In October 2009, U.S. President Barack Obama announced the largest single energy grid modernization investment in United States history. The government investment of $3.4 billion, which is matched by industry funding, will spur the nation’s transition to a smarter, stronger, more efficient and reliable electric system. Expected results include increased energy efficiency, significant growth in the use of renewable energy sources, the promotion of energy-saving choices for consumers, and job creation. Geospatial technologies will play a significant role, helping ensure timely, reliable, cost-effective, efficient, and sustainable execution of the energy delivery value chain spanning from generation to transmission, distribution, Home Area Networking and back to generation.
Editor’s Note: This story was first published in LBx Journal, Summer 2010, and can be read here.
While one short article cannot detail geospatial sensor technologies’ full spectrum of contributions to enabling the Smart Grid, three of the most significant areas of contribution – smart generation, smart transmission, and efficient management of energy consumption – are worth noting.
Smart Generation for the Smart Grid
Today, most energy production comes from nonrenewable energy sources such as oil and coal, both of which are controversial, geopolitically-influenced, greenhouse-gas-emitting resources. A third equally controversial energy source is nuclear energy. The Smarter Grid, as illustrated in Figure 1, using more advanced hardware, software, and communications technology, will be able to use a higher percentage of environmentally friendly renewable energy sources, such as wind, solar, biomass, tidal and geothermal energy.
Addressing Intermittency and Grid Congestion Challenges
Wind and solar energy have been difficult to integrate reliably into the grid. The flow of electric power from these renewable assets is as intermittent and difficult to predict as the availability and intensity of wind and sun. Currently, there’s no foolproof way to pre-determine and adequately plan for the timing and extent of their contribution to the grid.
A smarter electric grid will use the power of advanced hardware, software and communications systems to manage more optimally the integration of energy from renewable assets into the grid.
Most utilities have managed the unpredictability of renewable energy sources by lowering their expectations and limiting their reliance on them. As a result, utilities must always keep other energy sources – such as expensive peaker plants – available in case renewable production falls short. Further, transmission lines are often under-utilized, using less than 60% of capacity, to reduce the possibility of a transmission line overload caused by an unexpected spike in wind energy production, for example.
Timing for energy creation by wind turbines is usually poorly matched to peak energy demand. Wind tends to pick up and create the most energy at night, whereas peak energy demands typically occur in daytime hours. Moreover, sand and dust blowing at night can cover and dramatically reduce the efficiency of solar panels.
Grid congestion can prevent utility operators from taking full advantage of wind energy available. Over-production of energy can cause transmission line overload, leading to outages. Under-production requires substituting energy from non-renewable sources. While energy storage solutions are being explored, and some, including batteries and compressed air solutions, are promising, storage solutions are not yet sufficient to meet the needs of balancing renewable energy supply with demand. A smarter electric grid will use the power of advanced hardware, software and communications systems to manage more optimally the integration of energy from renewable assets into the grid.
Geospatial Value Added
Geospatial monitoring of wind turbines’ and solar energy panels’ condition and performance, correlated with forecasted versus actual wind speed and solar energy availability, all intuitively visualized on satellite images, can increase the accuracy and predictability of energy output from wind and solar energy sources. Real-time weather and sensor data, timed predictions and historical demand requirements provide the required assessments of actual and expected values for energy output and grid capacity. This sensor data combined with satellite imagery and data analytics gives operators increased situational awareness and confidence in allocating a greater percentage of transmission line capacity to energy from renewable sources – thus improving integration of more energy from renewable sources into the grid.
Geospatially visualized “where used” analysis further helps asset maintainers identify specific equipment by location, manufacturer, and failure cause, and is an enabler to condition-based maintenance of these typically remote renewable resources. Real-time alerts of failing equipment by location and manufacturer notify operators of locations where assets require remedial action (such as shutting down wind turbines during severe storms or before out-of-band performance damages equipment). Today, these incident management processes can trigger automated documentation for audit and compliance purposes.
