Walter Scott, Jr. College of Engineering

Graduate Exam Abstract

Fathalla Eldali
Ph.D. Preliminary
Mar 23, 2018, 1:30 pm - 3:30 pm
Civil Environmental Engineering Conference Room
Abstract: Wind energy generation is growing
significantly because of its favorable
attributes such as cost-effectiveness
and environment-friendness. 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 Hybrid
Electric Vehicles (PHEVs) are
recognized as one of the assets to
integrate energy storage on the
distribution side of the electricity grid.
An accurate wind power forecasting
(WPF) in the day-ahead market leads
to a more predictable dispatch and
unit-commitment (UC), 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 source (e.g., wind, 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
requirement is that 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 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 since 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, a metric based on
risk-adjusted metrics used in
investments assessments is
developed and applied on operation
cost (OC). OC is the result of running
the economic dispatch (ED) on
realistic 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
applied in another application of
deferring the capital investment on the
distribution systems by using the
combination of photovoltaic (PV) and
battery energy storage system (BESS)
at the residential section of the
distribution grid as explained in
appendix A.
After determining the needed energy
storage system (ESS) to work as an
energy buffer for the wind energy
resources for each area in the ERCOT
system, future work will focus on
developing the energy management
algorithm to schedule PHEVs in the
distribution system to buffer the excess
of wind energy. A distributed
optimization methodology, namely the
alternating direction method of
multipliers (ADMM) is intended to be
used for this part of the work as it is
applicable for distributed convex
optimization, especially very large
problems. The results of the
algorithm should be the power profile
(schedule) for PHEVs to be charged or
discharged based on the wind energy
resources taking into consideration the
transmission and the distribution
Adviser: Siddharth Suryanarayanan
Co-Adviser: NA
Non-ECE Member: Salah Abdel-Ghany, Dept of Biology
Member 3: George J Collins, ECE Dept
Addional Members: Dan Zimmerle
[1] F. Eldali, M. Samper, and S. Suryanarayanan, "Risk-adjusted Cost Ratios for Quantifying Improvements in Wind Power Forecasting," under review, Renewable Energy Focus Journal, Aug. 2017.
[2] M. E. Samper,F. Eldali, and S. Suryanarayanan, “Risk assessment in planning high penetrations of solar photovoltaic installations in distribution systems,” under review, International Journal of Electrical Power and Energy Systems,Nov. 2017.
[3] M. Samper, A. Vargas, F. Eldali, and S. Suryanarayanan, “Assessments of Battery Storage Options for Distribution Expansion Planning Using an OpenDSS-Based Framework,” 2017 IEEE Manchester PowerTech, Manchester, 2017, pp. 1-6.
[4] F. Eldali, T. Kirk and D. Pinney, "Application of AMI data to anomaly detection and dynamic power flow analysis," 2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, 2017, pp. 1-4.
[5] F. Eldali, T. M. Hansen, S. Suryanarayanan, and E. K. P. Chong, "Employing ARIMA Models to Improve Wind Power Forecasts: A Case Study in ERCOT," in Proc. 2016 North American Power Symposium (NAPS), Denver, CO, Oct. 2016, pp. 1-6.
[6] F. Eldali, T. Hardy, C. Corbin, D. Pinney, and M. Javid, "Cost-Benefit Analysis of Demand Response Programs Incorporated in Open Modeling Framework," in Proc. 2016 IEEE Power and Energy Society General Meeting (PESGM), Boston, MA, Jul. 2016, pp. 1-5
Program of Study:
ECE 509
ECE 565
ECE 566
ECE 666
ENGR 510