Smart fusion terminal-based substation load peak shaving and valley filling collaborative regulation method

Abstract

The application discloses a transformer area load peak shaving and valley filling cooperative regulation method based on an intelligent fusion terminal, and relates to the technical field of transformer area load regulation.The application obtains a load prediction curve and a controllable load curve of a target transformer area through historical load data prediction, identifies a peak-valley section in the load prediction curve in combination with a target optimization range, performs peak shaving on a peak section in combination with the controllable load curve, generates a load scheduling strategy for the purpose of valley filling on a valley section, and executes the load scheduling strategy by using the deployed intelligent fusion terminal, so as to realize the peak shaving and valley filling of the load of the target transformer area.The application can accurately predict by using a hyperbolic curve, avoid traditional load blind regulation, ensure the accuracy of the regulation direction by dynamic peak-valley identification, cooperatively reduce transformer overload and voltage drop by shifting and reducing strategies, improve the stability of a power distribution network, reduce the waste of high-peak light abandonment and low-valley resource idling, and significantly improve energy utilization efficiency.

Description

Technical Field

[0001] This invention belongs to the field of transformer area load regulation technology, specifically a transformer area load peak shaving and valley filling collaborative regulation method based on intelligent fusion terminals. Background Technology

[0002] Load fluctuations have multi-dimensional negative impacts on distribution areas. During peak hours, the load exceeds the rated capacity of the transformer, which can cause the transformer to overheat, the insulation to age, the lifespan to be shortened, and even cause the distribution cable to trip and the power outage. During off-peak hours, the load is too low, which makes the power generation resources of the grid idle. If the household photovoltaic system is generating power when the distribution area is under low load, the excess power cannot be connected to the grid.

[0003] Peak shaving and valley filling are the core objectives of power load management. However, the existing collaborative control technologies for peak shaving and valley filling in distribution areas have not been implemented effectively and have several key problems: 1) Load forecasting and control lack precision. Traditional solutions often rely on macro load data for "blind adjustment," making it difficult to define the boundaries of adjustable resources. This leads to a disconnect between peak and valley segment identification and actual optimization goals, resulting in deviations in control direction and problems such as transformer overload and voltage drops. At the same time, the phenomenon of energy waste, such as peak curtailment and idle resources during off-peak hours, is prominent. 2) User participation is low and trust is insufficient. Existing control is mostly a one-way command operation, directly controlling the start and stop of equipment. This makes it difficult to meet users' special electricity needs. Moreover, most users are worried about the impact on their electricity experience because they do not understand the control logic. Some users are concerned about data privacy leaks or believe that the benefits of control are unclear, further resisting participation in control. Ultimately, the goal of peak shaving and valley filling cannot be achieved efficiently, and the stability of the distribution network and energy utilization efficiency cannot be effectively improved.

[0004] This invention provides a method for coordinated control of transformer area load peak shaving and valley filling based on intelligent fusion terminals to solve the above-mentioned technical problems. Summary of the Invention

[0005] This invention aims to solve at least one of the technical problems existing in the prior art. To this end, this invention proposes a method for coordinated control of distribution area load peak shaving and valley filling based on intelligent fusion terminals. This method avoids traditional blind load adjustment through accurate hyperbolic prediction, ensures accurate control direction through dynamic peak-valley identification, and reduces transformer overload and voltage drop problems through coordinated transfer and reduction strategies, thereby improving the stability of the distribution network. At the same time, it reduces the waste of peak-hour curtailment and off-peak resource idleness, significantly improving energy utilization efficiency. Moreover, by generating load dispatching strategies through prediction, users have sufficient time to provide feedback and objections during the generation process. The load dispatching strategies are adjusted and updated in a timely manner based on the objections, ensuring the completion of peak shaving and valley filling of the target distribution area while increasing user participation, thereby improving user experience and the rationality of load coordination and dispatching.

[0006] To achieve the above objectives, a first aspect of the present invention provides a method for coordinated control of transformer area load peak shaving and valley filling based on an intelligent fusion terminal, comprising:

[0007] The load forecast curve and controllable load curve of the target distribution area are obtained; the controllable load curve is generated by superimposing the load change curves of all controllable equipment in the target distribution area.

[0008] Identify peak and valley sections of the load forecast curve based on the target optimization range; generate load scheduling strategies based on the peak and valley sections of the controllable load curve and the load forecast curve;

[0009] The target distribution area is controlled by intelligent converged terminals according to the load scheduling strategy; the load scheduling strategy includes load transfer strategy and load reduction strategy.

[0010] In one possible implementation, the load forecast curve for the target transformer area is obtained, including:

[0011] Obtain historical load data and historical environmental data for the target transformer area, and train an LSTM model based on the historical load data and historical environmental data to obtain a load prediction model;

[0012] Obtain forecast environmental data, standardize and process the forecast environmental data and historical load data, and input them into the load forecast model to obtain the load forecast curve for the target transformer area.

