Demand side load optimization scheduling method and system based on electricity consumption increment of special transformer user

Abstract

The invention provides a demand side load optimization scheduling method and system based on special transformer user power consumption increment, and relates to the technical field of load management, and the method comprises the steps: collecting a real-time power consumption data set through power information monitoring modules installed at a plurality of special transformer user sides; transmitting the real-time power consumption data set to a demand side intelligent terminal through a remote communication link, and comparing the real-time power consumption data set with the baseline load curve; performing trend prediction based on the plurality of real-time increment sets in combination with an external influence factor, calling an internally pre-stored adjustable load file, and generating a special transformer increment graph; extracting negative increment users and aggregating the negative increment users into a virtual power plant resource pool; and generating a scheduling instruction set according to the virtual power plant resource pool in combination with a power grid optimization target, and performing load scheduling. According to the method and the device, the technical problem of inaccurate demand side load scheduling caused by rough sensing granularity and lack of cooperative regulation and control capability in the prior art is solved, and flexible and accurate scheduling is realized by constructing the special variable increment graph.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of load management, and particularly relates to a demand side load optimization scheduling method and system based on power consumption increment of special transformer users. BACKGROUND

[0002] In the prior art, demand side load optimization scheduling usually relies on monitoring and analyzing the absolute value of total load of users, and the perception granularity stays at the total power consumption entry level of special transformer users, forming a black box control model. The existing method cannot effectively identify the running state and dynamic change of specific power consumption equipment in the user, and lacks unified collaborative control ability among multiple special transformer users, so that the load state of each user is considered in isolation, and it is difficult to realize the overall response of demand side resources, resulting in low precision of demand side load scheduling. The scheduling strategy often cannot accurately match the adjustable ability of the user, so that the execution efficiency of peak clipping and valley filling or demand response is significantly reduced, further affecting the power balance and scheduling stability of the power grid side.

[0003] In summary, in the prior art, due to the coarse perception granularity and lack of collaborative control ability, the demand side load scheduling is not accurate, which further affects the efficiency of demand side load scheduling. SUMMARY

[0004] The purpose of the present application is to provide a demand side load optimization scheduling method and system based on power consumption increment of special transformer users, to solve the technical problem that in the prior art, due to the coarse perception granularity and lack of collaborative control ability, the demand side load scheduling is not accurate, which further affects the efficiency of demand side load scheduling.

[0005] In view of the above problems, the present application provides a demand side load optimization scheduling method and system based on power consumption increment of special transformer users.

[0006] In a first aspect, the application provides a demand side load optimization scheduling method based on special transformer user electricity consumption increment, which is realized by a demand side load optimization scheduling system based on special transformer user electricity consumption increment, wherein the demand side load optimization scheduling method based on special transformer user electricity consumption increment comprises: collecting real-time electricity consumption data sets through an electricity information monitoring module installed on the side of a plurality of special transformer users; transmitting the real-time electricity consumption data sets to a demand side intelligent terminal through a long-distance communication link; determining a baseline load curve based on historical data by the demand side intelligent terminal, comparing the real-time electricity consumption data sets with the baseline load curve to obtain a plurality of real-time increment sets, wherein the plurality of real-time increment sets comprise real-time increment sets of the plurality of special transformer users; performing trend prediction based on the plurality of real-time increment sets in combination with external influencing factors, calling an internally pre-stored adjustable load archive, and generating a special transformer increment graph containing a plurality of increment graphs corresponding to the plurality of special transformer users; extracting negative increment users based on the special transformer increment graph and aggregating them into a virtual power plant resource pool; generating a scheduling instruction set in combination with a power grid optimization target according to the virtual power plant resource pool, and issuing it to the corresponding special transformer user side for load scheduling.

[0007] Optionally, the demand side intelligent terminal comprises a local communication interface for collecting local electricity consumption data of the plurality of special transformer user sides, and an isolation circuit is arranged inside the local communication interface, wherein the isolation circuit comprises a signal isolation module, an isolation power supply module, and a bus protection module.

[0008] Optionally, a data request command is sent to the electricity information monitoring module through the long-distance communication link according to a preset period; the electricity information monitoring module encapsulates the real-time electricity consumption data set according to the data request command, sends the encapsulated data frame back to the demand side intelligent terminal through the long-distance communication link for verification and checking, determines the parsed real-time electricity consumption data set, determines the baseline load curve in the baseline load database based on the date type of the parsed real-time electricity consumption data set, selects a baseline load value at the same time for each real-time electricity consumption data of the parsed real-time electricity consumption data set in the baseline load curve, calculates the difference between each parsed real-time electricity consumption data and the baseline load value at the corresponding time to obtain the real-time increment at each time, and adds it to the plurality of real-time increment sets.

[0009] Optionally, the historical power consumption dataset stored in the demand side intelligent terminal is called, preprocessed by cleaning and outlier rejection to obtain a standard power consumption dataset; based on the timestamp of the historical power consumption dataset, a date type label is identified for the standard power consumption dataset; based on the real-time power consumption dataset, the date type of the target day is determined, and a plurality of reference power consumption datasets with consistent date types are screened in the standard power consumption dataset; the plurality of reference power consumption datasets are aligned according to the timestamp, the load value at each time point is extracted and mean value calculation is performed to obtain a plurality of baseline values at a plurality of time points; the plurality of baseline values are connected in chronological order to form a full-day baseline load curve; the full-day baseline load curve is subjected to smoothness check, and the full-day baseline load curve that passes the check is integrated with the corresponding date type label to form a baseline load database, which is pre-stored in the demand side intelligent terminal.

