Zero carbon target-based park carbon reduction path planning model and optimization method

Abstract

The invention relates to a park carbon reduction path planning model and optimization method based on a zero carbon target, and the model comprises a data collection and preprocessing module which is used for constructing an energy and carbon emission database; the carbon emission accounting module is used for determining the current situation of carbon emission and a main emission source; the energy system modeling sub-module is used for establishing a mathematical model of the energy system; the industrial structure adjustment sub-module is used for analyzing the carbon emission intensity and the development trend of different industries in the park and establishing an industrial adjustment model; the constraint condition setting sub-module is used for setting various constraint conditions which need to be met by the park carbon reduction path planning; and the objective function construction sub-module is used for constructing a multi-objective optimization function by taking realization of a zero carbon objective as a guide, and converting the multi-objective optimization function into a single-objective optimization problem. According to the method, the carbon emission can be comprehensively calculated, the emission reduction key point is defined, and the scientific, economic and operable optimal carbon reduction scheme is planned for the park through the multi-objective optimization algorithm under the condition that the actual constraint condition is met.

Description

Technical Field

[0001] This invention relates to the field of energy management and carbon emission reduction in industrial parks, and more specifically, to a planning model and optimization method for carbon reduction pathways in industrial parks based on a zero-carbon objective. Background Technology

[0002] Zero-carbon industrial parks have received increasing attention in recent years. A zero-carbon industrial park does not mean that it emits no greenhouse gases at all, but rather that it achieves net carbon emissions of zero (net carbon emissions refer to the difference between the carbon emissions generated by the park and the carbon emissions absorbed by the carbon sinks within the park over a certain period). Zero-carbon industrial parks are the ultimate goal for all industrial park upgrades, and their evolution can generally be divided into three stages: low-carbon industrial parks, near-zero-carbon industrial parks, and zero-carbon industrial parks.

[0003] Achieving carbon neutrality in industrial parks is a systematic project that requires addressing aspects such as energy, buildings, transportation, carbon sequestration, and management. Currently, there is a lack of fundamental theories and key technologies for zero-carbon industrial parks. Summary of the Invention

[0004] The technical problem to be solved by this invention is to provide a planning model and optimization method for carbon reduction pathways in industrial parks based on the zero-carbon target. This model can accurately locate key emission sources and dynamically optimize carbon reduction measures and investments at different stages, ensuring that the zero-carbon target of the industrial park is achieved at the lowest cost and highest efficiency.

[0005] The technical solution adopted by this invention to solve its technical problem is: constructing a carbon reduction path planning model for industrial parks based on a zero-carbon target, including: The data acquisition and preprocessing module is used to build a complete energy and carbon emission database; The carbon emission accounting module is used to calculate the direct and indirect carbon emissions within the park based on the collected data, and to clarify the current status of carbon emissions and the main emission sources. The energy system modeling submodule is used to build mathematical models of energy systems; The industrial restructuring submodule is used to analyze the carbon emission intensity and development trend of different industries in the park, determine the direction and focus of industrial restructuring, and establish an industrial restructuring model. The constraint setting submodule is used to set various constraints that the carbon reduction pathway planning of the park must meet. The objective function construction submodule is used to construct multi-objective optimization functions with the goal of achieving zero carbon emissions, and transform them into single-objective optimization problems.

[0006] According to the above scheme, the implementation method of the data acquisition and preprocessing module is as follows: energy meters and data acquisition terminals are installed in the park to collect energy consumption data, production data and equipment operation data in real time, and the data is transmitted to the data center through the data acquisition system. Then, abnormal data, missing data and duplicate data are removed by data cleaning tools, and normalization algorithms are used to normalize different types of data to build a complete energy and carbon emission database.

[0007] According to the above scheme, the formula for calculating the direct carbon emissions of a certain enterprise in the park in the carbon emission accounting module is as follows:

[0008] in, Indicates the first The company's direct carbon emissions Indicates the first Enterprise consumption of the first Fuel quantity, Indicates the first Carbon emission factors of various fuels Total number of fuel types; The formula for calculating indirect carbon emissions is:

[0009] in, Indicates the first Indirect carbon emissions of enterprises Indicates the first Electricity purchased by enterprises Indicates the carbon emission factor of electricity; Indicates the first Enterprise's external heat consumption; Indicates the thermal carbon emission factor; The total carbon emissions of the park are the sum of the direct and indirect emissions of all enterprises:

[0010] in, This indicates the total carbon emissions of the park; This represents the total number of companies within the park.

[0011] According to the above scheme, in the mathematical model of the established energy system when solar photovoltaic power generation is used, the formula for calculating renewable energy power generation is as follows:

[0012] in, Indicates at time The amount of solar photovoltaic power generation; Indicates the area of ​​the solar panel; Indicates the photoelectric conversion efficiency of a solar panel; Indicates time The intensity of solar radiation; The formula for calculating wind power generation is:

[0013] in, Indicates at time The amount of wind power generated; Indicates air density; This indicates the swept area of ​​the wind turbine. This indicates the power generation efficiency of the wind turbine. Indicates time wind speed; The formula for calculating the power generation efficiency of a combined heat and power (CHP) system is: in, This indicates the power generation efficiency of a combined heat and power (CHP) system. Indicates the electrical power emitted; Indicates the emitted heat power; This indicates the amount of fuel consumed.

[0014] According to the above scheme, the formula for calculating industrial carbon emission intensity in the industrial adjustment model within the industrial restructuring submodule is as follows:

[0015] in, Indicates the first The carbon emission intensity of the industry; Indicates the first Carbon emissions from the industry; Indicates the first The output or production value of an industry.

