An annotated literature review on
DEMAND RESPONSE AND ENERGY MANAGEMENT
IN BUILDINGS
This document presents an annotated list of papers selected from those that I have reviewed during my research activity. The addressed topics are related to Demand Response and energy management for buildings. The list includes presentations, technical reports, and articles from journals and conferences. Web links are also provided.
- Smart Grid: architectures and frameworks
- Energy management for buildings
- Household energy consumption models
The
power grid is an extremely complex system requiring assurance that electricity
is generated in the exact moment that is consumed. In order to meet this
requirement in a more efficient way, energy system operators are investing in
advances in the field of ICT. In particular, the so-called two-way
communication technologies are enabling new control and monitoring
functionalities, thus renewing the production, transportation, and distribution
of electricity. As a consequence, is taking place an important discussion on
how those new technologies should be integrated in the future Smart Grid
architecture.
7.
B. Nordman, “The Case Against
the Smart Grid”, presentation
and video available on YouTube, October 2009. This presentation (slides are available here) addresses several architectural
issues related to the evolving of the Smart Grid. The Author’s thesis is
that the choices made today will have a great influence on tomorrow’s
energy consumption in residential and commercial buildings. Lessons can be
learned from development of the Internet. Architectural principles are: the
interoperability of devices, distributed architecture, price information used
as Demand Respond control signals, and the focus on end users and user
interfaces. An important assertion is that “the Smart Grid should not
extend through the meter” into buildings, so the load control has to be
implemented locally.
8.
N. Gershenfeld, S. Samouhos, B. Nordman, “Intelligent infrastructure for energy
efficiency”, Science, AAAS, Vol. 327,
pp. 1086-1088, 26 February 2010. In this short
article, architectural concepts from the Internet are imported to the proposed
Intelligent Infrastructure for Energy Efficiency (I2E), an energy management
test bed made of nodes (of 1 $ cost) with interfaces for: sensors, IPv6
network, and loads. The Authors state that the management intelligence should
be placed into devices in order to obtain a distributed and scalable
infrastructure.
9. B. Nordman “Nanogrids: Evolving our electricity systems from the bottom up”, Environmental Energy Technologies Division - Lawrence Berkeley National Laboratory, May. 2010. This paper proposes the Nanogrid paradigm as an alternative to the Smart Grid “top-down” approach that adopts the point of view of the grid. Nanogrids extend the concept of Micorgrid and their goal is to improve energy utilization. They are defined as administrative domains that include loads, a controller (that provides or denies power to loads), gateways (for communication and price-based power exchange with other –grids), but no sources (which may be connected to a single Nanogrid). The Nanogrid concepts are recognizable in current technologies like USB, Power over Ethernet, and vehicles.
10. B. Nordman “Beyond the Smart Grid: Building Networks”, Environmental Energy Technologies Division - Lawrence Berkeley National Laboratory, May. 2010. Illustrates the characteristics of building networks and their relation with the Smart Grid. Devices need to adjust their operation according to consumer’s preferences, in particular regarding the way they respond to price changes. The point of view of building networks is compared with that of the Smart Grid. The role of the meter, besides measuring, should be limited to communicating pricing information and two-way communication functionalities should be only used for charging electric vehicles. The adoption of dynamic pricing is not sufficient to guarantee the needed level of dynamism for load consumption adjustment, but it is a necessary change to improve the pricing model.
The
following articles are related to Demand Side Management technique. The first
subsection is dedicated to energy management frameworks for DSM within
buildings. The second one presents papers that deal with the control of smart devices.
Finally, some papers related to behavior modification are listed.
1.
M. Hatori, “Peak-Shift Methods for Notebook Computers”, Proceedings
of the International Symposium on Electronics and the Environment, pp.
117-121, May 2004. This paper presents a peak shift
method that employs the electricity storage capacity of notebook batteries and
uses a power management tool to switch AC power. The implementation of the
proposed system in real devices is illustrated, together with relevant test
results that show the achieved peak shift effect.
2.
