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

Abstract

This invention relates to the field of operation and management technology, and discloses an electricity spot market environment analysis system for virtual power plant management. The system includes screening effective participants from the original participants, analyzing the real-time stability level of the electricity spot market, collecting aggregated environmental data of the electricity spot market within the analysis period, predicting the supply and demand status value for the next analysis period, identifying management data from the aggregated environmental data, and providing management suggestions to effective participants in the virtual power plant. Compared with existing technologies, this invention raises the threshold for electricity spot market environment analysis, and can predict in advance whether the future supply and demand status of the electricity spot market is balanced. Based on the prediction results, it provides management suggestions to the power suppliers and consumers in the virtual power plant, thereby avoiding the negative phenomenon of imbalance in the electricity spot market environment and solving the lag caused by current real-time analysis and judgment methods of the electricity spot market.

Description

Technical Field

[0001] This invention relates to the field of operation management technology, and more specifically, to a power spot market environment analysis system for virtual power plant management. Background Technology

[0002] The electricity spot market refers to the market where qualified operators conduct day-ahead, intraday, and real-time electricity energy transactions. By observing and understanding relevant data on changes in the electricity spot market, the stability of the trading environment can be analyzed and evaluated. Based on the results of the analysis and evaluation, the participants in virtual power plants can be managed in a timely and effective manner, ultimately improving the efficiency of electricity resource transactions.

[0003] Referring to patent application CN116720885A, a distributed virtual power plant control method and system in an electricity spot market environment are disclosed, including: S1, designating each power generation enterprise participating in the electricity resource trading market as a designated power generation enterprise, and comparing and screening the designated power generation enterprises that are eligible to enter the market based on the information of each designated power generation enterprise, and designating them as licensed power generation enterprises; S2, obtaining the declaration data of each licensed power generation enterprise and the demand data of each electricity user on the trading day, analyzing the day-ahead electricity resource supply and demand balance index of the electricity resource trading market, and then formulating the day-ahead unit electricity price of the electricity resource trading market; S3, obtaining the real-time supply data of each licensed power generation enterprise and the real-time demand data of each electricity user on the trading day, and then coordinating the electricity resources of the electricity resource trading market; S4, obtaining the integrated supply data of each licensed power generation enterprise and the integrated demand data of each electricity user on the trading day, and analyzing the deviation assessment index of each licensed power generation enterprise and each electricity user; S5, processing the deviation assessment index of each licensed power generation enterprise and each electricity user accordingly;

[0004] When conducting environmental analysis of the existing electricity spot market, the stability of the trading environment can be determined by collecting and analyzing real-time comprehensive transaction data. This allows for the provision of management suggestions for virtual power plants. For example, the aforementioned patent application coordinates the electricity resource trading market by obtaining real-time supply data from licensed power generation companies and real-time demand data from electricity users on the trading day. While this method can accurately analyze the trading environment of the electricity spot market, it cannot predict the future trading environment and therefore cannot anticipate negative trading conditions. Consequently, it cannot coordinate and manage the participants in the virtual power plant in advance, resulting in a lag in the analysis of the electricity spot market environment and reducing the security and rationality of electricity resource trading.

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

[0006] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution: a power spot market environment analysis system for virtual power plant management, applied to a virtual management server, comprising:

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

[0008] The real-time judgment module is used to acquire the real-time environmental characteristics of the electricity spot market, including the effective trading 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.

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

[0010] The prediction and prompting module is used to input the aggregated environmental data into the trained supply and demand state prediction model, predict the supply and demand state value for the next analysis interval, including the balanced state and the unbalanced state, and determine whether to issue an environmental early warning prompt.

[0011] The management recommendations module is used to identify management data from the environmental aggregate data and, based on the management data, provide management recommendations to the effective participants in the virtual power plant.

[0012] Furthermore, participating attributes include persistent attributes, phase attributes, and temporary attributes. The methods for identifying persistent attributes, phase attributes, and temporary attributes include:

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

[0014] Natural language processing technology is used to identify the annotation values ​​in the attribute annotation box and to separate the numerical part of the annotation value.

[0015] The attribute note box with the number part being 11 is designated as a permanent box, and the participation attribute of the original participant corresponding to the permanent box is designated as a permanent attribute.

[0016] The attribute note box with a numerical 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;

[0017] The attribute notes box with a numeric part of 00 is designated as a temporary box, and the participation attribute of the original participant corresponding to the temporary box is designated as a temporary attribute.

[0018] Furthermore, the screening criteria are as follows: the original participants who are within the valid screening period are included in the screening scope;

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

[0020] By querying the power database one by one, all analytical events that participated in the analysis of the power spot market environment in the past time period are retrieved, and the duration value of each analytical event is retrieved one by one by timestamp. The effective duration is obtained by summing all the duration values ​​and averaging them.