[0016] According to the above scheme, the constraint conditions that the carbon reduction path planning of the park must meet in the constraint condition setting submodule include the energy supply balance constraint formula: in, Indicates at time No. Power generation capacity of various energy generation equipment; This represents the total number of types of energy generation equipment. Indicates at time The discharge power of the energy storage system; Indicates at time No. The power consumption of each energy load; Total number of energy load types; Indicates at time The charging power of the energy storage system; The equipment capacity constraint formula is: in, Indicates the first The minimum power output of this type of energy generation equipment; Indicates the first The maximum power generation capacity of this type of energy power generation equipment; The formula for the total carbon emission constraint is:

[0017] in, This indicates the upper limit of the park's total carbon emissions during the planning period.

[0018] According to the above scheme, the construction of the multi-objective optimization function in the objective function construction submodule includes: Multi-objective optimization function formula:

[0019] in, These represent different optimization objective functions, such as the total carbon emissions of the park, the construction and operation cost of the energy system, the utilization rate of renewable energy, and the economic benefits of the park. The weighted summation method is used to process the formulas for multi-objective optimization functions:

[0020] in, Let be the weight coefficients of each objective function, and satisfy . .

[0021] This invention also provides an optimization method for a carbon reduction pathway planning model for industrial parks based on a zero-carbon objective, comprising the following steps: S1. An improved genetic algorithm is used to encode each decision variable in the carbon reduction path planning of the park by combining real-number encoding of parameters in the energy system modeling submodule with binary encoding of discrete decisions in the industrial structure adjustment submodule. This forms individual chromosomes, generates an initial population, selects superior individuals to enter the breeding population, designs an adaptive crossover probability strategy and a crossover operator with multi-point crossover operation, and introduces a mutation operator that combines Gaussian mutation and polynomial mutation to improve the search efficiency and accuracy of the algorithm. S2. Using the current optimal solution obtained by the improved genetic algorithm as the initial solution, a new solution in the neighborhood is generated based on the initial solution through a neighborhood search strategy; the improved Metropolis criterion is used to decide whether to accept the new solution; a cooling progress combining exponential cooling and adaptive cooling is designed to balance the global search capability and local search capability of the algorithm, so that the obtained carbon reduction path planning scheme is closer to the global optimal solution.

[0022] According to the above scheme, in step S1, The decision variables are encoded as follows: continuous variables in the energy system modeling submodule are encoded with real numbers; discrete decision variables in the industrial structure adjustment submodule are encoded with binary numbers, together forming a chromosome that represents the complete carbon reduction path; The initial population and constraint adaptation are as follows: 70% of the individuals in the initial population are generated based on historical operational data, and the remaining 30% are randomly generated to enhance diversity. All individuals undergo hard validation through the constraint setting submodule to ensure that the following conditions are met: Equipment operating constraints: P min,t ≤ P t ≤ P max,t ; Energy supply and demand balance constraints: ; Carbon emission limits:

[0023] Fitness function: based on fitness values As an evaluation criterion, it guides the population to evolve towards the optimal solution, where: Total carbon emissions of the park: ; Total system cost: ; Reciprocal of renewable energy utilization rate: ; Crossover and mutation are as follows: crossover operation uses arithmetic crossover for continuous variables in the energy system and uniform crossover for discrete variables in the industry. Mutation operation introduces Gaussian mutation and bit-flip mutation.

[0024] According to the above scheme, the characteristic is that, in step S2, The initial solution is the optimal solution using the improved genetic algorithm. As the initial solution, the initial solution satisfies the energy supply balance constraint condition. And the direction of industrial restructuring; Neighborhood search is: in the current solution Random perturbations within the neighborhood to generate new solutions The perturbation strategy is linked to the priority of the objective function; if the priority is to improve the utilization rate of renewable energy, the focus is on perturbing the energy system parameters; if the priority is to optimize costs, the focus is on perturbing the industry adjustment variables. Metropolis Criterion and Cooling: Define the difference between the weighted sum of the objective functions of the new solution and the current solution as...

[0025] Acceptance probability:

[0026] Cooling coefficient The adjustment is made dynamically based on the degree of constraint violation.

[0027] The implementation of the zero-carbon target-based industrial park carbon reduction pathway planning model and optimization method of the present invention has the following beneficial effects: This invention effectively addresses the core pain points of existing industrial park carbon planning—namely, insufficient systematic approach, crude decision-making, and difficulty in balancing economic efficiency and emission reduction effects—by constructing a data-driven, multi-module collaborative systematic model. It achieves a leap from macro-level qualitative to micro-level quantitative carbon reduction pathways. Through multi-objective optimization, it significantly improves the economic efficiency and operability of planning schemes while ensuring the attainability of zero-carbon goals. It provides industrial parks with a scientific, forward-looking, and dynamically adaptable precision carbon reduction decision support system. Attached Figure Description

[0028] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings: Figure 1 This is a schematic diagram of the structure of the park carbon reduction path planning model and optimization method based on the zero-carbon goal of the present invention. Detailed Implementation

[0029] To provide a clearer understanding of the technical features, objectives, and effects of the present invention, specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0030] Example 1 like Figure 1 As shown, the carbon reduction path planning model for industrial parks based on zero-carbon targets of the present invention includes a data acquisition and preprocessing module, a carbon emission accounting module, an energy system modeling submodule, an industrial structure adjustment submodule, a constraint setting submodule, and an objective function construction submodule.

[0031] Data Acquisition and Preprocessing Module: Collects various energy consumption data, production data, equipment operation data, etc. within the park, and performs preprocessing steps such as cleaning and normalization to build a complete energy and carbon emission database.

