CN-122022601-A - Low-carbon park collaborative optimization method and system based on dynamic electric carbon index evaluation

CN122022601ACN 122022601 ACN122022601 ACN 122022601ACN-122022601-A

Abstract

The invention discloses a low-carbon park collaborative optimization method and a system based on dynamic electric carbon index evaluation, wherein the method comprises the steps of constructing a low-carbon park comprehensive energy system model, covering an electric power, heating power, a gas subsystem and an oxygen-enriched combustion carbon capture system; the method comprises the steps of collecting multi-source electricity/carbon data of a park, constructing a dynamic carbon emission factor model based on a tracing of an hour-level power generation structure, calculating three-dimensional indexes of park low carbonization evaluation, namely a direct and indirect carbon emission comprehensive energy consumption ratio gamma, a direct and indirect carbon emission quantity ratio delta and a carbon emission counteracting proportion epsilon, judging the park as one of a V level and an I level according to a preset grading reference threshold, and matching with an energy collaborative management strategy. The invention solves the problems of hysteresis and precision of carbon emission accounting through dynamic monitoring and accurate grading.

Inventors

ZHANG GUIHONG
REN JUNZHI
ZENG YUAN
QIN CHAO
SUN BING
JIA HONGJIE

Assignees

天津大学

Dates

Publication Date: 20260512
Application Date: 20260414

Claims (10)

