Plant area planning method and device and electronic equipment

Abstract

The application provides a plant planning method and device, electronic equipment, computer readable storage medium and computer program product. The method comprises: a reduction measure set construction step, the reduction measure set comprising at least one of a building reduction measure set, an energy utilization measure set, a greenhouse gas reduction measure set and a carbon credit measure set; a stage optimization model construction step, constructing an optimization model of a plurality of implementation stages of the reduction measure set; a model solving step, based on the target score of the plurality of implementation stages, solving the optimization model to obtain an optimal total cost, the optimal total cost comprising the sum of the target costs corresponding to the target scores of all implementation stages. The application solves the problem that the existing plant planning method does not consider the influence of the relevant policies of zero-carbon plants on the plant planning process.

Description

Technical Field

[0001] This application belongs to the technical field of plant planning, specifically relating to a plant planning method, apparatus, electronic equipment, computer-readable storage medium, and computer program product. Background Technology

[0002] With increasing global focus on climate change and growing demand for renewable energy, zero-carbon industrial zones have gained widespread attention as an important model for achieving low-carbon development. A "zero-carbon industrial zone" refers to a situation where a company's greenhouse gas emissions during its production processes reach a state of zero overall carbon emissions through carbon reduction and offsetting measures. Furthermore, beyond its own carbon reduction efforts, any remaining carbon emissions are eliminated or offset to achieve net-zero carbon emissions. In recent years, the number of national-level policies regarding zero-carbon industrial zones has been increasing, demonstrating the high level of importance the government places on this issue.

[0003] "Plant planning" refers to the systematic work of rationally arranging and laying out various production factors within a plant area. It mainly includes plant layout, production process optimization, lean layout, and digital transformation. However, existing plant planning methods are often based on traditional planning ideas, without taking into account the impact of relevant policies on zero-carbon plant areas on the plant planning process, nor the importance and necessity of incorporating zero-carbon indicators into plant planning. Summary of the Invention

[0004] The main purpose of this application is to provide a plant planning method, apparatus, electronic device, computer-readable storage medium, and computer program product, in order to solve the problem that existing plant planning methods do not take into account the impact of zero-carbon plant policies and zero-carbon indicators.

[0005] To address the aforementioned technical problems, in a first aspect, this application provides a plant planning method, comprising: a step of constructing an emission reduction measure set, wherein the emission reduction measure set includes at least one of a building emission reduction measure set, an energy utilization measure set, a greenhouse gas emission reduction measure set, and a carbon credit measure set; a step of constructing a stage optimization model, wherein an optimization model is constructed for several implementation stages of the emission reduction measure set; and a model solving step, wherein the optimization model is solved based on the target scores of several implementation stages to obtain the optimal total cost, wherein the optimal total cost includes the sum of the target costs corresponding to the target scores of all implementation stages.

[0006] Furthermore, the optimization model is a mixed-integer programming model, which includes decision variables based on the set of emission reduction measures, a cost optimization objective function based on the set of emission reduction measures, and cost constraints based on the set of emission reduction measures.

[0007] Furthermore, the decision variables include binary decision variables and / or continuous variables; binary decision variables are used to characterize the results of any emission reduction measure at any implementation stage, and continuous variables are used to characterize the amount of any emission reduction measure implemented at any implementation stage.

[0008] Furthermore, the cost optimization objective function is represented by the following relationship: ; Where t represents the implementation phase. Let be the total cost of the t-th implementation phase. Let be the implementation cost of the set of building emission reduction measures for the t-th implementation phase. Let be the implementation cost of the energy utilization measures set in the t-th implementation phase. Let be the implementation cost of the set of greenhouse gas emission reduction measures for the t-th implementation phase. Let be the implementation cost of the carbon credit measures set in the t-th implementation phase.

