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.

Singapore's standard for location of underground utilities

Straits Times

In August 2017 the Singapore Land Authority (SLA) released a standard and specifications for the procedure and practice of utility surveying in Singapore.  The standard specifies the data that should be captured for new underground utilities including telecommunication, sewerage, water supply, electric power, gas, and drainage. It specifies that after installing utilities and prior to filling the trenches an as-built survey should be performed by a registered surveyor to capture the location of the utilities with horizontal accuracy of ±100mm and vertical accuracy of ±100mm. It specifies that the survey equipment to be used should be a total station, GNSS Real Time Kinematic (RTK), or 3D Laser Scanning.

This standard is not mandated by the Singapore Government but is intended to provide a baseline to be incorporated in construction contracts. Since 85% of land is owned by the Singapore government, if a major government agency such as the Urban Redevelopment Authority (URA) routinely incorporated this standard in its contracts as part of standard procurement practice, it would effectively become a de facto mandate. 

In addition to location, the standard also defines the attribute information to be captured.  For example, for an electric power cable, the standard specifies that x,y,z should be surveyed at 20 meter intervals for a straight line cable and more frequently for a curving cable.  In addition, height, width, number of columns, number of rows, number of ducts, number of cables and quality level should also be captured. The quality standard used in Singapore is; 1) ±100mm, 2) ±300mm, 3) ±500mm, 4) Unknown accuracy and 5) Trenchless method.  In Singapore most underground utility work is open trench, although there is some trenchless which requires gyro surveying.

The standard does not specify format or what is to be done with the as-built information.  Currently in Singapore there is nothing in place to require sharing of information about the underground like Netherland's BRO or the British National Underground Asset Registry (NUAR) project.  But there is a Digital Underground project underway to create an ecosystem for a digital twin of the underground as part of the Mapping Singapore in 3D project.  The digital underground project was initiated by the Singapore Land Authority with the the support of the Urban Redevelopment Authority.  The standard for underground utility surveying would create a basic data model for sharing information about underground utilities.

Applying machine learning to identifying housing starts from satellite imagery

Deep learning is increasingly finding practical applications using geospatial data, specifically, satellite imagery. At the GeoIgnite conference in Ottawa, Nicolas Martinez of Statistics Canada described a project scheduled to kick-off in July to use machine learning to identify housing starts across Canada.  The goal of the project is to improve the accuracy and coverage of housing starts survey, specifically to fill data gaps for smaller remote and indiginous communities which are generally excluded from the current survey program. 

Canada Mortgage and Housing Corporation (CMHC) currently employs a significant number of people at their head office in addition to field agents across the country who physically confirm residential starts and completions.  The objective of the project is to apply convolutional neural network (CNN) machine learning to satellite imagery to extract information about housing starts and completions. The imagery will be preprocessed to prepare it to be used in developing a machine learning processing model.  The largest challenge will be developing training and testing imagery data sets.

The original application of deep learning to photo imagery was by Geoffrey Hinton at Google who used it to distinguish dogs and other objects in photos that people uploaded.  More recent applications of satellite imagery targeted landuse and agricultural identification. For example, remote sensed imagery from a satellite can be used to create a model that will differentiate between corn and potatoes, using factors that can be calculated from satellite imagery such as normalized difference vegetation index (NDVI) (min, max, mean), texture (min, max, mean), vegetation height, and geometric factors such as orientation.  I blogged about how open source code and publicly available training data has been applied to track deforestation and reforestation in Mato Grosso, a state in the central amazon region of Brazil. A study in the Netherlands used CNN to identify blocked waterways using overflight imagery with a success rate of 97 %.

An important application of satellite imagery is identifying built structures. I have blogged about how satellite imagery has been used to identify buildings and transportation networks by applying a neural network model together with OpenStreetMap layers and high resolution Worldview multispectral imagery.  NVIDIA has demonstrated the ability to automate detection of many road networks using deep learning algorithms and multi-spectral high resolution imagery.  Another application of CNN and imagery in the Netherlands was able to differentiate residential roofs with dormer windows from those without.