[0013] In one possible implementation, the controllable load curve of the target transformer area is predicted, including:

[0014] Historical load data and historical environmental data of several controllable devices within the target area are obtained, and the load change curves of the controllable devices are predicted based on the historical load data and historical environmental data; wherein, the load change curves include power change curves and time change curves;

[0015] The controllable load curve is obtained by superimposing the load change curves of several controllable devices.

[0016] In one possible implementation, the load change curves of several controllable devices are superimposed, including:

[0017] Extract the power variation curves of the power-controllable devices and the time variation curves of the time-controllable devices from a number of controllable devices;

[0018] Several power variation curves are superimposed to obtain a power load curve; several time variation curves are superimposed to obtain a time load curve.

[0019] The controllable load curve is obtained by superimposing the power load curve and the time load curve.

[0020] In one possible implementation, identifying the peak and trough sections of the load forecast curve based on the target optimization range includes:

[0021] Extract the upper and lower limits of the target optimization range;

[0022] Identify the curve segments in the load forecast curve that are above the upper limit of the range as peak segments; and identify the curve segments in the load forecast curve that are below the lower limit of the range as trough segments.

[0023] In one possible implementation, a load scheduling strategy is generated based on the peak-valley sections of the controllable load curve and the load forecast curve, including:

[0024] The statistics include the load surplus during peak periods and the load gap during off-peak periods; the load surplus includes controllable load and uncontrollable load, and the controllable load includes power surplus and time load.

[0025] A load transfer strategy is generated based on the time load and load gap, and then the load transfer strategy is transformed into a load scheduling strategy.

[0026] In one possible implementation, a load transfer strategy is generated based on the time load and the load gap, including:

[0027] The load transfer amount is determined by comparing the time load amount and the load gap amount, and the target control equipment is determined from the time-controllable equipment based on the load transfer amount;

[0028] A load transfer strategy is generated based on the controllable load corresponding to the target control equipment.

[0029] In one possible implementation, the target control device is determined from time-controllable devices based on the load transfer amount, including:

[0030] Calculate the operating time and unit load of several time-controllable devices during peak periods, and construct a load graph of the time-controllable devices with operating time as the longest and unit load as the longest.

[0031] By filling in the low-valley section using the load graphs of several time-controllable devices, and determining several equipment groups based on the principle that the sum of controllable loads is not less than the load gap;

[0032] When the number of time-controllable devices in a device group is the minimum, the time-controllable devices in that device group are marked as target control devices.

[0033] In one possible implementation, a load transfer strategy is generated based on the controllable load corresponding to the target control device, including:

[0034] Extract the load graphs of several of the target control devices;

[0035] The load graphs of several time-controllable devices are used to fill in the low-valley sections, and a load transfer strategy is generated based on the running time of several target control devices in the filling results.

[0036] In one possible implementation, a load reduction strategy is generated based on power surplus and the upper limit of transformer area load, including:

[0037] The load forecast curve for peak areas is updated based on load transfer strategies;

[0038] Determine whether the updated load forecast curve exceeds the upper limit of the transformer area load; if so, select power-controllable devices based on the upper limit of the range, and generate a load reduction strategy based on the selected power-controllable devices.

[0039] Compared with the prior art, the beneficial effects of the present invention are:

[0040] 1. This invention obtains the load forecast curve and controllable load curve of the target distribution area through historical load data prediction. It identifies peak-valley sections in the load forecast curve by combining the target optimization range with the controllable load curve. A load dispatching strategy is generated with the aim of peak shaving in the peak sections and valley filling in the valley sections. The deployed intelligent fusion terminal executes the load dispatching strategy to achieve peak shaving and valley filling of the target distribution area's load. This invention avoids traditional blind load adjustment through accurate hyperbolic prediction, ensures precise control direction through dynamic peak-valley identification, and reduces transformer overload and voltage drop problems through coordinated transfer and reduction strategies, improving distribution network stability. It also reduces waste from peak-hour curtailment and valley-hour resource idleness, significantly improving energy utilization efficiency. Furthermore, by generating the load dispatching strategy through prediction, users have sufficient time to provide feedback and objections during the strategy generation process. The load dispatching strategy is adjusted and updated promptly based on these objections, ensuring peak shaving and valley filling of the target distribution area while increasing user participation, thereby improving user experience and the rationality of load coordination and dispatching.

