The GridWise Olympic Peninsula Project

The Olympic Peninsula Project consisted of a field demonstration and test of advanced price signal-based control of distributed energy resources (DERs). Sponsored by the U.S. Department of Energy (DOE) and led by the Pacific Northwest National Laboratory, the project was part of the Pacific Northwest Grid- Wise Testbed Demonstration.

Other participating organizations included the Bonneville Power Administration, Public Utility District (PUD) #1 of Clallam County, the City of Port Angeles, Portland General Electric, IBM’s T.J. Watson Research Center, Whirlpool and Invensys Controls. The main objective of the project was to convert normally passive loads and idle distributed generation into actively participating resources optimally coordinated in near real time to reduce stress on the local distribution system.

Planning began in late 2004, and the bulk of the development work took place in 2005. By late 2005, equipment installations had begun, and by spring 2006, the experiment was fully operational, remaining so for one full year.

The motivating theme of the project was based on the GridWise concept that inserting intelligence into electric grid components at every point in the supply chain – from generation through end-use – will significantly improve both the electrical and economic efficiency of the power system. In this case, information technology and communications were used to create a real-time energy market system that could control demand response automation and distributed generation dispatch. Optimal use of the DER assets was achieved through the market, which was designed to manage the flow of power through a constrained distribution feeder circuit.

The project also illustrated the value of interoperability in several ways, as defined by the DOE’s GridWise Architecture Council (GWAC). First, a highly heterogeneous set of energy assets, associated automation controls and business processes was composed into a single solution integrating a purely economic or business function (the market-clearing system) with purely physical or operational functions (thermostatic control of space heating and water heating). This demonstrated interoperability at the technical and informational levels of the GWAC Interoperability Framework (www.gridwiseac.org/about/publications.aspx), providing an ideal example of a cyber-physical-business system. In addition, it represents an important class of solutions that will emerge as part of the transition to smart grids.

Second, the objectives of the various asset owners participating in the market were continuously balanced to maintain the optimal solution at any point in time. This included the residential demand response customers; the commercial and municipal entities with both demand response and distributed generation; and the utilities, which demonstrated interoperability at the organizational level of the framework.

PROJECT RESOURCES

The following energy assets were configured to respond to market price signals:

  • Residential demand response for electric space and water heating in 112 single-family homes using gateways connected by DSL or cable modem to provide two-way communication. The residential demand response system allowed the current market price of electricity to be presented to customers. Consumers could also configure their demand response automation preferences. The residential consumers were evenly divided among three contract types (fixed, time of use and real time) and a fourth control group. All electricity consumption was metered, but only the loads in price-responsive homes were controlled by the project (approximately 75 KW).
  • Two distributed generation units (175 KW and 600 KW) at a commercial site served the facility’s load when the feeder supply was not sufficient. These units were not connected in parallel to the grid, so they were bid into the market as a demand response asset equal to the total load of the facility (approximately 170 KW). When the bid was satisfied, the facility disconnected from the grid and shifted its load to the distributed generation units.
  • One distributed microturbine (30 KW) that was connected in parallel to the grid. This unit was bid into the market as a generation asset based on the actual fixed and variable expenses of running the unit.
  • Five 40-horsepower (HP) water pumps distributed between two municipal water-pumping stations (approximately 150 KW of total nameplate load). The demand response load from these pumps was incrementally bid into the market based on the water level in the pumped storage reservoir, effectively converting the top few feet of the reservoir capacity into a demand response asset on the electrical grid.

Monitoring was performed for all of these resources, and in cases of price-responsive contracts, automated control of demand response was also provided. All consumers who employed automated control were able to temporarily disable or override project control of their loads or generation units. In the residential realtime price demand response homes, consumers were given a simple configuration choice for their space heating and water heating that involved selecting an ideal set point and a degree of trade-off between comfort and price responsiveness.

