Linear programming method, device and medium for peak energy consumption reduction
By constructing a mixed-integer linear programming model, the problems of timing decision-making for key energy-consuming equipment and synergistic effects of energy storage systems were solved, reducing peak energy consumption and improving the energy security of manufacturing systems and grid stability.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHONGQING UNIV
- Filing Date
- 2025-12-02
- Publication Date
- 2026-04-21
AI Technical Summary
Existing technologies have failed to effectively address the timing decision-making issues during the startup of critical energy-consuming equipment and the synergistic effect between energy storage systems and production scheduling, resulting in significant peak energy consumption and impacting grid stability and energy security for manufacturing enterprises.
Construct a mixed-integer linear programming model, including parameters of production tasks, energy storage systems, and key energy-consuming equipment. With the goal of minimizing peak energy consumption, construct linear constraints and output the optimal scheduling scheme, which includes the start-up and shutdown plans of key energy-consuming equipment and the charging and discharging strategies of the energy storage system.
It has achieved a reduction in peak energy consumption, improved the energy security and reliability of the manufacturing system, balanced system energy, and reduced the peak load of the power grid.
Smart Images
Figure CN121906570A_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of industrial software technology, and specifically relates to a linear programming method, device and medium for peak energy consumption reduction. Background Technology
[0002] To cope with the cyclical fluctuations in electricity supply and demand, "peak shaving and valley filling" has become a key strategy to ensure grid stability and efficient operation of manufacturing enterprises. On the one hand, under the tiered electricity pricing system, peak energy consumption determines energy costs, and reducing peak energy consumption is an important means for manufacturing enterprises to "reduce costs and increase efficiency".
[0003] Furthermore, short-term peak energy consumption will significantly exacerbate the pressure on the power grid's peak regulation, weaken the system's regulation margin and dynamic response capability, induce operational risks such as frequency fluctuations and voltage deviations, and may even lead to regional power rationing. According to survey data, peak energy consumption mainly consists of the simultaneous startup of multiple devices within a short period of time and the start-up and shutdown process of high-energy-consuming devices. Previous studies have mostly focused on the problem of simultaneous startup of multiple devices and proposed feasible optimization methods.
[0004] However, the startup process of critical energy-consuming equipment can generate large-scale step energy consumption in a short period of time, which is essentially a timing-based decision-making problem, requiring more refined optimization decision-making methods. Furthermore, the application of energy storage systems provides crucial support and broader operational flexibility for manufacturing enterprises in peak shaving and valley filling. Therefore, there is an urgent need for a method that considers both energy storage systems and critical energy-consuming equipment to reduce peak energy consumption, thereby improving the energy security and reliability of manufacturing systems. Summary of the Invention
[0005] This application provides a linear programming method, device, and medium for peak energy consumption reduction, which addresses the problem that existing technologies do not adequately consider timing issues and do not take into account the synergistic effect between energy storage systems and production scheduling.
[0006] According to a first aspect of this application, this application provides a linear programming method for peak energy consumption reduction. Methods, including: Define a mixed-integer linear programming model for a manufacturing enterprise; wherein the mixed-integer linear programming model includes model parameters and decision variables for the manufacturing enterprise, and the model parameters include relevant parameters corresponding to production tasks, energy storage systems and key energy-consuming equipment respectively; To minimize the peak energy consumption of manufacturing enterprises on the social power grid, a mixed-integer linear programming model is constructed with the objective function as the optimization goal. Based on model parameters and decision variables, linear constraints are constructed for a mixed-integer linear programming model; among which, the linear constraints include at least the occupancy constraints of key energy-consuming equipment, task scheduling and sequence constraints, and constraints on the operation process of the energy storage system. Actual production data is substituted into a mixed-integer linear programming model as decision variables to output the optimal scheduling scheme. The optimal scheduling scheme includes the start-up and shutdown plans of key energy-consuming equipment and the charging and discharging strategies of the energy storage system, so as to reduce the peak energy consumption corresponding to the objective function.
