Between the Poles

Distributech 2014: Duke Energy's journey toward a location aware "big data" architecture

One of the hot topics, if not the hottest, at this year's Distributech in San Antonio was big data analytics.  David Lawrence of Duke energy and David Mulder of Leidos gave an insightful presentation on the challenges Duke has encountered in cleaning,  merging and managing operational data, combining it with other types of data including social media, and developing analytical tools to extract meaningful information. 

By way of background, Duke is the largest elecric power utility in the U.S. with 7.2 million distribution customers, 32,200 miles of transmission lines, and  4.9 GW of generation capacity.

David Lawrence's perspective on where utilties are with repect to smart grid is that many utilities are moving beyond their initial AMI deployments into advanced automation. Improved operational performance, reduced costs, and better cybersecurity are driving the convergence of IT(information technology) and OT (operations technology). In addition utilities are experiencing the rapid adoption by their customers of distributed energy resources (DERs) such as wind and solar PV.  Solid state transformers are accelerating this trend.  Dynamic load management is becoming more prevalent and will effectively become another DER.  The convergence of IT and OT is making cybersecurity even more critical.  Microgrids are on the radar but require faster response times and better integration with a variety of equipment.  Utilities expect that “big data” analytics offers significant benefits, but  most don’t yet have a solid strategy for implementing it.

A major challenge is that many operational systems including outage management (OMS), customer information systems (CIS), billing systems, GIS and others are packaged for a single “silo” and are not easily or cheaply interoperable.  Duke has hundreds of these siloed systems.

Sandbox 1.0

To begin the journey toward big data analytics and the benefits expected from this, Duke Energy has undertaken a series of data mining activities by mapping, monitoring, collecting, validating, storing, visualizing, and discovering new relationships with the operational data from its smart grid test area. Duke has data stored in hundreds of databases and systems.  As a first step Duke has implemented a dedicated data warehouse called “the Sandbox” to mine data across operational siloes for interesting insights and to discover new analytical tools.

The data sources that Duke has integrated in a data warehouse include operational sensors and systems such as AMI, transformers, line sensors, capacitor banks and reclosers, outage management systems, billing systems, and smart grid communication nodes and data sources such as weather stations, socioeconomic databases and social network feeds.  For their Sandbox  warehouse they included about 3 months of data from June to August 2012 which comprise about 30 GB of data.

To help find and develope the analytical tools to mine the data that Duke hs assembled, Duke invited the vendor community to participate in a collaborative, non-competitive data mining exercise.  The objective was to mine this data to develop new use cases.  To prime the analytics pump Duke provided six use cases.  Participants were asked to provide new use case ideas, insights into what other types of data should be collected, data quality and interoperabiity issues, and data models appropriate for different types of analysis.

The most important conclusions that came from this effort are that

• no single vendor had all the answers,
• interoperability issues between siloed operational systems limits the value of the data,
• custom data models are needed for different types of analysis (descriptive statistics, time series analysis, logistic regression and so on) of structured data,
• unstructured data such as social network feeds from outside Duke can add significant value.

The collaborative data mining exercise was very fruitful, generating over 150 new use cases.  Some examples of use cases include usage outlier analysis on meter interval data, customer peak analysis on meter interval data, outage analysis using outage data, predicting customer energy usage by correlating historical weather and customer energy usage data (predictive analytics).

Some of the most interesting insights into data requirements that were suggested by vendors include

• Need for more historical data
• Linking data from different datasets across siloed systems was a major challenge and severely limited the analysis
• Semantics - part of the linkage problem is mapping different terminology for the same data across application silos (this is similar to what the CB-NL concept library initiative is attempting to address for the construction lifecycle)
• GPS data (geolocation) for all devices would add significant value
• Skill sets and staffing: power system and data science experts are needed to develop successful big data analytics.

Next steps

The next steps are to consolidate vendor data issues, comments, and modeling methodologies to develop requirements for Sandbox 2.0. The long term goal of the exercise is to define Duke Energy's big data architecture.