[0009] Furthermore, the implementation cost of building emission reduction measures. This can be represented by the following relation: ; in, The result of the use of the i-th building emission reduction measure in the t-th implementation phase is given. Let i be the unit implementation cost of the emission reduction measure for the i-th building. Let represent the amount of emission reduction measures implemented in the i-th building during the t-th implementation phase; Implementation costs of energy utilization measures This can be represented by the following relation: ; in, For the fixed cost of the j-th energy emission reduction measure, For the result of using the j-th energy emission reduction measure, The unit operating cost of the j-th energy emission reduction measure is... Let be the amount of energy emission reduction measure implemented in the j-th phase of implementation; The implementation cost of a set of greenhouse gas emission reduction measures This can be represented by the following relation: ; in, The unit implementation cost of the k-th greenhouse gas emission reduction measure. Let be the amount of greenhouse gas emission reduction measure implemented in the k-th phase of implementation; Implementation costs of carbon credit measures This can be represented by the following relation: ; in, Let be the carbon credit price in the t-th implementation phase. Let t represent the amount of carbon credits purchased in the t-th implementation phase.

[0010] Furthermore, cost constraints include emission constraints, carbon offsetting constraints, production assurance constraints, technology implementation constraints, and green electricity ratio constraints. Emission constraints are characterized by the following relationship: ; in, Let t be the actual total emissions in the t-th implementation phase. This is the annual baseline emission level. Let be the maximum emissions for the target star rating in the t-th implementation phase. Let m be the efficiency of the emission reduction measure. Let M be the amount of emission reduction measure implemented in the m-th stage of implementation, and M = I + J + K; The carbon offsetting constraint is characterized by the following relationship: ; in, Let be the upper limit coefficient for the carbon offset ratio in the t-th implementation phase; Production assurance constraints are represented by the following equation: ; in, Let be the energy supply for the t-th implementation phase. Let be the total production demand in the t-th implementation phase. For energy supply security margin; The technical implementation constraints are represented by the following relation: , ; in, The result of the use of the m-th emission reduction measure in the t-th implementation phase is... Let m be the sustainability coefficient of the m-th emission reduction measure; The green electricity ratio constraint is represented by the following relationship: ; in, Let be the green electricity consumption in the t-th implementation phase. Let be the total electricity consumption in the t-th implementation phase. Let be the proportion of green electricity consumption in the t-th implementation phase.

[0011] Furthermore, the model solution steps include: a scoring function determination step, which determines the scoring function for the plant area based on the decision variables. ,in, Let n be the amount of emission reduction measure implemented in the t-th implementation phase. Let be the carbon credit purchase amount for the t-th implementation phase; cost determination step, based on a scoring function. Given the given conditions, solve the optimization model corresponding to the t-th implementation stage to determine the optimal total cost min. ,in, The target score for the t-th implementation phase is: The corresponding target cost is min .

[0012] Secondly, this application also provides a plant planning device, comprising: an emission reduction measure set construction module, the emission reduction measure set including at least one of a building emission reduction measure set, an energy utilization measure set, a greenhouse gas emission reduction measure set, and a carbon credit measure set; a stage optimization model construction module, configured to construct optimization models for several implementation stages; and a model solving module, configured to solve the optimization models based on the target scores of several implementation stages to obtain the optimal total cost, the optimal total cost including the sum of the target costs of all implementation stages.

[0013] Thirdly, this application provides an electronic device including a processor and a memory for storing processor-executable instructions, wherein the processor executes the instructions to implement the steps of the method described in the first aspect.

[0014] Fourthly, this application provides a computer-readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method as described in the first aspect.

[0015] Fifthly, this application provides a computer program product comprising a computer program that, when executed by a processor, implements the steps of the method as described in the first aspect.