[0007] Preferably, the linear programming method described above for peak energy consumption reduction defines a mixed-integer linear programming model for the manufacturing enterprise; wherein, the mixed-integer linear programming model includes model parameters and decision variables for the manufacturing enterprise, including: The model parameters for manufacturing enterprises include: the task set of production tasks, the set of sub-tasks contained in each production task, and the set of key energy-consuming equipment; the duration, energy consumption per unit time, and maintenance interval of each sub-task; the energy consumption of other equipment in the manufacturing enterprise excluding key energy-consuming equipment in each time period; and the maximum charging and discharging power, charging and discharging efficiency, upper and lower capacity limits, and initial energy of the energy storage system. The decision variables for manufacturing enterprises include: binary variables representing whether a subtask is executed or begins in a specific time period; binary variables representing the allocation of production tasks to specific equipment; binary variables representing the execution order of production tasks on equipment; integer variables representing the start and end times of production tasks and subtasks; continuous variables representing the charging and discharging capacity of the energy storage system and the state of charge of the battery in each time period; and continuous variables representing the peak energy consumption of the system.
[0008] Preferably, the linear programming method described above for peak energy consumption reduction aims to minimize the peak energy consumption of the manufacturing enterprise on the social power grid. The objective function of the mixed-integer linear programming model includes: According to the peak energy consumption calculation formula:
[0009] Ensure that the total energy consumption demand of manufacturing enterprises at any given time does not exceed peak energy consumption; among which, Indicates peak energy consumption. express t Energy consumption of equipment other than critical energy-consuming equipment within a manufacturing enterprise during a given period. Indicates task n The Middle j Each sub-task is t Energy consumption requirements at any time In task n, the first... j Each sub-task is t Whether the time is executed or not. Indicates that the energy storage system is in t The amount of charge during a given time period. Indicates that the energy storage system is in t Discharge amount during a given period.
[0010] Preferably, the linear programming method described above for peak energy consumption reduction includes the following task scheduling and sequence constraints: Ensure that each subtask corresponding to each production task must be uniquely started within the scheduling cycle; Ensure that the execution status of each subtask is strictly consistent with its start time and duration; Ensure that there is a correlation between the actual start time of each subtask and a binary variable indicating whether the subtask is executed or started during a specific time period; Ensure that adjacent subtasks within the same production task are executed sequentially and that the minimum time interval requirement is met; Ensure that the overall time interval of each production task includes the time intervals of all its subtasks.
[0011] Preferably, the above-mentioned linear programming method for peak energy consumption reduction includes the following occupancy constraints on key energy-consuming equipment: Ensure that each production task can be assigned to only one energy-consuming device to guarantee the exclusive allocation of tasks and resources; Ensure that subtasks can only be executed on the energy-consuming equipment to which the production task is assigned, so as to guarantee the logical consistency of allocation and scheduling; Ensure that each energy-consuming device can process at most one subtask at any given time, and that each subtask can only be processed by one device at any given time; Ensure that the production tasks and their corresponding subtasks conform to the ternary linear constraints, so that the mixed-integer linear programming model can be solved by the solver.
[0012] Preferably, the linear programming method described above for peak energy consumption reduction includes the following constraints during the operation of the energy storage system: Ensure that the energy storage system complies with the energy conservation constraint; By using energy conservation constraints, the state of charge of the battery in the energy storage system is calculated and limited at different times to ensure that the energy storage system operates within a safe range.
[0013] Preferably, the above-mentioned linear programming method for peak energy consumption reduction, which substitutes actual production data as decision variables into a mixed-integer linear programming model and outputs the optimal scheduling scheme, includes the following steps: Acquire actual production data, which includes the total time window, time step interval, energy consumption of other energy-consuming equipment in the manufacturing enterprise within the time window, duration of production tasks and their sub-tasks, energy consumption per unit time, and relevant parameters of the energy storage system. The total time window, time step interval, energy consumption of other energy-consuming equipment in the manufacturing enterprise within the time window, duration of production tasks and their sub-tasks, energy consumption per unit time, and relevant parameters of the energy storage system are combined with the model parameters of the mixed integer linear programming model and input into the GUROBI solver to calculate the minimum peak energy consumption of the manufacturing enterprise to the social power grid.
[0014] Preferably, the linear programming method for peak energy consumption reduction described above, after substituting actual production data as decision variables into the mixed-integer linear programming model and outputting the optimal scheduling scheme, further includes: By inputting real-world case data into the GUROBI solver, the Gantt charts of key energy-consuming equipment, the total energy consumption curve of the manufacturing enterprise, and the battery state-of-charge curve of the energy storage system are obtained to verify the effectiveness of the mixed-integer linear programming model.
