Energy load dynamic adaptation method and system considering production maintenance plan

Abstract

The present invention discloses a method and system for dynamic adaptation of energy load taking into account production and maintenance plans, belonging to the field of information processing technology. Aiming at the problem that regional power supply is difficult to adapt to sudden changes in enterprise production needs, a double-layer optimization scheduling structure of a monthly cycle model and a weekly cycle model is constructed to determine the monthly regular production plan and the weekly regular production plan within the cycle, and the energy is dynamically matched to the production and operation plan within the cycle with the week as the time cycle scale, and the daily peak shaving strategy and the daily valley filling strategy are determined. In response to the daily power supply of the power grid, the production plan is dynamically adjusted to balance the load fluctuation of the power grid and reduce the impact on the power grid caused by excessive load peak-valley difference. The solution comprehensively considers the dynamic adaptation of regional power supply and enterprise production and operation plans, realizes the refined formulation and dynamic adjustment of production plans based on regional power supply conditions, and significantly improves the production efficiency and market responsiveness of enterprises.

Description

Technical Field

[0001] The present invention relates to the field of information processing technology, and in particular to a method and system for dynamically adapting energy loads taking into account production and maintenance plans. Background Art

[0002] In modern industrial production, the production and operation activities of enterprises are constrained by many factors, among which the stability of energy supply and cost control are key links in enterprise operations. At present, enterprises often lack comprehensive and long-term considerations when formulating production plans, and often only focus on the arrangement of production tasks in the short term, while ignoring the comprehensive planning and dynamic adjustment of monthly, weekly and even daily schedules. This fragmented production plan is not only difficult to effectively cope with the cyclical changes in grid load, resulting in inefficient use of power resources, but also lacks flexibility and cannot respond to sudden market demands or sudden changes in grid load in a timely manner. How enterprises can reasonably arrange production plans to cope with the peak and valley fluctuations of regional power grids while ensuring the economy and safety of production has become an urgent problem to be solved.

[0003] Chinese patent, publication number: CN117689160A, publication date: March 12, 2024, provides a window visualization method based on the daily plan of power dispatching. By combining advanced forecasting technology and intelligent scheduling, as well as real-time visual feedback, it greatly improves the efficiency and reliability of power dispatching and meets the power grid's needs for intelligence and automation. At the same time, it can ensure that the work of the daily plan will not affect the timely supply of electricity, and the equipment indicators that need to be dispatched can be obtained according to the algorithm, which can ensure the stability of power supply. This solution does not take into account the complex adaptation of the uncertainty of the company's production and operation plan and the uncertainty of power supply, and it is difficult to improve the company's production efficiency and market responsiveness.

[0004] The above information disclosed in the Background section is only for enhancement of understanding of the background of the present application and therefore it may contain information that does not constitute the prior art that is already known to a person of ordinary skill in the art. Summary of the invention

[0005] The purpose of the present invention is to solve the problem that regional power supply is difficult to adapt to the sudden changes in enterprise production demand. A method and system for dynamic adaptation of energy load taking into account production and maintenance plans are proposed. By constructing a two-layer optimization scheduling structure of a monthly cycle model and a weekly cycle model, the monthly regular production plan and the weekly regular production plan within the cycle are determined, and the energy dynamic matching of the production and operation plan within the cycle is carried out with the week as the time cycle scale, and the daily peak shaving strategy and the daily valley filling strategy are determined. The dynamic adaptation of regional power supply and enterprise production and operation plans is fully considered, and the production plan is refined and dynamically adjusted based on the regional power supply situation, which significantly improves the production efficiency and market responsiveness of the enterprise.

[0006] In a first aspect, a technical solution provided in an embodiment of the present invention is a method for dynamic adaptation of energy load taking into account a production maintenance plan, comprising the following steps:

[0007] A monthly cycle model is constructed based on the peak-to-valley difference of the enterprise's electricity consumption during monthly scheduling and the production cost of regular orders, and the monthly cycle constraints of the monthly cycle model are set; the monthly regular production plan is obtained by solving the monthly cycle model through mixed integer linear programming;

[0008] A weekly cycle model is constructed based on the peak-to-valley difference of the enterprise's electricity consumption during weekly scheduling, the production cost of regular orders, and the production cost of emergency orders, and the weekly cycle constraints of the weekly cycle model are set; the weekly regular production plan is obtained by solving the weekly cycle model through mixed integer linear programming combined with the monthly regular production plan;

[0009] The daily power adjustable space is constructed through the daily power supply of the regional power grid and the daily power consumption of power users; the daily peak shaving strategy and the daily valley filling strategy are generated according to the weekly regular production plan combined with the daily power adjustable space; the energy load is dynamically adapted in response to the daily peak shaving strategy and the daily valley filling strategy.

[0010] In this plan, through the progressive three-level scheduling (monthly plan, weekly plan, daily plan), combined with the mixed integer linear programming method and real-time power supply and demand analysis, dynamic and efficient adaptation of the enterprise's energy load is achieved, taking into account the optimization of production costs and power adaptation, and fully considering the flexibility and response speed of the production plan, the production plan is realized based on the regional power supply situation. The detailed formulation and dynamic adjustment of the production plan can significantly improve the production efficiency and market responsiveness of the enterprise.

[0011] As a preferred embodiment, the monthly cycle model formula is expressed as follows:

[0012] ;

[0013] in is the electricity consumption weight coefficient, is the cost weight coefficient, For the The unit production cost of a regular order for a company, The first i Enterprise No. t The daily production of regular order products, N is the total number of enterprises in the region, is the number of days in the monthly cycle, and They are the historical maximum and minimum daily power consumption of the regional power grid within the monthly cycle.

[0014] Preferably, the monthly cycle constraints include: daily production constraints for regular orders, maintenance status constraints, enterprise maintenance times constraints, weekly production constraints for regular orders, and daily electricity consumption constraints.

[0015] As a preferred option, the daily output constraints for regular orders are as follows:

[0016] ;

[0017] in For the i The maximum daily production output of an enterprise, For the i The company in t The production status of the day, when Expressed as i The company in t The production was stopped for maintenance. Expressed as i The company in t Normal production every day can be expressed as:

[0018] .

[0019] As a preference, the maintenance status constraints are as follows:

[0020] ;

[0021] ;

[0022] In the formula, Indicates t Whether the company has started maintenance; Indicates t Whether the enterprise stops maintenance can be expressed by the formula:

[0023] ;

[0024] .

[0025] As a preferred embodiment, assuming that only one maintenance is required per month, the constraint on the number of maintenance times of the enterprise can be expressed as follows:

[0026] .

