A low-carbon analysis and optimization planning method for industrial parks

Abstract

The present invention provides a low-carbon analysis and optimization planning method for industrial parks, which uses the equivalent full-load operating time method and probability distribution to perform load forecasting on specific scenarios. A planning and construction cost model, an equipment operation and maintenance cost model, and a scrapping cost model are established for each energy unit. The optimization target of the upper-level planning model is to minimize the annual discounted cost over the entire life cycle, and the optimization target of the lower-level optimization model is to minimize the carbon footprint cost. A two-layer optimization planning model for the energy system of the industrial park is established, while satisfying various balance and restrictive constraints. Low-carbon analysis is performed by solving carbon footprint and carbon footprint, and the upper and lower models are connected. An improved genetic algorithm is used to solve the two-layer model and perform solution optimization. The solution is iteratively solved until convergence, and the production plan, equipment installed capacity, and carbon footprint offset plan of the industrial park system are obtained through optimization. The present invention can be applied to the planning and design of industrial parks and the optimization design of planning and emission reduction plans under low-carbon analysis.

Description

Technical Field

[0001] The present invention belongs to the field of combining low-carbon analysis and optimization planning of industrial parks in integrated energy systems, and specifically relates to an optimization planning method for industrial parks. Background Art

[0002] Industrial parks are the driving force behind my country's urban development. The intelligent upgrade of their energy systems is essential for building modern smart cities based on integrated energy systems and is a prerequisite for ensuring the overall safe, stable, friendly, and harmonious operation of cities. Currently, my country's industrial parks are experiencing an accelerating development trend, with their scale continuously expanding and tending towards a complementary operation model of centralized and distributed energy sources. This approach utilizes a centralized energy supply paradigm that combines multi-energy synergy and complementarity, multi-terminal supply and demand interaction, and information-energy integration, while integrating with unique distributed energy supply methods such as wind, solar, and electricity. This is of great significance for improving the reliability and flexibility of industrial park energy supply.

[0003] The planning and design of industrial parks is the primary key technology to ensure their safe, efficient, economical and reliable operation. Industrial parks contain multiple energy supply methods and multiple energy loads, and their planning and design is a complex system engineering. Industrial parks account for 31% of the country's carbon dioxide emissions. Under the requirements of the national dual-carbon strategy, facing the huge and complex integrated energy system of industrial parks, enterprises need to balance economic and environmental benefits, which poses a major challenge to the planning and design of industrial parks. Traditional planning and design methods are difficult to meet the current needs of industrial parks, mainly reflected in: (1) The production process of industrial parks is complex, and their energy systems include multiple energy units. In previous planning and design methods, there is a lack of complete low-carbon analysis and a lack of theoretical basis for low-carbon analysis; (2) The low-carbon analysis of industrial parks requires carbon accounting throughout the entire life cycle of the entire industrial chain. Currently, there is a lack of targeted accounting baselines. Carbon footprint verification only calculates the net value of carbon emissions within the industrial park itself, and cannot reflect the potential environmental benefits in the planning scheme and the environmental benefits brought by the products or services provided by reducing the carbon footprint of others; (3) Traditional planning and design methods relatively simply use carbon emissions as the optimization target to solve the solution, and the design of carbon reduction schemes remains in theory. There is an urgent need for planning and design methods that can optimize carbon reduction schemes.

[0004] Although there have been relevant studies on carbon baseline inventories of industrial production enterprises and two-layer optimization planning methods for distributed energy, such as Patent 202110769556.7 "A two-layer optimization planning method for distribution networks considering the operation of multiple distributed energy sources", Patent 201711092907.5 "An industrial park integrated energy system optimization scheduling and evaluation system and method with control strategy" and Patent 20180133288.8 "Integrated energy system design method for coordinated interaction of source, load and storage", which considered the comprehensive operating costs, energy loss, safety and environmental protection issues in the planning stage, and considered guiding the planning and design through optimizing operation strategies, but did not consider the capacity variability and periodicity of the equipment in the planning stage, resulting in limited comprehensive economic benefits when dealing with multi-cycle load changes, equipment upgrades and other issues during planning and design, and a lack of clear consideration of environmental benefits. Patent 201811497857.3, "A Carbon Inventory / Carbon Verification Management System and Method," primarily focuses on calculating, analyzing, and managing a company's greenhouse gas emissions throughout the entire process, but does not provide clear guidance on park planning and construction or energy-saving and emission reduction plans. Summary of the Invention

[0005] The purpose of the present invention is to overcome the fact that existing planning schemes do not consider in detail the impact of carbon emissions on the regulating capacity of the energy system of industrial parks, resulting in the inability of the installed capacity of each equipment in the existing planning schemes to fully adapt to the future national carbon quota requirements. A low-carbon analysis and optimization planning method for industrial parks that is coordinated by carbon footprint and carbon handwriting is proposed. Under the conditions of ensuring that the supply and demand balance constraints and good economic conditions are met, the present invention optimizes the installed capacity of equipment such as cogeneration boilers, energy storage systems, ground source heat pumps, and photovoltaic systems, and then optimizes the scheme based on the establishment of comprehensive cost-economic evaluation indicators. This reduces the problems of excessive installed capacity and ultra-high greenhouse gas emissions that may arise from the planning and design of traditional energy systems, and improves the planning and design level of the energy system of industrial parks.