[0015] According to a second aspect of this application, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the linear programming method for peak energy consumption reduction provided by any of the above technical solutions.
[0016] According to a third aspect of this application, this application also provides a computer storage medium storing a computer program thereon, which, when executed, implements the linear programming method for peak energy consumption reduction provided by any of the above-described technical solutions. The technical solutions of this application have at least the following technical effects: The linear programming scheme for peak energy consumption reduction provided in this application defines a mixed-integer linear programming model for a manufacturing enterprise. This model includes model parameters and decision variables. The model parameters include parameters related to production tasks, energy storage systems, and key energy-consuming equipment. The objective function of this mixed-integer linear programming model is constructed by minimizing the peak energy consumption of the manufacturing enterprise on the social power grid. This allows for the construction of linear constraints on the mixed-integer linear programming model, based on the aforementioned model parameters and decision variables, to constrain the peak energy consumption of relevant important energy-consuming equipment. These constraints include occupancy constraints of key energy-consuming equipment, task scheduling and sequence constraints, and constraints on the operation of the energy storage system. By scheduling energy-consuming equipment using the objective function, timing issues can be fully considered, and coordination between the energy storage system and production scheduling can be maintained. Finally, by inputting actual production data as decision variables into the mixed-integer linear programming model, an optimal scheduling scheme can be output. This optimal scheduling scheme includes the start-up and shutdown plans of key energy-consuming equipment and the charging and discharging strategies of the energy storage system. Through this method, the peak energy consumption corresponding to the objective function can be reduced, thereby improving the energy security and reliability of the manufacturing system. Attached Figure Description
[0017] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments of this application and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 A flowchart illustrating a linear programming method for peak energy consumption reduction provided in an embodiment of this application; Figure 2 A schematic diagram of the energy consumption curve of other equipment in a manufacturing system provided for an embodiment of this application; Figure 3 A Gantt chart provided for embodiments of this application; Figure 4 A schematic diagram of system load provided for an embodiment of this application; Figure 5 A schematic diagram of the structure of an energy storage system SOC provided in this application embodiment; Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation
[0018] To more clearly illustrate the overall concept of this application, a detailed explanation is provided below with reference to the accompanying drawings.
[0019] Many specific details are set forth in the following description to provide a thorough understanding of this application. However, this application may also be implemented in other ways different from those described herein. Therefore, the scope of protection of this application is not limited to the specific embodiments disclosed below. It should be noted that, unless otherwise specified, the embodiments of this application and the features thereof can be combined with each other.
[0020] In this application, unless otherwise expressly specified and limited, the terms "above" and "below" the second feature can refer to direct contact between the first and second features, or indirect contact between the first and second features through an intermediate medium. In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described can be combined in any suitable manner in one or more embodiments or examples.
[0021] The existing technology has the following drawbacks: The startup process of critical energy-consuming equipment can generate a large-scale step energy consumption in a short period of time, which is essentially a timing-based decision-making problem, requiring more refined optimization methods. Furthermore, the application of energy storage systems provides crucial support and broader operational flexibility for manufacturing enterprises in peak shaving and valley filling. However, current technologies do not consider timing-based decision-making and energy storage-related issues. In summary, 1) Existing scheduling methods give little consideration to timing issues; 2) Existing scheduling methods do not take into account the synergistic effect between energy storage systems and production scheduling.
[0022] To overcome the above technical difficulties, see Figure 1 , Figure 1 This is a flowchart illustrating a linear programming method for peak energy consumption reduction provided in an embodiment of this application. Figure 1 As shown, the linear programming method for peak energy consumption reduction includes: S110: Define a mixed-integer linear programming model for a manufacturing enterprise; wherein, the mixed-integer linear programming model includes the model parameters and decision variables of the manufacturing enterprise, and the model parameters include relevant parameters of production tasks, energy storage systems and key energy-consuming equipment.