[0021] Starting from the current time, count backwards for the duration corresponding to one valid time period to obtain the end time, and record the time period between the start time and the end time as the valid filtering time period;

[0022] Within the effective filtering period, mark the original participants whose participation attributes are long-term and stage-based, and query the identity type of the original participants.

[0023] The original participants whose identity types are power generation and power consumption are respectively recorded as valid power generation enterprises and valid power users, resulting in A valid power generation enterprises and B valid power users.

[0024] Furthermore, methods for obtaining the effective transaction rate include:

[0025] The electricity generated and invested in the electricity spot market by A valid power generation companies within the valid screening period is obtained by querying the power database one by one, and then the total investment value is obtained by summing up the A investment values ​​one by one.

[0026] The power database is used to query all transaction events of B valid electricity users within the valid screening period, and the transaction electricity in each transaction event is marked to obtain B transaction electricity. The total transaction value is obtained by summing up the B transaction electricity.

[0027] The effective transaction rate is obtained by comparing the total transaction value with the total investment value.

[0028] The expression for the effective trading rate is:

[0029]

[0030] In the formula, YX jyFor effective transaction rate, JY zz TR represents the total transaction value. zz Total input value.

[0031] Furthermore, methods for obtaining effective online traffic include:

[0032] Within the effective screening period, C non-adjacent sampling times are randomly marked, and the real-time status of effective power generation enterprises and effective power users at the C sampling times is queried one by one through the online management system.

[0033] The sampling time when both the effective power generation enterprise and the effective power user are in the online state in real time is recorded as the effective time, and D effective times are obtained;

[0034] The number of valid power generation enterprises and the number of valid electricity users are counted at each of the D valid time points. The effective online quantity is obtained by adding the number of valid power generation enterprises and the number of valid electricity users together.

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

[0036] The effective trading rate is compared with the standard trading rate. When the effective trading rate is greater than the standard trading rate, the effective trading rate is recorded as an effective feature.

[0037] The effective online quantity is compared with the standard online quantity. When the effective online quantity is greater than the standard online quantity, the effective online quantity is recorded as an effective feature.

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

[0039] The methods for determining whether to enter environmental analysis mode include:

[0040] When the real-time stability level is high, the system will enter environmental analysis mode.

[0041] When the real-time stability level is low, it is determined not to enter the environment analysis mode.

[0042] Furthermore, methods for obtaining the electricity supply-demand gap include:

[0043] By querying the power database, the power generation in the power spot market within E analysis intervals is obtained, resulting in E raw power generation figures.

[0044] The rated demand that meets the basic operating conditions of the electricity spot market is obtained by querying the power trading center, and the total power generation is obtained by subtracting each of the E original power generation from the rated demand.

[0045] The total demand in the electricity spot market within E analysis intervals is obtained by querying the electricity database, and the total electricity generation is subtracted from the corresponding total demand for each of the E intervals to obtain the supply and demand difference of the electricity.

[0046] The expression for the difference between electricity supply and demand is:

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

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

[0049] Furthermore, methods for obtaining electricity price fluctuation values ​​include:

[0050] Based on the preset transaction duration, mark F non-adjacent transaction moments within each of the E analysis intervals;

[0051] By querying the power trading center one by one the real-time electricity prices of the spot market at F trading times within E analysis intervals, F unit electricity prices are obtained.

[0052] Mark the maximum and minimum unit electricity price for each of the E analysis intervals, and obtain the E electricity price fluctuation values ​​by subtracting the maximum and minimum unit electricity prices.

[0053] The expression for electricity price fluctuation is:

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

[0055] In the formula, DJ bde Let DJ be the electricity price fluctuation value for the e-th analysis interval. zde DJ represents the maximum unit electricity price in the e-th analysis interval. zxe This represents the minimum unit electricity price for the e-th analysis interval.

[0056] Furthermore, training methods for supply and demand state prediction models include:

[0057] Multiple sets of environmental summary data and corresponding supply and demand status values ​​are collected in advance.

[0058] The environmental summary data is transformed 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. Each feature vector corresponds to one label and forms a set of training data. Multiple sets of training data form a training set. The environmental summary data is arranged in the order of collection time. The prediction time step size Q, the sliding step size W, and the sliding window length Y are preset.

[0059] The model uses the feature vector as input, predicts the running state value of the next analysis interval after time step Q as output, and uses the subsequent running state value of each training set as the prediction target. The model is trained with the goal of minimizing the sum of prediction errors, and generates a supply and demand state prediction model that predicts the supply and demand state value of the next analysis interval based on the environmental summary data of the previous analysis interval.

[0060] Furthermore, methods for identifying management data include:

[0061] The difference between electricity supply and demand is compared with the safe value of the difference between supply and demand. When the difference between electricity supply and demand is greater than the safe value of the difference between supply and demand, the difference between electricity supply and demand is recorded as management data.