[0027] As a preferred option, the conventional order weekly production constraint is expressed as follows:

[0028] ;

[0029] in For the i The company inj The weekly output of products required according to the initial regular order production plan; For Enterprises i In the j The production capacity reserved for the week.

[0030] As a preferred embodiment, the daily power consumption constraint within a monthly cycle is expressed as follows:

[0031] ;

[0032] Electricity consumption curve of regional power grid It can be expressed as:

[0033] ;

[0034] Total electricity consumption curve of enterprises participating in regulation It can be expressed as:

[0035] ;

[0036] in For the i Unit electricity consumption of a company's regular orders.

[0037] As a preferred embodiment, the cycle model formula is as follows:

[0038] ;

[0039] in, For the i The unit production cost of a company producing urgent orders is For the i Enterprise No. t Daily production of urgent orders, N is the total number of enterprises in the region, is the electricity consumption weight coefficient, is the cost weight coefficient, For the i The unit production cost of a regular order for a company, The first i Enterprise No. t The daily production of regular order products, N is the total number of enterprises in the region, is the number of days in the weekly cycle, and The historical maximum and minimum daily power consumption of the regional power grid within the weekly cycle respectively.

[0040] Preferably, the weekly constraints include: a modified order daily output constraint, an order output range constraint, a production product quantity constraint, and a daily electricity consumption constraint.

[0041] As a preferred method, the power forecast model is used to predict the future Wind power generation per day , photovoltaic daily power generation and the daily electricity consumption of electricity users , modify the regular order production plan for the current week , formulate the production plan for the current week's emergency orders , where the daily order output constraint is expressed as follows:

[0042] ;

[0043] The order output range constraint is expressed as follows:

[0044] ;

[0045] The production quantity constraint is expressed as follows:

[0046] ;

[0047] in, For the i The output of products that an enterprise needs to produce according to the revised regular order production plan, For the i The output of products that an enterprise needs to produce according to the emergency order production plan, For the i The estimated start time of the emergency order for each enterprise.

[0048] As a preferred embodiment, the daily power consumption constraint within a weekly cycle is expressed as follows:

[0049] ;

[0050] Electricity consumption curve of regional power grid It can be expressed as

[0051] ;

[0052] ;

[0053] in For the i Unit electricity consumption of urgent orders of an enterprise.

[0054] As a preference, the monthly routine production plan includes the company's production maintenance status value And the daily output of regular orders ; Weekly regular production plan includes the revised daily output of the company's regular orders And the daily output of urgent orders .

[0055] In this solution, in the process of dynamic adaptation of energy load, the weekly regular production plan is used as a medium-term plan, which fully considers the peak-valley difference of enterprise electricity consumption, the production cost of regular orders and the production cost of emergency orders, and is solved by the mixed integer linear programming method combined with the monthly regular production plan to ensure the rationality and economy of the production plan. The production plan not only includes the revised daily output of the enterprise's regular orders, but also covers the daily output of emergency orders, providing a basis for subsequent energy load adjustments.

[0056] Preferably, the daily power adjustable space is constructed by using the daily power supply of the power supply end and the daily power consumption of the power user, including the following steps:

[0057] An energy supply and demand coordinate system is constructed with time as the horizontal axis and power consumption as the vertical axis, and the daily power consumption data curve of power users and the regional power supply curve are plotted; the daily power adjustable space is constructed based on the integral operation of the non-overlapping areas of the daily power consumption data curve and the regional power supply curve on the time scale.

[0058] In this scheme, first, a two-dimensional coordinate system is established with time as the horizontal axis (usually a 24-hour time axis) and power consumption as the vertical axis. In this coordinate system, a daily power consumption data curve is drawn based on the actual daily power consumption data of power users. This curve reflects the power demand of users in different time periods. According to the daily power supply capacity of the regional power grid, a regional power supply curve is drawn. This curve shows the total amount of power that the power grid can provide in different time periods. Next, the two curves are compared to identify the non-overlapping areas between them. These non-overlapping areas represent the mismatching periods between power grid supply and user demand, and are also potential sources of daily power adjustable space. The non-overlapping areas are integrated on a time scale. It is possible to quantify the difference between power supply and demand in these mismatching periods, that is, the size of the daily power adjustable space, and intuitively show the distribution of the adjustable space throughout the day, providing a basis for subsequent peak shaving and valley filling strategies.

[0059] As a preferred method, a daily peak shaving strategy and a daily valley filling strategy are generated according to the weekly regular production plan combined with the daily power adjustable space, including the following steps:

[0060] According to the adaptation relationship between the daily power adjustable space and the conventional order power consumption sequence, the adjustable scale and adjustable factor corresponding to the power adjustable space are determined; when the adjustable factor is greater than 1, the daily peak shaving strategy is determined to be implemented within the adjustable scale; when the adjustable factor is less than 1, the daily valley filling strategy is determined to be implemented within the adjustable scale;

[0061] Among them, the conventional order power consumption sequence is adapted to the adjustable scale and adjustable factor to determine the daily valley filling strategy;

[0062] The corresponding regular order production factor is determined according to the daily valley filling strategy, and the emergency order power consumption sequence is adapted to determine the daily peak shaving strategy in combination with the regular order production factor, adjustable scale, and adjustable factor.

[0063] In this solution, in order to further optimize energy use and reduce energy waste and cost increases caused by peak-valley differences, the system will construct a daily power adjustable space based on the daily power supply of the regional power grid and the daily power consumption of power users, providing operating space for subsequent peak-shaving and valley-filling strategies. When generating daily peak-shaving strategies and daily valley-filling strategies, the power consumption sequence of regular orders is calculated based on the daily output of regular orders of the enterprise in the weekly regular production plan and its energy consumption coefficient. Similarly, the power consumption sequence of emergency orders is also determined based on the daily output of emergency orders and their energy consumption coefficients. The two power consumption sequences are the key basis for subsequent strategy formulation. Subsequently, the system will determine the adjustable scale and adjustable factor corresponding to the adjustable power space according to the adaptation relationship between the daily adjustable power space and the power consumption sequence of regular orders. These two parameters are key indicators for evaluating and adjusting energy loads. The adjustable scale represents the power supply time zone that can participate in industrial production, and the adjustable factor represents the ratio of the power supply in the current time zone to the amount of electricity required for industrial production when the enterprise carries out regular order production. When the adjustable factor is greater than 1, it means that there is a surplus of electricity within the adjustable scale. At this time, the system determines to implement the daily peak-cutting strategy, and by adjusting the production plan, more emergency orders are put into this time scale for industrial production, so as to implement the peak-cutting strategy to balance the load of the power grid. When the adjustable factor is less than 1, it means that the power supply is tight within the adjustable scale. At this time, the system determines to implement the daily valley-filling strategy, and by adjusting the production plan (part of the regular orders are put into the next time scale for production), the power consumption during the valley period is reduced to fully match the capacity balance of the power grid. In the process of formulating the daily valley filling strategy, the system will determine the corresponding regular order production factors based on the adaptation relationship between the regular order power consumption sequence and the adjustable scale and adjustable factor, and then guide the enterprise to reduce production during the valley period to adapt to the valley situation of the power load. At the same time, the emergency order power consumption sequence will also be adapted in combination with the regular order production factor, adjustable scale, and adjustable factor to determine the production arrangement of emergency orders under the daily peak shaving strategy. In this way, the system can balance the load of the power grid to the maximum extent while ensuring that the emergency orders are completed in time. Through the combination of weekly regular production plans and daily power adjustable space, the system can dynamically generate daily peak shaving strategies and daily valley filling strategies to achieve accurate adaptation and optimization of energy loads, thereby improving energy utilization efficiency, reducing production costs, and promoting the stable operation of the power grid.

