot market during the effective screening period one by one through the power database to obtain A input electricity quantity values, and after accumulating the A input electricity quantity values one by one, obtain the total input value;

[0026] Query all the transaction events of B effective electricity users during the effective screening period one by one through the power database, and mark the transaction electricity quantities in all the transaction events one by one to obtain B transaction electricity quantities. After accumulating the B transaction electricity quantities, obtain the total transaction value;

[0027] After comparing the total transaction value with the total input value, obtain the effective transaction rate;

[0028] The expression of the effective transaction rate is:

[0029]

[0030] In the formula, YX jyis the effective transaction rate, JY zz is the total transaction value, TR zz is the total input value.

[0031] Furthermore, the method for obtaining the effective online quantity includes:

[0032] Randomly mark C non - adjacent sampling moments within the effective screening period, and query the real - time status of effective power generation enterprises and effective electricity consumers at the C sampling moments one by one through the online management system;

[0033] Record the sampling moments when the real - time status of both effective power generation enterprises and effective electricity consumers is the online status as effective moments, and obtain D effective moments;

[0034] Respectively count the number of effective power generation enterprises and the number of effective electricity consumers at the D effective moments, and after adding the number of effective power generation enterprises and the number of effective electricity consumers, obtain the effective online quantity.

[0035] Furthermore, the real - time stability levels include a high - stability level and a low - stability level. The analysis methods for the high - stability level and the low - stability level include:

[0036] Compare the effective transaction rate with the standard transaction rate. When the effective transaction rate is greater than the standard transaction rate, record the effective transaction rate as an effective feature;

[0037] Compare the effective online quantity with the standard online quantity. When the effective online quantity is greater than the standard online quantity, record the effective online quantity as an effective feature;

[0038] Count the number of effective features. When the number of effective features is 2, the real - time stability level is the high - stability level; when the number of effective features is 0 or 1, the real - time stability level is the low - stability level;

[0039] The determination method for whether to enter the environmental analysis mode includes:

[0040] When the real - time stability level is the high - stability level, determine to enter the environmental analysis mode;

[0041] When the real - time stability level is the low - stability level, determine not to enter the environmental analysis mode.

[0042] Furthermore, the method for obtaining the difference between power supply and demand includes:

[0043] Query the power generation amounts in the power spot market within E analysis intervals through the power database respectively, and obtain E original power generation amounts;

[0044] Query the rated demand that meets the basic operating status of the power spot market through the power trading center, and after subtracting each of the E original power generation amounts from the rated demand one by one, obtain E total power generation amounts;

[0045] Query the total demand in the electricity spot market within E analysis intervals through the electricity database respectively. After subtracting the E total power generations from the corresponding E total demands one by one, obtain E power supply-demand differences.

[0046] The expression for the power supply-demand difference is:

[0047] GX cze = FD yse - XQ ed - XQ zle ;

[0048] In the formula, GX cze is the power supply-demand difference in the e-th analysis interval, where e = 1, 2... E, FD yse is the original power generation in the e-th analysis interval, XQ ed is the rated demand, and XQ zle is the total demand in the e-th analysis interval.

[0049] Furthermore, the method for obtaining the electricity price fluctuation value includes:

[0050] Mark F non-adjacent trading moments in E analysis intervals respectively with a preset trading duration as the standard;

[0051] Query the real-time electricity prices at the F trading moments in the electricity spot market within E analysis intervals through the power trading center one by one to obtain F unit electricity prices;

[0052] Mark the maximum value and the minimum value of the unit electricity prices in E analysis intervals one by one. After subtracting the minimum value of the unit electricity price from the maximum value of the unit electricity price, obtain E electricity price fluctuation values;

[0053] The expression for the electricity price fluctuation value is:

[0054] DJ bde = DJ zde - DJ zxe ;

[0055] In the formula, DJ bde is the electricity price fluctuation value in the e-th analysis interval, DJ zde is the maximum value of the unit electricity price in the e-th analysis interval, and DJ zxe is the minimum value of the unit electricity price in the e-th analysis interval.

[0056] Furthermore, the training method for the supply-demand state prediction model includes:

[0057] Collect multiple groups of environmental summary data and the corresponding supply-demand state values in advance;

[0058] Convert the environmental summary data into multiple feature vectors using the sliding window method. Convert the supply-demand status values into labels corresponding to the environmental summary data according to the sliding step. Convert the balanced state to 0 and the unbalanced state to 1. One feature vector corresponds to one label, and a set of training data is formed. Multiple sets of training data constitute the training set. Arrange the environmental summary data in the order of collection time, and preset the prediction time step Q, the sliding step W, and the sliding window length Y.

[0059] Use the feature vectors as the input of the model, and the operating status value of the next analysis interval after the prediction time step Q as the output. The subsequent operating status values of each training set are used as the prediction targets, and the sum of the minimized prediction errors is used as the training target to train the model, generating a supply-demand status prediction model that predicts the supply-demand status value of the next analysis interval based on the environmental summary data of the previous analysis interval.

[0060] Furthermore, the identification method of management data includes:

[0061] Compare the power supply-demand difference with the safety value of the supply-demand difference. When the power supply-demand difference is greater than the safety value of the supply-demand difference, record the power supply-demand difference as management data.