[0062] The electricity price fluctuation value is compared with the safe fluctuation value. When the electricity price fluctuation value is greater than the safe fluctuation value, the electricity price fluctuation value is recorded as management data.

[0063] The market liquidity ratio is compared with the safe liquidity value. When the market liquidity ratio is less than the safe liquidity value, the market liquidity ratio is recorded as management data.

[0064] Methods for providing management advice to effective stakeholders include:

[0065] When the management data represents the difference between electricity supply and demand, it is recommended to reduce the power generation of A effective power generation companies;

[0066] When the management data is the value of electricity price fluctuation, 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 companies or B effective electricity users.

[0068] The technical effects and advantages of the electricity spot market environment analysis system for virtual power plant management proposed in this invention are as follows:

[0069] This invention identifies the participation attributes of each original participant in a virtual power plant and, based on screening criteria, selects effective participants from among them. It then obtains real-time environmental characteristics of the electricity spot market, analyzes its real-time stability level, and determines whether to enter environmental analysis mode. The invention divides the set evaluation and analysis period into continuous analysis intervals and collects summary environmental data of the electricity spot market within each interval. This summary data is input into a trained supply and demand prediction model to predict the supply and demand status values ​​for the next analysis interval and determine whether to issue an environmental warning. Furthermore, it identifies management data from the summary environmental data and provides management suggestions to effective participants in the virtual power plant based on this data. Compared to existing technologies, this invention, through the collection and analysis of real-time environmental characteristics, can identify the real-time stability level of the electricity spot market. This ensures that the electricity spot market entering environmental analysis mode has a certain degree of stability, avoiding the useless operation of environmental analysis in an unstable electricity spot market. This raises the threshold for environmental analysis and reduces its burden. Simultaneously, combined with supply and demand forecasting models, it can predict the future supply and demand balance of the electricity spot market based on aggregated environmental data. It allows for early detection of imbalances before they occur and provides management suggestions to power suppliers and consumers in virtual power plants based on the forecast results. This avoids the negative effects of imbalances in the electricity spot market environment and solves the lag caused by current real-time analysis and judgment methods in the electricity spot market. It ensures that virtual power plants can manage in advance according to real-time changes in the electricity spot market, guaranteeing the security and rationality of electricity resource transactions. Attached Figure Description

[0070] Figure 1 This is a schematic diagram of the architecture of an electricity spot market environment analysis system for virtual power plant management provided in Embodiment 1 of the present invention;

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

[0072] Figure 3 This is a flowchart illustrating a method for analyzing the electricity spot market environment for virtual power plant management, provided in Embodiment 2 of the present invention.

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

[0074] Figure 5 This is a schematic diagram of the structure of a computer-readable storage medium provided in Embodiment 4 of the present invention. Detailed Implementation

[0075] 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, and 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.

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

[0077] The participant screening module identifies the participation attributes of the original participants in the virtual power plant one by one, and selects valid participants from the original participants based on the screening criteria. Valid participants include valid power generation companies and valid electricity users.

[0078] Original participants refer to any participant that can play any role in the electricity spot market and can form a virtual power plant. They serve as the original objects for the management and control of the virtual power plant, enabling them to represent the power supply and consumption parties of the virtual power plant. The participation attribute is used to represent the specific type of role played by the original participants in the electricity spot market, thereby providing a basis for subsequent analysis of the electricity spot market and management of the virtual power plant.

[0079] Participation attributes include long-term attributes, phase attributes, and temporary attributes; long-term attributes refer to the original participants' long-term role in the electricity spot market, phase attributes refer to the original participants' phased role in the electricity spot market, and temporary attributes refer to the original participants' temporary role in the electricity spot market.

[0080] Methods for identifying persistent, phased, and temporary attributes include:

[0081] All original participants in the virtual power plant are marked one by one, and the attribute notes of each original participant are retrieved one by one from the power database. The attribute notes are used to represent the overall type of role played by the original participants in the electricity spot market, and can integrate and summarize the relevant data of the role played by the original participants.

[0082] Natural language processing technology is used to identify the notes in the attribute notes box and to separate the numerical part of the notes. The notes are the smallest unit that makes up the attribute notes box and also a specific representation of the role played by the original participants in the electricity spot market, thus making it easier to distinguish the type of role played by the original participants.

[0083] The attribute note box with the number part being 11 is designated as a permanent box, and the participation attribute of the original participant corresponding to the permanent box is designated as a permanent attribute.

[0084] The attribute note box with a numerical 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;

[0085] The attribute notes box with a numeric part of 00 is designated as a temporary box, and the participation attribute of the original participant corresponding to the temporary box is designated as a temporary attribute.