For real-time price contracts, the space heater demand response involved automated bidding into the market by the space heating system. Since the programmable thermostats deployed in the project didn’t support real-time market bidding, IBM Research implemented virtual thermostats in software using an event-based distributed programming prototype called Internet- Scale Control Systems (iCS). The iCS prototype is designed to support distributed control applications that span virtually any underlying device or business process through the definition of software sensor, actuator and control objects connected by an asynchronous event programming model that can be deployed on a wide range of underlying communication and runtime environments. For this project, virtual thermostats were defined that conceptually wrapped the real thermostats and incorporated all of their functionality while at the same time providing the additional functionality needed to implement the real-time bidding. These virtual thermostats received
the actual temperature of the house as well as information about the real-time market average price and price distribution and the consumer’s preferences for set point and comfort/economy trade-off setting. This allowed the virtual thermostats to calculate the appropriate bid every five minutes based on the changing temperature and market price of energy.

The real-time market in the project was implemented as a shadow market – that is, rather than change the actual utility billing structure, the project implemented a parallel billing system and a real-time market. Consumers still received their normal utility bill each month, but in addition they received an online bill from the shadow market. This additional bill was paid from a debit account that used funds seeded by the project based on historical energy consumption information for the consumer.

The objective was to provide an economic incentive to consumers to be more price responsive. This was accomplished by allowing the consumers to keep the remaining balance in the debit account at the end of each quarter. Those consumers who were most responsive were estimated to receive about $150 at the end of the quarter.

The market in the project cleared every five minutes, having received demand response bids, distributed generation bids and a base supply bid based on the supply capacity and wholesale price of energy in the Mid-Columbia system operated by Bonneville Power Administration. (This was accomplished through a Dow Jones feed of the Mid-Columbia price and other information sources for capacity.) The market operation required project assets to submit bids every five minutes into the market, and then respond to the cleared price at the end of the five-minute market cycle. In the case of residential space heating in real-time price contract homes, the virtual thermostats adjusted the temperature set point every five minutes; however, in most cases the adjustment was negligible (for example, one-tenth of a degree) if the price was stable.

KEY FINDINGS

Distribution constraint management. As one of the primary objectives of the experiment, distribution constraint management was successfully accomplished. The distribution feeder-imported capacity was managed through demand response automation to a cap of 750 KW for all but one five-minute market cycle during the project year. In addition, distributed generation was dispatched as needed during the project, up to a peak of about 350 KW.

During one period of about 40 hours that took place from Oct. 30, 2006, to Nov. 1, 2006, the system successfully constrained the feeder import capacity at its limit and dispatched distributed generation several times, as shown in Figure 1. In this figure, actual demand under real-time price control is shown in red, while the blue line depicts what demand would have been without real-time price control. It should be noted that the red demand line steps up and down above the feeder capacity line several times during the event – this is the result of distributed generation units being dispatched and removed as their bid prices are met or not.

Market-based control demonstrated. The project controlled both heating and cooling loads, which showed a surprisingly significant shift in energy consumption. Space conditioning loads in real-time price contract homes demonstrated a significant shift to early morning hours – a shift that occurred during both constrained and unconstrained feeder conditions but was more pronounced during constrained periods. This is similar to what one would expect in preheating or precooling systems, but neither the real nor the virtual thermostats in the project had any explicit prediction capability. The analysis showed that the diurnal shape of the price curve itself caused the effect.

Peak load reduced. The project’s realtime price control system both deferred and shifted peak load very effectively. Unlike the time-of-use system, the realtime price control system operated at a fine level of precision, responding only when constraints were present and resulting in a precise and proportionally appropriate level of response. The time-of-use system, on the other hand, was much coarser in its response and responded regardless of conditions on the grid, since it was only responding to preconfiured time schedules or manually initiated critical peak price signals.

Internet-based control demonstrated. Bids and control of the distributed energy resources in the project were implemented over Internet connections. As an example, the residential thermostats modified their operation through a combination of local and central control communicated as asynchronous events over the Internet. Even in situations of intermittent communication failure, resources typically performed well in default mode until communications could be re-established. This example of the resilience of a well-designed, loosely coupled distributed control application schema is an important aspect of what the project demonstrated.

Distributed generation served as a valuable resource. The project was highly effective in using the distributed generation units, dispatching them many times over the duration of the experiment. Since the diesel generators were restricted by environmental licensing regulations to operate no more than 100 hours per year, the bid calculation factored in a sliding scale price premium such that bids would become higher as the cumulative runtime for the generators increased toward 100 hours.

