基于碳排放的水电建设项目多目标决策模型与策略分析

Multi-objective Optimization Problem for Hydroelectric Construction Project and Strategy Analysis Based on Carbon Emissions

摘要:

在全球变暖的趋势下,积极发展水电项目是实现我国节能减排的重要手段。随着水电项目的快速发展,建设期间所产生的碳排放是非常关键的,不仅量大,而且对于后期碳排放有很大影响。明确建设期的碳排放影响因素,发展低碳水电建设项目管理决策方法,具有重要的理论与现实意义。本文以锦屏二级水电建设项目为案例,提出了其建设项目决策中的工期—成本—碳排放平衡的问题,并建立多目标决策模型,并提出了求解算法。提出的改进自适应性混和遗传算法用以求解该多目标优化问题,设计了单点交叉和变异的修复式策略来避免不可行解的产生。通过锦屏二级水电建设项目的案例说明了模型和算法的有效性和合理性,通过灵敏度分析以及与其他算法的比较说明该优化方法的高效性、灵活性和适应性。实证结果表明,降低待工时间、提高使用效率是降低碳排放的关键因素,揭示了在方案优化过程中,碳排放和成本、进度间的变化机理；可产生多个帕累托最优解；决策者可根据三个目标的偏好选择最终方案。

Abstract:

Currently, many hydroelectric projects will be constructed in the next twenty years to respond to the increasing demand for clean energy and commitment of carbon reduction. Along with the rapid development of hydroelectric construction projects, more attention needs to paid more attention to the emissions from constructions because it is important to later stage and are worth evaluating. To meet the need of low carbon construction development, there is a need to study the management decision methods for carbon emissions of construction, analyzing emission factors. A discrete time-cost-carbon emission trade-off problem is presented and its corresponding multi-objective modeling is established. Furthermore, an improved adaptive-hybrid genetic algorithm is developed to find feasible solutions. The one-point crossover and repairing strategy for mutations are designed to avoid infeasible solutions. And then, the Jinping-II Hydroelectric Project is used as a case study to demonstrate the practicality and efficiency of the model. Results and a sensitivity analysis are presented to highlight the performance of the optimization method, which proves to be very effective and efficient compared to other algorithms. The result indicated that the most effective way to reduce the carbon emission of construction processes is to lower the idling time and promote the operation efficiency and proposed the relativity carbon emission quantity, time and cost; the model and algorithm could be used to find proper Pareto optimization solutions which can help the decision maker with different objective preference to make decision.