[0010] Optionally, based on the plurality of real-time increment sets, a first real-time increment set corresponding to a first special transformer user is extracted; for the first real-time increment set, short-term load prediction is performed in combination with external influence factors to obtain a first predicted increment value, wherein the external influence factors at least include environmental meteorological factors; based on the user identifier of the first special transformer user, a corresponding first adjustable load profile is called; the first predicted increment value and the first adjustable load profile are matched to determine the adjustable load potential value at each time, and a first increment atlas corresponding to the first special transformer user is obtained; by analogy, based on the plurality of real-time increment sets, a plurality of increment atlases corresponding to a plurality of special transformer users are generated, and the first increment atlas and the plurality of increment atlases are integrated to obtain the special transformer increment atlas.

[0011] Optionally, according to the adjustable load potential values of the plurality of special transformer users in the virtual power plant resource pool, an initial scheduling instruction space is generated; multi-objective optimization is performed in the initial scheduling instruction space to determine the scheduling instruction set, with minimizing the peak load of the power grid and minimizing the total scheduling cost as multi-objective functions.

[0012] Optionally, after load scheduling, the demand side intelligent terminal continuously collects actual load data of the plurality of special transformer users through the long-distance communication link; the state of the power consumption execution equipment on the plurality of special transformer user sides is synchronously monitored; based on the actual load data and the state of the power consumption execution equipment, scheduling effect evaluation is performed, a load scheduling event evaluation report is generated and stored in the demand side intelligent terminal.

[0013] In a second aspect, the application also provides a demand side load optimization scheduling system based on the electricity consumption increment of special transformer users, which is used to execute the method for demand side load optimization scheduling based on the electricity consumption increment of special transformer users as described in the first aspect. The demand side load optimization scheduling system based on the electricity consumption increment of special transformer users comprises: a data acquisition module, which is used to acquire real-time electricity consumption data sets through electricity information monitoring modules installed on the sides of multiple special transformer users; an increment calculation module, which is used to transmit the real-time electricity consumption data sets to a demand side intelligent terminal through a long-distance communication link, and the demand side intelligent terminal determines a baseline load curve based on historical data, compares the real-time electricity consumption data sets with the baseline load curve, and obtains multiple real-time increment sets, wherein the multiple real-time increment sets comprise real-time increment sets of multiple special transformer users; a graph generation module, which is used to perform trend prediction based on the multiple real-time increment sets in combination with external influencing factors, call internally pre-stored adjustable load archives, and generate special transformer increment graphs comprising multiple increment graphs corresponding to the multiple special transformer users; a resource pool establishment module, which is used to extract negative increment users based on the special transformer increment graphs and aggregate them into a virtual power plant resource pool; and a load scheduling optimization module, which is used to generate a scheduling instruction set according to the virtual power plant resource pool in combination with a power grid optimization target, and issue the scheduling instruction set to corresponding special transformer user sides for load scheduling.

[0014] The one or more technical solutions provided in the application have at least the following beneficial effects: The real-time electricity consumption data sets are acquired through electricity information monitoring modules installed on the sides of multiple special transformer users, the real-time electricity consumption data sets are transmitted to a demand side intelligent terminal through a long-distance communication link, the demand side intelligent terminal determines a baseline load curve based on historical data, compares the real-time electricity consumption data sets with the baseline load curve, and obtains multiple real-time increment sets, wherein the multiple real-time increment sets comprise real-time increment sets of multiple special transformer users, trend prediction is performed based on the multiple real-time increment sets in combination with external influencing factors, internally pre-stored adjustable load archives are called, special transformer increment graphs comprising multiple increment graphs corresponding to the multiple special transformer users are generated, negative increment users are extracted based on the special transformer increment graphs and aggregated into a virtual power plant resource pool, and a scheduling instruction set is generated according to the virtual power plant resource pool in combination with a power grid optimization target, and the scheduling instruction set is issued to corresponding special transformer user sides for load scheduling. That is, real-time electricity consumption data acquired through electricity information monitoring modules installed on the sides of multiple special transformer users are transmitted to a demand side intelligent terminal through a long-distance communication link to construct special transformer increment graphs for flexible and accurate scheduling, fine management of demand side load is realized, the efficiency of demand side load scheduling is improved, and thus the operation efficiency and reliability of the power grid are improved.

[0015] The above description is only a summary of the technical solutions of the present application. In order to more clearly understand the technical means of the present application, the specific embodiments of the present application can be implemented according to the content of the specification, and in order to make the above and other purposes, characteristics and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only exemplary, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of the provided drawings.

[0017] Figure 1 The flowchart of the demand side load optimization scheduling method based on the power consumption increment of the special transformer user of the present application.

[0018] Figure 2 The structure diagram of the demand side load optimization scheduling system based on the power consumption increment of the special transformer user of the present application.

[0019] Explanation of reference numerals: data acquisition module 11, increment calculation module 12, atlas generation module 13, resource pool establishment module 14, load scheduling optimization module 15. DETAILED DESCRIPTION

[0020] The present application provides a demand side load optimization scheduling method and system based on the power consumption increment of the special transformer user, which solves the technical problem that the demand side load scheduling is not accurate due to the coarse perception granularity and lack of collaborative control capability in the prior art, which further affects the efficiency of demand side load scheduling. By installing an electric information monitoring module at the side of multiple special transformer users, the collected real-time power consumption data is transmitted to the demand side intelligent terminal through a long-distance communication link to build a special transformer increment atlas for flexible and accurate scheduling, realizing fine management of the demand side load and improving the efficiency of demand side load scheduling, thereby improving the operation efficiency and reliability of the power grid.