[0086] Once the participation attributes of the original participants are obtained, the effective participants can be selected from the original participants based on the different participation attributes. However, when selecting effective participants from the original participants, it is necessary to screen the different types of original participants that constitute the virtual power plant separately to ensure that the selected effective participants can meet the needs of the electricity spot market environment analysis. Therefore, it is necessary to use screening criteria for screening.

[0087] The screening criterion is to select the original participants who are within the effective screening period as the screening scope; this ensures that the original participants participating in the screening of effective participants are within a reasonable and accurate time range, thereby ensuring that the selected effective participants are all within the correct and reasonable range.

[0088] Effective participants include effective power generation companies and effective electricity users;

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

[0090] By querying the power database one by one, all analytical events that participated in the analysis of the power spot market environment in the past time period are retrieved, and the duration value of each analytical event is retrieved one by one by timestamp. The effective duration is obtained by summing all the duration values ​​and averaging them.

[0091] Starting from the current time, count backwards for the duration corresponding to one valid time period to obtain the end time, and record the time period between the start time and the end time as the valid filtering time period;

[0092] Within the effective filtering period, the original participants with the attributes of long-term and phase are marked one by one, and the identity type of the original participants is queried. The identity type is used to specifically represent the application terminal represented by the original participant. Specifically, the identity type includes power generation, power consumption, and dispatching, etc.

[0093] The original participants whose identity types are power generation and power consumption are respectively recorded as valid power generation enterprises and valid power users, resulting in A valid power generation enterprises and B valid power users.

[0094] The real-time judgment module acquires 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] Real-time environmental characteristics are used to represent the real-time environmental data of effective power generation companies and effective electricity users participating in the electricity spot market. They can be used to distinguish the stability level of the electricity spot market at the current moment and serve as the basis for whether the electricity spot market needs to conduct further environmental analysis.

[0096] Real-time environmental characteristics include effective transaction rate and effective online volume;

[0097] The effective trading rate refers to the percentage of electricity generated and put into the electricity spot market by an effective power generation enterprise A within the effective screening period that is successfully traded. It can be used to represent the actual situation of electricity trading in the electricity spot market.

[0098] Methods for obtaining the effective transaction rate include:

[0099] The electricity generated and invested in the electricity spot market by A valid power generation companies within the valid screening period is obtained by querying the power database one by one, and then the total investment value is obtained by summing up the A investment values ​​one by one.

[0100] The system retrieves all transaction events of B valid electricity users within the valid screening period by querying the power database one by one, and marks the transaction volume of each transaction event to obtain B transaction volumes. The total transaction value is obtained by summing up the B transaction volumes. A transaction event is used to summarize the time of electricity transactions between valid power generation companies and valid electricity users in the electricity spot market, and can summarize the data involved in electricity transactions in a unified manner.

[0101] The effective transaction rate is obtained by comparing the total transaction value with the total investment value.

[0102] The expression for the effective trading rate is:

[0103]

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

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

[0106] Methods for obtaining effective online traffic include:

[0107] Within the effective screening period, C non-adjacent sampling times are randomly marked, and the real-time status of effective power generation enterprises and effective power users at each of the C sampling times is queried one by one through the online management system. The real-time status is used to indicate whether the effective power generation enterprises and effective power users are currently online, thereby distinguishing online and offline participants. Specifically, the real-time status includes online status and offline status.

[0108] The sampling time when both the effective power generation enterprise and the effective power user are in the online state in real time is recorded as the effective time, and D effective times are obtained;

[0109] The number of valid power generation enterprises and the number of valid electricity users are counted at each of the D valid time points. The effective online quantity is obtained by adding the number of valid power generation enterprises and the number of valid electricity users together.

[0110] Once the real-time environmental characteristics are obtained, they need to be identified and judged. The results of the identification and judgment of the real-time environmental characteristics are used to conduct a preliminary analysis of the real-time stability of the electricity spot market and serve as the basis for determining whether to enter the environmental analysis mode.

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

[0112] Analysis methods for high stability and low stability levels include:

[0113] Compare the effective trading rate with the standard trading rate; the standard trading rate is the minimum effective trading rate under normal conditions in the electricity spot market, and can be used as the basis for determining whether the effective trading rate is an effective characteristic.

[0114] When the effective trading rate is greater than the standard trading rate, it means that the effective trading rate of the electricity spot market has exceeded the minimum value under normal circumstances, and the effective trading rate is recorded as an effective characteristic.

[0115] Compare the effective online quantity with the standard online quantity; the standard online quantity is the minimum effective online quantity under normal conditions in the electricity spot market, which can be used as the basis for determining whether the effective online quantity is an effective characteristic;

[0116] When the effective online quantity is greater than the standard online quantity, it indicates that the effective online quantity in the electricity spot market has exceeded the minimum value under normal circumstances, and the effective online quantity is recorded as an effective characteristic.

[0117] The number of valid features is counted. When the number of valid features is 2, the stability of the electricity spot market is relatively high, and the real-time stability level is high stability level.