[0041] 2. This invention first categorizes controllable and uncontrollable devices with user confirmation. When generating a load transfer strategy, it constructs a load graph for time-controllable devices and fills in the off-peak sections. Target control devices are selected based on "load ≥ shortage and minimum number of devices". When generating a load reduction strategy, it first updates the peak load forecast curve. When exceeding limits, it adjusts the power controllable devices. Energy storage devices can also be introduced to cope with extreme situations. The control strategy requires user confirmation and has a fallback mechanism for special scenarios. In this technical solution, user-participatory device classification improves user acceptance, reduces resistance, minimizes the number of devices to be controlled to reduce the impact on the user's electricity experience, the tiered load reduction strategy avoids over-control, and energy storage and fallback mechanisms enhance control reliability, solving the pain points of low user participation and high control risks in existing solutions. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a schematic diagram of the method flow of the load peak shaving and valley filling coordinated regulation method in the embodiment of the present invention;

[0044] Figure 2 This is a schematic diagram of the load forecast curve for the target transformer area in an embodiment of the present invention;

[0045] Figure 3 This is a schematic diagram of the controllable load curve of the target area in an embodiment of the present invention;

[0046] Figure 4 This is a schematic diagram illustrating the division of peak and valley sections in the load forecast curve in an embodiment of the present invention.

[0047] Figure 5 This is a schematic diagram illustrating the generation of the load transfer strategy in an embodiment of the present invention. Detailed Implementation

[0048] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. 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.

[0049] The load of a distribution area refers to the total power consumption of all electricity users within that area, including residents, shops, and small factories, at a given moment. Its characteristic is that it fluctuates over time. For example, residential load surges before work (before 8 a.m.) and after get off work (6 p.m. to 10 p.m.), and drops sharply in the early morning (2 a.m. to 6 a.m.); industrial and commercial loads show a pattern of being high during the day and low at night, depending on the production period.

[0050] Load fluctuations have multifaceted impacts on transformer substations. During peak hours, loads exceeding the transformer's rated capacity can lead to transformer overload and overheating, insulation aging, shortened lifespan, and even overload tripping of distribution cables, causing power outages in the area. For example, during the summer, when residents turn on their air conditioners en masse (7-9 PM), the load on the substation can surge by 30%-50%, exceeding the transformer's capacity. Simultaneous electricity consumption by a large number of users during peak hours can cause voltage drops in the substation, potentially leading to problems such as air conditioners not cooling, refrigerators shutting down, and smart home appliance malfunctions. For instance, during the evening peak hours, in older residential areas, excessive load can cause some residents to experience dimmed lights and slower washing machine spinnerets. When the load is too low during off-peak hours, the power generation resources of the grid are idle. Similarly, when a household photovoltaic system is working, if the load in the distribution area is low, the excess power may not be able to be connected to the grid. For example, at 3 a.m., the load in the distribution area is only 100kW (500kW at peak), and the grid thermal power still needs to maintain an output of 300kW (minimum output), so the excess 200kW cannot be absorbed. At 12 p.m., the total power generation of the household photovoltaic system is 200kW, but the load in the distribution area is only 150kW, so 50kW of photovoltaic power needs to be abandoned.

[0051] Peak shaving and valley filling are the core objectives of power load management. This involves using technical means to reduce peak loads and increase valley loads, resulting in a smoother overall load curve for the distribution area and reducing extreme fluctuations. Specifically, peak shaving refers to reducing unnecessary electricity consumption or shifting demand during peak load periods to prevent the distribution area load from exceeding transformer capacity or the distribution network's carrying capacity, thus preventing overload tripping and voltage drops. Valley filling refers to improving the utilization rate of the distribution area load during off-peak load periods by guiding users to utilize energy storage discharge and electric vehicle charging, thereby preventing the grid from idling and wasting energy.

[0052] The intelligent converged terminal is a core device for realizing the Internet of Things (IoT) and intelligent management of power distribution substations (referring to low-voltage power consumption areas supplied by 10kV distribution lines, covering residential, commercial, and small industrial users). It breaks through the limitations of traditional power terminals' single function and data silos, integrating multiple types of sensing, communication, computing, and control capabilities to become a key node connecting the grid side, user side, and cloud platform side. It serves as the nerve center supporting core operations such as substation load monitoring, accurate metering, fault self-healing, and peak shaving and valley filling coordinated regulation. The intelligent converged terminal has full-data sensing and acquisition capabilities, enabling real-time collection of multi-dimensional data from both the grid and user sides. It also possesses edge computing and decision-making capabilities, allowing for analysis and rapid decision-making based on real-time substation data. Furthermore, the intelligent converged terminal also has data communication and data storage functions.

[0053] Please see Figure 1 The first aspect of the present invention provides a method for coordinated control of transformer area load peak shaving and valley filling based on intelligent fusion terminals, comprising:

[0054] S100: Predict and obtain the load forecast curve and controllable load curve of the target distribution area; wherein, the controllable load curve is generated by superimposing the load change curves of all controllable equipment in the target distribution area;

[0055] S200: Identify peak and valley sections of the load forecast curve based on the target optimization range; generate load scheduling strategies based on the peak and valley sections of the controllable load curve and the load forecast curve;

[0056] S300: The target distribution area is controlled by the intelligent converged terminal according to the load scheduling strategy; the load scheduling strategy includes load transfer strategy and load reduction strategy.

