Emerging Smart Grid Technologies

ZigBee

The ZigBee protocol enables communication using multiple network topologies, including star, tree and mesh [1].  It is particularly challenging to ensure reliability of the communication channel for smart meter designs, especially with the use of wireless-based backhaul channels and ZigBee is particularly suited to this application with its mesh network topology.  In case a meter is out of range of a central tower or otherwise obstructed by buildings or other objects, ZigBee-based meters enable communication between meters and relaying of information back to the data collection point [2].  Furthermore, since ZigBee devices utilize the unlicensed 2.4GHz spectrum, they have a very low cost of deployment and allow seamless integration and networking of many ZigBee devices.  Although they have many benefits, ZigBee devices are designed primarily for short-range communication and low device power requirements [3], requiring a separate wireless protocol for long-range transmission.  ZigBee is a key technology enabling the OpenHAN networking standard discussed later in this paper.

OpenADR (Automated Demand Response)

Because electricity is charged at a constant price in the current power system, regardless of time of use, consumers therefore have little incentive to put forth effort to change their usage patterns.  Introducing smart grids will allow for dynamic billing based on market pricing at the time, and thus give more incentive to customers to plan their energy usage [4].  With the proposed automated demand response, individual smart meters will have the capability of monitoring system wide conditions, determining when the system is stressed and appropriately allocate power to different appliances.  Automated demand response will aim for reducing high loading during peak times, in an attempt to remove excess stress from the power system [5].

Open Automated Demand Response is a standard currently under development [6], which aims to ensure interoperability between various smart meter infrastructure devices.  It will provide a way for users program appliances to operate according to current electricity prices, for example to do laundry when power is cheapest.

OpenHAN (Home Area Network)

Open Home Area Network is a proposed standard to interface with the smart meter in residences with appliances in the home.  OpenHAN can allow for utility control of the appliance, customer coordination and timing of appliance activation, and operational states of appliances based on set-points such as price.  Upon completion and implementation of this standard, residents will be capable of having appliances automatically run during times when electricity is cheapest, and utilities will be able to cease operation of appliances during peak loading times.  OpenHAN is the fundamental idea behind automated demand response, where there exists a link between the smart meter of the customer and the customer’s appliance [7].

Worldwide Interoperability for Microwave Access (WiMAX)

WiMAX is an industrial wireless interoperability standard related to the existing technology known as the Global System for Mobile Communications (GSM) [8].  It is typically used for land-based wireless Internet service providers, particularly those serving rural communities; however, it is finding applications within power systems as a backhaul for smart meter telemetry data [9].

Broadband over Power Lines

Several different startup companies have explored the use of Broadband over Power Lines (BPL) as an Internet service delivery or as a backhaul for telemetry from smart meters [10].  While it is no longer a serious contender for delivering Internet access to remote communities, the technology still has its niche applications, particularly within the realm of power systems.  Some smart meter vendors continue to sell smart metering equipment that transmits telemetry over power lines [11] rather than using its own radio frequency, which requires the purchase of costly spectrum.

Furthermore, using BPL couplers traditionally used for sending and receiving data across power lines can be used to listen for types of noise characteristic of certain types of equipment failures; for example, a cracked insulator beginning to fail will induce a specific signature pattern that can be detected using BPL couplers [12].