The essential key to effective application of deep learning is good training and test data - ground-truthed data that involves, for example, someone on the ground identifying whether the fields seen in the imagery are corn or potatoes or something else. There are  publicly available training data sets that can be used to train satellite imagery CNN models. For example, I blogged about the System for Terrrestrial Ecosystem Parameterization (STEP) dataset which has 2000 manually labeled sites covering 17 different land cover types scattered across all continents. A large publicly available training dataset derived from Sentinel 2 imagery contains 30,000 polygons of land use training data for ten classes of land use: annual crop, forest, herbaceous vegetation, highway, industrial, pastures, permanent crop, residential, river and sea or lake.

The Statistics Canada project is scheduled to begin in July with initial work on a proof of concept and CNN testing to begin in August and September.  The communities chosen for the initial phase are  Kitchener-Cambridge-Waterloo, Red Deer, and Iqaliut.  The first phase is scheduled to be completed by winter 2019.  The project plans to use Tensorflow and Keras, open source libraries and a python framework for applying machine learning.  Artificial intelligence (AI) has often suffered from inflated expectations, complex code, heavy processing requirements and not very practical applications. There is a growing body of practical applications in the geospatial domain that shows that as a result of the immense processing power available in the cloud machine learning is able to generate fairly easily practical and useful results.  As Chris Holmes pointed out in his talk at FOSS4GNA, the challenge for Statistics Canda will be to develop reliable and publicly available training and test datasets to enable deep learning models to be created for a broader range of applications using the huge volumes of satellite imagery and other geospatial data that are now available.

Deep learning is increasingly finding practical applications using geospatial data, specifically, satellite imagery. At the GeoIgnite conference in Ottawa, Nicolas Martinez of Statistics Canada described a project scheduled to kick-off in July to use machine learning to identify housing starts across Canada.  The goal of the project is to improve the accuracy and coverage of housing starts survey, specifically to fill data gaps for smaller remote and indiginous communities which are generally excluded from the current survey program. 

Canada Mortgage and Housing Corporation (CMHC) currently employs a significant number of people at their head office in addition to field agents across the country who physically confirm residential starts and completions.  The objective of the project is to apply convolutional neural network (CNN) machine learning to satellite imagery to extract information about housing starts and completions. The imagery will be preprocessed to prepare it to be used in developing a machine learning processing model.  The largest challenge will be developing training and testing imagery data sets.

The original application of deep learning to photo imagery was by Geoffrey Hinton at Google who used it to distinguish dogs and other objects in photos that people uploaded.  More recent applications of satellite imagery targeted landuse and agricultural identification. For example, remote sensed imagery from a satellite can be used to create a model that will differentiate between corn and potatoes, using factors that can be calculated from satellite imagery such as normalized difference vegetation index (NDVI) (min, max, mean), texture (min, max, mean), vegetation height, and geometric factors such as orientation.  I blogged about how open source code and publicly available training data has been applied to track deforestation and reforestation in Mato Grosso, a state in the central amazon region of Brazil. A study in the Netherlands used CNN to identify blocked waterways using overflight imagery with a success rate of 97 %.

An important application of satellite imagery is identifying built structures. I have blogged about how satellite imagery has been used to identify buildings and transportation networks by applying a neural network model together with OpenStreetMap layers and high resolution Worldview multispectral imagery.  NVIDIA has demonstrated the ability to automate detection of many road networks using deep learning algorithms and multi-spectral high resolution imagery.  Another application of CNN and imagery in the Netherlands was able to differentiate residential roofs with dormer windows from those without.

The essential key to effective application of deep learning is good training and test data - ground-truthed data that involves, for example, someone on the ground identifying whether the fields seen in the imagery are corn or potatoes or something else. There are  publicly available training data sets that can be used to train satellite imagery CNN models. For example, I blogged about the System for Terrrestrial Ecosystem Parameterization (STEP) dataset which has 2000 manually labeled sites covering 17 different land cover types scattered across all continents. A large publicly available training dataset derived from Sentinel 2 imagery contains 30,000 polygons of land use training data for ten classes of land use: annual crop, forest, herbaceous vegetation, highway, industrial, pastures, permanent crop, residential, river and sea or lake.

