Data-driven Energy Ecosystems for a Sustainable Future
It is that time of the year when policy makers, business leaders and academicians meet to discuss global challenges at the World Economic Forum (WEF) Annual Meeting. Shaping the future
Data-driven Energy Ecosystems for a Sustainable Future
It is that time of the year when policy makers, business leaders and academicians meet to discuss global challenges at the World Economic Forum (WEF) Annual Meeting. Shaping the future of energy is one of the focus areas of the forum. In the context of energy, big data and digital technologies will drive new efficiencies and open up possibilities, thereby playing a pivotal role in its future. Leveraging data in newer and more advanced ways will be a fundamental driver in creating an energy ecosystem for a sustainable future. This ecosystem could be predicated on what The WEF Global Energy Architecture Performance Index Report 2016 calls the "Energy Triangle", which benchmarks energy systems against three primary goals: Economic Growth and Development, Environmental Sustainability, and Energy Access and Security. The question is, how can data-driven energy ecosystems help achieve these goals?
Economic growth and development
Economic growth, in many ways, is based on having a robust energy ecosystem that includes an appropriate mix of fossil fuels and renewables. Creating a sustainable future for such an ecosystem is often talked about in terms of increasing adoption of renewables and reducing emissions. However, I would like to emphasize another aspect of sustainability ─ a relentless focus on economic efficiency of energy. This is not just about energy savings across the utility value chain, but rather, a broad focus on efficiency across the energy spectrum - from exploration and production of Oil & Gas, to every aspect of generation, distribution, and consumption of energy.
In this context, there are critical questions that need to be addressed. How can we reduce the break-even price of oil by decreasing the cost of exploration, production and distribution, thereby reducing the impact of oil price fluctuations? How can we maximize recovery in conventional reserves, and formulate field depletion plans to extend plateau time, rationalize cost of midstream operations, and mitigate risks in energy trading? How can we reduce losses from generation to distribution? How can we optimize usage by empowering consumers and providing advanced monitoring techniques? How can we improve grid operations with better grid planning, voltage regulation, customer and field operations, and improved fault detection systems?
These perplexing questions are in some ways "use cases" for leveraging advanced digital technologies and for harnessing big data techniques. Increasing instrumentation, connected devices, rapid adoption of sensor technologies and the Internet of Things (IoT) network lead to better data collection and analysis, which in turn results in better and more efficient ways of managing demand-supply in the energy sector. The need of the hour is a well-defined and executed information and data management strategy that focuses on insightful analysis of data. Economies who adopt this - including the possibilities of predictive analytics, new frontiers in automation and big data, and advanced industrial control networks - improve their chances to strengthen a sustainable energy ecosystem.
While energy drives economic development, it is also responsible for more than 65% of greenhouse gas emissions. The approaches to mitigating the environmental consequences of the production and consumption of fossil fuels continue to dominate discussions on climate change. Here again, data plays a pivotal role.
Rigorous data analysis drives automated environmental management systems and enables dynamic management of international environmental regulations. Further, it helps strengthen industrial safety. In this context, Infosys implements digital Environment, Health, and Safety (EHS) systems to minimize carbon footprint of organizations. Further, we are also a member of the Carbon Pricing Leadership Coalition (CPLC), an alliance of governments, corporations, and civil society to drive effective carbon pricing policy adoption and accelerate implementation.
Soil conservation and restoration with advanced chemical analysis and continuous monitoring is another area where a superior data management strategy can prove to be advantageous. Automated pollution control systems with mobile-enabled dashboards to track measurements ensure superior management of water, land and air pollution. This is another example of how digital technologies can drive a stronger energy ecosystem.
Energy access and security
Secure and reliable supply of low-cost energy holds the key to an equitable industrial and social ecosystem. A fine balance of physical, technological, societal, and regulatory elements is required to match global supply and demand. The growth of distributed generation of renewable energy has helped make progress in this direction. Needless to mention, digital technologies coupled with advanced data analytics can play a bigger role in ensuring sustainable growth and affordable energy.
With increasing adoption of smart meters, data can be leveraged to empower consumers in the context of electricity/power usage. A growing green conscience among today's power consumers, especially millennials, will make such a move very effective. Here again, there are enormous benefits to a data-driven approach. For instance, Infosys is working on employing advanced machine learning techniques to reduce errors and increase the generation efficiency for wind turbines in wind farms.
Statistical modeling and forecasting tools help eliminate functional and structural inefficiencies across energy systems. Use cases of functional modeling in oil and gas include predictive maintenance of pipelines and equipment. Further, predictive analysis can b