Production scheduling and energy utilization planning method and system considering wind and solar power output uncertainty

Abstract

The present application belongs to the technical field of energy optimization, and provides a production scheduling and energy utilization planning method and system considering wind and light output uncertainty, establishes an operation state machine model for each process, and defines mode switching logic; a dynamic process-energy coupling model is constructed, the process electrical load and thermal load of the period are dynamically calculated according to the operation mode selected by each process in each period; a wind and light output uncertainty set is constructed; a two-stage robust optimization model is constructed in combination with the mode switching logic, the process electrical load and thermal load and the wind and light output uncertainty set, the two-stage robust optimization model is iteratively solved by using a column and constraint generation algorithm, the worst scenario that maximizes the operation cost is identified and added to the scenario list through the alternative solving of the main problem and the sub-problem, until convergence, and the optimal process mode sequence and equipment capacity are output. Thus, the production energy consumption and energy equipment configuration cost are effectively reduced.

Description

Technical Field

[0001] This invention belongs to the field of energy optimization technology, and in particular relates to a production scheduling and energy planning method and system that takes into account the uncertainty of wind and solar power output. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] In industrial systems, the energy demand of production processes constitutes the main load. Different processes not only have different requirements for temperature grade and energy type, but also have multiple operating modes such as continuous operation, batch start-up and shutdown, and standby. Switching between modes will bring about significant changes in production capacity and energy consumption characteristics.

[0004] Extensive research has been conducted on the joint optimization of industrial processes and energy supply systems, but these methods generally suffer from the following shortcomings. In process modeling, existing methods typically categorize processes simply as continuous or intermittent based on production continuity, assigning fixed energy consumption coefficients per unit of capacity, such as consuming a fixed amount of electricity and heat per unit of product. However, in actual production, the same process may switch between high-power, low-power, or standby modes depending on the batch size and product specifications. The capacity limits, unit electricity consumption, unit heat consumption, and inherent electricity consumption vary significantly across different modes. More importantly, many processes do not operate continuously at full capacity 24 hours a day, but only need to complete a given production volume within a scheduling cycle, thus exhibiting significant flexibility in their production periods. However, existing technologies generally do not utilize this flexibility as a means of adjustment, treating the electricity consumption sequence of each process as fixed. This leads to rigid production load curves, failing to proactively match the time-of-use pricing and renewable energy output variations, resulting in high energy costs and forcing the configuration of oversized energy storage and converter equipment, increasing overall investment and operating costs.

[0005] Regarding uncertainty handling, existing industrial integrated energy system planning studies are mostly based on deterministic assumptions, using typical daily data of wind and solar power output as precisely known input conditions. However, photovoltaic and wind power output are significantly affected by weather, exhibiting overall deviations around baseline forecasts. Deterministic production and energy consumption schemes are highly susceptible to risks such as forced high-price energy purchases, insufficient energy supply, or even the inability to execute predetermined production tasks when faced with adverse scenarios such as persistently weak wind and solar power output. The robustness and economic efficiency of these schemes are poor. While a few studies have introduced robust optimization concepts, they have not yet incorporated the shiftable characteristics of production periods for processes not operating at full 24-hour capacity into decision variables, failing to fully leverage the potential of adjusting process timing to offset uncertainties in wind and solar power output. Summary of the Invention

[0006] In order to solve at least one of the technical problems existing in the background art, the present invention provides a production scheduling and energy planning method and system that takes into account the uncertainty of wind and solar power output.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: The first aspect of the present invention provides a production scheduling and energy planning method that takes into account the uncertainty of wind and solar power output, comprising the following steps: Acquire industrial enterprise production process parameters, energy supply equipment technical and economic parameters, and wind and solar power output data; Establish a state machine model for each process and define the mode switching logic; A dynamic process-energy coupling model is constructed, and the electrical and thermal loads of each process are dynamically calculated based on the operating mode selected by each process in each time period. Construct an uncertain set of wind and solar power output; A two-stage robust optimization model is constructed by combining mode switching logic, process electrical and thermal loads, and the uncertainty set of wind and solar power output. The decision variables in the first stage include binary variables of process operation mode, production time arrangement variables of non-24-hour full-load processes, and equipment capacity variables, which determine the equipment capacity and the operation mode and production time of each time period of adjustable processes. In the second stage, for each identified worst-case scenario, production scheduling and energy scheduling are optimized to minimize operating costs. The two-stage robust optimization model is iteratively solved using a column and constraint generation algorithm. By alternately solving the main problem and sub-problems, the worst-case scenario that maximizes the operating cost is identified and added to the scenario list until convergence, and the optimal process mode sequence and equipment capacity are output.