T. Rausch,
P. Palensky, “PROFESY: intelligent global energy management”, Proceedings
of the IEEE International Conference on Intelligent Engineering Systems (INES
05), pp. 59-64, September 2005. This paper
focuses on load management for large energy costumers, aiming at peak
reduction. The reference scenario is a single customer that has multiple loads
deployed in different sites. The load profile of each site is locally managed by a Maximum Demand
Monitor (MDM). PROFESY is a Java based software that implements load prediction
algorithms based on artificial intelligence and statistic processing, in order
to globally coordinate the
customer’s local MDMs by sending scheduling information.
3.
K. Wacks, “Energy Management Rediscovered”, iHomes &
Buildings, CABA, pp. 10-12, Spring 2007. This article presents the concepts of DSM. A use case
highlights the importance of user interfaces: consumption information messages
are shown on the appliance display to let the consumer decide the best schedule
on the basis of the service delay and cost. A domestic energy management system
for remote load control is presented, which consists of a centralized
controller that schedules appliances according to control signals received by a
Utility Gateway.
4.
F. Kupzog, C Roesener, “A closer look on load management”, Proceedings
of the 5th IEEE International Conference on Industrial Informatics, pp.
1151-1156, November 2007. This article focuses on
the load shift potential of thermal appliances. Two different methods are
proposed: varying the temperature set-point variation in order to pre-charge or
post-charge the thermal appliance (before or after the load reduction);
switching the appliance on and/or off in case the set-point is not accessible.
A model for inert thermal processes is proposed by which system losses due to
DSM are analyzed.
5.
M. LeMay, R. Nelli,
G. Gross, and C. A. Gunter, “An integrated architecture for demand
response communications and control”, Proceedings
of the 41st Annual IEEE Hawaii International Conference on System Sciences
(HICSS '08), Waikola, Hawaii, January 2008. This paper
presents the Meter Gateway Architecture, a framework that integrates Building
Automation System and Advanced Meter Infrastructure technologies in order to
implement DSM functionalities. The proposed architecture is centralized and the
main component is the Unified Hub, which provides control and communication (ZigBee is the reference technology). The hub receives
utility price signals in real time by the Smart Meter. The signals are then
relayed to the building appliances that may control themselves (Smart
Appliances) or delegate the DR controls to the hub. The Authors present a
prototype and test results that show the effectiveness of DR automated control
for the charging of laptop battery and the operation cycle of an air
conditioner.
6.
K. Wacks, “Home Area
Networks for Electricity Demand Management”, iHomes & Buildings, CABA, pp. 15-17, Summer 2008. This article focuses on the development of technologies
for residential DSM systems. The Author’s opinion is that the energy
management architecture should be based on a centralized controller, separated
from the Smart Meter.
7.
M. LeMay, J. J. Haas, and C.A.
Gunter, “Collaborative
recommender systems for building automation”, Proceedings of the 42nd Annual IEEE Hawaii International Conference on
System Sciences (HICSS '09), Waikola, Hawaii,
January 2009. This paper resents a Building
Automation System for the energy management of building devices. The objective
is to maximize the user comfort while complying with energy consumption
constraints. The system is based on a blackboard architectural pattern and
relies on a collaborative recommender. According to ratings given by building
managers, the recommender selects among multiple building control algorithms
the more suitable ones to be installed in the blackboard. The system includes
appliance usage detectors, set point generators, sensors, usage predictors, and
other components. Experimental results show the effectiveness of the
recommender and how the system is capable of determining what appliances are in
use by analyzing the aggregated consumption.
8.
A. Molderink, V. Bakker, M.G.C. Bosman,
J. L. Hurink, G.J.M. Smit, “A
three-step methodology to improve domestic energy efficiency”, Proceedings of Innovative Smart Grid
Technologies (ISGT), January 2010. This paper
introduces the current research for improving energy efficiency of power
systems (distributed generation, energy storage, and DSM solutions to enable
peak shaving, virtual power plants, and Microgrids)
and presents a general management methodology for residential DSM that consists
of the following steps: the system predicts the energy demand of single houses
(offline) with neural network approach; a global controller aggregates the
individual load profiles (offline) and generates a global planning; the local
schedulers of each house schedule the appliances in real time on the basis of
the global planning. A performance evaluation based on prototypes proves the
effectiveness of the methodology.