[0057] In S100, the load forecast curve for the target transformer area is obtained, including:

[0058] S111: Obtain historical load data and historical environmental data for the target transformer area, train an LSTM model based on the historical load data and historical environmental data, and obtain a load prediction model;

[0059] S112: Obtain the predicted environmental data, standardize the predicted environmental data and historical load data, and input them into the load prediction model to obtain the load prediction curve of the target transformer area.

[0060] The load forecast curve is based on historical load data and predicts the load change data of the target transformer area in the next control cycle. Figure 2 This is a schematic diagram of the load forecast curve. Figure 2 A schematic diagram of the total load change over time within the target distribution area was drawn using one control cycle. It can be seen that the electricity load of the target distribution area has peak and off-peak periods throughout the day (from 0:00 to 24:00).

[0061] For example, assuming historical load data for the target area over the past three months is extracted, with a data collection interval of 15 minutes, each data entry would have 96 load record points. Simultaneously, daily environmental data, including temperature, humidity, and weather conditions, is extracted. The historical load data is arranged according to the collection date and time to form a load time series. Features such as temperature and humidity are extracted from the environmental information, and weather conditions are converted into numerical codes, such as 1 for sunny days, 2 for cloudy days, 3 for overcast days, and 4 for light rain. The collection dates also need to be marked: 0 for weekdays and 1 for non-weekdays.

[0062] The data from the past 7 days can be used as a prediction window, that is, the load of the next day can be predicted by using the historical load data and environmental data of the past 7 days. The load sample has 7 days × 96 points / day = 672 features. The feature dimensions corresponding to the environmental data are the total dimensions of the model input. The feature dimensions of the environmental data include daily average temperature, daily maximum / minimum temperature, daily average humidity, weather conditions, and weekday / non-weekday.

[0063] By feeding the data into a Long Short-Term Memory (LSTM) network and training it, a load forecasting model can be obtained. This load forecasting model can then be used to predict the load forecast curve for a target transformer area.

[0064] In S100, the controllable load curve of the target transformer area is predicted and obtained, including:

[0065] S121: Obtain historical load data and historical environmental data of several controllable devices within the target area, and predict the load change curve of the controllable devices based on the historical load data and historical environmental data; wherein, the load change curve includes power change curve and time change curve;

[0066] S122: Overlay the load change curves of several controllable devices to obtain a controllable load curve.

[0067] The electrical equipment in the target distribution area can be mainly divided into two types: one is controllable equipment, which means that its operating power or operating time can be adjusted without affecting the user's normal production and life; the other is uncontrollable equipment, which means that its operating power and operating time cannot be adjusted, and once adjusted, it will affect the user's normal production and life.

[0068] In the coordinated regulation of peak shaving and valley filling in distribution transformer areas, the accurate classification of controllable and uncontrollable equipment is a prerequisite for the implementation of regulation strategies. The essential difference between controllable and uncontrollable equipment lies in whether the operating time and power are adjustable without affecting user experience. Whether the operating time is adjustable refers to whether the load's operating time can be adjusted; if so, the load is classified as controllable equipment. Whether the power is adjustable refers to whether the load's operating power can be adjusted; if so, the load is classified as controllable equipment. If the load's operating time and operating power are not adjustable, the corresponding load is classified as uncontrollable equipment.

[0069] The following are examples of controllable and uncontrollable equipment:

[0070] Controllable equipment: 1) Residential side: electric water heaters, electric vehicle charging stations, air source heat pumps, washing machines, dishwashers, etc.; 2) Commercial side: central air conditioning (during off-peak hours), cold storage (when there is sufficient cooling capacity), etc.; 3) Industrial side: pumps / fans, energy storage equipment, etc. in non-critical production processes.

[0071] Uncontrollable equipment: 1) Residential side: refrigerators, lighting, televisions, computers, home medical equipment, etc.; 2) Commercial side: POS systems, refrigerated cabinets, etc.; 3) Industrial side: key production equipment, precision instruments, etc.

[0072] By utilizing smart meters and smart sensors, historical load data of controllable devices can be collected from the user side. This historical load data refers to the load change patterns of controllable devices over a past period. Based on this historical load data, the load change curve of controllable devices within a future control cycle can be predicted. This process can refer to the acquisition steps of the load forecasting model, and will not be detailed here. After identifying the load change curves of each controllable device, overlay processing can be performed to obtain the controllable load curve corresponding to all controllable devices within the target distribution area. This controllable load curve is the curve showing the change of the total load of all controllable devices within the target distribution area over time.

[0073] It is worth noting that, if necessary, historical load data of uncontrollable equipment can also be collected, and the load change curve of uncontrollable equipment in the next control cycle can be predicted based on this historical load data, and then superimposed to obtain the uncontrollable load curve. Furthermore, the load prediction curve is actually a superposition of the controllable load curve corresponding to controllable equipment, the uncontrollable load curve corresponding to uncontrollable equipment, and load losses.