[0008] Furthermore, the obtained industrial enterprise production process parameters include a process list, selectable operating modes for each process, maximum capacity per unit time under each mode, power consumption per unit output, heat consumption per unit output, inherent power consumption of the mode, minimum mode holding time, and type identifier for whether each process is operating at full load for 24 hours. The acquired technical and economic parameters of the energy supply equipment include investment cost coefficient, operation and maintenance cost coefficient, efficiency curve, and capacity limit; the wind and solar power output data include typical daily time-of-use electricity price, natural gas price, photovoltaic benchmark predicted output curve and wind power benchmark predicted output curve, as well as the production tasks that each process needs to complete within the scheduling cycle.

[0009] Furthermore, the mode switching logic specifically includes: defining a set of optional operating modes for each process, introducing binary variables to represent the operating mode of each process in each time period, constraining each process to be uniquely in one operating mode in each time period, and establishing a duration constraint after mode switching based on the minimum holding time of each operating mode.

[0010] Furthermore, the electrical load is equal to the unit output power consumption in the current mode multiplied by the output plus the inherent power consumption of the mode, and the thermal load is equal to the unit output heat consumption in the current mode multiplied by the output; the additional energy consumption generated by mode switching is included in the electrical load of the corresponding time period as an additional item.

[0011] Furthermore, the uncertain set of wind and solar power output adopts the form of discrete multipliers. The upper limit of available photovoltaic power output is equal to the photovoltaic power output coefficient multiplied by the photovoltaic reference curve and then multiplied by the photovoltaic capacity. The upper limit of available wind power output is equal to the wind power output coefficient multiplied by the wind power reference curve and then multiplied by the wind power capacity.

[0012] Furthermore, the objective of the two-stage robust optimization model is to minimize the sum of the annualized investment cost in the first stage and the maximum operating cost in each scenario in the second stage. The constraints of the two-stage robust optimization model include capacity constraints, inventory balance constraints, electrical power balance constraints, thermal power balance constraints, equipment output constraints, and energy storage dynamic constraints. Among these, the equipment handling constraints include gas turbine output constraints, photovoltaic output constraints, wind power output constraints, and electric boiler output constraints.

[0013] Furthermore, the column and constraint generation algorithm is used to iteratively solve the two-stage robust optimization model, including: solving the main problem to obtain the current first-stage decision and lower bound; fixing the first-stage decision, traversing all joint scenarios to solve the linear programming subproblems, finding the scenario with the highest annual operating cost as the worst scenario; if the cost of the worst scenario is greater than the proxy variable of the main problem plus the tolerance, then the scenario and its corresponding second-stage variables and constraints are added to the main problem, and the iteration continues; otherwise, convergence is achieved, and the optimal process mode sequence and equipment capacity are output.

[0014] A second aspect of the present invention provides a production scheduling and energy planning system that takes into account the uncertainty of wind and solar power output, comprising: The data acquisition module is used to acquire production process parameters of industrial enterprises, technical and economic parameters of energy supply equipment, and wind and solar power output data. The process modeling module is used to build a state machine model for each process and define the mode switching logic; The load calculation module constructs a dynamic process-energy coupling model, which dynamically calculates the process electrical load and thermal load for each time period based on the operating mode selected by each process in each time period. The uncertainty set construction module is used to construct the uncertainty set of wind and solar power output; The optimization model building module is used to construct a two-stage robust optimization model by combining mode switching logic, process electrical and thermal loads, and the uncertainty set of wind and solar power output. The decision variables in the first stage include binary variables of process operation mode, production time arrangement variables of non-24-hour full-load processes, and equipment capacity variables, which determine the equipment capacity and the operation mode and production time of each time period of adjustable processes. In the second stage, for each identified worst-case scenario, production scheduling and energy scheduling are optimized to minimize operating costs. The optimization solution module is used to iteratively solve the two-stage robust optimization model using a column and constraint generation algorithm. By alternately solving the main problem and sub-problems, it identifies the worst scenario that maximizes the operating cost and adds it to the scenario list until convergence, outputting the optimal process mode sequence and equipment capacity.