[0006] This invention is primarily applicable to integrated energy systems in industrial parks. These systems include energy routers, gas turbines, combined heat and power (CHP) units, ground-source heat pumps, thermal energy storage devices, building-integrated photovoltaic systems, industrial production lines, and newly constructed carbon sinks. The system is connected to the external power grid and external heat network via a pipeline network. Both the park's renewable energy and industrial loads have certain uncertainties, and designing the installed capacity of each device to address these uncertainties is a key issue.

[0007] The industrial park planning method of the present invention includes load forecasting for specific scenarios, establishing a two-layer optimization planning model for the industrial park energy system, establishing a solution model for the carbon footprint and carbon footprint of the solution, and solving the two-layer optimization planning model to obtain a planning solution that includes the installed capacity of each device. The details are as follows:

[0008] (1) Generate a specific scenario.

[0009] The method for generating specific scenarios is to generate the output probability distribution of each energy source and load through probability statistics based on the historical operating data of energy routers, equipment units (such as gas turbines, renewable energy, CHP units, heat pumps, energy storage devices), thermal loads, electrical loads, and cooling loads. At the same time, based on the statistically obtained mean and standard deviation of the energy source and load in each time period, the equivalent full-load operating time method is used to predict and calculate the corresponding operating power probability of each device in each time period to obtain a specific scenario.

[0010] (2) Establish a two-layer optimization planning model for the industrial park energy system.

[0011] The two-layer optimization planning model for the industrial park energy system includes an upper-layer planning model and a lower-layer optimization model. First, a planning and construction cost model, an equipment operation and maintenance cost model, and a scrapping cost model are established for each energy router and equipment unit (gas turbine, renewable energy, CHP unit, heat pump, energy storage device). On this basis, the optimization target of the upper-layer planning model is to minimize the annualized life cycle cost, which is the sum of the annualized planning and construction cost, equipment operation and maintenance cost, and annualized scrapping cost. The optimization target of the lower-layer optimization model is to minimize the carbon footprint cost, and a two-layer model of low-carbon analysis and optimization planning for the industrial park is established, while satisfying various balance constraints and restrictive constraints (including capacity constraints, time constraints, and planned land constraints).

[0012] (3) Solve the two-level optimization planning model of the industrial park energy system.

[0013] Based on the carbon baseline inventory form and accounting list, carbon emission sources are identified, the system boundary diagram and accounting method are determined, and the accounting factor method is used to calculate the carbon footprint of the planning scheme throughout its life cycle and feedback is given to the lower-level model. An improved genetic algorithm and a fitness function based on simulated annealing correction are used to interactively and iteratively solve the bi-level planning model. The specific method is as follows: a bi-level interactive iterative solution method combining a multi-objective optimization algorithm with an hourly linear optimization scheduling algorithm is used to solve the upper-level planning model to obtain the capacity planning scheme for the industrial park energy system. The lower-level model is solved using a deterministic algorithm solver to obtain the output time series of each device corresponding to the solution set of the upper-level model. The bi-level interactive iteration is repeated until convergence. Through the above steps, the installed capacity of each device in the integrated energy system is optimized and the emission reduction scheme for the low-carbon analysis and optimization planning of the industrial park is obtained.

[0014] In the above technical solution, further, in the step (1), a scene reduction method is used to cluster the scenes;

[0015] The equivalent full load operating time method is specifically:

[0016] The equivalent full-load operating time refers to the ratio of the total annual operating load of the energy unit to the maximum output of the energy unit, that is:

[0017]

[0018]

[0019] Where, τ i is the equivalent full-load operating time of the i-th type energy unit, h; q is the total annual load of the energy unit, kJ / a; q m,i is the maximum output of the i-th type energy unit, kJ / h; load rate ε i It represents the ratio of the total annual load of the i-th energy unit to the total maximum output of the corresponding energy unit during the cumulative operating time; T i The accumulated operating time of the energy unit equipment of category i.

[0020] Furthermore, in step (2), the constraints satisfied by the upper-level planning model are energy structure constraints, namely, the installed capacity constraints of each energy unit and the balance constraints of electric, heating and cooling load supply and demand; in the optimization target of the upper-level planning model, the annualized planning and construction costs and equipment operation and maintenance costs in the annual discounted costs of the entire life cycle are obtained by converting the planning and construction costs and equipment operation and maintenance costs according to the annual interest rate; the planning and construction costs include the costs of system pre-planning and design and energy unit equipment procurement, and the equipment operation and maintenance costs include the costs of installation verification and commissioning confirmation, operating energy consumption, equipment maintenance, energy consumption purchase and carbon footprint costs; the scrapping costs include the annual depreciation of fixed assets and the final residual value recovery costs; the lower-level optimization model satisfies the following constraints: time constraints, energy transmission loss constraints, and planning land constraints;

[0021] Emission reduction options include: obtaining carbon quotas, building new carbon sinks, nationally mandated green and low-carbon behaviors, improving energy efficiency, reducing material use, using environmentally friendly materials, developing product recyclability, reducing waste, extending product life, and improving product availability.

