Navigant Research Blog

AMI Data Brings New Possibilities for Energy Efficiency Measurement and Verification: Part 2

— August 4, 2017

Coauthored by Emily Cross and Peter Steele-Mosey

Part 1 of this blog series covered operational improvements and provided background on the role of advanced metering infrastructure (AMI) data in energy efficiency program evaluation, measurement, and verification (EM&V). This blog continues the discussion with a focus on program impact evaluation. Navigant Research examines these topics in detail in its report, Utility Strategies for Smart Meter Innovation: Energy Efficiency Measurement and Verification.

Program Impact Evaluation

The use of AMI data for program evaluation has the potential to substantially reduce evaluation costs. A major cost associated with the evaluation of large customer (commercial and industrial) energy efficiency program evaluation is onsite verification and metering. For some programs, it is possible to reduce the number of site visits required or reduce the frequency of site visits. Another opportunity for faster program evaluation using AMI data is large-scale validation of coincident demand savings for energy efficiency programs. With AMI data, it is a straightforward matter to isolate demand impacts occurring during utility system peak performance hours.

Programs operating in utility services areas with full penetration of smart meters, deploying energy efficiency measures with consistent load reduction patterns, are excellent candidates for evaluation using hourly or subhourly AMI data. Energy efficiency impact analysis using utility data assumes enough of the program participant savings are above a minimum measurable threshold. That is, the signal-to-noise, savings-to-baseline ratio must be high enough to see the savings in the meter data for a substantial number of participants in the program. Otherwise, program savings estimates may be statistically non‑significant even if savings are being achieved.

A hybrid EM&V approach, using a combination of advanced, automated AMI data analytics and targeted in‑depth evaluation, provides the most value to utility clients and regulators. Automated impact analysis using AMI data (M&V 2.0) can serve as an initial screening of participants. It can quantify realized savings measured at each participant meter using high accuracy pre-post time-of-week/time-of-year and temperature normalized savings models. Participant projects screened out of the automated analysis can be identified and sampled for deeper analysis, providing targeted insights to utility clients and regulators for more complex projects.

Where automated screening methods provide sufficient program feedback without further investigation into the reasons for measured savings, evaluation costs could be reduced relative to traditional methods. Automated M&V2.0 screening provides a high level, statistically significant measure of program performance without the need for follow-up evaluation for programs with established performance. The evaluator using such methods must demonstrate there is no bias introduced by using only projects with measurable savings to characterize program performance.

For mass-market (residential and small commercial) programs, traditional evaluation often involves the application of survey findings to validate and update deemed savings values. In many cases, an empirical econometric approach can deliver an answer with just customer AMI data and program tracking data if the key question to be answered is simply: how many kilowatt-hours (kWh) and kilowatts (kW) is this program giving me? Such empirically-based savings could reduce program implementation and evaluation costs by streamlining or eliminating the ex ante customer application process, provided savings are measurable at the meter.

 

New Analytics Solutions Give Consumers More Energy Choice

— July 13, 2017

Residential consumers are becoming increasingly aware of their energy consumption and are interested in how they can reduce their use, save money on energy bills, and become more environmentally conscious. More and more customers are receiving home energy reports, which detail energy consumed and compare usage to that of neighbors. Opower (Oracle) achieved more than 11 TWh of energy reduction across 100 utility partners with these types of reports. Consumers are also logging into mobile apps that disaggregate devices to help them make smarter choices about where to target energy saving efforts.

Despite increasing efforts and awareness about energy, many consumers still do not know where their energy actually comes from. Most people may have a vague sense of their country’s energy mix and imports, such as the US energy mix depicted in the figure below, or that the UK imports 60% of its electricity-generating fuel. However, when a consumer flips a light switch, turns on their TV, or adjusts their thermostat, the energy that powers those actions is coming from whatever power plant is turned on to meet that incremental demand. This means the energy your light bulb is using could be drawing power from a coal plant, a natural gas facility, or a solar panel.

US Energy Mix: 2016

(Source: US Energy Information Administration)

New Technology Helps Track Generation Sources

In the past, there hasn’t been a method for determining the generation source that is meeting demand in real time. However, a non-profit called WattTime has developed a data analytics software that solves this problem. The software, which was the brainchild of a hackathon event in 2013, detects where the electricity powering the grid is coming from and the actual emissions impacts of people and companies using electricity. Not only does it detect this information, but it can also automatically power devices when energy sources are the cleanest. It can be installed in any Internet-connected device, making it flexible and easy to implement. This tool empowers customers to have a choice in the type of energy they are using and how much they are emitting when they consume electricity. WattTime’s software is gaining traction, having partnered with companies like Microsoft, Energate, and most recently, the Rocky Mountain Institute (RMI). WattTime has joined RMI as a subsidiary organization to foster the transition to a cleaner, more decentralized grid.

Looking Forward to a Cleaner Energy Future

Data analytics solutions like these are empowering consumers to make smarter energy choices, facilitating the transition to a cleaner, more decentralized and optimized grid, and solving challenges associated with reducing carbon emissions. Currently, emissions are calculated based on average factors, not based on the actual emissions that are generated depending on the source providing the next kilowatt-hour of power. As countries and organizations around the world move forward with reducing greenhouse gases, real, data-based information on emissions can help consumers understand how their actions directly affect greenhouse gas emissions and contribute to the overall goal of a cleaner, greener world.