[0015] A third aspect of the present invention provides a computer-readable storage medium.

[0016] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the production scheduling and energy planning method for taking into account the uncertainty of wind and solar power output as described above.

[0017] A fourth aspect of the present invention provides a computer device.

[0018] A computer device 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 steps in the production scheduling and energy planning method that takes into account the uncertainty of wind and solar power output as described above.

[0019] Compared with the prior art, the beneficial effects of the present invention are: This invention adapts and utilizes the shiftable production periods of non-24-hour full-load processes to dynamically adjust the load curve. By combining multi-energy flow coupling relationship modeling and robust optimization theory, it obtains a process scheduling and equipment capacity planning scheme that can still guarantee economy and feasibility under the worst wind and solar scenarios. It can also quantitatively demonstrate the regulating role of energy storage in this scenario, thereby effectively reducing production energy costs and energy equipment configuration costs.

[0020] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0021] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0022] Figure 1This is a flowchart of a production scheduling and energy planning method that takes into account the uncertainty of wind and solar power output, provided in an embodiment of the present invention. Figure 2 This is a flowchart of the iterative solution of the two-stage robust optimization model using a column and constraint generation algorithm provided in an embodiment of the present invention. Detailed Implementation

[0023] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0024] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0025] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0026] like Figure 1 As shown, this invention proposes a production scheduling and energy planning method that takes into account the uncertainty of wind and solar power output. By adaptively utilizing the shiftable characteristics of production periods of non-24-hour full-load processes, it dynamically constructs multi-energy flow coupling relationships and introduces robust optimization theory to obtain a process scheduling and equipment capacity planning scheme that balances economy and feasibility under the worst wind and solar scenarios. This effectively reduces production energy costs and energy equipment configuration costs, and quantifies the regulatory and supporting role of energy storage.

[0027] As an embodiment of the present invention, this embodiment provides a production scheduling and energy planning method that takes into account the uncertainty of wind and solar power output, including the following steps: Step 1: Obtain production process parameters of industrial enterprises, technical and economic parameters of energy supply equipment, and wind and solar power output data; In this embodiment, the obtained industrial enterprise production process parameters include a process list, selectable operating modes for each process, maximum capacity per unit time under each mode, power consumption per unit output, heat consumption per unit output, inherent power consumption of the mode, minimum mode holding time, and type identifier for whether each process is operating at full load for 24 hours. The acquired technical and economic parameters of the energy supply equipment include investment cost coefficient, operation and maintenance cost coefficient, efficiency curve, capacity limit, etc. In this embodiment, the energy supply equipment includes gas turbine units, electric boilers, photovoltaic power generation equipment, wind power generation equipment, electric energy storage equipment, and thermal energy storage equipment, etc. The wind and solar power output related data include typical daily time-of-use electricity price, natural gas price, photovoltaic benchmark predicted output curve and wind power benchmark predicted output curve, as well as the production tasks that each process needs to complete within the scheduling cycle.

[0028] In the above data, the production process parameters are used for the process operation status mechanism construction in step 2 and the dynamic energy flow load calculation in step 3, and serve as the input for capacity constraints and inventory balance constraints in step 5; the technical and economic parameters of the energy supply equipment are used for the equipment output constraint construction and objective function cost accounting in step 5; the wind and solar energy price data are used for the wind and solar output uncertainty set construction in step 4, and participate in the energy balance constraint and operating cost calculation in step 5.

[0029] Step 2: Based on the acquired industrial enterprise production process parameters, establish an operating state machine model for each process and define the mode switching logic; In this embodiment, based on the process parameters such as the selectable operating modes and minimum mode hold time obtained in step 1, a two-state operating state machine model (start-up or shutdown) is established for each process, and mode switching logic is defined. Specifically, this includes: Defining the mode switching logic specifically includes: defining the set of optional operating modes for each process. These correspond to the power-on mode and standby mode, respectively. A binary variable is introduced. Indicates the first Each process in time Is it in mode? This constrains each process to be in a unique mode of operation (either powered on or in standby) at any given time period. And based on the minimum hold time of each operating mode, establish the duration constraint after mode switching, if in the time period Once a certain mode is switched to, that mode must be maintained for a certain number of subsequent periods. By establishing a process operation state machine model and making full use of the shiftable production time of processes that are not at full load 24 hours a day, the active shifting and flexible adjustment of the production load curve were realized.