[0062] Compare the electricity price fluctuation value with the safety value of the fluctuation value. When the electricity price fluctuation value is greater than the safety value of the fluctuation value, record the electricity price fluctuation value as management data.

[0063] Compare the market liquidity rate with the safety value of the liquidity rate. When the market liquidity rate is less than the safety value of the liquidity rate, record the market liquidity rate as management data.

[0064] The method of providing management suggestions to effective participants includes:

[0065] When the management data is the power supply-demand difference, it is recommended to reduce the power generation of A effective power generation enterprises.

[0066] When the management data is the electricity price fluctuation value, it is recommended to increase the power generation of A effective power generation enterprises.

[0067] When the management data is the market liquidity rate, it is recommended to increase the market subsidy amount for A effective power generation enterprises or B effective electricity users.

[0068] The technical effects and advantages of a power spot market environment analysis system for virtual power plant management according to the present invention:

[0069] The present invention identifies the participation attributes of the original participants in the virtual power plant one by one, and based on the screening criteria, screens out the effective participants from the original participants, obtains the real-time environmental characteristics of the electricity spot market, analyzes the real-time stability level of the electricity spot market, and determines whether to enter the environmental analysis mode. The set evaluation and analysis period is divided into continuous analysis intervals, and the environmental summary data of the electricity spot market in the analysis intervals is collected. The environmental summary data is input into the trained supply-demand state prediction model to predict the supply-demand state value of the next analysis interval, and it is determined whether to issue an environmental warning prompt. The management data is identified from the environmental summary data, and according to the management data, management suggestions are provided to the effective participants in the virtual power plant. Compared with the prior art, through the collection and analysis of real-time environmental characteristics, the real-time stability level of the electricity spot market can be identified, so as to ensure that the electricity spot market entering the environmental analysis mode has a certain stability performance, that is, the useless operation of environmental analysis for the electricity spot market with unqualified stability performance can be avoided, thereby improving the threshold of environmental analysis and reducing the burden of environmental analysis. At the same time, combined with the supply-demand state prediction model, it is possible to predict in advance whether the future supply-demand state of the electricity spot market is balanced based on the environmental summary data, obtain the result in advance before the imbalance phenomenon occurs in the electricity spot market, and provide management suggestions to the power supply side and power consumption side in the virtual power plant according to the prediction result, thereby avoiding the negative phenomenon of imbalance in the electricity spot market environment, and also solving the lag caused by the current real-time analysis and judgment methods of the electricity spot market, ensuring that the virtual power plant can manage in advance according to the real-time changes of the electricity spot market, and guaranteeing the safety and rationality of the electricity resource transaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 FIG. 1 is a schematic structural diagram of a power spot market environment analysis system for virtual power plant management provided in Embodiment 1 of the present invention;

[0071] Figure 2 FIG. 2 is a schematic diagram of the modules of the virtual management server provided in Embodiment 1 of the present invention;

[0072] Figure 3 FIG. 3 is a schematic flow diagram of a power spot market environment analysis method for virtual power plant management provided in Embodiment 2 of the present invention;

[0073] Figure 4 FIG. 4 is a schematic diagram of the structure of an electronic device provided in Embodiment 3 of the present invention;

[0074] Figure 5 FIG. 5 is a schematic diagram of the structure of a computer-readable storage medium provided in Embodiment 4 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0075] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0076] Embodiment 1: Please refer to Figure 1 and Figure 2 As shown, the power spot market environment analysis system for virtual power plant management described in this embodiment is applied to a virtual management server and includes:

[0077] A participant screening module that identifies the participation attributes of the original participants in the virtual power plant one by one, and based on the screening criteria, screens out the effective participants from the original participants. The effective participants include effective power generation enterprises and effective electricity users;

[0078] The original participants refer to the participants who can play any role in the power spot market and can form a virtual power plant, and are the original objects for the management and control of the virtual power plant, so that the original participants can represent the power supply side and the electricity consumption side of the virtual power plant. The participation attribute is used to represent the type of role that the original participant plays in the power spot market, so as to provide a basis for the subsequent analysis of the power spot market and the management of the virtual power plant;

[0079] The participation attributes include long-term attributes, stage attributes, and temporary attributes; the long-term attribute means that the original participant plays a role permanently in the power spot market, the stage attribute means that the original participant plays a role periodically in the power spot market, and the temporary attribute means that the original participant plays a role temporarily in the power spot market;

[0080] The identification methods for long-term attributes, stage attributes, and temporary attributes include:

[0081] Mark all the original participants in the virtual power plant one by one, and query the attribute comment boxes of the original participants one by one through the power database; the attribute comment box is used to represent the overall role type of the original participant in the power spot market and can integrate and summarize the relevant data of the role played by the original participant;

[0082] Identify the comment values in the attribute comment box through natural language processing technology, and split the numerical part of the comment values; the comment value is the smallest unit that makes up the attribute comment box and is also a specific representation of the role played by the original participant in the power spot market, so as to facilitate the distinction of the role types played by the original participants;

[0083] Record the attribute note box with the digital part being 11 as the long-term box, and record the participation attribute of the original participant corresponding to the long-term box as the long-term attribute;

[0084] Record the attribute note box with the digital part being 10 as the stage box, and record the participation attribute of the original participant corresponding to the stage box as the stage attribute;

[0085] Record the attribute note box with the digital part being 00 as the temporary box, and record the participation attribute of the original participant corresponding to the temporary box as the temporary attribute.