Distributech 2014: Combining utility GIS and weather data to manage extreme weather events

Utilities have been using geospatial data and technology for many years for mapping and asset management.  But GIS is now being used in many other areas from network design to marketing to outage management to energy conservation.  With the rise of the smart grid utilities are combining GIS and AMI to monitor transformer loading, identify and reduce theft and for disaster management.  Many people in the industry expect that under the impetus of the smart grid the expanding role of geospatial technology will accelerate and that geospatial will become a foundational technology for the electric power industry.  At Distributech, Danny Petrecca, Director of Geospatial Product Management at Schneider Electric made the case that the role of utility GIS is touching every aspect of a utilties business, affecting customers, operations and management.

Disaster management

As an example Danny gave an overview of how GIS, weather data, and the integration of weather data and GIS are helping utilities prepare for and deal with extreme weather events.  The frequency and severity of extreme weather events is increasing so disaster management has become a priority with most utilities. With GIS and weather services utilities are able to plan crew resources and network assets, reduce restoration time and costs, ensure crew and public safety, optimize damage assessment, and keep the public informed during storms.

Hurricane Sandy severely impacted New Jersey Natural Gas (NJNG) infrastructure, 30,000 homes and over 275 miles of main were damaged.  NJNG found that the biggest reason that they were able to restore service so quickly was their ability to analyze and manage the restoration process using their utility GIS system.  That they had been using GIS for a decade prior to Sandy meant that they were better prepared for such an extreme weather event than they had expected. The GIS enabled engineers to view the network in the context of the damage caused by the storm and it enabled them to identify critical valves and shut off gas service in a matter of hours.  It helped them restore over 255 miles of pipeline in under two months.  It helped them resectionalize their network into smaller sections which they were able to repressurize in one day.  A very important benefit of the GIS was that NJNG were able to provide timely and accurate information to the public on the status of the network including estimated time of service restoration.

Weather services

Weather services can provide information, (historical, current and forecasts), about lightning strikes, wind, temperature and humidity, ice and snow, and storms.  They can predict precisely when and where a storm will hit in a utility's service territory.  This enables a utility to schedule crews on-call, holdover, and dispatching and the type of equipment that will be required.

Before a storm weather services can provide Before:high quality forecasts, expert meteorological consultation, and real time alerting. When a storm hits, weather services provide real-time lightning strike information (where and when), nowcasting tools, and current situational awareness.  And after the storm weather services can help with post-storm inspection and assessment.  Weather data is available in digital form and some provide it as a web service compliant with the Open Geospatial Consortium's (OGC) web services standards such as WMS.

Integrated utility GIS and weather services

By integrating weather services and utility GIS allows you to do even more interesting things, like quantitatively monitoring storm impact on  infrastructure like transformers in real-time, using geofences to setup alerts for lightning strikes, and correlating storm and outage events to be able to predict where issues can be expected as a storm moves across the utility' service territory.

California extends net metering for customers with solar PV and other renewable energy generation

Under existing law, the California Public Utilities Commission (CPUC) has regulatory authority over public utilities and authorizes the commission to fix the rates and charges for every public utility.

Net-metering

Currently California law requires every electric utility in the state to develop a standard contract or tariff providing for net metering and to make this contract or tariff available to eligible customer generators for a renewable electrical generation facility, typically rooftop solar PV.  This is made available to all customers on a first-come-first-served basis until the aggregate generation through this program exceeds 5% of the utility's peak demand.

Assembly Bill No. 327 (AB 327), which was passed toward the end of 2013, requires large power utilities to provide net energy metering to new customer-generators in its service area through July 1, 2017.

The bill requires the CPUC to develop a standard contract or tariff for eligible customer-generators with a renewable electrical generation facility. The bill requires large utilities to offer the standard contract or tariff to all eligible customer-generators beginning July 1, 2017.  The bill stipulates that there will be no limitation on the number of new eligible customer-generators entitled to participate in the net metering program.

Restricted time-of-use pricing

In addition the bill prohibits the CPUC from requiring or allowing a power utility to implement mandatory or default time-of-use pricing prior to January 1, 2018.  After that time the CPUC will be permitted to require or authorize a power utility to implement default time-of-use pricing to residential customers.  The bill would permit the CPUC to authorize a power utility to offer residential customers the option of time-of-use pricing and other demand response programs, but the residential customer would have the option to decline time-of-use pricing.