[0030] In step 3, based on the output of the state machine The values ​​are obtained, and the electrical and thermal loads of the process are calculated. In step 5, the relevant constraints generated by the state machine are used as constraints for the two-stage robust optimization model and solved together. By adopting the above method, when faced with time-of-use electricity price fluctuations and changes in wind and solar power output, transferable production tasks can be concentrated in periods of low electricity prices and abundant wind and solar power, effectively reducing energy costs for production. At the same time, it reduces the rigid demand for energy storage, converter and other energy supply equipment capacity, directly reducing the investment and configuration costs of equipment.

[0031] Step 3: Construct a dynamic process-energy coupling model. Based on the mode switching logic, dynamically calculate the process electrical load and thermal load for each process according to the operating mode selected by each process in each time period, and transfer the calculation results to the subsequent electrical / thermal power balance constraints. In this embodiment, the construction process of the dynamic process-energy coupling model includes: Based on the parameters such as unit output power consumption, unit output heat consumption, and inherent power consumption of processes that are not operating at full capacity for 24 hours obtained in step 1, as well as the production tasks that each process needs to complete, a dynamic process-energy coupling model is constructed.

[0032] In this embodiment, the electrical load equals the unit output power consumption multiplied by the output plus the inherent power consumption under the current mode, and the thermal load equals the unit output heat consumption multiplied by the output plus the inherent heat consumption under the current mode, that is: , , In the formula, For the first Step 1 Hourly electrical load, For the first Step 1 Hourly heat load, and The first Electricity consumption and heat consumption per unit output in each process For the first Hourly output and The first The inherent power consumption and inherent heat consumption of each process.

[0033] For processes that do not operate at full capacity 24 hours a day, their production time is shifted within the scheduling cycle to meet the production task requirements of that process, the output of that process meets the usage requirements of the next process (i.e., the output connection requirements between processes), and meets the overall inventory requirements.

[0034] Step 4: Construct the wind and solar power output uncertainty set based on the acquired wind and solar power output data; In this embodiment, based on the photovoltaic (PV) benchmark predicted output curve and the wind power benchmark predicted output curve obtained in step 1, a wind and solar power output uncertainty set is constructed. The wind and solar power output uncertainty set adopts a discrete multiplier form, where the upper limit of available PV output is equal to the PV output coefficient multiplied by the PV benchmark curve and then multiplied by the PV capacity, and the upper limit of available wind power output is equal to the wind power output coefficient multiplied by the wind power benchmark curve and then multiplied by the wind power capacity. Furthermore, the upper limit of available photovoltaic power output And the upper limit of available wind power output Represented as: (1), (2), in, Indicates the photovoltaic output coefficient. Represents the photovoltaic baseline curve. Indicates photovoltaic capacity, Indicates the wind power output coefficient. This represents the wind power baseline curve. This represents the wind power capacity. The upper limit of available photovoltaic output is calculated using formulas (1) and (2). And the upper limit of available wind power output It is directly used in the photovoltaic output constraints and wind power output constraints of the two-stage robust optimization model in step 5 as the upper limit of the actual output.

[0035] Step 5: Transfer the uncertainties of process electrical load, thermal load, and wind and solar power output to the corresponding constraints, and establish a two-stage robust optimization model; In this embodiment, a two-stage robust optimization model is established based on the technical and economic parameters of the energy supply equipment, time-of-use electricity price, natural gas price, process parameters, and wind and solar power output data obtained in step 1. The energy supply equipment parameters are used to determine investment costs and equipment output constraints; the time-of-use electricity price and gas price are used to calculate operating costs; and the process type identifier is used to distinguish between relocatable and non-relocatable processes.