[0064] Preferably, dynamically adapting the energy load in response to the daily peak shaving strategy and the daily valley filling strategy comprises the following steps:

[0065] Determine the emergency order production factor according to the daily peak shaving strategy, and determine the order day production sequence according to the emergency order production factor and the regular order production factor; dynamically adapt the energy load according to the order day production sequence; wherein, the order day production sequence includes the order type and order production volume corresponding to each time scale in the daily plan.

[0066] In this solution, first, the system determines the emergency order production factor based on the guiding principles of the daily peak shaving strategy. The emergency order production factor is calculated based on the power supply during the peak load period of the power grid and the priority of the emergency order. It aims to ensure that the emergency order can be completed on time when the power resources are tight, while minimizing the impact on the power grid load. Subsequently, the emergency order production factor is combined with the regular order production factor, and the production capacity, production cost and supply and demand of the power grid are comprehensively considered to generate the order day production sequence. This sequence details the order type (regular order or emergency order) and order production volume corresponding to each time scale (such as hour, half hour, etc.) in the daily plan. Through this refined time scale division, the system can achieve precise control of energy load and ensure that production activities are coordinated with the fluctuation trend of power grid load. Finally, the energy load is dynamically adapted according to the order day production sequence. This means that in the actual production process, the system will adjust the operating status of each production equipment in real time according to the instructions of the production sequence, ensuring that production is increased when the power supply is sufficient, and appropriately reducing production or adjusting the production plan when the power supply is tight to balance the load of the power grid. At the same time, the system will monitor the actual load of the power grid and adjust the production strategy in real time as needed to ensure the smooth progress of production activities and the stable operation of the power grid. By dynamically adapting the energy load in response to the daily peak shaving strategy and daily valley filling strategy, the system can achieve accurate matching and optimal adjustment of production plans and power grid loads, which not only improves energy utilization efficiency and reduces production costs, but also helps promote the stable operation and sustainable development of the power grid.

[0067] In a second aspect, a technical solution provided in an embodiment of the present invention is a dynamic adaptation system for energy load, which is applicable to a dynamic adaptation method for energy load taking into account a production maintenance plan, comprising:

[0068] The first model building module: build a monthly cycle model based on the peak-valley difference of enterprise electricity consumption during monthly scheduling and the production cost of regular orders, and set the monthly cycle constraints of the monthly cycle model; solve the monthly cycle model through mixed integer linear programming to obtain the monthly regular production plan;

[0069] The second model building module: build a weekly cycle model based on the peak-to-valley difference of enterprise electricity consumption, regular order production cost, and emergency order production cost during weekly scheduling, and set the weekly cycle constraints of the weekly cycle model; solve the weekly cycle model through mixed integer linear programming combined with the monthly regular production plan to obtain the weekly regular production plan;

[0070] Analysis module: construct daily power adjustable space through the daily power supply of the regional power grid and the daily power consumption of power users; generate daily peak shaving strategy and daily valley filling strategy based on the weekly regular production plan and the daily power adjustable space;

[0071] Execution module: dynamically adapts energy load in response to daily peak shaving strategy and daily valley filling strategy.

[0072] In the third aspect, a technical solution provided in an embodiment of the present invention is an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor calls the computer program in the memory, it implements the steps of the method for dynamic adaptation of energy load taking into account production maintenance plans.

[0073] In a fourth aspect, a technical solution provided in an embodiment of the present invention is a storage medium, in which computer executable instructions are stored. When the computer executable instructions are loaded and executed by a processor, the steps of the method for dynamic adaptation of energy load taking into account production maintenance plans are implemented.

[0074] The present invention has at least the following substantial beneficial effects:

[0075] (1) The present invention realizes the refinement of the enterprise production plan by constructing a monthly cycle and a weekly cycle model and combining it with a mixed integer linear programming method. On this basis, the daily peak shaving and valley filling strategies are generated in combination with the daily power adjustable space. The production plan can be dynamically adjusted according to the actual power supply and demand situation to avoid excessive consumption during peak power periods and make full use of the remaining power during low power periods, thereby significantly improving energy utilization efficiency and reducing energy waste.

[0076] (2) Through dynamic adaptation of energy load, the present invention can effectively reduce the cost increase caused by the difference between peak and valley power while ensuring the smooth completion of production tasks. Reducing production during peak hours can reduce high electricity price costs, while increasing production during valley hours can utilize low-priced electricity, thereby reducing overall production costs. At the same time, by reasonably arranging the production of regular orders and emergency orders, it can further optimize the allocation of production resources, reduce unnecessary production delays and cost expenditures, and significantly improve the production efficiency and market responsiveness of enterprises.

[0077] (3) This application not only focuses on the production efficiency and cost of the enterprise itself, but also fully considers the supply and demand balance and stable operation of the power grid. By responding to the daily power supply of the power grid and dynamically adjusting the production plan, the load fluctuation of the power grid can be balanced and the impact on the power grid caused by excessive load peak-valley difference can be reduced. This helps to maintain the stable operation of the power grid, improve the reliability and safety of the power grid, and provide flexible scheduling guarantee for the power supply of the entire society.

[0078] The above invention content is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Other features, objects and advantages of the present invention will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are only for the purpose of illustrating preferred embodiments and are not to be considered as limiting the present invention. Also, the same reference symbols are used throughout the drawings to represent the same parts.

[0080] Figure 1 This is a flow chart of the method for dynamic adaptation of energy load taking into account production maintenance plan of the present invention.