[0086] After obtaining the participation attribute of the original participant, according to the different participation attributes, the direct objects that can be used for subsequent analysis of the electricity spot market environment can be screened out from the original participants, that is, the effective participants. When screening out the effective participants from the original participants, it is necessary to screen different types of original participants that make up the virtual power plant separately to ensure that the screened effective participants can meet the requirements of the electricity spot market environment analysis. Therefore, it is necessary to use the screening criteria for screening;

[0087] The screening criteria are: take the original participants within the effective screening period as the screening scope; this can ensure that the original participants participating in the screening of effective participants are within a reasonable and accurate time range, and then ensure that the screened effective participants are all within the correct and reasonable category;

[0088] The effective participants include effective power generation enterprises and effective electricity users;

[0089] The screening methods for effective power generation enterprises and effective electricity users include:

[0090] Query all analysis events that participated in the electricity spot market environment analysis during the past time period one by one through the power database, and query the duration values of all analysis events one by one through the time stamp. After accumulating all the duration values and taking the average, obtain the effective duration;

[0091] Taking the current moment as the starting moment, after backward deducing a duration corresponding to the effective duration, obtain the ending moment, and record the time period between the starting moment and the ending moment as the effective screening period;

[0092] During the effective screening period, mark the original participants with long-term attributes and stage attributes one by one, and query the identity types of the original participants; the identity type is used to specifically represent the application end represented by the original participant. Specifically, the identity types include power generation, electricity consumption, and dispatching, etc.;

[0093] Record the original participants with identity types of power generation and electricity consumption as effective power generation enterprises and effective electricity users respectively, and obtain A effective power generation enterprises and B effective electricity users.

[0094] A real-time determination module that obtains the real-time environmental characteristics of the electricity spot market, analyzes the real-time stability level of the electricity spot market, and determines whether to enter the environmental analysis mode;

[0095] The real-time environmental characteristics are used to represent the real-time environmental data of the effective power generation enterprises and effective electricity users participating in the electricity spot market, that is, they can distinguish the current stability level of the electricity spot market and be used as the basis for whether further environmental analysis is required in the electricity spot market;

[0096] The real-time environmental characteristics include the effective transaction rate and the effective online quantity;

[0097] The effective transaction rate refers to the proportion of the electricity quantity sent by A effective power generation enterprises and put into the electricity spot market during the effective screening period that is successfully traded, that is, it can represent the actual situation of the electricity quantity transaction in the electricity spot market;

[0098] The methods for obtaining the effective transaction rate include:

[0099] Query the electricity quantity sent by A effective power generation enterprises and put into the electricity spot market during the effective screening period one by one through the power database, obtain A input electricity quantity values, and after accumulating the A input electricity quantity values one by one, obtain the total input value;

[0100] Query all the transaction events of B effective electricity users during the effective screening period one by one through the power database, and mark the transaction electricity quantity in all the transaction events one by one to obtain B transaction electricity quantities. After accumulating the B transaction electricity quantities, obtain the total transaction value; A transaction event is used to summarize the time of the electricity consumption transaction between the effective power generation enterprises and effective electricity users in the electricity spot market and can unify and summarize the data involved in the electricity consumption transaction;

[0101] After comparing the total transaction value with the total input value, obtain the effective transaction rate;

[0102] The expression of the effective transaction rate is:

[0103]

[0104] In the formula, YX jy is the effective transaction rate, JY zz is the total transaction value, TR zz is the total input value.

[0105] The effective online quantity refers to the number of A effective power generation enterprises and B effective electricity users that are in a real-time online state during the effective screening period, that is, it can represent the actual situation of the number of participants online in the electricity spot market;

[0106] The methods for obtaining the effective online quantity include:

[0107] Randomly mark C non - adjacent sampling moments within the effective screening period, and query the real - time status of effective power generation enterprises and effective electricity consumers at the C sampling moments one by one through the online management system; the real - time status is used to represent the current online or offline status of effective power generation enterprises and effective electricity consumers, so as to distinguish between online and offline participants; specifically, the real - time status includes the online status and the offline status.

[0108] Record the sampling moments when the real - time status of both effective power generation enterprises and effective electricity consumers is the online status as effective moments, and obtain D effective moments.

[0109] Respectively count the number of effective power generation enterprises and the number of effective electricity consumers at the D effective moments, and after adding the number of effective power generation enterprises and the number of effective electricity consumers, obtain the effective online quantity.

[0110] When the real - time environmental characteristics are obtained, it is necessary to identify and judge the real - time environmental characteristics, and based on the identification and judgment results of the real - time environmental characteristics, preliminarily analyze the high or low real - time stability of the power spot market, and use it as the basis for determining whether to enter the environmental analysis mode.

[0111] The real - time stability levels include the high - stability level and the low - stability level; among them, the stability of the high - stability level is higher than that of the low - stability level.

