Navigant Research Blog

Clearing the Data Hurdle for Effective Asset Performance Management

— June 9, 2017

Few in the utility industry today disagree with the notion that technical advances in terms of sensing and analytics are yielding powerful new solutions for asset performance management (APM) and predictive maintenance. Many would also agree, however, that there are challenges for utilities ready to digitize their asset management program. Indeed, finding, consolidating, mapping, cleansing, and storing the data from a multitude of sources can seem like a daunting challenge.

Best practices are emerging as major utilities take the APM plunge, and meaningful benefits to holistic APM strategies are now clear. One transmission operator, for example, has avoided five major transformer failures since the implementation of its APM program—and said that “just one or two saves paid for the system.”

With growing emphasis on reliability from regulators, aging infrastructure, and accelerating workforce retirement at utilities, the need for a utilitywide APM program has never been greater. Understanding the data challenges utilities are likely to face is an important first step to putting a plan in place.

Who, What, Where, When, and Why?

When preparing to deploy an APM solution, the five Ws should be asked in the context of company assets and data:

  • Who: Which operating divisions house data needed for the desired analytics? What institutional knowledge is held by which actors? How can it be incorporated into the APM system for preservation? How do IT and operational personnel coordinate efforts?
  • What: What datasets exist today? In what format? Electronic or paper-based? Is the data accuracy good? Is it verifiable? Are there new datasets that need to be developed?
  • Where: Where has asset data historically been housed? Where should it be stored going forward? Do I need a data lake? Can I store my data in the cloud? How should the transition be orchestrated? Is there data available in the field that is not communicated to the operations center? Should asset analytics be performed centrally or in the field?
  • When: How often should asset data be updated? Is there connectivity to the asset, allowing for real-time or on-demand reads?
  • Why: For what applications do I need this data? For what applications might I want the data in the future? What are my primary goals for the APM system—reducing maintenance expenses with proactive repairs and replacements? Reducing outage frequency/duration? Shoring up grid stability where solar penetration is high and growing? All of the above?

As the APM planning team drills down into each of these questions, new questions will become apparent. Testing and validation of analytics algorithms must be thorough and must be completed on an ongoing basis—rather than one and done. As new data becomes available, adjustments may be needed due to previously unforeseen situations.

Is It Worth It?

It’s still early days in the APM world, but clear benefits have been reported by utilities that have done pilots or full-scale deployments. As more utilities invest in APM solutions, it seems likely that the benefits—in terms of avoiding unnecessary repairs, preventing outages, averting capital investment, and efficiently managing field crews—will become apparent. New applications that can be created with a robust, agile APM platform and complete, quality datasets will also emerge.

Join the Webinar

If you’d like to learn more about the nitty gritty details of the APM world, attend the Navigant Research webinar, The Digital Future of Asset Performance Management. Join me, ABB’s Matthew Zafuto, and FirstEnergy’s Dana Parshall for an interactive discussion of the data challenges and lessons learned in FirstEnergy’s implementation of ABB’s Asset Health Center solution.

 

Envision Charlotte: Putting Data at the Heart of Smart City Programs

— March 10, 2017

Established in 2011 as a non-profit, public-private partnership to improve energy efficiency and sustainability in the City of Charlotte in North Carolina, Envision Charlotte has a particular place in the growing list of smart city projects in the United States. The founding project was a collaboration between Duke Energy, Charlotte Center City Partners, and a number of supplier partners, including Cisco, Itron, and Verizon, to make 61 large commercial buildings in downtown Charlotte more energy efficient. Today, the program has expanded to tackle a range of projects and sustainability goals, including energy efficiency, water conservation, and air quality. Moreover, the project is having a direct influence on other US cities through the development of the Envision America program.