[1]	Peng Ran, Mao-heng Sun, and You-min Zou, “ZigBee ROuting Selection Strategy Based on Data Services and Energy-Balanced ZigBee Routing,” in IEEE Asia-Pacific Conference on Services Computing, Xi’an, China, 2006, pp. 400-404.
[2]	Hoi Yan Tung, Kim Fung Tsang, and Ka Lun Lam, “ZigBee Sensor Network for Advanced Metering Infrastructure,” in Power Electronics and Drive Systems, Taipei, Taiwan, 2009, pp. 95-96.
[3]	ZigBee Alliance Inc. (2007, October) ZigBee Specification. [Online]. http://zigbee.org/ZigBeeSpecificationDownloadRequest/tabid/311/Default.aspx
[4]	David Andrew, “National Grid’s use of Emergency Diesel Standby Generator’s in Dealing with Grid Intermittency and Variability,” in Open University Conference on Intermittency, Milton Keynes, UK, 2006.
[5]	Dan Yang and Yanni Chen, “Demand Response and Market Performance in Power Economics,” in Power and Energy Society General Meeting, Calgary, AB, 2009, pp. 1-6.
[6]	Ivin Rhyne et al., “Open Automated Demand Response Communications Specification,” Public Interest Energy Research Program (PIER), California Energy Commission, Berkeley, CA, PIER Final Project Report 2009.
[7]	UtilityAMI OpenHAN Task Force. (2007, December) Requirements Working Group Specification Briefing. [Online].  http://osgug.ucaiug.org/sgsystems/openhan/HAN%20Requirements/OpenHAN%20Specification%20Dec.ppt
[8]	Zheng Ruiming, Zhang Xin, Pan Qun, Yang Dacheng, and Li Xi, “Research on coexistence of WiMAX and WCDMA systems,” in IEEE 19th Internetional Symposium on Personal, Indoor and Mobile Radio Communications, Cannes, France, 2008, pp. 1-6.
[9]	G.N. Srinivasa Prasanna et al., “Data communication over the smart grid,” in IEEE International Symposium on Power Line Communications and Its Applications, Dresden, Germany, 2009, pp. 273-279.
[10]	X. Qiu, “Powerful talk,” IET Power Engineer, vol. 21, no. 1, pp. 38-43, February-March 2007.
[11]	Echelon Corporation. (2010, March) Energy Management Control Networks. [Online].   http://www.echelon.com/products/energyproducts.htm
[12]	Larry Silverman, “BPL shouldn’t mimic DSL/cable models,” BPL Today, pp. 1-7, July 2005.

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One of my partners wrote the majority of this article for a report submitted to ECE4439: Conventional, Renewable and Nuclear Energy, taught by Professor Amirnaser Yazdani at the University of Western Ontario. It is included here for completeness with the rest of the articles. I edited the article and wrote the sections entitled: Worldwide Interoperability for Microwave Access (WiMAX) and Broadband over Power Lines.

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Emerging Issues in Power Systems

Over a century ago, power engineers designed the majority of what we see in the today’s power system infrastructure, based upon significant research during the infancy of wide-scale electric power generation, transmission and distribution.  At the time, utilities built centralized electrical generation under the assumption of unidirectional power flow from the plant to the customer.  These concepts were appropriate for the demand and complexity of the power system during that time; however, with growing electrical demand of modern society, we must take a closer look at these assumptions.  Increasing fuel costs for centralized generation as well as changing social attitudes is leading to increased distributed generation from renewable resources including solar and wind.

Distributed Generation

Distributed generation has changed the way that the power system operates, allowing many small generation facilities to contribute power in order to meet current electricity demand collectively.  Consequently, utilities anticipate that distributed generation systems will introduce new problems since it violates the previous assumption of unidirectional power flow.  Distributed generation introduces the phenomenon of bidirectional power flow, resulting in adverse effects on conventional protection and voltage regulation equipment in the existing power system.

Indeed, many American states have adopted renewable portfolio standards, which require a pre-determined amount of electricity to come from renewable sources by as early as 2013 (for details, see Appendix A: Renewable Portfolio Standards).

Plug-in Electric Vehicles

With dwindling supplies of fossil fuels and increasing prices for crude oil and petroleum products, electric vehicles are steadily gaining momentum.  Although electric vehicles are not yet mainstream, they are expected to have a significant impact on the method and amount of power distribution in the near future as drivers begin switching from gasoline-fuelled vehicles to their electric equivalents en masse.  The increasing popularity of plug-in electric and hybrid-vehicles introduces issues to the power system since it effectively doubles or triples power consumption in already strained residential areas.

There are several problems faced due to the way in which the current power system is configured.  The problem lies not with the method by which the electric car is charged, but rather the number of electric cars being charged, as well as the total amount of energy required to charge each car on a daily basis (see Appendix B: PHEV Demand Increase Example).  This large increase in electrical demand will require additional generation facilities to meet the demand, and require new equipment to deal with the increased demand of consumers.  This paradigm shift will severely affect distribution utilities since the current generation of residential transformers is not rated for such high peak demands.