[0112] The analysis methods for the high - stability level and the low - stability level include:

[0113] Compare the effective transaction rate with the standard transaction rate; the standard transaction rate is the minimum value of the effective transaction rate under normal circumstances in the power spot market, and can be used as the basis for determining whether the effective transaction rate is an effective feature.

[0114] When the effective transaction rate is greater than the standard transaction rate, it means that the effective transaction rate in the power spot market exceeds the minimum value under normal circumstances, then record the effective transaction rate as an effective feature.

[0115] Compare the effective online quantity with the standard online quantity; the standard online quantity is the minimum value of the effective online quantity under normal circumstances in the power spot market, and can be used as the basis for determining whether the effective online quantity is an effective feature.

[0116] When the effective online quantity is greater than the standard online quantity, it means that the effective online quantity in the power spot market exceeds the minimum value under normal circumstances, then record the effective online quantity as an effective feature.

[0117] Count the number of effective features. When the number of effective features is 2, at this time, the stability of the power spot market is relatively high, and the real - time stability level is the high - stability level.

[0118] When the number of effective features is 0 or 1, the stability of the electricity spot market is relatively low at this time, and the real-time stability level is the low stability level.

[0119] After obtaining the real-time stability level, it is possible to analyze and determine whether the electricity spot market enters the environmental analysis mode according to the specific level;

[0120] The determination method of whether to enter the environmental analysis mode includes:

[0121] When the real-time stability level is the high stability level, the electricity spot market at this time can meet the subsequent environmental analysis requirements, and it is determined to enter the environmental analysis mode;

[0122] When the real-time stability level is the low stability level, the electricity spot market at this time does not meet the subsequent environmental analysis requirements, and it is determined not to enter the environmental analysis mode.

[0123] The data acquisition module divides the set evaluation and analysis period into continuous analysis intervals, and collects the environmental summary data of the electricity spot market in the analysis intervals. The environmental summary data includes the difference between electricity supply and demand, the electricity price fluctuation value, and the market liquidity rate;

[0124] The evaluation and analysis period refers to the collection duration of data that can affect the environmental analysis results of the electricity spot market, so as to serve as the collection duration limit of the environmental summary data. Due to the large time span of the evaluation and analysis period, the number of data included in the evaluation and analysis period is too large. In order to subdivide the excessive data and at the same time shorten the collection duration of each data, it is necessary to perform an equal-duration continuous division operation on the evaluation and analysis period, so as to divide the evaluation and analysis period into E continuous and equal-duration analysis intervals. The evaluation and analysis period is set according to the average value of the duration corresponding to the increase change period of each price or trading volume in the electricity spot market. Exemplarily, when the evaluation and analysis period is 1 week, the analysis interval is 1 day; when the evaluation and analysis period is 1 day, the analysis interval is 1 hour;

[0125] The environmental summary data refers to the difference between electricity supply and demand, the electricity price fluctuation value, and the market liquidity rate;

[0126] The difference between electricity supply and demand refers to the size of the difference between the total power generation and the total demand of electric energy in the electricity spot market within the analysis interval, so as to represent the supply and demand relationship in the electricity spot market. When the difference between electricity supply and demand is larger, it means that the difference between the total power generation and the total demand in the electricity spot market is larger, and the environmental stability state in the electricity spot market is worse;

[0127] The acquisition method of the difference between electricity supply and demand includes:

[0128] Query the power generation in the electricity spot market within E analysis intervals through the power database respectively to obtain E original power generations;

[0129] Query the rated demand that meets the basic operating state of the electricity spot market through the power trading center. After subtracting each of the E original power generations from the rated demand one by one, obtain E total power generations; The basic operating state refers to the most basic state when there is no market transaction in the electricity spot market. At this time, there is no excess power in the electricity spot market except to meet its own basic demand. Therefore, the rated demand is the minimum power to meet its own basic demand;

[0130] Query the total demand in the electricity spot market within E analysis intervals through the power database respectively. After subtracting each of the E total power generations from the corresponding E total demands one by one, obtain E power supply-demand differences;

[0131] The expression for the power supply-demand difference is:

[0132] GX cze =FD yse -XQ ed -XQ zle ;

[0133] In the formula, GX cze is the power supply-demand difference in the e-th analysis interval, e = 1, 2... E, FD yse is the original power generation in the e-th analysis interval, XQ ed is the rated demand, XQ zle is the total demand in the e-th analysis interval.

[0134] The electricity price fluctuation value refers to the magnitude of the increase and decrease of the electricity price in the electricity spot market within the analysis interval, so as to represent the change of the electricity price in the electricity spot market. When the electricity price fluctuation value is larger, it means that the increase and decrease amplitude of the electricity price in the electricity spot market is larger, and the environmental stability state in the electricity spot market is worse;

[0135] The method for obtaining the electricity price fluctuation value includes:

[0136] Taking the preset trading duration as the standard, mark F non-adjacent trading moments in E analysis intervals respectively; The preset trading duration refers to the maximum duration that can cause a complete fluctuation phenomenon of the unit electricity price in the electricity spot market, so as to ensure that the data at adjacent two trading moments is sufficient to have a complete fluctuation phenomenon, which can not only ensure the basis for the change of the data at each trading moment, but also ensure the independence of the data at each trading moment;

[0137] Query the real-time electricity prices at F trading moments in E analysis intervals of the electricity spot market one by one through the power trading center to obtain F unit electricity prices;

[0138] Mark the maximum value and the minimum value of the unit electricity price in E analysis intervals one by one. After taking the difference between the maximum value and the minimum value of the unit electricity price, obtain E electricity price fluctuation values;

[0139] The expression of the electricity price fluctuation value is:

[0140] DJ bde = DJ zde - DJ zxe ;

[0141] In the formula, DJ bde is the electricity price fluctuation value of the e-th analysis interval, DJ zde is the maximum value of the unit electricity price in the e-th analysis interval, DJ zxe is the minimum value of the unit electricity price in the e-th analysis interval.