9.
A Misra, H Schulzrinne, “Policy-Driven
Distributed and Collaborative Demand Response in Multi-Domain Commercial
Buildings”, Proceedings of the
1st International Conference on Energy-Efficient Computing and Networking,
Passau, Germany, April 2010. The Authors state
the need to go beyond the current model of energy management based on a central
controller (inadequate to manage multi-domain environments such as commercial
buildings) and on static device configuration (inadequate to manage a large set
of devices). DRACHMA is an architectural framework for decentralized DR in
which devices organize themselves in autonomic domains. Each domain has a
controller in charge of negotiating the needed DR adaptation actions. This is
done according to the aggregate consumption and policies of the particular
domain.
10. T. J. Lui, W. Stirling, H. O. Marcy, “Get
Smart”, Power and Energy
Magazine, IEEE, Volume 8, Issue 3, pp. 66-78, May 2010. The paper focuses on the integration of Smart Appliances
into the Smart Grid. The Whirlpool Smart Device Network (WSDN) is a DR
architecture that includes both the Home Area Network and the Smart Grid
domains. WSDN proposes three levels of DR operation: (1) appliances
individually respond to signals; (2) the operation of all smart devices
(appliances, micro generation, EV chargers) is coordinated by a home energy
management system; (3) the Smart Grid coordinates the DR of the houses (through
the Internet, since the technology of Smart Meters does not offer sufficient
bandwidth yet). Consumers should ultimately control how appliances response to
the DR signals. The networking technology and the functional blocks of the home
energy management system are described. The appliances are managed by a
centralized Smart Device Controller that also operates as gateway towards the
Smart Grid. The paper also provides typical load profiles of several
appliances.
11. J. Xiao, J. Y. Chung, J. Li, R. Boutaba, J W. Hong, “Near
Optimal Demand-Side Energy Management Under Real-time Demand-Response Pricing”,
POSTECH University, Pohang, South Korea,
2010. The Authors focus on DSM and formulate a
scheduling problem for daily consumption tasks characterized by deadlines and a
given power demand. The objectives are peak shaving and cost minimization. The
reference DR program is based on Local Control through Real Time Pricing
signals (the most economically efficient solution). The scheduling is
analytically formulated as a min-max problem (NP-hard, since it is a particular
case of a well-known problem). The proposed algorithm is based on the Longest
Process Time greedy search algorithm, but processes the task with the largest
energy consumption first according to the cumulative cost of each timeslot.
Through the analytical formulation, the Author proves that the algorithm finds
near optimal solutions. The benefits of energy storage are also considered.
Simulation results prove that the system achieves peak shaving.
12. A-H. Mohsenian-Rad,
V.W.S. Wong, J. Jatskevich, R. Schober,
“Optimal
and Autonomous Incentive-based Energy Consumption Scheduling Algorithm for
Smart Grid”, Proceedings of the
IEEE Conference on Innovative Smart Grid Technologies, January 2010.
The paper resents a DSM scheduler for domestic
energy consumption (Smart Appliances and EV charging) to be implemented into
Smart Meters. The reference scenario is a ToU DR
program that controls the aggregated consumption of multiple houses. The
scheduler is distributed (implemented locally in each Smart Meters of the
controlled domain) and collaborative (the local algorithm relies on the
knowledge of the current overall schedule of the domain). The Authors present
an analytical formulation of the problem based on Game Theory. The distributed
algorithm aims at reducing the total energy cost and the peak to average ratio.
Thanks to the analytical formulation, it is shown that the system reaches the
global optimum when the local algorithm reaches the local optimum. Simulation
results prove cost and peak reductions.
