A method for dynamic simulation and optimization decision of low-carbon policy in industrial park based on energy-carbon-economy synergy
By constructing a coupled dynamic model of energy flow, carbon flow, and economic flow, and a multi-objective optimization algorithm, the problem of multi-dimensional evaluation in the formulation of low-carbon policies for industrial parks was solved, realizing dynamic simulation and optimization decision-making of policies, and improving the scientific nature and feasibility of policies.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NORTHEASTERN UNIV CHINA
- Filing Date
- 2026-05-11
- Publication Date
- 2026-06-09
AI Technical Summary
Existing technologies have limitations in the formulation and evaluation of low-carbon policies for industrial parks, including single-indicator evaluation, static analysis, economic effect analysis, insufficient policy combination optimization, and insufficient localization adaptability, making it difficult to achieve precise policy design and full-process decision support.
A coupled dynamic model of energy flow, carbon flow, and economic flow in industrial parks is constructed. A multi-dimensional collaborative evaluation index system and a multi-objective optimization algorithm are adopted, combined with a policy sandbox intelligent recommendation mechanism, to achieve dynamic simulation and optimization decision-making of low-carbon policies.
It enables multi-dimensional dynamic evaluation of low-carbon policies in industrial parks, accurately quantifies the comprehensive impact of policies on energy, carbon emissions and economic benefits, identifies systemic costs of the power grid, outputs the optimal policy combination, and improves the scientific nature and feasibility of policies.
Smart Images
Figure CN122175461A_ABST
Abstract
Claims
1. A dynamic simulation and optimization decision-making method for low-carbon policies in industrial parks based on energy-carbon-economic synergy, characterized in that, Includes the following steps: Step 1, Park Boundary Identification and Carbon Emission Responsibility Unit Delineation: Through boundary delineation algorithms, the park's boundaries are accurately delineated in multiple dimensions to clarify the park's carbon emission accounting boundaries and responsibility attribution; Step 2: Multi-source data collection and construction of energy-carbon-economy collaborative database in the park: Collect core micro data of the park from all dimensions, clean, correct, denoise, standardize and unify the time stamp of the collected raw data, remove invalid data and outliers, and construct the basic database of energy-carbon-economy collaborative database of the park through the fusion of multi-source heterogeneous data; Step 3: Construct a dynamic coupling simulation model of energy flow, carbon flow, and economic flow: Through the full-dimensional micro-core data of the park's energy-carbon-economic synergy basic database, track the temporal evolution of energy flow, carbon flow, and economic flow in the park, construct a dynamic simulation model of policies, and quantify the marginal changes brought about by policy implementation; Step 4: Construct an energy-carbon-economy synergistic evaluation model: Perform dimensionless processing on energy, carbon, and economic indicators under different policy scenarios, and obtain the park synergy index by weighted fusion calculation of weight coefficients to form a standardized policy comprehensive performance evaluation system; Step 5: Construct a policy net economic effect model: Define the accounting boundaries and scope of the direct costs, indirect benefits and systemic costs of policy implementation. Based on the policy quantification model and the energy substitution elasticity model, introduce the full life cycle economic cost-benefit analysis method to quantify the comprehensive economic effect of the marginal substitution rate of renewable energy for thermal power, construct a policy net economic effect accounting model, and evaluate the economic feasibility of a single policy or a combination of policies. Step 6: Establish a set of policy instrument variables and a policy scenario library: Based on the design requirements of the park's low-carbon policy, set policy instrument variables and clarify their reasonable value ranges, construct a standardized policy scenario library, and classify the data collected in Step 2 according to the policy scenario library to provide a standardized scenario basis for subsequent simulation analysis and optimization solutions; Step 7: Construct a multi-objective optimization model to solve the Pareto optimal frontier: Integrate multi-objective optimization algorithms, simulate the Pareto optimal frontier under different policy combinations, and set multi-dimensional constraints to find the global optimal balance point among the three major objectives of improving energy efficiency, achieving carbon emission reduction targets, and stable economic growth. Step 8: Establish a policy sandbox and intelligent recommendation mechanism: Develop a policy sandbox simulation environment that allows users to set their own constraints. Through machine learning prediction engines and reinforcement learning algorithms, it automatically recommends the optimal carbon reduction path that meets all constraints. Step 9: Visualization of Results and Interpretation of Decisions: Based on the full-process results of policy simulation in Step 6, multi-objective optimization in Step 7, and intelligent recommendation in Step 8, generate multi-dimensional visualization outputs to intuitively display the comprehensive performance of policy solutions in the three dimensions of energy, carbon, and economy, and mark the deviation of each indicator from the baseline scenario.