Flattening power rates

California law requires the commission to establish an assistance program for low-income electric and gas customers, referred to as the California Alternate Rates for Energy (CARE) program.  Basically this creates a tiered rate structure.  The first two tiers, 1 and 2, are for ratepayers who use average or below average amounts of electricity. The upper tiers 4 and 5 are for heavy electricity users. Tiered pricing is not based on income, just usage.  Tier 1 users pay $0.14/kWh whereas Tier 4 users pay $0.31/kWh.

AB 327 repeals the limitations upon increasing the electric service rates of CARE customers. It removes the long-standing restriction on raising rates for those users in the two lowest tiers and lowering rates for those users in the upper tiers. The effect of AB 327 is to flatten rates between the different tiers.

Maryland's Utility 2.0 proposes "utility of the future" business model

In response to a recommendation from the Governor’s Task Force on Grid Resiliency to “scope out a Utility 2.0 pilot proposal", the Energy Future Coalition (EFC) prepared recommendations for a smart grid pilot project that would address key business objectives including eliability and resiliency, residential customer empowerment, large customer flexibility, infrastructure upgrading, a regulatory framework for the smart grid, and a new utility business model that would keep utilities financially viable even as they delivered less electric power.  EFC's "Utility 2.0" recommendations include

• Utility investment in an interoperable, integrated suite of smart-grid technologies, for the utility's own system, but also on customer sites.  This would include time-of-use rates, automated demand response, conservation voltage regulations and other technolgies.
• Allowing utilities to provide low-cost loans to consumers to finance new technology to be repaid by customers on their utility bills.
• Automated system sectionalizing and reclosing (self-healing networks)
• Support for micro-grids in areas where customers could safely provide their own energy during an outage.
• Utility-controlled vehicle battery charging.

Utility of the future

But the key recommendation is a proposal for a new utility business model that would decouple utility revenue from selling electric power.  EFC is proposing that a set of five performance parameters measured through objective metrics would be used to vary the utility’s rate of return on equity by up to one percent above or below the current rate of return determined by the regulator. The performance parameters would be: cost, reliability, customer service, adoption of smart grid technologies and services, and support for alternate energy.

EFC is proposing that the relative weighting of these five factors would be determined by customers’ own rankings of their relative importance. This would provide utilities with a geographically detailed understanding of their customers’ priorities, and a direct incentive to serve those priorities. It would also encourage a potentially mutually beneficial utility-customer dialog.

World's first 3D printed house begins construction March 2

January 6 KamerMaker XL, a 3D printer for printing rooms, was moved to the construction site of the 3D Print Canal House in Amsterdam.  The printer is using a type of plastic 80% bio-based hotmelt developed by a German chemical company.  

According to DAS who developed the printer "Each room is printed separately on site before being assembled into one house. This way the rooms can be carefully tested in a safe and easy accessible manner. Each room is different and consists of complex and tailormade architecture and unique design features. The structure is scripted and this creates its proper strength but also generates ornament, and allows for new types of smart features, such as angled shading scripted to the exact solar angle. Each printed room consists of several parts, which are joined together as large Lego-like blocks. Both the outside façade as the interior are printed at once, in one element. Within the 3D printed walls are spares for connecting construction, cables, pipes, communication technique, wiring etc."

Each room is structurally independent of the others.  After the rooms are printed, they are assembled into connected floors, and then the floors are stacked to form the entire house.  The printing of the home and its assembly is projected to take about three years. The grand opening of the construction site will be on Sunday March 2, 2014 in Amsterdam.

EIA maps power generator additions and retirements by fuel type and location

Beginning in December 2013, the Energy Information Administration (EIA) has begun reporting detailed accounting of generator additions and retirements as well as their effects on total generation capacity by fuel and technology type on a state-by-state basis.  Power plant owners and operators report plans to modify or retire existing generators, or build new generators, every year as part of EIA's Annual Electric Generator Report.  All generators at utility, industrial and commercial plants with at least one megawatt (MW) of capacity are included. Smaller installations such as residential rooftop solar PV are not broken out in these reports.

The state-level detail in the tables can be represented using maps like the one included here. Maps make it easier to see how different fuel types are reflected in different regions, for example, new solar generators in the Southwest, natural gas generators in Texas and along the Atlantic Coast, and wind generators in the Plains region that are expected to come online between December 2013 and November 2014.