[0142] The market turnover rate refers to the proportion between the number of electricity volume transactions completed in the analysis interval of the electricity spot market and the total number of consultations, so as to represent the liquidity performance in the electricity spot market. When the market turnover rate is larger, it means that the number of electricity volume transactions completed in the electricity spot market is more, and then the environmental stability state in the electricity spot market is better; the market turnover rate is obtained by querying through the power trading center.

[0143] The prediction and prompt module inputs the environmental summary data into the trained supply and demand state prediction model, predicts the supply and demand state value of the next analysis interval, and determines whether to issue an environmental warning prompt;

[0144] When the environmental summary data of the electricity spot market is obtained, the environmental summary data can be input into the supply and demand state prediction model, so as to predict the supply and demand state value of the next analysis interval, and represent the supply and demand stability performance of the electricity spot market through the supply and demand state value;

[0145] The supply and demand state value is used to represent the supply and demand state stability performance of the electricity spot market. Specifically, the supply and demand state value includes a balanced state and an unbalanced state, and the supply and demand state value is obtained by collecting a large number of historical electricity supply and demand differences, electricity price fluctuation values and market turnover rates in the balanced state and the unbalanced state;

[0146] The training method of the supply and demand state prediction model includes:

[0147] Pre-collect multiple groups of environmental summary data and the corresponding supply and demand state values;

[0148] Convert the environmental summary data into multiple feature vectors using the sliding window method, and convert the supply-demand status values into labels corresponding to the environmental summary data according to the sliding step. Exemplarily, convert the balanced state to 0 and the unbalanced state to 1. One feature vector corresponds to one label, and a set of training data is formed. Multiple sets of training data form a training set. Arrange the environmental summary data in the order of the collection time, and preset the prediction time step Q, the sliding step W, and the sliding window length Y.

[0149] Use the feature vector as the input of the model, and use the operating status value of the next analysis interval after the prediction time step Q as the output. The subsequent operating status values of each training set are used as the prediction targets, and the sum of the minimized prediction errors is used as the training target to train the model, and generate a supply-demand status prediction model that predicts the supply-demand status value of the next analysis interval based on the environmental summary data of the previous analysis interval.

[0150] Exemplarily, the supply-demand status prediction model adopts any one of CNN or AlexNet.

[0151] The calculation formula for the prediction error is:

[0152] zk = (ak - wk) 2 ;

[0153] In the formula, zk is the prediction error, and k is the group number of the feature vector; ak is the predicted status value corresponding to the k-th group of feature vectors, and wk is the actual status value corresponding to the k-th group of training data.

[0154] In the supply-demand status recognition model, the feature vector is the environmental summary data, and the status value is the supply-demand status value.

[0155] By importing the environmental summary data into the supply-demand status recognition model, the supply-demand status value of the next analysis interval can be predicted, and based on the predicted supply-demand status value, it can be determined whether to issue an environmental warning prompt, that is, an early warning message can be issued when there is a phenomenon of supply-demand imbalance in the electricity spot market.

[0156] The method for determining whether to issue an environmental warning prompt includes:

[0157] When the output of the supply-demand status prediction model is 0, the supply-demand status value of the next analysis interval in the electricity spot market is in a balanced state, then it is determined not to issue an environmental warning prompt;

[0158] When the output of the supply-demand status prediction model is 1, the supply-demand status value of the next analysis interval in the electricity spot market is in an unbalanced state, then it is determined to issue an environmental warning prompt.

[0159] The management advice module identifies management data from the environmental summary data and provides management advice to the effective participants in the virtual power plant based on the management data;

[0160] Management data refers to the specific environmental summary data that causes an environmental warning prompt to be issued in the electricity spot market and serves as the basis for subsequent management of the effective participants in the virtual power plant to ensure that the electricity spot market can maintain normal environmental conditions;

[0161] The methods for identifying management data include:

[0162] Compare the electricity quantity supply-demand difference with the supply-demand difference safety value; the supply-demand difference safety value refers to the maximum value of the electricity quantity supply-demand difference in the electricity spot market without issuing an environmental warning prompt, which can be used as the basis for judging whether the electricity quantity supply-demand difference is management data;

[0163] When the electricity quantity supply-demand difference is greater than the supply-demand difference safety value, at this time, the electricity quantity supply-demand difference in the electricity spot market exceeds the maximum value of the electricity quantity supply-demand difference without issuing an environmental warning prompt, then record the electricity quantity supply-demand difference as management data;

