Between the Poles

Above and underground infrastructure comprise Rotterdam's open digital twin

The City of Rotterdam is developing a digital twin of the city.  3D vector and image data of the above- and underground infrastructure, zoning regulations and city by-laws, data streams from real-time sensors, applications and simulations will comprise Rotterdam's digital twin. It is envisaged that the digital twin will enable the city to assess the impact of proposed changes in bylaws and regulations by simulating them before they are implemented. Rotterdam, and in fact the whole of the Netherlands have adopted transparency and open data as a pillar of government and are basing 3D models on open national and international standards, intend for the 3D data to be open and accessible to the public, and intend to use open source software wherever possible.

Many cities have 3D models of their aboveground infrastructure; buildings, transportation networks, parks, and other infrastructure typically captured in 2D or 3D imagery from overflights or in 2D GIS maps.  Imagery is not very smart and extracting 3D vector objects such as building footprints, heights, profiles and transportation networks requires sophisticated post-processing.  Even more importantly, underground infrastructure is usually neglected, even though it is recognized that water and wastewater, energy (gas, electric power, and district heating),  and communications (fibre and copper) networks provide the life blood of the city.

Rotterdam has been developing its 3D model, which underpins the digital twin, for several years.  Currently it contains street furniture such as street lights, trees and underground parts of street furniture, a large scale basemap, a complete map of underground networks of cables and pipes, buildings, and non-physical content such as zoning regulations and  bylaws.  The 3D model is kept in sync with data repositories maintained by city departments and others.  In the future it will also include real-time data streams from a variety of sensors. 

The 3D model is open, based on open standards and many of the applications that have been developed for it are open source projects. For example, visualizations rely on Cesium, an open source initiative which has just announced support for visualizing underground utilities, mines, and geological layers. The rationale for the data being based on open standards is that an standards-based environment creates an open data commons where developers can devote their time to developing innovative applications rather than spending time converting data from one format to another.

The Dutch 3D standard is an integration of CityGML Application Domain Extension (ADE) and the existing national IMGeo standard for large-scale representations of roads, water bodies, and land use. The Dutch Kadaster, Geonovum, the Netherlands Geodetic Commission and the Dutch Ministry of Infrastructure and Environment initiated a "3D Pilot NL" to develop a national 3D standard.  Existing intnernational standards including CityGML(Open Geospatial Consortium), IFC (buildingSMART), and others were investigated. The findings of the pilot  resulted in a national 3D standard implemented as a CityGML Application Domain Extension (ADE) that integrates the latest version of the existing Dutch national standard IMGeo 2.0. 

Current applications (virtualcitySYSTEMS, ESRI, IMAGEM and Unity) use the 3D model as input for analytics and visualization. One can imagine many use cases for the data in the Rotterdam 3D model. Among these three use cases have been identified as low-hanging fruit; climate adaptation, viewsheds and energy performance of buildings. 

Much of Rotterdam is below sea level and managing rain water accumulation during heavy rainfalls is a major issue. which affects building basements, subway entrances, low-lying roads, electrical transformers, and other infrastructure.  To address this problem, the concept of "water squares" has been developed and the 3D model used to determine the optimum location for these..  Water squares are open areas where water can safely be allowed to accumulate during heavy rainfalls to avoid overstressing storm sewers. 

In another application of the 3D model data, building data can be filtered and streamed to energy performance applications that provide insights into how buildings perform.

Open standards and open data also enables the sharing of data among cities in the Netherlands.  The Dutch Kadaster has developed an open 3D map of the whole of the Netherlands, with scales ranging from 1:500 to 1:25,000 in collaboration with academic, private sector, and public sector partners.  All data in the 3D map is built on the basis of open standards.  The map was created by combining existing 2D data with the Elevation Model of the Netherlands resulting from a LiDAR survey which covers the whole of the Netherlands with a resolution of eight elevation points per square meter.

Open data and open standards also relate to a critical issue that is of growing importance. Many governments have adopted transparency and open data as a cornerstone of democratic policy.  But often digital twins for smart cities are being developed by private-sector firms or by public-private initiatives.  As William Davies points out in Nervous States, "the IT infrastructure used to build e-geovernment projects or "smart cities" comes from the commercial sector, and the companies that provide it often retain control over the data that results." 

For example, recently a joint Alphabet/Google/Sidewalk Labs and City of Toronto initiative to develop a smart city on Toronto's waterfront foundered and was cancelled.  One of the reasons, it is argued, is that a technology giant such as Google (or Facebook, Amazon, ...) is not a vendor in the traditional sense, but a "surveillance capitalist" that collects and analyzes behavioural data captured from users for its own economic imperatives.  In the context of a smart city the opportunities for data collection from citizens and from systems such as water and wastewater, transit, waste removal, traffic management, and others are immense.  If this data is appropriated and becomes proprietary, it puts a private entity in a position to supplant democratic government, which doesn't have access to this huge volume of data and the analytics which extracts information from it.  Relying on open standards and ensuring that data is open and accessible to the public are essential to avoid citizens' and municipal data becoming proprietary.  Furthermore, using open source software and, I would add, open training data for machine learning applications helps avoid the problem of the analytics also becoming proprietary.