[0023] Specifically, as a preferred embodiment, the linear programming method for peak energy consumption reduction described above includes step S110: defining a mixed-integer linear programming model for the manufacturing enterprise; wherein the mixed-integer linear programming model includes model parameters and decision variables for the manufacturing enterprise, including: S111: The model parameters for manufacturing enterprises include: the task set of production tasks, the set of sub-tasks included in each production task, and the set of key energy-consuming equipment; the duration, energy consumption per unit time, and maintenance interval of each sub-task; the energy consumption of other equipment in the manufacturing enterprise excluding key energy-consuming equipment in each time period; and the maximum charging and discharging power, charging and discharging efficiency, upper and lower capacity limits, and initial power of the energy storage system.
[0024] Specifically, the model parameters for manufacturing enterprises are as follows: N - Task set for production tasks ( N ); J n - Production tasks n The set of subtasks in ( j =1,…, J n ); M - Equipment set of key energy-consuming equipment (m=1,…, M ); T - Preset scheduling time window (t=1,…,T); - The duration of the j-th subtask in production task n; - The energy consumption requirement of the j-th subtask in production task n at time t; - Maintenance interval for subtask m; - Energy consumption of other equipment in the manufacturing enterprise during period t; - The maximum amount of charge the energy storage system can generate per unit time; - The maximum discharge rate of the energy storage system per unit time; - Charging efficiency of energy storage systems; - Discharge efficiency of the energy storage system; - Minimum capacity of energy storage systems; - Maximum capacity of the energy storage system; - The initial charge of the energy storage system.
[0025] S112: The decision variables of manufacturing enterprises include: binary variables used to indicate whether a subtask is executed or begins in a specific time period; binary variables used to indicate the assignment of a task to a specific device; binary variables used to indicate the execution order of tasks on the device; integer variables used to indicate the start and end times of tasks and subtasks; continuous variables used to indicate the charging and discharging amount and battery state of charge of the energy storage system in each time period; and continuous variables used to indicate the peak energy consumption of the system.
[0026] The specific decision variables include the following: - Binary variables, task n subtasks j Whether to execute during time slot t; - Binary variables, task n subtasks j Does it start during time period t? - Binary variables, task n Is it assigned to a machine? m ; - Binary variables, task n subtasks j Is it during time period t on the machine? m implement; - Binary variables, task n 1 and Task n 2 in the machine m The order relationship above; - Integer variables, task n The actual start time of subtask j; tst n - Integer variables, task n Start time; tet n - Integer variables, task n End time; - A continuous variable, representing the amount of energy charged by the energy storage system during time period t; - A continuous variable, representing the discharge amount of the energy storage system during time period t; SOC t - Continuous variable: the state of charge of the battery in the energy storage system during time period t; P peak – Continuous variable, system peak energy consumption.
[0027] Figure 1 The technical solution provided in the illustrated embodiment, after step S110: defining the mixed-integer linear programming model of the manufacturing enterprise, further includes: S120: The objective function of a mixed-integer linear programming model is constructed with the goal of minimizing the peak energy consumption of manufacturing enterprises on the social power grid.
[0028] The optimization objective of the mixed-integer linear programming model is to minimize the peak energy consumption of the manufacturing enterprise on the social power grid. This objective ensures that the total energy demand of the manufacturing enterprise at any given time will not exceed the peak energy consumption.
[0029] Specifically, as a preferred embodiment, the linear programming method for peak energy consumption reduction described above, step S120: constructing the objective function of a mixed-integer linear programming model with the optimization objective of minimizing the peak energy consumption of manufacturing enterprises on the social power grid, including: According to the peak energy consumption calculation formula:
[0030] Ensure that the total energy consumption demand of manufacturing enterprises at any given time does not exceed peak energy consumption; among which, Indicates peak energy consumption. express t Energy consumption of equipment other than critical energy-consuming equipment within a manufacturing enterprise during a given period. Indicates task n The Middle j Each sub-task is t Energy consumption requirements at any time In task n, the first... j Each sub-task is t Whether the time is executed or not. Indicates that the energy storage system is in t The amount of charge during a given time period. Indicates that the energy storage system is in t Discharge amount during a given period.
[0031] S130: Based on model parameters and decision variables, construct the linear constraints of the mixed-integer linear programming model; wherein, the linear constraints include at least the occupancy constraints of key energy-consuming equipment, task scheduling and sequence constraints, and constraints on the operation process of the energy storage system; In one preferred embodiment, the linear programming method for peak energy consumption reduction described above includes the following task scheduling and sequence constraints: (1) Ensure that each subtask corresponding to each production task must be uniquely started within the scheduling cycle;
[0032] This means the task must be executed within the scheduled time slot to ensure that each task is completed within the specified timeframe. n Each subtask j It can only be started once within a scheduling cycle. That is, the variable. The sum of all possible start times t is 1, thus ensuring that each subtask is not scheduled repeatedly.