[0016] Compared with the prior art, this application has the following advantages: By establishing a set of emission reduction measures, an optimization model is constructed for several implementation stages of each set of emission reduction measures. The optimization model is solved to obtain the target score for each implementation stage, and then the optimal total cost corresponding to the plant's planning process is determined based on the target score. This approach fully considers the impact of policy factors on plant operations, helping the plant quickly assess the emission reduction strategies and costs required to achieve zero-carbon plant status, and formulate a suitable zero-carbon pathway to achieve the zero-carbon goal. Attached Figure Description

[0017] Figure 1This is a flowchart illustrating a factory area planning method according to an embodiment of this application; Figure 2 This is a schematic diagram illustrating the specific process of model solving in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a factory planning device according to an embodiment of this application. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. However, the embodiments described below are only some embodiments of this application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the protection scope of this application. Unless obvious from the context or otherwise, the same reference numerals in the figures represent the same structures or operations.

[0019] As indicated in this application and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not specifically singular and may include plural forms. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0020] Flowcharts are used in this application to illustrate the operations performed by the system according to embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, various steps can be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more steps may be removed from these processes.

[0021] Please see Figure 1 An embodiment of the first aspect of this application provides a method for planning a factory area, comprising the following steps: S1, the steps for constructing a set of emission reduction measures, which includes at least one of a set of building emission reduction measures, a set of energy utilization measures, a set of greenhouse gas emission reduction measures, and a set of carbon credit measures; S2, the steps for constructing the phased optimization model, involves building optimization models for several implementation phases of the emission reduction measures set; S3, the model solution step, is to solve the optimization model based on the target scores of several implementation stages to obtain the optimal total cost, which includes the sum of the target costs of all implementation stages.

[0022] This application establishes a set of emission reduction measures, constructs optimization models for several implementation stages of each set of emission reduction measures, solves the optimization models to obtain the target score for each implementation stage, and then determines the optimal total cost corresponding to the planning process of the plant area based on the target score. This application fully considers the impact of policy factors on plant operation, helps the plant area quickly assess the emission reduction strategies and costs required to achieve zero-carbon plant requirements, and formulates a suitable zero-carbon path to achieve the zero-carbon goal.

[0023] In some specific embodiments, the set of building emission reduction measures includes measures such as optimizing building materials, increasing green coverage, installing water-saving appliances, upgrading lighting systems, updating low-energy equipment, and configuring metering instruments. Among them, "optimizing building materials" refers to using low-carbon building materials such as low-carbon cement, recycled aggregate concrete, and steel structures; "increasing green coverage" refers to increasing the green coverage rate of the plant area from the baseline value to the target value; "installing water-saving appliances" refers to gradually achieving 100% installation rate of water-saving sanitary appliances in the plant area in stages; "upgrading lighting systems" refers to gradually achieving 100% LED lighting coverage in the plant area in stages from the current proportion; "updating low-energy equipment" refers to gradually increasing the proportion of energy efficiency level 1 (lowest energy consumption) equipment in the plant area in stages; and "configuring metering instruments" refers to gradually achieving 100% configuration of energy metering instruments in the plant area in stages.

[0024] The energy utilization measures include measures such as waste heat and pressure utilization and clean energy use. Among them, "waste heat and pressure utilization" refers to the installation of waste heat recovery devices in the plant area, and "clean energy use" refers to the phased increase of photovoltaic installed capacity in the plant area, as well as the phased increase of the proportion of clean energy such as wind power and geothermal energy.

[0025] Greenhouse gas emission reduction measures include energy conservation and emission reduction, carbon-negative technologies, and low-carbon feedstock substitution. Among them, "energy conservation and emission reduction" refers to emission reduction measures such as process optimization and energy efficiency improvement; "carbon-negative technologies" refers to technologies such as carbon capture, utilization and storage (CCUS) and biomass carbon sinks; and "low-carbon feedstock substitution" refers to the use of low-carbon feedstocks such as bio-based feedstocks and recycled materials.

[0026] Purchase of external carbon credits includes purchasing carbon credits that meet the China Certified Emission Reduction (CCER) standard and carbon credits that meet the international Verified Carbon Standard (VCS).

[0027] In some specific embodiments, the optimization model in step S2, “Stage Optimization Model Construction Step”, is a Mixed Integer Programming (MIP) model, which includes decision variables V based on the emission reduction measure set A, a cost optimization function F based on the emission reduction measure set A, and cost constraints C based on the emission reduction measure set A.