[0032] Various energy meters and data acquisition terminals are installed within the park to collect real-time energy consumption, production, and equipment operation data. This data is then transmitted to the data center via the data acquisition system. In the data center, data cleaning tools are used to remove outlier, missing, and duplicate data. Normalization algorithms are employed to normalize different types of data, constructing a complete energy and carbon emission database to provide data support for subsequent carbon emission accounting and energy system modeling.

[0033] Carbon emission accounting module: Based on the collected data, and in accordance with relevant standards and methods, calculate the direct and indirect carbon emissions of each link and enterprise in the park, and clarify the current status of carbon emissions and the main emission sources.

[0034] The formula for calculating the direct carbon emissions of a company within the industrial park is as follows:

[0035] in, Indicates the first The company's direct carbon emissions Indicates the first Enterprise consumption of the first Fuel quantity, Indicates the first Carbon emission factors of various fuels This represents the total number of fuel types.

[0036] The formula for calculating indirect carbon emissions is:

[0037] in, Indicates the first Indirect carbon emissions of enterprises Indicates the first Electricity purchased by enterprises Indicates the carbon emission factor of electricity; Indicates the first Enterprise's external heat consumption; This represents the thermal carbon emission factor.

[0038] The total carbon emissions of the park are the sum of the direct and indirect emissions of all enterprises:

[0039] in, This indicates the total carbon emissions of the park; This represents the total number of companies within the park.

[0040] Energy System Modeling Submodule: Considering various forms of energy production and consumption within the park, establish mathematical models of the energy system, including renewable energy power generation models, traditional energy power generation models, combined heat and power models, etc., to describe the processes of energy generation, conversion, transmission and use.

[0041] Taking solar photovoltaic power generation as an example, the formula for calculating renewable energy power generation is as follows:

[0042] in, Indicates at time The amount of solar photovoltaic power generation; Indicates the area of ​​the solar panel; Indicates the photoelectric conversion efficiency of a solar panel; Indicates time The intensity of solar radiation.

[0043] Formula for calculating wind power generation:

[0044] in, Indicates at time The amount of wind power generated; Indicates air density; This indicates the swept area of ​​the wind turbine. This indicates the power generation efficiency of the wind turbine. Indicates time The wind speed.

[0045] Formula for calculating the power generation efficiency of a combined heat and power (CHP) system: in, This indicates the power generation efficiency of a combined heat and power (CHP) system. Indicates the electrical power emitted; Indicates the emitted heat power; This indicates the amount of fuel consumed.

[0046] The industrial restructuring submodule analyzes the carbon emission intensity and development trends of different industries within the park, determines the direction and focus of industrial restructuring, and establishes industrial restructuring models, such as industrial transfer models and industrial upgrading models.

[0047] Formula for calculating industrial carbon emission intensity:

[0048] in, Indicates the first The carbon emission intensity of the industry; Indicates the first Carbon emissions from the industry; Indicates the first The output or production value of an industry.

[0049] The constraint setting submodule sets various constraints that the park's carbon reduction path planning must meet, such as energy supply balance constraints, production equipment capacity constraints, environmental emission standard constraints, and policy and regulatory constraints.

[0050] Energy supply balance constraint formula: in, Indicates at time No. Power generation capacity of various energy generation equipment; This represents the total number of types of energy generation equipment. Indicates at time The discharge power of the energy storage system; Indicates at time No. The power consumption of each energy load; Total number of energy load types; Indicates at time The charging power of the energy storage system.

[0051] Taking power generation equipment as an example, the equipment capacity constraint formula is: in, Indicates the first The minimum power output of this type of energy generation equipment; Indicates the first The maximum power output of a type of energy generation equipment.

[0052] Carbon emission total constraint formula:

[0053] in, This indicates the upper limit of the park's total carbon emissions during the planning period.

[0054] Objective function construction submodule: Guided by the goal of achieving zero carbon, construct multi-objective optimization functions, including minimizing the total carbon emissions of the park, minimizing the construction and operation costs of the energy system, maximizing the utilization rate of renewable energy, and maximizing the economic benefits of the park, and transform them into single-objective optimization problems through weighted summation or other multi-objective processing methods.

[0055] Multi-objective optimization function formula:

[0056] in, These represent different optimization objective functions, such as the total carbon emissions of the park, the construction and operation costs of the energy system, the utilization rate of renewable energy, and the economic benefits of the park.

[0057] The weighted summation method is used to process the formulas for multi-objective optimization functions:

[0058] in, Let be the weight coefficients of each objective function, and satisfy . .

[0059] Taking a certain comprehensive industrial park as an example, the park covers an area of ​​5 km² and has 12 companies that have achieved zero-carbon operation.

[0060] 1. Data Acquisition and Preprocessing Module Collect various energy consumption data, production data, equipment operation data, etc. within the park, and perform preprocessing steps such as cleaning and normalization to build a complete energy and carbon emission database.

[0061] Specific implementation: 150 smart meters, 80 gas meters, 30 heat meters, and 50 production data acquisition terminals will be installed in key locations such as the main production workshops, office buildings, and energy stations of 12 companies in the industrial park to collect the following data in real time (sampling frequency: 1 hour / time): ① Energy consumption data: Annual raw coal consumption 23,000 tons, natural gas 1.8 million m³, purchased electricity 5.6 million kWh, purchased heat 1.2 million GJ; ② Production data: Annual output value of equipment manufacturing enterprises was 850 million yuan, annual output value of chemical enterprises was 620 million yuan, and annual output value of service enterprises was 330 million yuan; ③ Equipment operation data: There are currently two 10t / h coal-fired boilers (7200 hours of operation per year) and one 5MW gas turbine (6000 hours of operation per year). Data is transmitted to the park's data center through the industrial internet platform. Using Python Pandas, 237 abnormal data entries (such as instantaneous meter values ​​exceeding the range) are removed, 156 missing data entries are filled (using the average of adjacent time periods), and duplicate data are removed. The min-max normalization algorithm is used to map data of different dimensions, such as raw coal consumption (unit: tons) and power generation (unit: kWh), to the [0,1] interval. Finally, an energy and carbon emission database containing more than 100,000 records is constructed to provide data support for subsequent processes.