Based on a Webinar by Christian Veldhuis, Advisor City of Rotterdam, Rick Klooster, CCO, FUTURE INSIGHT and Antje Kunze, Director Sales & Marketing, virtualcitySYSTEMS.

IoT platform for integrating two decades of Hexagon's technology acquisitions announced at HxGNLIve

In May of last year Hexagon acquired a company based in Alpharetta, Georgia called Catavolt that combines internet of things (IoT), analytics and mobility. At HxGNLIve in Las Vegas Ola Rollén in his exhilarating opening keynote - beginning with incredible graphics showing every foot of a young gosling's 400 foot fall down a cliff (which happily it survived) and ending with an incredible slalom ahead of an avalanche punctuated with the skier executing a back flip - announced its new name Xalt and showed a unique example of what it is capable of.  Xalt runs in the cloud on Amazon Web Services and collects data from sensors, which could be a metrology (measuring) device, an Oracle database or SAP. It then analyzes the input stream looking for outliers and raises an alarm that causes something else to happen. It could be informing a human that there is a problem or it could be linking to another machine. In the example that Ola showed a measuring device could be seen measuring the precision of what a robot did and when the precision became worse than a threshold, it raised an alert which caused the robot to recalibrate itself.  Another example of what the Xalt platform is capable of was demonstrated on the exhibition floor where we were invited to develop an app in seven minutes that integrated data from several different sources. The development platform was a laptop and the resulting app ran on an iPad.

Xalt is an internet of things platform supporting many different sensors and types of sensors, big data analytics and different clients especially smart phones. Xalt supports multiple AI data sources including imagery, video and big data for applications such as predictive maintenance, change and anomaly detection. The platform monitors, senses and acts on all connected data inputs in real time. It accesses sensors from remote locations and utilizes the data to optimize business processes. It includes AI capabilities including deep learning with digital neural networks. From Ola's presentation it is clear it is not intended as a standalone product but rather as a platform that integrates the many and disparate devices and systems that Hexagon has acquired since Hexagon began its incredible journey in 2000 from a Swedish contract metals manufacturer and producer of valves, antennas and pressure vessels to a global geolocation and measurement, design and construction application and visualization technology company with $4.11 billion in revenue and 18,315 employees in 2017. During these nearly two decades Hexagon acquired 64 companies many with geospatial capabilities including Leica Geosystems in 2005 and Intergraph in 2010. The big question for observers of Hexagon's meteoric rise has been how was it to integrate these disparate acquisitions into a synergistic whole that is bigger than its component parts.  It is clear from Ola's keynote that Xalt will be the foundation for Hexagon's big leap to achieve this.

Low cost sensor enables citizen scientists to measure and share urban air pollution data over the web

The World Health Organization (WHO) estimates that more than seven million deaths every year are linked to air pollution exposure from household and ambient (outdoor) air pollution. But in spite of these statistics the average city has only one or two air quality monitoring devices which are very expensive costing about $1.5 million each. At this year's FOSS4GNA (Free and Open Source Software for Geospatial North America) get together in St Louis Steve Liang, a professor at the University of Calagary and CEO of SensorUp, described a way that citizen scientists can contribute to measuring air pollution using a low cost board and CPU and share via open source geospatial web software to map the resulting measured air pollution in real time.

Based on the latest results from WHO air pollution is now the world’s largest single environmental health risk, linked to 12% of all global deaths. Around 4.3 million deaths every year are attributed to exposure to household (indoor) air pollution, from heating, cooking and lighting using solid fuels. Around 3.7 million deaths every year are linked to outdoor air pollution , including exposure to fine particulate matter from fuel combustion from vehicles and from power plants, industry and biomass burning.

Steve described the sensors, which are typically built by interested people in workshops led by staff from SensorUp, a startup that Steve leads. The devices consist of a sensor that measures PM2.5 (particles less than 2.5 microns in size), temperature and humidity, a CPU and Wifi. Each device costs less than $100. After building the device, each participant in the workshop brings it home and installs it outside within wifi range of their internet router. The sensor reports ambient PM2.5, temperature and humidity every 5 minutes to a central server maintained by SensorUp. It uses the Open Geospatial Consortium (OGC) standard SensorThings API which provides an open and unified way to interconnect Internet of Things (IoT) devices, data, and applications over the Web. SensorUp SensorThings platform is the most advanced OGC SensorThings API implementation.