[0033] (2) Ensure that the execution status of each subtask is strictly consistent with the start time and duration of the subtask;
[0034] This formula defines the activity state of a subtask. That is, a subtask is active only if t is in a certain started state. When within the corresponding duration interval, Otherwise, it is 0. This ensures that the execution status of the subtask is strictly consistent with its actual start time and duration.
[0035] (3) Ensure that there is a correlation between the actual start time of each subtask and the binary variable of whether it starts in a specific time period;
[0036] This formula defines the actual start time of the subtask. With binary variables The relationship between them.
[0037] (4) Ensure that adjacent subtasks within the same production task must be executed sequentially and meet the minimum time interval requirement;
[0038] This ensures that adjacent subtasks within the same task must be executed in a given order, and that there is a minimum time interval between subtasks under the same task that is not less than the maintenance time.
[0039] (5) Ensure that the overall time interval of each production task includes the time intervals of all its sub-tasks.
[0040]
[0041] The above formula defines the overall start / end time of the task and the time relationship between all subtasks, ensuring that the time interval of each subtask is contained within the overall task interval.
[0042] Furthermore, as a preferred embodiment, the linear programming method for peak energy consumption reduction described above includes the following occupancy constraints on key energy-consuming equipment: (6) Ensure that each production task can only be assigned to one energy-consuming device to guarantee the exclusive allocation of tasks and resources;
[0043] This formula means that each production task can only be uniquely assigned to one machine, ensuring the exclusive allocation of tasks and resources and reducing the number of times products are transferred.
[0044] (7) Ensure that subtasks can only be executed on the energy-consuming equipment to which the production task is assigned, so as to ensure the logical consistency of allocation and scheduling.
[0045]
[0046] This formula guarantees that subtasks of task n can only be executed on device m if task n is assigned to that device. Otherwise, this term is always 0, which ensures the logical consistency of allocation and scheduling.
[0047] (8) Ensure that each energy-consuming device can process at most one subtask at any given time, and that each subtask can only be processed by one device at any given time;
[0048] The formula stipulates that each device can process at most one subtask at any given time, and each subtask can only be processed by one device at any given time.
[0049]
[0050] The above formulas are used to constrain each subtask to be processed by only one device at any given time, utilizing 0-1 variables. Determine the order of priority.
[0051] (9) Ensure that the production tasks and their corresponding sub-tasks conform to the ternary linear constraints so that the mixed integer linear programming model can be solved by the solver.
[0052]
[0053] The above formula is used to ensure that a task is only assigned to the device and that subtask is active at that moment. Otherwise, it is 0. Additionally, the following set of inequalities is a ternary linear constraint obtained by linearizing the above formula, so that the model can be solved using the GUROBI solver.
[0054] In addition, the linear constraints also include constraints related to the operation of the energy storage system, i.e., capacity management-related constraints. Specifically, as a preferred embodiment, the linear programming method for peak energy consumption reduction described above includes the following constraints related to the operation of the energy storage system: (10) Ensure that the energy storage system complies with the energy conservation constraint; By using energy conservation constraints, the state of charge of the battery in the energy storage system is calculated and limited at different times to ensure that the energy storage system operates within a safe range.
[0055]
[0056] The above formula is the energy conservation constraint of the energy storage system, used to calculate the battery state of charge of the energy storage system at different times. Through the above formula, the operation process of the energy storage system can be constrained to ensure that the energy storage system operates within a safe range.
[0057] Figure 1 The embodiment shown, after constructing the linear constraints of the mixed-integer linear programming model based on model parameters and decision variables, further includes: S140: Substitute actual production data as decision variables into the mixed-integer linear programming model to output the optimal scheduling scheme; wherein, the optimal scheduling scheme includes the start-up and shutdown plan of key energy-consuming equipment and the charging and discharging strategy of the energy storage system, so as to achieve the reduction of peak energy consumption corresponding to the objective function.
