Power load control method and device, electronic equipment and storage medium

Abstract

The invention discloses a power load control method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring historical load data, current power demand and a production power consumption constraint condition set of a preset area to be subjected to power load control; predicting the power load of the preset area at a preset future time point based on the historical load data to obtain a power demand prediction result; based on the power demand prediction result, the current power demand and the production power consumption constraint condition set, a power consumption scheduling plan is determined, and the power consumption scheduling plan is used for adjusting the production activity of the preset area so as to control the power load of the preset area; and controlling the power load of the preset area based on the power utilization scheduling plan to obtain a power load control result. According to the invention, the technical problem of low efficiency of controlling the power load in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of power load control, and in particular to a power load control method, device, electronic equipment and storage medium. Background Art

[0002] With the gradual advancement of the "dual carbon" goals, energy conservation and emission reduction issues in high-energy-consuming parks, which are large energy consumers in the industrial field, have become increasingly important. Promoting industrial structure optimization and improving energy efficiency have become the focus of relevant research. By managing demand for power-consuming parks, electricity costs can be effectively saved, while reducing the load impact on the power grid. Load control technology is the main means of demand management, which regulates output loads through economic and other incentives, reduces the maximum demand peak, and improves energy utilization.

[0003] At present, the control of power load in related technologies usually needs to be completed by technical personnel relying on expert experience, which is easily interfered by human factors, or inappropriate power load control is easy to interfere with the normal production activities of the park or enterprise, resulting in low efficiency of power load control in related technologies.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present invention provide a method, device, electronic device and storage medium for controlling an electric load, so as to at least solve the technical problem of low efficiency in controlling an electric load in the related art.

[0006] According to one aspect of an embodiment of the present invention, a method for controlling power load is provided, comprising: obtaining historical load data, current power demand and a set of production power constraint conditions of a preset area to be subjected to power load control, wherein the set of production power constraint conditions is used to represent a set of constraint conditions that need to be satisfied for normal production operation of the preset area; predicting the power load of the preset area at a preset future time point based on the historical load data to obtain a power demand prediction result; determining a power dispatching plan based on the power demand prediction result, the current power demand and the set of production power constraint conditions, wherein the power dispatching plan is used to adjust the production activities of the preset area to control the power load of the preset area; and controlling the power load of the preset area based on the power dispatching plan to obtain a power load control result.

[0007] Optionally, the power load of a preset area at a preset future time point is predicted based on historical load data to obtain a power demand forecast result, including: constructing a long-term and short-term load forecasting model based on historical load data, wherein the long-term and short-term load forecasting model is used to represent a network model for predicting power load demand based on a time series; based on the long-term and short-term load forecasting model, the power load of a preset area at a preset future time point is predicted to obtain a power demand forecast result.

[0008] Optionally, based on the electricity demand forecast results, current electricity demand and a set of production electricity constraints, a power scheduling plan is determined, including: constructing a standard demand optimization scheduling model based on the electricity demand forecast results, current electricity demand and a set of production electricity constraints; generating a power scheduling plan based on the standard demand optimization scheduling model.

[0009] Optionally, based on the electricity demand forecast results, current electricity demand and a set of production electricity constraints, a standard demand optimization scheduling model is constructed, including: based on the electricity demand forecast results and current electricity demand, constructing scheduling impact characteristic parameters; based on the scheduling impact characteristic parameters and a set of production electricity constraints, constructing a standard demand optimization scheduling model.

[0010] Optionally, generating a power scheduling plan based on a standard demand optimization scheduling model includes: obtaining an initial power scheduling plan for a preset area; and adjusting the initial power scheduling plan based on the standard demand optimization scheduling model to obtain a power scheduling plan.

[0011] Optionally, the production electricity constraint condition set includes at least one of the following: a production batch status constraint in a preset area, an output load range constraint, a task total capacity demand constraint, and a production indicator timeliness constraint.

[0012] According to another aspect of an embodiment of the present invention, there is also provided a power load control device, comprising: an acquisition module, used to acquire historical load data, current power demand and a set of production power constraint conditions for a preset area to be subjected to power load control, wherein the set of production power constraint conditions is used to represent a set of constraint conditions that need to be satisfied for normal production operation in the preset area; a prediction module, used to predict the power load of the preset area at a preset future time point based on the historical load data, and obtain a power demand prediction result; a determination module, used to determine a power scheduling plan based on the power demand prediction result, the current power demand and the set of production power constraint conditions, wherein the power scheduling plan is used to adjust the production activities in the preset area to control the power load in the preset area; and a control module, used to control the power load in the preset area based on the power scheduling plan, and obtain a power load control result.