[0164] Compare the electricity price fluctuation value with the fluctuation value safety value; the fluctuation value safety value refers to the maximum value of the electricity price fluctuation value in the electricity spot market without issuing an environmental warning prompt, which can be used as the basis for judging whether the electricity price fluctuation value is management data;

[0165] When the electricity price fluctuation value is greater than the fluctuation value safety value, at this time, the electricity price fluctuation value in the electricity spot market exceeds the maximum value of the electricity price fluctuation value without issuing an environmental warning prompt, then record the electricity price fluctuation value as management data;

[0166] Compare the market liquidity rate with the liquidity rate safety value; the liquidity rate safety value refers to the minimum value of the market liquidity rate in the electricity spot market without issuing an environmental warning prompt, which can be used as the basis for judging whether the market liquidity rate is management data;

[0167] When the market liquidity rate is less than the liquidity rate safety value, at this time, the market liquidity rate in the electricity spot market does not reach the minimum value of the market liquidity rate without issuing an environmental warning prompt, then record the market liquidity rate as management data.

[0168] After identifying the management data, it is necessary to manage and control the effective participants in the virtual power plant according to the differences in the management data, and be able to provide it to the virtual management server as the basis for managing and optimizing the virtual power plant. Furthermore, it is possible to manage the virtual power plant targeted according to the real-time situation of the effective participants in the electricity spot market, and finally provide management advice;

[0169] The methods for providing management advice to the effective participants include:

[0170] When the management data is the difference between power supply and demand, if the total power generation in the electricity spot market exceeds the maximum value under normal circumstances at this time, it indicates that the power generation of the effective power generation enterprises in the virtual power plant greatly exceeds the power consumption of the effective electricity users. Then it is recommended to reduce the power generation of A effective power generation enterprises;

[0171] When the management data is the electricity price fluctuation value, if the unit electricity price in the electricity spot market exceeds the maximum value under normal circumstances at this time, it indicates that the power generation of the effective power generation enterprises in the virtual power plant is lower than the power consumption of the effective electricity users. Then it is recommended to increase the power generation of A effective power generation enterprises;

[0172] When the management data is the market liquidity rate, if the number of transactions completed in the electricity spot market is lower than the minimum value under normal circumstances at this time, it indicates that the transaction success probability of the effective electricity users in the virtual power plant is relatively low. Then it is recommended to increase the market subsidy amount for A effective power generation enterprises or B effective electricity users.

[0173] In this embodiment, by identifying the participation attributes of the original participants in the virtual power plant one by one, and based on the screening criteria, screening out the effective participants from the original participants, obtaining the real-time environmental characteristics of the electricity spot market, analyzing the real-time stability level of the electricity spot market, determining whether to enter the environmental analysis mode, dividing the set evaluation and analysis period into continuous analysis intervals, collecting the environmental summary data of the electricity spot market in the analysis interval, inputting the environmental summary data into the trained supply and demand state prediction model, predicting the supply and demand state value of the next analysis interval, determining whether to issue an environmental warning prompt, identifying the management data from the environmental summary data, and providing management suggestions to the effective participants in the virtual power plant according to the management data; compared with the prior art, through the collection and analysis of the real-time environmental characteristics, the real-time stability level of the electricity spot market can be identified, so as to ensure that the electricity spot market entering the environmental analysis mode has a certain stability performance, that is, it can avoid the useless operation of environmental analysis for the electricity spot market with unqualified stability performance, thereby improving the threshold of environmental analysis and reducing the burden of environmental analysis. At the same time, combined with the supply and demand state prediction model, it can predict in advance whether the future supply and demand state of the electricity spot market is balanced based on the environmental summary data, obtain the result in advance before the imbalance phenomenon appears in the electricity spot market, and provide management suggestions to the power supply side and the power consumption side in the virtual power plant according to the prediction result, thereby avoiding the negative phenomenon of imbalance in the electricity spot market environment, and also solving the lag caused by the current real-time analysis and judgment method of the electricity spot market, ensuring that the virtual power plant can manage in advance according to the real-time changes of the electricity spot market, and guaranteeing the safety and rationality of the electricity resource transaction.

[0174] Embodiment 2: Please refer to Figure 3As shown, for the parts not described in detail in this embodiment, refer to the description in Embodiment 1. A method for analyzing the electricity spot market environment for virtual power plant management is provided, which is applied to a virtual management server and implemented based on a system for analyzing the electricity spot market environment for virtual power plant management, including:

[0175] S1: Identify the participation attributes of the original participants in the virtual power plant one by one, and based on the screening criteria, screen out the effective participants from the original participants. The effective participants include effective power generation enterprises and effective electricity users;

[0176] S2: Obtain the real-time environmental characteristics of the electricity spot market. The real-time environmental characteristics include the effective transaction rate and the effective online volume, analyze the real-time stability level of the electricity spot market, and determine whether to enter the environmental analysis mode;

[0177] S3; If entering the environmental analysis mode, divide the set evaluation analysis period into continuous analysis intervals, and collect the environmental summary data of the electricity spot market in the analysis intervals. The environmental summary data includes the difference between electricity supply and demand, the electricity price fluctuation value, and the market liquidity rate;

[0178] S4: Input the environmental summary data into the trained supply-demand state prediction model to predict the supply-demand state value of the next analysis interval. The supply-demand state value includes the balanced state and the unbalanced state, and determine whether to issue an environmental warning prompt;

[0179] S5: If an environmental warning prompt is issued, identify the management data from the environmental summary data, and based on the management data, provide management suggestions to the effective participants in the virtual power plant.