The Statistics Canada project is scheduled to begin in July with initial work on a proof of concept and CNN testing to begin in August and September.  The communities chosen for the initial phase are  Kitchener-Cambridge-Waterloo, Red Deer, and Iqaliut.  The first phase is scheduled to be completed by winter 2019.  The project plans to use Tensorflow and Keras, open source libraries and a python framework for applying machine learning.  Artificial intelligence (AI) has often suffered from inflated expectations, complex code, heavy processing requirements and not very practical applications. There is a growing body of practical applications in the geospatial domain that shows that as a result of the immense processing power available in the cloud machine learning is able to generate fairly easily practical and useful results.  As Chris Holmes pointed out in his talk at FOSS4GNA, the challenge for Statistics Canda will be to develop reliable and publicly available training and test datasets to enable deep learning models to be created for a broader range of applications using the huge volumes of satellite imagery and other geospatial data that are now available.

Bentley goes open source for its digital twin products

One of the biggest announcements in recent memory in the design and construction world was made by Keith Bentley of Bentley Systems this morning at Year in Infrastructure 2018 in London.  After describing some of the key technologies that Bentley plans to underpin its digital twin offering iTwin Services including ContextCapture for reality modeling and iModelHub for sharing  model information, Keith really opened up when he acknowledged that the world had gone open source and that Bentley was following Microsoft's lead in joining Google, Amazon and many other major companies in embracing open source. 

Bentley intends to build it products supporting digital twins around an open source project already on Github called iModel.js.  Keith has committed Bentley resources to developing an ecosystem around iModel.js. I have heard Keith Bentley at a number of events talking about his vision for Bentley technology, but I have never seen him so excited and committed to a new direction like this morning's announcement. To quote Keith this is a huge thing - for him the biggest thing in 35 of these Bentley events that he has spoken at since founding Bentley Systems.

Bentley had puts its toes in the open source water previously.  Bentley used open source geospatial components to develop a new front-end for its leading road and rail asset management system AssetWise Lifecycle Information Management solution (ALIM).  Bentley also partnered with AGI to support the open source Cesium project.

Bentley has clearly thought through open sourcing its digital twin products.  They have a business model for iTwin Services - not surprisingly for the rest of the IT world, but a significant step for Bentley - it is a subscription model.

To start the iModel.js ecosystem off, Bentley is working with Hack Partners to sponsor and support an Infrahack Hackathon early in 2019.

Bentley open sources its web-based digital twin javascript support library

I am at the Year in Infrastructure 2018 conference in London this week.  This morning there have been some very interesting announcements relating to digital twins.  First of all Greg Bentley announced Bentley's foray into infrastructure digital twins.  iTwin Services provides cloud-based support for digital twins and integrates with ProjectWise and AssetWise.  Secondly, Keith Bentley announced what I think is Bentley's first open source project.  iModel.js, which is hosted on Github, is a javascript library to support web-based visualization and analytics for infrastructure digital twins. 

Previously Bentley used open source geospatial components to develop a new front-end for its leading road and rail asset management system AssetWise Lifecycle Information Management solution (ALIM).  Bentley also partnered with AGI to support the open source Cesium project.

Microsoft becomes a fully fledged open source company

Things do change. Microsoft, once the perceived enemy of open source, has been moving into open source over the last two decades.  The first infamous Halloween document requested by Jim Allchin was leaked to Eric Raymond in October 1998.  This and other internal documents recognized that that open software products such as Linux were technologically competitive with Microsoft's products and set out a strategy to combat them. But there were folks within Microsoft that disagreed with this strategy.  Microsoft began partnering with open source companies back in 2006. It announced support for Git in 2013.  It now has over 2,000 open-source projects on Github and claims to be the largest open-source project supporter in the world. As further evidence that It is now a fully fledged open source company, Microsoft has joined the Open Invention Network (OIN), an open-source patent consortium that includes the Open Source Geospatial Foundation (OSGEO), Google, IBM, Red Hat, Oracle and over 2600 other organizations as members or licencees. Microsoft is bringing something on the order of 60,000 patents to OIN, effectively granting royalty-free and unrestricted license to its patent portfolio to all other OIN members.  Since in the past Microsoft has derived significant royalty revenue from protecting these patents including many used in Android, this really is a case of putting its money where its mouth is.

An open source web-based platform for integrating BIM and 3D geospatial

BIM+geospatial integration is being driven by a trend toward infrastructure construction projects that require maintenance and/or operations during an extended period post-construction.  At the GEO|Design+BIM conference in Amsterdam, Sanghee Shin, CEO of Gaia3D Inc., gave a riveting presentation about a web-based geo-platform integrating massive, complex BIM models using open source geospatial libraries and tools including PostGIS, GeoServer, and Cesium and NASA WorldWind virtual globes.