[0036] In the two-stage robust optimization model, the decision variables in the first stage include binary variables of process operation mode, production time arrangement variables of non-24-hour full-load processes, and equipment capacity variables; the second stage, for each identified worst-case scenario, decides on process output, inventory, electricity purchase, output of each piece of equipment, and energy storage charging and discharging power. Furthermore, the variables in the first stage include a binary variable of process operation mode and a variable of production time arrangement for processes that are not operating at full capacity 24 hours a day. and equipment capacity Decision-making equipment capacity and the operating mode and production period of each adjustable process; The second phase addresses each identified worst-case scenario. Optimize process output ,in stock Energy scheduling, including production scheduling, electricity purchase, power output of various equipment, and energy storage charging and discharging power, minimizes operating costs.

[0037] The goal of the two-stage robust optimization model is to minimize the sum of the annualized investment cost in the first stage and the maximum operating cost for each scenario in the second stage. (3), in, This represents the capital recovery factor, a coefficient used to even out the initial investment cost over each year. ,in It is the discount rate. It is the project's lifespan; Indicates the first The unit capacity investment cost coefficient of similar energy supply equipment Indicates the first The planned installation capacity of similar energy supply equipment This represents the maximum value of the minimum operating cost for each scenario in the second phase.

[0038] The constraints of the two-stage robust optimization model include capacity constraints, inventory balance constraints, electrical power balance constraints, thermal power balance constraints, equipment output constraints, and energy storage dynamic constraints. Capacity constraints: Process output is coupled with operating mode; different modes have different capacity limits, and actual output cannot exceed the capacity limit corresponding to the current mode. (4), in, Indicates the first Hourly output This indicates the upper limit of production capacity.

[0039] Inventory balance constraints: (5), In addition to the above relationships, the inventory must also be able to be no less than zero, i.e. And inventory at the end of the scheduling cycle ,in, and Indicates the first and Hourly inventory.

[0040] Electric power balance constraints: (6), in, , , These represent the output of gas turbine units, photovoltaic power, and wind power, respectively. , These represent the energy release and energy storage power of electrical energy storage, respectively. Indicates hourly electrical load. This indicates the power of the electric boiler.

[0041] Thermal power balance constraint: (7), in, These represent the heat output of the gas turbine unit and the electric boiler, respectively. This indicates the energy release and storage power of thermal energy storage.

[0042] Equipment output constraints include: Gas turbine unit output constraints: (8), (9), Photovoltaic output constraints: (10) Wind power output constraints: (11), Electric boiler output constraints: (12) (13) in, and These represent the capacities of the gas turbine unit and the electric boiler, respectively. For electric boiler efficiency.

[0043] Energy storage dynamic constraints: (14) (15) (16) (17) (18) (19) in, , These represent the electrical energy storage in the first... and Hourly energy storage capacity , These represent the thermal energy storage in the first... and Hourly heat storage This indicates the heat loss of electrical energy storage and thermal energy storage. These are the charge / discharge efficiencies for electrical energy storage and thermal energy storage, respectively. These are two variables representing the charge and discharge states, respectively. , These represent the capacity of electrical energy storage and thermal energy storage, respectively.

[0044] To address the uncertainty of wind and solar power output, a method for constructing uncertainty sets in the form of discrete multipliers is adopted. Production scheduling and energy supply planning are incorporated into a two-stage robust optimization framework. Combined with column and constraint generation algorithms for iterative solution, a robust solution can be obtained within a finite number of iterations that can guarantee the completion of production tasks and the balance of energy supply and demand under all possible worst-case wind and solar power scenarios. This avoids the risk of being forced to purchase energy at high prices or even experiencing insufficient energy supply when the overall wind and solar power output is weak, and significantly improves the system's ability to cope with the uncertainty of renewable energy.

[0045] Step 6: The column and constraint generation algorithm is used to iteratively solve the two-stage robust optimization model. By alternately solving the main problem and sub-problems, the worst scenario that maximizes the operating cost is identified and added to the scenario list until convergence, and the optimal process mode sequence and equipment capacity are output. In this embodiment, the main problem is solved to obtain the current first-stage decision and lower bound; the first-stage decision is fixed, and all joint scenarios are traversed to solve the linear programming subproblems. The scenario with the highest annual operating cost is identified as the worst scenario. If the cost of the worst scenario is greater than the proxy variable of the main problem plus the tolerance, the scenario and the corresponding second-stage variable and constraints are added to the main problem, and the iteration continues; otherwise, convergence is achieved, and the optimal process mode sequence and equipment capacity are output.