[0058] Specifically, in a preferred embodiment, step S140 involves substituting actual production data as decision variables into a mixed-integer linear programming model to output the optimal scheduling scheme, including: S141: Obtain actual production data, which includes the total time window, time step interval, energy consumption of other energy-consuming equipment in the manufacturing enterprise within the time window, duration of production tasks and their sub-tasks, energy consumption per unit time, and relevant parameters of the energy storage system.
[0059] S142: Input the total time window, time step interval, energy consumption of other energy-consuming equipment in the manufacturing enterprise within the time window, duration of production tasks and their sub-tasks, energy consumption per unit time, relevant parameters of the energy storage system, and model parameters of the mixed integer linear programming model into the GUROBI solver to calculate the minimum peak energy consumption of the manufacturing enterprise to the social power grid.
[0060] In summary, the linear programming method for peak energy consumption reduction provided in this application defines a mixed-integer linear programming model for a manufacturing enterprise. This model includes model parameters and decision variables. The model parameters include relevant parameters of production tasks, energy storage systems, and key energy-consuming equipment. The objective function of this mixed-integer linear programming model is constructed with minimizing the peak energy consumption of the manufacturing enterprise on the social power grid as the optimization objective. This allows for the construction of linear constraints on the mixed-integer linear programming model based on the aforementioned model parameters and decision variables, thus constraining the peak energy consumption of key energy-consuming equipment. These constraints include occupancy constraints of key energy-consuming equipment, task scheduling and sequence constraints, and constraints on the operation of the energy storage system. By scheduling energy-consuming equipment using the objective function, timing issues can be fully considered, and coordination between the energy storage system and production scheduling can be maintained. Finally, by inputting actual production data as decision variables into the mixed-integer linear programming model, an optimal scheduling scheme can be output. This optimal scheduling scheme includes the start-up and shutdown plans of key energy-consuming equipment and the charging and discharging strategies of the energy storage system. Through this method, the peak energy consumption corresponding to the objective function can be reduced, thereby improving the energy security and reliability of the manufacturing system.
[0061] In addition, as a preferred embodiment, Figure 1 The linear programming method for peak energy consumption reduction shown, after substituting actual production data as decision variables into the mixed-integer linear programming model and outputting the optimal scheduling scheme, also includes: S150: By inputting real-world case data into the GUROBI solver, the Gantt charts of key energy-consuming equipment, the total energy consumption curve of the manufacturing enterprise, and the battery state-of-charge curve of the energy storage system are obtained to verify the effectiveness of the mixed-integer linear programming model.
[0062] To verify the effectiveness of the linear programming method for peak energy consumption reduction provided in the above embodiments, numerical simulation experiments were conducted in this application. By inputting real-world case data into the GUROBI solver, Gantt charts of key energy-consuming equipment, total energy consumption curves of manufacturing enterprises, and battery state-of-charge curves of energy storage systems were obtained to verify the effectiveness of the mixed-integer linear programming model.
[0063] Specifically, this study selects real historical data as the case study, with a total time window of 24 hours and a time step interval of 15 minutes. The energy consumption of other equipment in the manufacturing system within the time window is also considered. like Figure 2 As shown. There are N=3 tasks in total, each task is assigned by J. n =It consists of 3 sub-tasks, and each task can be processed on any of the 3 devices. The duration of each sub-task is... As shown in Table 1, the task energy consumption requirements are as follows. As shown in Table 2, maintenance time Based on actual operating conditions, the relevant parameters of the 1MW / 1MWh energy storage system are shown in Table 3. This embodiment of the application uses Gurobi Optimizer version 11.0.3 on a computer with a six-core 2.5 GHz Intel i5 processor and 16 GB RAM to solve the above problem.
[0064] Table 1 – Duration of the Task
[0065] Table 2 – Energy Consumption per Unit Time for the Task
[0066] Table 3 – Relevant parameters of the energy storage system
[0067] Additionally, see Figures 3 to 5 , Figure 3 The optimized Gantt chart for key energy-consuming equipment is shown. Figure 4 The system load curve and Figure 5 The SOC trajectory of the energy storage system is shown.
[0068] exist Figure 3 In the Gantt chart of critical energy-consuming equipment shown, the tasks of all critical energy-consuming equipment are allocated in an orderly manner. Each sub-task is rationally arranged under the premise of satisfying the process sequence and resource constraints, and maintenance intervals are reserved between different sub-tasks under the same task. Figure 4The system load curve shown has a peak load of 662.1011 kWh. The load distribution is more balanced across time periods, and the maximum load is significantly reduced. The optimized scheduling has a suppressive effect on peak energy consumption, achieving the typical peak shaving and valley filling target.