[0021] Below, the technical solutions in the present application will be described clearly and completely with reference to the drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. It should be understood that the present application is not limited by the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application. In addition, it should be noted that, for the convenience of description, only parts related to the present application are shown in the drawings, not all.

[0022] Embodiment one, please refer to the attached Figure 1 The present application provides a demand side load optimization scheduling method based on special transformer user power consumption increment, wherein the demand side load optimization scheduling method based on special transformer user power consumption increment is executed by a demand side load optimization scheduling system based on special transformer user power consumption increment, and the demand side load optimization scheduling method based on special transformer user power consumption increment specifically includes the following steps: S100: Collecting real-time power consumption data set through the electric information monitoring module installed at the side of multiple special transformer users.

[0023] Specifically, the special transformer user refers to a large power consumer or important user directly connected with a transformer substation, and usually these users have large power load, such as industrial enterprises, large commercial facilities, etc. The special transformer user side refers to the part from the special transformer to the user terminal, including power access and power consumption equipment. In the power distribution room of the selected multiple special transformer users, the electric information monitoring module is installed on the low-voltage side outlet cabinet of the special transformer and the power supply loop of the key high-power equipment. That is, by installing the electric information monitoring module at the side of multiple special transformer users, high-frequency collection of power quality parameters is realized, including but not limited to voltage, current, frequency, power factor, harmonic component, etc. The electric information monitoring module collects data simultaneously according to the unified set collection period through the built-in clock synchronization function, instantaneously captures the power consumption state of all network participating users, forms a real-time power consumption data set covering multiple users including current, voltage, active power, reactive power, etc., and updates at a certain time interval to reflect the power consumption of the user in real time. By installing the electric information monitoring module and collecting the real-time power consumption data set, the power consumption data of each user can be obtained instantly, and the monitoring accuracy and real-time response capability of the demand side load management system are significantly improved.

[0024] S200: Transmitting the real-time power consumption data set to the demand side intelligent terminal through a long-distance communication link, determining a baseline load curve based on historical data by the demand side intelligent terminal, comparing the real-time power consumption data set with the baseline load curve to obtain multiple real-time increment sets, wherein the multiple real-time increment sets include real-time increment sets of multiple special transformer users.

[0025] Further, the S200 includes a local communication interface configured to collect the local power consumption data of the plurality of special transformer users, and an isolation circuit is arranged inside the local communication interface, and the isolation circuit includes a signal isolation module, an isolation power module, and a bus protection module.

[0026] Further, the application further includes the following steps: sending a data request command to the electric information monitoring module through the long-distance communication link according to a preset period; the electric information monitoring module encapsulates the real-time power consumption data set according to the data request command, sends the encapsulated data frame back to the demand side intelligent terminal through the long-distance communication link for verification and checking, determines the analysis real-time power consumption data set; based on the date type of the analysis real-time power consumption data set, determines the baseline load curve in the baseline load database; for each real-time power consumption data of the analysis real-time power consumption data set, selects the baseline load value at the same time in the baseline load curve; calculates the difference between each analysis real-time power consumption data and the baseline load value at the corresponding time, obtains the real-time increment at each time, and adds to the plurality of real-time increment sets.

[0027] Further, the application further includes the following steps: calling the historical power consumption data set stored in the demand side intelligent terminal, pre-processing the historical power consumption data set to obtain a standard power consumption data set; based on the time stamp of the historical power consumption data set, date type label identification is performed on the standard power consumption data set; based on the real-time power consumption data set, determine the date type of the target day, and filter a plurality of reference power consumption data sets with consistent date types in the standard power consumption data set; align the plurality of reference power consumption data sets according to the time stamp, extract the load value at each time point and perform mean value calculation to obtain a plurality of baseline values at a plurality of time points; connect the plurality of baseline values in time sequence to form a whole-day baseline load curve; perform smoothness check on the whole-day baseline load curve, integrate the whole-day baseline load curve that passes the check with the corresponding date type label into a baseline load database, and pre-store in the demand side intelligent terminal.

[0028] Specifically, running with strict timing, the clock controller inside the demand side intelligent terminal triggers a communication task every preset period, and broadcasts a data request command to all electric information monitoring modules on the bus through the local communication interface. The preset period is a fixed data collection time interval set in advance, such as every 15 minutes. The data request command is an instruction frame sent by the demand side intelligent terminal, which is used to ask the electric information monitoring module for real-time data of a specific address. After receiving the command, the electric information monitoring module immediately reads the latest real-time power consumption data set from its own register, and packs it according to the standard communication protocol format to form a complete data frame, with the CRC check code calculated based on the data content attached to the frame tail. The data frame contains not only the valid payload data, but also the frame header, device address, command code, error check code and frame tail, ensuring the integrity and recognizability of the data.

[0029] The long-distance communication link is a long-distance data transmission network for connecting various special transformer user sites distributed in a wide geographical area with the data center or master station, including optical fiber private network, 4G / 5G wireless network, industrial Ethernet, etc. The local communication interface is provided with an isolation circuit inside, like a firewall, which only allows data signals to pass through and blocks dangerous current and voltage. The isolation circuit includes a signal isolation module, an isolation power module, and a bus protection module. The signal isolation module is responsible for electrically isolating the digital control signals and data signals transmitted between the terminal and the field device, ensuring the correct transmission of logic signals, while completely cutting off the direct electrical connection. The isolation power module is an independent energy conversion unit that powers the bus drive unit outside the interface circuit. The input and output sides are completely isolated electrically, thereby cutting off the interference path formed through the power supply circuit. The bus protection module is a combination of protection devices deployed at the entrance of the communication line, forming a multi-level defense system against transient overvoltage and overcurrent impact, used to absorb and discharge transient high-energy disturbances from the field line.