Spatial analysis finds that methane emissions from fossil fuel extraction may be 5X larger than current estimates

Methane is one of the more potent greenhouse gases for global warming, but it is not clear just how much more potent methane is than CO₂.  The EPA has estimated a factor of  21 times compared to carbon dioxide. But Robert Howarth, an environmental biology professor at Cornell University, has argued that it is actually 72 times as powerful as carbon dioxide in terms of its warming potential.  Furthermore  Howarth has argued that the type of shale gas drilling taking place in Texas, New York and Pennsylvania generates particularly high emissions of methane. A study has estimated that between 3.6% to 7.9% of the methane from shale-gas production escapes to the atmosphere in venting and leaks over the lifetime of a well.

A study just published has assessed the spatial distribution of anthropogenic methane sources in the United States by combining comprehensive atmospheric methane observations, extensive spatial datasets, and a high-resolution atmospheric transport model. Based on the results of this analysis the authors conclude that the US Environmental Protection Agency (EPA) underestimates methane emissions nationally by a factor of ∼1.5.

Generally the study finds that methane emissions due to the animal husbandry and fossil fuel industries have larger greenhouse gas impacts than indicated by existing inventories.

The study concludes that there is a wide regional variation in the discrepancies in methane emissions and that the discrepancy is particularly large in the south-central United States.  There the study reports its results are ∼2.7 times greater than EPA estimates.  South-central emissions account for 24 ± 3% of national emissions.  Based on their analysis of the the spatial patterns of methane emissions and correlations between methane and propane,  the authors conclude that fossil fuel extraction and refining are major contributors (45 ± 13%) to methane emissions in the south-central United States.

Furthermore based on their analysis the authors  suggests that regional methane emissions due to fossil fuel extraction and processing could be 4.9 ± 2.6 times larger than in the Emissions Database for Global Atmospheric Research (EDGAR), an international inventory of past and present day anthropogenic emissions of greenhouse gases maintained by the European Commission JRC Joint Research Centre and the Netherlands Environmental Assessment Agency (PBL).

Massachusetts report recommends alternative regulatory framework for electric utilities based on societal benefit

The Massachusetts Department of Public Utilities (DPU) has decided to aggressively accelerate the deployment of smart grid technologies in the state.  In July, 2013, the grid modernization Working Group submitted a report to the Department covering on a wide array of grid modernization issues entitled “Report to the Department of Public Utilities from the Steering Committee”.

One of the most interesting parts of this report is that it presents recommendations for alternative regulatory frameworks that could be used for planning, reviewing and implementing grid modernization projects. 

All of the utilities operating in Massachusetts with the exception of NSTAR operate under a regime that is based on fully decoupled base distribution rates.  So there has been a recognition for some time in Massachusetts that the traditional approach to utility cost-recovery does not work in a modern environment where utilities are expected to encourage energy conservation, consumer deployment of solar PV, and other programs that actually reduce revenue.

The report discusses alternative regulatory frameworks that range from tweaking the existing framework to changing the fundamental  groundrules.

Enhanced Regulatory Model

The Enhanced Regulatory Model provides a regulatory model that ensures maximum flexibility in addressing cost recovery for individual, targeted programs. It provides five submodels each of which is designed to facilitate recovery of costs associated with one of the five main initiatives of grid modernization, reliability, advanced metering, time-of-use rates, distributed generation, and direct load control.

Grid Modernization Expansion - Pre-approval Process

Distribution Companies would be permitted to request recovery of grid modernization investments through mechanisms outside of base rates, as determined by the DPU. Cost recovery could be enabled consistent with existing precedent regarding historic test-years (decoupling) but may also be modified to accommodate a future test-year approach.

Expansion of Investment Caps

This model would allow a utility with a capital investment recovery mechanism, such as National Grid’s annual mechanism for in-service capital investments up to $170 million made in a preceding calendar year, to request an increase to its capital investment budget cap outside of a base rate proceeding for additional investment for grid modernization.

Expansion of Investment Caps with a Multi-Year Plan

This model builds on the Expansion of Investment Caps model and additionally allows a utility to propose spending levels for a multi-year period instead of one year at a time.