Drivers of BIM+geospatial integration

Since the world became aware of the importance of human impact on the environment, it has become increasingly difficult to design buildings without reference to geographic context.  Many cities have environmental zoning and other types of by-laws that require designers to take into account neighboring buildings, infrastructure and vegetation.  States and national governments are developing building codes that require designers to assess the energy, water, emissions, and other environmental impacts of buildings.  This means that when designing and constructing a building or renovating existing buildings, the traditional approach ( often associated with a 0,0 coordinate system ) of design in isolation is no longer adequate for sustainable design.

Furthermore integrated geospatial+BIM is essential during the operations and maintenance phase of a building.  Utilities, transportation, emergency planning, first-responder access, energy performance, and evacuation management all require information about internal and external infrastructure.  A common geospatial coordinate system enables a comprehensive operational view of all infrastructure including internal and external structures. 

BIM+geospatial integration provide greater value to projects that involve not just design and construction but also operations and maintenance.  A leader in this space, Rijkswaterstaat, the Dutch transportation authority, began offering design-build-finance-maintain (DBFM) projects a number of years ago which has motivated private Dutch engineering and construction companies to adopt an integrated geospatial+BIM approach to construction.  For example, the firm Royal BAM Group nv/BAM Infraconsult adopted integrated BIM+geospatial because of market developments including more complex construction assignments and an increasing demand from customers for service provision throughout the entire life cycle of a project.

It is indicative of the importance of the integration of these technologies that Autodesk and ESRI, 800 lb gorillas in their respective areas of BIM and GIS, have announced an agreement to collaborate to help bridge the gap between BIM and 3D GIS.  This is their seond attempt.  The first attempt, an agreement between Jack Dangermond and Carol Bartz in the mid 90s, resulted in a product called ArcCAD, intended for civil engineers and planners.

An open source geo-platform for integrating BIM+geospatial

mago3D is a new 3D geo-platform developed by Gaia3D.  It is open source and integrates many well-known open source geospatial products and APis. It is web-based so it is not necessary to install anything to run it. It has an engine for handling massive, complex BIM objects based on an internal format (F4D) that was created to reduce network data traffic, enable LOD (level of detail) control, support octrees, a 3D spatial structure that enables efficient spatial queries and (frustrum and occultation) culling.  Culling refers to the removal of objects that are not visible to the observer and is key to efficient visualization or large, complex structures. It integrates with two of the best known open source 3D virtual globe tools, Cesium (supported by AGI and Bentley) and NASA WorldWind, for visualization in a geospatial context.  Viewing BIM structures in mago3D has some similarities with a Google Earth experience. The source code is available under Apache and AGPL (Affero GPL) open source licenses.  Cesium is an open source project supported by AGI and Bentley.

mago3D includes a F4D Converter that converts BIM models in IFC (buildingSmart Industry Foundation Classes) format as well as 3D objects in .3ds (Autodesk 3ds Max), .obj (Alias/Wavefront) and .dae (Collada) formats into its internal F4D format.  Industry Foundation Classes (IFC) is a vendor neutral and open model that most industry software BIM tools including Revit and Tekla are able to export, although there is typically some information loss. The bimserver.org open source project provides an open repository of IFC models and offers a Java API to extract partial building information models from a repository.

The main components of mago3D are mago3D.js, a javascript plug-in for WebGL virtual globes and open source web server (NGiNX), map server (GeoServer), geospatially-enabled database (PostgreSQL+PostGIS), 2D mapping (OpenLayers, Leaflet) and 3D visualization (Cesium or NASA WorldWind). The mago3d.js javascript plugin is designed to run in a WebGL virtual globe environment in a browser so that moving/rotating objects such as BIM models and heading adjustment happen in-browser and are very fast. 

I went to the mago3D site and viewed a BIM model of a chemical manufacturing facility in South Korea.  I was impressed with the ability to zoom ala Google Earth from the entire virtual globe down to viewing the chemical facility in its geospatial context.  I could continue down to a very detailed, almost nuts and bolts, level showing that the LoD control and the octree-based culling algorithms make visualization very efficient, even for large complex objects.