A web application built on the open source geospatial software Leaflet allows users to view the data on a map in real time, investigate air pollution historically and compare different cities. The data is open and accessible to anyone.  To date about 500 people across Canada have built their own devices and are sharing measured PM2.5. The real-time feed from these devices is mapped here.

Spar 3D: 3D mapping the inside of multi-story buildings with a backpack

At the Spar 3D Expo and Conference in Houston The Woodlands, Texas, Avideh Zakhor from the University of California Berkeley was onsite with some remarkable products from her new startup Indoor Reality.  I blogged previously about the technology when it was still under development.  The "man portable system" she has developed is a 32 pound backback with a collection of sensors.  The most important feature of the technology are sensors and algorithms for accurately tracking the six degrees of spatial freedom which determine the location and orientation of the backpack in the building.  In addition there are sensors for recording optical and thermal point clouds. She has also developed a handheld device that supplements  the capabilities of the backpack.

The technology can track accurately the location and orientation of the backpack as the operator walks through the building going into all rooms, halls, up and down stairs and into other human accessible spaces.  Software tools then generate different 3D data products including optical and thermal 3D point clouds, a colourized 3D point cloud, surface texture map,  and triangulated mesh surfaces.  The package includes analytical software tools for automatically generating floor plans, distinguishing rooms, identifying staircases, identifying windows, finding heat sources such as computers and human occupants, and identifying lights. Surface textures can be added to create a 3D photo-realtistic rendering.  The sensors and software can capture and generate all required input for the Department of Energy's EnergyPlus energy performance analysis program.  It has GPS and can generate real world coordinates.  Indoor Reality is an ESRI Emerging Business Partner.

I had a chance to sit down with Avideh to talk about her new company and the remarkable products she was demonstrating at SPAR2016.

Avideh, what are the key features of the products you are demonstrating here that distinguish them from some of the other devices vendors are showing here at SPAR ?

We have developed a unique way to track where the operator is in a building and the orientation of the backpack.  We have a lot of sensors. That is the key to all of our intellectual property (IP). It's called sensor fusion and most of our IP is in that area. It does not suffer from drift. It tracks accurately where you are in the building even if you go upstairs. You can even go up in an elevator because it has a barometer.  In addition we detect wifi.  It can tell you what the wifi strength is in different parts of the building. We have an infrared sensor and we have developed an app that will give you real-time occupancy of the building, for example, we can tell you there are four people in this room, two people in that room. We have patents on that.

Based on the data collected by the operator's walk through the building, we allow users to virtually navigate through the space and spatially collaborate with each other through our web based visualization tool. During virtual navigation, you can look around in 3D as if you were there.  That's our second data product. We don't just record dumb point clouds.  We generate intelligent meshes.  Not only can I can pan around, look up and down at each location, but the shape of the cursor shows you the orientation of the surface - for a horizontal surface it's a circle, for vertical surfaces it's a square.  The cursor gets bigger as it gets nearer, smaller as it goes farther away.  We know the normals of all the surfaces because of the 3D mesh behind what you see.  We recognize doors and windows.  Of course, we can measure distances and we allow users to annotate objects and locations.   So this mesh is very intelligent, so intelligent that we can automatically generate a Revit model with a floor plan, rooms, ceilings, doors and windows. That is our third data product.

How do you foresee people will use the tool ?

We went to the University of Illinois just a month ago and we did an energy model for a 14 story bldg. We can do 3d geometry, we can detect lights automatically, we can detect windows automatically, and we can detect AC plugs automatically.   This gets fed into an energy simulation model which is a complete model for an Energy Plus performance audit.  Typically when people do an energy audit they are only interested in the lowest hanging fruit, for example, replacing incandescent light bulbs with LEDs.   This is because collecting the data for a full Energy Plus model has been time consuming.  With the Indoor Reality backpack you can collect whatever you need for a complete Energy Plus audit very simply. For example, we can superimpose the infrared record on a ceiling to show a hot area which is very likely a hot water pipe behind the dry wall.

We have just come out with a visual building documentation tool for stakeholders to collaborate  with each other throughout the lifecycle of a building - during design, construction and operations and maintenance.  If I want to remodel a building using an architect, designer and contractor they can communicate with each other by leaving notes for each other in the virtual building.  For example, I can put a tag on a door for John the contractor. "Please fix this door."  John can come along in the virtual building and edit that note "Avideh, I can do this Tuesday."  So this makes it possible to collaborate virtually on a building project in 3D,  in much the same way that we can collaborate on a Word document.