[0030] The demand side intelligent terminal establishes a stable master-slave or multi-point peer-to-peer communication network with each monitoring unit distributed on the user side through its local communication interface. When the terminal initiates data collection polling or receives active reporting, all local power consumption data interacting in the local network must pass through the safety processing of the isolation circuit before entering the terminal core processing unit. The signal isolation module uses electromagnetic or optoelectronic conversion principle to ensure that the data byte stream is lossless, while maintaining the electrical insulation strength of kilovolts; at the same time, the isolation power module provides a pure power source completely floating with the terminal mainboard for the interface chip responsible for bus signal transmission and reception, which fundamentally eliminates the loop current and conduction interference caused by ground potential difference; and the bus protection module serves as the first physical defense line, continuously monitoring the communication line and quickly clamping and energy discharging the transient peak pulse caused by lightning induction and operating overvoltage. The signal isolation module, isolation power module and bus protection module work together to ensure the integrity of the data link and the safety of the terminal equipment in the industrial environment with strong electromagnetic interference and ground potential difference.

[0031] After receiving the data frame, the demand side intelligent terminal performs verification and checking, i.e. recalculates the CRC check code of the data and compares it with the received check code. If consistent, it is determined that the data is error-free in the transmission process, and then the data frame is parsed to extract the parsed real-time power consumption data set. According to the date type of the parsed real-time power consumption data set, i.e. whether it is a weekday, weekend or holiday, the baseline load curve matching the date type is called from the baseline load database.

[0032] For each real-time power consumption data of the parsed real-time power consumption data set, the baseline load value at the same time is selected in the baseline load curve, i.e. the baseline load value at the same time point is found, real-time increment = real-time power consumption data - corresponding baseline load value, and the increment data of each time point of each user is added to multiple real-time increment sets to form a user's load fluctuation data set, which together constitutes multiple real-time increment data sets. Multiple real-time increment data sets include the increment data of multiple special transformer users at each time point. For example, assuming that the target day is a weekday, a request is sent to the monitoring module of the commercial complex at address 03, and a frame of data is returned. After passing the CRC check, the central air conditioner total power of the commercial complex at 14:15 is parsed as 638.5kW. According to the date type of the weekday, the load data of the commercial complex at the same time 14:15 on the past weekdays is called from the baseline load database, and the baseline load value is calculated as 585.0kW, so the real-time increment at 14:15 on the weekday is 53.5kW.

[0033] The real-time increment is calculated by comparing with the baseline load curve, which accurately quantifies the load deviation of the user at each time point; the real-time increment of each user is summarized to form multiple increment sets, realizing park-level collaborative scheduling; through data frame packaging, verification and isolation bus communication, the data is ensured to be complete and accurate, and the risk of misjudgment is reduced.

[0034] The historical power consumption data set is retrieved from the demand side intelligent terminal, which is strictly cleaned and the abnormal values are removed to form a high-quality standard power consumption data set. The timestamp of each data point is analyzed, and a date type label is marked. The date type label is a label marked according to the timestamp of each power consumption data, which usually includes at least weekdays, weekends and statutory holidays, and is used to distinguish the differentiated power consumption mode of the user under different date types.

[0035] When a baseline for a target day is needed, the date type of the target day is first determined, and then multiple reference power consumption data sets with the same date type are selected from the standard power consumption data set. The multiple reference power consumption data sets are aligned according to the timestamp, and at each same time point, the load values of all historical days at that moment are extracted, and a baseline value is obtained by mean calculation. Apply this calculation process to each time point throughout the day to obtain the baseline value at each time point. Connect these baseline values in chronological order to form a complete baseline load curve. The baseline load curve is checked for smoothness to check whether there are curve mutations or burrs caused by incomplete removal of a small amount of abnormal data. The baseline load curve that passes the check is integrated with its corresponding date type label and stored in the baseline load database. Through rigorous data preprocessing, the quality of the basic data is guaranteed, and through date type subdivision and mean aggregation algorithm, the typical load characteristics of the user on different dates are accurately described, and the interference of accidental factors and abnormal events is effectively removed.

[0036] S300: Based on the multiple real-time increment sets, trend prediction is performed in combination with external influence factors, an internally pre-stored adjustable load archive is called, and a special transformer increment spectrum containing multiple increment spectra corresponding to the multiple special transformer users is generated.

[0037] Further, the S300 includes: based on the plurality of real-time incremental sets, extracting a first real-time incremental set corresponding to a first special variable user; for the first real-time incremental set, combining an external influence factor to perform short-term load prediction to obtain a first predicted incremental value, wherein the external influence factor at least includes environmental meteorological factors; based on a user identifier of the first special variable user, calling a corresponding first adjustable load profile; matching the first predicted incremental value and the first adjustable load profile to determine the adjustable load potential value at each time to obtain a first incremental graph corresponding to the first special variable user; and in the same way, based on the plurality of real-time incremental sets, generating a plurality of incremental graphs corresponding to a plurality of special variable users, integrating the first incremental graph and the plurality of incremental graphs to obtain the special variable incremental graph.

