Between the Poles

GeoBuiz 2016: High volume geospatial imagery processing requires new architecture

DigitalGlobe owns and operates a constellation of commercial earth observation satellites.  The latest is Worldview-3 which is capable of about 30 cm precision.  Digital Globe's satellites collect 3,000,000 square kilometers of Earth imagery every day, that's about 70 terabytes of data per day.  Digital Globe also provides online access to 15 years of earth imagery, about 90 petabytes.  Of all high-resolution commercial imagery collected since 2010, Digital Globe has collected approximately 80% of it.  Clearly as a user you can't download all of this data, so how can users do things like search through this huge data set and generate a product such as a vegetation map for Tanzania ?

At the GeoBuiz 2016 Summit in Bethesda this week, Walter Scott, CTO and Founder, outlined how  Digital Globe is making this vast amount of high resolution data accessible to and usable by customers.  To make it possible for customers to search and process this data efficiently, Digital Globe has put all of the data on the public cloud (Amazon) and has provided a platform that supports efficient search, processing and workflows. GBDX is an online environment for efficiently running advanced algorithms for information extraction from imagery datasets at scale.  It is a set of RESTful APIs, and algorithms that have already been implemented by Digital Globe or its partners include car counting, orthorectification, land use, land cover, and atmospheric compensation. 

One of the most important efficiencies of the GBDX architecture is that it avoids having to move huge volumes of data over the internet.  It does this by bringing the application to the data, rather than the data to the application.  For example, to search through the data, GBDX provides a catalog API that uses 39 different image attributes such as geographic location, cloud cover percentage, sensor type, sun angle and others.  The search runs locally on Amazon, avoiding pushing data over the internet.

The GBDX API includes support for workflows.   A workflow is a sequence of tasks executed against sets of data. A typical workflow is initiated with a search for the data you want to process.  The selected data is assigned to a working set, and then processed  through a sequence of steps to generate your desired product.   For example, a typical product would be an orthorectified vegetation map.  Again the workflow runs locally on Amazon, avoiding pushing data over the internet during processing.  Only the final product resulting from the workflow, such as a vegetation map, would be transferred over the internet to the customer.

GBDX uses the cloud infrastructure of Amazon Web Service (AWS).  Data is stored in S3 ‘buckets’ that are accessed through GBDX RESTful web service APIs. GBDX includes built in tools supporting remote sensing algorithms from DigitalGlobe and ENVI.  GBDX is python-friendly and allows you create your own tools.

One of the application areas that the platform makes possible is deep learning.  The basic concept is that software can simulate the brain's large array of neurons as an artificial “neural network".  Large volumes of input data and some way of quantitatively assessing the results are required to train the system.  With the greater depth enabled by new computers and algorithms, remarkable advances in speech and image recognition are being achieved.  For example, a Google deep-learning system that had been shown 10 million images from YouTube videos proved almost twice as good as any previous image recognition effort at identifying objects such as cats.  Microsoft has demonstrated speech software that transcribes spoken words into English text with an error rate of only 7 percent.

But deep learning requires a lot of compute power and a lot of training.  GBDX provides the computational power to apply deep learning to earth observation and Digital Globe's data library can provide the training data sets.  What is required is a scalable way of assessing and quantifying the results. Walter Scott suggested crowd sourcing as a way of achieving the scale required for earth observation training.

RICS BIM 2016: April 4th BIM mandate looms for the UK construction industry

At this year's RICS BIM conference the elephant in the room was April 4^th 2016, the deadline for all public construction projects to be BIM Level 2 compliant. This is the culmination of a five year process starting in 2011 focussed on developing a set of documents defining BIM Level 2 and providing best practices for achieving compliance.

This year's conference suggests that for many in the industry they are now wrestling with implementation issues and are reporting a shortage of BIM skills.  They are finding that BIM is really about data and are realizing that data management and analytics are essential to handling and interpreting the huge volume of data that is being generated. A shortage of data scientists to help them to do this has become an acute problem.

Transforming the UK Construction Industry in Five Years

The government's focus from the beginning has been on full life cycle BIM.  Back in 2010/2011, the Government's BIM roadmap was perceived to be very aggressive, mandating BIM Level 2 compliance for all national government projects after just five years. In the UK public projects comprise about 50% of the construction industry so this amounted to radically transforming the UK construction industry in half a decade. Put another way the construction industry had to move from thinking analog to thinking digital in just five years. 