CONCLUSION

The Olympic Peninsula Project was unique in many ways. It clearly demonstrated the value of the GridWise concepts of leveraging information technology and incorporating market constructs to manage distributed energy resources. Local marginal price signals as implemented through the market clearing process, and the overall event-based software integration framework successfully managed the bidding and dispatch of loads and balanced the issues of wholesale costs, distribution congestion and customer needs in a very natural fashion.

The final report (as well as background material) on the project is available at www.gridwise.pnl.gov. The report expands on the remarks in this article and provides detailed coverage of a number of important assertions supported by the project, including:

  • Market-based control was shown to be a viable and effective tool for managing price-based responses from single-family premises.
  • Peak load reduction was successfully accomplished.
  • Automation was extremely important in obtaining consistent responses from both supply and demand resources.
  • The project demonstrated that demand response programs could be designed by establishing debit account incentives without changing the actual energy prices offered by energy providers.

Although technological challenges were identified and noted, the project found no fundamental obstacles to implementing similar systems at a much larger scale. Thus, it’s hoped that an opportunity to do so will present itself at some point in the near future.

Trilliant: Advanced Metering Infrastructure Solutions for Utilities and Green Energy Markets

Trilliant Incorporated provides wireless network solutions and software for advanced metering, demand response, smart grid and submetering. With more than 20 years’ experience solving utility meter communications needs, the company empowers flexibility and choice through the adoption and integration of open standards-based technologies.

ADVANCED METERING

Trilliant SecureMesh™ AMI solutions enable utilities to introduce services and programs such as time-of-use (TOU) metering, CIS initiated real-time meter reads and customer disconnect/ reconnect. These programs are transforming the traditional customer-utility relationship through interval-based consumption data and two-way messaging, resulting in reduced operational costs and improved reliability.

DEMAND RESPONSE

Many utilities are initiating smart metering and AMI programs with a primary goal of ad dressing critical peak demand challenges using TOU pricing, critical peak pricing and demand response programs. Trilliant is the first AMI supplier to provide an open standards-based platform for AMI-integrated demand response (i.e., load control) incorporating smart thermostats – and thus air conditioning equipment – and other loads such as pool pumps and water heaters. The Trilliant Demand Response solution also supports in-premise (“in-home”) displays that offer consumers real-time information on energy usage and utility-initiated messages.

SMART GRID

By leveraging Smart Grid solutions from Trilliant, utilities can realize dramatic improvements in system performance and cost. System operational challenges such as outage detection and restoration verification are supported through a combination of network-based intelligence and operations center applications. Trilliant’s Smart Grid solutions enable operations to more effectively identify faults and rapidly restore service on the basis of real-time readings of on-premise conditions. These offerings may also be integrated with extended enterprise systems supporting the mobile field force. Smart Grid solutions from Trilliant provide the foundation for advanced applications such as utility asset life cycle management and others that can benefit from the use of actual loading data.

SUBMETERING

Trilliant Energy Services offerings include turnkey submetering solutions, utility data profiling and online presentment to meet the needs of electric and natural gas utilities. Because Trilliant is an expert in energy technology the company’s solutions offer benefits to all stakeholders – from condo developers and corporations to owners and managers and directly to residential suite owners.

Ontario Pilot

Smart metering technologies are making it possible to provide residential utility customers with the sophisticated “smart pricing” options once available only to larger commercial and industrial customers. When integrated with appropriate data manipulation and billing systems, smart metering systems can enable a number of innovative pricing and service regimes that shift or reduce energy consumption.

In addition, by giving customers ready access to up-to-date information about their energy demand and usage through a more informative bill, an in-home display monitor or an enhanced website, utilities can supplement smart pricing options and promote further energy conservation.

SMART PRICES

Examples of smart pricing options include:

  • Time-of-use (TOU) is a tiered system where price varies consistently by day or time of day, typically with two or three price levels.
  • Critical peak pricing (CPP) imposes dramatically higher prices during specific days or hours in the year to reflect the actual or deemed price of electricity at that time.
  • Critical peak rebate (CPR) programs enable customers to receive rebates for using less power during specific periods.
  • Hourly pricing allows energy prices to change on an hourly basis in conformance with market prices.
  • Price adjustments reflect customer participation in load control, distributed generation or other programs.