I recently had a chance to catch up with Amy Aussieker, the executive director for Envision Charlotte, to discuss progress. Aussieker outlined the four key pillars the program currently focuses on:

  • Energy: The program continues to build on the success of its initial project with the city’s commercial buildings. The aim of that project was to reduce building energy consumption by 20%, and it has so far delivered around $20 million in energy savings. A grant from the US Department of Energy is now enabling the project to be rolled out to an additional 200 buildings. The project also seeded a commercial program from Duke Energy to address the potential for energy savings in offices across its service territories.
  • Water: Improving water efficiency and quality is the next priority for the program. Itron, for example, has deployed smart water meters in 22 downtown buildings that are part of the original energy savings program. The goal is to collect data on water consumption for a year to help shape water management programs and to develop benchmarks for building managers. Envision Charlotte is also working with Charlotte Water, the local water company, as it looks to meet growing pressures on the regional water system.
  • Air quality: A growing area of focus for Envision Charlotte is air pollution. Car and truck usage are the biggest contributors to air quality problems in the city, and projects are being established to encourage people to reduce vehicle miles and use local transit systems. However, there is little data available on local air quality conditions, so it is difficult to monitor the impact of specific interventions. The team is examining how it can create benchmarks to show the effectiveness of different programs.
  • Waste reduction: The fourth pillar of the program is waste reduction. Envision Charlotte is trying to help reduce the 5 million pounds of solid waste sent to landfills by the City of Charlotte and Mecklenburg County residents and businesses every day. This is another area where the team is looking to collect more data, particularly around recycling rates and how to improve them.

Looking Ahead, Data Is Key

Envision Charlotte is building on its initial successes, looking to scale up proven solutions and identifying new issues to address. The program also continues to extend its links in the community and has developed close ties with the University of North Carolina, which is hoping to develop a smart city center of excellence.

One thing common to all of the program’s initiatives is the importance given to data collection and analysis. Data is seen as key to understanding the root causes of the issues being addressed and to developing solutions that are effective and viable. Using sensors and smart devices to gather that data is not a technical demonstration exercise, but rather, a necessary step to developing effective programs for change. This helps ensure that investments are made in the right projects while also helping to build momentum and ensure successful programs feed enthusiasm for the next project.

The recent announcement of the 2017 Envision America award winners provides further evidence of the Charlotte team’s impact. The program leverages the success of Envision Charlotte to accelerate deployment of innovative technologies in other cities. The aim is for cities to learn from the experience of Charlotte, but also to find their own model fitting local circumstances and priorities. Charlotte is becoming an important node in the growing network of smart cities worldwide that are sharing ideas and developing robust and effective approaches to common city problems.

 

Data Analysis Key to Unlocking EV Demand

— January 5, 2017

The term big data has quickly entered the lexicon of technologists in energy, IT, transportation, healthcare, security, and other industries for the potential of using data to get a better systems-level understanding of how industries function. In the nascent industry of plug-in electric vehicles (PEVs), sharing data on how these vehicles are driven in comparison to gasoline vehicles, as well as vehicle charging habits and requirements, are viewed as critical to growing the market beyond today’s less than 2% penetration rates.

Recognizing this, the White House assembled a group of government and private sector data enthusiasts (from automakers, charging networks, and others) for a Datathon in late November last year. The event featured presentations by many leading researchers who shared their latest work to get their peers interested in comparing, processing, and combining these data sets to increase the understanding of market requirements. Participants heard from the following:

  • The Idaho National Laboratory—the granddaddy of EV data, having housed and analyzed EV data since the early EV Project, and most recently included recommendations on residential and workplace charging based on its extensive experience.
  • The Argonne National Lab offers the Downloadable Dynamometer Database, which houses test data evaluating the energy consumption of PEVs as well as conventional drive vehicles in cold, average, and warm driving temperatures.
  • The National Renewable Energy Laboratory (NREL) offers the Transportation Secure Data Center, providing access to regional travel surveys and studies to understand the differences in the driving patterns in the United States. This data for all types of vehicles can be used to see how PEV driving habits compare to gasoline cars, and how PEV usage may evolve once the promised 200+ mile range EVs hit the market. As an example, NREL hosts the 2014-2015 Puget Sound Regional Travel Study, which contains records of more than 10,000 individual driving trips in the area, including time of day, the distance of the trips, and the time required for the trips.
  • Another great resource is the US Department of Transportation’s (DOT’s) Bureau of Transportation Statistics, which has a bevy of travel and fuel consumption data about vehicles of all sizes, from cars to buses to trucks and rail.