1. A low-carbon park collaborative optimization method based on dynamic electric carbon index evaluation is characterized by comprising the following steps: S1, constructing a low-carbon park comprehensive energy system model, wherein the comprehensive energy system model comprises an electric subsystem, a thermodynamic subsystem, a gas subsystem and an oxygen-enriched combustion carbon capture system, and acquiring multi-source electricity/carbon data of the park; s2, based on the traceability of the hour-level power generation structure, constructing a power supply dynamic carbon emission factor model, and calculating a real-time carbon emission factor of the power purchased outside the park; S3, calculating three-dimensional indexes of park low carbonization evaluation based on the comprehensive energy system model and the real-time carbon emission factor of the park outsourcing power, wherein the three-dimensional indexes comprise a direct carbon emission and indirect carbon emission comprehensive energy consumption ratio gamma, a direct carbon emission and indirect carbon emission ratio delta and a carbon emission offset ratio epsilon; s4, presetting a grading reference threshold value of each dimension, and judging the green low-carbon grade of the park according to the comparison result of the three-dimensional index obtained through calculation and the grading reference threshold value; and S5, according to the determined green low-carbon level, automatically matching a corresponding energy collaborative management strategy, and adjusting equipment operation parameters or planning configuration in the comprehensive energy system model.
2. The low-carbon park collaborative optimization method based on dynamic electric carbon index evaluation according to claim 1, wherein in step S2, the power supply dynamic carbon emission factor model includes a provincial grid average carbon emission factor and a residual carbon emission factor; The provincial power grid average carbon emission factor is obtained by weighting calculation based on the provincial fossil fuel combustion emission, the net power-in emission and the regional power grid interactive electric quantity; the residual carbon emission factor is an average emission factor of the power grid after the renewable energy electric quantity is removed, and is used for representing the electric power carbon intensity after the environmental benefit is deducted; Real-time carbon emission factor for the off-shore power The calculation formula of (2) is as follows: ; Wherein, the Is the remaining carbon emission factor at time t, The average carbon emission factor of the provincial power grid at the time t; For the proportion of the conventional electricity purchased externally at time t, For the proportion of outsourcing green electricity with environmental rights at the time t, The proportion of outsourcing mixed power at t time is satisfied 。
3. The low-carbon park collaborative optimization method based on dynamic electric carbon index evaluation according to claim 1, wherein the three-dimensional index in step S3 is calculated by the following method: the ratio of direct carbon emission to indirect carbon emission integrated energy consumption is gamma=c 1 /C 2 , the ratio of direct carbon emission to indirect carbon emission is delta=e scope1 /(E scope2 +E scope3 ), the carbon emission offset ratio epsilon=o sinks /(E scope1 +E scope2 +E scope3 ); Wherein C 1 is the comprehensive energy consumption of fossil fuel and raw material consumption, C 2 is the comprehensive energy consumption of outsourcing electric power and heating power, E scope1 is the direct carbon emission, E scope2 is the indirect carbon emission of outsourcing electric power and heating power, E scope3 is the other indirect carbon emission, O sinks is the carbon offset, and the outsourcing electric power and heating power indirect carbon emission E scope2 is obtained by using outsourcing electric quantity And real-time carbon emission factor Multiplying the two times by each other hour and accumulating the two times.
4. The method for collaborative optimization of a low carbon park based on dynamic electric carbon index evaluation according to claim 1, wherein the process of determining the green low carbon grade of the park in step S4 is to set a reference value γ th of γ, a reference value δ th of δ, and a reference value ε th of ε, wherein ε th defaults to 100%, meaning that the carbon offset is equal to the total emission, to achieve a net zero emission; if gamma > gamma th , rating as V grade; if gamma is less than or equal to gamma th and delta is more than delta th , the grade IV is rated; If gamma is less than or equal to gamma th , delta is less than or equal to delta th , epsilon is less than or equal to epsilon th , the grade III is rated; Grade II if epsilon th < epsilon < 200%; if epsilon is more than or equal to 200 percent, the grade I is rated.
5. The method for collaborative optimization of a low-carbon park based on dynamic electric carbon index evaluation according to claim 4, wherein the energy collaborative management strategy in step S5 comprises one or more of electric energy substitution, process improvement, green electricity trade proportioning adjustment and carbon offset configuration, and the automatic matching of the corresponding energy collaborative management strategy comprises: When the evaluation is V-level, generating an electric energy substitution strategy for improving the electric energy duty ratio; When the evaluation is IV level, generating an energy-saving strategy for improving the process and reducing the comprehensive energy consumption; when the evaluation is grade III, generating a scheduling strategy for increasing the green electricity duty ratio and the green electricity transaction amount; When rated as class II, a configuration strategy is generated that increases carbon offset project investment and carbon asset reserves.
6. The low-carbon park collaborative optimization method based on dynamic electric carbon index evaluation according to claim 1, wherein the comprehensive energy system model adopts a multi-target double-layer planning model, and the step S5 is realized by adjusting equipment operation parameters or planning configuration in the comprehensive energy system model through solving the multi-target double-layer planning model; The upper planning layer aims at optimizing the full life cycle economy of the comprehensive energy system, and the optimization variables comprise the installed capacity of new energy equipment, the energy storage configuration capacity and the configuration capacity of the oxygen-enriched combustion carbon capture system; The lower operation layer is aimed at minimizing the operation cost and the carbon emission, and simulates energy production, conversion and consumption strategies of the park in different periods based on the real-time carbon emission factors calculated in the step S2; and the double-layer planning model converts the energy collaborative management strategy generated in the step S5 into a constraint condition input model for solving.
7. The method for collaborative optimization of a low-carbon park based on dynamic electric carbon index evaluation according to claim 6, wherein in the lower operating layer, an operating admission coefficient is introduced for the operating control of the oxycombustion carbon capture system The operation permission coefficient And real-time carbon emission factor And real-time electricity price The oxygen-enriched combustion carbon capture system is used for increasing the carbon capture amount in a period of low power grid carbon intensity and low electricity price, and comprises an air separation unit, an oxygen-enriched combustion unit and a carbon dioxide compression and purification unit.
8. A low-carbon park collaborative optimization system based on dynamic electric carbon index evaluation, which is based on the low-carbon park collaborative optimization method based on dynamic electric carbon index evaluation according to any one of claims 1-7, and is characterized by comprising: The system comprises a data acquisition and modeling module, a data acquisition and modeling module and a data processing module, wherein the data acquisition and modeling module is used for constructing a comprehensive energy system model of a low-carbon park, the comprehensive energy system model comprises an electric subsystem, a thermodynamic subsystem, a gas subsystem and an oxygen-enriched combustion carbon capture system, and acquiring multisource electric/carbon data of the park; The dynamic factor calculation module is used for constructing a power supply dynamic carbon emission factor model based on the traceability of the hour-level power generation structure and calculating the real-time carbon emission factor of the power purchased outside the park; The evaluation index calculation module is used for calculating three-dimensional indexes of park low carbonization evaluation based on the comprehensive energy system model and the real-time carbon emission factor of the park outsourcing power, wherein the three-dimensional indexes comprise a direct carbon emission and indirect carbon emission comprehensive energy consumption ratio gamma, a direct carbon emission and indirect carbon emission ratio delta and a carbon emission offset ratio epsilon; the grade judging module is used for presetting a grading reference threshold value of each dimension and judging green low-carbon grade of the park according to the comparison result of the three-dimensional index obtained through calculation and the grading reference threshold value; and the decision optimization module is used for automatically matching the corresponding energy collaborative management strategy according to the determined green low-carbon level and adjusting the equipment operation parameters or planning configuration in the comprehensive energy system model.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method for co-optimization of low carbon parks based on dynamic electric carbon indicator assessment according to any one of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements the low carbon park co-optimization method based on dynamic electric carbon index assessment of any of claims 1 to 7.

