Impact of Wind Energy on Cost and Balancing Reserves

Wind energy offers a significant advantage such as no
fuel costs and no emissions from generation. However, wind energy
sources are variable and non-dispatchable. The utility grid is able to
accommodate the variability of wind in smaller proportion along with
the daily load. However, at high penetration levels, the variability can
severely impact the utility reserve requirements and the cost
associated with it. In this paper the impact of wind energy is
evaluated in detail in formulating the total utility cost. The objective
is to minimize the overall cost of generation while ensuring the
proper management of the load. Overall cost includes the curtailment
cost, reserve cost and the reliability cost, as well as any other penalty
imposed by the regulatory authority. Different levels of wind
penetrations are explored and the cost impacts are evaluated. As the
penetration level increases significantly, the reliability becomes a
critical question to be answered. Here we increase the penetration
from the wind yet keep the reliability factor within the acceptable
limit provided by NERC. This paper uses an economic dispatch (ED)
model to incorporate wind generation into the power grid. Power
system costs are analyzed at various wind penetration levels using
Linear Programming. The goal of this study is show how the
increases in wind generation will affect power system economics.

• A. Khanal
• A. Osareh
• G. Lebby

• Balancing Reserves
• Optimization
• Wind Energy.

[1] Yuri Makarov, Shuai Lu, Bart Mcmanus, and John Pease. The future
impact of wind on BPA power system ancillary services. In
Transmission and Distribution Conference and Exposition. IEEE/PES,
IEEE/PES, April 2008.
[2] Wiser, Ryan, Mark Bolinger, June 2011: 2010 Wind Technologies
Market Report. Department of Energy. (Available online at
http://www.windpoweringamerica.gov/pdfs/2010_annual_wind_
market_ report.pdf)
[3] Das, Trishna, 2013: Performance and economic evaluation of storage
technologies, PhD dissertation, Iowa State University, 237 pp.
(Available online at http://lib.dr.iastate.edu/etd/13047)
[4] John Hetzer, David C. Yu, and Kalu Bhattarai. An economic dispatch
model incorporating wind power. IEEE Transactions on Energy
Conversion, 23(2):603–611, June 2008
[5] Sorotomme, E., Ali Al-Awami, M. El-Sharkawi, April 2010: Multi
Objective Optimization for Wind Energy Integration.Available online at:
http://sgpubs.ieee.org/publications/latest-ieee-xplore-publications/plugin-
hybrid-electric-vehicle/961-multi-objective-optimization-for-windenergy-
integration
[6] Post, Willem, November 2013: A More Realistic Cost of Wind Energy.
The Energy Collective. (Available online at
http://theenergycollective.com/willem-post/310631/more-realistic-costwind-
energy)
[7] F. Milano. (2002) PSAT, Matlab-Based Power System Analysis
Toolbox. (Online) Available at: http://thunderbox.uwaterloo.ca/~fmilano
[8] Freris, L. and D. Infield (2008). Renewable Energy in Power Systems,
Wiley.
[9] Y. Makarov, B. Yang, J. G. DeSteese, S. Lu, C. H. Miller, P. Nyeng,
J.Ma, D. J. Hammerstrom, and V. V. Viswanathan, "Wide-Area Energy
Storage and Management System to Balance Intermittent Resources in
the Bonneville Power Administration and California ISO Control
Areas," Pacific Northwest National Laboratory, Richland, WA,
Tech.Rep….. June 2008.
[10] Gurobi Optimization (Online). Available: http://www.gurobi.com/
[11] DOE/EPRI 2013 Electricity Storage Handbook in Collaboration with
NRECA-Abbas A. Akhil, Georgianne Huff, Aileen B. Currier, Benjamin
C. Kaun, Dan M. Rastler, Stella Bingqing Chen, Andrew L. Cotter, Dale
T. Bradshaw, and William D. Gauntlett