[0069] exist Figure 5 In the SOC curve of the energy storage system shown, the energy storage device actively regulates the load on the energy consumption side. By charging during off-peak hours and discharging during peak hours, it balances the system's energy and reduces the peak-to-valley difference. The dynamic change process of the SOC conforms to the law of energy conservation, verifying the physical rationality and operability of the model.
[0070] In summary, the numerical simulation results fully demonstrate the effectiveness and superiority of the proposed scheduling optimization method in improving resource utilization, reducing system load peaks, and achieving efficient energy storage management, and it has good practical application value.
[0071] In addition, based on the same concept of the above method embodiments, this application also provides a linear programming system for peak energy consumption reduction to implement the above method of this application. Since the principle and method of solving the problem in this system embodiment are similar, it has at least all the beneficial effects brought by the technical solutions of the above embodiments, which will not be described in detail here.
[0072] See below. Figure 6 , Figure 6 An electronic device provided in this application includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the linear programming method for peak energy consumption reduction provided in any of the above embodiments.
[0073] The following is for reference. Figure 6 The diagram illustrates a structural schematic of an electronic device suitable for implementing embodiments of this application. The electronic devices in the embodiments of this application may include, but are not limited to, mobile terminals and / or fixed terminals. Figure 6 The device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.
[0074] like Figure 6As shown, the electronic device can include a processing unit 1001, such as a central processing unit and / or a graphics processing unit, which can perform various appropriate actions and processes according to a program stored in ROM 1002 or a program loaded from storage device 1003 into RAM 1004. RAM 1004 also stores various programs and data required for the operation of the electronic device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. Input / output interface 1006 is also connected to bus 1005. Typically, the following systems can be connected to input / output interface 1006: input devices 1007, such as touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, and / or gyroscopes; output devices 1008, such as liquid crystal displays (LCDs), speakers, and / or vibrators; storage devices 1003, such as magnetic tape and / or hard disks; and communication devices 1009. Communication device 1009 is capable of enabling the electronic device to exchange data with other devices wirelessly or via wired communication. Although the diagram shows a model building device with various systems, it should be understood that it is not required to implement or have all of the systems shown. It is possible to implement or have more or fewer systems alternatively.
[0075] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.
[0076] This application provides a computer-readable storage medium having computer-readable program instructions stored thereon, namely the computer program described above, which is used to perform the methods in the above embodiments. The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram can represent a module, program segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks can occur in a different order than those marked in the drawings. For example, two consecutively indicated blocks can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.
[0077] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.
[0078] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.
[0079] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.
Claims
1. A linear programming method for peak energy consumption reduction, characterized in that, include: Define a mixed-integer linear programming model for a manufacturing enterprise; wherein the mixed-integer linear programming model includes model parameters and decision variables for the manufacturing enterprise, and the model parameters include relevant parameters corresponding to production tasks, energy storage systems, and key energy-consuming equipment, respectively; With the goal of minimizing the peak energy consumption of manufacturing enterprises on the social power grid, the objective function of the mixed-integer linear programming model is constructed. Based on the model parameters and decision variables, the linear constraints of the mixed-integer linear programming model are constructed; wherein, the linear constraints include at least the occupancy constraints of key energy-consuming equipment, task scheduling and sequence constraints, and constraints on the operation process of the energy storage system; The actual production data is substituted into the mixed-integer linear programming model as the decision variable to output the optimal scheduling scheme; wherein, the optimal scheduling scheme includes the start-up and shutdown plan of key energy-consuming equipment and the charging and discharging strategy of the energy storage system, so as to achieve the peak energy consumption reduction corresponding to the objective function.
