A Stochastic Optimization Model for Carbon-Emission Reduction Investment and Sustainable Energy Planning under Cost-Risk Control

IF 6 1区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES Journal of Environmental Informatics Pub Date : 2020-03-04 DOI:10.3808/jei.202000428
L. Ji, G. Huang, D. Niu, Y. Cai, J. Yin
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引用次数: 55

Abstract

Restricted by conventional energy resources and environmental space, the sustainable development of urban power sector faces enormous challenges. Renewable energy generation and carbon capture and storage (CCS) are attractive technologies for reducing conventional energy resource consumption and improving CO2 emission mitigation. Considering the limitation of expensive investment cost on their wide application, a stochastic optimization model for the optimal design and operation strategy of regional electric power system is proposed to achieve conventional resource-consumption reduction and CO2 emission mitigation under cost-risk control. The hybrid method integrates interval two-stage stochastic programming with downside risk theory. It can not only effectively deal with the complex uncertainties expressed as discrete intervals and probability distribution, but also help decision-makers make cost-risk tradeoff under predetermined budget. The proposed model is applied in the electric power system planning of Zhejiang Province, an economically developed area with limited fossil energy resources. The influences of different resource and environmental policies on the investment portfolio and power system operation are analyzed and discussed under various scenarios. The results indicated that different policies would lead to different generation technology portfolios. The aggressive CO2 emission reduction policy could stimulate the development of CCS technology, and the electric power system would still heavily rely on coal resource, while the tough coal-consumption control policy could directly promote regional renewable energy development and electric power structure adjustment.
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成本-风险控制下碳减排投资与可持续能源规划的随机优化模型
受常规能源资源和环境空间的制约,城市电力行业的可持续发展面临巨大挑战。可再生能源发电和碳捕获与封存(CCS)是减少常规能源消耗和改善二氧化碳减排的有吸引力的技术。考虑到投资成本昂贵对其广泛应用的限制,提出了一种成本-风险控制下区域电力系统优化设计与运行策略的随机优化模型,以实现常规的资源消耗降低和CO2排放减缓。混合方法将区间两阶段随机规划与下行风险理论相结合。它不仅可以有效地处理离散区间和概率分布的复杂不确定性,而且可以帮助决策者在预先确定的预算下进行成本-风险权衡。将该模型应用于浙江省电力系统规划中,浙江省是经济发达地区,化石能源资源有限。分析和讨论了不同资源环境政策在不同情景下对投资组合和电力系统运行的影响。结果表明,不同的政策会导致不同的发电技术组合。积极的CO2减排政策会刺激CCS技术的发展,电力系统仍将严重依赖煤炭资源,而严格的煤耗控制政策则会直接促进区域可再生能源发展和电力结构调整。
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来源期刊
Journal of Environmental Informatics
Journal of Environmental Informatics ENVIRONMENTAL SCIENCES-
CiteScore
12.40
自引率
2.90%
发文量
7
审稿时长
24 months
期刊介绍: Journal of Environmental Informatics (JEI) is an international, peer-reviewed, and interdisciplinary publication designed to foster research innovation and discovery on basic science and information technology for addressing various environmental problems. The journal aims to motivate and enhance the integration of science and technology to help develop sustainable solutions that are consensus-oriented, risk-informed, scientifically-based and cost-effective. JEI serves researchers, educators and practitioners who are interested in theoretical and/or applied aspects of environmental science, regardless of disciplinary boundaries. The topics addressed by the journal include: - Planning of energy, environmental and ecological management systems - Simulation, optimization and Environmental decision support - Environmental geomatics - GIS, RS and other spatial information technologies - Informatics for environmental chemistry and biochemistry - Environmental applications of functional materials - Environmental phenomena at atomic, molecular and macromolecular scales - Modeling of chemical, biological and environmental processes - Modeling of biotechnological systems for enhanced pollution mitigation - Computer graphics and visualization for environmental decision support - Artificial intelligence and expert systems for environmental applications - Environmental statistics and risk analysis - Climate modeling, downscaling, impact assessment, and adaptation planning - Other areas of environmental systems science and information technology.
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