为应对全球气候治理背景下可再生能源消纳与多能负荷协同供给的技术挑战,提出一种新型的风电-光伏-熔盐储热-电加热器联合可再生能源热电联产系统。该系统可通过电加热-储热罐-汽轮机组联合调节风电光伏出力满足电负荷需求,并通过储热罐-换热器满足热负荷需求。基于能源枢纽建模理论,构建涵盖经济性（平准化能源成本）、可靠性（失负荷概率）、可持续性（弃风弃光率）及低碳性（全生命周期碳排放）的四维性能评价体系,建立多能流耦合约束下的高维多目标容量优化模型。利用非劣排序遗传算法（NSGA-Ⅲ）求解四维多目标问题,然后利用主成分分析（PCA）进行维度削减实现可视化,最后利用逼近理想点法（TOPSIS）决策得出最优权衡解。案例分析结果表明,所提热电联产系统的目标函数(ELCOE、LLSP、Rc、Ec)相较独立供电系统分别降低4.7%、31.8%、7.5%和26.4%。
Abstract
In order to alleviate the technical challenges of renewable energy accommodation and synergistic supply of multi-energy loads in the context of global climate governance, this paper proposes a wind-photovoltaic-molten salt thermal energy storage-electric heater hybrid renewable cogeneration system, which uses electric heater-thermal energy storage-power block to regulate wind and photovoltaic output and supply power load, as well as uses thermal energy storage-heat exchanger to supply heat load. This paper comprehensively considers the economy, reliability, energy efficiency and environmental performance to establish a many-objective capacity optimization model. The model aims to minimize levelized cost of energy, loss of power and heat supply probability, curtailment rate of wind and PV power and whole lifecycle equivalent carbon emissions as objectives, uses the non-dominated sorting genetic algorithm（NSGA-Ⅲ） to solve the four-objective problem, then uses principal component analysis to reduce dimension, finally uses TOPSIS decision-making method to obtain the optimal-compromise solution. The results of case study show that the objective function of the cogeneration system proposed in this paper is reduced by 4.7%, 31.8%, 7.5% and 26.4%, respectively, compared to the stand-alone power system.
关键词
风光互补热电联产系统 /
熔盐储热 /
高维多目标优化 /
主成分分析 /
TOPSIS决策
Key words
wind-solar cogeneration system /
molten salt thermal storage /
high-dimensional multi-objective optimization /
principal component analysis /
TOPSIS
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基金
中国大唐集团科学技术研究总院有限公司科技项目（DTKY-2025-20254）; 中国大唐集团有限公司科技项目（DTSN-2024-10233）
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