By implementing smart grids, local distribution utilities will be able to mitigate the problem by staggering the charging sequence of each electric vehicle.  Furthermore, utilities can explore the use of hybrid vehicles as a distributed storage technology or as a power factor controller.  Indeed, the smart grid has the potential to reduce loading on residential substations and small distribution transformers, which eliminate the necessity for expensive high-capacity equipment.

Appendix A: Renewable Portfolio Standards

State	Amount	Deadline	Program Administrator
Arizona	15%	2025	Arizona Corporation Commission
California	20%	2010	California Energy Commission
Colorado	20%	2020	Colorado Public Utilities Commission
Conneticut	23%	2020	Department of Public Utility Control
District of Columbia	11%	2022	DC Public Service Commission
Delaware	20%	2019	Delaware Energy Office
Hawaii	20%	2020	Hawaii Strategic Industries Division
Iowa	105MW		Iowa Utilities Board
Illinois	25%	2025	Illinois Department of Commerce
Massachusetts	4%	2009	Massachusetts Division of Energy Resources
Maryland	9.5%	2022	Maryland Public Service Commission
Maine	10%	2017	Maine Public Utilities Commission
Minnesota	25%	2025	Minnesota Department of commerce
Missouri	11%	2020	Missouri Public Service Commission
Montana	15%	2015	Montana Public Service Commission
New Hampshire	16%	2025	New Hampshire Office of Energy and Planning
New Jersey	22.5%	2021	New Jersey Board of Public Utilities
New Mexico	20%	2020	New Mexico Public Regulation Commission
Nevada	20%	2015	Public Utilities Commission of Nevada
New York	24%	2013	New York Public Service Commission
North Carolina	12.5%	2021	North Carolina Utilities Commission
Oregon	25%	2025	Oregon Energy Office
Pennsylvania	18%	2020	Pennsylvania Public Utility Commission
Rhode Island	15%	2020	Rhode Island Public Utilities Commission
Texas	5880 MW	2015	Public Utility Commission of Texas
Utah	20%	2025	Utah Department of Environmental Quality
Vermont	10%	2013	Vermont Department of Public Service
Virginia	12%	2022	Virginia Department of Mines, Minerals and Energy
Washington	15%	2020	Washington Secretary of State
Wisconsin	10%	2015	Public Service Commission of Wisconsin

Source: The Smart Grid: An Introduction – For Utilities.  Published by the Office of Electricity Delivery and Energy Reliability, United States Department of Energy.  Page 19.  Retrieved on March 20, 2010 from http://www.smartgrid.gov

Appendix B: PHEV Demand Increase Example

Gasoline car energy

Energy density of gasoline = 32MJ/L*50L/tank = 1600MJ/tank

Gasoline energy per month = 1600MJ/tank * 4tank/month = 6400MJ/month

Note that 50L/week = 200L/month would result in a monthly cost of: 200L/month @ $1.00/L = 200$/month

Electric car energy

1kWh = 3.6MJ

6400MJ/3.6MJ = 1778kWh/month

1778kWh/month @ $0.058/kWh = $103/month

Not only is the total electrical energy usage of the family almost tripled for every month, but charging peaks at night-time would exceed the peaks during the daytime and also prevent transformers from cooling down at night (in case they are being run above rated conditions during the daytime).

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A partner and I originally wrote this article for a report submitted to ECE4439: Conventional, Renewable and Nuclear Energy, taught by Professor Amirnaser Yazdani at the University of Western Ontario.

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Toward a Smarter Grid

The smart grid is born of modern necessity; this article discusses a brief history and establishes practical relevance for a smarter grid.

History

The term smart grid has been in use since at least 2005, when the article “Toward a Smart Grid,” written by S. Massoud Amin and Bruce F. Wollenberg, appeared in the September-October issue of Power and Energy Magazine.  For decades, engineers have envisioned an intelligent power grid system with many of the capabilities mentioned in formal definitions of today’s smart grids.  Indeed, while the development of modern microprocessor technologies has only recently begun making it economical for utilities to deploy smart measurement devices at a large scale, its humble beginnings can be traced as far back as the late 1970s, when Theodore Paraskevakos patented the first remote meter reading and load management system [1].