Description

Low-carbon park collaborative optimization method and system based on dynamic electric carbon index evaluation Technical Field The invention relates to the technical fields of comprehensive energy system management, carbon emission accounting and evaluation, in particular to a low-carbon park collaborative optimization method and system based on dynamic electric carbon index evaluation. Background The industrial park is used as an important unit for industrial aggregation and energy consumption, and the low-carbonization transformation of the industrial park has a key meaning for realizing energy conservation and emission reduction of the whole society. Currently, collaborative optimization operation technology of a low-carbon park comprehensive energy system (INTEGRATED ENERGY SYSTEM, IES) has become a research hotspot. In the aspect of a low-carbon evaluation system of a park, the current evaluation system focuses on post-hoc authentication and statistics, and lacks a set of quantitative grading decision model capable of directly guiding planning and operation adjustment of an energy system of the park. Most of the existing optimization technologies only pay attention to how to operate equipment, but cannot dynamically monitor and accurately grade, and cannot solve the problems of carbon emission accounting hysteresis and precision and further scientifically optimize the technical scheme of carbon emission. In view of the foregoing, there is a need in the art to construct a low-carbon park integrated energy system operation technology system integrating fine monitoring, multi-dimensional grading and collaborative optimization. Disclosure of Invention The invention aims to overcome the defects in the prior art and provides a low-carbon park collaborative optimization method and system based on dynamic electric carbon index evaluation. The invention aims at realizing the following technical scheme: A low-carbon park collaborative optimization method based on dynamic electric carbon index evaluation comprises the following steps: S1, constructing a low-carbon park comprehensive energy system model, wherein the comprehensive energy system model comprises an electric subsystem, a thermodynamic subsystem, a gas subsystem and an oxygen-enriched combustion carbon capture system, and acquiring multi-source electricity/carbon data of the park; s2, based on the traceability of the hour-level power generation structure, constructing a power supply dynamic carbon emission factor model, and calculating a real-time carbon emission factor of the power purchased outside the park; S3, calculating three-dimensional indexes of park low carbonization evaluation based on the comprehensive energy system model and the real-time carbon emission factor of the park outsourcing power, wherein the three-dimensional indexes comprise a direct carbon emission and indirect carbon emission comprehensive energy consumption ratio gamma, a direct carbon emission and indirect carbon emission ratio delta and a carbon emission offset ratio epsilon; s4, presetting a grading reference threshold value of each dimension, and judging the green low-carbon grade of the park according to the comparison result of the three-dimensional index obtained through calculation and the grading reference threshold value; and S5, according to the determined green low-carbon level, automatically matching a corresponding energy collaborative management strategy, and adjusting equipment operation parameters or planning configuration in the comprehensive energy system model. Further, in step S2, the power supply dynamic carbon emission factor model includes an average carbon emission factor and a residual carbon emission factor of the provincial grid; The provincial power grid average carbon emission factor is obtained by weighting calculation based on the provincial fossil fuel combustion emission, the net power-in emission and the regional power grid interactive electric quantity; the residual carbon emission factor is an average emission factor of the power grid after the renewable energy electric quantity is removed, and is used for representing the electric power carbon intensity after the environmental benefit is deducted; Real-time carbon emission factor for the off-shore power The calculation formula of (2) is as follows: ; Wherein, the Is the remaining carbon emission factor at time t,The average carbon emission factor of the provincial power grid at the time t; For the proportion of the conventional electricity purchased externally at time t, For the proportion of outsourcing green electricity with environmental rights at the time t,The proportion of outsourcing mixed power at t time is satisfied。 Further, in step S3, the three-dimensional index is calculated by: the ratio of direct carbon emission to indirect carbon emission integrated energy consumption is gamma=c 1/C2, the ratio of direct carbon emission to indirect carbon emission is delta=e scope1/(Esc