Demand response has become a headliner in the current utility and regulatory lexicon for a number of reasons. State regulators are mandating that utilities examine how demand response programs can reduce or shave peak loads as opposed to building additional generation. Regulators are in turn being pressured by federal legislation to pursue time-varying rates and support smart metering and smart grid activities. What’s more, utilities continue to be internally driven to improve efficiency through technological innovations such as demand response. Yet, the success of demand response programs rests extensively on residential and small commercial consumers who generally know little about their energy use, have not traditionally had tools to understand the link between their lifestyle and energy consumption, and cannot fathom what the difference between a kWh and kW is and why they should care.
So, why should they care? Simply stated, demand response programs are less expensive for customers than the alternatives. During peak system or regional loading events the increasingly deregulated electricity market will increase the price of energy, so it is less expensive for the utility to reduce load as opposed to buying energy on the open market. Demand response also can improve the reliability of electric service during times of peak loading when high demand can result in localized or widespread system failure. Whatever the reason, demand response can help society and the utilities meet the challenges of today’s evolving utility landscape.
Much has been written on the actual definition of demand response, and although the specifics may vary, it is generally agreed that it incorporates pricing programs, customer-enabling technologies, or efforts to encourage short-duration load reduction during specific periods of peak energy usage. The demand response programs being designed today are either based on new critical-peak-pricing (CPP) tariffs or other technology-enabled pricing programs. Most discussion on the subject has been relatively academic or exploratory, as only a limited number of demand response programs have been fully implemented.
The limited experience does not mean that results are theoretical or unproven. There is a significant amount of available information from pilots, customer trials, and initial implementations that have all supported the position that mass-market customers -- given appropriate pricing signals and/or enabling technology -- will provide the demand reductions required by utilities to make these programs effective and reduce the need for operating or construction of expensive and environmentally impactful new generation assets. It should also be noted that when the narrow definition of demand response that is commonly used today is expanded to include traditional air-conditioning cycling and older demand-side management programs, the industry has significant experience in how customers respond to load-management programs.
One of the main differences between the core objectives of advanced metering infrastructure (AMI) initiatives -- as compared to automated meter reading (AMR) objectives -- is the ability to deliver timely high-resolution energy usage information to consumers, allowing them to understand their consumption patterns and the connection between appliance use or lifestyle decisions and cost. This information is normally in the form of hourly data for residential and small commercial customers and 15-minute data for large commercial and industrial users. After being provided just a single monthly usage value and corresponding bill for so long, it is imperative that utilities not underestimate the enormous transition that consumers be facing when they are asked to modify or reduce their energy consumption as well as react to pricing changes from the utility.
Success Requires Customer Participation
Demand response programs all depend on utility-customer participation to deliver the benefits that are envisioned. Yet, how involved does the “average” residential customer wish to be? Is the average residential consumer more concerned with reducing monthly energy cost or being environmentally conscious by reducing their carbon footprint? How intrusive can the enabling technology be without impacting the consumer’s comfort? Do consumers want the utility to control the operation of the demand response devices or have control over their consumption reduction? The customer’s acceptance will ultimately determine the success of the programs, which could well be based on regional demographics.
Given the significant emphasis that utilities and regulators have placed on getting AMI programs implemented, one would expect to see a significant amount of demand response programs in existence that are based on using the available AMI interval data to support CPP programs or similar innovative alternatives. Unfortunately, this is not yet a widespread reality. While there are CPP programs being implemented by investor-owned utilities such as Southern California Edison, Pacific Gas & Electric Company, and Dominion, the large scale and industry-wide launch of these programs is waiting for the availability of the smart meter systems required to collect and manage the interval data.
Presenting Customers with Usage Data
Interval data, which is the core deliverable of AMI, is essential for customers to participate in demand response programs. Interval data provides customers with information on their consumption patterns and shows them the impact of their actions. A noteworthy example of the use of interval data, and the largest effort of its kinds to date, was undertaken by PPL Electric Utilities of Allentown, Pennsylvania, which recently announced that it is making interval data available to its 1.4 million electric customers. The data is presented on a website using software from Aclara that helps them understand how they are using energy.
Figure 1 illustrates the presentment of the hourly data that a residential customer of PPL Electric Utilities might receive when a smart meter is installed and the data made available to the customer via the utility’s web portal.
Usage data can be presented in several formats. Data can be shown in actual energy-consumption values such as kWh. Most people in the energy industry like this approach because it is more common and traditional. However, energy-consumption values are generally meaningless to the average residential customer. Unless they understand energy units and can convert these units to dollars and then compare against their perceived benefits, the ability for the average residential customers to make intelligent decisions using this data is nominal.
One way to help consumers understand energy usage is to describe it in dollars – a measure that the consumer can understand and evaluate -- as opposed to engineering units, which are far less meaningful or relevant to the average consumer. It also provides a direct connection between the information being presented and the eventual bill the customer will pay.