SMART INFORMATION

Although time-sensitive pricing is designed primarily to reduce peak demand, these programs also typically result in a small reduction in overall energy consumption. This reduction is caused by factors independent of the primary objective of TOU pricing. These factors include the following:

  • Higher peak pricing causes consumers to eliminate, rather than merely delay, activities or habits that consume energy. Some of the load reductions that higher peak or critical peak prices produce are merely shifted to other time periods. For example, consumers do not stop doing laundry; they simply switch to doing it at non-peak times. In these cases the usage is “recovered.” Other load reductions, such as those resulting from consumers turning off lights or lowering heat, are not recovered, thus reducing the household’s total electricity consumption.
  • Dynamic pricing programs give participants a more detailed awareness of how they use electricity, which in turn results in lower consumption.
  • These programs usually increase the amount of usage information or feedback received by the customer, which also encourages lower consumption.

The key challenge for utilities and policy makers comes in deciding which pricing and communications structures will most actively engage their customers and drive the desired conservation behaviors. Studies show that good customer feedback on energy usage can reduce total consumption by 5 to 10 percent. Smart meters let customers readily access more up-to-date information about their hourly, daily and monthly energy usage via in-home displays, websites and even monthly bill inserts.

The smart metering program undertaken by the province of Ontario, Canada, presents one approach and serves as a useful example for utility companies contemplating similar deployments.

ONTARIO’S PROGRAM

In 2004, anticipating a serious energy generation shortfall in coming years, the government of Ontario announced plans to have smart electricity meters installed in 800,000 homes and small businesses by the end of 2007, and throughout Ontario by 2010. The initiative will affect approximately 4.5 million customers.

As the regulator of Ontario’s electricity industry, the Ontario Energy Board (OEB) was responsible for designing the smart prices that would go with these smart meters. The plan was to introduce flexible, time-of-use electricity pricing to encourage conservation and peak demand shifting. In June 2006, the OEB commissioned IBM to manage a pilot program that would help determine the best structure for prices and the best ways to communicate these prices.

By Aug. 1, 2006, 375 residential customers in the Ottawa area of Ontario had been recruited into a seven-month pilot program. Customers were promised $50 as an incentive for remaining on the pilot for the full period and $25 for completing the pilot survey.

Pilot participants continued to receive and pay their “normal” bimonthly utility bills. Separately, participants received monthly electricity usage statements that showed their electricity supply charges on their respective pilot price plan, as illustrated in Figure 1. Customers were not provided with any other new channels for information, such as a website or in-home display.

A control group that continued being billed at standard rates was also included in the study. Three pricing structures were tested in the pilot, with 125 customers in each group:

  • Time-of-use (TOU). Ontario’s TOU pricing includes off-peak, mid-peak and peak prices that changed by winter and summer season.
  • TOU with CPP. Customers were notified a day in advance that the price of the electricity commodity (not delivery) for three or four hours the next day would increase to 30 cents per kilowatt hour (kWh) – nearly six times the average TOU price. Seven critical peak events were declared during the pilot period – four in summer and three in winter. Figure 2 shows the different pricing levels.
  • TOU with CPR. During the same critical peak hours as CPP, participants were provided a rebate for reductions below their “baseline” usage. The base was calculated as the average usage for the same hours of the five previous nonevent, non-holiday weekdays, multiplied by 125 percent.

The results from the Ontario pilot clearly demonstrate that customers want to be engaged and involved in their energy service and use. Consider the following:

  • Within the first week, and before enrollment was suspended, more than 450 customers responded to the invitation letter and submitted requests to be part of the pilot – a remarkable 25 percent response rate. In subsequent focus groups, participants emphasized a desire to better monitor their own electricity usage and give the OEB feedback on the design of the pricing. These were in fact the primary reasons cited for enrolling in the pilot.
  • In comparison to the control group, total load shifting during the four summertime critical peak periods ranged from 5.7 percent for TOU-only participants to 25.4 percent for CPP participants.
  • By comparing the usage of the treatment and control groups before and during the pilot, a substantial average conservation effect of 6 percent was recorded across all customers.
  • Over the course of the entire pilot period, on average, participants shifted consumption and paid 3 percent, or $1.44, less on monthly bills with the TOU pilot prices, compared with what they would have paid using the regular electricity prices charged by their utility. Of all participants, 75 percent saved money on TOU prices. Figure 3 illustrates the distribution of savings.
  • When this shift in consumption was combined with the reduction in customers’ overall consumption, a total average monthly savings of more than $4 resulted. From this perspective, 93 percent of customers would pay less on the TOU prices over the course of the pilot program than they would have with the regular electricity prices charged by their utility.
  • Citing greater control of their energy costs and benefits to the environment, 7 percent of participants surveyed said they would recommend TOU pricing to their friends.