[0028] This application adopts a mixed integer programming model as the optimization model for the planning process of zero-carbon plant areas. This model combines the characteristics of linear programming and integer programming. Some of its decision variables are continuous variables (that is, they can take any real value), while the other part are discrete variables (that is, they can only take integer values). This conforms to the characteristics of the decision variables in the emission reduction measure set A of the zero-carbon plant area, and effectively improves the planning efficiency of the plant area.

[0029] In some specific embodiments, the decision variable V includes a binary decision variable V1 and / or a continuous variable V2. The continuous variable V2 is used to characterize the implementation amount of any emission reduction measure at any implementation stage, while the binary decision variable V1 corresponds to the discrete variable in the mixed integer programming model and is used to characterize the usage result of any emission reduction measure at any implementation stage. It is usually expressed as a binary "0" and "1", where "0" indicates that an emission reduction measure was not used at a certain stage, and "1" indicates that an emission reduction measure was used at a certain stage.

[0030] In some specific embodiments, the cost optimization function F based on the set of emission reduction measures A is characterized by the following relationship: ; In the above relationship, t represents the implementation stage. In this application, T=4, meaning that the set A of all emission reduction measures includes a total of 4 implementation stages. Let be the total cost of the t-th implementation phase. Let be the implementation cost of the set of building emission reduction measures for the t-th implementation phase. Let be the implementation cost of the energy utilization measures set in the t-th implementation phase. Let be the implementation cost of the set of greenhouse gas emission reduction measures for the t-th implementation phase. Let be the implementation cost of the carbon credit measures set in the t-th implementation phase.

[0031] In some specific embodiments, the implementation cost of a set of building emission reduction measures This can be represented by the following relation: ; in, The binary decision variable V1 corresponds to the outcome of the i-th building emission reduction measure in the t-th implementation phase. Let i be the unit implementation cost of the emission reduction measure for the i-th building. Let V2 be the amount of emission reduction measures implemented for the i-th building in the t-th implementation phase, corresponding to the continuous variable V2.

[0032] Taking the implementation cost of "building material optimization" measures in the second implementation stage as an example (where i=2 and t=2): In this stage, alkali-activated low-carbon cement and C30 recycled aggregate concrete were used. The purchase cost of alkali-activated low-carbon cement is 260 yuan / ton, transportation cost is 10 yuan / ton, and processing cost is 20 yuan / ton, so the unit implementation cost of alkali-activated low-carbon cement is 290 yuan / ton. The purchase cost of C30 recycled aggregate concrete is 400 yuan / cubic meter, transportation cost is 15 yuan / cubic meter, and processing cost is 30 yuan / cubic meter, so the unit implementation cost of C30 recycled aggregate concrete is 445 yuan / cubic meter. The total usage of alkali-activated low-carbon cement in this stage is 50 tons, and the total usage of C30 recycled aggregate concrete in this stage is 50 cubic meters. Therefore, the total implementation cost of "building material optimization" in this second implementation stage is 1. 290 50+1 445 50 = 36,750 yuan.

[0033] In some specific embodiments, the implementation cost of the energy utilization measures set This can be represented by the following relation: ; in, For the fixed cost of the j-th energy emission reduction measure, The result of using the j-th energy emission reduction measure corresponds to the binary decision variable V1. The unit operating cost of the j-th energy emission reduction measure is... Let V2 be the amount of energy emission reduction measure implemented in the t-th implementation phase, corresponding to the continuous variable V2.