The utility industry has been going through a similar transition in moving to what is called a smart grid and distributed generation.  Some think that this process will take 20 years.  But new technology and a younger generation steeped in digital is stepping in to make this happen much sooner than that.  Energy is going digital more rapidly than expected.  I think it likely that something similar will happen in the construction industry because digital enables all sorts of efficiencies that we often cannot even imagine.  Uber and Airbnb are classic examples.  Both are the largest or second largest companies in their respective industries.  Gartner has forecasted a similar type of company will emerge for managing energy flows in the electric power industry by 2020.

For 2012 the Government's strategy focussed on discovery through pilot projects, notably the Ministry of Justice Cookham Wood project, a £20 mllion project where it is estimated that BIM saved hundreds of thousands of pounds in CAPEX costs.  

At the 2013 conference David Philp,Head of BIM Implementation, Cabinet Office, UK Government, announced PAS 1192-2 a key document supporting the Construction BIM Strategy to achieve Level 2 compliance.  It specified requirements for achieving BIM Level 2, sets set out the framework for collaborative working on BIM enabled projects and provided specific guidance for the information management requirements associated with projects delivered using BIM.  David Philp made it clear that although the initial focus is on the design/build part of the lifecycle with the goal of saving 20% of Capex and reducing the 30% waste in most construction projects, the "largest prize for BIM lies in the operational stages of the project life-cycle".

At the 2014 conference Peter Hansford, Government Chief Construction Advisor, made a link between the overwhelming interest in BIM as represented by the sold-out RICS BIM conference and the industry’s enhanced interest and appetite for BIM. He saw a shift in emphasis from asking ‘’should we do BIM?’’ to ‘’how do we do BIM?’’  This was happengin inspite of the fact that as, Rich Saxon, the UK Government's BIM Ambassador for Growth, pointed out In a panel discussion on the future of BIM that some aspects of BIM Level 2 were still  incomplete. 

Deborah Rowland, Head of Facilities Management, Government Property Unit, Cabinet Office in the UK Government outlined the Government's motivation and plans for a program to improve the post construction handover and operation of newly constructed buildings. She described the Government Soft Landings (GSL) program which was intended to rectify this and was designed to put in place a legal, contractual, and technical framework (based on the BSRIA Soft Landings Framework)  incorporating building information modeling (BIM) to fix the problem by ensuring continuity thoughout the buiiding lifecycle from inception, though design, construction, commissioning, training and handover through to operations and maintenance for 1-3 years after handover.

At the 2015 conference seven key BIM Level 2 documents (PAS 1192:2, PAS 1192:3, BS 1192-4, CIC BIM Protocol, Classification (Uniclass), Digital Plan of Works / Levels of Detail (LoD), Government Soft Landings) which collectively represented the current repository of BIM best practices were announced.  Every speaker at the conference referenced these documents.

Seven major government projects using BIM, worth about £ 10 billion, have maintained records of the savings they have seen from using BIM. The measured benefits of BIM and factors associated with BIM range between15-20 %.   By 2014 the government estimated that it had achieved savings of £ 1.4 billion through BIM and related improvements in the 2013/2014 fiscal year.  But with full lifecyle BIM, the government saw an upside potential of 40%+ with the biggest benefits being realized in the operations phase of the building lifecycle.

This year it was announced that Government statistics show that in 2015 Her Majesty's Government saved GBP 3 billion on these demonstration projects as a result of gains in construction productivity related to BIM. 

Full Life Cycle BIM

But BIM Level 3 is where the gold is.  Level 3 will enable the interconnected digital design of different elements in a built environment and will extend BIM into the operation of assets over their lifetime. It will support the accelerated delivery of smart cities, services and grids. As the AGI has pointed out in a recent Foresight Report integrated BIM and geospatial technology will be fundamental for BIM Level 3.

Is the UK construction industry ready for April 14 2016 ? 

An industry survey in the UK in 2013 reported that 54% of respondents said that they were using BIM on projects.  Most of those not yet using BIM said that they would be within one to two years.  70 % of the respondents reporting using BIM said that BIM has given them a competitive advantage. But this year's conference suggests that most in the industry are doing BIM although for some it is not really clear what BIM Level 2 is and how one demonstrates compliance. 