[0022] Furthermore, in step (2), the method for establishing the planning and construction cost model, equipment operation and maintenance cost model, and scrapping cost model of each energy router and equipment unit is as follows:

[0023] 1) Planning and construction cost and equipment operation and maintenance cost model

[0024]

[0025]

[0026] Where: is the planned construction cost of the i-th type of equipment, is the operation and maintenance cost of the i-th type of equipment; C in.i is the annualized planned construction cost of the i-th type of equipment; R is the discount rate; l is the expected operating life of the equipment; U i P is the unit power construction cost of the i-th type of equipment; i Plan the rated load for equipment of category i;

[0027] 2) Scrap cost model

[0028] The annualized scrap cost is obtained by converting the accumulated depreciation of fixed assets into the equipment investment cost using the average life method according to the corresponding proportional coefficient. The difference between the scrap disposal cost and the scrap asset residual value recovery income determines whether the accumulated depreciation of fixed assets is a positive or negative value when included in the total cost.

[0029]

[0030] Where: SC i is the scrapping cost of the i-th energy unit, SC EAC.i —Annualized retirement cost of energy unit type i.

[0031] Furthermore, in step (2), the optimization objectives of the upper-level planning model and the lower-level optimization model are as follows:

[0032] F lca (x)=F1(x)+F2(x) (6)

[0033]

[0034]

[0035]

[0036] Where, F lca (x) is the annual discounted cost of the entire life cycle; x is the set of equipment capacities of each energy unit to be optimized, that is, the set of installed planned capacities of each energy router and equipment unit; F1(x) is the total cost of planning, construction, and equipment operation and maintenance of all energy units, is the planned construction cost of the i-th type of equipment, is the operation and maintenance cost of the i-th type of equipment, F2(x) is the total scrapping cost of all energy units, F c (x,y) is the total carbon footprint cost under the planning scheme, which is determined by each energy unit Process optimization saves energy and costs, Energy transmission loss reduction cost, C mat.i Raw material replacement cost, C cfor.i New carbon sink greening cost, C cpro.iCost of new carbon sink project, C tra.i Energy saving cost of transportation mode, C life.i Life extension cost, C ele.i Other stipulated module cost components, y is the lower-level decision variable that can be optimized, D is the annual operating days, and δ is the time conversion coefficient.

[0037] Furthermore, before calculating the carbon footprint cost, it is necessary to first calculate the carbon footprint. The carbon footprint is an indicator that reflects the emission reduction capability, expressed in CO2 equivalents, and is used to describe the emission reduction generated when the planning solution is used to replace the baseline solution. The calculation formula of the carbon footprint is:

[0038] H=H1+H ini +H ele (10)

[0039]

[0040] Where: H is the carbon footprint, H1 is the carbon footprint achieved in the process of providing products / services in the industrial park, and H ini The carbon footprint of the industrial park achieved during the initiative process, H ele is the carbon footprint of the industrial park by providing products / services that reduce the carbon footprint of others, Wi is the annual output of the i-th product / service in the industrial park, FE i is the benchmark carbon footprint output corresponding to the unit output of the i-th product / service, F i is the carbon footprint output corresponding to the unit output of the i-th product / service, ε i is the annual production share of category i products / services.

[0041] Furthermore, in step (2), the balance constraint includes an electrical balance constraint, a thermal balance constraint, and a cold balance constraint, which are specifically as follows:

[0042] Electricity load supply and demand balance constraints

[0043]

[0044] Where: P i is the power supply of the i-th type equipment, E demand is the electricity demand;

[0045] Heat load supply and demand balance constraints

[0046]

[0047] Where: Q h.i is the heat supply of the i-th type equipment, Q h.demand is the heat demand;

[0048] Cooling load supply and demand balance constraints

[0049]

[0050] Where: Q c.i is the cooling capacity of the i-th type of equipment, Q c.demand The cooling demand.

[0051] Furthermore, in step (3), the solution process of the upper-level planning model is specifically as follows:

[0052] 1) Call the lower-level optimization model operation results and calculate the objective function response value;

[0053] 2) Based on the current objective function response value, output the planned capacity of the machine assembly;

[0054] 3) Using the improved genetic algorithm to solve the upper-level planning model to obtain the solution set;

[0055] The improved genetic algorithm is specifically:

[0056] The crossover probability Pc and mutation probability Pm in the genetic algorithm are key parameters that affect the search performance and convergence. By introducing adaptive crossover probability and mutation probability, the crossover and probability operations are adjusted as the fitness of the population changes, and are no longer fixed values. When the population gradually falls into the local optimal solution, the crossover probability and mutation probability are increased accordingly. When the population tends to diverge, the crossover probability and mutation probability are reduced accordingly, thereby achieving adaptive adjustment of genetic operations and improving the search ability of the genetic algorithm. The adaptive crossover probability and mutation probability are expressed as follows:

[0057]

[0058]

[0059] Where: fit max is the maximum individual fitness value in the population; fit avg is the average fitness value of the population, fit c is the fitness value of the better individual in the crossover operation, fit m is the fitness value of the individual in the mutation operation; P c1 、P c2 、P m1 、P m2 are the upper and lower limits of the adaptive crossover probability and mutation probability, respectively.