Usage data also needs to be matched with the appropriate rate structure to which the customer is assigned. Traditionally, most residential electric customers are on flat or inverted-tier rate structures. As the industry transitions to more appropriate time-based rate strategies and interval metering data is available to support these strategies, the presentation of the data must match the customer’s rate profile. Figure 2 adds the date, or time of use (TOU), to the underlying interval data, highlighting periods when the customer experienced the highest cost for energy. Figure 3 adds CPP to the TOU rate structure.
Rate design is critical to modifying consumer behavior. Program results have shown that improperly designed time-based rates do not send the appropriate signals to customers, nor do they provide the financial incentives to encourage reduced energy consumption. However, it is difficult to create rates that are not punitive but do reflect the anticipated costs for electricity generation in various time categories. Many regulators also want new rates to be revenue neutral, which could be counterproductive to the ultimate goal of incenting consumers to reduce load during peak periods, since revenue neutral rates may not accurately reflect the true cost of energy being consumed during high-demand periods.
Analytical Tools Necessary for Acceptance
Since industrial and commercial customers have enjoyed the availability of interval metering for many years, an entire industry has developed to assist the plant and facility management business with obtaining, analyzing, and understanding their energy use and providing effective options to reduce costs through equipment replacement, lighting retrofits, and other energy-reduction techniques. Residential and small commercial customers do not need this level of sophistication, but they do require interval data and analytical tools to make effective choices.
Studies have consistently indicated that when consumers are provided with detailed energy-consumption information and tools to understand their use, there is a statistically significant overall reduction in load. This energy-conservation effect should be repeated in the area of demand reduction by further educating consumers about their usage patterns during peak periods and by providing analytic tools and technology to reduce their demand.
Many utilities have been using bill presentment programs that provide powerful yet easy-to-understand analytic tools for customers to disaggregate their energy consumption into specific usage buckets. This provides the end-use consumer with the ability to understand the components of their bills and make energy conservation choices, as in Figure 4.
Over 240,000 customers of PPL Electric Utilities have taken advantage of this tool to better understand their energy use in preparation of being exposed to time-based rates once the rate caps in Pennsylvania are removed in 2012. The next step is likely to be the use of the actual customer interval data to provide more refined profiles and analytics as well as an extension of the analytic tools to highlight activities that will have the most impact on demand response efforts.
Enabling Technologies for Demand Response
There has been a tremendous amount of work in the industry related to the technology enabling systems required to support demand response. Home-area networks (HAN) are envisioned to enable controllable thermostats, consumer display devices, load- management nodes and other customer-premise devices that will automatically take actions to reduce load. Much has recently been written about the impact that these technologies could have, and pilots have conclusively shown that automated controls triggered by pricing signals may have a significant impact on load. However, industry should not have to wait for the ubiquitous availability of the HAN-based devices in order to enable innovative customer-focused demand response programs.
Effective solutions exist today that combine interval usage data available from AMI systems with load-control devices. In these systems, the AMI networks create technology-enabled pricing programs. The consumer can choose which appliances to enroll in the utility-managed programs and enable the utility to manage these loads during critical periods to both reduce the utility system load and the impact on the consumer’s bill. One example of this synergy is a recent analysis by Rappahannock Electric Cooperative, Fredericksburg, Virginia, that concluded the cooperative had saved more than $50 million for the utility as well as generated savings for their customers with a hybrid load-management system. In this system, customers signed up to have the utility place a load-control device on their water heaters that could be remotely controlled by the utility through the AMI solution.
In addition to providing consumers with an understanding of their energy usage, interval data can be used to provide confirmation that the anticipated load reduction from the enabling technology was received. Rather than accumulating just device operating statistics, the reduction in actual load provides the “bottom line” answer to how the system is performing. In aggregate, the load data for all of the customers using a particular time-base rate or specific enabling technology will provide performance information to the utility for planning or future distribution needs. In particular, the interval data provides important information to individual customers on how they performed during CPP events or other specific periods. This feedback is extremely important in terms of both demand response system performance and customer satisfaction.
There are many questions to be answered and program designs that require more investigation. The industry does not yet understand what level of involvement most people will want to have in the management of their energy use. Will the average consumer, once they have a better understanding of how they use and what demand-reduction changes that they are willing to undertake, pay attention to the available information or be interested in continuing to tweak their energy use? How will the desire to reduce carbon footprint impact both energy conservation and demand reduction? What are the most effective ways to communicate with consumers? The key to all of these and other issues is that the industry must ensure that the focus of the debate is on the customer – for it is this group that will accept the challenges in understanding the information presented and take the actions on which society is depending.
About the Author
Kevin Cornish is the Director of Technical Support for Aclara with responsibility for supporting the Aclara STAR Network system, TWACS technology, and Aclara Software solutions to the utility industry. He has more than 20 years of experience in utility operations and engineering, product management, AMI system sales and project management, and business development roles. Kevin holds a BSEE degree from the University of California at Berkeley and a Master of Engineering degree in Power Systems from Santa Clara University as well as an MBA in Telecommunications Management from California State University (Hayward).
For further information or to provide comments on this article, please contact Scott Schaffer, Manager of Marketing & Communications, Aclara (www.aclara.com), 945 Hornet Drive, Hazelwood, MO 63042 Tel: 314-895-7397 Email: sschaffer@aclara.com.
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