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

[0119] Once the real-time stability level is obtained, the specific level can be used to analyze and determine whether the electricity spot market has entered the environmental analysis mode.

[0120] The methods for determining whether to enter environmental analysis mode include:

[0121] When the real-time stability level is high, the electricity spot market can meet the needs of subsequent environmental analysis, and then it is determined to enter the environmental analysis mode.

[0122] When the real-time stability level is low, the electricity spot market does not meet the requirements for subsequent environmental analysis, so it is determined that the environmental analysis mode will not be entered.

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

[0124] The assessment and analysis period refers to the duration of data collection that can affect the environmental analysis results of the electricity spot market. It serves as a limit on the collection duration of environmental summary data. Due to the large duration of the assessment and analysis period, the amount of data included in the period is excessive. In order to subdivide the excessive data and shorten the collection time of each data point, the assessment and analysis period needs to be divided into consecutive analysis intervals of equal duration. The assessment and analysis period is set based on the average duration of each price or trading volume increase cycle in the electricity spot market. For example, when the assessment and analysis period is 1 week, the analysis interval is 1 day; when the assessment and analysis period is 1 day, the analysis interval is 1 hour.

[0125] Environmental aggregate data refers to the electricity supply-demand gap, electricity price fluctuations, and market liquidity.

[0126] The electricity supply-demand difference refers to the difference between the total power generation and the total demand in the electricity spot market within the analysis period, thus representing the supply and demand relationship in the electricity spot market. The larger the electricity supply-demand difference, the larger the difference between the total power generation and the total demand in the electricity spot market, and the worse the environmental stability of the electricity spot market.

[0127] Methods for obtaining the difference between electricity supply and demand include:

[0128] By querying the power database, the power generation in the power spot market within E analysis intervals is obtained, resulting in E raw power generation figures.

[0129] The rated demand that meets the basic operating conditions of the electricity spot market is obtained by querying the power trading center, and the total power generation is obtained by subtracting each of the E original power generation from the rated demand. The basic operating conditions refer to the most basic state when there is no market transaction in the electricity spot market. At this time, the electricity spot market does not have any excess electricity other than meeting its own basic demand. Therefore, the rated demand is the minimum amount of electricity to meet its own basic demand.

[0130] The total demand in the electricity spot market within E analysis intervals is obtained by querying the electricity database, and the total electricity generation is subtracted from the corresponding total demand for each of the E intervals to obtain the supply and demand difference of the electricity.

[0131] The expression for the difference between electricity supply and demand is:

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

[0133] In the formula, GX cze Let FD be the electricity supply and demand difference for the e-th analysis interval, where e = 1, 2...E. yse Let XQ be the raw power generation for the e-th analysis interval. ed XQ is the rated demand. zle Let be the total demand for the e-th analysis interval.

[0134] Electricity price fluctuation refers to the magnitude of the rise and fall of electricity prices in the electricity spot market within the analysis period, thus representing the changes in electricity prices in the electricity spot market. The larger the electricity price fluctuation value, the greater the magnitude of the rise and fall of electricity prices in the electricity spot market, and the worse the environmental stability of the electricity spot market.

[0135] Methods for obtaining electricity price fluctuation values ​​include:

[0136] Using a preset trading duration as the standard, F non-adjacent trading moments are marked within E analysis intervals. The preset trading duration refers to the maximum duration that can cause a complete fluctuation in the unit electricity price in the electricity spot market, thereby ensuring that the data at two adjacent trading moments are sufficient to cause a complete fluctuation. This ensures that the data at each trading moment has a basis for change and also ensures the independence of the data at each trading moment.

[0137] By querying the power trading center one by one the real-time electricity prices of the spot market at F trading times within E analysis intervals, F unit electricity prices are obtained.

[0138] Mark the maximum and minimum unit electricity price for each of the E analysis intervals, and obtain the E electricity price fluctuation values ​​by subtracting the maximum and minimum unit electricity prices.

[0139] The expression for electricity price fluctuation is:

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

[0141] In the formula, DJ bde Let DJ be the electricity price fluctuation value for the e-th analysis interval. zde DJ represents the maximum unit electricity price in the e-th analysis interval. zxe This represents the minimum unit electricity price for the e-th analysis interval.

[0142] Market liquidity ratio refers to the ratio between the number of electricity transactions completed in the electricity spot market within the analysis period and the total number of inquiries. It represents the liquidity performance of the electricity spot market. The higher the market liquidity ratio, the more electricity transactions are completed in the electricity spot market, and the better the stability of the electricity spot market environment. The market liquidity ratio can be obtained by querying the electricity trading center.

[0143] The prediction and alert module inputs the environmental aggregated data into the trained supply and demand status prediction model, predicts the supply and demand status value for the next analysis interval, and determines whether to issue an environmental early warning alert.