[0081] Figure 2 Flowchart of the load peak shaving and valley filling adjustment method for industrial enterprises based on monthly maintenance plans and weekly production plans.

[0082] Figure 3 It is a schematic diagram of the monthly cycle scheduling results taking into account the monthly maintenance plan in the present invention.

[0083] Figure 4 It is a schematic diagram of the weekly cycle scheduling results taking into account the weekly production plan in the present invention.

[0084] Figure 5 This is a block diagram of the energy load dynamic adaptation system of the present invention. DETAILED DESCRIPTION

[0085] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific implementation method described herein is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0086] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations (or steps) therein can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the drawings; the process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0087] Embodiment 1: Figure 2 As shown in the flow chart of the load peak shaving and valley filling adjustment method for industrial enterprises taking into account the monthly maintenance plan and weekly production plan, the first step is to determine the overall framework of the optimal scheduling, establish a two-layer optimal scheduling structure model with the upper layer being the monthly cycle scheduling and the lower layer being the weekly cycle scheduling, and determine the scheduling cycle, time granularity, and decision variables of each layer.

[0088] The second step is to take into account the monthly maintenance plan of the monthly cycle scheduling strategy, according to the predicted wind power daily power generation in January , photovoltaic daily power generation and the daily electricity consumption of electricity users , and regular order weekly production schedule , establish an optimization scheduling model based on the scheduling objectives and constraints, and determine the daily production / maintenance status of industrial enterprises , and the initial regular order daily production output of industrial enterprises .

[0089] The third step is weekly scheduling taking into account the weekly production plan, based on the latest forecast of wind power daily power generation within a week. , photovoltaic daily power generation , daily electricity consumption of electricity users , and the latest regular order production plan for the current week , urgent order production plan , production / maintenance status of industrial enterprises , establish an optimal scheduling model based on the scheduling objectives and constraints, and determine the daily production output of regular orders of industrial enterprises after correction , and daily production output for urgent orders , as the basis for the production and load peak-shaving and valley-filling of enterprises. Through the above steps, while realizing the load peak-shaving and valley-filling of industrial enterprises, the maintenance plan and weekly production plan are taken into account to reduce the impact on the normal production of industrial enterprises; on the basis of this weekly production plan, combined with the daily power adjustable space to generate daily peak-shaving and daily valley-filling strategies, the production plan can be dynamically adjusted according to the actual power supply and demand situation, avoiding excessive consumption during peak power periods, and making full use of the remaining power during low power periods, thereby significantly improving energy utilization efficiency and reducing energy waste.

[0090] Figure 2 For the specific construction and execution process of the double-layer optimization scheduling structure model, this application further provides a dynamic adaptation method for energy load taking into account the production maintenance plan, such as Figure 1 As shown, the following steps are included:

[0091] S1. According to the peak-to-valley difference of the enterprise's electricity consumption during monthly scheduling and the production cost of regular orders, a monthly cycle model is constructed, and the monthly cycle constraints of the monthly cycle model are set; the monthly cycle model is solved by mixed integer linear programming to obtain the monthly regular production plan, where the monthly regular production plan includes the enterprise's production maintenance status value s(i, t) and the daily output of regular orders. .

[0092] Specifically, the monthly cycle model formula is expressed as follows:

[0093] ;

[0094] in, is the electricity consumption weight coefficient, which reflects the sensitivity of the enterprise to changes in electricity costs. is the cost weight coefficient, which reflects the importance that the enterprise attaches to the production cost of regular orders; it is set according to the actual situation, and the initial parameter is generally set to =0.5, =0.5, is the unit production cost of the regular order of the ith enterprise, is the number of enterprises in the monthly cycle t The output of regular order products produced per day, N is the total number of enterprises in the region, is the number of days in the monthly cycle, and They are the historical maximum and minimum daily power consumption of the regional power grid within the monthly cycle.

[0095] In this embodiment, the objective function of the model is first determined to be the minimization of the total cost function, including electricity cost and production cost. The objective function and constraints are converted into a MILP model, and then the constraints are constructed and solved using an appropriate solver (such as CPLEX, Gurobi, etc.). The solver will automatically find a solution that satisfies all constraints and minimizes the objective function. According to the solution results and actual conditions, the model parameters (such as electricity weight coefficient, cost weight coefficient, etc.) are adjusted to further optimize the production plan to obtain the production maintenance status value of the enterprise. And the daily output of regular orders .

[0096] Figure 3 This is a schematic diagram of the monthly cycle scheduling results taking into account the monthly maintenance plan in this embodiment. From top to bottom, the daily production output of regular orders of three enterprises are shown. Graph, daily production / maintenance status Graph and graph of total daily electricity consumption of industrial enterprises Graph, total electricity usage The curve chart shows that the optimized scheduling arranges the maintenance time of industrial enterprises around the 18th to 21st, thus using the total electricity curve The low point occurred on the 18th to 21st.

[0097] Furthermore, the monthly cycle constraints include: daily production constraints for regular orders, maintenance status constraints, enterprise maintenance times constraints, weekly production constraints for regular orders, and daily electricity consumption constraints.

[0098] Specifically, the daily output constraints for regular orders are as follows:

[0099] ;

[0100] in For the i The maximum daily production output of an enterprise, For the i The production status of an enterprise on day t is =0 means the i The enterprise stops production for maintenance on day t, and =1 means the i The company in t Normal production every day can be expressed as:

[0101] .

[0102] Specifically, the maintenance status constraints are as follows:

[0103] ;

[0104] ;

[0105] In the formula, Indicates t Whether the company has started maintenance; Indicates t Whether the enterprise stops maintenance can be expressed by the formula:

[0106] ;

[0107] .

[0108] Specifically, assuming that only one maintenance is required per month, the constraint on the number of maintenance times of an enterprise can be expressed as:

[0109] .

[0110] Specifically, the regular order weekly output constraint is expressed as follows:

[0111] ;

[0112] in For the i The company in j The weekly output of products required according to the initial regular order production plan; For enterprise i j The production capacity reserved for the week.

[0113] Specifically, the daily power consumption constraint within a monthly cycle is expressed as follows:

[0114] ;

[0115] Electricity consumption curve of regional power grid It can be expressed as:

[0116] ;

[0117] Total electricity consumption curve of enterprises participating in regulation It can be expressed as:

[0118] ;

[0119] in For the i Unit electricity consumption of a company's regular orders.

