Walter Scott, Jr. College of Engineering

Graduate Exam Abstract

Fathalla Eldali
Ph.D. Final
May 03, 2019, 2:00 pm - 4:00 pm
LSC 304
Abstract: Wind energy generation is growing significantly because of its favorable attributes such as cost-effectiveness and environment-friendliness.
Electricity is the most perishable commodity as it must be consumed almost instantaneously as it is produced. Because of that, the variable nature
of wind power generation and the challenges in forecasting the output power of wind impose problems of curtailment (excess of available wind
energy than forecast) and deployment of reserves (deficit of available wind energy than forecast). Energy storage for wind power installations is a
potential solution; however, storing large amounts of energy over long time periods is an expensive and inefficient solution. Plug-in electric vehicles
(PEVs) are recognized as one of the assets to integrate energy storage on the distribution side of the electricity grid. Thus, PEVs charging presents
an alternative solution for managing this excess energy in wind energy-rich grids.

An accurate wind power forecasting (WPF) in the day-ahead market leads to a more predictable dispatch and unit-commitment (UC) of generators,
thus reducing the need for reserves and storage. Typically, reserves to match the imbalance in supply and demand of electricity are provided by
generators that are more expensive than the ones engaged in primary services. Markets in different regions of the world have specific designs,
operation policies, and regulations when it comes to variable sources (e.g., wind, and solar). Independent system operators (ISOs), tasked with
handling electricity markets in the US, must meet regulating reserve as directed by the North America Electric Reliability Council (NERC). One of
these requirements is that the sufficient reserve must be available to cover the generation deficit. This deficit can be due to under-forecasting. There
is also a case when ISOs need to curtail wind energy generation because of over-forecasting. In the first part of this dissertation, wind power data
from the Electric Reliability Council of Texas (ERCOT) market is used to improve WPF as Texas has the highest installed wind energy capacity in
the North American electricity grid. Autoregressive integrated moving average (ARIMA) model is used for WPF improvement.

There is also a need to develop a coherent metric to quantify the improvements to WPF because different studies use different metrics. Also, using
the statistical representation of the reduction in error does not necessarily reflect the overall benefit, especially the economic benefit, for ISOs. In
the second part of this dissertation work, modifications of on risk-adjusted metrics used in investments assessments are developed and applied to
the operation cost (OC). OC is the result of running the economic dispatch (ED) on realistic synthetic models of the actual Texas grid to evaluate the
impact of the WPF improvement on the cost of operation. The modifications of the above-mentioned risk-adjusted metrics are also applied to
deferring the capital investment on the distribution systems. Then, the metrics are used to assess the combination of photovoltaic (PV) and battery
energy storage system (BESS) at the residential section of the distribution grid as explained in appendix A.
The third part of this dissertation uses a data-driven approach to investigate existing pricing mechanisms for a Texan city (i.e., Austin) located in a
wind energy-rich grid such as ERCOT with an increased adoption rate of PEVs. The study performed indicates the need for an alternative dynamic
pricing mechanism dedicated to PEVs than the existing choices for maximizing the utility of available energy from wind in the absence of grid-level
energy storage.

Dynamic pricing produces an opportunity to avoid high costs for the power provider and benefits the consumers if they respond to the change of the
price. However, achieving these benefits needs smart rate design and real data. After justifying the need for fair pricing mechanisms to benefit the
utility and the customers for the coordination of wind energy and PEVs charging in wind energy-rich grid, this dissertation designs a time-varying
pricing mechanism. This dissertation employs a data decomposition technique to design a dedicated pricing mechanism for PEVs. We use real data
of a city with high projections of PEVs (Austin, Texas) located in a wind-rich electricity grid (ERCOT) to demonstrate this design of a dynamic pricing
Adviser: Siddharth Suryanarayanan
Co-Adviser: NA
Non-ECE Member: Salah Abdel-Ghany
Member 3: George J Collins
Addional Members: Dan Zimmerle
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Program of Study:
ECE 509
ECE 565
ECE 566
ECE 666
ENGR 510