Data and People

This year's RICS BIM conference was quite different from previous conferences.  The focus was heavily on implementation, not on progress in completing specifications or developing and working with 3D models as in previous RICS BIM conferences.

The event was led off by a panel discussion that set the tone for the rest of the conference - the focus was on data and people rather than 3D BIM models.  The industry people on the panel were patently up to their elbows in real world BIM projects including data, the analytics to make sense of it, and the shortage of the required skills (data scientists and people with BIM skills).

Alex Jones, Interserve, said that within the construction industry there is now a focus on data and how data is used downstream.  He said that clients (owners) are now aware of BIM and that both private and public procurements are asking for BIM.  But he qualified this - they are not so much asking for BIM (as in 3D models) as asking for data that can be used downstream. David Throssel, Skanska, added that deliverables now means data.

In the context of data, Simon Rawlinson, Arcadis UK, gave an example of utilities where smart devices and automated capture has resulted in a database of asset conditions, and which also includes geospatial data for geolocating those assets. This information allows utilities to move forward to condition-based maintenance where prioritizing maintenance and replacement depends on asset condition not the calendar.  Real-time monitoring is being adopted by virtually all industries.  For example, devices are being implanted in structures such as bridges to monitor distortions. He added that the leadership has not yet recognized that sensors and analytics enable a major change that will revolutionize asset maintenance.

Also the new digital economy is disintermediating certain skills. For example, digitalized construction requires fewer layers. Simon cited a KPMG study of disintermediation by technology. As an example, he mentioned data such as that collected by utilities from smart devices that allows owners/clients/utilities to do condition-based maintenance, which can lower field staff requirements significantly.

In the construction industry as a whole one of the major issues that emerged is a shortage of BIM related skills.Simon emphasized that now that we have lots of data, what we need urgently are data scientists to help make sense of it.  He identified this as a major problem because in his experience data scientists are as rare as “hens teeth”. 

David Hancock, UK Government Construction, said that construction industry still thinks analog. Nowadays we can all produce data, but analyzing it is the challenge.  We need people who can analyze to produce actionable items. He also identified BIM skills shortage as a major problem, and repeated that data scientists people are “as rare as hens' teeth.  He suggested that one solution is upskilling – training existing construction/bldg maintenance/utility folks in BIM and data science.  Another may be raiding the U.S. baseball industry where teams such as the Oakland Atheletics starting using analytics several years ago.  "Moneyball" analytics has been recommended as a strategy for the utility sector in the U.S.

In the question period a speaker argued that the 3D BIM model has been "massively overplayed".  BIM is really about data and collaboration, not about 3D. To maximize the benefits of BIM clients  (owners) have to be clear about what the model, and much more importantly the data associated with the model, are going to be used for. For example, if for facilities management (FM) then clients (owners) have to be specific about what data is required for maintaining and operating the building as part of defining deliverables.

BIM Level 2 for BIM Novices

What if you are a small company with no more experience with BIM than discovering what the acronym stands for ?  At RICS BIM 2016 there was a sequence of presentations by a group of mostly quantity surveyors (people who do quantity takeoff), all BIM novices, who came together to try to understand and share openly what BIM Level 2 means practically. (As one speaker pointed out, there is no legal definition of BIM Level 2 compliance.  They called their project KT4BIM, which you can find on LinkeIn. The objective is to answer the questions

• how do we do BIM Level 2

• how do we demonstrate compliance.

The target audience is BIM novices and for both the residential and commercial sectors.  The objective is to share all the material used and developed as part of this project with the industry.  More information about this very important project will be provided in my next blog post.

BIM Level 2 Documents

I've included this list just to show how comprehensive this set of BIM and BIM-related documents is.

Key documents

• BS 1192:2007+A1:2015     Collaborative production of architectural, engineering and construction information
• PAS 1192-2:2013     Specification for information management for the capital/delivery phase of construction projects using Building Information Modelling
• PAS 1192-3:2014      (Corrigendum No. 1) Specification for information management for the operational phase of assets using building information modelling
• BS 1192-4:2014     Collaborative production of information Part 4: Fulfilling employer’s information exchange requirements using COBie
• PAS 1192-5:2015     Specification for security minded building information modelling, digital built environments and smart asset management
• BS 7000-4:2013     Design management systems. Part 4. Guide to managing design in construction
• BS 8536-1:2015     Briefing for design and construction. Code of practice for facilities management (Buildings infrastructure)
• Uniclass 2015     Classification embedded within the NBS Toolkit
• Digital Plan of Work     Delivery plan embedded within the NBS Toolkit
• CIC/BIM Pro first edition 2013     Building Information Model (BIM) Protocol