[0034] Taking the implementation cost of "waste heat and waste pressure utilization" in the third implementation stage as an example (where j=1 and t=3): In this stage, one flue gas waste heat recovery device is installed in the plant area, then... The fixed cost of the flue gas waste heat recovery device includes equipment purchase, installation and commissioning, design and development, auxiliary and control systems, totaling 1.25 million yuan; its operating cost includes energy consumption, maintenance, labor costs, consumable replacement, depreciation, etc., totaling 360,000 yuan / year. The maximum annual operating time is 8,000 hours, and the total operating time of the third implementation phase is 3,000 hours. Therefore, the unit operating cost of this phase is 360,000 / 8,000 = 45 yuan / hour, and the implementation volume is 3,000 hours. Therefore, the total implementation cost of "waste heat and waste pressure utilization" in the third implementation phase is 1. 1250000+45 3000=1.385 million yuan.

[0035] In some specific embodiments, the implementation cost of a set of greenhouse gas emission reduction measures This can be represented by the following relation: ; in, The unit implementation cost of the k-th greenhouse gas emission reduction measure. V2 represents the implementation amount of the k-th greenhouse gas emission reduction measure in the t-th implementation phase, corresponding to the continuous variable V2.

[0036] Taking the implementation cost of "carbon negative technology" in the first implementation phase as an example (where k=2 and t=1): This phase uses bioenergy with carbon capture and storage (BECCS) technology for carbon capture, with a unit implementation cost of 10 million yuan / ton CO2. The implementation volume in this phase (within one year) is 100,000 tons of CO2. Therefore, the implementation cost of "carbon negative technology" in this phase is 10 million yuan / ton CO2. 100,000 tons of CO2 = 10 billion yuan.

[0037] In some specific embodiments, the implementation cost of the carbon credit measure set This can be represented by the following relation: ; in, Let be the carbon credit price in the t-th implementation phase. Let V2 be the amount of carbon credits purchased in the t-th implementation phase, corresponding to the continuous variable V2.

[0038] Taking the implementation cost of purchasing CCER-compliant carbon credits in the fourth implementation phase as an example (corresponding to t=4), the unit price of carbon credits is 70 yuan / ton, and the purchase quantity is 10,000 tons. Therefore, the implementation cost of this phase is 70 yuan / ton. 10,000 tons = 700,000 yuan.

[0039] It is understandable that the cost optimization objective function guides the optimization of this mixed-integer programming model. By setting an expression that includes linear and / or nonlinear variables, the cost planning objective of the zero-carbon plant area in this application is defined, thereby quantifying the progress of emission reduction measures at each stage of the zero-carbon plant area planning process.

[0040] In some specific embodiments, the cost constraints C based on the set of emission reduction measures A include emission constraints C1, carbon offset constraints C2, production assurance constraints C3, technology implementation constraints C4, and green electricity ratio constraints C5.

[0041] In some specific embodiments, emission constraint C1 is characterized by the following relationship: ; in, Let t be the actual total emissions in the t-th implementation phase. This is the annual baseline emission level. Let be the maximum emissions for the target star rating in the t-th implementation phase. Let m be the efficiency of the emission reduction measure. Let M be the amount of emission reduction measure implemented in the m-th stage of implementation, and M = I + J + K.

[0042] In some specific embodiments, the carbon offsetting constraint C2 is characterized by the following relationship: ; in, is the upper limit coefficient for the carbon offset ratio in the t-th implementation phase.

[0043] In some specific embodiments, the production assurance constraint C3 is represented by the following relation: ; in, Let be the energy supply for the t-th implementation phase. Let be the total production demand in the t-th implementation phase. To ensure a margin of security for energy supply.

[0044] Production assurance constraint C3 reflects the energy supply limit that must be met to meet minimum production requirements, including the energy supply security margin. Most importantly, taking the power system as an example, the energy supply security margin... This represents the power system's ability to maintain stable operation under expected or unexpected disturbances, reflecting its resilience to disturbances. It is the energy supply security margin of the power system. It includes static safety margin and dynamic safety margin. Static safety margin represents the ability of a power system to stabilize at a new equilibrium point after being disturbed, including generation power margin, line load factor margin, voltage margin, frequency margin, etc. Dynamic safety margin represents the ability of a power system to operate stably after being disturbed, including transient stability margin, angle margin, frequency margin, etc.