[0038] Specifically, according to the plurality of real-time incremental sets, a first real-time incremental set corresponding to a first special variable user is extracted, and a short-term load prediction is performed on the first real-time incremental set in combination with an external influence factor to predict the load change trend in the future several minutes or hours to obtain a first predicted incremental value. That is, the first real-time incremental set is input into a pre-trained short-term load prediction model together with the external influence factor, and the short-term load prediction model calculates the first predicted incremental value by learning the correlation between temperature and user load found in historical data. The external influence factor is an external environmental data that has a significant impact on user electricity load but is not from the internal production plan of the user, and the core is the environmental meteorological factor, including temperature, humidity, and light intensity. The first predicted incremental value is the load incremental value at a certain specific time in the future calculated by the short-term load prediction model, which is a prediction of the direction of the load change of the user.

[0039] The first adjustable load profile of the first special variable user is called from the local database through the user identifier of the first special variable user. The user identifier is a code that uniquely represents a special variable user, like an ID number, which is used to accurately index and call specific information of the user in the database. The adjustable load profile is a structured data file that records detailed parameters of electricity-using equipment on the user side that can participate in dispatch, including device name, rated power, minimum switching time, maximum interruptible time, adjustment response delay, belonging production process, and cost / comfort sensitivity to adjustment, etc.

[0040] The first predicted incremental value is matched with the first adjustable load profile to determine the adjustable load potential value at each time, that is, the maximum load power value that can be theoretically scheduled for the user at each time. The adjustable load potential values of the first dedicated transformer user at each time are integrated to obtain the first incremental atlas corresponding to the first dedicated transformer user. Each user is generated corresponding to the incremental atlas, and finally all user atlases are integrated to form a dedicated transformer incremental atlas, which indicates the load change prediction and the size of the schedulable energy of each user node in the future period of time.

[0041] By introducing short-term load forecasting, the potential scheduling target is locked in advance, the predicted increment is accurately matched with the detailed adjustable load profile, and the generated dedicated transformer incremental atlas not only indicates the load increment, but also indicates the schedulability of the dedicated transformer user.

[0042] S400: Based on the dedicated transformer incremental atlas, extract negative incremental users and aggregate into a virtual power plant resource pool.

[0043] Specifically, the generated dedicated transformer incremental atlas is scanned and analyzed in real time, and all user entries with a negative predicted incremental value in the atlas prediction time period are screened out. Each identified negative incremental user represents a potential and available flexible adjustment unit. The negative incremental user means that the actual load of the user at a future time is expected to be lower than its baseline load, indicating that the user is in or about to enter a natural load decline state.

[0044] According to the schedulable load potential value of the negative incremental user, a virtual power plant resource pool is formed. The virtual power plant resource pool is a resource collection aggregated by multiple negative incremental users. Generally, the schedulable load of multiple users is aggregated to form a unified scheduling resource pool, which is similar to a small virtual power plant for optimizing grid scheduling and load balancing. The resource pool of the virtual power plant generally includes the schedulable load of multiple users, and the total adjustment capacity depends on the total load adjustment range of the negative incremental users. The adjustment capacity of multiple negative incremental users is weighted and summarized to generate a scheduling scheme of the virtual power plant. According to the grid load demand and the response capacity of the user schedulable load, the load output of the virtual power plant resource pool is dynamically adjusted. By accurately identifying and aggregating negative incremental users, the negative increment is converted into a large-scale adjustment resource, improving the total amount and quality of available demand-side resources and significantly reducing the scheduling complexity and transaction cost.

[0045] S500: According to the virtual power plant resource pool, a scheduling instruction set is generated in combination with the grid optimization target and is issued to the corresponding dedicated transformer user side for load scheduling.

[0046] Further, the S500 includes: generating an initial scheduling instruction space according to the schedulable load potential values of the plurality of dedicated users in the virtual power plant resource pool; and performing multi-objective optimization seeking in the initial scheduling instruction space with minimization of grid peak load and minimization of total scheduling cost as multi-objective functions, to determine the scheduling instruction set.

[0047] Specifically, according to the schedulable load potential values of all users in the virtual power plant resource pool, an initial scheduling instruction space is generated, which enumerates all possible load reduction combination modes, forming a huge decision option library. The initial scheduling instruction space contains a set of all theoretically feasible scheduling schemes, which is composed of the schedulable load potential values of each dedicated user in the virtual power plant resource pool.

[0048] A multi-objective optimization model is established, with minimization of grid peak load and minimization of total scheduling cost as two objective functions. The total scheduling cost is usually the sum of the compensation fees paid to all scheduled users, and the compensation unit price varies for different users. In the initial scheduling instruction space, search is performed to find non-inferior solutions that can significantly reduce the peak load while the total cost is relatively low. In the optimization process, the algorithm will continuously adjust the load adjustment amount of each user and evaluate the grid peak load and scheduling cost of each scheme, and finally obtain the optimal scheduling instruction set.

[0049] Minimizing the grid peak load means that, in the load scheduling process, by reasonably allocating the load adjustment capacity of each user, the peak load of the grid is reduced, thereby avoiding grid overload or instability; minimizing the total scheduling cost means that, by optimizing scheduling, the cost generated by scheduling operation is reduced, including but not limited to power purchase cost, operation cost of scheduling system, etc.

[0050] According to the optimization result, the final scheduling instruction set is generated, which clearly specifies the load amount that each user should adjust at a specific time. The scheduling instruction set is the final scheduling scheme generated based on the optimization result, which contains specific scheduling instructions and informs each user of the load amount that needs to be adjusted in different time periods, i.e., clearly specifies the time, user, and specific load adjustment instruction. By constructing the initial scheduling instruction space, the comprehensiveness and feasibility of the decision are ensured. Through multi-objective optimization seeking, the scheduling benefit is maximized and unnecessary scheduling cost is reduced under the premise of ensuring the stability of the grid. The optimized scheduling instruction set can make the load adjustment of each user more reasonable and reduce the operation cost of the energy procurement and scheduling system.