Future Test Year Model

A forecasted rate year approach to cost of service provides utilities with greater incentive to invest in modernizing the grid because it aligns the cost of service with the time period in which the costs would be incurred so that revenues would be set to match expected costs in the year they are incurred instead of costs incurred two years earlier.

Future Test Year with Multi-Year Plan Model

This model takes the same form as the Future Test Year Model with a forecasted rate year. However, it would extend the plan for a number of years, usually three to five years. The benefit from multi-year plans  is that the utility’s capital investment plan can be reviewed and approved for a number of years which allows the utility to plan several years in advance.

Utility of the Future, Today

Grid modernization promises to bring substantial net benefits to customers and society including improved reliability, reduced costs of service and customer bills, improved capacity utilization, reduced environmental costs, and increased customer choice. But since the incremental benefits of grid modernization investments tend to accrue to others such as customers and society in general and not the utility, the risk of disallowance under traditional ratemaking practices discourages utilities from pursuing grid modernization investments.

To address the fundamental shortcoming in the incentive structure of traditional utility ratemaking practice, it is proposed that a new regulatory model be adopted by the DPU – one that requires the utility to analyze grid modernization investments from a broader societal point of view, gives the utility a degree of certainty regarding  cost-recovery before it makes investments, and evaluates and rewards good planning,  implementation and performance. The regulatory model that it is suggested will encourage cost-effective grid modernization efforts includes pre-approval and performance-based ratemaking (PBR) elements.

Massachusetts mandates smart grid for electric power utilities

Massachusetts has not been among the states that have led the smart grid revolution in the United States.  In September 2012 National Grid began a small smart grid pilot involving 5,000 meters in Worcester, Massachusetts.  National Grid is expanding this to 15,000 customers to evaluate integrating home automation, dynamic pricing and distribution automation.  At this point Massachusetts looks like a late adopter, but this is set to change. The Massachusetts Department of Public Utilities (DPU) has decided to aggressively accelerate the deployment of smart grid technologies in the state.

Background

DPU is responsible for ensuring that electric distribution companies provide safe and reliable electric service to customers and expand the deployment of clean energy technologies.  As part of that mandate In October, 2012, the DPU issued a Notice of Investigation (“NOI”) into the modernization of the electric grid with the goal of developing policies to  ensure that electric distribution companies adopt grid modernization technologies and practices.  The expected benefits of grid modernization include improved reliability, reduced operating costs, reducing rate increases and volatility for customers, and empowering customers to better manage their use of electricity.  The NOI also specifically mentions increasing the resiliency of the grid in response to "increasingly extreme weather".  Other benefits of grid modernization that the DPU foresees include providing support for energy efficiency, demand response, distributed generation, storage, electric vehicles (“EVs”), and renewable energy resources.

In November 2012, the DPU organized a workshop attended by over 125 stakeholders which resulted in the formation of a Working Group tasked with providing input to the DPU on approaches to grid modernization over the short, medium, and long terms and with helping the DPU in prioritizing grid modernization investments.

In July, 2013, the Working Group submitted a report to the Department covering on a wide array of grid modernization issues entitled “Report to the Department of Public Utilities from the Steering Committee”.

Enterprise systems required for smart grid include GIS

This report explicitly identifies what it calls network system enablers, enterprise systems that it considers essential for implementing a smart grid. These include GIS in addition to Distribution Management System (DMS)/SCADA, Outage Management System (OMS), Billing System, Metering System and Meter Data Management System (MDMS), and Communication Systems (Fiber, Microwave, Radio, etc.).

The report sees the role of GIS as the repository for asset information.  It feeds system planning models, system operations models, outage management models and work-order/financial systems and can also 

Electricity  distribution companies required to prepare Grid Modernization Plans (GMP)

Based on the report the DPU established four grid modernization objectives;  reduce the effects of outages; optimize demand (which includes reducing system and customer costs); integrate distributed resources, and improve workforce and asset management.

The DPU then proposed requiring each electric distribution company to develop and submit to the DPU a ten-year strategic grid modernization plan within six months.  The GMPs must lay out plans to make measureable progress towards all of these grid modernization objectives.