[0062] 2. Carbon Emission Accounting Module Based on the collected data, in accordance with the GB / T 32151-2015 standard "Requirements for Greenhouse Gas Emission Accounting and Reporting for Industrial Enterprises", the direct and indirect carbon emissions of each link and each enterprise in the park were calculated, and the current status of carbon emissions and the main emission sources were identified.

[0063] Specific calculation formulas and example assignments: (1) Direct carbon emissions of enterprises:

[0064] In formula (1): Let be the direct carbon emissions (tons of CO2) of the i-th enterprise. The amount of the j-th type of fuel consumed by the i-th enterprise; Let be the carbon emission factor of the j-th fuel (refer to the IPCC 2022 guidelines); n is the total number of fuel types (in this example, n=2, i.e., raw coal and natural gas).

[0065] Example calculation: Taking a certain equipment manufacturing enterprise i=1 in the park as an example, its annual raw coal consumption is 800 tons ( ), raw coal carbon emission factors Tons of CO2 per 10,000 m³; natural gas consumption of 300,000 m³ ( ), natural gas carbon emission factors Tons of CO2 per 10,000 m³. Substituting this into the calculation, the company's direct carbon emissions are: Tons of CO2.

[0066] (2) Indirect carbon emissions from enterprises:

[0067] In the formula: Let represent the indirect carbon emissions (tons of CO2) of the i-th enterprise. For the electricity purchased by the i-th company (in ten thousand kWh); Tons of CO2 / 10,000 kWh (National Power Grid Average Factor); For the i-th enterprise, the purchased heat consumption (ten thousand GJ); ton CO2 / GJ (industry average factor).

[0068] Example calculation: The aforementioned equipment manufacturing enterprises purchase 220,000 kWh of electricity annually. Purchased heat (50,000 GJ) Substituting into the above formula, we obtain the indirect carbon emissions: Tons of CO2. Example: The aforementioned equipment manufacturing company purchases 220,000 kWh of electricity annually. Purchased heat (50,000 GJ) Indirect carbon emissions: Tons of CO2.

[0069] (3) Total carbon emissions of the park:

[0070] In the formula: is the total carbon emissions of the park (tons of CO2); m is the total number of enterprises in the park (m=12 in this example).

[0071] Example calculation: After calculating the carbon emissions of each enterprise using the above formula, substituting the values, we get the total direct carbon emissions of the 12 enterprises in the park are 32,800 tons of CO2, and the total indirect carbon emissions are 4,200 tons of CO2. The total carbon emissions of the park are... tons of CO2 per year. Major emission sources: fine chemical enterprises (58%), equipment manufacturing enterprises (32%), coal-fired boilers (7%), and others (3%).

[0072] 3. Energy System Modeling Submodule Considering various forms of energy production and consumption within the park, establish mathematical models of the energy system, including renewable energy power generation models, traditional energy power generation models, and combined heat and power models, to describe the processes of energy generation, conversion, transmission, and use.

[0073] Specific model and instance assignments: (1) Solar photovoltaic power generation model:

[0074] In the formula: Let be the solar photovoltaic power generation (kW) at time t. The area of ​​the solar panels is (m², in this example, the planned installation is 50,000 m²). =0.18 (conversion efficiency of mainstream monocrystalline silicon cells); Let be the solar radiation intensity at time t (kW / m², taken as the annual average value of 0.12 kW / m² in the park).

[0075] Example calculation: Substituting the parameters into the above equation, we get... =50000×0.18×0.12=1080kW. Annual photovoltaic power generation =1080kW×8760h×0.8 (reduction factor)=7603200 kWh≈760.32 million kWh.

[0076] (2) Wind power generation model:

[0077] In formula (5): Let t be the wind power generation (kW) at time t. =1.225 kg / m³ (air density); Given the swept area (m²) of the wind turbine, a 2.5MW wind turbine with a rotor diameter of 130m is selected. )); =0.42 (wind turbine power generation efficiency); Let t be the wind speed (m / s, taking the average annual wind speed of the park as 5.2 m / s).

[0078] Example calculation: Substituting the parameters, we get: kW. Annual power generation of a single wind turbine = 2410kW × 8760h × 0.9 (reduction factor) ≈ 19.15 million kWh. With the plan to install 2 wind turbines, the total annual power generation is approximately 38.3 million kWh.

[0079] (3) Power generation efficiency model of cogeneration system:

[0080] In the formula: The power generation efficiency of a combined heat and power (CHP) system; =5000kW (electric power generated); =8000kW (heat power generated); =1.2t / h (amount of natural gas consumed); LHV=35.588MJ / m³ (lower heating value of natural gas, 1.2t / h natural gas ≈ 1680 m³ / h).

[0081] Example calculation: Input energy = 1680 m³ / h × 35.588 MJ / m³ ≈ 59788 MJ / h; Output energy = (5000kW + 8000kW) × 3.6 MJ / (kW·h) = 46800 MJ / h; Substituting into the equation, we get: =46800 / 59788≈0.783 (i.e. 78.3%).

[0082] 4. Industrial Restructuring Submodule Analyze the carbon emission intensity and development trend of different industries in the park, determine the direction and focus of industrial restructuring, and establish industrial restructuring models, such as industrial transfer models and industrial upgrading models.