[0074] Figure 3 This is a schematic diagram of a controllable load curve. Figure 3 The solid line in the diagram represents the load forecast curve for the target distribution area, while the dashed line represents the controllable load curve for the target distribution area. As mentioned earlier, the load forecast curve is the superposition of the controllable load curve, the uncontrollable load curve, and load losses. Therefore, the difference between the load forecast curve and the controllable load curve is the sum of the uncontrollable load curve and load losses. Load losses mainly consist of transformer losses, winding losses, and line losses within the target distribution area.

[0075] The load change curves of several controllable devices are superimposed, including:

[0076] S122-1: Extract the power change curve of the power controllable device and the time change curve of the time controllable device from a number of controllable devices;

[0077] S122-2: Superimpose several power variation curves to obtain a power load curve; superimpose several time variation curves to obtain a time load curve;

[0078] S122-3: Superimpose the power load curve and the time load curve to obtain the controllable load curve.

[0079] When overlaying the load change curves of controllable equipment (after time alignment), the controllable equipment is first distinguished as either power-controllable or time-controllable. The load change curves corresponding to all power-controllable equipment are overlaid to obtain the power load curve; the load change curves corresponding to all time-controllable equipment are overlaid to obtain the time load curve. Finally, the power load curve and the time load curve are overlaid to obtain the controllable load curve.

[0080] In obtaining the controllable load curve, the electrical equipment within the target distribution area is first divided into controllable and uncontrollable equipment. Controllable equipment is further divided into power-controllable and time-controllable equipment. Equipment within the target distribution area that has no specific requirements for operating time is marked as time-controllable; equipment that has requirements for operating time, but whose operating power can be appropriately adjusted without affecting normal production or daily life, is marked as power-controllable.

[0081] For example, when using a washing machine in daily life, users can wash clothes at night or in the morning, so the washing machine can be labeled as a time-controlled device. In contrast, the time for using an air conditioner cannot be flexibly adjusted, but the operating power of the air conditioner can be adjusted appropriately, such as reducing the operating power of the air conditioner to reduce its energy consumption. Therefore, the air conditioner can be labeled as a power-controlled device.

[0082] It's important to understand that when classifying electrical equipment in a target distribution area into controllable or uncontrollable devices, the equipment can be matched and labeled according to a pre-set classification table. After labeling, the labeling results (e.g., some equipment is controllable, some is uncontrollable) can be sent to the user. If the user has no objection, the equipment is classified as controllable or uncontrollable according to the labeling results. If the user has objections, they can provide feedback, such as in special circumstances where a certain piece of equipment cannot be classified as controllable. After receiving user feedback, the classification of the equipment can be adjusted as appropriate. This feedback mainly addresses specific user needs, i.e., the equipment is controllable from a classification perspective but uncontrollable from the user's perspective, otherwise it would affect the user experience. Such objections should be analyzed in conjunction with the reasons for the objection. If the reasons for the objection are valid, the equipment can be directly classified as uncontrollable, and no load transfer will be performed on that equipment in subsequent control operations.

[0083] In the process of generating load control strategies, peak and off-peak segments in the load forecast curve are identified based on the target optimization range. The target optimization range is the desired load target in the distribution area load control; it is an actively pursued optimization objective. The target optimization range differs significantly from the distribution area load limits (upper and lower limits), which are rigid boundaries set to ensure the safe and stable operation of the power system in the distribution area and prevent load breaches. Generally, the upper limit of the target optimization range is lower than the upper limit of the distribution area load, and the lower limit is higher than the lower limit of the distribution area load.

[0084] In S200, the peak and valley sections of the load forecast curve are identified based on the target optimization range, including:

[0085] S211: Extract the upper and lower limits of the target optimization range;

[0086] S212: Identify the curve segments in the load forecast curve that are above the upper limit of the range as peak segments; and identify the curve segments in the load forecast curve that are below the lower limit of the range as trough segments.

[0087] The load optimization target is the area within the target optimization range. The load forecast curve is compared with the target optimization range. During peak electricity consumption, the load forecast curve will be higher than the upper limit of the target optimization range. This part is called the peak segment. Similarly, during off-peak electricity consumption, the load forecast curve will be lower than the lower limit of the target optimization range. This part is called the off-peak segment.

[0088] In the coordinated regulation of peak shaving and valley filling of transformer area load, the load in the peak period is transferred to the valley period. This reduces the electricity load in the peak period and increases the electricity load in the valley period, making the actual load change curve of the transformer area smoother and minimizing the impact of transformer area load fluctuations on the power grid equipment and users' electricity experience in the target transformer area.

[0089] It should be noted that within a control cycle, the target distribution area may have multiple peak periods or multiple trough periods. The load control objective is to transfer the excess load from multiple peak periods to multiple trough periods in order to reduce the volatility of the actual load curve of the target distribution area.