There is another dimension to this.  Let's say we want to remodel a space in a building. The first thing the architect will want to see is the existing floor plan and the other professionals will also want to see other aspects of the space.  Normally at this point the building owner will ask everyone involved in the planning to come over for a  walk-through the building.   That requires a lot of valuable time from very busy people.  With the virtual model that we can generate, most of this won't be necessary any more. The owner can ask a relatively junior person equipped with the backpack to walk the building to collect the data.  Then the resulting model can be put online and everyone can virtually walk through it.  For bidding on a big renovation project, a relatively junior operator does the walk through and puts it on the cloud and the construction companies who are interested in the project can virtually walk through it online for bidding purposes.

For a new building,  during the actual construction you can conduct a walk through every day and update visually what's going on.  Then if there are lawsuits in the future, you will have a permanent record of who did what and when.   You can show the legal folks where a component was supposed to go and where it was actually installed. It is also useful for monitoring progress, so you know who did what when to determine who gets paid and how much.   If there is a lawsuit and insurance companies get involved, you can step through the record to see who did what.  Then when you want to sell the finished building, the real estate folks can do a virtual walk through of the building with prospective customers.  Property managers and facility managers can use the virtual model for maintaining the building. For example, we talked about the energy performance audit.  Facility managers can use it to identify lights that need to be replaced with more efficient bulbs. Then they can put a virtual tag on the lights for the maintenance folks.  With a virtual model like this, owners, contractors, designers, and facility managers can all collaborate throughout the lifecycle of the building.

There seems to be a lot of areas where the technology you have developed could be used ?

There are quite a number of other application areas for the technology we have identified including historical building and monument recording, scan-to-BIM,  variance detection and virtual inspection, real estate valuations, building plans for first responders, change management and dispute resolution, and detecting thermal anomalies including water leaks and insulation deficiencies to name just a few.

Crossrail's huge BIM+geospatial model targeted on operations and maintenance

The UK Government as part of its building information modeling (BIM) initiative has said repeatedly that it expects the big payoff of a digital model, estimated at more than 40% savings, will be during operations and maintenance, typically representing 80% of the total cost of a facility.  Companies such as BAM who do Design, Build, Finance and Maintain (DBFM) projects report significant benefits from full lifecycle BIM + geospatial.  But there is little if any quantitative evidence supporting this conjecture.  I have asked people from Finland familiar with the very early BIM developments in that country if there were studies of the benefits of BIM for operations and maintenance, but apparently the BIM focus there has been entirely on design and build.

 

Crossrail with a budget of £14.8 billion is the biggest engineering project in Europe.  It involves 42 km of tunnels beneath one of the most densely populated parts of Europe.  It has wider tunnels  and its 40 stations have longer station platforms than the Tube has.  Crossrail trains are expected to start running next year and the full network should be open by 2019.

 

But the most interesting aspect of the Crossrail project is a 3D digital model with associated asset data that has not only been used during design and construction, but is intended to be used for operations and maintenance.  Crossrail appears to be the first major project that may be able to provide support for the conjecture that the biggest benefits of BIM are for operations and maintenance.

 

The Crossrail model is comprised of spatial and non-spatial data with links between the two.  The spatial data is made up of more than 250,000 3D BIM models as well as as-builts, together comprising a few terabytes. As construction of each facility is completed as-builts are collected by point-cloud survey using laser scanners.  The point clouds captured in the survey are compared to the design and divergences that need resolving are recorded for fixing. The detailed asset data and documentation add an additional 5 terabytes.  This represents one of the World's largest BIM model.  A critical aspect of the spatial database is that all assets are geolocated so that workers can query a particular location of London on a map and then navigate to the Crossrail assets there.

 

The model is intended to become a crucial tool for monitoring, operating and maintaining Crossrail’s systems once the railway is running.  Sensors monitor various aspects of the railway's operation and remote-controlled devices can change operating parameters from a central control room or from a handheld device.  Managers can view this information within the 3D model and can zoom in on an area which needs attention.  Crossrail is testing low-power wireless smart sensors called Utterberries that can monitor strain, temperature, humidity, acceleration, and other aspects of a facility. Utterberries weigh 15 grams and are smart - they have an ARM processor on-board and can operate for more than a year on one charge.  One of the coolest capabilities of the digital infrastrucure is an augmented-reality interface which allows workers to hold an iPad up to a wall or floor and see a view of the infrastructure (electricity, water, and communications) under the floor or behind the wall.

For the future of distributed energy watch the Hawaiian power utilities

Keep a close watch on the Hawaiian power utilities (HECO) because Hawaii has committed to 100% renewables by 2045 and 65% by 2030. That was the recommendation of Sharon Allan, CEO of the Smart Grid Interoperability Panel (SGIP) at a DistribuTECH2016 MegaSession on distributed energy resources (DER) in response to a question from the audience asking what should utilities do to get ready for GRID 3.0.

