1. One challenge of integrating renewable energy generation into the
electric grid is that it may induce deviations in the frequency of power
resulting in instability in the electrical service quality. This has the
potential to damage equipment and lead to power outages. We propose a
method that uses frequency as a signifier of the current state of the grid
and regulates this quantity by way of deferrable ‘smart’ loads employed at
the residential level. These loads can be directly controlled to mediate the
frequency excursion by deferring consumption whilst preserving their
respective duty cycles. Here, we discuss the characterization of potential
‘smart’ loads as well as the design and implementation of embedded
systems that perform the logic to control load behavior and sense
frequency in a home-built test micro grid. Thermal loads, refrigerators
and water heaters, were selected for their energy storage and their
regulation will not cause a noticeable disruption in service. We measure
frequency of the mains power using a microcontroller that transmits data
wirelessly to a single-board computer for processing. Once data is
analyzed, an ancillary algorithm rooted at the specified deferrable load
responds to queries and commands from the computer to advance or
postpone power usage. This manner of demand regulation allows the test
bed to successfully stabilize frequency and improve the state of the power
delivered with no disturbance to the consumer. Furthermore, this test bed
can be modified for other investigations when direct implementation into
the grid is neither practical nor an available resource.
Introduction
The utility service is responsible for ensuring that the supplied electricity is
constantly in balance with the electricity in demand. To guarantee that the
grid can accommodate for the increase or decrease in power generation
due to renewable sources, frequency must be regulated to stay with in a
threshold of 60 ± 0.01 Hz.
Renewable Energy Generated in CA ISO service area
• Frequency of AC power is an indicator of balance between power
generation from generators and power withdrawal from loads.
• When generation and consumption are not in balance, the frequency
changes at the rate of
∆𝜔 =
𝑃𝑔 − 𝑃𝑙
𝑀
• 𝑃𝑔 is the power provided by the generators
• 𝑃𝑙 is power consumed by loads
• M is the system’s inertia
• ∆𝜔 is the change in angular Frequency
Acknowledgements
Hartnell College Advanced Technology Center
Jack Baskin School of Engineering at U.C. Santa Cruz
Zachary Graham , Mentor
Tela Favaloro , Mentor
This publication was developed with support from a
Hispanic Serving STEM & Articulation program funded by
the U.S. Department of Education
Load Characterization Smart Load Result
Andres Aranda
Electrical Engineering, UCSC, Salinas, CA 93901
Zachary W. Graham and Tela Favaloro, Center for Sustainable Energy and Power
Systems, UCSC, Santa Cruz, CA 95064
For further information
Optimal candidates for the deferral of consumption for frequency regulation
have the means of energy storage and thus do not directly affect the
consumer
• We identified two residential loads for use as frequency controlled
reserve: a water heater and a refrigerator.
• These appliances have the means to store electricity as thermal energy
and thus defer consumption
• The necessity to make these deferrable loads smart, allows control to the
grid to accommodate for fluctuation in frequency
• Smart control and response is implemented through microcontrollers with
temperature sensor attached to a relay switch within each deferrable load
[refer to next section]
Designing Smart Demand for Frequency Regulation in a Micro-Grid Test Bed
Algorithm/Design
Abstract
Please contact the following:
zwgraham@soe.ucsc.edu
tela@soe.ucsc.edu
Reference papers:
Kondoh, J.; Ning Lu; Hammerstrom, D.J., "An evaluation of the
water heater load potential for providing regulation
service," Power and Energy Soc., IEEE , pp.1,8, 24-29 2011
Andres Aranda is an Electrical Engineering major at University of
California, Santa Cruz
XBEE Command
Smart Response
• Arduino Mega, Node XBEE, temperature sensor and heating element are connected in an
integrated circuit
• Base XBEE configured on the single board computer commands Node XBEE wirelessly through
Arduino ‘s serial monitor
• Arduino algorithm analyzes command received, and Node XBEE responds with data accordingly
• Algorithm controls Water Heater’s turn on/off within a threshold of its assigned set point.
• Implementation of design permits manipulation of the water heater relay, regulating duty cycle
• Further development will allow water heater to be completely independent and self-regulating
allowing direct response to a change in frequency .
• Additional expansion includes applying concepts of smart loads into more deferrable thermal
appliances as well as electrical storage, such as an electric vehicle battery
𝐶𝑚𝐿
𝑑𝑇
𝑑𝑡
= 𝑃𝑒 − 𝑃𝑎𝑖𝑟 − 𝑃𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑑
• C = heat capacity of substance
• m = mass of object
• L = volume
• T = temperature
• t = time
• 𝑃𝑒 = energy drawn from heating element
• 𝑃𝑎𝑖𝑟 = energy lost to outside surroundings
• 𝑃𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑑 = energy lost to water being used by consumer
This equation illustrates the amount of energy supplied vs energy
lost over time. The period it takes to reach 37 percent of its original
heat is represented by τ.
𝑃𝑎𝑖𝑟 =
𝐶𝑚𝐿(𝑇𝑖 − 𝑇𝑓)
τ
This equation was used to find our rate of decay to our particular
water heater. Our τ was found to be 6 and half hours.
• Graph above illustrates power consumption of chosen appliances
• The typical duty cycle for these appliances is ~35% and can be used for
both normal reserve and disruptive reserve scenarios