[0090] Figure 4 This is a schematic diagram showing the division of peak and valley sections in the load forecast curve. Figure 4 The dashed lines A and B represent the lower and upper limits of the target optimization range, respectively. As shown in the figure, the upper limit of the range crosses the load forecast curve, and two peak segments, F1 and F2, can be obtained above the upper limit. Similarly, a curve segment below the lower limit of the range, i.e., a trough segment, can be obtained.

[0091] In S200, load scheduling strategies are generated based on peak-valley sections of controllable load curves and load forecast curves, including:

[0092] S221: Statistically calculate the load surplus during peak periods and the load gap during off-peak periods; whereby the load surplus includes controllable load and uncontrollable load, and the controllable load includes power surplus and time load.

[0093] S222: Generate a load transfer strategy based on the time load and load gap, and convert the load transfer strategy into a load scheduling strategy.

[0094] After determining the peak and off-peak periods of the target distribution area within a control cycle, it is necessary to assess the excess load in the peak period and the missing load in the off-peak period, and generate specific load dispatch strategies based on the assessment results.

[0095] When calculating the excess load during peak periods, the essence is to determine the load volume within the peak period that exceeds the upper limit of the target optimization range. This excess load is the load volume that needs to be "peak-shaving". Since this excess load may need to be transferred, it should include the controllable load volume in the peak period. If the excess load is greater than the controllable load volume in the peak period, then it should also include the uncontrollable load volume.

[0096] If transferring a portion of the controllable load can achieve the peak shaving target, then the amount of controllable load to be transferred needs to be determined. If transferring all the controllable load cannot achieve the peak shaving target, then while transferring all the controllable load, it is also necessary to determine whether the remaining uncontrollable load exceeds the area's load limit. If it does not exceed the area's load limit, no action is needed. If it exceeds the area's load limit, then additional energy storage equipment needs to be activated to supply power to the uncontrollable equipment in that peak period to ensure normal power consumption in the target area.

[0097] like Figure 4 As shown, the load forecast curve forms two peak segments, F1 and F2, above the upper limit of the range. The load corresponding to these peak segments F1 and F2 is the excess load of the corresponding segments, which can be solved by integration. Similarly, the load forecast curve forms a trough segment below the lower limit of the range, and the load corresponding to this trough segment is the load gap. The purpose of peak shaving and valley filling is to transfer the excess load of peak segments F1 and F2 to trough segment G1.

[0098] It's also important to understand that load scheduling strategies control the power and timing of relevant controllable equipment. Although the controllable equipment has been confirmed by the user, the load scheduling strategy should still be sent to the user for confirmation, and adjusted based on user feedback. For example, if a user believes that the operating time of a controllable device affects their experience, the device can be replaced instead of being moved during peak hours. A new target control device can be selected from other controllable devices during peak hours to fill the valley. Of course, when sending the load scheduling strategy to the user, the benefits the user can obtain under the load scheduling should also be sent, allowing the user to confirm with the help of these benefits.

[0099] Furthermore, controllable load includes power surplus and time load. Power surplus refers to the load that a power-controllable device can reduce by adjusting its operating power; it is not equal to the actual load of the electrical equipment. For example, if the load of a power-controllable device G operating at its rated power is FH1, and the load operating at its minimum power is FH2, then the power surplus of this device is FH1 - FH2. In contrast, time load refers to the total load of a time-controllable device within a given time period. For example, if the operating load of a time-controllable device S during a given time period is S1, then the time load of this device is S1. The time loads of multiple time-controllable devices can be summed according to their time intervals.

[0100] Load shifting strategies are generated based on time load and load gap, including:

[0101] S222-1: Compare the time load and the load gap to determine the load transfer amount, and determine the target control device from the time-controllable devices based on the load transfer amount;

[0102] S222-2: Generate a load transfer strategy based on the controllable load corresponding to the target control device.

[0103] The excess load during peak periods includes various types of load, of which only time-based load can be transferred to fill the load gap during off-peak periods. Therefore, it is necessary to determine whether the time-based load during peak periods is greater than the load gap. If it is greater, a portion of the time-based load needs to be transferred; if it is less than the load gap, all of the time-based load needs to be transferred.

[0104] It's important to clarify that if the time-based load exceeds the load gap, only the controllable load corresponding to some time-controlled devices within the peak period needs to be transferred. In this case, some controllable devices need to be selected as target control devices, and transferring these target control devices to operate in the off-peak period will complete peak shaving and valley filling. If the time-based load is less than the load gap, then all time-controlled devices within the peak period become target control devices, and the electrical load of all time-controlled devices needs to be transferred to the off-peak period.