[0144] Once the environmental summary data of the electricity spot market is obtained, it can be input into the supply and demand state prediction model to predict the supply and demand state value for the next analysis period, and the supply and demand state value can be used to represent the supply and demand stability performance of the electricity spot market.

[0145] Supply and demand status values ​​are used to represent the stability of supply and demand in the electricity spot market. Specifically, supply and demand status values ​​include balanced and unbalanced states. Supply and demand status values ​​are obtained by collecting a large amount of historical data on the difference between electricity supply and demand, electricity price fluctuations, and market liquidity under balanced and unbalanced states.

[0146] Training methods for supply and demand prediction models include:

[0147] Multiple sets of environmental summary data and corresponding supply and demand status values ​​are collected in advance.

[0148] The environmental summary data is transformed 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. For example, the balanced state is converted into 0 and the unbalanced state is converted into 1. Each feature vector corresponds to a label and forms a set of training data. Multiple sets of training data constitute a training set. The environmental summary data is arranged in the order of collection time. The prediction time step Q, the sliding step size W, and the sliding window length Y are preset.

[0149] The model uses the feature vector as input, predicts the running state value of the next analysis interval after time step Q as output, and uses the subsequent running state value of each training set as the prediction target. The model is trained with the goal of minimizing the sum of prediction errors, and generates a supply and demand state prediction model that predicts the supply and demand state value of the next analysis interval based on the environmental summary data of the previous analysis interval.

[0150] For example, the supply and demand state prediction model can use either CNN or AlexNet;

[0151] The formula for calculating prediction error is:

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

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

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

[0155] By importing the environmental aggregated data into the supply and demand status identification model, the supply and demand status value of the next analysis interval can be predicted. Based on the predicted supply and demand status value, it can be determined whether to issue an environmental early warning. This allows for the early warning information to be issued in advance when there is a supply and demand imbalance in the electricity spot market.

[0156] The methods for determining whether to issue an environmental early warning include:

[0157] When the output of the supply and demand state prediction model is 0, the supply and demand state value of the next analysis interval of the electricity spot market is in equilibrium, and it is determined that no environmental warning will be issued.

[0158] When the output of the supply and demand state prediction model is 1, the supply and demand state value of the next analysis interval of the electricity spot market is in an unbalanced state, and an environmental warning is issued.

[0159] The management recommendation module identifies management data from the environmental aggregate data and provides management recommendations to the effective participants in the virtual power plant based on the management data.

[0160] Management data refers to the specific environmental aggregate data that causes the electricity spot market to issue environmental warnings, and serves as the basis for subsequent management of effective participants in virtual power plants to ensure that the electricity spot market can maintain normal environmental conditions;

[0161] Methods for identifying management data include:

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

[0163] When the power supply and demand difference exceeds the safe value of the supply and demand difference, the power supply and demand difference in the spot market exceeds the maximum value of the power supply and demand difference under the condition of not issuing environmental warnings. In this case, the power supply and demand difference is recorded as management data.

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

[0165] When the electricity price fluctuation exceeds the safe fluctuation value, the electricity price fluctuation in the spot market exceeds the maximum value of the electricity price fluctuation without issuing an environmental warning. In this case, the electricity price fluctuation is recorded as management data.

[0166] Compare the market liquidity ratio with the safe liquidity value; the safe liquidity value refers to the minimum market liquidity ratio in the electricity spot market without issuing environmental warnings, which can be used as the basis for judging whether the market liquidity ratio is management data.

[0167] When the market liquidity ratio is less than the safe liquidity value, and the market liquidity ratio of the electricity spot market has not reached the minimum market liquidity ratio under the condition of not issuing an environmental warning, the market liquidity ratio is recorded as management data.

[0168] Once the management data is identified, it is necessary to manage and control the effective participants in the virtual power plant according to the different management data, and provide it to the virtual management server as the basis for managing and optimizing the virtual power plant. Then, based on the real-time situation of the effective participants in the electricity spot market, the virtual power plant can be managed in a targeted manner, and finally management suggestions can be provided.

[0169] Methods for providing management advice to effective stakeholders include:

[0170] When the management data is the difference between electricity supply and demand, the total power generation in the electricity spot market exceeds the maximum value under normal circumstances. This indicates that the power generation of the effective power generation enterprises in the virtual power plant greatly exceeds the electricity consumption of the effective electricity users. Therefore, 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, the unit electricity price in the electricity spot market exceeds the maximum value under normal circumstances. This indicates that the power generation of the effective power generation enterprises in the virtual power plant is lower than the electricity consumption of the effective electricity users. It is recommended to increase the power generation of A effective power generation enterprises.

