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Value chain optimization of hydroelectric power generation systems

Value chain optimization of hydroelectric power generation systems
水力发电系统价值链优化
批准号:
522126-2017
负责人:
Arsenault, Richard
金额:
$5.9万
依托单位国家:
加拿大
项目类别:
Collaborative Research and Development Grants
财政年份:
2021
资助国家:
加拿大
项目状态:
已结题
起止时间:
2021-01-01 至 2022-12-31

项目摘要

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中文摘要
翻译
水力发电是加拿大最重要的资产之一,覆盖了该国总电力需求的60%以上。在不久的将来,由于电动汽车的大规模营销,一些以石油为基础的需求将转向电力,并转向电动公共交通。这将对加拿大的电力需求产生补充压力。本项目旨在查明水力发电系统运行方面有待改进的领域,并开发工具来衡量拟议改进的效果。更具体地说,该项目旨在改善水力发电业务的价值链。这包括从短期和中期天气预报和观测到的气候学中收集尽可能多的信息,将这些信息转化为对未来水电站水库流入的最佳估计,然后从水库进行最佳放水。这个多步骤过程在本质上是随机的,为了达到目标,必须实施和改进概率方法。本项目的新颖之处在于对多种预测工具组合进行技能评估,并利用水电系统模拟器对改进进行量化。拟议的研究和模拟工具将开发和调查4个水库、6个发电站的水力发电系统,该系统由里约热内卢Tinto为其铝冶炼厂操作。该系统的衍生品也将被独立研究,例如通过增加或减少水库,这将允许将结果推广到不同的水电安排。
英文摘要
Hydroelectric power generation is one of Canada's most important assets, covering over 60% of the country's total electricity demand. In the near future, some of the oil-based demand will transition to electricity due to the mass-marketing of electric vehicles and shift to electric public transit. This will exert supplementary pressure on the electricity demand in Canada. This project aims to identify areas of improvement in the operation of hydroelectric power generation systems and develop tools to measure the effects of proposed improvements. More specifically, this project aims to improve the value chain of the hydroelectric power generation operations. This includes gathering as much information as possible from the short-term and medium-term weather forecasts and observed climatology, transforming that information into the best possible estimates of future inflows to hydropower reservoirs and then making the optimal water releases from the reservoirs. This multi-step process is stochastic in nature and probabilistic approaches will have to be implemented and improved upon to attain the objective. The novelty of this project lies in the skill evaluation of multiple combinations of forecasting tools and quantifying the improvements using a hydropower system simulator. The proposed research and simulation tools will be developed and investigated for a 4-reservoir, 6-generating station hydropower system in central Québec which is operated by Rio Tinto for its aluminium smelting plants. Derivatives of this system will also be independently investigated, such as by adding or removing reservoirs, which will allow generalizing the results to different hydropower arrangements.
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Pushing back the boundaries to automating the operational hydrological forecasts used in hydropower reservoir management.
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Pushing back the boundaries to automating the operational hydrological forecasts used in hydropower reservoir management.
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    RGPIN-2018-04872
  • 项目类别:
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  • 依托单位:
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  • 批准号:
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