[0120] S2. According to the peak-to-valley difference of the enterprise's electricity consumption during weekly scheduling, the production cost of regular orders, and the production cost of emergency orders, a weekly cycle model is constructed, and the weekly cycle constraints of the weekly cycle model are set; the weekly regular production plan is obtained by solving the weekly cycle model through mixed integer linear programming combined with the monthly regular production plan; the weekly regular production plan includes the revised daily output of the enterprise's regular orders And the daily output of urgent orders .

[0121] Specifically, the weekly cycle model formula is as follows:

[0122] ;

[0123] in, For the i The unit production cost of a company producing urgent orders is For the i Enterprise No. t Daily production of urgent orders, N is the total number of enterprises in the region, is the electricity consumption weight coefficient, which reflects the sensitivity of the enterprise to changes in electricity costs. is the cost weight coefficient, which reflects the importance that the enterprise attaches to the production cost of regular orders; it is set according to the actual situation, and the initial parameter is generally set to =0.5, =0.5, For the i The unit production cost of a regular order for a company, The first i Enterprise No. t The daily production of regular order products, N is the total number of enterprises in the region, is the number of days in the weekly cycle, and The historical maximum and minimum daily power consumption of the regional power grid within the weekly cycle respectively.

[0124] In this embodiment, the objective function of the weekly cycle model minimizes the total cost including electricity cost and production cost. Specifically, it is the production cost of regular orders and emergency orders, as well as the cost caused by electricity use (affected by the peak and valley difference of electricity). The decision variable is defined as the revised daily output of regular orders of the enterprise, and then the weekly cycle constraints are set. The objective function and constraints are converted into a MILP model, and a professional solver (such as CPLEX, Gurobi, etc.) is used to solve it; the solver will find a solution that satisfies all constraints and minimizes the total cost, that is, the revised daily output of regular orders and daily output of emergency orders. By solving the weekly cycle model through mixed integer linear programming, a weekly regular production plan that meets both the cost minimization requirements and the power demand of the power grid can be obtained, thereby optimizing the production process and improving economic benefits.

[0125] Figure 4 This is a schematic diagram of the weekly scheduling results taking into account the weekly production plan in this embodiment, from top to bottom, the daily production output of the three companies' regular orders Graph, daily production output for urgent orders Graph, total daily electricity consumption of industrial enterprises Graph and total electricity usage As can be seen from the curve chart, optimizing the scheduling and producing the emergency orders of industrial enterprises around the 4th to 5th day can achieve the effect of reducing the peak and filling the valley.

[0126] Furthermore, the weekly constraints include: modified order daily output constraint, order output range constraint, production product quantity constraint, and daily electricity consumption constraint.

[0127] Specifically, the power forecasting model predicts the future Wind power generation per day , photovoltaic daily power generation and the daily electricity consumption of electricity users , modify the regular order production plan for the current week , formulate the production plan for the current week's emergency orders , where the daily order output constraint is expressed as follows:

[0128] ;

[0129] The order output range constraint is expressed as follows:

[0130] ;

[0131] The production quantity constraint is expressed as follows:

[0132] ;

[0133] in, For the i The output of products that an enterprise needs to produce according to the revised regular order production plan, For the i The output of products that an enterprise needs to produce according to the emergency order production plan, is the estimated start time of the urgent order of the i-th enterprise.

[0134] Specifically, the daily power consumption constraint within a weekly cycle is expressed as follows:

[0135] ;

[0136] Electricity consumption curve of regional power grid It can be expressed as:

[0137] ;

[0138] ;

[0139] in For the i Unit electricity consumption of urgent orders of an enterprise.

[0140] S3. Construct a daily power adjustable space through the daily power supply of the regional power grid and the daily power consumption of power users; generate a daily peak shaving strategy and a daily valley filling strategy based on the weekly regular production plan combined with the daily power adjustable space; dynamically adapt the energy load in response to the daily peak shaving strategy and the daily valley filling strategy.

[0141] The daily power adjustable space is constructed by using the daily power supply of the power supply end and the daily power consumption of the power user, including the following steps:

[0142] An energy supply and demand coordinate system is constructed with time as the horizontal axis and power consumption as the vertical axis, and the daily power consumption data curve of power users and the regional power supply curve are plotted; the daily power adjustable space is constructed based on the integral operation of the non-overlapping areas of the daily power consumption data curve and the regional power supply curve on the time scale.

[0143] In this embodiment, first, a two-dimensional coordinate system is established with time as the horizontal axis (usually a 24-hour time axis) and power consumption as the vertical axis. In this coordinate system, a daily power consumption data curve is drawn based on the actual daily power consumption data of the power user. This data curve reflects the power demand of users in different time periods. According to the daily power supply capacity of the regional power grid, a regional power supply curve is drawn. This curve shows the total amount of power that the power grid can provide in different time periods. Next, the two curves are compared to identify the non-overlapping areas between them. These non-overlapping areas represent the mismatching periods between power grid supply and user demand, and are also potential sources of daily power adjustable space. The non-overlapping areas are integrated on a time scale. It is possible to quantify the difference between power supply and demand in these mismatching periods, that is, the size of the daily power adjustable space, and intuitively show the distribution of the adjustable space throughout the day, providing a basis for subsequent peak shaving and valley filling strategies.

[0144] As a further elaboration of this embodiment, illustratively, it is assumed that the daily power supply data of the regional power grid has been given, including the power supply in each time period of the day, in hours, for example: [800MW, 850MW, 900MW, ..., 750MW] (24-hour data). The daily power consumption data of each enterprise in the region has been obtained, also in hours, for example: the daily power consumption data of enterprise A is [100MW, 120MW, 130MW, ..., 90MW] (24-hour data). The power consumption data of all enterprises are summarized to obtain the total daily power consumption data of regional power users. Through steps S1 and S2, the weekly regular production plan is obtained, including the daily output of regular orders and emergency orders of each enterprise including enterprise A, and the corresponding energy consumption coefficient. A two-dimensional coordinate system is established with time as the horizontal axis (0-23 hours) and power consumption as the vertical axis. The daily power consumption data curve of the power user and the regional power supply curve are drawn in the coordinate system. The two curves respectively show the power demand and power supply capacity in each time period of the day. Identify the non-overlapping area between the daily power consumption data curve and the regional power supply curve. The non-overlapping area represents the mismatch period between the power supply of the power grid and the user demand; perform integral calculations on the non-overlapping area on a time scale to quantify the difference between power supply and demand during the mismatch period, and obtain the size of the daily power adjustable space. For example, assuming that in a certain period of time, the power supply is 900MW and the user demand is 800MW, then the adjustable space for this period is 100MW.