[0060] Furthermore, in step (3), the solution process of the lower-level optimization model specifically includes the following steps:

[0061] 1) For the sampling points in the optimal solution set output by the upper model, resample in the area that may contain the optimal solution and calculate the objective function Fc (x, y); compare and calculate with the current optimal sampling point. If the objective function value decreases, that is, the optimization target H1 of the corresponding lower-level optimization model increases, then the optimal solution set will be updated, otherwise it will remain unchanged;

[0062] 2) Based on the fitness function modified by simulated annealing, call the upper layer to output the optimal solution set

[0063] The simulated annealing algorithm is used to improve the genetic algorithm to achieve continuous correction of fitness during the optimization process, which is expressed as follows:

[0064]

[0065] Where: fit(x) im is the fitness function modified based on simulated annealing, k is the simulated annealing function coefficient, which is less than 1.0, t is the evolutionary generation of the genetic algorithm, T0 is the initial temperature of simulated annealing, which is the same order of magnitude as the objective function, and f(x) is the objective function value of individuals in the population;

[0066] 3) Repeat the sampling process, continuously call the upper-level model, and iteratively calculate to narrow the optimal solution set until the global optimal solution is found.

[0067] The two-layer model iterates interactively until convergence. The convergence speed depends on the termination conditions set by the upper-layer model. Through the above steps, the installed capacity of each equipment in the integrated energy system is optimized and the low-carbon analysis and optimization planning scheme for the industrial park is obtained.

[0068] The carbon footprint refers to the potential positive environmental impact a system can achieve by replacing a baseline scenario with an optimized scenario.

[0069] This innovative evaluation mechanism enables the evaluation of positive environmental impacts throughout the entire life cycle. The evaluation is based on achieving beneficial environmental impacts by providing products or services that reduce the carbon footprint of others. The size of the footprint refers to the difference between the two options. One can achieve one's own footprint by reducing the footprint of others, such as producing more energy-efficient products and using carbon footprints to offset carbon footprints to achieve emission reduction targets.

[0070] The present invention has the following advantages:

[0071] (1) In terms of planning scenarios for the energy system of an industrial park, the present invention takes into account the load data and environmental parameters of typical scenarios, as well as the long-term planning needs of the industrial park for emission reduction solutions.

[0072] (2) Based on the analysis of the equipment capacity and annual discounted cost of the entire life cycle of the industrial park energy system, the present invention considers the important impact of carbon footprint in planning and establishes a multi-objective solution model based on annual discounted cost and carbon footprint.

[0073] (3) The present invention proposes a two-layer interactive iterative solution method that combines a multi-objective optimization algorithm with an hourly linear optimization scheduling algorithm. The upper-layer model solves the capacity planning scheme of the industrial park energy system, and the lower-layer model is solved by a deterministic algorithm solver to obtain the output time series of each device corresponding to each upper-layer scheme. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 This is a schematic diagram of the carbon emission system accounting boundary of a chemical production park;

[0075] Figure 2 This is a schematic diagram of the carbon emission verification process for plasticizer manufacturers;

[0076] Figure 3 It is a schematic diagram of the steps for solving the double-layer model in the present invention. DETAILED DESCRIPTION

[0077] The method of the present invention is described in detail below with reference to the accompanying drawings and specific embodiments. The present invention is mainly applied to the energy system of industrial parks.

[0078] In this example, the industrial park energy system primarily comprises renewable energy generation devices, including building-integrated photovoltaic systems and photovoltaic panels. The combined heat and power (CHP) system primarily includes CHP units, ground-source heat pumps, lithium bromide absorption heat pumps, refrigeration units, electric refrigeration units, and electrical energy storage devices. The system can meet diverse load demands for electricity, heat, and cooling. The heating and cooling networks are coupled via energy routers, and the electrical energy storage devices generate revenue by inputting off-peak electricity and renewable energy power and distributing it during peak hours.

[0079] The industrial park integrated energy system planning method of the present invention includes the following steps: generating a typical daily planning scenario, establishing a two-layer optimization planning model, and solving the two-layer model. The specific steps are as follows:

[0080] 1. First, carry out load forecasting for specific scenarios and selection of typical days.

[0081] Based on the historical operating data of energy routers, equipment units (such as gas turbines, renewable energy, CHP units, heat pumps, energy storage devices), thermal loads, electrical loads, and cooling loads, the output probability distribution of each energy source and load is generated through probability statistics. At the same time, based on the statistically obtained mean and standard deviation of the energy source and load in each time period, the equivalent full-load operating time method is used to predict and calculate the corresponding operating power probability in each time period to obtain specific planning scenarios.

[0082] The clustering method was used to select typical days, and boundary data such as electric load, heating load, cooling load, light intensity and ambient wind speed were used as input data.