[0046] like Figure 2 As shown, the specific steps include the following: Step 601: Set the initial scene as the nominal scene, where , , Initialize the iteration counter. Upper and lower boundaries , ; Step 602: Fix the current scene set Optimize the capacity of variable devices in the first stage With process mode and second-stage cost proxy variables This yields the current optimal capacity configuration, mode sequence, and lower bound. ; Step 603: Solve the subproblems: fix the output of the main problem. , Traverse the joint scene set Solve for the optimal runtime scheduling cost in each scenario. Find the worst-case scenario that maximizes operating costs. Get the upper bound update value ; Step 604: Convergence Judgment and Scene Addition: If If the maximum number of iterations is reached, the process will terminate; otherwise, it will... join in Return to step 602 and continue iterating.

[0047] After obtaining the robust optimal solution, the operation optimization model is solved under the nominal scenario and the worst scenario respectively, and the hourly output, energy storage charging and discharging power and energy storage changes of each device are extracted.

[0048] The optimization model established in this invention is a mixed-integer linear programming model with a clear structure. It can be efficiently solved by commercial solvers and has strong engineering feasibility. It is particularly suitable for industrial scenarios with high process energy consumption, large fluctuations in wind and solar resources, flexible production task adjustments, and high requirements for energy supply reliability, such as structural component welding and die casting processes in machining, catalytic cracking and air separation oxygen production processes in petrochemicals, and fermentation and bottling processes in food and beverage. It can significantly reduce enterprises' energy costs and energy equipment configuration costs while ensuring energy supply security, resulting in significant economic benefits.

[0049] To verify the advancement and accuracy of the method of this invention, a mechanical processing industrial project was used as an example, and three comparison strategies were employed for quantitative comparison. The definitions of each strategy are as follows: Strategy A: Deterministic planning, using only unbiased baseline forecasts of wind and solar power output for process scheduling and equipment capacity planning. This strategy ignores the uncertainty of wind and solar power output and fixes the power consumption sequence of each process to a typical daily load curve, without utilizing the shiftable production periods of processes that are not at full load 24 hours a day.

[0050] Strategy B: Robust planning without production time adjustments. This strategy fixes the power consumption sequence of each process, disallowing load shifting based on the shiftability of production time periods for processes not operating at full capacity 24 hours a day. Under this constraint, a robust optimization framework containing wind and solar uncertainties, similar to that used in this invention, is employed to solve for equipment capacity and scheduling schemes. This strategy is used to quantify the value of production time flexibility.

[0051] Strategy C: The method of this invention is a complete multi-mode operation state machine for the process. It combines the transferability of the production period of non-24-hour full-load processes with robust optimization based on the uncertainty of wind and solar power output. It adopts a two-stage robust optimization and column and constraint generation algorithm for iterative solution.

[0052] By running three strategies under the same computational data, the optimal equipment capacity, annualized investment, nominal annual operating cost, worst-case annual operating cost, and supply shortage under the worst-case scenario are extracted for each strategy, thereby comprehensively evaluating the improvements of this invention in terms of robustness, economy, and power supply reliability.

[0053] By running three strategies under the same example data, the optimal equipment capacity, annualized investment, nominal annual operating cost, worst-case annual operating cost (taking the maximum operating cost in the joint uncertain scenario), and supply shortage (electricity / heat shortage) of each strategy are extracted, as shown in Table 1, thereby comprehensively evaluating the improvements of this invention in terms of robustness, economy, and energy supply reliability.

[0054] Table 1 shows the project data for each of the three strategies.

[0055] Strategy A has the lowest cost in nominal scenarios because it does not take into account the uncertainty of wind and solar power output and does not take advantage of the shiftable production periods of non-24-hour full-load processes. However, when faced with unfavorable scenarios such as persistently weak wind and solar power output, it is forced to purchase large amounts of electricity at high prices, resulting in extremely high operating costs in the worst-case scenario and poor economic efficiency and reliability.

[0056] Although Strategy B takes into account the uncertainty of wind and solar power, it fixes the electricity consumption time of each process and cannot actively match the peak output of wind and solar power and the low electricity price period by shifting the production period of non-24-hour full-load processes. The production load curve is rigid, and it is forced to configure a larger capacity of energy storage to smooth out supply and demand fluctuations. The investment cost is the highest among the three strategies, and the operating cost is also high.