Geospatial solutions also play a role in proper siting of wind and solar energy farms, as illustrated in Figure 2. A visually intuitive location assessment using key metrics important to site selection includes appropriateness of terrain, transmission line accessibility, environmental feasibility, road access, and safety, as well as wind speed and direction, if meteorological towers are in place during site assessment. Proper siting helps to prevent disasters, such as experienced by the Maple Ridge Wind Farm in New York, where grid congestion has frequently necessitated energy production shutdowns exactly when the wind is briskly blowing.
Smart Transmission Management
SCADA (supervisory control and data acquisition) sensors today communicate a large percentage of the signals electric utility operators use to monitor asset status, energy production, and grid status. However, Phase Measurement Units (PMU), solid state devices that sit on transmission lines, provide even clearer real-time grid status and reliability details, transmitting information at millisecond intervals. The information they deliver improves operators’ understanding of grid status, including data about voltage, frequency spikes, and phase angle differences. PMUs offer the promise of earlier grid stability warnings that can help operators take faster, more informed action to prevent widespread and cascading power outages. As such, synchrophasor initiatives have received a notable portion of recent Smart Grid funding.
In the past, PMU data was relegated to engineering offices, because control room operators found it difficult to interpret non-intuitive charts and graphs changing at sub-second intervals and requiring time-consuming analysis. Operators need a quick, intuitive grasp of what is happening, where and when. They need a fast take on how quickly a situation is evolving (or devolving), the direction it is spreading, and the location of at-risk assets that need attention or must be shut down to prevent a cascading outage throughout the grid. This requirement is ready-made for geospatial, visual analytics.
Visual Intelligence and Ability to Take Action
Visualizing location-based alerts and impact assessments regarding frequency spikes, phase angle differences, oscillations, and correlating weather events such as storms and lightning strikes allows operators to quickly identify and prioritize critical locations and causes of stress on the grid. System status is shown geospatially with transmission lines and assets overlaid on a satellite image of terrain, with real-time phasor data and interactive displays of operating of assets in their correct locations, plus colors and symbols that present the technical data in an easily digestible and intuitive form.
Clicking on an asset offers drill-down views of its attributes and current condition. A playback capability lets operators view anything they may have missed. See how in Figure 3, a visual overlay of weather conditions across the landscape completes the picture. Operators can initiate remedial workflows directly from the geospatial screen, including islanding portions of the grid to prevent a cascading outage.
More Efficient Management and Use of Energy
Electric utilities are challenged to manage consumer consumption in response to ever-changing supply conditions. Demand-Response (DR) programs engage utilities, businesses, and consumers in a partnership to balance energy supply and demand. DR participants are motivated to allow their electrical power to be curtailed during peak energy demand conditions to ensure energy reliability – for everyone. Air conditioners or refrigerators, for example, can receive a signal to stop or lessen their power usage for brief periods during peak energy demand conditions. This frees up energy for higher-priority uses.
Congestion, load pockets, and emergency conditions across the landscape can be visualized with location-based awareness of load and supply imbalances on the electric grid. This capability allows for a visual simulation of demand-response options, including integration with data from the independent system operator (ISO) or demand aggregator and information from demand response contracts. In addition, it offers filtering and specification of DR customers based on a variety of criteria, including service area, customer type and frequency of power curtailment.
Our Smart Grid solution, the Space-Time Demand Response Composite, allows visual balancing of energy consumption and supply conditions. The geospatial composite application captures and correlates real-time operations data with asset and customer data to support informed projection and simulation of available demand-response scenarios. This support enables fast reaction to changing supply conditions, whether supply constraints are caused by transmission congestion, unexpected load increases or critical component failures.
Geospatial features that enable more efficient DR programs must include configurable dashboards that alert the utility company of the need for emergency demand-response, ISO load drop signals to demand aggregators and utilities for consolidated demand drop requests, and integration with customer relationship systems for visualization of demand-response contracts by customer, location and service level agreements. Other important features include selection filters to identify DR customers by visualization of load shave potential, location, frequency of DR requests and type.
The role of geospatial technologies is growing in significance, as energy efficiency, cost and reliability grow in importance to the economy, public safety and security.