[0083] Specific formulas and examples: Formula for calculating industrial carbon emission intensity:

[0084] In the formula: The carbon emission intensity of industry k (tons of CO2 / ten thousand yuan); The carbon emissions (tons of CO2) for industry k. Let be the output value (in ten thousand yuan) of the kth industry.

[0085] Example values: For the equipment manufacturing industry in the park (k=1), calculated using the above formula, the carbon emissions are 12,040 tons of CO2, with an output value of 850 million yuan and a carbon intensity of 0.142 tons of CO2 / 10,000 yuan; for the fine chemical industry (k=2), the carbon emissions are 21,460 tons of CO2, with an output value of 620 million yuan and a carbon intensity of 0.346 tons of CO2 / 10,000 yuan; for the productive service industry (k=3), the carbon emissions are 3,500 tons of CO2, with an output value of 330 million yuan and a carbon intensity of 0.106 tons of CO2 / 10,000 yuan.

[0086] Adjusting Direction and Model: Based on carbon emission intensity analysis, establishing an industrial upgrading + relocation model: ① Fine chemical industry (high carbon emission intensity): shut down 1 high-pollution production line (accounting for 15% of carbon emissions) and introduce green chemical technologies (reducing carbon emission intensity by 30%). ② Equipment manufacturing industry: Upgrade intelligent production lines (reduce carbon emission intensity by 18%). ③ Expand the scale of productive service industries (increase output value by 20%, and keep carbon emission intensity unchanged).

[0087] 5. Constraint Setting Submodule The planning of carbon reduction pathways for the industrial park must meet various constraints, such as energy supply balance constraints, production equipment capacity constraints, environmental emission standards constraints, and policy and regulatory constraints.

[0088] Specific constraint formulas and example thresholds: (1) Energy supply balance constraints:

[0089] In the formula: Let t be the power generation capacity (kW) of the j-th energy source generator. =4, namely photovoltaic, wind power, combined heat and power, and standby gas turbine); =2000kW (upper limit of discharge power of energy storage system at time t). Let be the power consumption (kW) of the k-th energy load at time t. =3, i.e., industrial load, commercial load, and residential load, with a total load of 8500 kW at time t). =1500kW (the upper limit of the charging power of the energy storage system at time t).

[0090] Example Verification: At time t, the power output of each generating unit is: photovoltaic 1080kW, wind power 4820kW (2 wind turbines), combined heat and power 5000kW, and standby gas turbine 0kW. The total power output is 1080 + 4820 + 5000 = 10900kW; energy storage discharge is 2000kW, and charging is 0kW. Substituting these values ​​into the formula, we get: total power on the supply side = 10900 + 2000 = 12900kW; total power on the load side = 8500 + 0 = 8500kW, satisfying the balance constraint.

[0091] (2) Power generation equipment capacity constraints:

[0092] In the formula: Let be the minimum power generation capacity of the j-th type of energy power generation equipment; This represents the maximum power generation capacity. Example assignment: Photovoltaics. kW kW; wind power kW kW; Cogeneration kW kW.

[0093] (3) Total carbon emission constraints:

[0094] In the formula: The upper limit for the park's total carbon emissions (tons of CO2) during the planning period is set in conjunction with the zero-carbon target: 2026. =32,000 tons of CO2, by 2030 =15,000 tons of CO2, by 2035 =0 tons of CO2.

[0095] 6. Objective Function Construction Submodule Guided by the goal of achieving zero carbon, a multi-objective optimization function is constructed, including minimizing the total carbon emissions of the park, minimizing the construction and operation costs of the energy system, maximizing the utilization rate of renewable energy, and maximizing the economic benefits of the park. The function is then transformed into a single-objective optimization problem through a weighted summation method.

[0096] Specific functions and instance weights: (1) Multi-objective optimization function: Multi-objective optimization function:

[0097] In the formula: This represents the total carbon emissions of the park (tons of CO2). Cost of energy system construction and operation (ten thousand yuan / year); 1 - Renewable energy utilization rate (dimensionless, the higher the utilization rate, the higher the efficiency). (smaller) - Economic benefits of the park (ten thousand yuan / year, negative value indicates maximum benefit).

[0098] (2) Weighted summation method (weights are determined based on expert scores, and the sum is 1), the specific formula is as follows:

[0099] Instance weight assignment in the formula: (Zero-carbon goals have the highest priority) (Cost control) (Renewable Energy Utilization) (Economic benefits).

[0100] Example 2 This invention also provides an optimization method for a carbon reduction pathway planning model for industrial parks based on a zero-carbon objective, comprising the following steps: S1. Improved Genetic Algorithm: The parameters in the energy system modeling submodule are encoded using real-number encoding combined with the discrete decision binary encoding in the industrial structure adjustment submodule to encode each decision variable in the park's carbon reduction pathway planning, forming individual chromosomes. The chromosome structure directly maps to the normalized key parameters in the data acquisition and preprocessing module. An initial population is generated through a combination of empirical data and random generation. 70% of the individuals in the initial population are based on historical operating data from the data acquisition and preprocessing module, while the remaining individuals are randomly generated. An improved selection operator combining tournament selection and roulette wheel selection is used to select superior individuals for the breeding population. An adaptive crossover probability strategy and a multi-point crossover operator are designed. A mutation operator combining Gaussian mutation and polynomial mutation is introduced, and an adaptive variable asynchronous long adjustment mechanism is adopted to improve the algorithm's search efficiency and accuracy. This algorithm effectively overcomes the shortcomings of traditional genetic algorithms in solving the park carbon reduction pathway planning problem, such as premature convergence and low search efficiency. Employing an efficient hybrid coding mechanism, a highly feasible initial population generation strategy, and powerful global search capabilities, this approach not only maintains population diversity and effectively avoids getting trapped in local optima, but also enhances the ability to perform fine-grained searches in complex solution spaces, laying the foundation for finding globally optimal or near-optimal carbon reduction pathways. Specifically: Decision variable coding: Continuous variables in the energy system modeling submodule are coded with real numbers, such as photovoltaic panel area. Rated power of the fan Energy storage system capacity For example, binary encoding is used for discrete decision variables in the industrial restructuring submodule. For instance, whether a certain type of high-carbon industry should be shut down or upgraded can be represented by binary variables. These together constitute the chromosome that characterizes the complete carbon reduction pathway.