Supporting documents

• CIC/INF MAN/S first edition 2013    Outline Scope of Services for the Role of Information Management
• Government Soft Landings    "Design for operation" to encourage better outcomes using BIM for built assets during the design and construction stages to ensure value is achieved in the operational lifecycle of an asset.
• AEC (UK) BIM Technology Protocol Version 2.1.1 June 2015    Practical implementation of BIM for the UK Architectural, Engineering and Construction (AEC) industry
• BS ISO 16757-1:2015    Data structures for electronic product catalogues for building services. Concepts, architecture and model.
• BS 8541-1:2012, BS 8541-2:2011, BS 8541-3:2012, BS 8541-4:2012, BS 8541-5:2015, BS 8541-6:2015    Library objects for architecture, engineering an construction

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.

DistribuTECH2016: Dramatically cutting utility costs with unmanned aerial vehicles (UAVs) flying beyond visual line of sight

If you would ask me among all the new technologies on display here and at other events which will likely dramatically reduce the bottom line of utilities in the transmission business I would say unmanned aerial vehicles (UAVs) or drones.  For example, every utility which has transmission lines spends large sums on overflying, typically with very expensive helicopter flights, transmission lines for vegetation management to monitor the trees to prevent them from interfering with transmissions lines.  If instead this could be done with much less expensive UAV flights, the savings would be huge. But right now this is not possible, because commercial operation of UAVs is only allowed by the Federal Aviation Authority (FAA) with visual line of sight (VLOS) rules.

 

The potential ROI of using UAVs for vegetation monitoring are expected to be sizable, but this requires the FAA to change regulations to permit operating of UAVs with beyond visual line of sight (BVLOS) rules.  Today at DistribuTECH2016 Eileen Lockhart of Xcel Energy with partners Environmental Consultants Inc and Flot Systems gave a presentation that showed just how close we are to UAVs operating under BVLOS rules becoming a commercial reality for electric power utilities.

 

Xcel Energy, which has electric and gas assets in eight states, is the fourth largest utility in the U.S.  They have partnered with EEI, EPRI, INL and others in the utility sector to show the way in the application of UAVs in the utility sector.   This includes working with the FAA to push forward the practical application of UAVs in the utility sector.  Their objectives are improved safety for utility employees and the public, reducing risk to and improving reliability of the grid, and reducing the cost of operating and maintaining grid infrastructure.  The technologies involved include GIS and geospatial analytics in addition to the UAVs themselves.  Expected sources of major savings are reducing the need for expensive helicopters and plane flights and the time utility employees have to spend in the field.

 

Xcel launched their UAV Proof of Concept project in 2015 with seven UAV missions in transmission, gas operations, energy supply, and electricty distribution.

• Transmission line and tower inspections
• Substation facility rating data collection
• Pipeline bridge and river crossings inspections
• High pressure pipeline inspections
• Coal/ash storage inventory (volumetric)
• Wind turbine blade inspections
• Storm damage assessment

Just last week (Feb 3, 2016) Xcel with its partners completed the first beyond visual line of sight mission with UAV flights over 20 miles of transmission lines.  They are convinced that BVLOS flights will become a commercial reality in the near future.   The UAV flights will not  be flown by Xcel itself but by contractors such as Flot Systems operating with FAA licences.  Xcel has not yet calculated ROIs for the applications using UAVs, but plan to do so soon.

Be Inspired Awards at Year in Infrastructure 2015: Operational analytics reduces water utility power bill by A$3 million

At the Year in Infrastructure conference in London SA Water is one of the finalists for the annual Be Inspired Innovation in Roads Awards. Today Rowan Steele presented an overview of how SA Water, based in Adelaide, South Australia, has applied operation analytics to reduce the water utility's power bill by A$3 million.

South Australia has been suffering from an extended drought for nearly a decade and has just built a desalinization plant to provide more reliable water to their customers. 

About 40% of the South Australian power generation is now renewable.  Most of this is wind (33%) and solar (8%).  Fluctuating sources of power generation means that the price of electricity can range widely from -A$1000 to A$13,000 per megawatt hour (Mwh).