[0105] Assumption Figure 4 The excess load corresponding to peak segment F1 and peak segment F2 are both time-based loads. The time-based load of peak segment F1 is less than the load gap of valley segment G1, while the time-based load of peak segment F2 is greater than the load gap of valley segment G1. If the time-based load of peak segment F1 is transferred to valley segment G1, then all time-controllable devices within peak segment F1 can be transferred. If the time-based load of peak segment F2 is transferred to valley segment G1, then some time-controllable devices within peak segment F2 need to be selected for load transfer.

[0106] Determining the target control device from time-controllable devices based on load transfer amount includes:

[0107] S222-1-1: Calculate the operating time and unit load of several time-controllable devices in the peak period, and construct the load graph of the time-controllable devices with the operating time as the longest and the unit load as the longest.

[0108] S222-1-2: Fill in the low-valley section using the load graphs of several time-controllable devices, and determine several equipment groups based on the principle that the sum of controllable loads is not less than the load gap.

[0109] S222-1-3: When the number of time-controllable devices in a device group is the minimum, mark the time-controllable devices in that device group as target control devices.

[0110] When transferring the controllable load of time-controlled devices to off-peak periods, it is necessary to consider minimizing the number of time-controlled devices transferred to ensure user experience, while also ensuring that the transferred controllable load can fill the off-peak periods.

[0111] Based on this principle, first calculate the controllable load corresponding to all time-controllable devices within the peak period. Determine several device groups based on the principle that the sum of the controllable loads is not less than the load gap. Each device group includes several time-controllable devices, and the sum of the controllable loads of these devices equals the load gap. It can also be slightly greater than the load gap, but the difference should be less than a set threshold. Simultaneously, identify the number of time-controllable devices in each device group, select the group with the fewest devices, and mark all time-controllable devices in that group as the target control devices.

[0112] It is important to understand that when the controllable load in the peak section is transferred to the valley section, if the controllable load is greater than the load gap in the valley section, it will not affect the safe operation of the power grid in the target area. This is because the electricity load in the valley section after the transfer will be greater than the lower limit of the range, which will not affect the operation of the power grid.

[0113] Load transfer strategies are generated based on the controllable load corresponding to the target control equipment, including:

[0114] S222-2-1: Extract the load diagrams of several target control devices;

[0115] S222-2-2: Fill the low-valley section using the load graphs of several time-controllable devices, and generate a load transfer strategy based on the running time of several target control devices in the filling result.

[0116] Figure 5 This is a schematic diagram illustrating the generation of a load transfer strategy. Assume that five target control devices are needed to fill in the low-load area, and the load diagram for each target control device is rectangular. During the filling process, the strategy is as follows: Figure 5 The load patterns are filled in the order listed. Load pattern number 1, with the longest running time, is filled at the position closest to the lower azimuth limit. Load patterns numbered 2 through 5 are filled sequentially according to their running time, covering the entire valley section. After filling, the times corresponding to the two ends of each rectangle are the running times of the corresponding target control equipment, thus obtaining the running times of 5 target control equipment. These 5 target control equipment running times are converted into control commands, meaning that executing the control commands can control the target control equipment in... Figure 5 It operates at the corresponding time to fill the valley section.

[0117] It should be noted that since the unit load (i.e., operating power) of the target control equipment is not fixed, its load pattern may not be rectangular. However, by filling in the low-load sections according to the above technical solution, it can be ensured that the total load after filling does not exceed the upper limit of the range.

[0118] After generating the load shifting strategy, it is determined that the load corresponding to some or all of the controllable equipment within the peak period will be shifted. Based on this, the load forecast curve within the peak period is updated, and the value of the updated load forecast curve will decrease. However, if the value of the updated load forecast curve within the peak period still exceeds the load limit of the transformer area, it is necessary to adjust the power controllable equipment within the peak period to reduce the load risk within the peak period.

[0119] Load reduction strategies are generated based on power surplus and the upper limit of transformer area load, including:

[0120] S222-3: Update the load forecast curve for peak areas based on load transfer strategies;

[0121] S222-4: Determine whether the updated load forecast curve exceeds the upper limit of the transformer area load; if yes, select power controllable devices based on the upper limit of the range, and generate a load reduction strategy based on the selected power controllable devices.

[0122] The update of the load forecast curve actually involves removing the load variation curves of the target controlled equipment during peak periods. At this point, the total load during peak periods includes both uncontrollable loads and partially controllable loads. This partially controllable load includes the total load of power-controllable equipment and the total load of time-controllable equipment.

[0123] If the total load in the peak period still exceeds the load limit of the distribution area, it will still affect the load safety of the target distribution area. Therefore, it is necessary to control the operating power of the power-controlled equipment to reduce the total load. When controlling the operating power of the power-controlled equipment, the power-controlled equipment is selected according to the load that needs to be regulated. The operating power of the selected power-controlled equipment is reduced at the corresponding time. The operating time and power control parameters are converted into control commands, and the control is executed at the corresponding time to reduce the operating power, thereby reducing the total load in the peak period and ensuring the grid operation safety of the target distribution area.

