Optimization Modeling for Regional Energy System Management Coupled with Energy–Water Nexus and Carbon Emission Reduction: A Case Study

环境科学 可再生能源 水能关系 温室气体 Nexus(标准) 环境经济学 发电 环境工程 自然资源经济学 计算机科学 功率(物理) 工程类 经济 生态学 物理 量子力学 嵌入式系统 电气工程 生物
作者
Jiliang Zhen,Yang Zhang,Xiaoran Liu,Guohe Huang,Jingbo Li
出处
期刊:Environmental Engineering Science [Mary Ann Liebert, Inc.]
卷期号:40 (3): 114-126 被引量:7
标识
DOI:10.1089/ees.2022.0204
摘要

Under global climate change and resource crisis, the correlation and restriction between energy, water, and carbon emission are increasingly prominent, which has posed great challenges for the sustainable development of energy systems. This article develops a dual-interval stage-fuzzy credibility-constrained programming model for regional energy system management problems with energy–water nexus (EWN) and carbon emission reduction, in which uncertainties expressed as discrete intervals, probability distributions, and dual-interval fuzzy numbers can be addressed. It can provide a trade-off among economic cost, system risk, and environmental objective. Then, the hybrid programming method is applied to a case of Guangdong Province, China to verify its applicability, where a series of scenarios with respect to water resources and carbon emission constraints are explored and examined. The robust solutions of optimal strategies for electricity generation, capacity expansion, water supply, and carbon capture are investigated and obtained. Results disclose that different scenarios associated with water resource availability and carbon emissions would deeply affect the EWN system planning: (1) Compared with low carbon emission standard, the proportion of renewable power generation under high carbon emission scenario would increase by [1.39, 1.68] %, [1.69, 2.35] %, and [1.49, 1.94] % in periods 1–3, respectively. (2) Scarce water availability scenario would restrict coal-fired power generation and simulate the development of renewable energy. These findings can be useful for decision makers to gain deep insights into energy system management in a sustainable way considering energy structure adjustment, multiple uncertainties, EWN, and carbon emission reduction.
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