[0172] When the management data is the market liquidity rate, the number of transactions in the electricity spot market is lower than the minimum under normal circumstances, indicating that the success rate of transactions for effective electricity users in virtual power plants is low. Therefore, it is recommended to increase the market subsidy amount for A effective power generation companies 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 selecting effective participants based on screening criteria, the real-time environmental characteristics of the electricity spot market are obtained, the real-time stability level of the electricity spot market is analyzed, and it is determined whether to enter the environmental analysis mode. The set evaluation and analysis period is divided into continuous analysis intervals, and environmental summary data of the electricity spot market in the analysis interval is collected. The environmental summary data is input into a trained supply and demand state prediction model to predict the supply and demand state value of the next analysis interval, and it is determined whether to issue an environmental warning. Management data is identified from the environmental summary data, and management suggestions are provided to the effective participants in the virtual power plant based on the management data. Compared with the prior art, by collecting and analyzing real-time environmental characteristics, the real-time stability level of the electricity spot market can be identified. This approach ensures that the electricity spot market entering environmental analysis mode has a certain level of stability, avoiding the useless operation of environmental analysis in an unstable electricity spot market. This raises the threshold for environmental analysis and reduces its burden. Furthermore, combined with supply and demand forecasting models, it can predict the future supply and demand balance of the electricity spot market based on aggregated environmental data. This allows for early detection of imbalances before they occur, and provides management suggestions to power suppliers and consumers in virtual power plants based on the forecast results. This avoids the negative effects of imbalances in the electricity spot market environment and solves the lag caused by current real-time analysis and judgment methods in the electricity spot market. It ensures that virtual power plants can manage in advance based on real-time changes in the electricity spot market, guaranteeing the security and rationality of electricity resource transactions.

[0174] Example 2: Please refer to Figure 3As shown, the parts not described in detail in this embodiment are described in Embodiment 1. This embodiment provides a method for analyzing the electricity spot market environment for virtual power plant management. It is applied to a virtual management server and implemented based on an electricity spot market environment analysis system 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 select the effective participants from the original participants based on the screening criteria. The effective participants include effective power generation companies and effective electricity users.

[0176] S2: Obtain real-time environmental characteristics of the electricity spot market, including effective transaction rate and 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 environmental analysis mode, the set assessment and analysis period will be divided into continuous analysis intervals, and environmental summary data of the electricity spot market during the analysis intervals will be collected. The environmental summary data includes the electricity supply and demand difference, electricity price fluctuation value, and market liquidity rate.

[0178] S4: Input the environmental aggregated data into the trained supply and demand state prediction model to predict the supply and demand state values ​​for the next analysis interval. The supply and demand state values ​​include the balanced state and the unbalanced state, and determine whether to issue an environmental early warning.

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

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

[0181] The memory stores computer programs that can be called by a processor;

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

[0183] Since the electronic device described in this embodiment is the same electronic device used in implementing the electricity spot market environment analysis method for virtual power plant management in Embodiment 2 of this application, those skilled in the art can understand the specific implementation methods and various variations of the electronic device in this embodiment based on the electricity spot market environment analysis method for virtual power plant management described in this application. Therefore, how the electronic device implements the method in this application embodiment will not be described in detail here. Any electronic device used by those skilled in the art in implementing the electricity spot market environment analysis method for virtual power plant management in this application embodiment falls within the scope of protection of this application.

[0184] Example 4: Please refer to Figure 5 As shown, this embodiment discloses a computer-readable storage medium having an erasable and rewritable computer program stored thereon;

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

[0186] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A 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 to screen out the valid participants from the original participants based on the screening criteria. Valid participants include valid power generation companies and valid electricity users. The real-time judgment module is used to acquire the real-time environmental characteristics of the electricity spot market, including the effective trading 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. The data acquisition module is used to divide the set evaluation and analysis period into continuous analysis intervals and collect environmental summary data of the electricity spot market during the analysis intervals. The environmental summary data includes the electricity supply and demand difference, electricity price fluctuation value and market liquidity rate. The prediction and prompting module is used to input the aggregated environmental data into the trained supply and demand state prediction model, predict the supply and demand state value for the next analysis interval, including the balanced state and the unbalanced state, and determine whether to issue an environmental early warning prompt. The management recommendations module is used to identify management data from the environmental aggregate data and, based on the management data, provide management recommendations to the effective participants in the virtual power plant.

2. The electricity spot market environment analysis system for virtual power plant management according to claim 1, characterized in that, Participation attributes include persistent attributes, phase attributes, and temporary attributes. Methods for identifying persistent, phase, and temporary attributes include: Mark all the original participants in the virtual power plant one by one, and query the attribute notes of each original participant one by one through the power database; Natural language processing technology is used to identify the annotation values ​​in the attribute annotation box and to separate the numerical part of the annotation value. The attribute note box with the number part being 11 is designated as a permanent box, and the participation attribute of the original participant corresponding to the permanent box is designated as a permanent attribute. The attribute note box with a numerical 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 notes box with a numeric part of 00 is designated as a temporary box, and the participation attribute of the original participant corresponding to the temporary box is designated as a temporary attribute.