[0045] In some specific embodiments, the technical implementation constraint C4 is represented by the following relation: , ; in, The result of the use of the m-th emission reduction measure in the t-th implementation phase is... , which is the sustainability coefficient for the m-th emission reduction measure, used for the assessment of depreciation or technology obsolescence of Kallu equipment.

[0046] In some specific embodiments, the green electricity ratio constraint C5 is characterized by the following relationship: ; in, Let be the green electricity consumption in the t-th implementation phase. Let be the total electricity consumption in the t-th implementation phase. Let be the proportion of green electricity consumption in the t-th implementation phase.

[0047] Understandably, the constraints define feasible boundary conditions for the mixed-integer programming model. Based on resource constraints and technical requirements in actual application scenarios, constraint equations of equality and / or inequality are constructed to select combinations of emission reduction measures that are in line with reality.

[0048] Please see Figure 2 Step S3, "Model Solving Step," of this application further includes: S31, Steps for determining the scoring function: Based on the decision variables, determine the scoring function for the plant area. ,in, Let n be the amount of emission reduction measure implemented in the t-th implementation phase. Let t represent the amount of carbon credits purchased in the t-th implementation phase; S32, Cost Determination Step, Based on Scoring Function Given the given conditions, solve the optimization model corresponding to the t-th implementation stage to determine the optimal total cost min. ,in, The target score for the t-th implementation phase is: The corresponding target cost is min .

[0049] In step S31, the scoring function of the plant area The function expression is constructed based on the amount of emission reduction measures implemented and the amount of carbon credits purchased in each implementation stage. In the embodiments of this application, there are 4 implementation stages, which correspond to 4 different scoring functions.

[0050] In step S32, based on The solution conditions are used to solve the optimization model corresponding to each implementation stage, where... The target score for the t-th implementation phase is the target score for each phase in the plant area. They are respectively , , , .

[0051] Specifically, for the first implementation phase (t=1), the aforementioned optimization model is solved until... And the corresponding target cost at this time is min( For the second implementation phase (t=2), solve the aforementioned optimization model until... And the corresponding target cost at this time is min( ); and so on, until the fourth implementation stage (t=4), at which point the corresponding target cost is min ( Thus, the optimal total cost min is obtained. = min( ).

[0052] The planning methodology for this plant area, combined with the star-rating evaluation system for zero-carbon plants, established a set of emission reduction measures. It constructed optimization models for several implementation stages of each emission reduction measure set, quantified the constraints and game relationships of the stage requirements, and sought the cost-optimal solution with the goal of minimizing costs. It fully considered the impact of policy factors on plant operation, helped the plant area quickly assess the emission reduction strategies and costs required to meet the requirements of a zero-carbon plant area, and formulated a suitable zero-carbon path to achieve the zero-carbon goal.

[0053] The model solving process involved in the plant planning method of this application can be processed by mathematical optimization solvers such as CPLEX, Gurobi, SCIP or Xpress. The above solvers and their specific solution principles are common knowledge in this field and will not be elaborated here.

[0054] Please see Figure 3Another embodiment of this application provides a plant planning device, mainly including: an emission reduction measure set construction module, which includes a building emission reduction measure set, an energy utilization measure set, a greenhouse gas emission reduction measure set, and a carbon credit measure set; a stage optimization model construction module, configured to construct an optimization model for several implementation stages; and a model solving module, configured to solve the optimization model based on the target scores of several implementation stages to obtain the optimal total cost, which includes the sum of the target costs of all implementation stages.

[0055] For details of other operations performed by each module in this embodiment, please refer to the foregoing embodiments, which will not be elaborated here.

[0056] The plant planning device in this application embodiment can be a device, or a component, integrated circuit, or chip in a terminal. The plant planning device in this application embodiment can be a chip, including FPGA (Field Programmable Gate Array), MCU (Microcontroller Unit), etc., but this application embodiment does not specifically limit the specific implementation.