[0051] Further, the application further comprises the following steps: after the load scheduling, the demand side intelligent terminal continuously collects the actual load data of the plurality of special transformer user sides through the long-distance communication link; synchronously monitors the power consumption execution equipment state of the plurality of special transformer user sides; based on the actual load data and the power consumption execution equipment state, performs scheduling effect evaluation, generates a load scheduling event evaluation report and stores it in the demand side intelligent terminal.

[0052] Specifically, after the scheduling instruction set is issued and the load scheduling is performed, the real-time power consumption data of the special transformer user is collected again through the electric information monitoring module. The demand side intelligent terminal continues to collect the actual load data of all related special transformer users through the long-distance communication link at a high frequency, and synchronously monitors the states of the controlled power consumption execution equipment through a special monitoring loop. The power consumption execution equipment state is the state feedback of the terminal equipment directly responsible for executing the load adjustment, including monitoring the operating frequency of the frequency converter, the switch state of the digital output module, and the opening and closing state of the intelligent circuit breaker.

[0053] The actual load data after scheduling is compared with the baseline load value before scheduling to calculate the actual load reduction or increase. The cross-verification of the collected power consumption execution equipment state quantifies the actual execution effect of the scheduling instruction, including whether the load reduction meets the standard, whether the response time is timely, and sustainability, etc. Through scheduling effect evaluation, key performance indicators such as actual response load, response deviation rate, response delay time, and instruction execution success rate are calculated to generate a detailed load scheduling event evaluation report and store it in the demand side intelligent terminal. The load scheduling event evaluation report is a document recording the execution of the load scheduling event, including scheduling instructions, actual load response, equipment state information, and scheduling effect analysis results.

[0054] Through actual load data and equipment state monitoring, it can be accurately judged whether the scheduling instruction is executed, the states of the frequency converter, the switch module and the circuit breaker are synchronously monitored to ensure that the load adjustment operation is safe and reliable. Through the evaluation of the scheduling effect, the response rate and the scheduling deviation are calculated to improve the accuracy of future load scheduling.

[0055] In summary, the demand side load optimization scheduling method based on special transformer user power consumption increment provided by the application has the following beneficial effects: The real-time power consumption data set is collected through the electric information monitoring module installed on the multiple special transformer user sides; the real-time power consumption data set is transmitted to the demand side intelligent terminal through a long-distance communication link; the demand side intelligent terminal determines a baseline load curve based on historical data, compares the real-time power consumption data set with the baseline load curve, and obtains multiple real-time increment sets, wherein the multiple real-time increment sets include real-time increment sets of multiple special transformer users; trend prediction is performed based on the multiple real-time increment sets in combination with external influence factors, an internally pre-stored adjustable load archive is called, and a special transformer increment graph including multiple increment graphs corresponding to the multiple special transformer users is generated; based on the special transformer increment graph, negative increment users are extracted and aggregated into a virtual power plant resource pool; based on the virtual power plant resource pool, a dispatch instruction set is generated in combination with a power grid optimization target, and is issued to the corresponding special transformer user side for load dispatching. That is, the real-time power consumption data collected through the electric information monitoring module installed on the multiple special transformer user sides is transmitted to the demand side intelligent terminal through a long-distance communication link to construct a special transformer increment graph for flexible and accurate scheduling, fine management of the demand side load is realized, the demand side load scheduling efficiency is improved, and the operation efficiency and reliability of the power grid are improved.

[0056] In the embodiment two, based on the same inventive concept as the demand side load optimization scheduling method based on the power consumption increment of the special transformer user in the aforementioned embodiment one, the application also provides a demand side load optimization scheduling system based on the power consumption increment of the special transformer user. Please refer to the attached Figure 2 The demand side load optimization scheduling system based on the power consumption increment of the special transformer user includes: A data acquisition module 11 is configured to collect real-time power consumption data sets through electric information monitoring modules installed on multiple special transformer user sides; an increment calculation module 12 is configured to transmit the real-time power consumption data sets to a demand side intelligent terminal through a long-distance communication link; the demand side intelligent terminal determines a baseline load curve based on historical data, compares the real-time power consumption data set with the baseline load curve, and obtains multiple real-time increment sets, wherein the multiple real-time increment sets include real-time increment sets of multiple special transformer users; a graph generation module 13 is configured to perform trend prediction based on the multiple real-time increment sets in combination with external influence factors, call an internally pre-stored adjustable load archive, and generate a special transformer increment graph including multiple increment graphs corresponding to the multiple special transformer users; a resource pool establishment module 14 is configured to extract negative increment users based on the special transformer increment graph and aggregate them into a virtual power plant resource pool; and a load dispatch optimization module 15 is configured to generate a dispatch instruction set based on the virtual power plant resource pool in combination with a power grid optimization target, and issue it to the corresponding special transformer user side for load dispatching.

[0057] Further, the increment calculation module 12 in the demand side load optimization scheduling system based on the power consumption increment of the special transformer user is further used for: the demand side intelligent terminal includes a local communication interface for collecting local power consumption data of the plurality of special transformer user sides, an isolation circuit is arranged inside the local communication interface, and the isolation circuit includes a signal isolation module, an isolation power supply module, and a bus protection module.