[0101] Initial Population and Constraint Fitting: 70% of the individuals in the initial population are generated based on historical operational data, while the remaining 30% are randomly generated to enhance diversity. All individuals must undergo hard validation through the constraint setting submodule to ensure that the following conditions are met: Equipment operating constraints: P min,t ≤ P t ≤ P max,t ; Energy supply and demand balance constraints: ; Carbon emission limits: .

[0102] Fitness function: based on fitness values As an evaluation criterion, it guides the population to evolve towards the optimal solution, where: Total carbon emissions of the park: ; Total system cost: ; Reciprocal of renewable energy utilization rate: .

[0103] Crossover and Mutation: Crossover operations employ arithmetic crossover for continuous variables in the energy system and uniform crossover for discrete variables in the industry. Mutation operations introduce Gaussian mutagenesis and bit-flip mutagenesis. Gaussian mutagenesis is used for continuous variables, such as... Bit flip mutation for discrete variables. Variable asynchronous length. The parameters are adaptively adjusted based on their actual fluctuation range. All newly generated individuals must undergo the above constraint verification again to ensure the feasibility of the solution.

[0104] S2. Simulated Annealing Algorithm-Assisted Optimization: The current optimal solution obtained from the improved genetic algorithm is used as the initial solution. This initial solution satisfies the operational logic of the energy system modeling submodule and the transformation direction of the industrial structure adjustment submodule. A neighborhood search strategy is used to generate new solutions within the neighborhood based on the initial solution. If the energy system cost of the current solution is not optimal, the neighborhood search focuses on the equipment construction cost parameters in the energy system modeling submodule. An improved Metropolis criterion is used to determine whether to accept a new solution. Before accepting a new solution, the submodule needs to undergo dual verification through constraint conditions: one is the energy supply balance constraint, and the other is the total carbon emission constraint. A cooling schedule combining exponential cooling and adaptive cooling is designed to balance the global and local search capabilities of the algorithm, making the obtained carbon reduction path planning scheme closer to the global optimal solution. The switching between exponential cooling and adaptive cooling is based on the optimization schedule of the objective function construction submodule. When the renewable energy utilization rate increases by more than or equal to 5%, a slower cooling rate is used. When each objective function enters a plateau, a faster cooling rate is switched to avoid ineffective iterations. This algorithm starts by improving the output of genetic algorithms, aiming to further enhance the quality and robustness of solutions. Its core advantages lie in its superior local fine-grained search capability, efficient convergence characteristics, and constraint handling ability. While ensuring high-quality solutions, it significantly improves the algorithm's convergence efficiency and stability. Specifically: Initial solution: The optimal solution using the improved genetic algorithm. As the initial solution, this solution already satisfies the energy supply balance constraint. And the direction of industrial restructuring.

[0105] Neighborhood search: in the current solution Random perturbations within the neighborhood to generate new solutions The perturbation strategy is linked to the priority of the objective function. For example, if the priority is to improve the utilization rate of renewable energy, then the focus is on perturbing the energy system parameters (such as...). If cost optimization is required, the focus should be on variables that disrupt industry adjustments. (New solution) The energy supply balance and total carbon emission constraints must be re-evaluated.

[0106] Metropolis Criterion and Cooling: Define the difference between the weighted sum of the objective functions of the new solution and the current solution as... Acceptance probability Cooling coefficient The system is dynamically adjusted based on the degree of constraint violation. For example, if a new solution... If total carbon emissions exceed the limit, temperature will drop significantly. Perform a detailed search.

[0107] Specific implementation examples: (1) Decision variable coding: ① Continuous variable (real number encoding): Photovoltaic panel area (Code range 50,000-80,000 m²), number of wind turbines (Code range 2-5 units), energy storage system capacity (Encoding range 1000-5000 kWh); ② Discrete variables (binary encoding): Shutting down production lines in fine chemical enterprises (1 = shut down, 0 = retain), upgrading equipment manufacturing enterprises (1 = upgrade, 0 = no upgrade), expanding the service industry (1 = expansion, 0 = no expansion). Example chromosome structure: [6.5,3, 3000, 1, 1, 1] (corresponding to: 65,000 m² of photovoltaic panels, 3 wind turbines, 3000 kWh of energy storage, shutting down production lines, upgrading manufacturing enterprises, and expanding the service industry).

[0108] (2) Initial population and constraint adaptation: The initial population size is set to 100, of which 70 individuals are generated based on the historical operation data of the park from 2023 to 2025 (e.g., the photovoltaic panel area refers to the historical installation efficiency), and 30 individuals are generated randomly.

[0109] (3) Fitness function: As an evaluation criterion, among which: (Initial value of the example: 37,000 tons of CO2); (Initial annual cost of energy system: 28 million yuan); (Initial renewable energy utilization rate 12%;) (Initial annual total output value of the industrial park: 1.8 billion yuan). Example of initial fitness value: .

