Surrogate Models for Maximizing Net Present Value of Renewable Energy Sources
最大化可再生能源净现值的替代模型
基本信息
- 批准号:485500-2015
- 负责人:
- 金额:$ 1.82万
- 依托单位:
- 依托单位国家:加拿大
- 项目类别:Engage Grants Program
- 财政年份:2015
- 资助国家:加拿大
- 起止时间:2015-01-01 至 2016-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Hydro-Québec supplies electricity to customers in remote off-grid locations by operating diesel plants. Cost
and environmental concerns motivate the consideration of using several types of renewable energy sources. The
optimal combination of different energy sources can be determined by solving an optimization problem to
maximize the net present value (NPV) generated by the off-grid electrical network over its lifetime while
accounting for construction, fuel, maintenance and dismantling costs. However, this requires numerical
simulations that are associated with high computational cost and discontinuities in both functions and
variables, which does not allow the computation of gradients. IREQ has tried conventional techniques to
address these challenges in order to solve the simulation-based optimization problem in practical time frames,
but has not had any success.
This project aims at developing and implementing a surrogate-assisted, derivative-free optimization framework
to alleviate these challenges. We will develop surrogate models that are based on Kernel Smoothing (KS) and
Radial Basis Functions (RBF) and exploit our knowledge of the problem and its structure. These surrogate
models will then be integrated with a particular class of derivative-free optimization algorithms termed
Mesh-Adaptive Direct Search (MADS). Our optimization framework will use the surrogate models to explore
the design space locally and to accelerate local convergence, and will use the expensive simulations for final
decision-making at each iteration.
The outcome of this research will provide Hydro-Québec with a powerful tool for off-grid network
optimization that can consider renewable energy sources to offer economically viable, robust and
environmentally benign energy solutions for isolated communities.
魁北克水电通过运营柴油发电厂向偏远离网地点的客户供电。成本
对环境的担忧促使人们考虑使用几种类型的可再生能源。这个
不同能源的最佳组合可以通过求解一个优化问题来确定
使离网电网在其生命周期内产生的净现值(NPV)最大化
核算建筑、燃料、维护和拆卸费用。然而,这需要数值计算
模拟与较高的计算成本和函数和
变量,这不允许计算渐变。IREQ已经尝试了传统技术来
解决这些挑战以便在实际时间范围内解决基于模拟的优化问题,
但并未取得任何成功。
该项目旨在开发和实现一个代理辅助的、无需派生的优化框架
来缓解这些挑战。我们将开发基于核平滑(KS)和
径向基函数(RBF),并利用我们对问题及其结构的知识。这些代孕妈妈
然后将模型与一类特殊的无导数优化算法集成在一起,称为
网格自适应直接搜索(MADS)。我们的优化框架将使用代理模型来探索
设计空间的局部收敛和加速局部收敛,并将使用昂贵的仿真进行最终
在每一次迭代中做出决策。
本文的研究成果将为魁北克水电离网提供有力的工具
可以考虑可再生能源的优化,以提供经济上可行、强大和
为与世隔绝的社区提供环境友好的能源解决方案。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Kokkolaras, Michael其他文献
Optimization of Infectious Disease Prevention and Control Policies Using Artificial Life
- DOI:
10.1109/tetci.2021.3107496 - 发表时间:
2021-09-08 - 期刊:
- 影响因子:5.3
- 作者:
Al Handawi, Khalil;Kokkolaras, Michael - 通讯作者:
Kokkolaras, Michael
Design Optimization of Tumor Vasculature-Bound Nanoparticles
- DOI:
10.1038/s41598-018-35675-y - 发表时间:
2018-12-11 - 期刊:
- 影响因子:4.6
- 作者:
Chamseddine, Ibrahim M.;Frieboes, Hermann B.;Kokkolaras, Michael - 通讯作者:
Kokkolaras, Michael
Scalable Set-Based Design Optimization and Remanufacturing for Meeting Changing Requirements
- DOI:
10.1115/1.4047908 - 发表时间:
2021-02-01 - 期刊:
- 影响因子:3.3
- 作者:
Al Handawi, Khalil;Andersson, Petter;Kokkolaras, Michael - 通讯作者:
Kokkolaras, Michael
Kokkolaras, Michael的其他文献
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{{ truncateString('Kokkolaras, Michael', 18)}}的其他基金
Data-driven optimization for enhanced computational engineering design
用于增强计算工程设计的数据驱动优化
- 批准号:
RGPIN-2018-05298 - 财政年份:2022
- 资助金额:
$ 1.82万 - 项目类别:
Discovery Grants Program - Individual
Digital multidisciplinary analysis and design optimization platform for aeroderivative gas turbines
航改燃气轮机数字化多学科分析与设计优化平台
- 批准号:
513922-2017 - 财政年份:2021
- 资助金额:
$ 1.82万 - 项目类别:
Collaborative Research and Development Grants
Data-driven optimization for enhanced computational engineering design
用于增强计算工程设计的数据驱动优化
- 批准号:
RGPIN-2018-05298 - 财政年份:2021
- 资助金额:
$ 1.82万 - 项目类别:
Discovery Grants Program - Individual
Digital multidisciplinary analysis and design optimization platform for aeroderivative gas turbines
航改燃气轮机数字化多学科分析与设计优化平台
- 批准号:
513922-2017 - 财政年份:2020
- 资助金额:
$ 1.82万 - 项目类别:
Collaborative Research and Development Grants
Data-driven optimization for enhanced computational engineering design
用于增强计算工程设计的数据驱动优化
- 批准号:
RGPIN-2018-05298 - 财政年份:2020
- 资助金额:
$ 1.82万 - 项目类别:
Discovery Grants Program - Individual
Digital multidisciplinary analysis and design optimization platform for aeroderivative gas turbines
航改燃气轮机数字化多学科分析与设计优化平台
- 批准号:
513922-2017 - 财政年份:2019
- 资助金额:
$ 1.82万 - 项目类别:
Collaborative Research and Development Grants
Data-driven optimization for enhanced computational engineering design
用于增强计算工程设计的数据驱动优化
- 批准号:
RGPIN-2018-05298 - 财政年份:2019
- 资助金额:
$ 1.82万 - 项目类别:
Discovery Grants Program - Individual
Data-driven optimization for enhanced computational engineering design
用于增强计算工程设计的数据驱动优化
- 批准号:
RGPIN-2018-05298 - 财政年份:2018
- 资助金额:
$ 1.82万 - 项目类别:
Discovery Grants Program - Individual
Digital multidisciplinary analysis and design optimization platform for aeroderivative gas turbines
航改燃气轮机数字化多学科分析与设计优化平台
- 批准号:
513922-2017 - 财政年份:2018
- 资助金额:
$ 1.82万 - 项目类别:
Collaborative Research and Development Grants
Digital multidisciplinary analysis and design optimization platform for aeroderivative gas turbines
航改燃气轮机数字化多学科分析与设计优化平台
- 批准号:
513922-2017 - 财政年份:2017
- 资助金额:
$ 1.82万 - 项目类别:
Collaborative Research and Development Grants
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