2. The method according to claim 1, characterized in that, The definition of the mixed-integer linear programming model for the manufacturing enterprise includes: model parameters and decision variables for the manufacturing enterprise. The model parameters for the manufacturing enterprise include: The set of production tasks, the set of sub-tasks contained in each production task, and the set of key energy-consuming equipment; Duration, energy consumption per unit time, and maintenance interval for each subtask; Energy consumption of other equipment in the manufacturing enterprise, excluding the key energy-consuming equipment, during each time period; The maximum charging and discharging power, charging and discharging efficiency, upper and lower capacity limits, and initial charge of the energy storage system; The decision variables for the manufacturing company include: A binary variable used to indicate whether a subtask is executed or begins during a specific time period; A binary variable used to represent the assignment of production tasks to specific equipment; A binary variable used to represent the order in which tasks are executed on the device; Integer variables used to represent the start and end times of production tasks and subtasks; Continuous variables used to represent the charging and discharging amounts and battery state of charge of an energy storage system at different times; A continuous variable used to represent the peak energy consumption of a system.
3. The method according to claim 1 or 2, characterized in that, The objective function of the mixed-integer linear programming model, which aims to minimize the peak energy consumption of the power grid by manufacturing enterprises, includes: According to the peak energy consumption calculation formula: Ensure that the total energy consumption demand of the manufacturing enterprise at any given time does not exceed the peak energy consumption; wherein, Indicates peak energy consumption. express t Energy consumption of other equipment within the manufacturing enterprise during the specified period, excluding the key energy-consuming equipment. Indicates task n The Middle j Each sub-task is t Energy consumption requirements at any time In task n, the first... j Each sub-task is t Whether the time is executed, Indicates that the energy storage system is in t The amount of charge during a given time period. Indicates that the energy storage system is in t Discharge amount during a given period.
4. The method according to claim 2, characterized in that, The task scheduling and sequence constraints include: Ensure that each subtask corresponding to each production task must be uniquely started within the scheduling cycle; Ensure that the execution status of each subtask is strictly consistent with the start time and duration of the subtask; Ensure that there is a correlation between the actual start time of each subtask and a binary variable indicating whether the subtask is executed or started during a specific time period; Ensure that adjacent subtasks within the same production task are executed sequentially and that the minimum time interval requirement is met; Ensure that the overall time interval of each production task includes the time intervals of all its subtasks.
5. The method according to claim 1, characterized in that, The occupancy constraints of the key energy-consuming equipment include: Ensure that each production task can be assigned to only one energy-consuming device to guarantee the exclusive allocation of tasks and resources; Ensure that subtasks can only be executed on the energy-consuming equipment to which the production task is assigned, so as to guarantee the logical consistency of allocation and scheduling; Ensure that each energy-consuming device can process at most one subtask at any given time, and that each subtask can only be processed by one device at any given time; Ensure that the production tasks and their corresponding subtasks conform to the ternary linear constraints, so that the mixed-integer linear programming model can be solved by a solver.
6. The method according to claim 1, characterized in that, The constraints on the operation of the energy storage system include: Ensure that the energy storage system complies with the energy conservation constraint; By applying the energy conservation constraint, the state of charge of the battery in the energy storage system is calculated and limited at different times to ensure that the energy storage system operates within a safe range.
7. The method according to claim 1, characterized in that, The step of substituting actual production data as the decision variable into the mixed-integer linear programming model and outputting the optimal scheduling scheme includes: The actual production data is obtained, including the total time window, time step interval, energy consumption of other energy-consuming equipment in the manufacturing enterprise within the time window, duration of production tasks and their sub-tasks, energy consumption per unit time, and relevant parameters of the energy storage system. The total time window, time step interval, energy consumption of other energy-consuming equipment in the manufacturing enterprise within the time window, duration of production tasks and their sub-tasks, energy consumption per unit time, and relevant parameters of the energy storage system, combined with the model parameters of the mixed integer linear programming model, are input into the GUROBI solver to calculate the minimum peak energy consumption of the manufacturing enterprise to the social power grid.
8. The method according to claim 7, characterized in that, After substituting actual production data as the decision variable into the mixed-integer linear programming model and outputting the optimal scheduling scheme, the process also includes: By inputting real-world case data into the GUROBI solver, the Gantt chart of the key energy-consuming equipment, the total energy consumption curve of the manufacturing enterprise, and the battery state-of-charge curve of the energy storage system are obtained to verify the effectiveness of the mixed-integer linear programming model.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the linear programming method for peak energy consumption reduction as described in any one of claims 1 to 8.
10. A computer storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the linear programming method for peak energy consumption reduction as described in any one of claims 1 to 8.