[0110] (4) Crossover and mutation: The crossover probability is set to 0.7. Arithmetic crossover is used for continuous variables (such as photovoltaic panel area) (parent generation [6.5, 3] and [7.2, 4] cross to generate offspring [6.85, 3.5]), and uniform crossover is used for discrete variables. The mutation probability is set to 0.05. Gaussian mutation is used for continuous variables (such as photovoltaic panel area of ​​65,000 m² mutating to 6.5 + N(0,0.3) ≈ 67,200 m²), and bit-flip mutation is used for discrete variables (such as [1,1,1] mutating to [1,0,1]).

[0111] S2, Simulated Annealing Algorithm-Assisted Optimization Using the current optimal solution obtained by the improved genetic algorithm as the initial solution, a new solution is generated through a neighborhood search strategy. An improved Metropolis criterion is adopted to determine whether to accept the new solution. A cooling progress combining exponential cooling and adaptive cooling is designed to make the obtained carbon reduction path planning scheme closer to the global optimal solution.

[0112] Implement this using a specific example: (1) Initial solution: After 100 iterations of the improved genetic algorithm, the optimal solution S00 is obtained: [7.2, 4, 4200, 1, 1, 1], corresponding to: 72,000 m² of photovoltaic panels, 4 wind turbines, 4200 kWh of energy storage, shutdown of 1 production line, upgrading of all manufacturing enterprises, and expansion of service industries; at this time Tons of CO2 (2030 target) The park has an annual output value of 2.2 billion yuan, a renewable energy utilization rate of 38%, and a total annual output value of 2.2 billion yuan.

[0113] (2) Neighborhood search: Since the current renewable energy utilization rate has not reached the medium-term target of 50%, we focus on the parameters of the disturbed energy system and generate a new solution S': [7.8, 5, 4800, 1, 1, 1] (photovoltaic panels increased to 78,000 m², wind turbines increased to 5 units, and energy storage increased to 4,800 kWh). We verify the energy balance: After adding photovoltaic / wind power, the total power on the supply side increases to 15,200 kW, which meets the maximum demand of 10,000 kW on the load side, and the total carbon emissions decrease to 14,200 tons of CO2, which meets the constraints.

[0114] (3) Metropolis criterion and cooling: initial temperature T0=100, the objective function difference between the new solution S' and the current solution S0 ( If E<0, S' is accepted directly.

[0115] Cooling coefficient α: Since the rate of increase in renewable energy utilization = (52%-38%) / 1 iteration = 14%≥5%, a slower cooling rate α=0.95 is adopted, T1=0.95×100=95.

[0116] (3) Metropolis Criterion and Cooling: Define the difference between the weighted sum of the objective functions of the new solution and the current solution as:

[0117] In equation (14): The weights are those defined in equation (12); For a new interpretation The value of the i-th objective function; Let be the i-th objective function value of the current solution \( S \).

[0118] The probability of acceptance follows the Metropolis criterion:

[0119] Example calculation: Initial temperature \( T_0 = 100 \), substituting the objective function difference between the new solution \( S' \) and the current solution \( S_0 \) into equation (14), we get, From the above formula, we know Accept directly Cooling coefficient Since the rate of increase in renewable energy utilization is (52%-38%) / 1 = 14% ≥ 5%, a slower cooling rate is adopted. The next round of temperature .

[0120] (4) Final optimization result: After 50 iterations of the simulated annealing algorithm, the globally optimal carbon reduction path is obtained: ① Energy System: 78,000 m² photovoltaic panels, 5 x 2.5MW wind turbines, 5000 kWh energy storage system, and 1 x 50MW combined heat and power system; ② Key Indicators: Total carbon emissions of the park in 2035 = 0 tons of CO2 (achieving zero carbon), annual cost of energy system = 18 million yuan, renewable energy utilization rate = 92%, annual output value of the park = 2.5 billion yuan, and user satisfaction = 96%.

[0121] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A carbon reduction pathway planning model for industrial parks based on a zero-carbon objective, characterized in that, include: The data acquisition and preprocessing module is used to build a complete energy and carbon emission database; The carbon emission accounting module is used to calculate the direct and indirect carbon emissions within the park based on the collected data, and to clarify the current status of carbon emissions and the main emission sources. The energy system modeling submodule is used to build mathematical models of energy systems; The industrial restructuring submodule is used to analyze the carbon emission intensity and development trend of different industries in the park, determine the direction and focus of industrial restructuring, and establish an industrial restructuring model. The constraint setting submodule is used to set various constraints that the carbon reduction pathway planning of the park must meet. The objective function construction submodule is used to construct multi-objective optimization functions with the goal of achieving zero carbon emissions, and transform them into single-objective optimization problems.

2. The industrial park carbon reduction pathway planning model based on a zero-carbon target as described in claim 1, characterized in that, The data acquisition and preprocessing module is implemented as follows: energy meters and data acquisition terminals are installed in the park to collect energy consumption data, production data, and equipment operation data in real time. The data is then transmitted to the data center through the data acquisition system. Abnormal, missing, and duplicate data are removed using data cleaning tools. Normalization algorithms are used to normalize different types of data to build a complete energy and carbon emission database.

3. The industrial park carbon reduction pathway planning model based on a zero-carbon target as described in claim 1, characterized in that, In the carbon emission accounting module, the formula for calculating the direct carbon emissions of a company within the industrial park is as follows: in, Indicates the first The company's direct carbon emissions Indicates the first Enterprise consumption of the first Fuel quantity, Indicates the first Carbon emission factors of various fuels Total number of fuel types; The formula for calculating indirect carbon emissions is: in, Indicates the first Indirect carbon emissions of enterprises Indicates the first Electricity purchased by enterprises Indicates the carbon emission factor of electricity; Indicates the first Enterprise's external heat consumption; Indicates the thermal carbon emission factor; The total carbon emissions of the park are the sum of the direct and indirect emissions of all enterprises: in, This indicates the total carbon emissions of the park; This represents the total number of companies within the park.