13. J. Howard, H. Ham, N. F. Maxemchuk,
“Smart Air Conditioners”, Proceedings
of the Global Smart Grid Symposium, US-Korea Conference on Science, Technology,
and Entrepreneurship (UKC), August 2010. This
paper focuses on the scheduling of periodic appliances (such as refrigerators
and air conditioners) through the control of their duty cycles. The proposed
algorithm (Tetris-like) sets the phase of the tasks related to a common time
period in order to achieve peak shaving. A simulation-based evaluation compares
the algorithm results in terms of peak reduction with a lower bound given by
the average consumption. The Authors present the problem of how to reduce the
cooling of multiple air conditioners in a fair manner and propose a max-min
approach based on temperature.
1.
M. Newborough, S. D. Probert,
“Intelligent
automatic electrical-load management for networks of major domestic appliances”,
Applied Energy, Elsevier, Volume 37,
Issue 2, pp. 151-168, 1990. The paper’s
thesis is that peak demand may be reduced by the introduction of intelligent
load-management facilities. The Authors presents the problem of energy balance
and the influence of domestic consumption on the peak demand, for which several
statistics are given. Benefits can be achieved by increasing the energy
efficiency of appliances. Load management can be implemented through home
automation. Appliances are classified by the required level of automation for
domestic load management.
2. A. Schmidt and K. Van Laerhoven, “How to build smart appliances?”, IEEE Personal Communications, Vol. 1070-9916/01, pp. 66-71, August 2001. The paper focuses on several issues on context awareness (the relation between the real world and the data provided by sensors). The Authors provide a terminology on context awareness (situational context, sensor data, context, context-aware application) and define smart devices as those that “are not ignorant about their environment and context.” A layered architecture to build context-aware devices is presented.
3.
A. Harris, “Charge
of the electric car”, Engineering
& Technology, IET Journals, pp. 52-53, 2009. This
short article deals with the “ability of the UK power grid to support the
growth of electric cars.” The Author presents the results of two
different studies. The first study concludes that the electrification of the
vehicle fleet will be manageable, but localized problems may arise, in
particular in areas of high concentration of EVs. The second study affirms that
EVs will contribute to the long-term reduction of the UK’s CO2 emissions.
4.
T. Reddoch, “Marrying
Electric Transportation to the Electric System: EVSEs and Distribution Impacts”,
Electric Power Research Institute
presentation for Moving Ahead 2010, May 2010. The
slides of this presentation focus on the EV market (a forecast chart for the EV
adoption in California in the 2010-2020 decade is presented), charging
technologies, and on the impact of EV charging on the power system. The EV
charging can “alleviate issues related to night time over
generation.” Smart Grid functionalities will manage the charging (Smart
Charging). Time of Use rates help mitigate the sensitivity to overloads.
1. T. Ueno, F. Sano, O. Saeki, K. Tsuji, “Effectiveness of an energy-consumption information system on energy savings in residential houses based on monitored data”, Applied Energy, Elsevier, Volume 83, Issue 2, pp. 166-183, February 2006. This paper focuses on the effects of consumption awareness on energy savings. An information system has been deployed in 9 households and power consumption data has been collected. The results show how the information system led to a 9% consumption reduction and other beneficial behavior changes.
2. G. Wood, M. Newborough, “Energy-use information transfer for intelligent homes: Enabling energy conservation with central and local displays”, Energy and Buildings, Elsevier, Volume 39, Issue 4, pp. 495-503, April 2007. This article focus on energy consumption displays. Methods to motivate consumers to save energy are presented, such as comparative data, goal settings, and rewards. The Authors classify appliances regarding the consumption information to be displayed.
3. W. Abrahamse, L. Stega, C. Vleka, and T. Rothengatter, “The effect of tailored information, goal setting, and tailored feedback on household energy use, energy-related behaviors, and behavioral antecedents”, Journal of Environmental Psychology, Elsevier, Vol. 27, Issue 4, pp. 265-276, December 2007. This paper presents a study on the influence of tailored information, individual feedback about energy savings, and goal setting over 189 households for 5 months. The information was provided by a web site. The analyzed outcome, regarding energy savings and changes in energy utilization, was compared to that obtained from a control group. Results show a 5.1% consumption reduction.
Insights on the electricity market are needed to understand the mechanisms of Demand Response. The main problem relies on the old pricing model, by which consumers are used to pay averaged tariff prices while the real energy cost significantly varies on an hour basis.