[0058] Further, the increment calculation module 12 in the demand side load optimization scheduling system based on the power consumption increment of the special transformer user is further used for: sending a data request command to the electric information monitoring module through the long-distance communication link according to a preset period; the electric information monitoring module encapsulates the real-time power consumption data set according to the data request command, sends the encapsulated data frame back to the demand side intelligent terminal through the long-distance communication link for verification and checking, determines the parsed real-time power consumption data set, determines the baseline load curve in the baseline load database based on the date type of the parsed real-time power consumption data set, selects a baseline load value at the same time in the baseline load curve for each real-time power consumption data of the parsed real-time power consumption data set, calculates the difference between each parsed real-time power consumption data and the baseline load value at the corresponding time, obtains the real-time increment at each time, and adds to the plurality of real-time increment sets.

[0059] Further, the increment calculation module 12 in the demand side load optimization scheduling system based on the power consumption increment of the special transformer user is further used for: calling the historical power consumption data set stored in the demand side intelligent terminal, pre-processing the historical power consumption data set to obtain a standard power consumption data set, and performing cleaning and outlier elimination; based on the time stamp of the historical power consumption data set, date type label identification is performed on the standard power consumption data set; based on the real-time power consumption data set, the date type of the target day is determined, and a plurality of reference power consumption data sets with consistent date types are screened from the standard power consumption data set; the plurality of reference power consumption data sets are aligned according to the time stamp, the load value at each time point is extracted and mean value calculation is performed, a plurality of baseline values at a plurality of time points are obtained; the plurality of baseline values are connected in time sequence to form a whole-day baseline load curve; the whole-day baseline load curve is subjected to smoothness inspection, the whole-day baseline load curve that passes the inspection is integrated with the corresponding date type label to form a baseline load database, and is pre-stored in the demand side intelligent terminal.

[0060] Further, the graph generating module 13 in the demand side load optimization scheduling system based on the power consumption increment of the special transformer user is further used for: based on the plurality of real-time increment sets, extracting a first real-time increment set corresponding to a first special transformer user; for the first real-time increment set, combining an external influence factor to perform short-term load prediction, to obtain a first predicted increment value, wherein the external influence factor at least includes environmental meteorological factors; based on a user identifier of the first special transformer user, calling a corresponding first adjustable load archive; matching the first predicted increment value and the first adjustable load archive to determine the adjustable load potential value at each time, to obtain a first increment graph corresponding to the first special transformer user; and based on the plurality of real-time increment sets, generating a plurality of increment graphs corresponding to a plurality of special transformer users, integrating the first increment graph and the plurality of increment graphs to obtain the special transformer increment graph.

[0061] Further, the load scheduling optimization module 15 in the demand side load optimization scheduling system based on the power consumption increment of the special transformer user is further used for: generating an initial scheduling instruction space according to the adjustable load potential values of the plurality of special transformer users in the virtual power plant resource pool; performing multi-objective optimization optimization in the initial scheduling instruction space with the minimum grid peak load and the minimum total scheduling cost as multi-objective functions, to determine the scheduling instruction set.

[0062] Further, the demand side load optimization scheduling system based on the power consumption increment of the special transformer user further comprises: after load scheduling, the demand side intelligent terminal continuously collects actual load data of the plurality of special transformer users through the long-distance communication link; synchronously monitors the power consumption execution equipment state of the plurality of special transformer users; based on the actual load data and the power consumption execution equipment state, performs scheduling effect evaluation, generates a load scheduling event evaluation report and stores it in the demand side intelligent terminal.

[0063] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The foregoing Figure 1 The demand side load optimization scheduling method based on the power consumption increment of the special transformer user in the first embodiment and the specific examples are also applicable to the demand side load optimization scheduling system based on the power consumption increment of the special transformer user in the present embodiment. Through the foregoing detailed description of the demand side load optimization scheduling method based on the power consumption increment of the special transformer user, those skilled in the art can clearly know the demand side load optimization scheduling system based on the power consumption increment of the special transformer user in the present embodiment. Therefore, for the sake of brevity of the specification, it will not be described in detail here.

[0064] The above description of disclosed embodiments enables one of ordinary skill in the art to make or use the application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein can be applied to other embodiments without departing from the spirit or scope of the application. Therefore, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0065] Obviously, many modifications and changes can be made to the application as set forth above without departing from the spirit and scope of the application. It is to be understood that the above description is intended to be illustrative, and not restrictive. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description. The scope of the application should, therefore, be determined not with reference to the above description, but instead with reference to the appended claims, along with their full scope of equivalents.