[0124] It is also important to note that energy storage devices are installed in the target distribution area or power grid system. If the load dispatching scheme still cannot guarantee grid security, the energy storage devices will participate to ensure the normal operation of the grid. For example, when peak-hour segment F2 transfers part of its load to off-peak segment G1 and adjusts its power equipment, if the load of peak-hour segment F2 still exceeds the grid load limit of the target distribution area, then energy storage devices are needed to supplement the power. When peak-hour segment F2 transfers all of its load to off-peak segment G1, and off-peak segment G1 still cannot consume the power generated by the grid, then energy storage devices are needed to absorb the power.

[0125] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for coordinated control of transformer area load peak shaving and valley filling based on intelligent fusion terminals, characterized in that, include: The load forecast curve and controllable load curve of the target distribution area are obtained; the controllable load curve is generated by superimposing the load change curves of all controllable equipment in the target distribution area. Identify the peak and valley sections of the load forecast curve based on the target optimization range; generate a load scheduling strategy based on the controllable load curve and the peak and valley sections of the load forecast curve; The target distribution area is load regulated by the intelligent converged terminal according to the load scheduling strategy; wherein, the load scheduling strategy includes load transfer strategy and load reduction strategy; Predicting and obtaining the controllable load curve for the target transformer area includes: Historical load data and historical environmental data of several controllable devices within the target area are obtained, and the load change curves of the controllable devices are predicted based on the historical load data and historical environmental data; wherein, the load change curves include power change curves and time change curves; The load change curves of several controllable devices are superimposed to obtain a controllable load curve. The load variation curves of several controllable devices are superimposed, including: Extract the power variation curves of the power-controllable devices and the time variation curves of the time-controllable devices from the aforementioned controllable devices; The power variation curves are superimposed to obtain the power load curve; the time variation curves are superimposed to obtain the time load curve. The power load curve and the time load curve are superimposed to obtain the controllable load curve; Identifying the peak and valley sections of the load forecast curve based on the target optimization range includes: Extract the upper and lower limits of the target optimization range; The curve segments in the load forecast curve that are above the upper limit of the range are identified as peak segments; and the curve segments in the load forecast curve that are below the lower limit of the range are identified as trough segments. Based on the controllable load curve and the load forecast curve, a load scheduling strategy is generated for peak-valley sections, including: The statistics include the load surplus during peak periods and the load gap during off-peak periods; the load surplus includes controllable load and uncontrollable load, and the controllable load includes power surplus and time load. A load transfer strategy is generated based on the time load and the load gap, and the load transfer strategy is converted into a load scheduling strategy. A load transfer strategy is generated based on the time load and the load gap, including: The load transfer amount is determined by comparing the time load amount and the load gap amount, and the target control device is determined from the time-controllable devices based on the load transfer amount; A load transfer strategy is generated based on the controllable load corresponding to the target control device; A load transfer strategy is generated based on the controllable load corresponding to the target control device, including: Extract the load graphs of several of the target control devices; The load graphs of several time-controllable devices are used to fill the low-valley section, and a load transfer strategy is generated based on the running time of several target control devices in the filling result. Each target control device load graphic is a rectangle. When filling, the load graphic with the longest running time is filled at the position closest to the lower limit of the azimuth, and the remaining load graphics are filled in sequence according to the running time to cover the entire valley section.

2. The method for coordinated control of transformer area load peak shaving and valley filling based on intelligent fusion terminals according to claim 1, characterized in that, The load forecast curve for the target transformer area is obtained by prediction, including: Historical load data and historical environmental data of the target transformer area are obtained, and an LSTM model is trained based on the historical load data and the historical environmental data to obtain a load prediction model; Obtain the predicted environmental data, and after standardizing the predicted environmental data and the historical load data, input them into the load prediction model to obtain the load prediction curve of the target transformer area.

3. The method for coordinated control of transformer area load peak shaving and valley filling based on intelligent fusion terminals according to claim 1, characterized in that, Determining the target control device from the time-controllable devices based on the load transfer amount includes: Calculate the operating time and unit load of several time-controllable devices in the peak period, and construct a load graph of the time-controllable devices with the operating time as the length and the unit load as the height. The low-valley section is filled using the load graphs of several time-controllable devices, and several device groups are determined based on the principle that the sum of controllable loads is not less than the load gap. When the number of time-controllable devices in the device group is the minimum, the time-controllable devices in the device group are marked as target control devices.

4. The method for coordinated control of transformer area load peak shaving and valley filling based on intelligent fusion terminals according to claim 1, characterized in that, The load reduction strategy is generated based on the power surplus and the upper limit of the transformer area load, including: The load forecast curve for the peak section is updated based on the load transfer strategy. Determine whether the updated load forecast curve exceeds the upper limit of the transformer area load; if so, select power-controllable devices based on the upper limit of the range, and generate a load reduction strategy based on the selected power-controllable devices.

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