2. The dynamic simulation and optimization decision-making method for low-carbon policies in industrial parks based on energy-carbon-economic synergy as described in claim 1, characterized in that: The core data in step 2 includes enterprise energy consumption and cost data, distributed photovoltaic / wind power generation operation data, real-time carbon emission factor database of the power grid, enterprise financial statement data, and policy-related parameter data.
3. The dynamic simulation and optimization decision-making method for low-carbon policies in industrial parks based on energy-carbon-economic synergy as described in claim 1, characterized in that, Step 3 specifically involves constructing three sub-models based on the park's energy-carbon-economic synergy database. These include an energy flow model that describes the entire process of energy purchase, conversion, transmission, distribution, consumption, and substitution in the park; a carbon flow model that depicts the mapping relationship between energy consumption and carbon emissions in the park, as well as the emission reductions generated by low-carbon substitution measures; and an economic flow model that quantifies the dynamic changes in investment, costs, benefits, profits, and industrial added value in the park before and after policy implementation. By temporally coupling the energy flow model, carbon flow model, and economic flow model along a unified time axis, a dynamic policy simulation model is constructed to quantify the marginal changes brought about by policy implementation. The model expression is as follows: ； in: The park's energy-carbon-economy synergy index is defined by a higher value, which indicates better synergy in the park's energy efficiency improvement, carbon emission reduction effectiveness, and economic benefit guarantee. The change in the industrial added value of the park is a core economic output indicator used to measure the new value created by the park's production activities. This represents the change in the park's overall energy consumption, covering all types of energy, including electricity and heat. The change in the total carbon emissions of the park is a core environmental impact indicator. The change in the total profit of enterprises in the park is a direct indicator of the park's economic vitality. This represents the change in the total cost of enterprises in the park, including energy costs, pollution control costs, and equipment investment depreciation. , and For policy weighting coefficients, satisfying The coefficient is flexibly set by policymakers based on policy guidance.
4. The dynamic simulation and optimization decision-making method for low-carbon policies in industrial parks based on energy-carbon-economic synergy as described in claim 1, characterized in that, In step 4, the weighting coefficients are determined using a combination of the Analytic Hierarchy Process (AHP) and the entropy weighting method. Subjective weights are determined using the AHP based on policy guidance and expert experience, while objective weights are calculated using the entropy weighting method based on historical operational data of the park. The final combined weighting formula is as follows: ； in: For the final combined weights; This is a subjective weighting adjustment coefficient, which can be flexibly adjusted according to the development stage of the park; These are the subjective weights obtained from the Analytic Hierarchy Process (AHP). The objective weights obtained by the entropy weight method; By flexibly adjusting the weighting coefficients, the system can adapt to different regions and development stages of industrial parks, generating standardized evaluation results that meet localized needs.