[0013] According to another aspect of an embodiment of the present invention, there is further provided an electronic device, comprising: a memory storing an executable program; and a processor for running the program, wherein the method in each embodiment of the present invention is executed when the program is running.

[0014] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the methods in various embodiments of the present invention.

[0015] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the method in each embodiment of the present invention is implemented.

[0016] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method in each embodiment of the present invention is implemented.

[0017] According to another aspect of the embodiments of the present invention, a computer program is further provided. When the computer program is executed by a processor, the methods in the embodiments of the present invention are implemented.

[0018] In an embodiment of the present invention, historical load data, current power demand and a set of production power constraint conditions for a preset area to be subjected to power load control are obtained, wherein the set of production power constraint conditions is used to represent a set of constraint conditions that need to be satisfied for normal production operation in the preset area; based on the historical load data, the power load of the preset area at a preset future time point is predicted to obtain a power demand prediction result; based on the power demand prediction result, the current power demand and the set of production power constraint conditions, a power dispatching plan is determined, wherein the power dispatching plan is used to adjust the production activities in the preset area to control the power load in the preset area; based on the power dispatching plan, the power load in the preset area is controlled to obtain a power load control result. It is easy to notice that this application obtains historical load data, predicts future power loads, and takes into account the set of production power constraints, so as to ensure that the normal production operation of the preset area is not affected during the power load control process, thereby ensuring the normal progress of production activities. By adjusting the power load in real time based on the power dispatch plan, it is possible to respond to changes in power demand in a timely manner and ensure the stability and reliability of the power grid operation. The entire process can be automated and controlled, reducing manual intervention, improving efficiency and accuracy, and thus solving the technical problem of low efficiency in controlling power loads in related technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0020] Figure 1 is a flow chart of a method for controlling an electric load according to an embodiment of the present invention;

[0021] Figure 2 is a schematic diagram of a control device for an electric load according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] According to one aspect of an embodiment of the present invention, a method for controlling an electric load is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0025] Figure 1 is a flow chart of a method for controlling an electric load according to an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0026] Step S102, obtaining historical load data, current power demand and a set of production power constraint conditions of a preset area to be subjected to power load control.

[0027] The production electricity constraint condition set is used to represent a set of constraint conditions that need to be met for normal production operation in a preset area.

[0028] The above-mentioned preset area may refer to a pre-set area, which may be an area where one or more enterprises are located, or may be an industrial park. The preset area may be specifically determined according to actual needs and is not limited here.

[0029] In an optional embodiment, the historical load data of the preset area to be subjected to power load control can be obtained. First, the power load data of the preset area in the past period of time can be collected, which may include hourly or daily load conditions, and can be collected through power monitoring equipment, smart meters and other equipment. The current power demand can be obtained, and the current power demand of the preset area can be monitored in real time, including real-time load, power consumption, etc., which can be collected in real time through smart meters, remote monitoring systems and other equipment. And the production power constraint condition set can be obtained, and the constraints that need to be met for normal production operation in the preset area can be determined, including the minimum power demand, maximum power demand and other constraints of the production equipment. These constraints can be determined by the technical parameters of the production equipment, production plan and other information. By analyzing the historical load data and the current power demand, the future power load situation can be predicted more accurately, which is helpful to formulate a more effective load control strategy. Obtaining the production power constraint condition set can ensure that the power load control is carried out under the premise of meeting the production demand, and avoid affecting the production operation due to load control.

[0030] Step S104, predicting the power load of a preset area at a preset future time point based on the historical load data to obtain a power demand prediction result.

[0031] In an optional embodiment, the power load of a preset area at a preset future time point can be predicted based on historical load data. Specifically, first, historical power load data can be collected, which may include information such as the load size in different time periods, seasonal changes, and the impact of factors such as holidays on the load. The collected data can be cleaned and processed to remove outliers and noise to ensure the accuracy and integrity of the data. According to the characteristics of historical data, factors that have a greater impact on the power load, such as weather, holidays, working days / non-working days, etc., can be extracted. Next, a suitable prediction model, such as a time series model, a regression model, etc., can be selected to establish a power demand prediction model, and the selected model can be trained using historical data to obtain the parameters of the model. Finally, the trained model can be used to predict the power load at a future time point to obtain the power demand prediction result. By accurately predicting future power demand, the power system can make corresponding adjustments in advance to avoid excessive or underload situations and ensure the stable operation of the power system. By accurately predicting power demand, the power system can be flexibly adjusted according to demand changes to avoid unnecessary energy waste and cost increases, thereby reducing power supply costs. Accurate power demand forecasting can help the power system identify potential problems in a timely manner and take corresponding measures to ensure the reliability and stability of the power system.