There were also some unexpected results. For instance, there was no pattern of customers shifting demand away from the dinnertime peak period in winter. In addition, TOU-only pricing alone did not result in a statistically significant shifting of power away from peak periods.

CONCLUSION

In summary, participants in the Ontario Energy Board’s pilot program approved of these smarter pricing structures, used less energy overall, shifted consumption from peak periods in the summertime and, as a result, most paid less on their utility bills.

Over the next decade, as the utility industry evolves to the intelligent utility network and smart metering technologies are deployed to all customers, utilities will have many opportunities to implement new electricity pricing structures. This transition will represent a considerable technical challenge, testing the limits of the latest communications, data management, engineering, metering and security technologies.

But the greater challenge may come from customers. Much of the benefit from smart metering is directly tied to real, measurable and predictable changes in how customers use energy and interact with their utility provider. Capturing this benefit requires successful manipulation of the complex interactions of economic incentives, consumer behavior and societal change. Studies such as the OEB Smart Pricing Pilot provide another step in penetrating this complexity, helping the utility industry better understand how customers react and interact with these new approaches.

About Alcatel-Lucent

Alcatel-Lucent’s vision is to enrich people’s lives by transforming the way the world communicates. Alcatel-Lucent provides solutions that enable service providers, enterprises and governments worldwide to deliver voice, data and video communication services to end users. As a leader in carrier and enterprise IP technologies; fixed, mobile and converged broadband access; applications and services, Alcatel-Lucent offers the end-to-end solutions that enable compelling communications services for people at work, at home and on the move.

With 77,000 employees and operations in more than 130 countries, Alcatel-Lucent is a local partner with global reach. The company has the most experienced global services team in the industry and includes Bell labs, one of the largest research, technology and innovation organizations focused on communications. Alcatel-Lucent achieved adjusted revenues of €17.8 billion in 2007, and is incorporated in France, with executive offices located in Paris.

YOUR ENERGY AND UTILITY PARTNER

Alcatel-Lucent offers comprehensive capabilities that combine carrier-grade communications technology and expertise with utility industry- specific knowledge. Alcatel-Lucent’s IP transformation expertise and utility market-specific knowledge have led to the development of turnkey communications solutions designed for the energy and utility market. Alcatel-Lucent has extensive experience in:

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Working with Alcatel-Lucent enables energy and utility companies to realize the increased reliability and greater efficiency of next-generation communications technology, providing a platform for – and minimizing the risks associated with – moving to SmartGrid solutions. And Alcatel-Lucent helps energy and utility companies achieve compliance with regulatory requirements and reduce operational expenses while maintaining the security, integrity and high availability of their power infrastructure and services.

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Utility companies are experienced at building and operating reliable and effective networks to ensure the delivery of essential information and maintain fl awless service delivery. The Alcatel-Lucent IP/MPLS solution can enable utility operators to extend and enhance their networks with new technologies like IP, Ethernet and MPLS. These new technologies will enable the utility to optimize its network to reduce both capital expenditures and operating expenses without jeopardizing reliability. Advanced technologies also allow the introduction of new applications that can improve operational and workflow efficiency within the utility. Alcatel-Lucent leverages cutting-edge technologies along with the company’s broad and deep experience in the utility industry to help utility operators build better, next-generation networks with IP/MPLS.

THE ALCATEL-LUCENT ADVANTAGE

Alcatel-Lucent has years of experience in the development of IP, MPLS and Ethernet technologies. The Alcatel-Lucent IP/MPLS solution offers utility operators the flexibility, scale and feature sets required for mission-critical operation. With the broadest portfolio of products and services in the telecommunications industry, Alcatel-Lucent has the unparalleled ability to design and deliver end-to-end solutions that drive next-generation communications networks.