[0083] 2. Establish a two-tier planning model for the industrial park energy system:

[0084] (1) Establish the equipment mechanism model of each energy unit

[0085] CHP unit model

[0086] Q GT =B GT Q net ·η GT (1)

[0087]

[0088]

[0089] Where: Q b is the total heat input; B GT is the fuel consumption; Q net is the low calorific value of the fuel; GT is the thermal efficiency of the gas turbine; r is the thermal-to-electricity ratio; is the rated efficiency; is the rated power generation efficiency; P GT Output power

[13] .

[0090] Solar photovoltaic system model

[0091] Solar photovoltaic power generation systems usually adopt the MPPT control strategy, and the steady-state model is used in energy system planning. The model expression is as follows:

[0092] P PV =ξη m A p η p (4)

[0093] Where: P PV is the rated power of photovoltaic power generation; ξ is the average light radiation intensity; η m is the efficiency of the MPPT controller; A p is the utilization area of ​​photovoltaic panels; η p is the average photovoltaic panel efficiency.

[0094] Ground source heat pump model

[0095]

[0096] Where: COP is the energy efficiency ratio of the ground source heat pump; E is the output heating power; P is the input power consumption.

[0097] Lithium bromide absorption heat pump model

[0098] Q h =COPh Q ls (6)

[0099] Q ls =G ls (h lsin -h lsout ) (7)

[0100] Where: Q h is the output thermal power of lithium bromide absorption heat pump; COP h is the heating energy efficiency ratio of the unit; Q ls is the input power of driving the heat source; G is is the working fluid flow rate driving the heat source; h lsih Enthalpy before driving heat source utilization; h lsout The post enthalpy value is utilized as the driving heat source.

[0101] Lithium bromide absorption refrigerator model

[0102] Q c =COP c Q ls (8)

[0103] Q ls =G ls (h lsin -h lsout ) (9)

[0104] Where: Q c is the output thermal power of lithium bromide absorption heat pump; COP c is the cooling energy efficiency ratio of the unit; Q ls is the input power of driving the heat source; G ls is the working fluid flow rate driving the heat source; h lsin Enthalpy before driving heat source utilization; h lsout The post enthalpy value is utilized as the driving heat source.

[0105] Electric energy storage system model

[0106] Q sto =Q ch γ ch Δt ch -Q dis γ dis Δt dis (10)

[0107] Where: Q sto is the storage capacity of the tank; Q ch is the storage power of the tank; Q dis is the heat release power of the storage tank; γ ch is the energy storage efficiency; Δt ch is the storage time; γdis is the heat release efficiency; Δt dis The exothermic time.

[0108] Electric refrigeration unit model

[0109] Q ce =COP ce P ce (11)

[0110] Where: Q ce is the output power of the electric refrigeration unit; COP ce is the cooling energy efficiency ratio of the unit; P ce For power consumption.

[0111] CCUS system model

[0112] C trans =(0.015N+1)×(C pipe +nI pre ) (12)

[0113] Where: C trans is the total investment cost of the transportation process; N is the expected service life of the pipeline; C pipe is the pipeline investment cost; I pre Compressor station investment costs.

[0114] (2) Use the comprehensive cost method to establish an annualized discounted cost model for the entire life cycle of each equipment:

[0115] Economic evaluation models constructed using the comprehensive cost method typically consist of system planning and construction costs, equipment operation and maintenance cost models, and scrapping cost models, using an average annual cost for evaluation. System planning and construction costs include the costs of system initial planning and design, and energy unit equipment procurement; equipment operation and maintenance costs include installation verification and commissioning confirmation, operating energy consumption, equipment maintenance, energy purchases, and carbon footprint costs; and scrapping costs include annual depreciation of fixed assets and the final recovery of residual value.

[0116] The method for establishing the planning and construction cost model, equipment operation and maintenance cost model, and scrapping cost model for each energy router and equipment unit is as follows:

[0117] 1) Planning and construction cost and equipment operation and maintenance cost model

[0118]

[0119]

[0120] Where: is the planned construction cost of the i-th type of equipment, is the operation and maintenance cost of the i-th type of equipment; C in.i is the annualized planned construction cost of the i-th type of equipment; R is the discount rate; l is the expected operating life of the equipment; U i P is the unit power construction cost of the i-th type of equipment; i Plan the rated load for equipment of category i;

[0121] 2) Scrap cost model

[0122] The annualized scrap cost is obtained by converting the accumulated depreciation of fixed assets into the equipment investment cost using the average life method according to the corresponding proportional coefficient. The difference between the scrap disposal cost and the scrap asset residual value recovery income determines whether the accumulated depreciation of fixed assets is a positive or negative value when included in the total cost.

[0123]

[0124] Where: SC i is the scrapping cost of the i-th energy unit, SC EAC.i —Annualized retirement cost of energy unit type i.

[0125] Taking the lowest carbon footprint cost as the optimization goal of the lower optimization model, a two-layer model of low-carbon analysis and optimization planning of industrial parks is established, while satisfying various balance constraints and restrictive constraints.