The drought, the new desalinization plant and the fluctuating cost of power introduced complexity into managing what used to be a fairly simple water network.

To address these issues SA Water decided to invest significantly in IT to help manage the water network better.  They acquired a hydraulic model that allowed them to simulate the network under different conditions.  They also invested in an operational analytics tool. 

Together these applications have helped them optimize their network in various ways, such as optimizing chlorine dosing (water from the desal plant has very little organics compared to river water), minimizing electric power costs and reducing water age in some parts of the network.  The benefits have been significant.  They not only have been able to reduce their power bill by A$3 million, but also have cut their network operating costs by nearly a A$ million.  It has also resulted in improved water quality.  For example, they can map water age geographically for their entire service area.  More fundamentally it has given them much greater insight into sources of revenue and the costs of various aspects of operating a water network.

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.

EPRI's Smart Grid Demonstration Project reveals value of volt-var control

Conservation voltage reduction (CVR) or volt-var is an approach to demand response (DR) and energy efficiency (EE) that can provide major benefits without significant alterations to the power distribution system,  unlike other DR/EE approaches.  Volt-var is used to reduce demand and energy consumption during peak load when electricity prices are inflated and demand may exceed the available energy while maintaining customer voltage power quality according to tolerances mandated by the regulator to protect consumer and utility devices.  Peak demand can be reduced typically by 2 to 4 %.  The business benefits are a greater percentage of energy delivered to paying customers, reduced investment in peaking generation plants or in buying power from generators at peak market prices, and a reduction in the environmental impact of energy delivery (reduced emissions).

For the past seven years the Electric Power Research Institute (EPRI) has been leading a smart grid demonstration project initiative that includes regional demonstrations and supporting research focusing on smart grid activities related to integration of distributed energy resources (DER) including distributed generation, storage, renewables, and demand response technology. A number of well-known electric power utilities have participated in this program by developing and reporting on smart grid demonstration projects.

1. AEP Smart Grid Demonstration
2. Con Edison Smart Grid Demonstration
3. Duke Energy Smart Grid Demonstration
4. EDF Smart Grid Demonstration
5. Ergon Energy EPRI Smart Grid Demonstration
6. ESB Smart Grid Demonstration
7. Exelon (ComEd/PECO) Smart Grid Demonstration
8. Hawaiian Electric Company (HECO) Smart Grid Demonstration Project
9. FirstEnergy Smart Grid Demonstration
10. Hydro Quebec Smart Grid Demonstration
11. KCP&L Smart Grid Demonstration
12. PNM Smart Grid Demonstration
13. Sacramento Municipal Utility District Smart Grid Demonstration     
14. Southern California Edison Smart Grid Demonstration
15. Southern Company Smart Grid Demonstration

As an example, AEP's smart grid demonstration project is based on a 10,000 customer pilot that includes smart meters, communications, end-use tariffs and controls, and distribution automation and volt/var control. It integrates other distributed and end-use technologies that are being evaluated by AEP including four MW scale sodium sulfur battery installations, two 70-kW roof-top photovoltaic systems, a new 5.7 kW concentrating solar technology, three 60 kW natural gas-fired reciprocating engines capable of combined heat and power generation, two plug-in hybrid electric vehicles, a large air conditioning system, two 10 kW wind turbines, and several 25 kW community energy storage systems (CES).

Currently a significant amount (about 10 %) of electric energy produced by power plants is lost during transmission and distribution to consumers. About 40 % of this total loss occurs on the distribution network. 

In a recent blog, Jared Green, Project Manager for EPRI's Smart Grid Demonstration Initiative reported a surprising finding from the Initiative.  Through the Smart Grid Demonstration Initiative EPRI found that volt-var control was was one of the most effective technologies in reducing energy demand. EPRI reported that most of the utilities that ran demonstration projects had already deployed or were deploying volt-var technologies to optimize their distribution grids. Results from the demonstration projects showed that a significant reduction in energy demand could be  achieved. EPRI's analyses showed that for each 1 %  reduction in voltage a corresponding 0.4-0.7 % reduction in energy demand was achieved. 

For utilities that used volt-var control for targeted demand reduction, volt-var can reduce peak demand as well.  Duke Energy, for example, reported reducing peak demand by about two hundred megawatts (MW) during the 2014 cold period in the Eastern U.S.  It has been estimated that in the U.S. with every one percent reduction in peak demand there would be a reduced need to build a 7,900 MW power plant.

