Gartner has identified the top ten technology trends affecting the global energy and utility markets in 2013, as the industry faces significant challenges from ongoing environmental sensitivity, changing policymaker attitudes and consumer expectations.
“Searching for successful business models that will address these issues and generate anticipated shareholder returns in uncertain regulatory settings is a top priority for this industry,” said report author Kristian Steenstrup, Vice President and Gartner Fellow. “This is in addition to protecting the security of critical generation and delivery infrastructure, as well as maintaining system reliability with aging physical assets. Public and private utilities are looking at how technology can reduce cost, drive efficiency and enhance competitive advantage.”
According to Steenstrup, what was once considered a conservative and slow-moving industry has seen a wide range of innovation from within and outside the traditional IT organization. He believes that understanding business trends is a crucial requirement for energy and utility organisations to create a successful IT strategy in this market.
The top ten technology trends are (in no particular order):
Social Media and Web 2.0
Utility IT leaders have opportunities to use social media as a customer acquisition and retention medium for competitive energy retailers, as a consumer engagement channel to drive customer participation in energy efficiency programs and as the emerging area of crowd-sourcing distributed energy resources coordination. Social media for outage communications is also rising in importance and value for utilities and customers experiencing outages. Opportunities to use social media to improve internal utility business processes are starting to emerge.
Smart grid development will increase data quantity by several orders of magnitude, driven by a host of edge devices, as well as new IT and OT applications such as advanced metering infrastructure (AMI), synchrophasors, smart appliances, microgrids, advanced distribution management, remote asset monitoring, event avoidance and self-healing networks. In addition to significantly impacting data volume, smart grid initiatives will produce a different variety of data, such as temporal, spatial, transactional, streaming, structured and unstructured.
Mobile and Location-Aware Technology
Lowering costs and improving the accuracy and effectiveness of the field force are the main drivers for utilities to deploy mobile and wireless technologies. Mobile and location-aware technology spans hardware (such as ruggedized laptops, PDAs and smartphones), communication products (such as navigation, routing and tracking technologies like GPS) and services (such as cellular digital packet data and general packet radio service, using high-speed terrestrial data networks, Wi-Fi and satellites).
Cloud Computing and SaaS
Although the utility industry trails other sectors in cloud adoption due to security and reliability concerns, solutions are beginning to emerge in areas such as smart meter, big data analytics, demand response coordination and GIS. Early implementers of utility cloud and SaaS include organizations interested in providing common application and data services to multiple utility entities, such as cooperative associations and transmission system operators, smaller municipal and cooperatives without extensive infrastructure or budgets, and investor-owned utilities (IoUs) conducting short-term smart grid pilots interested in quick time-to-market, with minimal impact on production systems.
Sensors are applied extensively throughout the entire supply, transmission and distribution domains of utilities. Sensor fusion — the addition of onboard digital signal processing and associated software development capabilities — is accelerating potential applications. Widespread utility adoption is challenged by specific implementation requirements, such as ruggedization, electromagnetic shielding, temperature extremes, cybersecurity and remote access.
Increasing use of in-memory computing (IMC) application infrastructure technologies as enablers inside multiple types of software and hardware products will result in rapid IMC adoption by mainstream, risk-averse IT organizations. The ability of IMC to support high-scale, high-throughput and low-latency use cases will make it possible for IT organizations to implement innovative scenarios, such as those addressing processing of the smart-grid-generated metering and real-time sensor data.
IT and OT Convergence
Virtually all new technology projects in utilities will require a combination of IT and OT investment and planning, such as AMI or advanced distribution management systems (ADMSs). More than any industry, the utility sector faces the challenge of the separation between IT and OT management, coupled with the importance of hybrid projects that link IT and OT systems. The industry will benefit by aligning their OT support, standards and procedures with those used for IT, shortening the time to develop governance over OT. This will ensure that when integration of IT and OT is inevitably done there is already some alignment in standards.
Advanced Metering Infrastructure
AMI constitutes a cornerstone of the smart grid by potentially providing a communication backbone for low-latency data aimed at improving distribution asset utilisation failure detection, and facilitating consumer inclusion in energy markets. Different market structures, regulatory drivers and benefit expectations create different ownership models for components of the AMI technology stack, which favor different technology solutions across the globe.
The distributed nature of utility assets, combined with the need for more efficient asset management and labor use, makes mobility and supporting communication technologies high investment priority areas for utilities. The smart grid drive toward better observability of the distribution network requires machine-to-machine (M2M) monitoring systems that are similar in function to low bandwidth SCADA, but use different communication technologies and approaches (such as personal-area networks (PANs), HANs, FANs, substation, control center and enterprise LANs, and shared wide-area networks (WAN).
Predictive analytics has become generally used to describe any approach to data mining with four attributes: an emphasis on prediction, rapid time to insight, an emphasis on the business relevance of the resulting insights and an increasing emphasis on ease of use, thus making the tools accessible to business users. Common applications include understanding the future failure patterns of equipment, or the likely load from certain customer groups or regions. By understanding likely future circumstances, organizations are better able to allocate investments to maximise returns.