[0145] As a preferred method, a daily peak shaving strategy and a daily valley filling strategy are generated according to the weekly regular production plan combined with the daily power adjustable space, including the following steps:

[0146] Determine the power consumption sequence of regular orders based on the daily output of regular orders of the enterprise and its energy consumption coefficient in the weekly regular production plan;

[0147] Determine the power consumption sequence of emergency orders based on the daily output of emergency orders and their energy consumption coefficients in the weekly regular production plan;

[0148] According to the adaptation relationship between the daily power adjustable space and the conventional order power consumption sequence, the adjustable scale and adjustable factor corresponding to the power adjustable space are determined; when the adjustable factor is greater than 1, the daily peak shaving strategy is determined to be implemented within the adjustable scale; when the adjustable factor is less than 1, the daily valley filling strategy is determined to be implemented within the adjustable scale;

[0149] Among them, the conventional order power consumption sequence is adapted to the adjustable scale and adjustable factor to determine the daily valley filling strategy;

[0150] The corresponding regular order production factor is determined according to the daily valley filling strategy, and the emergency order power consumption sequence is adapted to determine the daily peak shaving strategy in combination with the regular order production factor, adjustable scale, and adjustable factor.

[0151] In this embodiment, in the process of dynamic adaptation of energy load, the weekly regular production plan is used as a medium-term plan, which fully considers the peak-valley difference of enterprise electricity consumption, the production cost of regular orders and the production cost of emergency orders, and is solved by the mixed integer linear programming method combined with the monthly regular production plan to ensure the rationality and economy of the production plan. The production plan not only includes the revised daily output of the enterprise's regular orders, but also covers the daily output of emergency orders, which provides a basis for the subsequent energy load adjustment; next, in order to further optimize energy use and reduce energy waste and cost increase caused by the peak-valley difference, the system will construct a daily power adjustable space based on the daily power supply of the regional power grid and the daily power consumption of power users, which provides an operating space for the subsequent peak-cutting and valley-filling strategies. When generating the daily peak-cutting strategy and the daily valley-filling strategy, the power consumption sequence of the regular order is calculated according to the daily output of the enterprise's regular orders and their energy consumption coefficients in the weekly regular production plan. Similarly, the power consumption sequence of the emergency order is also determined based on the daily output of the emergency order and its energy consumption coefficient. The two power consumption sequences are the key basis for the formulation of subsequent strategies. Subsequently, the system will determine the adjustable scale and adjustable factor corresponding to the adjustable power space according to the adaptation relationship between the daily adjustable power space and the power consumption sequence of regular orders. These two parameters are key indicators for evaluating and adjusting energy loads. The adjustable scale represents the power supply time zone that can participate in industrial production, and the adjustable factor represents the ratio of the power supply in the current time zone to the amount of electricity required for industrial production when the enterprise carries out regular order production. When the adjustable factor is greater than 1, it means that there is a surplus of electricity within the adjustable scale. At this time, the system determines to implement the daily peak-cutting strategy, and by adjusting the production plan, more emergency orders are put into this time scale for industrial production, so as to implement the peak-cutting strategy to balance the load of the power grid. When the adjustable factor is less than 1, it means that the power supply is tight within the adjustable scale. At this time, the system determines to implement the daily valley-filling strategy, and by adjusting the production plan (part of the regular orders are put into the next time scale for production), the power consumption during the valley period is reduced to fully match the capacity balance of the power grid. In the process of formulating the daily valley filling strategy, the system will determine the corresponding regular order production factors based on the adaptation relationship between the regular order power consumption sequence and the adjustable scale and adjustable factor, and then guide the enterprise to reduce production during the valley period to adapt to the valley situation of the power load. At the same time, the emergency order power consumption sequence will also be adapted in combination with the regular order production factor, adjustable scale, and adjustable factor to determine the production arrangement of emergency orders under the daily peak shaving strategy. In this way, the system can balance the load of the power grid to the maximum extent while ensuring that the emergency orders are completed in time. Through the combination of weekly regular production plans and daily power adjustable space, the system can dynamically generate daily peak shaving strategies and daily valley filling strategies to achieve accurate adaptation and optimization of energy loads, thereby improving energy utilization efficiency, reducing production costs, and promoting the stable operation of the power grid.

[0152] As a further elaboration of this embodiment, illustratively, the power consumption sequence of regular orders is calculated based on the daily output of regular orders of enterprises in the weekly regular production plan and their energy consumption coefficients. For example, if the daily output of regular orders of enterprise A on a certain day is 1,000 pieces and the energy consumption coefficient is 0.1MW / piece, then the power consumption sequence of regular orders on that day is 100MW (assuming that the production is evenly distributed). Similarly, the power consumption sequence of emergency orders is determined based on the daily output of emergency orders and their energy consumption coefficients. According to the adaptation relationship between the daily power adjustable space and the power consumption sequence of regular orders, the adjustable scale and adjustable factor corresponding to the power adjustable space are determined. For example, assuming that in a certain period of time, the daily power adjustable space is 200MW, and the power consumption of regular orders in this period is 150MW, then the adjustable scale is this period, and the adjustable factor is 200MW / 150MW=1.33. When the adjustable factor is greater than 1 (such as 1.33), it is determined to execute the daily peak shaving strategy within the adjustable scale. For example, some emergency orders can be arranged to be produced in this period to balance the load on the power grid. When the adjustable factor is less than 1 (such as 0.8), it is determined to execute the daily valley filling strategy within the adjustable scale. For example, some regular orders can be allocated to the next time scale for production to reduce the power consumption in the current period. According to the daily peak shaving strategy and the daily valley filling strategy, the order production sequence of each period is adjusted. For example, the production volume of emergency orders is increased during the period of sufficient power supply, and the production volume of regular orders is reduced during the period of tight power supply. Assuming that during the period of 14:00-15:00 on a certain day, the power supply of the regional power grid is 900MW, and the total power consumption of regional power users is 800MW, then the daily power adjustable space of this period is 100MW. At the same time, it is assumed that the power consumption of regular orders of enterprise A during this period is 150MW (calculated based on the weekly regular production plan), and the power consumption of emergency orders is 50MW. Since the power supply during this period is sufficient (adjustable factor = 900MW / 800MW = 1.125>1), the system determines to execute the daily peak shaving strategy. The system can arrange some of the emergency orders of enterprise A (such as 30MW of electricity demand) to be produced during this period to balance the load on the power grid. At the same time, the production volume of emergency orders in other periods can be reduced or the production time of regular orders can be adjusted. Through the above steps and examples, we can clearly understand how to build a daily power adjustable space based on the daily power supply of the regional power grid and the daily power consumption of power users, and generate daily peak shaving strategies and daily valley filling strategies according to the weekly regular production plan to dynamically adapt the energy load.