The importance of this technology is that it shows that it is possible using the available open source, geospatial APis and tools - together with some genuine innovative development - to create an open, non-proprietary 3D geospatial platform for integrating geospatial and BIM.  Given the critical importance of addressing the cultural and technical divide between the AEC and geospatial worlds, a viable open source alternative is essential for developing the innovative solutions to the challenge of interfacing the two worlds. 

The other part of the solution is common, open standards. There has been encouraging progress on that front.  The major breakthrough in bringing the architectural and geospatial views onto a common footing is the OGC LandInfra Conceptual Model developed by the OGC in cooperation with buildingSMART International and approved as an OGC standard in August, 2016.  LandInfra  was developed by Bentley Systems, Leica Geosystems, Trimble, Australian Government Department of Communications, Autodesk, Vianova Systems AS, and buildingSMART International and provides a unifying basis for land and civil engineering standards including the OGC's InfraGML and buildingSmart International's IFC for infrastructure standards.  

Open source blockchain development to enable transactive energy

The world is rapidly developing the technology that allows energy flows to be  determined by market forces.  There is little doubt that transactive energy is in the future of electric power in North America, which already has continental grids, and in the EU with the development of the EU's supergrid. 

The Rocky Mountain Institute and Grid Singularity (a blockchain technology developer) have created the Energy Web Foundation (EWF), a non-profit organization whose mission is to further the development of transactive energy by accelerating the commercial deployment of blockchain technology in the energy sector.  Initial supporters are Centrica plc, Elia, Engie, Royal Dutch Shell plc, Sempra Energy, SP Group, Statoil ASA, Stedin, Technical Works Ludwigshafen AG (TWL), and Tokyo Electric Power Co (Tepco) who have provided a first round of funding of $2.5 million.

Blockchain is a decentralized, distributed database for managing transactions.  Blockchains provide a global infrastructure untethered from the stability or permission of governments and institutions.  It is the technology underlying Bitcoin.   Nasdaq and other financial institutions including banks have been experimenting with and developing applications based on blockchain technology. Nasdaq has opened its blockchain services framework to more than 100 of its market operator clients around the world.  Earlier this year Nasdaq demonstrated a blockchain service that lets solar power generators sell energy certificates. 

Blockchain technology has the potential to play a significant and potentially game-changing role in the energy sector by providing the financial infrastructure for transactive energy. Blockchain technology could provide the transactional infrastructure enabling millions of energy generators (for ex, rooftop solar PV) and energy consumers (HVAC systems, water heaters, electric vehicles) to transact with each other. Blockchain also provides higher levels of cybersecurity.  Grid Singularity with its partner Parity Technologies is leading the development of an open-source, energy-specific blockchain infrastructure that will be maintained by EWF and supporting affiliates.

OpenGridMap: open, crowdsourced project to map power grid infrastructure

Earlier this year on a Sunday renewable power supplied almost all of Germany’s power demand, a major milestone in Germany's “Energiewende” policy.  The transformation of the grid (called by some a revolution) is being driven by distributed energy (DER) and smart devices.  It requires profound changes to how we monitor and manage the power grid and to be able to understand the impact of  the integration of more distributed intermittent sources and new consumer, industrial, and commercial electronics requires data and tools that only exist partially today.

If you are familiar with OpenStreetMap (OSM), then the OpenGridMap project is not that different except that it focusses on mapping electric power infrastructure instead of transportation.  I had heard rumours about an OSM-like project in Germany to map electric power infrastructure several years ago, but was unable to find more information about it.  OpenGridMap is an new open community that crowdsources power grid geospatial location and related data to be used for research purposes.  The goal is to collect reliable grid data to enable researchers to conduct simulation studies. OpenGridMap is intended to support the entire process from data collection to making grid data available for various applications. An OpenGridMap app for Android for people interested in helping to collect grid infrastructure data is available on Playstore.

Using OpenGridMap data analytics developed at the Technical University (TU) Munich generates a power grid model in the form of a Common Information Model (CIM) description file which provides the basis for a power grid simulation model.  Experiments with OpenGridMap data have demonstrated the effectiveness of this approach for grid modeling and simulation.  It is expected that as more data is collected it will be possible to generate power grid simulations of larger size, more variety and more accuracy than the currently available state-of-the-art test power grids.