[0032] Step S106, determining a power dispatching plan based on the power demand forecast result, the current power demand and the set of production power constraint conditions.

[0033] Among them, the electricity dispatch plan is used to adjust the production activities in the preset area to control the power load in the preset area.

[0034] In an optional embodiment, the power demand in the future can be predicted by historical data analysis, statistical methods, machine learning and other technical means, and the prediction results can be used as the data basis for the subsequent power dispatch plan. The current actual power demand and production power constraints can be collected, including the use of various types of equipment, production plans, power limits and other information. Then, a reasonable power dispatch plan can be formulated in combination with the power demand prediction results and the current constraint set. The power dispatch plan can include the on / off time of various types of equipment, the adjustment of production activities and other contents, which can meet the power demand while ensuring the stable operation of the power system. By reasonably dispatching power, the waste of power during peak hours can be avoided, the power resources can be used to a greater extent, and the energy utilization efficiency of the power grid can be improved. Through prediction and dispatching, the failure and power outage caused by overload of the power system can be avoided, and the stable operation of the power grid can be guaranteed. By reasonably dispatching power, it is possible to avoid the purchase of expensive electricity during peak hours and reduce the electricity cost of the enterprise. The operating efficiency and stability of the power system can be improved, and the electricity cost can be reduced.

[0035] Step S108, controlling the power load in the preset area based on the power dispatching plan to obtain a power load control result.

[0036] In an optional embodiment, the power load of a preset area can be controlled based on a power dispatch plan. Specifically, the power equipment in the preset area can be controlled and dispatched according to the formulated power dispatch plan to ensure that the power system operates within a predetermined load range. After the power dispatch plan is implemented, the operation of the power system can be continuously monitored, and the dispatch plan can be adjusted in time to adapt to the actual power load changes. Through reasonable power load control, the supply and demand relationship can be effectively balanced, the operating efficiency of the power system can be improved, and energy waste can be reduced. Through power load control, the peak power demand during high-load periods can be avoided, and the demand for expensive peak-shaving energy can be reduced, thereby reducing the operating cost of the power system. It can effectively improve the operating efficiency of the power system, reduce operating costs, and ensure the safe and stable operation of the power system.

[0037] In an embodiment of the present invention, historical load data, current power demand and a set of production power constraint conditions for a preset area to be subjected to power load control are obtained, wherein the set of production power constraint conditions is used to represent a set of constraint conditions that need to be satisfied for normal production operation in the preset area; based on the historical load data, the power load of the preset area at a preset future time point is predicted to obtain a power demand prediction result; based on the power demand prediction result, the current power demand and the set of production power constraint conditions, a power dispatching plan is determined, wherein the power dispatching plan is used to adjust the production activities in the preset area to control the power load in the preset area; based on the power dispatching plan, the power load in the preset area is controlled to obtain a power load control result. It is easy to notice that this application obtains historical load data, predicts future power loads, and takes into account the set of production power constraints, so as to ensure that the normal production operation of the preset area is not affected during the power load control process, thereby ensuring the normal progress of production activities. By adjusting the power load in real time based on the power dispatch plan, it is possible to respond to changes in power demand in a timely manner and ensure the stability and reliability of the power grid operation. The entire process can be automated and controlled, reducing manual intervention, improving efficiency and accuracy, and thus solving the technical problem of low efficiency in controlling power loads in related technologies.

[0038] Optionally, the power load of a preset area at a preset future time point is predicted based on historical load data to obtain a power demand forecast result, including: constructing a long-term and short-term load forecasting model based on historical load data, wherein the long-term and short-term load forecasting model is used to represent a network model for predicting power load demand based on a time series; based on the long-term and short-term load forecasting model, the power load of a preset area at a preset future time point is predicted to obtain a power demand forecast result.

[0039] In an optional embodiment, based on the collected historical load data, a long-term and short-term load forecasting model can be constructed by using time series analysis methods, neural network models, regression analysis and other methods. Long-term forecasting can use seasonal analysis or trend analysis methods, and short-term forecasting can be modeled with the help of neural networks and other methods. Then, the constructed long-term and short-term load forecasting model can be used to predict the power load within a preset time point in the future to obtain the prediction result of the power demand. The accuracy and reliability of the model can be evaluated by verifying the prediction ability of the model. By accurately predicting power demand, users can reasonably arrange power consumption plans during periods with lower prices, thereby reducing power costs, which can effectively improve the operating efficiency of the power system, reduce costs and ensure power supply reliability.