3. The electricity spot market environment analysis system for virtual power plant management according to claim 2, characterized in that, The screening criteria are: the original participants who are within the valid screening period are included in the screening scope; The screening methods for effective power generation companies and effective electricity users include: By querying the power database one by one, all analytical events that participated in the analysis of the power spot market environment in the past time period are retrieved, and the duration value of each analytical event is retrieved one by one by timestamp. The effective duration is obtained by summing all the duration values ​​and averaging them. Starting from the current time, count backwards for the duration corresponding to one valid time period to obtain the end time, and record the time period between the start time and the end time as the valid filtering time period; Within the effective filtering period, mark the original participants whose participation attributes are long-term and stage-based, and query the identity type of the original participants. The original participants whose identity types are power generation and power consumption are respectively recorded as valid power generation enterprises and valid power users, resulting in A valid power generation enterprises and B valid power users.

4. The electricity spot market environment analysis system for virtual power plant management according to claim 3, characterized in that, Methods for obtaining the effective transaction rate include: The electricity generated and invested in the electricity spot market by A valid power generation companies within the valid screening period is obtained by querying the power database one by one, and then the total investment value is obtained by summing up the A investment values ​​one by one. The power database is used to query all transaction events of B valid electricity users within the valid screening period, and the transaction electricity in each transaction event is marked to obtain B transaction electricity. The total transaction value is obtained by summing up the B transaction electricity. The effective transaction rate is obtained by comparing the total transaction value with the total investment value. The expression for the effective trading rate is: In the formula, YX jy For effective transaction rate, JY zz TR represents the total transaction value. zz Total input value.

5. The electricity spot market environment analysis system for virtual power plant management according to claim 4, characterized in that, Methods for obtaining effective online traffic include: Within the effective screening period, C non-adjacent sampling times are randomly marked, and the real-time status of effective power generation enterprises and effective power users at the C sampling times is queried one by one through the online management system. The sampling time when both the effective power generation enterprise and the effective power user are in the online state in real time is recorded as the effective time, and D effective times are obtained; The number of valid power generation enterprises and the number of valid electricity users are counted at each of the D valid time points. The effective online quantity is obtained by adding the number of valid power generation enterprises and the number of valid electricity users together.

6. The electricity spot market environment analysis system for virtual power plant management according to claim 5, characterized in that, Real-time stability levels include high stability and low stability. Analysis methods for high and low stability levels include: The effective trading rate is compared with the standard trading rate. When the effective trading rate is greater than the standard trading rate, the effective trading rate is recorded as an effective feature. The effective online quantity is compared with the standard online quantity. When the effective online quantity is greater than the standard online quantity, the effective online quantity is recorded as an effective feature. The number of effective features is counted. When the number of effective features is 2, the real-time stability level is high; when the number of effective features is 0 or 1, the real-time stability level is low. The methods for determining whether to enter environmental analysis mode include: When the real-time stability level is high, the system will enter environmental analysis mode. When the real-time stability level is low, it is determined not to enter the environment analysis mode.

7. The electricity 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: By querying the power database, the power generation in the power spot market within E analysis intervals is obtained, resulting in E raw power generation figures. The rated demand that meets the basic operating conditions of the electricity spot market is obtained by querying the power trading center, and the total power generation is obtained by subtracting each of the E original power generation from the rated demand. The total demand in the electricity spot market within E analysis intervals is obtained by querying the electricity database, and the total electricity generation is subtracted from the corresponding total demand for each of the E intervals to obtain the supply and demand difference of the electricity. The expression for the difference between electricity supply and demand is: GX cze =FD yse -XQ ed -XQ zle ; In the formula, GX cze Let FD be the electricity supply and demand difference for the e-th analysis interval, where e = 1, 2...E. yse Let XQ be the raw power generation for the e-th analysis interval. ed XQ is the rated demand. zle Let be the total demand for the e-th analysis interval.

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

9. A power spot market environment analysis system for virtual power plant management according to claim 8, characterized in that, Training methods for supply and demand prediction models include: Multiple sets of environmental summary data and corresponding supply and demand status values ​​are collected in advance. The environmental summary data is transformed 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. Each feature vector corresponds to one label and forms a set of training data. Multiple sets of training data form a training set. The environmental summary data is arranged in the order of collection time. The prediction time step size Q, the sliding step size W, and the sliding window length Y are preset. The model uses the feature vector as input, predicts the running state value of the next analysis interval after time step Q as output, and uses the subsequent running state value of each training set as the prediction target. The model is trained with the goal of minimizing the sum of prediction errors, and generates a supply and demand state prediction model that predicts the supply and demand state value of the next analysis interval based on the environmental summary data of the previous analysis interval.

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

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