The project was initiated by Professor Hans-Arno Jacobsen, Chair of Business Information Systems, and José Rivera at the Technical University of Munich.  It is supported by a German Federal Ministry of Education and Research grant and the Alexander von Humbolt Foundation.  Collaborators include Siemens AG, World Bank's ESMAP, the Technical University Berlin's VEREDELE project and Universität Erlangen-Nürnberg (FAU) KOSiNeK project.

Related links

OpenGridMap: An Open Platform for Inferring Power Grids with Crowdsourced Data, José Rivera , Christoph Goebel, David Sardari, Hans-Arno Jacobsen, Energy Informatics, Volume 9424 of the series Lecture Notes in Computer Science pp 179-191, 06 January 2016

OpenGridMap: towards automatic power grid simulation model generation from crowdsourced data Rivera, J., Leimhofer, J. & Jacobsen, HA. Comput Sci Res Dev (2016). doi:10.1007/s00450-016-0317-4

Revolution in the electric power industry: distributed energy and microgrids

Anne Pramaggiore, President and CEO of ComEd of Chicago, was the first at the Smart Gird Interoperability Panel 2016 Grid Modernization Summit in Washington DC to call the current business transformation that the electric power industry is experiencing a revolution.  But many other speakers agreed with this characterization.  Frequently mentioned were the regulatory and utility business transformations occurring in California and New York (REV).  Sacramento Municipal Utility District (SMUD) was mentioned as an example of a utility that was aggressively driving change in the utility business model. 

Robert Wilhite of Navigant Research pointed out that their recent research had found that 90% of utilities recognize that utility business models have to change.  But there are different opinions about the urgency of this transformation.  Navigant found that about half believe that it has to happen immediately, while the remainder believe it can happen later. 

Of course none of this would be possible without the enabling technology of the smart grid.  Dave Velazguez, President and CEO of Pepco, now part of Exelon, recollected that the first smart grid development at Pepco, by no means a laggard in adopting new technology, occurred only a decade ago.

Distributed energy

Virtually every speaker singled out distributed energy (DER), especially solarPV, as the main motivating driver that is forcing the business transformation of the utility industry.   Originally seen as a response to a warming climate, solarPV has achieved grid parity in several parts of the country and is seen as a way of providing customers not only with a choice of a green energy source but increasingly as a less expensive alternative to the local utility.   The price drop has been so substantial that even at a time of unusually low natural gas prices that are responsible for closing many coal-fired generation plants solarPV is achieving grid parity in many parts of North America.  Another important contributor to the revolution is the electrification of transportation, not only EVs but also buses, rail and even trucks.  

But none of this could have happened without the enabling platform of the smart grid.   The conjunction of technological advances in packet switched wired and wireless communications networks, intelligent electronic devices underlies the DER advances that have created choice for customers.  As we have seen previously in other industries such as telecom consumers have grasped the opportunity and are now beginning to drive changes in the energy market.

Microgrids

Another topic that came up repeatedly was microgrids.  Duke Energy has been running microgrid pilot for several years from which emerged the OpenFMB standard with an OpenFMB working group to support the effort. ComEd has five microgrid pilots and is starting the country's first microgrid cluster.  Pepco has two microgrid projects underway.

At the Distributech 2016 and at this SGIP Summit an implementation of OpenFMB demonstrated live by Green Energy Corp.  It was also announced at the summit that the source code was now available for download from the openFMB.io site. 

In addition a number of vendors were offering solutions for managing microgrids including the KEPCO Consortium from Korea who has built a comprehensive microgrid solution.

Geospatial

Although this was not a geospatially-focussed conference, GIS and geospatial technology came up in several areas.

Chanda Visweswanah, an IBM fellow, outlined in three topics what the the challenge is in transforming the grid.  First, there are several trends that are behaving exponentially, the number of sensors, the volume of data, and renewable energy sources were among them.  Secondly, we are increasingly relying on intermittent energy sources like wind and solarPV.  Third, this is not you grandmother's software.  This is software that provides a platform that supports sophisticated mathematical algorithms.  One of the most important of these are algorithms that nowcast and forecast weather at a very high resolution so that it is possible to predict wind velocity not at ground level but at the elevation of the turbines and at the location of the wind farm.  Similarly for insolation for solarPV.  These wind and insolation maps are effectively are the generation forecast minutes and hours in advance.