Claims

1. A demand side load optimization scheduling method based on the electricity consumption increment of a special variable user, characterized in that, The method comprises the following steps: Collecting real-time power consumption data sets through the electric information monitoring module installed on the multiple dedicated variable user sides; Transmitting the real-time power consumption data sets to the demand side intelligent terminal through a long-distance communication link, determining a baseline load curve based on historical data, comparing the real-time power consumption data sets with the baseline load curve, and obtaining multiple real-time incremental sets, wherein the multiple real-time incremental sets include real-time incremental sets of multiple dedicated variable users; Based on the multiple real-time incremental sets, trend prediction is performed in combination with external influencing factors, an internally pre-stored adjustable load archive is called, and a dedicated variable incremental atlas including multiple incremental atlases corresponding to the multiple dedicated variable users is generated; Based on the dedicated variable incremental atlas, negative incremental users are extracted and aggregated into a virtual power plant resource pool; According to the virtual power plant resource pool, in combination with the grid optimization target, a dispatching instruction set is generated and issued to the corresponding dedicated variable user side for load dispatching. 2.The demand side load optimization scheduling method based on the power consumption increment of the special user according to claim 1, characterized in that, The demand side intelligent terminal includes a local communication interface for collecting local power consumption data of the multiple dedicated variable user sides, and an isolation circuit is arranged inside the local communication interface, wherein the isolation circuit includes a signal isolation module, an isolation power supply module, and a bus protection module. 3.The method of claim 1, wherein, Transmitting the real-time power consumption data sets to the demand side intelligent terminal through a long-distance communication link, determining a baseline load curve based on historical data, comparing the real-time power consumption data sets with the baseline load curve, and obtaining multiple real-time incremental sets, comprising: Sending a data request command to the electric information monitoring module through the long-distance communication link according to a preset period; The electric information monitoring module encapsulates the real-time power consumption data set according to the data request command, sends the encapsulated data frame back to the demand side intelligent terminal through the long-distance communication link for verification and checking, and determines the parsed real-time power consumption data set; Based on the date type of the parsed real-time power consumption data set, the baseline load curve is determined in the baseline load database; For each real-time power consumption data of the parsed real-time power consumption data set, a baseline load value at the same time is selected in the baseline load curve; The difference between each parsed real-time power consumption data and the baseline load value at the corresponding time is calculated to obtain the real-time increment at each time, which is added to the multiple real-time incremental sets. 4.The method of claim 3, wherein, The construction process of the baseline load database comprises: Retrieve the historical power consumption data set stored in the demand side intelligent terminal, and perform preprocessing such as cleaning and outlier removal on the historical power consumption data set to obtain a standard power consumption data set; Based on the time stamp of the historical power consumption data set, date type label identification is performed on the standard power consumption data set; Based on the real-time power consumption data set, the date type of the target day is determined, and multiple reference power consumption data sets with consistent date types are filtered from the standard power consumption data set; Align the multiple reference power consumption data sets according to the time stamp, extract the load value at each time point, and perform mean value calculation to obtain multiple baseline values at multiple time points; Connect the multiple baseline values in time sequence to form a full-day baseline load curve; The all-day baseline load curve is subjected to smoothness check, the all-day baseline load curve passing the check is integrated with the corresponding date type label into a baseline load database, and is pre-stored in the demand side intelligent terminal.

5. The method of claim 1, wherein, Based on the multiple real-time increment sets, trend prediction is performed in combination with external influence factors, an internally pre-stored adjustable load archive is called, and a special transformer increment graph including the special transformer increment sets is generated, including: Based on the multiple real-time increment sets, a first real-time increment set corresponding to a first special transformer user is extracted; For the first real-time increment set, short-term load prediction is performed in combination with external influence factors to obtain a first predicted increment value, wherein the external influence factors at least include environmental meteorological factors; Based on a user identifier of the first special transformer user, a corresponding first adjustable load archive is called; The first predicted increment value and the first adjustable load archive are matched to determine a dispatchable load potential value at each time, and a first increment graph corresponding to the first special transformer user is obtained; By analogy, based on the multiple real-time increment sets, multiple increment graphs corresponding to multiple special transformer users are generated, the first increment graph and the multiple increment graphs are integrated, and the special transformer increment graph is obtained. 6.The method of claim 1, wherein, According to the virtual power plant resource pool, in combination with a power grid optimization target, a dispatch instruction set is generated, including: According to the dispatchable load potential values of the multiple special transformer users in the virtual power plant resource pool, an initial dispatch instruction space is generated; With minimization of power grid peak load and minimization of total dispatch cost as multi-objective functions, multi-objective optimization is performed in the initial dispatch instruction space to determine the dispatch instruction set. 7.The method of claim 1, wherein, Further comprising: After load dispatching, the demand side intelligent terminal continuously collects actual load data of the multiple special transformer user sides through the long-distance communication link; Synchronously monitoring states of power consumption execution equipment of the multiple special transformer user sides; Based on the actual load data and the states of the power consumption execution equipment, dispatching effect evaluation is performed, a load dispatching event evaluation report is generated, and is stored in the demand side intelligent terminal.

8. A demand side load optimization scheduling system based on the power consumption increment of a special transformer user, characterized in that, The special transformer user power consumption increment-based demand side load optimization dispatching system for implementing the steps of the special transformer user power consumption increment-based demand side load optimization dispatching method in any one of claims 1 to 7, including: A data collection module for collecting real-time power consumption data sets through power information monitoring modules installed on the multiple special transformer user sides; An increment calculation module for transmitting the real-time power consumption data sets to a demand side intelligent terminal through a long-distance communication link, the demand side intelligent terminal determining a baseline load curve based on historical data, comparing the real-time power consumption data sets with the baseline load curve, and obtaining multiple real-time increment sets, wherein the multiple real-time increment sets include real-time increment sets of the multiple special transformer users; A graph generation module for performing trend prediction in combination with external influence factors based on the multiple real-time increment sets, calling an internally pre-stored adjustable load archive, and generating a special transformer increment graph including multiple increment graphs corresponding to the multiple special transformer users; A resource pool establishment module for extracting negative increment users and aggregating them into a virtual power plant resource pool based on the special transformer increment graph; A resource pool establishment module for extracting negative increment users and aggregating them into a virtual power plant resource pool based on the special transformer increment graph; A load scheduling optimization module is configured to generate a scheduling instruction set according to the virtual power plant resource pool and in combination with a power grid optimization target, and to issue the scheduling instruction set to corresponding special transformer user sides for load scheduling.

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