The following papers present models for the energy consumption in domestic buildings.
1.
A. Capasso, W. Grattieri, R. Lamedica, A. Prudenzi, “A
bottom-up approach to residential load modeling”, Transactions of Power Systems, IEEE, Volume
9, Issue 2, pp. 957-964, May 1994. This seminal
paper presents a model for household electric energy consumption. By using Montecarlo extraction processes, the energy consumption is
obtained with a bottom-up approach over two levels of aggregation: multiple
appliances are aggregated into a household unit; multiple household are
aggregated in a group of houses. The relation between the operation of
appliances and human behavior is modeled by probability functions (behavioral
and engineering functions). The presented simulation results validate the
model.
2.
R. Yao, K.
Steemers, “A
method of formulating energy load profile for domestic buildings in the UK”,
Energy and Buildings, Elsevier, Volume 37, Issue 6, pp. 663-671, June 2005. This article presents a method for predicting household
load profiles. The model takes into account the people’s occupancy
pattern. It requires average consumption values as input; load profiles are
then generated randomly. Household and regional profiles are obtained by an
aggregated approach. The Authors present validation results that show how the
consumption of 100 households obtained by the model is close to UK statistical
data.
3.
J. V. Paatero, P. D. Lund, “A model for
generating household electricity load profiles”, International Journal of Energy Research, Wiley, Volume 30, Issue 5,
pp. 273–290, April 2006. This paper presents
a simple bottom-up model for household electric energy consumption that uses
statistical data as input. The model is used to demonstrate the effectiveness
of three different DSM programs in terms of load shift. Results show how
allowing cold appliances to shift for 1 hour, a reduction of 7.2% of the annual
mean peak is achieved.
4.
I.
Richardson, M. Thomson, D. Infield, C. Clifford, “Domestic
electricity use: A high-resolution energy demand model”, Energy and Buildings, Elsevier, Volume 42,
Issue 10, pp. 1878-1887, October 2010. This
paper presents a bottom-up model for energy consumption in households. The
model considers the active occupancy of the single households and employs a 1
min time resolution. Simulation results validate the model. The Authors make
available an example of the model implemented on a spreadsheet.
The problem of peak shift ultimately consists in scheduling non-periodic tasks, such as appliance operation cycles and the charging of electric vehicles. This problem involves deadlines and may require the rescheduling of already allocated tasks, as well as the interruption of running tasks. In the context of a Demand Response program, the scheduler may use the dynamic pricing as a control signal, so the scheduling objective becomes the minimization of the energy cost. Energy providers may also benefit from the introduction of resource requirements (set the available energy in a time slot) in order to control the peak consumption. The following papers provide a classification and algorithmic approaches for scheduling problems.
1.
T. L. Casavant, J. G. Kuhl, “A taxonomy
of scheduling in general-purpose distributed computing systems”, Transactions on Software Engineering, IEEE, Volume
14, Issue 2, pp. 141-154, February 1988. This
article resents the scheduling problem, the relevant terminology, and a hybrid
taxonomy (mostly hierarchical, but flat for common descriptors) of scheduling
systems. Examples taken from the literature are classified with the taxonomy.
Finally, an annotated bibliography is presented.
2.
K. Ramamritham, J. A. Stankovic, W.
Zhao, “Distributed
scheduling of tasks with deadlines and resource requirements”, Transactions on Computers, IEEE, Volume 38,
Issue 8, pp. 1110-1123, August 1989. The paper
presents different heuristics for real-time scheduling problems with priority,
deadlines, and resource requirements, in the context of CPU allocation. The
Author distinguishes between periodic and non-periodic tasks. Each node
involved in the scheduling problem has three components: a local scheduler, a
dispatcher, and a global scheduler.
1. N
Gershenfeld, R Krikorian, D Cohen, “The
Internet of things”, Scientific American, American Edition, CDS, Vol. 291; Number 4, pp.
76, 2004. This article introduces the motivations and requirements of Internet 0,
a low-rate physical layer technology that has the objective to route IP over
anything. The benefits of pervasive computing are presented, as well as the
motivations for the choosing a low-rate transmission.