Even greater control and reduced risk can be achieved by Integrating volt-var with advanced metering infrastructure (AMI) and GIS.  Volt-var/AMI/GIS provides greater precision in managing voltage reduction. At Distributech 2014, Tantalus reported using GIS is to produce voltage maps. Voltages reported by smart meters can be mapped geographically in real-time across the entire distribution network in the form of isovolt maps. This makes it possible to rapidly identify areas of low voltage and correct them in real time.

INSPIRE-Geospatial World Forum 2015 program released

INSPIRE-Geospatial World Forum 2015, a joint conference organized by the European Commission and Geospatial Media and Communications, has made available its full conference program. Almost 500 presentations are scheduled on topics including building information modelling (BIM), open data, big data analytics, open standards, linked data, cloud computing, crowdsourcing, Earth observation, indoor positioning, land information systems for smart cities, urban resilience and sustainability, health, agriculture and others.  Some 2000 delegates are expected to attend from more than 80 countries.  Top sponsors include Trimble, Topcon, ESRI, Digital Globe, Oracle and Bentley.

The theme of the conference is CONVERGENCE: Policies + Practices + Processes via PPP with a focus on improving coordination among policy-makers, technology providers and users.  Including geospatial data and technology in construction, agriculture, health and other industry workflows is an enabler for more successful public–private partnerships (PPP) by facilitating more informed decision making among the stakeholders.

GeoWave aims at linking popular geospatial tools to "big data" technology

A very exciting project has been proposed at LocationTech (it's in the Project Proposal Phase as defined in the Eclipse Development Process).  Simply put, GeoWave intends to do for "big data" databases (initially Apache Accumulo) what PostGIS does for SQL databases (PostgreSQL).  GeoWave is open source software (licensed under Apache 2.0) that adds support for geographic objects, multi-dimensional indexing and geospatial operators to Apache Accumulo.  

NoSQL databases

To deal with data volumes that are too large for traditional SQL databases, beginning in 2004 Google developed "BigTable" which is a compressed, high performance, and proprietary data storage system built on the Google File System that is used by a number of Google applications including Google Maps.  Apache Accumulo is a distributed database that is based on Google's BigTable design and is built on top of Apache Hadoop and other Apache projects.  For putting geospatial data into a key/value store like Accumulo the key concept is that of the "geospatial hash" which converts a 2D, 3D or 4D coordinate such as a lon and lat, lon, lat and elevation or a lon, lat, elevation, and time to an integer index, such as a quadtree or R-Tree index, that can be used to order and rapidly retrieve spatial data.  GeoWave means that you can manage massive amounts of geoinformation in key/value databases such as Accumulo and take advantage of programs such as MapReduce which Accumulo uses for distributed processing.

Connecting Accumulo to GeoServer, GeoTools, and PDAL

In addition GeoWave includes a GeoServer plugin to enable geospatial data in Accumulo to be shared and visualized via GeoServer OGC standard web services. It provides plugins to connect the popular geospatial toolset GeoTools and the point cloud library PDAL to an Accumulo based data store. The PDAL plugin makes it possible to interact with point cloud data in Accumulo through the PDAL library.
 
The GeoWave project Work plans to extend the same geospatial capabilities to other distributed key-value stores in addtition to Accumulo.  The next data store will be HBase.  It also will support other geospatial frameworks in addition to GeoTools/GeoServer.  Mapnik is the next geospatial framework targeted for GeoWave support.  GeoWave says it is very interested in GeoGig and support for this geospatial data versioning library is currently on their backlog.  GeoGig takes the concepts used in distributed version control such as Git and applies them to versioned spatial data.

Background
 
GeoWave was developed at the National Geospatial-Intelligence Agency (NGA) in collaboration with RadiantBlue Technologies and Booz Allen Hamilton.  The NGA released GeoWave under an open source license in June, 2014. The primary goal of GeoWave is to bridge the gap between well-known geospatial projects such as GeoTools and distributed databases.

I blogged previously about GeoMesa, the first LocationTech project that aims at providing a foundation for storing, querying, and transforming spatio-temporal data in Accumulo.  It implements interfaces that enable Geoserver and other Geotools projects to use Accumulo as a data store.