[0057] This application also provides an electronic device, including a processor and a memory for storing processor-executable instructions. When the processor executes the instructions, it implements the various processes of the above-described factory planning method and achieves the same technical effect. To avoid repetition, it will not be described in detail here.

[0058] The processor can be a central processing unit (CPU). The processor can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above types of chips.

[0059] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the plant planning method in the embodiments of this application. The processor executes various functional applications and data classification by running the non-transitory software programs, instructions, and modules stored in the memory, thereby realizing the plant planning method in the above method embodiments.

[0060] The memory may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the processor, etc. Furthermore, the memory may include high-speed random access memory and non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the processor, which can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0061] One or more modules are stored in memory and, when executed by the processor, perform the plant planning method in the implementation example.

[0062] This application also provides a computer-readable medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described factory planning method embodiments and achieve the same technical effects. To avoid repetition, further details are omitted here.

[0063] This application also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes a computer program that, when executed by a processor, implements the various processes of the above-described factory planning method embodiments and achieves the same technical effects. To avoid repetition, further details are omitted here.

[0064] Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible, non-transitory program carrier for execution by a data processing apparatus or for controlling the operation of a data processing apparatus. Alternatively or additionally, the program instructions may be encoded on artificially generated propagation signals, such as machine-generated electrical, optical, or electromagnetic signals, which are generated to encode information and transmit it to a suitable receiving device for execution by the data processing apparatus.

[0065] When the plant planning method of this application is implemented as a computer program, it can also be stored as an article of manufacture in a computer-readable storage medium. For example, a computer-readable storage medium may include, but is not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic stripes), optical discs (e.g., compact discs (CDs), digital multifunction discs (DVDs)), smart cards, and flash memory devices (e.g., electrically erasable programmable read-only memory (EEPROM), cards, sticks, key drives). Furthermore, the various storage media described herein can represent one or more devices and / or other machine-readable media for storing information. The term "machine-readable medium" may include, but is not limited to, wireless channels and various other media (and / or storage media) capable of storing, containing, and / or carrying code and / or instructions and / or data.

[0066] For those skilled in the art, the above disclosure is merely illustrative and does not constitute a limitation of this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this application. Such modifications, improvements, and corrections are suggested in this application and therefore remain within the spirit and scope of the exemplary embodiments of this application.

[0067] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of scope in some embodiments of this application are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0068] Although this application has been described with reference to specific embodiments, those skilled in the art should recognize that the above embodiments are only used to illustrate this application, and various equivalent changes or substitutions can be made without departing from the spirit of this application. Therefore, any changes or modifications to the above embodiments within the scope of the essential spirit of this application will fall within the scope of this application.

Claims

1. A method for planning a factory area, characterized in that, include: The steps for constructing a set of emission reduction measures include at least one of a set of building emission reduction measures, a set of energy utilization measures, a set of greenhouse gas emission reduction measures, and a set of carbon credit measures. The phased optimization model construction steps involve constructing optimization models for several implementation phases of the emission reduction measure set. The model solving steps involve solving the optimization model based on the target scores of several implementation stages to obtain the optimal total cost, which includes the sum of the target costs corresponding to the target scores of all implementation stages.

2. The factory area planning method according to claim 1, characterized in that, The optimization model is a mixed integer programming model. The mixed-integer programming model includes decision variables based on the set of emission reduction measures, a cost optimization objective function based on the set of emission reduction measures, and cost constraints based on the set of emission reduction measures.

3. The factory area planning method according to claim 2, characterized in that, The decision variables include binary decision variables and / or continuous variables; The binary decision variable is used to characterize the result of using any of the emission reduction measures in any of the implementation stages, and the continuous variable is used to characterize the amount of any of the emission reduction measures implemented in any of the implementation stages.