[0040] Optionally, based on the electricity demand forecast results, current electricity demand and a set of production electricity constraints, a power scheduling plan is determined, including: constructing a standard demand optimization scheduling model based on the electricity demand forecast results, current electricity demand and a set of production electricity constraints; generating a power scheduling plan based on the standard demand optimization scheduling model.

[0041] In an optional embodiment, the power demand forecast can help the power system to accurately predict the power demand in the future, so as to prepare for the power supply. Determining the power dispatch plan based on the power demand forecast results, the current power demand and the production power constraint set can effectively allocate power resources and achieve good power dispatch while ensuring power supply. Specifically, first, according to the power demand forecast results and the current power demand, combined with the production power constraint set, a standard demand optimization dispatch model can be established to describe the power interaction relationship between each node in the power system. The standard demand optimization dispatch model can include factors such as power demand, power supply capacity, transmission loss, and special constraints for production power. Then, based on the established standard demand optimization dispatch model, the power dispatch plan can be solved by using optimization algorithms such as linear programming and integer programming. The power dispatch plan can include the power supply plan, power transmission plan, peak and valley adjustment strategy, etc. of each node, so as to improve the efficiency and stability of the power system under the premise of meeting the power demand. By rationally allocating power resources, the overload and congestion of the power system can be effectively reduced, and the stability and reliability of the system can be improved. By optimizing the scheduling plan, the losses in the power production and transmission process can be reduced, thereby reducing the cost of power production and improving the economy of power supply.

[0042] Optionally, based on the electricity demand forecast results, current electricity demand and a set of production electricity constraints, a standard demand optimization scheduling model is constructed, including: based on the electricity demand forecast results and current electricity demand, constructing scheduling impact characteristic parameters; based on the scheduling impact characteristic parameters and a set of production electricity constraints, constructing a standard demand optimization scheduling model.

[0043] In an optional embodiment, the scheduling influence characteristic parameters can be constructed based on the power demand prediction results and the current power demand. First, the power demand prediction results can be obtained based on historical data and prediction algorithms, and then the scheduling influence characteristic parameters can be calculated in combination with the current power demand data, including indicators such as load prediction error and load volatility. Next, a standard demand optimization scheduling model can be constructed based on the scheduling influence characteristic parameters and the production power constraint condition set. According to different scheduling influence characteristic parameters and production power constraint condition sets, a mathematical model can be established to describe the optimization scheduling problem of the power system, including objective functions and constraints. By constructing the scheduling influence characteristic parameters based on the prediction results and the current demand, the load situation of the power system can be described more accurately, thereby improving the precision and accuracy of the scheduling. By constructing the standard demand optimization scheduling model, the appropriate scheduling of the power system can be achieved under the premise of considering the production power constraint conditions, making the operation of the system more efficient.

[0044] Optionally, generating a power scheduling plan based on a standard demand optimization scheduling model includes: obtaining an initial power scheduling plan for a preset area; and adjusting the initial power scheduling plan based on the standard demand optimization scheduling model to obtain a power scheduling plan.

[0045] In an optional embodiment, the initial power consumption scheduling plan for the preset area can be obtained first, which can be a plan formulated based on historical data and forecast information, including power consumption and load conditions in various time periods. Then, the initial power consumption scheduling plan can be adjusted according to the standard demand optimization scheduling model, which can include operations such as load balancing, supply and demand matching, and generator output adjustment to optimize system operation. By optimizing the scheduling plan, the supply and demand balance of the power system can be achieved, the situation of excessive or insufficient load can be avoided, and the operating efficiency of the power system can be improved. Through reasonable scheduling, the unstable factors in the system can be reduced, the stability and reliability of the power system can be improved, and the incidence of power outages and failures can be reduced.

[0046] Optionally, the production electricity constraint condition set includes at least one of the following: a production batch status constraint in a preset area, an output load range constraint, a task total capacity demand constraint, and a production indicator timeliness constraint.

[0047] In an optional embodiment, during the production process, the use of electricity can be controlled by setting a series of constraints to ensure the completion of production tasks and the stable operation of the power system. These constraints may include production batch status constraints in preset areas, output load range constraints, task total capacity demand constraints, and production index timeliness constraints. Specifically, for the production batch status constraints in preset areas, the production tasks can be first classified and managed in batches to determine the status and priority of each production batch. By monitoring and controlling the production progress and status of each batch, the power usage can be dynamically adjusted according to the actual situation to ensure that each batch can complete the production task on time. For the output load range constraints, a reasonable load range can be set according to the requirements of the production task and the load capacity of the power system. During the production process, the use of production equipment can be adjusted in time by monitoring the changes in the power load to ensure that the load fluctuates within a controllable range and avoid excessive or too small loads. For the total capacity demand constraints of the tasks, the corresponding power usage targets can be set according to the total capacity requirements of the production tasks. During the production process, the production progress and power usage plan can be adjusted in time by monitoring the completion of each task and the power consumption to ensure that the total capacity requirements are met. For the timeliness constraints of production indicators, corresponding production plans and power usage plans can be set according to the timeliness requirements of production indicators. By monitoring the real-time changes of production indicators and power consumption, the production progress and power usage strategies can be adjusted in time to ensure that the timeliness of production indicators is met.

