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Hydropower Generation Optimization i...

Dogan, Mustafa Sahin.

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Hydropower Generation Optimization in the Era of Renewables and Climate Change.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Hydropower Generation Optimization in the Era of Renewables and Climate Change./
Author:	Dogan, Mustafa Sahin.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	144 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-04, Section: A.
Contained By:	Dissertations Abstracts International81-04A.
Subject:	Water resources management. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13884391
ISBN:	9781085795951

Hydropower Generation Optimization in the Era of Renewables and Climate Change.
Dogan, Mustafa Sahin.

Hydropower Generation Optimization in the Era of Renewables and Climate Change. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 144 p.

Source: Dissertations Abstracts International, Volume: 81-04, Section: A.

Thesis (Ph.D.)--University of California, Davis, 2019.

This item must not be sold to any third party vendors.

Effects of expanding solar energy and non-stationary climate conditions on hydropower generation are explored with a hybrid linear programming-nonlinear programming (LP-NLP) hydropower optimization model. Additional operating algorithms are developed for pumped-storage hydropower optimization. The hybrid LP-NLP model is a reservoir operations model with an objective to maximize overall hydropower revenue. LP models are fast but less accurate, while NLP models are slow but have better nonlinear system representation. In a sequential optimization, the model solves the problem first with a linear approximation, then initializes the NLP model with the LP solution to reduce NLP iterations and runtime. LP and NLP models also can be used stand-alone. Energy prices are highly correlated with net energy load. Starting 2013, California's solar generation has been increasing to meet its renewable energy targets. As solar generation increases, net load decreases, reducing energy prices during solar generation hours. As a result, dispatchable hydropower becomes less profitable during mid-day hours, shifting hydropower generation to hours 6-10 in the morning and increasing evening peak generation. In the dry season with less inflow availability and higher evening peak prices, operations focus mostly on evening peak hours. Climate change effects on long-term hydropower planning from 2015 to 2100 with monthly time-step are evaluated with the LP model only as runtime of the NLP model significantly increases with large model sizes of this period. With climate change, temperature increases, more winter precipitation falls as rainfall rather than snowfall, and timing of snowmelt runoff shifts earlier. These changes slightly increase winter hydropower generation, while decreasing spring and early summer generation for hydropower plants with sizable storage capacities, which adds some operating flexibility and adaptability to changing conditions. Plants without storage capacities are more directly affected from climatic changes. Two algorithms are developed for pumped-storage hydropower (PSH) operations. The first algorithm uses a price-duration curve to find optimal pump and generate thresholds, while the second algorithm uses numerical reservoir system modeling, accounting for water mass balance. The second algorithm also is used to evaluate solar generation e↵ects on PSH operations. With the expansion of solar generation, water is pumped into the upper reservoir during night and solar generation hours with low energy prices, and energy is generated during the morning and evening peak hours with high energy prices.

ISBN: 9781085795951Subjects--Topical Terms:

794747
Water resources management.
Subjects--Index Terms:

Climate Change

Hydropower Generation Optimization in the Era of Renewables and Climate Change.
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300 $a 144 p.
500 $a Source: Dissertations Abstracts International, Volume: 81-04, Section: A.
500 $a Advisor: Lund, Jay R.
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520 $a Effects of expanding solar energy and non-stationary climate conditions on hydropower generation are explored with a hybrid linear programming-nonlinear programming (LP-NLP) hydropower optimization model. Additional operating algorithms are developed for pumped-storage hydropower optimization. The hybrid LP-NLP model is a reservoir operations model with an objective to maximize overall hydropower revenue. LP models are fast but less accurate, while NLP models are slow but have better nonlinear system representation. In a sequential optimization, the model solves the problem first with a linear approximation, then initializes the NLP model with the LP solution to reduce NLP iterations and runtime. LP and NLP models also can be used stand-alone. Energy prices are highly correlated with net energy load. Starting 2013, California's solar generation has been increasing to meet its renewable energy targets. As solar generation increases, net load decreases, reducing energy prices during solar generation hours. As a result, dispatchable hydropower becomes less profitable during mid-day hours, shifting hydropower generation to hours 6-10 in the morning and increasing evening peak generation. In the dry season with less inflow availability and higher evening peak prices, operations focus mostly on evening peak hours. Climate change effects on long-term hydropower planning from 2015 to 2100 with monthly time-step are evaluated with the LP model only as runtime of the NLP model significantly increases with large model sizes of this period. With climate change, temperature increases, more winter precipitation falls as rainfall rather than snowfall, and timing of snowmelt runoff shifts earlier. These changes slightly increase winter hydropower generation, while decreasing spring and early summer generation for hydropower plants with sizable storage capacities, which adds some operating flexibility and adaptability to changing conditions. Plants without storage capacities are more directly affected from climatic changes. Two algorithms are developed for pumped-storage hydropower (PSH) operations. The first algorithm uses a price-duration curve to find optimal pump and generate thresholds, while the second algorithm uses numerical reservoir system modeling, accounting for water mass balance. The second algorithm also is used to evaluate solar generation e↵ects on PSH operations. With the expansion of solar generation, water is pumped into the upper reservoir during night and solar generation hours with low energy prices, and energy is generated during the morning and evening peak hours with high energy prices.
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653 $a Solar Generation
653 $a Water Management
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