[0057] Strategy C adaptively adjusts the production periods of processes not operating at full capacity 24 hours a day, concentrating transferable production tasks during periods of abundant wind and solar power and low electricity prices. This effectively shifts the overall load curve, reduces reliance on energy storage capacity and electricity purchases, and utilizes energy storage to cope with residual fluctuations, significantly reducing operating costs under worst-case scenarios. This invention achieves a nominal total cost close to that of Strategy A, but far surpasses both Strategy A and Strategy B in worst-case total cost, achieving an optimal balance between robustness and economy.

[0058] Example 2 This embodiment provides a production scheduling and energy planning system that takes into account the uncertainty of wind and solar power output, including: The data acquisition module is used to acquire production process parameters of industrial enterprises, technical and economic parameters of energy supply equipment, and wind and solar power output data. The process modeling module is used to build a state machine model for each process and define the mode switching logic; The load calculation module constructs a dynamic process-energy coupling model, which dynamically calculates the process electrical load and thermal load for each time period based on the operating mode selected by each process in each time period. The uncertainty set construction module is used to construct the uncertainty set of wind and solar power output; The optimization model building module is used to construct a two-stage robust optimization model by combining mode switching logic, process electrical and thermal loads, and the uncertainty set of wind and solar power output. The decision variables in the first stage include binary variables of process operation mode, production time arrangement variables of non-24-hour full-load processes, and equipment capacity variables, which determine the equipment capacity and the operation mode and production time of each time period of adjustable processes. In the second stage, for each identified worst-case scenario, production scheduling and energy scheduling are optimized to minimize operating costs. The optimization solution module is used to iteratively solve the two-stage robust optimization model using a column and constraint generation algorithm. By alternately solving the main problem and sub-problems, it identifies the worst scenario that maximizes the operating cost and adds it to the scenario list until convergence, outputting the optimal process mode sequence and equipment capacity.

[0059] It should be noted that the specific implementation of the production scheduling and energy consumption planning system taking into account the uncertainty of wind and solar power output in this embodiment of the invention is similar to the specific implementation of the production scheduling and energy consumption planning method taking into account the uncertainty of wind and solar power output in this embodiment of the invention. For details, please refer to the description in the method section. In order to reduce redundancy, it will not be repeated here.

[0060] Example 3 This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the production scheduling and energy planning method that takes into account the uncertainty of wind and solar power output as described above.

[0061] Example 4 This embodiment provides a computer device, including 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 steps in the production scheduling and energy planning method that takes into account the uncertainty of wind and solar power output as described above.

[0062] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0063] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0064] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0065] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0066] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0067] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A production scheduling and energy planning method that takes into account the uncertainty of wind and solar power output, characterized in that, include: Acquire industrial enterprise production process parameters, energy supply equipment technical and economic parameters, and wind and solar power output data; Establish a state machine model for each process and define the mode switching logic; A dynamic process-energy coupling model is constructed, and the electrical and thermal loads of each process are dynamically calculated based on the operating mode selected by each process in each time period. Construct an uncertain set of wind and solar power output; A two-stage robust optimization model is constructed by combining mode switching logic, process electrical and thermal loads, and the uncertainty set of wind and solar power output. The decision variables in the first stage include binary variables of process operation mode, production time arrangement variables of non-24-hour full-load processes, and equipment capacity variables. The decision is based on the equipment capacity and the operation mode and production time of each time period of the adjustable processes. The second phase optimizes production and energy scheduling for each identified worst-case scenario to minimize operating costs. The two-stage robust optimization model is iteratively solved using a column and constraint generation algorithm. By alternately solving the main problem and sub-problems, the worst-case scenario that maximizes the operating cost is identified and added to the scenario list until convergence, and the optimal process mode sequence and equipment capacity are output.

2. The production scheduling and energy planning method considering the uncertainty of wind and solar power output as described in claim 1, characterized in that, The obtained industrial enterprise production process parameters include a process list, selectable operating modes for each process, maximum capacity per unit time under each mode, power consumption per unit output, heat consumption per unit output, inherent power consumption of the mode, minimum mode hold time, and type identifier for whether each process is operating at full load for 24 hours. The acquired technical and economic parameters of the energy supply equipment include investment cost coefficient, operation and maintenance cost coefficient, efficiency curve, and capacity limit; the wind and solar power output data include typical daily time-of-use electricity price, natural gas price, photovoltaic benchmark predicted output curve and wind power benchmark predicted output curve, as well as the production tasks that each process needs to complete within the scheduling cycle.