[0048] In the above process, by reasonably setting constraints and dynamically adjusting power usage, production efficiency can be effectively improved, production tasks can be completed on time, and production stagnation and delays can be reduced. By controlling the scope of power usage and optimizing the power usage plan, power costs can be effectively reduced and additional costs caused by excessive use or waste of power can be avoided. By monitoring and controlling the fluctuation range of power load, the instability of the power system caused by excessive or insufficient load can be avoided, and the safe operation of production equipment and power systems can be guaranteed. By setting the timeliness constraints of production indicators, the timely achievement of production indicators can be ensured, and the execution efficiency and accuracy of production plans can be improved. By implementing the production power constraint set, the power load can be effectively controlled, production efficiency can be improved, costs can be reduced, the stability of the power system can be guaranteed, and the timeliness requirements of production indicators can be achieved, thereby providing technical guarantee and support for enterprise production.

[0049] The technical solution proposed in this application is explained below in combination with an optional embodiment. At present, industrial electricity consumption in high-energy-consuming parks such as steel parks mainly follows the two-part electricity price billing method, that is, the total electricity cost is divided into two parts: the electricity price and the demand price. The electricity price is formed by free market bidding and is charged according to actual power consumption and time-of-use electricity prices. The demand price reflects the fixed compensation obtained in the power supply cost based on the transformer capacity or the maximum demand measurement with the demand cost, among which the maximum demand index can well judge the economic operation cost and energy utilization rate of the target. The embodiment of this application considers the demand price generated under the maximum demand index as the main body of the basic electricity price. By studying a production power load operation decision based on sliding demand pricing, combined with controllable power consumption planning, while meeting the long-term production task of high-energy-consuming parks, the appropriate demand price cost is dispatched through short-term loads, and the long-term reliability of short-term decisions is achieved through decision optimization of comprehensive time scales.

[0050] The present application provides a method for optimizing the operation of production electricity load based on sliding demand pricing, which can meet the basic production capacity requirements of the park and its time constraints, reduce the uncertainty changes in demand peaks, and improve the energy utilization efficiency of electricity loads, thereby achieving appropriate demand electricity price costs.

[0051] The method for real-time demand control of power load provided by the present application includes the following steps: obtaining the historical load data of the park and the current maximum demand; the embodiment of the present application is designed for the demand electricity price of the park, taking into account the cost brought by the maximum demand under the monthly unit. It should be noted that the measurement method of the demand electricity price is based on the user's maximum average load output in each demand metering cycle within a period of time, usually in months, as the maximum demand billing value, and then multiplied by the unit demand electricity price to obtain the final demand electricity fee. The peak demand value of the month is continuously updated over time as a state value. Whenever new load data is obtained, the peak demand value is updated once, but it should be noted that the value will only be updated when the peak value exceeds the original state.

[0052] Constructing long-term and short-term load forecasting models; the embodiment of the present application can obtain the changing characteristics of the park's historical load data, and thereby construct a model through neural networks and other means to predict the changing trend of the load in a certain period of time in the future. In the absence of electrical energy storage equipment, how to use the changing trend of the load to reasonably distribute the power supply load is an important issue to ensure the safe and stable operation of the park's power grid.

[0053] Obtain the predicted change of the standard demand peak; combined with the above load forecasting model, the embodiment of the present application further considers whether the demand change in the short term will incur costs. Due to the monotonic nature of the demand pricing method, the current maximum demand value is used as a necessary indicator for demand optimization and regulation to determine whether there will be an increase in demand electricity prices. Only when the demand peak exceeds the original maximum demand will a demand electricity price be further generated, which is used as a judgment indicator for whether regulation or filling of accumulated gaps is required under the demand optimization scheduling strategy.

[0054] The standard scheduling plan is obtained; the embodiment of the present application calls the optimization scheduling method to calculate the production schedule adjustment strategy under the current state. The control decision is made by considering the demand change trend with high accuracy within the prediction interval, and the constraints and objective functions are reasonably constructed according to the actual production demand, and the production plan set a few days ago is further introduced as the input factor of the decision. The decision amount is considered as an adjustment to the original production plan, and the demand optimization decision under the standard state is obtained.