Hawaii already has 487 MW of solar PV capacity, 90% of which is residential rooftop panels. At  DistribuTECH2016, Talin Sakugawa from HECO and Matthew Shawver from in2lytics gave an insightful presentation on distributed energy generation (DER), big data and analytics at the Hawaiian power utility.  Hawaii has seen an exponential rise in rooftop solar PV. As a result since 2010 the utility has been experiencing a reduction in net load and revenue. The most dramatic drop in load is during daytime hours as is readily apparent by comparing the utility's load on clear and cloudy days which reflects the impact of residential solar PV.

Challenges facing utilities

The Rocky Mountain Institute (RMI) has published  an analysis of the economics of leaving the grid.  The central thesis is that solar PV and electricity storage enables consumers to leave the grid completely.  Solar PV has already reached grid parity in about 10% of the U.S. and declining PV prices suggest that this trend will continue.  However, without the ability to store electric power, consumers with rooftop PV still require the grid for nights and cloudy days.  The development of combined solar PV and batteries from Tesla and others promises to make solar + storage accessible for increasing numbers of consumers.  These consumers could form their own microgrid, either by themselves or with their neighbours and disconnect from the grid.  Alternatively they could become a source of dispatchable power and become an energy provider.  Increasingly they could find that either of these options is economically advantageous.  This has serious implications for local utilities because if this trend develops it will seriously erode the traditional utility revenue base.   It could also lead to a completely decentralized grid comprised of many microgrids.  From an operations perspective it increases the complexity of managing the grid compared to the centralized model in use today.

The other challenge for a utility with a high penetration of intermittent, distributed generation (DER) is load balancing, ensuring that generation meets demand. The German power industry, the U.S. Department of Energy, Hawaiian power utilities,  California power utilities, ERCOT, and many others are working to address this challenge.  HECO's presentation at DistribuTECH gave an insightful overview of what they are doing to meet this technical challenge.

Data sources

HECO collects a large amount of data from diverse sources. It is comprised of internal operational data plus data from esternal sources. It includes weather forecasts, data from customer sited PV, consumer/public resource data, phasor (PMU) data, renewables power quality, feeder data, irradiance meters, generator and substations power quality, SCADA, and vendor data such as forecasts and data from Independent Power Producers (IPPs). The data has widely different time resolutions ranging from real time such as PMUs sampling 30 times per second, SCADA reporting every 2 seconds, calculated gross load every 2 seconds, PV inverter data reporting every 5 minutes and aggregated monthly, weather forecast reporting every 15 minutes and aggregated daily, transformers collecting data every 15 mins and aggregated monthly, and smart meters reporting every 15 minutes and aggregated quarterly. Much of the data includes location. The data comes in different formats and has different levels of quality.

Fundamental to distributed energy are weather maps.  Maps of irradiance and wind velocity can be directly used to estimate or predict solar and wind generation in different parts of the islands.  Temperature maps are also important because the efficiency of solar panels depends on temperature.  The more accurate the weather forecasts are, the more predictable the generation.  Knowing that there it will be cloudy over the south of Oahu, but sunny over the rest of the island, or cloudy over the whole island, but with moderate to strong winds allows operations to estimate how much backup generation is required and where. 

EMS Integration

Data from new sources relating to distributed renewable generation are integrated with traditional sources in a distributed energy management system which enables operations and planning visibility into how distributed generation is impacting thte grid. It not only provides a view into the status of the grid (situational awareness) but also allows simulations to assess the impact of different future renewable generation scenarios on the grid. This helps to determine where backup generation may be required because high demand and low generation from renewables or curtailment when there is excess generation and insufficient load.  Location is fundamental to the analytics, because wind and irradiance varies widely over the islands.

Traditional input sources include SCADA and data from transmission, generators, protection, and so on. The new input data sources include GIS-based infrastructure models and data from the distributed generation sources. Also new is weather forecasting which enables predicting generation from renewable sources.  It also includes historical and actual weather reports, and satellite and other weather data.

HECO uses an IT platform from In2lytics (a spinoff from Referentia) which integrates all of these time series data with EMS, SCADA, CIS, and GIS and makes it available to operational users, planners, modelers and the public. in2lytics is a high performance time series database that is specially designed to provide instant data accessibility for planning and operational decision making.  in2lytics enables load, query, analysis using MATLAB, and sharing of the "big data" required to monitor and manage today’s complex electric grid. in2lytics has native high performance interfaces for MATLAB in addition to programming languages.  It translates data from different sources into its internal time series database. All data is archived in a spatially enabled time series database for future analysis. Matthew said that in2lytics is able to analyze large volumes of historical time series data very rapidly. For example, a longitudinal analysis on two years worth of archived data can be run in an hour or two.

Applications

Accounting for renewables requires a lot of data. Some areas that use this data include generation planning, load forecasting, distribution planning, and contract administration. One of the first applications is a customer facing web site called Renewable Watch - Oahu that reports the total renewables generation, solar and wind, on Oahu in real-time.