[0153] Furthermore, dynamically adapting the energy load in response to the daily peak shaving strategy and the daily valley filling strategy includes the following steps:

[0154] Determine the emergency order production factor according to the daily peak shaving strategy, and determine the order day production sequence according to the emergency order production factor and the regular order production factor; dynamically adapt the energy load according to the order day production sequence; wherein, the order day production sequence includes the order type and order production volume corresponding to each time scale in the daily plan.

[0155] In this solution, first, the system determines the emergency order production factor based on the guiding principles of the daily peak shaving strategy. The emergency order production factor is calculated based on the power supply during the peak load period of the power grid and the priority of the emergency order. It aims to ensure that the emergency order can be completed on time when the power resources are tight, while minimizing the impact on the power grid load. Subsequently, the emergency order production factor is combined with the regular order production factor, and the production capacity, production cost and supply and demand of the power grid are comprehensively considered to generate the order day production sequence. This sequence details the order type (regular order or emergency order) and order production volume corresponding to each time scale (such as hour, half hour, etc.) in the daily plan. Through this refined time scale division, the system can achieve precise control of energy load and ensure that production activities are coordinated with the fluctuation trend of power grid load. Finally, the energy load is dynamically adapted according to the order day production sequence. This means that in the actual production process, the system will adjust the operating status of each production equipment in real time according to the instructions of the production sequence, ensuring that production is increased when the power supply is sufficient, and appropriately reducing production or adjusting the production plan when the power supply is tight to balance the load of the power grid. At the same time, the system will monitor the actual load of the power grid and adjust the production strategy in real time as needed to ensure the smooth progress of production activities and the stable operation of the power grid. By dynamically adapting the energy load in response to the daily peak shaving strategy and daily valley filling strategy, the system can achieve accurate matching and optimal adjustment of production plans and power grid loads, which not only improves energy utilization efficiency and reduces production costs, but also helps promote the stable operation and sustainable development of the power grid.

[0156] Embodiment 2: A technical solution also provided in the embodiment of the present invention is a dynamic adaptation system for energy loads, such as Figure 5 As shown, including:

[0157] The first model building module 101: constructs a monthly cycle model according to the peak-valley difference of the enterprise's electricity consumption during monthly scheduling and the production cost of regular orders, and sets the monthly cycle constraints of the monthly cycle model; solves the monthly cycle model through mixed integer linear programming to obtain a monthly regular production plan;

[0158] The second model building module 102: builds a weekly cycle model according to the peak-to-valley difference of the enterprise's electricity consumption during weekly scheduling, the production cost of regular orders, and the production cost of emergency orders, and sets weekly cycle constraints of the weekly cycle model; solves the weekly cycle model through mixed integer linear programming combined with the monthly regular production plan to obtain a weekly regular production plan;

[0159] Analysis module 103: constructing a daily power adjustable space through the daily power supply of the regional power grid and the daily power consumption of the power users; generating a daily peak shaving strategy and a daily valley filling strategy based on the weekly regular production plan and the daily power adjustable space;

[0160] Execution module 104: dynamically adapt energy load in response to daily peak shaving strategy and daily valley filling strategy.

[0161] This embodiment has at least the following technical effects: through the construction of the monthly cycle model and the weekly cycle model, the system can fully consider the peak-valley difference of the enterprise's electricity consumption, the production cost of regular orders and the production cost of emergency orders, so as to formulate a more reasonable production plan. The construction of the daily power adjustable space enables the system to dynamically adjust the production plan according to the daily power supply of the regional power grid and the daily power consumption of the power users, so as to maximize the use of power resources and avoid energy waste. By optimizing the production plan, the system reduces the energy waste caused by the peak-valley difference, thereby reducing the production cost. Increasing the production volume during the period of sufficient power supply and reducing the production volume during the period of tight power supply avoids the decline in production efficiency and increase in cost due to insufficient power. Through the implementation of the daily peak shaving strategy and the daily valley filling strategy, the system can balance the power grid load and reduce the fluctuation and instability of the power grid. It helps to improve the power supply quality and reliability of the power grid and provide power users with a more stable and reliable power supply. The system can dynamically adjust the production plan according to the real-time power supply and production demand, and improve the production flexibility of the enterprise. It enables enterprises to better respond to market changes and emergencies and improve market competitiveness. In summary, the energy load dynamic adaptation system achieves efficient and dynamic adaptation of energy load by comprehensively considering multiple factors. It not only helps to improve energy utilization efficiency and reduce production costs, but also promotes the stable operation of the power grid and enhances the production flexibility of enterprises. At the same time, the solution also realizes the intelligence and automation of energy management, providing strong support for the sustainable development of enterprises.

[0162] Embodiment 3: An optional embodiment also provided in the embodiments of the present invention is: an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor calls the computer program in the memory, it implements the steps of the energy load dynamic adaptation method taking into account the production maintenance plan.

[0163] Embodiment 4: An optional embodiment also provided in the embodiments of the present invention is: a storage medium, wherein the storage medium stores computer executable instructions, and when the computer executable instructions are loaded and executed by a processor, the steps of the method for dynamic adaptation of energy load taking into account production maintenance plans are implemented.

[0164] Through the description of the above implementation methods, technical personnel in the relevant field can understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the specific device can be divided into different functional modules to complete all or part of the functions described above.

[0165] In the embodiments provided in the present application, it should be understood that the disclosed structures and methods can be implemented in other ways. For example, the embodiments of the structure described above are only schematic. For example, the division of modules or units is only 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 structure, 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 an indirect coupling or communication connection through some interfaces, structures or units, which can be electrical, mechanical or other forms.

[0166] The units described as separate components may or may not be physically separated, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0167] In addition, each functional unit in the embodiment of the present application 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.

[0168] 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 readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially 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, which is stored in a storage medium, including several instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0169] The specific implementation method described above is a preferred implementation method of the energy load dynamic adaptation method and system taking into account the production maintenance plan of the present invention, and is not intended to limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to this specific implementation method. All equivalent changes made in accordance with the shape and structure of the present invention are within the protection scope of the present invention.