[0126] The calculation formula of the carbon trace is:

[0127] H=H1+H ini +H ele (16)

[0128]

[0129] Where: H is the carbon footprint, H1 is the carbon footprint achieved in the process of providing products / services in the industrial park, and H ini The carbon footprint of the industrial park achieved during the initiative process, H ele is the carbon footprint of the industrial park by providing products / services that reduce the carbon footprint of others, Wi is the annual output of the i-th product / service in the industrial park, FE i is the benchmark carbon footprint output corresponding to the unit output of the i-th product / service, F i is the carbon footprint output corresponding to the unit output of the i-th product / service, ε i is the annual production share of category i products / services.

[0130] Taking the lowest annualized comprehensive cost as the optimization goal of the upper-level planning model, and the lowest annualized carbon footprint cost under typical daily load parameters and environmental parameters as the lower-level optimization scheduling goal, a two-level planning model for the comprehensive energy system of the industrial park is established, while satisfying the balance constraints, carbon quota constraints, and restrictive constraints (including capacity constraints, time constraints, and planned land constraints).

[0131] The optimization objectives of the upper-level planning model and the lower-level optimization model are as follows:

[0132] F lca (x)=F1(x)+F2(x) (18)

[0133]

[0134]

[0135]

[0136] Where, F lca (x) is the annual discounted cost of the entire life cycle, which corresponds to the solution target of the established upper-level planning model. x is the set of equipment capacities of each energy unit to be optimized, that is, the set of installed planned capacities of each energy router, gas turbine, renewable energy, CHP unit, heat pump, and energy storage device. F1(x) is the total cost of planning, construction, operation, and maintenance of all energy units. is the planned construction cost of the i-th type of equipment, is the operation and maintenance cost of the i-th type of equipment, F2(x) is the total scrapping cost of all energy units, F c (x,y) is the total carbon footprint cost under the planning scheme, which is determined by each energy unit Process optimization saves energy and costs, Energy transmission loss reduction cost, C mat.i Raw material replacement cost, C cfor.i New carbon sink greening cost, C cpri.i Cost of new carbon sink project, C tra.i Energy saving cost of transportation mode, C life.i Life extension cost, C ele.i Other stipulated module cost components, y is the lower-level decision variable that can be optimized, D is the annual operating days, and δ is the time conversion coefficient.

[0137] The constraints considered are as follows:

[0138] Balance constraints mainly include electrical balance constraints, thermal balance constraints, and cold balance constraints as follows:

[0139] Electricity load supply and demand balance constraints

[0140]

[0141] Where: P i is the power supply of the i-th type equipment, E demand is the electricity demand;

[0142] Heat load supply and demand balance constraints

[0143]

[0144] Where: Q h.i is the heat supply of the i-th type equipment, Q h.demand is the heat demand;

[0145] Cooling load supply and demand balance constraints

[0146]

[0147] Where: Q h.i is the heat supply of the i-th type equipment, Q h.demand is the heat demand;

[0148] Carbon quota constraints

[0149] H all ≥H lim (25)

[0150] Where: H all is the total carbon emissions, H lim Carbon quotas for governments;

[0151] Restrictive constraints include capacity constraints, time constraints, and planned land constraints:

[0152] Time Constraints

[0153] T day ≤T max (26)

[0154] Where: T day T is the total time to achieve emission reduction in the planned scheme, max is the total time of the planning period;

[0155] Capacity constraints

[0156] P pv ≥P min (27)

[0157] Where: P pv is the equipment capacity of the planning scheme, P min The minimum capacity of the equipment specified;

[0158] Planning land constraints

[0159] S green ≤Smax (28)

[0160] Where: S green is the land area of ​​the planning scheme, S max The maximum available area.

[0161] 3. Solve the two-level optimization planning model of the industrial park energy system

[0162] like Figure 3 The present invention proposes a two-layer interactive iterative solution method that combines a multi-objective optimization algorithm with an hourly linear optimization scheduling algorithm.

[0163] Based on the carbon baseline inventory form and accounting list, identify carbon emission sources and determine the system boundary diagram (such as Figure 1 ) and accounting procedures (such as Figure 2 ), the accounting factor method is used to calculate the carbon footprint of the planning scheme throughout its life cycle and feed it back to the lower-level model.

[0164] The set of optional planning schemes in the industrial park energy system obtained by the multi-objective optimization solution of the upper-level model is input into the lower-level optimization scheduling model. Under the given planning scheme, the lower-level model uses a deterministic algorithm solver to obtain the output time series of each device corresponding to each upper-level scheme. The corresponding output time series obtained by the lower-level solution is returned to the upper-level planning model. Through hierarchical solution and alternating iteration, it is determined that the algorithm termination condition of the upper-level model is met and the optimal system planning scheme solution set is obtained.

[0165] The steps for solving the bi-level optimization planning model are as follows:

[0166] (1) Initialize parameter settings, including technical and economic parameters of the photovoltaic system, CHP unit, ground source heat pump, lithium bromide absorption heat pump, refrigeration unit, electric refrigeration unit, and electric energy storage equipment, to generate the initial system planning space:

[0167] (2) Based on the planning space, the equivalent full-load operating time method is used to obtain the load forecast results. The clustering method is used to select typical days, and the planning specific scenario is generated in combination with the environmental data to complete the initialization of the algorithm.