Smart grid priorities

In its first GMP each power utility must include a comprehensive plan for advanced metering (CAMP). The DPU is prioritizing advanced metering because it sees AMI as a basic platform upon which grid modernization will be developed.  It is being very aggressive in mandating completion of the rollout of AMI with three years of the DPU's approval of each company's GMP.  To support this effort the DPU has already developed a regulatory framework specifically targeted on advanced metering.

The DPU has identified its next top priorities;
(1) time varying rates (time-of-use rates)
(2) cybersecurity, privacy, and access to meter data
(3) electric vehicles.

California ISO releases energy efficiency and demand response roadmap

Recent technology trends indicate that there will be a greater reliance on demand response (DR) and enengy efficeincy (EE) as well as other forms of distributed energy resources (DER) such as microgrids, rooftop solar, electric vehicles as well as energy and thermal storage facilities in order to maintain a
reliable and affordable power supply while minimizing the environmental impacts of conventional electricity generation. 

Some of the immediate challenges Caliornia is facing are the shutting down of the San Onofre Nuclear Generating Station as well the potential loss of about 6 GW of coastal power plants because of new EPA rules for the use of sea water for cooling. 

Anther challenge is the addition of intermittent energy sources primarily wind and solar power to the grid. DR and EE are expected to play new roles in the future as flexible resources to balance fluctuations in the grid due to distributed intermittent energy sources.  California has committed to a goal of 33% renewable energy generation by 2020. Greater operational flexibility is required as more distributed intermittent renewable energy sources are added to the grid. Because the grid is increasingly comprised of many geographically distributed small energy sources, it is expected that geospatial technology will play an increasingly central role in managing the grid as it evolves from a small number of centralized power plants to a distributed generation system.

Germany is also addressing the challenge of balancing generation and demand with increasing intermittent energy sources distributed over larger areas.  The Deutsche Energie-Agentur GmbH (dena) - the German Energy Agency - commissioned a study led by Prof. Dr.-Ing. Christian Rehtanz, Technical University Dortmund/ef.Ruhr to determine the scope of grid system services in the context of an increasing supply of intermittent energy.  In particular, the study looks into the extent to which distribution grids can contribute to grid stability for the transmission grid, and the role renewable energy systems, storage facilities and demand-side management (DR and EE) need to play to ensure grid stability and resilience

The “duck curve” illustrate how the California power demand is likely to evolve as more energy is produced from renewables.  In the graphic “net load” is the the actual demand on the system minus generation from intermittent sources and is calculated by taking the forecasted total demand and subtracting the forecasted electricity production from wind and solar generation resources.

These curves represent the variability that the California Independent System Opertaor (CAISO) must balance with other resources including EE and DR. One way to enable reliable grid management is load reshaping, applying EE and DR resources as behavior modifying mechanisms to change the shape of the net load curve.  The load-modifying path aims to flatten the demand curve by attenuating the peaks and valleys and to avoid steep ramps in power generation.

The other approach is to manage procurement of resources in response to the grid needs.  The resource sufficiency path focusses on ensuring the needed flexible resources are available including dispatchable power capacity, storage and DR.

The CAISO is working with the California Public Utilities Commission (CPUC) and the California Energy Commission (CEC) to create a market for DR and EE based on smart grid technologies to transition the state toward a more reliable and environmentally-sustainable electric power system.  CAISO has just released a roadmap for EE and DR entitled “Demand Response and Energy Efficiency Roadmap: Maximizing Preferred Resources”.

The roadmap anticipates the size, timing, and geographic distribution of newer, more flexible energy generation resources and lays out the plan for lowering market barriers that inhibit DR/EE from competing alongside conventional resources.  The roadmap identifies four pathways which the CAISO sees as essential for enabling a viable online a DR and EE market over the next three years:

1. Load Reshaping Path - use incentives to modify consumption patterns to “flatten” demand and reduce the need for peaking generation capacity.
2. Resource Sufficiency Path - ensure enough generation or DR/EE resources are available at the right places and at the right times.
3. Operations Path - optimize use of resources (demand and supply). CAISO will have to change some existing policies as well as develope new market products to expand EE/DR market participation.
4. Monitoring Path  - monitor progress and outcomes