[0180] Embodiment 3: Please refer to Figure 4 As shown, this embodiment publicly provides an electronic device, including a processor and a memory;

[0181] Among them, the memory stores a computer program that can be called by the processor;

[0182] The processor executes the implemented method for analyzing the electricity spot market environment for virtual power plant management by calling the computer program stored in the memory.

[0183] Since the electronic device introduced in this embodiment is the electronic device used in the method for analyzing the electricity spot market environment for virtual power plant management in Embodiment 2 of the present application, based on the method for analyzing the electricity spot market environment for virtual power plant management introduced in the embodiments of the present application, those skilled in the art can understand the specific implementation manners and various forms of change of the electronic device in this embodiment. Therefore, the specific implementation of how this electronic device implements the method in the embodiments of the present application will not be described in detail here. As long as those skilled in the art implement the electronic device used in the method for analyzing the electricity spot market environment for virtual power plant management in the embodiments of the present application, it falls within the scope of protection of the present application.

[0184] Embodiment 4: Please refer to Figure 5 As shown, this embodiment discloses a computer-readable storage medium, on which a rewritable computer program is stored;

[0185] When the computer program is run, it implements the method for analyzing the electricity spot market environment for virtual power plant management described above.

[0186] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, and all should be covered by the protection scope of the present invention.

Claims

1. An electric power spot market environment analysis system for virtual power plant management, applied to a virtual management server, characterized in that: include: The participant screening module is used to identify the participation attributes of the original participants in the virtual power plant one by one, and screen out valid participants from the original participants based on the screening criteria. The valid participants include valid power generation enterprises and valid electricity users. The real-time determination module is used to obtain the real-time environmental characteristics of the power spot market, including the effective transaction rate and the effective online quantity, analyze the real-time stability level of the power spot market, and determine whether to enter the environmental analysis mode; The data collection module is used to divide the set evaluation and analysis cycle into continuous analysis intervals and collect environmental summary data of the power spot market in the analysis interval. The environmental summary data includes the difference between power supply and demand, power price fluctuation value and market liquidity; The prediction and prompt module is used to input the environmental summary data into the trained supply and demand status prediction model, predict the supply and demand status value of the next analysis interval, the supply and demand status value includes the balanced state and the unbalanced state, and determine whether to issue an environmental early warning prompt; The management suggestion module is used to identify management data from the environmental summary data and provide management suggestions to effective participants in the virtual power plant based on the management data.

2. The power spot market environment analysis system for virtual power plant management according to claim 1, characterized in that: Participation attributes include long-term attributes, stage attributes and temporary attributes. The identification methods of long-term attributes, stage attributes and temporary attributes include: Mark all the original participants in the virtual power plant one by one, and query the attribute comment boxes of the original participants one by one through the power database; Identify the remark value in the attribute remark box through natural language processing technology, and split the numerical part of the remark value; The attribute remark box with the number part of 11 is recorded as a permanent box, and the participation attribute of the original participant corresponding to the permanent box is recorded as a permanent attribute; The attribute remark box with the number part of 10 is recorded as the stage box, and the participation attribute of the original participant corresponding to the stage box is recorded as the stage attribute; The attribute remark box with the digital part being 00 is recorded as a temporary box, and the participation attribute of the original participant corresponding to the temporary box is recorded as a temporary attribute.

3. The power spot market environment analysis system for virtual power plant management according to claim 2, characterized in that: The screening criteria are: the original participants within the effective screening period are used as the screening scope; The screening methods for effective power generation enterprises and effective electricity users include: All analysis events participating in the power spot market environment analysis in the past time period are queried one by one through the power database, and the duration values ​​of all analysis events are queried one by one through the timestamps, and all the duration values ​​are accumulated and averaged to obtain the effective duration; Take the current time as the starting time, count back a time corresponding to the effective time, and get the end time, and record the time period from the starting time to the end time as the effective screening time period; During the effective screening period, the original participants whose participation attributes are long-term attributes and phase attributes are marked one by one, and the identity types of the original participants are queried; The original participants whose identity types are power generation and power consumption are recorded as effective power generation enterprises and effective power users respectively, and A effective power generation enterprises and B effective power users are obtained.

4. The power spot market environment analysis system for virtual power plant management according to claim 3, characterized in that: Methods for obtaining effective transaction rate include: The power database is used to query the power generated by A effective power generation enterprises and invested in the power spot market during the effective screening period, and A invested power values ​​are obtained. After accumulating the A invested power values ​​one by one, the total investment value is obtained; All transaction events of B effective electricity users in the effective screening period are queried one by one through the power database, and the transaction power in all transaction events is marked one by one to obtain B transaction power. After accumulating the B transaction power, the total transaction value is obtained; After comparing the total transaction value with the total investment value, the effective transaction rate is obtained; The expression of effective transaction rate is: In the formula, YX jy is the effective transaction rate, JY zz is the total transaction value, TR zz is the total investment value.