4. The plant area planning method according to claim 2 or 3, characterized in that, The cost optimization objective function is represented by the following relation: ； in, t represents the implementation stage. Let be the total cost of the t-th implementation phase. The implementation cost of the set of building emission reduction measures in the t-th implementation phase is... The implementation cost of the energy utilization measures set in the t-th implementation phase is... The implementation cost of the greenhouse gas emission reduction measures set for the t-th implementation phase is... The implementation cost of the set of carbon credit measures in the t-th implementation phase.

5. The factory area planning method according to claim 4, characterized in that, The implementation cost of the aforementioned set of building emission reduction measures This can be represented by the following relation: ； in, The result of the use of the i-th building emission reduction measure in the t-th implementation phase, The unit implementation cost of the i-th building emission reduction measure is... The amount of emission reduction measures implemented in the i-th building during the t-th implementation phase; The implementation cost of the energy utilization measures set This can be represented by the following relation: ； in, For the fixed cost of the j-th energy emission reduction measure, For the result of using the j-th energy emission reduction measure, The unit operating cost of the j-th energy emission reduction measure is... The amount of energy emission reduction measure described in the j-th case implemented in the t-th case implementation phase; The implementation cost of the aforementioned set of greenhouse gas emission reduction measures This can be represented by the following relation: ； in, The unit implementation cost of the k-th greenhouse gas emission reduction measure is... The amount of greenhouse gas emission reduction measure described in the k-th instance is implemented in the t-th implementation phase. The implementation cost of the carbon credit measures set This can be represented by the following relation: ； in, Let be the carbon credit price for the t-th implementation phase. Let t be the amount of carbon credits purchased in the t-th implementation phase.

6. The factory area planning method according to claim 5, characterized in that, The cost constraints include emission constraints, carbon offset constraints, production security constraints, technology implementation constraints, and green electricity ratio constraints. The emission constraints are characterized by the following relationship: ； in, Let t be the actual total emissions of the implementation phase described above. This is the annual baseline emission level. The maximum emissions for the target star level in the t-th implementation phase. The efficiency of the m-th emission reduction measure is... Let M be the amount of emission reduction measure implemented in the m-th implementation phase, and M = I + J + K; The carbon offsetting constraint is characterized by the following relationship: ； in, The upper limit coefficient for the carbon offset ratio in the t-th implementation phase; The production assurance constraints are represented by the following relation: ； in, Let be the energy supply for the t-th implementation phase. Let be the total production demand for the t-th implementation phase. For energy supply security margin; The technical implementation constraints are represented by the following relation: ， ； in, The result of the use of the m-th emission reduction measure in the t-th implementation phase. Let m be the sustainability coefficient of the m-th emission reduction measure; The green electricity ratio constraint is represented by the following relationship: ； in, Let be the green electricity consumption in the t-th implementation phase. Let be the total power consumption in the t-th implementation phase. Let be the proportion of green electricity consumption in the t-th implementation phase.

7. The factory area planning method according to claim 6, characterized in that, The model solving steps further include: The scoring function determination step involves determining the scoring function for the plant area based on the decision variables. ,in, The amount of emission reduction measure implemented in the nth implementation phase is [amount]. Let t be the amount of carbon credits purchased in the t-th implementation phase. The cost determination step is based on the scoring function. Given the conditions, solve the optimization model corresponding to the t-th implementation stage to determine the optimal total cost min. ,in, The target score for the t-th implementation phase is... The corresponding target cost is min .

8. A factory area planning device, characterized in that, include: The emission reduction measures set construction module includes at least one of the following: building emission reduction measures set, energy utilization measures set, greenhouse gas emission reduction measures set, and carbon credit measures set; The phase optimization model building module is configured to build optimization models for several implementation phases; The model solving module is configured to solve the optimization model based on the target scores of several implementation stages to obtain the optimal total cost, which includes the sum of the target costs of all implementation stages.

9. An electronic device, characterized in that, include: It includes a processor and a memory for storing processor-executable instructions, wherein the processor, when executing the instructions, implements the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program or instructions that, when executed by a processor, implement the method as described in any one of claims 1-7.

11. A computer program product, the computer program product comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-7.

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