[0055] Adjust demand to optimize scheduling decisions; based on the standard demand optimization decision with a limited forecast range, further introduce the long-term impact of the current decision on future demand, analyze the impact of the cumulative control amount, adjust on the basis of the original model, and obtain a reasonable decision that balances short-term scheduling and long-term impact through appropriate correlation coefficients.

[0056] This application proposes the overall process of the sliding demand scheduling model, including: extracting forecast data and historical load data; first extracting relatively accurate load forecast information in the short term, bringing it into the day-ahead production plan, and combining it with the demand calculation method to determine the subsequent short-term demand change status.

[0057] Determine whether the expected demand exceeds the existing peak demand; determine whether it will exceed the current maximum demand, obtain the current adjustable load, and production limit constraints, and analyze the impact of scheduling; obtain the current total adjustable load, introduce future impact parameters on the basis of meeting production limit requirements to build a linear solution model. Consider whether the cumulative amount of regulation needs to be supplemented in advance to avoid sudden load demand due to excessive cumulative lack of load near the end of the production task, and the resulting constraints are:

[0058] Compared with the model built by a single batch, the first thing to consider for the production batch status constraint is the status of each project. Here, the original production plan is used as the basis for judgment, that is, regulation actions are only taken when the project is planned to run. For the rest of the time periods, which have not yet started or have ended, the project is stopped by default, that is, the demand load is zero.

[0059]

[0060] Among them, W t i represents the output load of project i at time t, and also satisfies That is, the model's estimated output load is equal to the value of the planned output load after adjustment. The formula is obtained through the original scheduling plan It is determined whether the current moment is the running time of the project. If the project has not started or has ended, no control should be added, that is, it is determined that the project at this moment is not within the controllable range at the current moment.

[0061] The output load range constraints and production batch judgment constraints, as well as the current moment when the control action can be performed, are the parts that need to be considered in the model decision-making. Only by adding appropriate constraints to the corresponding decision quantities can a complex control model with multiple mixed projects be constructed.

[0062]

[0063] in, represents the minimum output of running project i, It represents the highest output of project i. The formula is also obtained through the original scheduling plan It is determined whether the task is running at the current moment. If the task has started running, the formula under the corresponding single task model adds reasonable constraints on the load control range for each ongoing project.

[0064] The total capacity demand constraint of the task, the specific control constraints of the multi-project batch model are similar to those of the single-task model, except that the total control quantity constraints are added to each project accordingly.

[0065]

[0066] in, represents the remaining time of project i at time t, and also satisfies That is, the remaining time is the end time of project i Calculated by subtracting the current time t; Represents the cumulative control amount of project i at time t. The formula shows that the total control amount of each task is zero. Since the output load and control action of each project are independent of each other, they will not directly affect each other. They will only change the total load output and indirectly affect the control decision of each project. Each project can meet the conditions by adding independent constraints.

[0067] The timeliness constraint of production indicators, in order to accurately describe the production indicator requirements, the formula under the original single task model is changed. When the remaining time of the project exceeds the longest period that can be controlled, it exceeds the maximum range that can be constrained. After this period of regulation, the project output, the remaining load demand can be reasonably completed within the normal load range within the remaining time. There will be no premature termination, and the cumulative load demand will not be too large to complete the indicators on time.

[0068]

[0069] in, represents the remaining total load demand of project i at time t, E i It represents the total power load demand of project i. The formula replaces the original sufficient condition constraint with the constraint condition that accurately describes the production index demand. When the remaining time of the task exceeds the current optimization control range, the control amount within the control range of each project is considered as a whole, and the impact on the future load control range is associated with the current control as a whole. The upper and lower limits are constructed according to the corresponding formula to construct the complete implementation conditions of any project.

[0070] Construct a linear solution model; after obtaining the simplified linear solution model, use the encapsulation package to solve and obtain the control strategy within the short-term prediction interval, as well as the peak demand expected to be reached under this scheme. The load forecast data under the day-ahead forecast is used as the basis for the ultra-short-term predictive control model to analyze the impact of future demand, and the entire demand target is divided into the part expected to exceed the current control period and the part predicted to exceed the current maximum demand in the future. The two work together to fully solve the demand control problem.

[0071] When there is no need to reduce short-term demand, consider filling the accumulated control costs, or reserving adjustable resources for future control; the above model will implement pre-emptive load increase measures during the period when the current load is relatively low. Combined with the change in the control state target, active load increase actions that will not affect the existing demand peak will also become feasible, providing load shedding margin in advance for subsequent demand peak control that may be affected. This also fully reflects the impact of the current control quantity on the future demand.