The Kepco Consortium demonstrated the μ-Grid platform.  Kepco have already partnered with PowerStream in Ontario to provide a utility-scale microgrid.  Earlier this year PowerStream and Kepco officially launched a utility-scaled Microgrid, which has the capacity to provide several hours of backup power supply for approximately 400 customers residing or owning a business in Penetanguishene. The Microgrid is helping to reinforce the existing grid by increasing resiliency and operational flexibility.  The platform provides a GIS Studio which allows grid 35 types of power facilities such as transformers to be visulaized and edited on a map, either imagery form satellites or overflights or a topographic map.

Another application of geloocation is in the area of cybersecurity where location is used by advanced cybersecurity applications for verifying identity.

Distributed intelligence

Several speakers pointed out that the volume of data has simply become to large for a centralized IT system that collects all the data at a central control point where analysis and visualization is performed.  By pushing intelligence out into the field, it reduces the data volumes that have to be handled at the central station and reduces the data flow over the network. 

Standards

Orange Button

Based on the very successful Green Button program, an important standards initiative is Orange Button.  It was initiated by the Department of Energy (DOE) with the objective of making it easier for investment to flow into solar power by standardizing electronic reporting of solar installation performance.  Orange Button targets a reduction in soft costs by streamlining the collection, security, management, exchange, and monetizing of solar datasets across the value chain of solar. It is expected that creating an industry-driven standardized data landscape will facilitate the growth and expansion of distributed solar. As the solar market continues to expand, it is critical that the collection, management, and exchange of solar datasets – especially those that affect the bankability (ability to secure loans for solar installations) of solar assets – are coordinated and streamlined to protect consumers, increase efficient pricing, and support new and existing businesses entering the solar marketplace. In April of this year four awardees (kWh Analytics, National Renewable Energy Laboratory,  Smart Grid Interoperability Panel, and SunSpec Alliance) were announced by the Department of Energy that will collaborate on the overall goal to standardize the way solar data is collected and exchanged.  SGIP was chosen to lead the effort.

At SGIP the effort is being led and coordinated by Dixon Wright of Wells Fargo.  Dixon is the only person that I was aware of representing a financial institution at the SGIP event.  I think that the presence of a major financial player such as Wells Fargo is a strong indication that solar is moving into the big leagues of financing. Dixon is passionate about the Orange Button initiative and believes that once a draft standard and implementation becomes available and a few users champion it, it will catch on rapidly with the large financial institutions and the solar industry.

OpenFMB

In March 2015, the Smart Grid Interoperability Panel (SGIP) formally kicked-off its OpenFMB effort to foster field interoperability drawing upon Duke Energy’s previous Coalition of the Willing I and Distributed Intelligence Platform work. By the 2015 SGIP Annual Conference in November, three microgrid use cases had been developed and demonstrated: optimization, unscheduled islanding, and grid reconnection.

In February 2016, the first OpenFMB reference implementation, utilizing SGIP’s microgrid use cases at Duke Energy’s Mount Holly microgrid test center, was demonstrated at the 2016 DistribuTECH Conference by Duke Energy’s Coalition of the Willing II (COW-II) vendor partners. For the annual SGIP Grid Modernization Summit in November 2016 the SGIP OpenFMB task force published a series of related foundational use cases that center on Distributed Energy Resources (DER) Circuit Segment Management for the active coordination of power systems equipment to DER, including a microgrid.  Green Energ Corp demonstrated an OpenFMB implementation connecting a solar PV installation with other equipment and applications.   This is an open source project and it was announced at the SGIP event that the source code is now available and can be downloaded from openfmb.io.  There is a free webinar replay "SGIP Grid Modernization Summit OpenFMB Demo Preview."  

Interoperability

Several speakers were on hand from the Department of Energy's Gris Modernization labs.  Since this was an SGIP event they focussed on the research underway at 13 Federal labs and 150 partner organizations that involves interoperability.  First of all research is underway to find a way to measure interoperability.  Secondly, there is an important initiative underwritten by several million dollars of financing to determine how to value interoperability.  If we can quantify the contribution it makes to business efficiencies by enhancing the opportunity for competitve products, by creating standards-based platforms that intelligent products from different vendors, whether smart meters, synchrophasors, reclosers, or substation components can plug and play with, that ultimately reduces prices for utilities, then it will be easier to justify and priorize greater investment  the standards and implementations that provide interoperability.