An example of an application that will be used for contract administration beginning next month estimates how much power is lost from Independent Power Producers (IPPs) in the case of curtailment. It uses weather information to estimate wind speed and irradiance meters to estimate wind and solar generation potential.

Another analytical application was used to study how solar PV variability affects transformer tap changes. This application allows HECO to relate frequency of tap changes to solar PV variability and to determine which transformers are most affected by solar variability.

HECO has a number of projects with the Department of Energy and other partners.

1. Department of Energy's Integrating System to Edge-of-Network Architecture and Management" (SEAMS) A federally-funded research initiative on high penetration grids which aims to streamline grid planning and operations for utilities in regions with high concentrations of distributed generation (DG) resources.

2. Department of Energy's Distributed Resource Energy Analysis and Management System (DREAMS) Department of Energy system to provide useful information about the distributed grid to grid operators.

   Partners: Siemens, Alstom, DNV, AWS Truepower, Referentia, Sunshot

   Advisory Team:SMUD, Southern California Edison, California ISO, TEP, APS, Kaua'i Island Utility Cooperative, Western APA

3. Making sense of synchrophasor data for utilities. The Synchrophasor Visual Integration and Event Evaluation for Utilities (SynchroVIEEU) with High Penetrations of Renewables. Accelerate the integration of synchrophasor information into production grade data visualization and analysis platforms/models. Leverage PMU capability at many substations – explore ways to tap resources and provide real-time visibility and real-time data. Make synchrophasor data accessible for efficient and reliable operations of a modern grid in light of high penetrations of renewable resources

   Partners: SEL, DNV GL, Referentia

4. Monitoring, planning, and modeling high PV penetration microgrid. Decision support for microgrid design and operation at Marine Corps Base Hawaii. The Office of Naval Research (ONR) has launched an ambitious program to demonstrate and evaluate energy technologies using Navy and Marine Corps facilities as test beds, known as the Energy Systems Technology and Evaluation Program (ESTEP). Program management is being handled by the Space and Naval Warfare Systems Command (SPAWAR) Systems Center Pacific (SSC Pacific). ESTEP was established in 2013. It brings together the Department of the Navy, academia, and private industry to investigate and test emerging energy technologies at Navy and Marine Corps installations. At present, ESTEP conducts over 20 in-house government energy projects, ranging from energy management to alternative energy and storage technologies.

Other projects include

• Remote light sensor technology at the Kahuku wind farm on Oahu to measure and forecast windspeed and direction to support reliability;
• Sonic detection and ranging technology to remotely measure wind speed and direction;
• Network of solar irradiance sensors to measure light and to make forecasts;
• EV Race to the Sun, a joint Japan-U.S. showcase event for electric vehicles,to take place on Haleakala;
• Battery storage technology development, including the JUMPSmartMaui project.

The ultimate objective for HECO is to become a company offering diversified services providing value to engaged customers, of course with satisfied regulators and sustainable costs and margins.  

Big data and spatial-temporal analytics

It was clear from this presentation that essential for HECO, or to any utility, with a high penetration of solar or wind are weather maps, forecasted, actual and historical because these enable the utility to project generation. The also allow utilities to analyze the historical record in conjunction with other data to discover trends that may help improve forecasting.  Collecting and analyzing temporal geospatial data is fundamental for distributed energy management.  This is in addition to the expanded application of geospatial technology for utility operations and planning in the smart grid era.

Powering up the smart grid with geospatial technology

The electricity industry is undergoing a transformation. With utilities embracing geospatial technology and turning into data driven enterprises in the Smart Grid scenario, the sector is staring at an innovative future.

The National Academy of Engineering identifies the electric power grid as the first of 20 major engineering achievements that has had the greatest impact on the quality of life in the 20th Century. Modern society has reached a point where virtually every crucial economic and social function depends on the secure, reliable operation of electric power infrastructure.  But because it has become so crucial for modern life, it faces major challenges.
   
The major drivers for the fundamental change underway in the electric power industry are increasing demand, universal access, decarbonizing electric power, reducing revenue losses, and grid reliability and resilience.  Some of the  technologies that are contributing to this transformation are intelligent devices integrated with a communications network, distributed renewable power generation especially wind and solar PV, net zero energy buildings, microgrids, and the new remote sensing technologies of subsurface utility engineering.  

For an industry not known historically for rapid change, many utilities are in the midst of transforming themselves into data driven enterprises. Recently IDC published its future predictions for the development of the electric power industry over the period 2015-2018.  For many in the industry these are quite startling and clearly reflect an industry that is rapidly evolving.

The technology roadmap for the smart grid involves the deployment of increasing numbers of intelligent electronic devices for sensing and for control.  The challenge is federating the data from all of these devices, extracting information from it, and dispatching the information to the right control devices. 
 
