Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/62390

Application of Signal Processing Methods in Energy and Water Sustainability Optimization.

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Title:Application of Signal Processing Methods in Energy and Water Sustainability Optimization.
Authors:Fatemi, Seyyed Abolhasan
Contributors:Electrical Engineering (department)
Date Issued:Aug 2017
Publisher:University of Hawaiʻi at Mānoa
Abstract:Solar irradiation is a non-stationary process with its mean and variance changing depending
on time and day of the year. For solar irradiation prediction we remove both
seasonal and time of day effects to make observations approximately stationary and then
use prediction methods. We propose to use the zenith angle (the angle between sun beam
and perpendicular line on horizontal surface) to remove both seasonal and time of day
effects. Our simulations using least-squares (LS), time-varying least squares (TVLS), exponentially
weighted recursive least squares (EWRLS) and one step estimation of second
order statistics shows that using zenith angle normalization gives lower mean square error
than traditional normalization by subtracting the mean and then dividing by deviation.
In electric power grids, generation must equal load at all times. Since wind and solar
power are intermittent, system operators must predict renewable generation and allocate
operating reserves to mitigate imbalances. If they overestimate the renewable generation
during scheduling, insufficient generation will be available during operation, which can
be very costly. However, if they underestimate the renewable generation, usually they
will only face the cost of keeping some generation capacity online and idle. Therefore
overestimation of renewable generation resources usually presents a more serious problem
than underestimation. Many researchers train their solar radiation forecast algorithms using
symmetric criteria like RMSE or MAE, and then a bias is applied to the forecast later to
reflect the asymmetric cost faced by the system operator - a technique we call indirectly
biased forecasting. We investigate solar radiation forecasts using asymmetric cost functions
(convex piecewise linear (CPWL) and LinEx) and optimize directly in the forecast training
stage. We use linear programming and a gradient descent algorithm to find a directly biased
solution and compare it with the best indirectly biased solution. We also modify the LMS
algorithm according to the cost functions to create an online forecast method. Simulation
results show substantial cost savings using these methods.
We also propose two parametric probabilistic forecast methods by using beta and two
sided power distribution for predicting solar irradiation and evaluate their performance. To
improve their performance metrics a combining procedure based on the beta transformed
linear opinion pool is utilized. Our simulations show that these methods -despite the simple
structure- can accurately describe the stochastic characterization of solar irradiation and
effectively reduce its uncertainty. The proposed approach is robust and algorithms can be
modified for other point forecast methods.
We also consider reliability of electric grid as a public good and we use an insurance
policy to implement a benefit taxation mechanism that provides a framework to achieve
optimal reliability levels. Finally, we examine energy efficient scheduling for pumping
water in water supply networks. This is formulated as a nonconvex optimization problem
and we find solutions and conduct simulations for small networks.
Description:Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017.
URI:http://hdl.handle.net/10125/62390
Rights:All UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner.
Appears in Collections: Ph.D. - Electrical Engineering


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