IEEE Electrification Magazine - March 2016 - 32

response). The dashed green line represents the substation
apparent power, in kilovolt-amperes, after the aggregator-based
residential demand response was performed. The peak system
power at 5:15 p.m. was reduced by 19.2 kVA, the total schedulable load available for demand response at that time. This corresponds to a 2.5% decrease in peak system load, which aligns
with the U.S. Federal Energy Regulatory Commission expectations for demand response in the residential sector.
The second example involves total customer cost minimization of the schedulable demand-response loads (i.e.,
smart appliances) in a time-of-use (TOU) market, data used
from Duke Energy in North Carolina, using Bus.py. The
resulting load of the controllable assets, which represents
450 kWh of the distribution feeder load, throughout the simulation period is presented in Figure 15. The solid blue line
represents the baseline schedulable loads of the customers.
The dashed green line represents the customer schedulable
loads, in kilowatts, after the aggregator-based residential
demand response was performed while optimizing for the
minimization of customer energy cost. The solid black curve
shows the TOU pricing used in the optimization, with the
corresponding y-axis values on the right, in cents/kWh.
During the TOU peak-pricing period, from 1:00 to 7:00 p.m.
(hours 13 to 19), the total schedulable loads of all the customers were pushed off peak and resulted in a reduction from
123 to 52 kWh. This makes sense because the only way to
reduce customer cost in the TOU pricing scheme is to move
load from on peak to off peak. The reason that all schedulable
load was not moved off peak is because of the customerdefined comfort constraints. It is interesting to note the
resulting rebound effect-the change in the consumption
pattern of electricity from the changing cost of electricity-
that occurs on either side of the transition from off-peak to
on-peak pricing at 1:00 p.m. and at 7:00 p.m.

conclusions
As more smart grid technologies are implemented in the distribution system, the infeasibility of a single tool simulating
different electric power system domains at a detailed level
limits the ability to properly study and quantify their impacts.
DEx.py and Bus.py enable the dynamic integration of multiple electric power system simulation tools, such as GridLABD and MATPOWER. The use of DEx.py, Bus.py, and IGMS
enables the cosimulation of transmission and distribution
systems, customer HEMSs and the distribution system, as
well as many other use cases that cross traditional electric
power system domain boundaries, leading to the rapid
design and development of the technologies and controls
required for the future electric power grid. Work is ongoing to
link DEx.py, Bus.py, and the constituent simulation environments to demonstrate simulations of large systems representative of smart cities connect to the bulk electricity grid.

Disclaimer
Material presented here is an anthology of research works
of the authors published or under review consideration by

I E E E E l e c t r i f i cati o n M agaz ine / March 2016

the IEEE, identified in the "For Further Reading" section
(denoted by "a").

For Further reading

aT. M. Hansen, B. Palmintier, S. Suryanarayanan, A. A. Maciejew-

ski, and H. J. Siegel, "Bus.py: A GridLAB-D communication
interface for smart distribution grid simulations," in Proc. IEEE
Power & Energy Society General Meeting, July 2015, pp. 1-5.
a T. M. Hansen, R. Roche, S. Suryanarayanan, A. A.
Maciejewski, and H. J. Siegel, "Heuristic optimization for an
aggregator-based resource allocation in the smart grid," IEEE
Trans. Smart Grid, vol. 6, no. 4, pp. 1785-1794, July 2015.
D. P. Chassin, K. Schneider, and C. Gerkensmeyer, "GridLAB-D: An open-source power systems modeling and simulation environment," in Proc. IEEE Power & Energy Society Transmission and Distribution Conf. and Expo., Apr. 2008, pp. 1-5.
R. D. Zimmerman, C. E. Murillo-Sánchez, and R. J. Thomas,
"MATPOWER: Steady-state operations, planning, and analysis
tools for power systems research and educations," IEEE Trans.
Power Syst., vol. 26, no. 1, pp. 12-19, Feb. 2011.
R. Billinton and S. Jonnavithula, "A test system for teaching
overall power system reliability assessment," IEEE Trans. Power
Syst., vol. 11, no. 4, pp. 1670-1676, Nov. 1996.
aT. M. Hansen, E. K. P. Chong, S. Suryanarayanan, A. A.
Maciejewski, and H. J. Siegel, "A partially observable Markov
decision process approach to residential home energy management," submitted for publication.
aR. Kadavil, T. M. Hansen, and S. Suryanarayanan, "An algorithmic approach for creating diverse stochastic feeder datasets
for power systems co-simulations," submitted for publication.
aB. Palmintier, E. Hale, T. M. Hansen, W. Jones, D. Biagioni, H.
Sorensen, and B.-M. Hodge, "IGMS: An integrated ISO-to-appliance scale grid modeling system," submitted for publication.

Biographies
Timothy M. Hansen (timothy.hansen@sdstate.edu) is an
assistant professor in the Electrical Engineering and Computer Science Department at South Dakota State University.
Rahul Kadavil (rahul.kadavil@colostate.edu) is a graduate student with the Department of Electrical and Computer Engineering at Colorado State University.
Bryan Palmintier (bryan.palmintier@nrel.gov) is a senior
research engineer in the Power Systems Engineering Center at the U.S. National Renewable Energy Laboratory.
Siddharth Suryanarayanan (sid@colostate.edu) is an
associate professor in the Department of Electrical and
Computer Engineering at Colorado State University.
Anthony A. Maciejewski (aam@colostate.edu) is a professor and the head of the Department of Electrical and
Computer Engineering at Colorado State University.
Howard Jay Siegel (hj@colostate.edu) is the George T.
Abell Endowed Chair Distinguished Professor of Electrical
and Computer Engineering at Colorado State University,
where he is also a professor of computer science.
Edwin K.P. Chong (edwin.chong@colostate.edu) is a professor in the Department of Electrical and Computer Engineering and Department of Mathematics at Colorado State
University.
Elaine Hale (elaine.hale@nrel.gov) is a senior engineer in
the Strategic Energy Analysis Center at the U.S. National
Renewable Energy Laboratory.

http://www.Bus.py http://www.Bus.py http://www.DEx.py http://www.Bus.py http://www.DEx.py http://www.Bus.py http://www.DEx.py http://www.Bus.py

Table of Contents for the Digital Edition of IEEE Electrification Magazine - March 2016