[0168] (3) Set the initial objective function of the upper model according to the technical parameters and economic parameters of the equipment, randomly generate the first generation parent population and calculate the objective function value, population selection, crossover, and mutation.

[0169] (4) Solve the upper model, calculate the objective function value of the population, calculate the crowding degree of the individuals in the population, generate the next generation parent population based on the results, determine whether the maximum evolutionary generation has been reached or evolution has stagnated, and output the Pareto solution set to be passed to the lower layer.

[0170] (5) Update the scheduling optimization model and boundary conditions of the lower-level optimization model, and use the deterministic optimization algorithm to solve and obtain the optimized scheduling results.

[0171] (6) Determine whether the scheduling optimization result of the lower-level model meets the convergence conditions. If so, stop the iteration and output the optimal planning scheme set. Otherwise, return the scheduling optimization result to the upper-level model, update the upper-level objective function value, and return to step (4).

Claims

1. A low-carbon analysis and optimization planning method for industrial parks, characterized in that: The following steps are involved: (1) Generate a specific scenario Based on the historical operating data of energy routers, equipment units, thermal loads, electrical loads, and cooling loads, the output probability distribution of each energy source and load is generated through probability statistics. At the same time, based on the statistically obtained mean and standard deviation of the energy source and load in each time period, the equivalent full-load operating time method is used to predict and calculate the corresponding operating power probability of each device in different time periods to generate specific scenarios; The scene reduction method is used to cluster the scenes; The equivalent full load operating time method is specifically: The equivalent full-load operating time refers to the ratio of the total annual operating load of the energy unit to the maximum output of the energy unit, that is: （1） （2） Where, is the equivalent full-load operating time of the i-th type energy unit, h; is the total annual load of the energy unit, kJ / a; is the maximum output of the i-th type energy unit, kJ / h; load rate It represents the ratio of the total annual load of the i-th energy unit to the total maximum output of the corresponding energy unit during the cumulative operating time; T i The accumulated operating time of the energy unit equipment of category i; (2) Establish a two-level optimization planning model for the industrial park energy system The two-layer optimization planning model for the industrial park energy system includes an upper-layer planning model and a lower-layer optimization model. First, a planning and construction cost model, an equipment operation and maintenance cost model, and a scrapping cost model are established for each energy router and equipment unit. On this basis, the optimization goal of the upper-layer planning model is to minimize the annualized lifecycle cost. The annualized lifecycle cost is the sum of the annualized planning and construction cost, equipment operation and maintenance cost, and annualized scrapping cost. Taking the lowest carbon footprint cost as the optimization goal of the lower optimization model, a two-layer model of low-carbon analysis and optimization planning of industrial parks is established, while satisfying various balance constraints and restrictive constraints; The optimization objectives of the upper-level planning model and the lower-level optimization model are as follows: （6） （7） （8） = （9） Where, The annual discounted cost for the entire life cycle; The set of equipment capacities of each energy unit to be optimized, i.e., the set of installed planned capacities of each energy router and equipment unit; The total cost of planning, construction, equipment operation and maintenance of all energy units, is the planned construction cost of the i-th type of equipment, is the operation and maintenance cost of the i-th type of equipment, is the total scrapping cost of all energy units, is the total carbon footprint cost under the planning scheme, which is calculated by each energy unit Process optimization saves energy and costs, Energy transmission loss reduction costs, Raw material replacement costs, Cost of new carbon sink greening, Cost of new carbon sequestration projects, Energy saving cost of transportation mode, Life extension costs, Other specified module cost components, is the lower-level decision variable that can be optimized, D is the number of operating days per year, and δ is the time conversion coefficient; Before calculating the carbon footprint cost, the carbon footprint must be calculated first. The calculation formula for the carbon footprint is: （10） （11） Where: For carbon handwriting, The carbon footprint achieved in the process of providing products / services to industrial parks, The carbon footprint of industrial parks achieved during the initiative. is the carbon footprint of the industrial park by providing products / services that reduce the carbon footprint of others, Wi is the annual output of the i-th product / service in the industrial park, is the benchmark carbon footprint output corresponding to the unit output of product / service of category i, is the carbon footprint output corresponding to the unit output of the i-th product / service, ε i is the annual production share of category i products / services; (3) Solve the two-level optimization planning model of the industrial park energy system Based on the carbon baseline inventory form and accounting list, carbon emission sources are identified, the system boundary diagram and accounting method are determined, and the accounting factor method is used to calculate the carbon footprint of the entire life cycle and feedback is given to the lower-level model. A two-layer interactive iterative solution method combining a multi-objective optimization algorithm with an hourly linear optimization scheduling algorithm is used to solve the upper-level planning model to obtain the capacity planning scheme of the industrial park energy system. The lower-level model is solved using a deterministic algorithm solver to obtain the output time series of each device corresponding to the solution set of the upper-level model. The two-layer interactive iteration is carried out until convergence. Through the above steps, the installed capacity of each device in the integrated energy system is optimized and the emission reduction plan for the low-carbon analysis and optimization planning of the industrial park is obtained. The solution process of the upper-level planning model is as follows: 1) Call the lower-level optimization model operation results and calculate the objective function response value; 2) Based on the current objective function response value, output the planned capacity of the machine assembly; 3) Using the improved genetic algorithm to solve the upper-level planning model to obtain the solution set; The improved genetic algorithm is specifically: The crossover probability Pc and mutation probability Pm in the genetic algorithm are key parameters that affect the search performance and convergence. By introducing adaptive crossover probability and mutation probability, the crossover and probability operations are adjusted as the fitness of the population changes, and are no longer fixed values. When the population gradually falls into the local optimal solution, the crossover probability and mutation probability are increased accordingly. When the population tends to diverge, the crossover probability and mutation probability are reduced accordingly, thereby achieving adaptive adjustment of genetic operations and improving the search ability of the genetic algorithm. The adaptive crossover probability and mutation probability are expressed as follows: (15) (16) Where: fit max is the maximum individual fitness value in the population; fit avg is the average fitness value of the population, fit c is the fitness value of the better individual in the crossover operation, fit m is the fitness value of the individual in the mutation operation; P c1 、P c2 、P m1 、P m2 are the upper and lower limits of the adaptive crossover probability and mutation probability respectively; The solution process of the lower-level optimization model specifically includes the following steps: 1) For the sampling points in the optimal solution set output by the upper model, resample in the area that may contain the optimal solution and calculate the objective function The value of ; compared with the current optimal sampling point, if the objective function value decreases, the optimization target of the corresponding lower optimization model is established. If increases, the optimal solution set will be updated, otherwise it will remain unchanged; 2) Based on the fitness function modified by simulated annealing, call the upper layer to output the optimal solution set; The simulated annealing algorithm is used to improve the genetic algorithm to achieve continuous correction of fitness during the optimization process, which is expressed as follows: (17) Where: fit(x) im is the fitness function modified based on simulated annealing, k is the simulated annealing function coefficient, which is less than 1.0, t is the evolutionary generation of the genetic algorithm, T0 is the initial temperature of simulated annealing, which is the same order of magnitude as the objective function, and f(x) is the objective function value of individuals in the population; 3) Repeat the sampling process, continuously call the upper-level model, and iteratively calculate to narrow the optimal solution set until the global optimal solution is found.