Not long after the Datathon, the DOT announced grants totaling $300 million for the nation’s dozens of University Transportation Centers, which share the common goal to “advance US technology and expertise in the many disciplines composing transportation through education, solutions-oriented research and technology transfer … .” These Centers contribute to the DOT’s research housed in the USDOT Research Hub, the central repository for research data not only for highway vehicles, but also aviation and maritime vehicles.

Transforming the US Highways

The Federal Highway Administration published a map that shows the recently designated Alternative Fuels Corridors, where signs will be posted to direct PEV drivers to the charging stations located near the highways. This map provides useful data for utilities to anticipate where additional DC fast charging stations are likely to be installed. This could affect grid operations and could also provide a new revenue stream.

Highway Information: Electric Vehicle

(Source: US Department of Transportation)

In late December 2016, the US Department of Energy announced that it is further committing $18 million to researching electric and other alternative fuel vehicles, which will no doubt generate some interesting additional data. By continuing to add new research and by diving deeper into this plethora of data, we can continue to chip away at burning PEV questions such as, “How is the range limitation of EVs preventing their expansion to selling in larger numbers?” and, based on where people, work, live, and recreate, “Where should charging stations be located to be frequently utilized and better serve EV drivers?”

Analyzing real-world data to better understand how PEVs can most appropriately fit into the overall transportation market will enable automakers, utilities, charging networks, and the other stakeholders to improve their decision-making and reduce the risk in this rapidly evolving market.

 

Stationary Fuel Cell Prices Falling Faster Than Wind, Close to PV

— August 1, 2016

CodeMany fuel cell manufacturers are stealthy about their costs and prices, protecting the data like it is intellectual property. But new data from Japan’s ENE-FARM program confirms what other analyses have shown: fuel cells are showing consistently steep cost declines as production increases.

Most technologies exhibit a similar cost decline pattern. For every doubling of cumulative installed capacity, a commensurate decline in cost is realized due to improvements in manufacturing, supply chain efficiencies, and economies of scale. Plotted on a log-log chart, this curve forms a straight line called the learning or experience curve, and the slope is correlated with the rate of cost decline. For these 0.7 kW proton exchange membrane (PEM) micro-combined heat and power fuel cells, the learning rate is 17.2%, a number in agreement with the 20% found for larger-scale fuel cells. These rates beat the 12% of wind power and approach the 23% of PV (based on global values from this meta-study). Japan’s Ministry of Economy, Trade and Industry also released price goals for ENE-FARM in 2019. If met, these goals will continue the trend and bring the unsubsidized payback period down to around 7 years, which could mean broad adoption in the target residential market. Europe has its own similar program ramping up as well, while the United States and South Korea are more focused on larger-scale fuel cells.

Unsubsidized Price and Capacity of ENE FARM PEM Fuel Cells, Japan: 2006-2019

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(Sources: Navigant Research; Imperial College London; Ministry of Economy, Trade and Industry)

Note that the ENE-FARM data is based on prices, not costs, and that the underlying marginal profitability of these units (produced mainly by Panasonic and Toshiba) is unknown. In addition, fuel cell systems have some components that are already mature and which may limit opportunities to squeeze out costs. Regardless, relative to PV and wind, fuel cells are far less commercially mature and are likely to fall faster in the near term. Each doubling in capacity becomes increasingly difficult for mature technologies. For example, at the end of 2015, wind had an installed base of 434 GW and solar PV had an installed base of 230 GW. This accounts for around 12% of global generating capacity, and even with the current fast growth rates, it is clear that future doublings will take even longer. Meanwhile, fuel cells (which have around 1 GW installed capacity) have the potential for greater price declines as adoption grows. As prices fall, these continuous output sources will become more attractive to a growing host of markets in the coming years.

 

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