[0072] Combined with the data from the day-ahead forecast, ultra-short-term real-time scheduling can fully achieve the goal of demand control. This also reflects that the comparative value of the maximum demand on a longer time scale must consider not only historical data and the current model status, but also the future numerical status under the forecast, and ultimately achieve the short-term rolling optimization that introduces future demand forecasts to improve the overall target value under the long cycle as much as possible.

[0073] In response to the problem of power system demand control in high-energy-consuming parks, the embodiments of the present application consider multiple time scales, including long-term and short-term. In the short term, the action cost is the main part, and in the long term, the continuous accumulation of the control amount is analyzed as the key point, and then the impact of the accumulated control amount on the long-term demand in the subsequent time period is analyzed. The long-term and short-term are combined to jointly judge whether demand optimization scheduling is needed at present, and the subsequent demand peak is estimated based on the decision-making plan obtained by the solution, so as to achieve a better cost under the condition of meeting the basic production constraints.

[0074] The embodiment of the present application also provides an overall demand optimization module unit architecture, which collects and acquires information at the bottom layer, and uses the processed information to train a prediction model to estimate future demand changes, determine whether further adjustments are needed under the current peak demand situation, and finally introduce long-term influencing factors to construct an objective function to achieve a globally stable control effect.

[0075] According to another aspect of an embodiment of the present invention, a device for controlling an electric load is also provided, which can execute the method for controlling an electric load of the above embodiment. The specific implementation method and preferred application scenario are the same as those of the above embodiment and will not be described in detail here.

[0076] Figure 2 is a schematic diagram of a control device for an electric load according to an embodiment of the present application, such as Figure 2 As shown, the device includes the following: an acquisition module 202 , a prediction module 204 , a determination module 206 and a control module 208 .

[0077] Among them, the acquisition module is used to obtain the historical load data, current power demand and production power constraint condition set of the preset area to be subjected to power load control, wherein the production power constraint condition set is used to represent the set of constraint conditions that need to be satisfied for the normal production operation of the preset area; the prediction module is used to predict the power load of the preset area at a preset future time point based on the historical load data, and obtain the power demand prediction result; the determination module is used to determine the power dispatching plan based on the power demand prediction result, the current power demand and the production power constraint condition set, wherein the power dispatching plan is used to adjust the production activities of the preset area to control the power load of the preset area; the control module is used to control the power load of the preset area based on the power dispatching plan, and obtain the power load control result.

[0078] Among them, the prediction module is also used to construct a long-term and short-term load prediction model based on historical load data, wherein the long-term and short-term load prediction model is used to represent a network model for predicting power load demand based on time series; based on the long-term and short-term load prediction model, the power load of a preset area at a preset future time point is predicted to obtain a power demand prediction result.

[0079] Among them, the determination module is also used to build a standard demand optimization scheduling model based on the power demand forecast results, current power demand and production power constraint condition set; and generate a power scheduling plan based on the standard demand optimization scheduling model.

[0080] Among them, the determination module is also used to construct scheduling impact characteristic parameters based on the power demand prediction results and the current power demand; and to construct a standard demand optimization scheduling model based on the scheduling impact characteristic parameters and the set of production power constraint conditions.

[0081] Among them, the determination module is also used to obtain the initial power scheduling plan for the preset area; based on the standard demand optimization scheduling model, the initial power scheduling plan is adjusted to obtain the power scheduling plan.

[0082] Among them, the production electricity constraint condition set includes at least one of the following: production batch status constraint in a preset area, output load range constraint, task total capacity demand constraint and production indicator timeliness constraint.

[0083] An embodiment of the present application further provides an electronic device, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of the present invention when running.

[0084] The above-mentioned memory may refer to a device inside a computer for storing data and programs, and may include memory, hard disk, etc., wherein the memory may be used to temporarily store running programs and data, the hard disk may be used to store programs and data for a long time, and the memory may be used to enable the computer to read and write data, and execute programs; the above-mentioned processor may be responsible for executing instructions in computer programs and performing data processing, and may be responsible for controlling and executing various operations, including arithmetic operations, logical operations, data transmission, etc.

[0085] An embodiment of the present application further provides a computer-readable storage medium, which includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the methods in various embodiments of the present invention.

[0086] The above-mentioned computer storage medium may refer to a medium in a computer memory used to store certain discontinuous physical quantities. Computer storage media mainly include semiconductors, magnetic cores, magnetic drums, magnetic tapes, laser disks, etc.; the stored program included in the computer-readable storage medium may be a set of instructions that can be recognized and executed by a computer, running on an electronic computer, and is an information tool that meets certain needs of people.

[0087] An embodiment of the present application further provides a computer program product, including a computer program, which implements the methods in various embodiments of the present invention when executed by a processor.