3. The production scheduling and energy planning method considering the uncertainty of wind and solar power output as described in claim 1, characterized in that, The mode switching logic specifically includes: defining a set of optional operating modes for each process, introducing binary variables to represent the operating mode of each process in each time period, constraining each process to be uniquely in one operating mode in each time period, and establishing a duration constraint after mode switching based on the minimum holding time of each operating mode.

4. The production scheduling and energy planning method considering the uncertainty of wind and solar power output as described in claim 1, characterized in that, The electrical load equals the unit output power consumption in the current mode multiplied by the output plus the inherent power consumption of the mode; the thermal load equals the unit output heat consumption in the current mode multiplied by the output; the additional energy consumption generated by mode switching is included in the electrical load of the corresponding time period as an additional item.

5. The production scheduling and energy planning method considering the uncertainty of wind and solar power output as described in claim 1, characterized in that, The uncertainty set of wind and solar power output adopts the form of discrete multipliers. The upper limit of available photovoltaic power output is equal to the photovoltaic power output coefficient multiplied by the photovoltaic reference curve and then multiplied by the photovoltaic capacity. The upper limit of available wind power output is equal to the wind power output coefficient multiplied by the wind power reference curve and then multiplied by the wind power capacity.

6. The production scheduling and energy planning method considering the uncertainty of wind and solar power output as described in claim 1, characterized in that, The goal of the two-stage robust optimization model is to minimize the sum of the annualized investment cost in the first stage and the maximum operating cost in each scenario in the second stage. The constraints of the two-stage robust optimization model include capacity constraints, inventory balance constraints, power balance constraints, thermal power balance constraints, equipment output constraints, and energy storage dynamic constraints. Among them, equipment handling constraints include gas turbine output constraints, photovoltaic output constraints, wind power output constraints, and electric boiler output constraints.

7. The production scheduling and energy planning method considering the uncertainty of wind and solar power output as described in claim 1, characterized in that, The column and constraint generation algorithm is used to iteratively solve the two-stage robust optimization model, including: solving the main problem to obtain the current first-stage decision and lower bound; fixing the first-stage decision, traversing all joint scenarios to solve the linear programming subproblems, finding the scenario with the highest annual operating cost as the worst scenario; if the cost of the worst scenario is greater than the proxy variable of the main problem plus the tolerance, then the scenario and its corresponding second-stage variables and constraints are added to the main problem, and the iteration continues; otherwise, convergence is achieved, and the optimal process mode sequence and equipment capacity are output.

8. A production scheduling and energy planning system that takes into account the uncertainty of wind and solar power output, characterized in that, include: The data acquisition module is used to acquire production process parameters of industrial enterprises, technical and economic parameters of energy supply equipment, and wind and solar power output data. The process modeling module is used to build a state machine model for each process and define the mode switching logic; The load calculation module constructs a dynamic process-energy coupling model, which dynamically calculates the process electrical load and thermal load for each time period based on the operating mode selected by each process in each time period. The uncertainty set construction module is used to construct the uncertainty set of wind and solar power output; The optimization model building module is used to construct a two-stage robust optimization model by combining mode switching logic, process electrical and thermal loads and wind and solar power output uncertainty sets. The decision variables in the first stage include binary variables of process operation mode, production time arrangement variables of non-24-hour full load processes and equipment capacity variables, and determine the equipment capacity and the operation mode and production time of each time period of the adjustable process. The second phase optimizes production and energy scheduling for each identified worst-case scenario to minimize operating costs. The optimization solution module is used to iteratively solve the two-stage robust optimization model using a column and constraint generation algorithm. By alternately solving the main problem and sub-problems, it identifies the worst scenario that maximizes the operating cost and adds it to the scenario list until convergence, outputting the optimal process mode sequence and equipment capacity.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the production scheduling and energy planning method that takes into account the uncertainty of wind and solar power output as described in any one of claims 1-7.

10. A computer 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 steps in the production scheduling and energy planning method that takes into account the uncertainty of wind and solar power output as described in any one of claims 1-7.

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