 

AMI Data Brings New Possibilities for Energy Efficiency Measurement and Verification: Part 1

— June 29, 2017

Coauthored by Emily Cross and Peter Steele-Mosey

Utility industry stakeholders have been debating whether the proliferation of advanced metering infrastructure (AMI), also known as smart meters, will change the way energy efficiency program evaluation, measurement, and verification (EM&V) are conducted. Many utilities remain unsure about what is realistically possible. This uncertainty is compounded by the fact that new firms seem to emerge each year, claiming to provide increasingly deep insights into customers’ energy reduction potential (such as appliance-level load disaggregation and building-specific identification and targeting) using little more than consumption data from the utility.

How Can AMI Data Be Used?

In the field of EM&V, what is AMI data good for? How can it be used by utilities, regulators, and stakeholders to reduce evaluation costs, deliver more accurate and precise estimated program results, and improve the effectiveness of program delivery?

To answer these questions, it is helpful to define the two key evaluation-driven use cases for AMI data:

  1. Operational improvements: Early indications of program achievement provide the opportunity for course correction. Due to the continual collection of AMI data, it should be possible to quantify the impacts of changes in marketing approach and customer targeting on energy efficiency achievement more quickly than is traditionally required for program evaluation.
  2. Program impact evaluation: What is the best estimate of the energy and demand savings that a program delivered? This type of information is required to track utilities’ progress against mandated energy efficiency targets, to enable energy efficiency programs to be bid into energy and capacity markets as resources, and to quantify overall program cost-effectiveness.

Part 1 of this blog covers operational improvements, while part 2 will cover program impact evaluation. This topic is covered in detail in Navigant Research’s new report, Utility Strategies for Smart Meter Innovation: Energy Efficiency Measurement and Verification.

Operational Improvements

Utilities are all too familiar with the frustration of waiting for results from evaluators. Typically, a full year of data is required and the evaluation itself may take several months. This lag between implementation and assessment limits the ability of program administrators to course correct underperforming programs or understand how to tailor messaging to maximize the recruitment of high potential customers.

AMI data is collected continually, and several firms have recently come to market with prebuilt software solutions designed to quickly plug and play with this data. In theory and depending on the type of program, it should be possible to obtain ongoing updates of program performance long before the actual evaluation even begins.

These software packages have their limitations and are no substitute for a custom econometric evaluation, as they tend to be one size fits most. Additionally, the innovative approaches they employ sometimes lack the support of academic and professional literature from which econometric approaches benefit.

There is no denying, however, that these prebuilt software solutions can deliver results much more quickly than the traditional approaches. The results may not be sufficiently robust for a regulatory environment, but they may (depending on the program and the vendor) be sufficient to allow program administrators to take greater control of their programs and monitor their progress in near real-time. Program administrators would have the opportunity to make more effective use of program budgets and increase the value of their programs for their shareholders and ratepayers. They could use these software solutions for programs where simply multiplying the implementer‑reported savings by the prior year’s realization rates is not expected to be accurate.

 

Postcard from Hawaii to Nation’s Capital

— June 29, 2017

The mood at the second annual VERGE conference in Honolulu, Hawaii last week was upbeat about the future of clean energy, despite pushback on the US mainland. Apparently, those committed to a clean energy agenda, including the private sector, are more motivated than ever to push forward with aggressive programs to bring renewables resources online. They aim to not only combat climate change, but also create jobs.

Conference attendees clearly supported the supposition that clean energy is here to stay, no matter what might be unfolding in Washington, DC. The proposed dismantling of the federal Environmental Protection Agency’s Clean Power Plan and recent withdrawal of the United States from the Paris Agreement on climate change only seemed to serve as motivation to push forward even harder.

Hawaii’s Renewable Energy Vision

Hawaii is the first (and so far) only state in the United States to commit to a 100% renewable energy future. Governor David Ige of Hawaii didn’t seem to blink in the face of counter currents flowing from the Trump administration. A confessed energy geek, he seemed to take particular delight in the fact that Hawaii has emerged as a key testing ground for bolstering commitments to infrastructure needed to integrate variable renewables for both power and transportation services. Since each island of Hawaii is its own separate electric grid control area and retail costs are high due to such a reliance upon imported sources of fossil fuel, Hawaii is in a unique spot. The economics in the state clearly favor renewable energy.

Industry Momentum Is for Renewables

Even Connie Lau, CEO of Hawaiian Electric Industries, reported that her investor-owned utilities brethren have all bought into the clean energy agenda. If the administrative about-face on clean energy had occurred 8 years ago, then the momentum for renewables and other clean energy may have been halted, but that time has passed. Past government and industry investments have driven down the price of solar PV, wind, and batteries while software innovation to manage such resources has scaled up.

Nevertheless, there are challenges in implementing aggressive clean energy goals. Just look at California, where the state is paying neighboring states to take excess solar production. Many models show that once one reaches 80%-90% renewables penetration, the cost of integration can jump dramatically.

One of the key tools Hawaii will rely upon to reach its 100% renewable energy goal is to integrate devices like energy storage into self-balancing distribution networks such as microgrids. As of now, over 90 MW of new energy storage devices has been authorized by state regulators to be installed among the Hawaiian islands, with the majority of that capacity—70 MW—to be installed in Oahu.

Continuing Conversation

I had the pleasure of helping to run a 4-hour workshop on how to overcome challenges to developing a microgrid at VERGE with cutting edge microgrid market makers such as ENGIE and Spirae. I also moderated a session on how microgrids boost clean energy on islands, with featured speakers from ABB—which is pushing forward with a 134 MW microgrid designed to reach 50% renewable energy on the island of Aruba by 2020—and representatives from Hawaii and the US Navy.

Ironically, there may still be some room for collaboration between Hawaii and Washington, DC in the clean energy space. As I noted in a previous in a previous blog, one area where the interests in promoting national security in DC and a clean energy agenda in Hawaii align is the microgrid space. Watch for a report on that topic later this year.

 

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