Claims

1. A method for dynamic adaptation of energy load taking into account production maintenance plan, characterized in that: The steps include: A monthly cycle model is constructed based on the peak-to-valley difference of the enterprise's electricity consumption during monthly scheduling and the production cost of regular orders, and the monthly cycle constraints of the monthly cycle model are set; the monthly regular production plan is obtained by solving the monthly cycle model through mixed integer linear programming; A weekly cycle model is constructed based on the peak-to-valley difference of the enterprise's electricity consumption during weekly scheduling, the production cost of regular orders, and the production cost of emergency orders, and the weekly cycle constraints of the weekly cycle model are set; The weekly regular production plan is obtained by solving the weekly cycle model through mixed integer linear programming combined with the monthly regular production plan; The daily power adjustable space is constructed through the daily power supply of the regional power grid and the daily power consumption of the power users; the daily peak shaving strategy and the daily valley filling strategy are generated according to the weekly regular production plan combined with the daily power adjustable space; the energy load is dynamically adapted in response to the daily peak shaving strategy and the daily valley filling strategy; The monthly cycle model formula is as follows: ； in is the electricity consumption weight coefficient, is the cost weight coefficient, For the i The unit production cost of a regular order for a company, The first i Enterprise No. t The daily production of regular order products, N is the total number of enterprises in the region, is the number of days in the monthly cycle, and They are the historical maximum and minimum daily power consumption of the regional power grid in the monthly cycle; The weekly cycle model formula is as follows: ; in, For the i The unit production cost of a company producing urgent orders is For the i Industrial enterprise t Daily production of urgent orders, N is the total number of enterprises in the region, is the electricity consumption weight coefficient, is the cost weight coefficient, For the i The unit production cost of a regular order for a company, The first i The output of regular order products produced by an enterprise on day t is N is the total number of enterprises in the region, is the number of days in the weekly cycle, and The historical maximum and minimum daily power consumption of the regional power grid within the weekly cycle respectively.

2. The method for dynamic adaptation of energy load taking into account production maintenance plan according to claim 1 is characterized in that: The monthly cycle constraints include: daily production constraints for regular orders, maintenance status constraints, enterprise maintenance times constraints, weekly production constraints for regular orders, and daily electricity consumption constraints.

3. The method for dynamic adaptation of energy load taking into account production maintenance plan according to claim 1, characterized in that: Weekly constraints include: modified order daily output constraint, order output range constraint, production product quantity constraint, and daily electricity consumption constraint.

4. The method for dynamic adaptation of energy load taking into account production maintenance plan according to claim 1, characterized in that: The monthly regular production plan includes the enterprise's production maintenance status value and the daily output of regular orders; The weekly regular production plan includes the revised daily output of the company's regular orders and emergency orders.

5. The method for dynamic adaptation of energy load taking into account production maintenance plan according to claim 1, characterized in that: The daily power adjustable space is constructed by the daily power supply of the power supply end and the daily power consumption of the power user, including the following steps: An energy supply and demand coordinate system is constructed with time as the horizontal axis and power consumption as the vertical axis, and the daily power consumption data curve of power users and the regional power supply curve are plotted; the daily power adjustable space is constructed based on the integral operation of the non-overlapping areas of the daily power consumption data curve and the regional power supply curve on the time scale.

6. The method for dynamic adaptation of energy load taking into account production maintenance plan according to claim 1, characterized in that: According to the weekly regular production plan and the daily power adjustable space, the daily peak shaving strategy and daily valley filling strategy are generated, including the following steps: Determine the power consumption sequence of regular orders based on the daily output of regular orders of the enterprise and its energy consumption coefficient in the weekly regular production plan; Determine the power consumption sequence of emergency orders based on the daily output of emergency orders and their energy consumption coefficients in the weekly regular production plan; According to the adaptation relationship between the daily power adjustable space and the conventional order power consumption sequence, the adjustable scale and adjustable factor corresponding to the power adjustable space are determined; when the adjustable factor is greater than 1, the daily peak shaving strategy is determined to be executed within the adjustable scale; When the adjustable factor is less than 1, the daily valley filling strategy is executed within the adjustable scale; Among them, the conventional order power consumption sequence is adapted to the adjustable scale and adjustable factor to determine the daily valley filling strategy; The corresponding regular order production factor is determined according to the daily valley filling strategy, and the emergency order power consumption sequence is adapted to determine the daily peak shaving strategy in combination with the regular order production factor, adjustable scale, and adjustable factor; Among them, the adjustable scale: represents the power supply time zone that can participate in industrial production; Adjustable factor: represents the ratio of the power supply in the current time zone to the amount of electricity required for industrial production when the enterprise carries out regular order production; Regular order production factor: In the process of formulating the daily valley filling strategy, it is determined based on the matching relationship between the regular order power consumption sequence and the adjustable scale and adjustable factor, and is used to guide enterprises to reduce production during the valley period to adapt to the valley situation of power load; Emergency order production factor: The emergency order production factor is determined based on the power supply situation during the peak load period of the power grid and the priority of the emergency order. It is used to ensure that emergency orders can be completed on time when power resources are tight to reduce the impact on the power grid load.

7. The method for dynamic adaptation of energy load taking into account production maintenance plan according to claim 6 is characterized in that: Dynamically adapting the energy load in response to the daily peak shaving strategy and the daily valley filling strategy includes the following steps: Determine the emergency order production factor according to the daily peak shaving strategy, and determine the order day production sequence according to the emergency order production factor and the regular order production factor; dynamically adapt the energy load according to the order day production sequence; wherein, the order day production sequence includes the order type and order production volume corresponding to each time scale in the daily plan.

8. An energy load dynamic adaptation system, applicable to the energy load dynamic adaptation method taking into account the production maintenance plan as described in any one of claims 1 to 7, characterized in that: include: The first model building module: build a monthly cycle model based on the peak-valley difference of enterprise electricity consumption during monthly scheduling and the production cost of regular orders, and set the monthly cycle constraints of the monthly cycle model; solve the monthly cycle model through mixed integer linear programming to obtain the monthly regular production plan; The second model building module: builds a weekly cycle model based on the peak-to-valley difference of enterprise electricity consumption during weekly scheduling, regular order production costs, and emergency order production costs, and sets weekly cycle constraints for the weekly cycle model; The weekly regular production plan is obtained by solving the weekly cycle model through mixed integer linear programming combined with the monthly regular production plan; Analysis module: construct daily power adjustable space through the daily power supply of the regional power grid and the daily power consumption of power users; generate daily peak shaving strategy and daily valley filling strategy based on the weekly regular production plan and the daily power adjustable space; Execution module: dynamically adapts energy load in response to daily peak shaving strategy and daily valley filling strategy.

9. An electronic device, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program, and when the processor calls the computer program in the memory, it implements the steps of the method for dynamic adaptation of energy load taking into account production maintenance plan as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores computer executable instructions, which, when loaded and executed by a processor, implement the steps of the method for dynamic adaptation of energy load taking into account a production maintenance plan as described in any one of claims 1 to 7.

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