2. N Gershenfeld, D
Cohen, “Internet
0: Interdevice Internetworking”, IEEE Circuits and Systems, Vol. 22, Issue 5,
pp 48 – 55, 2006. The authors present the
principles behind Internet 0: reduction of IP connectivity costs, open standards,
distributed architecture that does not require servers (“devices should
have the resources to independently implement their functionality”). The
physical layer is described (transmission big bits and end-to-end modulation).
3. “SMART 2020: Enabling low carbon economy in the
information age”, A
report by The Climate Group on behalf of the Global eSustainability
Initiative (GeSI), 2008. This report focuses on the relation between the ICT
and energy consumption. Several statistics and forecast charts are presented
together with suggested actions to achieve a more efficient use of ICT
equipment. The study concludes that ICT, counting for approximately 2% of the
current carbon footprint, can make a “major contribution to the global
response to climate change” by enabling new social behaviors
(dematerialization) and by new smart technologies (motor systems, logistics,
buildings, grid).
4. D. J. C. MacKay, “Without hot air”,
UIT Cambridge Ltd., 2009. This book addresses the problem of energy consumption,
sources of energy, and related policies. The utilization of renewable sources
of energy is investigated by using an engineering approach, in order to
determine if it is possible to exclusively rely on them. The Author highlights
the importance of having meaningful consumption values and identifies actions
to promote a better use of the energy resources related to transportation,
heating, and electricity use. Demand Response and the utilization of electric
vehicles for energy storage can ease the integration of renewables.
5. A. Berl, R. Weidlich, M. Schrank, H. Hlavacs, and H. de Meer, “Network Virtualization in Future Home Environments”, Proceedings of the International Workshop on Distributed Systems: Operations and Management (DSOM09), October 2009. This article presents the Virtual Home Environment (VHE), a peer-to-peer architecture that aims at virtualizing and consolidating end-user ITC residential resources (CPU, storage, and network) over multiple houses. Several issues related to the virtualization of the network (by a peer-to-peer overlay that relies on home gateway for continuous connectivity) and of the host system (resource availability) are described. A download sharing application is used to evaluate the network virtualization overhead.
a. Related CNET News article.
This
project is co- founded by the European Commission under the 7th FP. ADDRESS
stands for Active Distribution network with full integration of Demand and
distributed energy RESourceS. The project focuses on
enabling the Active Demand in the context of the Smart Grids of the future.
1.
E. Peeters, R. Belhomme, C. Batlle, F. Bouffard, S. Karkkainen, D. Six, M. Hommelberg,
“ADDRESS:
Scenarios and architecture for Active Demand development in the smart grids of
the future”, Proceedings of the
International Conference and Exhibition on Electricity Distribution
(CIRED2009), September 2009. This paper
introduces the ADDRESS project and identifies potential barriers for active
demand with the relative proposed solutions.
2.
“ADDRESS:
Technical and Commercial Conceptual Architectures – Core document”,
Deliverable D1.1, ADDREESS Project, October 2009.
Smart-A
is an European project that aims at “identifying and evaluating the
potential synergies that arise from coordinating energy demand of domestic
appliances with local sustainable energy generation but also with the
requirements of regional load management in electricity networks.”
EDISON
(Electric vehicles in a Distributed and Integrated market using Sustainable
energy and Open Networks) is an international project that focuses on
“developing optimal system solutions for EV system integration, including
network issues, market solutions, and optimal interaction between different
energy technologies.”
1. C. Binding, D. Gantenbein,
J. Bernhard, O. Sundström, P. B. Andersen, F. Marra, B. Poulsen, C. Træholt, “Electric
Vehicle Fleet Integration in the Danish EDISON Project - A Virtual Power Plant
on the Island of Bornholm”, EDISON
project research report, IBM Research – Zurich,
DTU Technical University of Denmark, January 2010. This paper presents a SOA-based
software for the integration of vehicle-to-grid technologies.
2. “The EDISON picture”, EDISON project definitions document, WP7, November 2010.