2. The method for low-carbon analysis and optimization planning of industrial parks according to claim 1, characterized in that: In step (2), the constraints satisfied by the upper-level planning model are energy structure constraints, i.e., the installed capacity constraints of each energy unit and the balance constraints of electric, heating and cooling load supply and demand; In the optimization objective of the upper-level planning model, the annualized planning and construction costs and equipment operation and maintenance costs in the annual discounted costs of the entire life cycle are obtained by converting the planning and construction costs and equipment operation and maintenance costs according to the annual interest rate; the planning and construction costs include the costs of system preliminary planning and design and energy unit equipment procurement, and the equipment operation and maintenance costs include the costs of installation verification and commissioning confirmation, operating energy consumption, equipment maintenance, energy purchase and carbon footprint costs; the scrapping costs include the annual depreciation of fixed assets and the final residual value recovery costs; the lower-level optimization model meets the following constraints: time constraints, energy transmission loss constraints, and planning land constraints; Emission reduction options include: obtaining carbon quotas, building new carbon sinks, nationally mandated green and low-carbon behaviors, improving energy efficiency, reducing material use, using environmentally friendly materials, developing product recyclability, reducing waste, extending product life, and improving product availability.

3. The low-carbon analysis and optimization planning method for industrial parks according to claim 1 is characterized in that: In step (2), the method for establishing the planning and construction cost model, equipment operation and maintenance cost model, and scrapping cost model of each energy router and equipment unit is as follows: 1) Planning and construction cost and equipment operation and maintenance cost model （3） （4） Where: is the planned construction cost of the i-th type of equipment, is the operation and maintenance cost of the i-th type of equipment; C in.i is the annualized planned construction cost of the i-th type of equipment; R is the discount rate; l is the expected operating life of the equipment; U i P is the unit power construction cost of the i-th type of equipment; i Plan the rated load for equipment of category i; 2) Scrap cost model The annualized scrap cost is obtained by converting the accumulated depreciation of fixed assets into the equipment investment cost using the average life method according to the corresponding proportional coefficient. The difference between the scrap disposal cost and the scrap asset residual value recovery income determines whether the accumulated depreciation of fixed assets is a positive or negative value when included in the total cost. （5） Where: is the scrapping cost of the i-th type energy unit, —Annualized retirement cost of energy unit type i.

4. The method for low-carbon analysis and optimization planning of industrial parks according to claim 1, characterized in that: In step (2), the balance constraints include electrical balance constraints, thermal balance constraints, and cold balance constraints, which are as follows: Electricity load supply and demand balance constraints （12） Where: The power supply for category i equipment, is the electricity demand; Heat load supply and demand balance constraints （13） Where: is the heat supply of the equipment of type i, is the heat demand; Cooling load supply and demand balance constraints （14） Where: is the cooling capacity of the i-th type equipment, The cooling demand.

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