[0088] The above-mentioned computer program product may refer to a software program that has been written, tested and released and can be run on a computer or other device. The computer program product may include an application, an operating system, tool software, etc., which is used to implement specific functions or solve specific problems.

[0089] An embodiment of the present application further provides a computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium is used to store a computer program, and when the computer program is executed by a processor, the method in each embodiment of the present invention is implemented.

[0090] The above-mentioned non-volatile computer-readable storage medium may refer to a medium for storing data. The non-volatile computer-readable storage medium can keep the data from being lost when the power is off, and can be used to store long-term data, such as operating systems, applications, and user files. The non-volatile storage medium may include hard disk drives, solid-state drives, optical disks, and flash memory storage devices, etc.

[0091] The embodiments of the present application further provide a computer program, which implements the methods in the above-mentioned embodiments of the present invention when executed by a processor.

[0092] The above-mentioned computer program may refer to a collection of instructions used to tell a computer to perform a specific task or operation. A computer program may be written by a programmer using a specific programming language and may include algorithms, data structures, logic, and control flows. A computer program may be used for a variety of purposes, including application software, operating systems, and the like.

[0093] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0094] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0095] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0096] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0097] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0098] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for controlling an electric load, characterized in that: include: Obtaining historical load data, current power demand, and a set of production power constraint conditions for a preset area to be subjected to power load control, wherein the set of production power constraint conditions is used to represent a set of constraint conditions that need to be satisfied for the preset area to perform normal production operations; Based on the historical load data, the power load of the preset area at a preset future time point is predicted to obtain a power demand prediction result; Determine a power dispatching plan based on the power demand forecast result, the current power demand and the set of production power consumption constraint conditions, wherein the power dispatching plan is used to adjust the production activities in the preset area to control the power load in the preset area; The power load of the preset area is controlled based on the power dispatching plan to obtain a power load control result.

2. The method for controlling electric load according to claim 1, characterized in that: Predicting the power load of the preset area at a preset future time point based on the historical load data to obtain a power demand prediction result includes: Constructing a long-term and short-term load forecasting model based on the historical load data, wherein the long-term and short-term load forecasting model is used to represent a network model for predicting power load demand based on time series; Based on the long-term and short-term load forecasting model, the power load of the preset area at the preset future time point is predicted to obtain the power demand forecast result.

3. The method for controlling electric load according to claim 1, characterized in that: Based on the power demand prediction result, the current power demand and the production power constraint condition set, a power dispatch plan is determined, including: Based on the power demand prediction result, the current power demand and the production power constraint condition set, a standard demand optimization scheduling model is constructed; The power consumption scheduling plan is generated based on the standard demand optimization scheduling model.

4. The method for controlling electric load according to claim 3, characterized in that: Based on the power demand prediction result, the current power demand and the production power constraint condition set, a standard demand optimization scheduling model is constructed, including: Constructing a scheduling impact characteristic parameter based on the power demand prediction result and the current power demand; Based on the scheduling impact characteristic parameters and the production power constraint condition set, the standard demand optimization scheduling model is constructed.

5. The method for controlling electric load according to claim 3, characterized in that: Generating the power consumption scheduling plan based on the standard demand optimization scheduling model includes: Obtaining an initial power consumption scheduling plan for the preset area; Based on the standard demand optimization scheduling model, the initial power consumption scheduling plan is adjusted to obtain the power consumption scheduling plan.

6. The method for controlling electric load according to claim 1, characterized in that: The production electricity constraint condition set includes at least one of the following: a production batch status constraint in the preset area, an output load range constraint, a task total capacity demand constraint, and a production indicator timeliness constraint.

7. A control device for electric load, characterized in that: include: An acquisition module, used to acquire historical load data, current power demand and a set of production power constraint conditions of a preset area to be subjected to power load control, wherein the set of production power constraint conditions is used to represent a set of constraint conditions that need to be satisfied for the preset area to perform normal production operation; A prediction module, used to predict the power load of the preset area at a preset future time point based on the historical load data to obtain a power demand prediction result; A determination module, configured to determine a power dispatching plan based on the power demand prediction result, the current power demand and the set of production power constraint conditions, wherein the power dispatching plan is used to adjust the production activities in the preset area to control the power load in the preset area; The control module is used to control the power load of the preset area based on the power dispatching plan to obtain a power load control result.

8. An electronic device, characterized in that: include: A memory storing an executable program; A processor is used to run the program, wherein the program, when running, executes the method for controlling the electric power load as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored executable program, wherein when the executable program is executed, the device where the storage medium is located is controlled to execute the power load control method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the method for controlling an electric power load according to any one of claims 1 to 6.

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