With the changes that the electric power industry is undergoing now, analysts see geospatial technology poised to become a foundation technology for the smart grid.  The role of utility GIS is expected to touch every aspect of a utilities business, affecting customers, operations and management because geospatial is the logical technology that can provide  the basis for integrating data from intelligent electronic devices such as smart meters and the  information silos associated with proprietary applications.

You can read more about smart grid and relevance of geospatial data and technology, real-time big spatial data, standards for interoperability, the importance of data quality, open source geospatial technology,  spatial analytics and other aspects of the role of geospatial technology in the smart grid in Geospatial World.

New journal for open geospatial data, standards and interoperability

A new open journal Open Geospatial Data, Software and Standards provides an advanced forum for the science and technology of open data, crowdsourced information, and sensor web through the publication of reviews and regular research papers. The journal publishes articles that address issues related, but not limited to, the analysis and processing of open geo-data, standardization and interoperability of open geo-data and services, as well as applications based on open geo-data. The journal is also meant to be a space for theories, methods and applications related to crowdsourcing, volunteered geographic information, as well as sensor web and related topics.

Very long range unmanned craft operates under all weather conditions

In the last few years I have come across a number of UAVs at the various conferences I have attended.  They range from hexacopters to fixed wing aircraft but are limited typically to flight times of 40-50 minutes maximum because of battery capacity.  I am also aware of a UAV with a daytime endurance estimated to be 5 to 12 hours depending on wing configuration, weather, and flight profile.  All of these have a serious constraint in common, flights are severely limited by weather conditions.

Wave Gliders

In 2009 a pair of unmanned Wave Gliders travelled from Hawaii to San Diego encountering a wide range of weather conditions on the way.  The 2,500 mile trip took 82 days.  As of 2013 the total Wave Glider fleet had exceeded 400,000 nautical miles at sea.

The Wave Glider is an unmanned vessel used for collecting data with a variety of sensors that uses both wave-powered and solar energy to navigate under any kind of ocean conditions from doldrums to hurricanes and cyclones.  The Wave Glider converts wave motion to propulsion by exploiting the difference between wave motion on the surface and below the surface where it is much less.  The Wave Glider has a two-part architecture that allows this difference in wave motion to be converted to propulsion.  It is also able to generate its own power.  It has a maximum payload power capacity of 260W and a battery capacity of up to 6.86 kWh.  The power system supports various types of energy sources and multiple powered sensors, as well as an auxiliary electric thruster and navigation, GPS, computing and communications equipment.

Everything is controlled by software. Since August, 2011, James Gosling, well-known as the "father of Java", has been the chief software architect at Liquid Robotics. The software environment has several components and can be used to controls either single vessels or fleets of Wave Gliders.  It has a web application for remote mission management that includes piloting and data management tools. This software provides browser and programmatic interfaces (APIs) to allow users and applications to send commands to and receive data from the Wave Glider and its sensors.  The Wave Glider has its own operating system for the onboard command-and-control computer, responsible for communications, navigation and collision avoidance, power and security.  Sensor Management Software  runs the onboard sensor management computer, managing the functions of and data collected from all the sensors on the Wave Glider.  The Data Services Environment software provides cloud-based infrastructure that manages the flow of data from the Wave Glider to the remote pilots, mission specialists, and other users and software applications.

Predictions of the future of the utility business 2015-2018

For the first time in a hundred years, the electric power utility industry is undergoing a momentous change. Distributed renewable power generation, especially solar photovoltaics (PV), is introducing competition into an industry that has been managed as regulated monopolies. Consumers with solar PV panels on their roofs are fundamentally changing the traditional utility business model. A recent report from the Edison Electric Institute (EEI) report refers to disruptive challenges that threaten to force electric power utilities to change or adapt the business model that has been in place since the first half of the 20th century.

Most utilities are in the midst of deploying smart grids, which basically amounts to applying the internet to the electric power grid to link intelligent electronic devices, sensors and grid control applications to enable data-driven decision making.  One of the most important changes driven by the implementation of smart grid is the much greater importance of location. Geospatial technology (location, geospatial data management and spatial analytics) is seen as foundational techology for the smart grid.

The other major global change in energy is the shift in energy demand from the world's advanced economies to emerging economies.   Energy demand from OECD countries has hit a plateau.  Currently China is driving world energy demand. The International Energy Agency's (IEA) World Energy Outlook 2014 projects that in the future  as demand slows from China, world energy demand will be driven by India, the Middle East, and Africa and Latin America.

Recently IDC Energy Insights released a report IDC FutureScape: Worldwide Utilities 2015 Predictions with predictions for the future of the utility business.  Some of these are startling, suggesting that the utility industry is going to experience fundamental changes in how they do business over the next few years.