5. The dynamic simulation and optimization decision-making method for low-carbon policies in industrial parks based on energy-carbon-economic synergy as described in claim 1, characterized in that, The basic model expression for the net economic effect of the policy in step 5 is: ； in: For the net economic effect of the policy, if This indicates that the policy is economically feasible throughout its entire life cycle; For energy-saving benefits, ,in For comprehensive energy prices; Carbon asset revenue includes income from the sale of surplus carbon emission allowances, savings from carbon tax / carbon emission compliance costs, green electricity premium revenue, and green special subsidies, quantifying the economic value of carbon reduction behavior; New investment costs refer to the capital expenditures incurred by enterprises in response to policy requirements, such as purchasing energy-saving equipment, investing in photovoltaic / wind power projects, and constructing energy storage facilities. The systemic cost of the power grid is used to quantify the indirect system-side costs generated by the grid connection of renewable energy. Specifically, it includes the cost of additional standby thermal power unit capacity to absorb fluctuating renewable energy, the amortization cost of grid upgrades and renovations, and the economic losses caused by the imbalance between power supply and demand. Furthermore, the model can be extended to a full-scope cost-benefit accounting form: ； in: For the special revenue from green electricity trading; For the benefits of special policy subsidies; To account for equipment operation and maintenance costs, and to achieve full-caliber accounting of all economic inflows and outflows throughout the entire policy lifecycle.
6. The method for dynamic simulation and optimization decision-making of low-carbon policies in industrial parks based on energy-carbon-economic synergy as described in claim 1, characterized in that, The policy tool variables in step 6 include carbon tax rate, carbon quota tightening ratio, green electricity subsidy amount, energy storage subsidy ratio, equipment retrofit subsidy ratio, time-of-use electricity price parameters, and demand response compensation parameters.
7. The method for dynamic simulation and optimization decision-making of low-carbon policies in industrial parks based on energy-carbon-economic synergy as described in claim 1, characterized in that, Step 6 includes the policy scenario library, which includes: the baseline scenario BAU with no new policies, single policy scenarios, and combined policy scenarios. The single policy scenarios include carbon quota tightening S1, green electricity subsidies / green electricity premiums S2, and energy efficiency improvement / equipment retrofit subsidies S4. The combined policy scenarios include carbon quotas + green electricity subsidies S5 and carbon quotas + green electricity subsidies + energy storage incentives S6.
8. The method for dynamic simulation and optimization decision-making of low-carbon policies in industrial parks based on energy-carbon-economic synergy as described in claim 1, characterized in that, The objective function of the multi-objective optimization problem in step 7 is: ； in: The core evaluation indicator in existing research is the minimization of comprehensive energy consumption intensity, with the optimization objective being the minimum comprehensive energy consumption intensity. Extend the indicators to the environmental dimension, with the optimization objective of minimizing carbon emission intensity; The core economic indicator is the maximization of return on assets as the optimization objective. The multi-dimensional constraints include total investment budget constraints for the park, regional power grid absorption capacity constraints, minimum economic growth requirements, average return on assets threshold constraints for enterprises, rigid constraints on emission reduction targets, and investment payback period constraints.
9. The dynamic simulation and optimization decision-making method for low-carbon policies in industrial parks based on energy-carbon-economic synergy as described in claim 1, characterized in that, The core constraint expression for the policy sandbox in step 8 is: ； in: The policy tool variable vector includes carbon emission intensity, carbon tax rate, green electricity subsidy amount, and energy storage electricity price policy; The feature vector for enterprises / parks includes park size, enterprise ownership, industry type, and geographical location, used to characterize the heterogeneity of policy transmission; To describe the complex functional relationship in which policies affect optimization objectives through enterprise / park characteristics; The vector of resource and economic constraints includes the total investment budget of the park, the regional power grid's absorption capacity, and the minimum economic growth requirement; and These are the lower and upper limits of the constraint conditions, respectively.
10. The method for dynamic simulation and optimization decision-making of low-carbon policies in industrial parks based on energy-carbon-economic synergy as described in claim 1, characterized in that, Step 9 provides multi-dimensional visualization outputs including an energy-carbon-economic synergy radar chart, a net economic effect change curve, a marginal emission reduction cost bar chart, a Pareto frontier distribution chart, and a multi-scenario indicator comparison bar chart. Simultaneously, it outputs sensitivity analysis of key influencing factors, explanations of constraint fulfillment, and policy implementation priorities.