5. The power spot market environment analysis system for virtual power plant management according to claim 4, characterized in that: Methods for obtaining effective online volume include: Randomly mark C non-adjacent sampling moments within the effective screening period, and query the real-time status of effective power generation enterprises and effective electricity users at the C sampling moments one by one through the online management system; The sampling time when the real-time status of the effective power generation enterprises and the effective power users are both online is recorded as the effective time, and D effective times are obtained; The number of effective power generation enterprises and the number of effective electricity users at D effective moments are counted respectively, and the effective online quantity is obtained by adding the number of effective power generation enterprises and the number of effective electricity users.

6. The power spot market environment analysis system for virtual power plant management according to claim 5, characterized in that: The real-time stability level includes a high stability level and a low stability level. The analysis methods of the high stability level and the low stability level include: Compare the effective transaction rate with the standard transaction rate. When the effective transaction rate is greater than the standard transaction rate, record the effective transaction rate as a valid feature. Compare the effective online quantity with the standard online quantity. When the effective online quantity is greater than the standard online quantity, the effective online quantity is recorded as a valid feature. The number of valid features is counted. When the number of valid features is 2, the real-time stability level is a high stability level; when the number of valid features is 0 or 1, the real-time stability level is a low stability level; The determination methods for whether to enter the environmental analysis mode include: When the real-time stability level is a high stability level, it is determined to enter the environment analysis mode; When the real-time stability level is a low stability level, it is determined not to enter the environment analysis mode.

7. The power spot market environment analysis system for virtual power plant management according to claim 6, characterized in that: Methods for obtaining the difference between electricity supply and demand include: The power generation in the power spot market in E analysis intervals is queried through the power database to obtain E original power generation; The rated demand that meets the basic operating status of the power spot market is queried through the power trading center, and the E original power generation is subtracted from the rated demand one by one to obtain the E total power generation; The total demand of the electricity spot market in E analysis intervals is queried through the electricity database, and the E total power generation is subtracted from the corresponding E total demand one by one to obtain E electricity supply and demand difference values; The expression of the difference between electricity supply and demand is: GX cze =FD yse -XQ ed -XQ zle ; In the formula, GX cze is the difference between electricity supply and demand in the e-th analysis interval, e=1,2...E,FD yse is the original power generation in the e-th analysis interval, XQ ed is the rated demand, XQ zle is the total demand in the e-th analysis interval.

8. The power spot market environment analysis system for virtual power plant management according to claim 7, characterized in that: The methods for obtaining the electricity price fluctuation value include: Based on the preset trading duration, mark F non-adjacent trading moments in E analysis intervals; Through the power trading center, the real-time electricity prices of the power spot market at F trading moments in E analysis intervals are queried one by one to obtain F unit electricity prices; Mark the maximum and minimum unit electricity prices of the E analysis intervals one by one, and obtain E electricity price fluctuation values ​​by subtracting the maximum and minimum unit electricity prices; The expression of electricity price fluctuation value is: DJ bde =DJ zde -DJ zxe ; In the formula, DJ bde is the electricity price fluctuation value of the e-th analysis interval, DJ zde is the maximum value of the unit electricity price in the e-th analysis interval, DJ zxe is the minimum unit electricity price in the e-th analysis interval.

9. The power spot market environment analysis system for virtual power plant management according to claim 8, characterized in that: The training method of the supply and demand status prediction model includes: Collect multiple sets of environmental summary data and supply and demand status values ​​corresponding to the environmental summary data in advance; The environmental summary data is converted into multiple feature vectors using the sliding window method. The supply and demand state values ​​are converted into labels corresponding to the environmental summary data according to the sliding step size. The balanced state is converted to 0, and the unbalanced state is converted to 1. One feature vector corresponds to one label and constitutes a set of training data. Multiple sets of training data constitute a training set. The environmental summary data are arranged in the order of collection time, and the prediction time step size Q, sliding step size W, and sliding window length Y are preset. The feature vector is used as the input of the model, the operating status value of the next analysis interval after the predicted time step Q is used as the output, the subsequent operating status value of each training set is used as the prediction target, and the model is trained with the minimized sum of prediction errors as the training target to generate a supply and demand status prediction model that predicts the supply and demand status value of the next analysis interval based on the environmental summary data of the previous analysis interval.

10. The power spot market environment analysis system for virtual power plant management according to claim 9, characterized in that: Methods for identifying management data include: Compare the power supply and demand difference with the supply and demand difference safety value. When the power supply and demand difference is greater than the supply and demand difference safety value, record the power supply and demand difference as management data. Compare the electricity price fluctuation value with the fluctuation value safety value. When the electricity price fluctuation value is greater than the fluctuation value safety value, record the electricity price fluctuation value as management data. Compare the market liquidity rate with the liquidity rate safety value. When the market liquidity rate is less than the liquidity rate safety value, record the market liquidity rate as management data. Methods for providing management advice to effective participants include: When the management data is the difference between electricity supply and demand, it is recommended to reduce the power generation of A effective power generation enterprises; When the management data is the electricity price fluctuation value, it is recommended to increase the power generation of A effective power generation enterprises; When the management data is market liquidity, it is recommended to increase the market subsidy amount for A effective power generation enterprise or B effective electricity users.

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