Utilities Middle East
THE ROAD TO DIGITAL POWER PLANTS
As industry 4.0 technology continues to advance, existing data can be harnessed to develop machine-learning solutions that deliver real value, say Rodolfo Maciel and Peter Safarik, consultant and partner respectively at McKinsey
Even before the outbreak of COVID-19, fossil-fuel power plants faced significant disruption from renewable energy sources, low gas prices, and ambitious decarbonisation goals, all of which are changing customer preferences.
Now, as the power-generation industry shifts to the next normal, adopting the latest digital and advanced-analytics technologies has become critical.
Many power companies began their digital transformations with technological solutions such as data models, which help optimize set points, enable better dispatch decisions, and support maintenance strategies and operating-mode selection.
Forward-thinking companies, however, have recently started using visualization tools to manage real-time generation performance and digital control software to relay predictive data to control rooms.
Yet these innovations are grounded in tangibly improving outcomes for plant operations and are therefore only part of a digitally enabled, next-generation power plant.
Chief among an organization’s most valuable assets are its data. And the first steps of any company’s journey are building a fact-based, data-driven culture and learning how recent advances in analytics can transform data into actionable insights. The next generation of digital and advanced-analytics tools has emerged alongside innovative technologies, such as artificial intelligence and machine learning. Such approaches seek to go beyond traditional multivariate regression analysis methods in terms of revealing hidden patterns and complex interdependencies.
For example, a next-generation power plant can use machine learning to account for significantly more inputs, thus enabling core plant operational functions to be modeled more precisely than previously thought possible.
Just a few years ago, performance-improvement models based on thermodynamic models and OEM set points were considered an adequate approach to optimizing a plant’s heat rate—the amount of energy needed to produce a single kilowatt-hour (kWh). Today, machine learning can