New business models

IDC predicts that utilities will be looking less at generation as a source of revenue.  IDC predicts that by 2018 45% of new data traffic in utilities' control systems will originate from distributed energy resources that are not owned by the utility.

To make up for this loss of generation revenue IDC predicts that utilities will be looking for new business opportunities such as services.  Specifically, IDC predicts that utilities will derive at least 40% of their earnings from new business models by 2017.

Technology

• Cloud - By 2018 cloud services will make up half of the IT portfolio for over 60% of utilities.
• Integration - In 2015 utilities will invest over a quarter of their IT budgets on integrating new technologies with legacy enterprise systems.
• Analytics - By 2017 45% of utilities' new investment in analytics will be used in operations and maintenance of plant and network infrastructure.
• Mobility - 60% of utilities will focus on transitioning enterprise mobility to capitalize on the consumer mobility wave.
• Smart systems - By 2018 cognitive systems will penetrate utilities' customer operation to improve service and reduce costs.

Some  of the important drivers for these trends include the global redistribution of energy demand from the world's advanced to the emerging economies, the rapid emergence of cloud-based provisioning and services, increasing regulatory pressure responding to customer demand to improve energy market transparency and competitiveness, cross-industry competition for technical, especially IT skills, smart analytics, and virtual and augmented reality beginning to be applied in business.

Top 10 technology trends

In March 2014 Gartner, Inc. identified the top ten technology trends which it saw impacting the global energy and utility markets.  There is considerable overlap between IDC's business predictions and the technology trends identified by Gartner, Inc.

Social Media

Social media are beginning to be used as a customer acquisition and retention medium, as a consumer engagement channel to drive customer participation in energy efficiency programs, a source of information about outages, and as the emerging area of crowd-sourcing distributed energy resources coordination. Social media are also being used by utilities for communicating information about outages with customers.

Big Data

Smart grid will increase the quantity of data that utilities have to manage by a factor of about 10,000 according to a recent estimate.  This trend is driven by intelligent devices, sensors, social networks, and new IT and OT applications such as advanced metering infrastructure (AMI), synchrophasors, smart appliances, microgrids, advanced distribution management, remote asset monitoring, and self-healing networks.  The type of data that utilities will need to manage will change: for example, real-time data from sensors and intelligent devices including smart phones and unstructured data from social networks will play a much greater role for utilities in the future.

Mobile and Location-Aware Technology

Mobile and location-aware technology which includes hardware (laptops and smartphones), communication products (GPS-based navigation, routing and tracking technologies), social networks (Twitter,Facebook and others) and services (WiFi, satellites, and packet switched networks) are transforming all industries.  Utilities for the most part have been slow to adopt consumer mobile technology, but  this is changing.

Cloud Computing

Acccording to Gartner, areas such as smart meter, big data analytics, demand response coordination and GIS are driving utilities to adopt cloud-based solutions.  Early adopters of cloud technologies include small utilities with limited in-house IT skills and budgets, organizations which provide application and data services to multiple utilities, such as cooperative associations and transmission system operators,  and investor-owned utilities (IoUs) conducting short-term smart grid pilots.

Sensor Technology

Sensors, which are being applied extensively throughout the entire supply, transmission and distribution domains of utilities, provide a stream of real-time information from which a real-time state of the grid can be derived.

IT and OT Convergence

Virtually all new technology projects in utilities will require a combination of IT and OT investment and planning, such as AMI or advanced distribution management systems (ADMSs).  This will be a challenge for many utilities, especially smaller ones, which don't have in-house IT skills.

Advanced Metering Infrastructure

AMI provides a communication backbone aimed at improving distribution asset utilization and facilitating consumer inclusion in energy markets.

Internet of Things

Sensors and actuators embedded in physical objects are linked through wired and wireless networks, using the same Internet Protocol (IP) that connects the Internet.  When intelligent objects can both sense the environment and communicate, they become tools for understanding utility grids and responding to changes in near real-time.  Following McKinsey there are two key benefits arising from the Internet of Things for utilities

• Enhanced situational awareness - acheiving real-time awareness of physical environment, in this case, the grid
• Sensor-driven decision analytics - assisting human decision making through deep analysis and data visualization

Asset performance management

Traditional asset management approaches are too limiting for today’s performance-based, data-driven utility environment. Asset performance management solutions need to deliver real-time equipment performance, reliability, maintenance and decision support for effective resource management so that operations and maintenance teams are empowered with real-time decision support information, providing the right information to the right people at the right time and in the right context.  The result is improved operational performance and better asset availability and utilization.

Business Intelligence and Advanced Analytics

Analytics will become essential as the volume of data generated by intelligent devices and sensors, mobile devices (the Internet of Things) and social media increases and huge pools of structured and unstructured data need to be analyzed to extract actionable information.  Analytics will become embedded everywhere, often invisibly.