4. The industrial park carbon reduction pathway planning model based on a zero-carbon target as described in claim 1, characterized in that, In the mathematical model of the established energy system when solar photovoltaic power generation is used, the formula for calculating renewable energy power generation is as follows: in, Indicates at time The amount of solar photovoltaic power generation; Indicates the area of ​​the solar panel; Indicates the photoelectric conversion efficiency of the solar panel; Indicates time The intensity of solar radiation; The formula for calculating wind power generation is: in, Indicates at time The amount of wind power generated; Indicates air density; This indicates the swept area of ​​the wind turbine. This indicates the power generation efficiency of the wind turbine. Indicates time wind speed; The formula for calculating the power generation efficiency of a combined heat and power (CHP) system is: in, Indicates the power generation efficiency of a combined heat and power system; Indicates the electrical power emitted; Indicates the heat output; This indicates the amount of fuel consumed.

5. The industrial park carbon reduction pathway planning model based on a zero-carbon target as described in claim 1, characterized in that, In the industrial restructuring model within the aforementioned industrial restructuring submodule, the formula for calculating industrial carbon emission intensity is as follows: in, Indicates the first The carbon emission intensity of the industry; Indicates the first Carbon emissions from the industry; Indicates the first The output or production value of an industry.

6. The industrial park carbon reduction pathway planning model based on a zero-carbon target as described in claim 1, characterized in that, The constraint setting submodule sets the constraints that the park's carbon reduction pathway planning must meet, including: Energy supply balance constraint formula: in, Indicates at time No. Power generation capacity of various energy generation equipment; This represents the total number of types of energy generation equipment. Indicates at time The discharge power of the energy storage system; Indicates at time No. The power consumption of each energy load; Total number of energy load types; Indicates at time The charging power of the energy storage system; Equipment capacity constraint formula: in, Indicates the first The minimum power output of this type of energy generation equipment; Indicates the first The maximum power output of this type of energy generation equipment; Carbon emission total constraint formula: in, This indicates the upper limit of the park's total carbon emissions during the planning period.

7. The industrial park carbon reduction pathway planning model based on a zero-carbon target as described in claim 1, characterized in that, The construction of the multi-objective optimization function in the objective function construction submodule includes: Multi-objective optimization function formula: in, These represent different optimization objective functions, such as the total carbon emissions of the park, the construction and operation cost of the energy system, the utilization rate of renewable energy, and the economic benefits of the park. The weighted summation method is used to process the formulas for multi-objective optimization functions: in, Let be the weight coefficients of each objective function, and satisfy . .

8. An optimization method for a park carbon reduction pathway planning model based on a zero-carbon target as described in claim 1, characterized in that, Includes the following steps: S1. An improved genetic algorithm is used to encode each decision variable in the carbon reduction path planning of the park by combining real-number encoding of parameters in the energy system modeling submodule with binary encoding of discrete decisions in the industrial structure adjustment submodule. This forms individual chromosomes, generates an initial population, selects superior individuals to enter the breeding population, designs an adaptive crossover probability strategy and a crossover operator with multi-point crossover operation, and introduces a mutation operator that combines Gaussian mutation and polynomial mutation to improve the search efficiency and accuracy of the algorithm. S2. Using the current optimal solution obtained by the improved genetic algorithm as the initial solution, a new solution in the neighborhood is generated based on the initial solution through a neighborhood search strategy; the improved Metropolis criterion is used to decide whether to accept the new solution; a cooling progress combining exponential cooling and adaptive cooling is designed to balance the global search capability and local search capability of the algorithm, so that the obtained carbon reduction path planning scheme is closer to the global optimal solution.

9. The optimization method for the park carbon reduction path planning model based on the zero-carbon target according to claim 8, characterized in that, In step S1 The decision variables are encoded as follows: continuous variables in the energy system modeling submodule are encoded with real numbers; discrete decision variables in the industrial structure adjustment submodule are encoded with binary numbers, together forming a chromosome that represents the complete carbon reduction path; The initial population and constraint adaptation are as follows: 70% of the individuals in the initial population are generated based on historical operational data, and the remaining 30% are randomly generated to enhance diversity. All individuals undergo hard validation through the constraint setting submodule to ensure that the following conditions are met: Equipment operating constraints: P min,t ≤ P t ≤ P max,t ; Energy supply and demand balance constraints: ; Carbon emission limits: Fitness function: based on fitness values As an evaluation criterion, it guides the population to evolve towards the optimal solution, where: Total carbon emissions of the park: ; Total system cost: ; Reciprocal of renewable energy utilization rate: ; Crossover and mutation are as follows: crossover operation uses arithmetic crossover for continuous variables in the energy system and uniform crossover for discrete variables in the industry. Mutation operation introduces Gaussian mutation and bit-flip mutation.

10. An optimization method for a park carbon reduction pathway planning model based on a zero-carbon target as described in claim 9, characterized in that, In step S2 The initial solution is the optimal solution using the improved genetic algorithm. As the initial solution, the initial solution satisfies the energy supply balance constraint condition. And the direction of industrial restructuring; Neighborhood search is: in the current solution Random perturbations within the neighborhood to generate new solutions The perturbation strategy is linked to the priority of the objective function; If the priority is to increase the utilization rate of renewable energy, the focus should be on perturbing the parameters of the energy system; if the priority is to optimize costs, the focus should be on perturbing the variables that adjust the industry. Metropolis Criterion and Cooling: Define the difference between the weighted sum of the objective functions of the new solution and the current solution as... Acceptance probability: Cooling coefficient The adjustment is made dynamically based on the degree of constraint violation.

Images