Relevance

For the next several decades, our global energy strategy will inevitably involve upgrading to a more intelligent grid system.  Three fundamental motivators are driving this change: current bulk generation facilities are reaching their limit; utilities must maximize operational efficiency today in order to postpone the costly addition of new transmission and distribution infrastructure; and they must do all of this without compromising reliability of the power system.  In fact, many governments, including the Essential Services Commission (ESC) of Victoria, Australia [2] are adopting legislation to make crucial components of a smarter grid system mandatory.  In Canada, Hydro One’s distribution system has millions of smart meters already installed [3] in preparation for time-of-use rates slated to become mandatory by 2011 [4].

Capacity

Over the next several decades, consumer advocacy groups and environmental concerns from the public will prevent the construction of centralized generation plants as a measure to meet quickly growing demand for electric power.  Moreover, global electricity demand will require the addition of 1000 MW of generation capacity as well as all related infrastructure every week for the foreseeable future [5].  Traditional bulk generation plants are now prohibitively expensive to construct due to cap-and-trade legislation, which places severe financial penalties on processes that continue to emit carbon dioxide and other harmful greenhouse gases.  In conjunction with the higher economic cost, there are also social pressures and widespread concerns about long-term sustainability.

Reliability

With the exception of hydroelectric and geothermal power, renewable energy sources such as wind and solar present unique challenges since they are unpredictable by nature and may vary significantly in their power output due to unpredictably- and rapidly-changing external factors.  Subsequently, we must retrofit the existing power grid to ensure that it can maintain system stability despite these fluctuations in power output.  Furthermore, utilities must have the ability to monitor key indicators of system reliability on a continual basis, particularly as we approach the grid’s maximum theoretical capacity.

Efficiency

A smarter grid can also improve operational efficiencies by intelligently routing different sources of energy.  Because we currently send electricity from distant power generation facilities to serve customers across hundreds of kilometres of transmission lines, approximately eight percent of the total generated electric power is lost as waste heat [6].  Moreover, we can make better use of the existing power generation infrastructure by reducing peak demand; in fact, the International Energy Agency found that a 5% demand response capability can reduce wholesale electricity prices by up to 50% [7].

[1]	Theodoros G. Paraskevakos and W. Thomas Bushman, “Apparatus and method for remote sensor monitoring, metering and control,” USPTO#4241237, December 30, 1980.
[2]	Essential Services Commission, “Mandatory Rollout of Interval Meters for Electricity Customers,” Essential Services Commission, Melbourne, Victoria, Draft Decision.
[3]	Hydro One. (2009, June) One Million Smart Meters Installed – Hydro One Networks and Hydro One Brampton Reach Important Milestone. [Online].  http://www.hydroone.com/OurCompany/MediaCentre/Documents/NewsReleases2009/06_22_2009_smart_meter.pdf
[4]	Ontario Energy Board. (2010, February) Monitoring Report: Smart Meter Deployment and TOU Pricing – 2009 Fourth Quarter. [Online].   http://www.oeb.gov.on.ca/OEB/Industry/Regulatory+Proceedings/Policy+Initiatives+and+Consultations/Smart+Metering+Initiative+%28SMI%29/Smart+Meter+Deployment+Reporting
[5]	The ABB Group. (2010, March) Performance of future [power] systems. [Online].      http://www.abb.com/cawp/db0003db002698/c663527625d66b1dc1257670004fb09f.aspx
[6]	Hassan Farhangi, “The Path of the Smart Grid,” Power and Energy Magazine, vol. 8, no. 1, pp. 18-28, January-February 2010.
[7]	International Energy Agency, “The Power to Choose: Demand Response in Liberalised Electricity Markets,” International Energy Agency, Paris, France, 2003.

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I originally wrote this article for a report submitted to ECE4439: Conventional, Renewable and Nuclear Energy, taught by Professor Amirnaser Yazdani
at the University of Western Ontario.

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