AI and Big Data in Future Energy Systems
- [Smart Grid - StateTech]
- Overview
As electricity demand grows and decarbonization efforts intensify, power systems become increasingly complex. In the past, the grid directed energy from centralized power stations. Today, power systems increasingly need to support multi-directional power flow between distributed generators, the grid and consumers.
The growing number of grid-connected devices, from electric vehicle (EV) charging stations to residential solar installations, makes traffic flow more unpredictable. At the same time, connections between power systems and the transport, industry, construction and industrial sectors are deepening. The result is a greatly increased need for information exchange, and as power systems continue to evolve, more powerful tools are needed to plan and operate power systems.
This need comes as the capabilities of artificial intelligence (AI) applications are rapidly evolving. As machine learning (ML) models become more advanced, the computing power required to develop them has doubled every five to six months since 2010.
So it’s no surprise that the energy industry is taking early steps to harness the power of AI to increase efficiency and accelerate innovation. The technology is uniquely positioned to support the simultaneous growth of smart grids and the massive amounts of data they generate.
Smart meters generate and send thousands of times more data points to utilities than their analog predecessors. New equipment for monitoring grid power flows delivers an order of magnitude more data to operators than the technology they are replacing. It is estimated that wind turbines around the world generate more than 400 billion data points each year.
This volume is a key reason why energy companies view AI as an increasingly important resource. One recent estimate suggests that AI already serves more than 50 different purposes in energy systems, and that the technology market in this area could be worth as much as $13 billion.
- Big Data in Energy Systems and Applications
Energy systems are becoming more complex and advanced as new concepts for energy production and utilization stem from technological developments. Sensors collect vast amounts of data during the generation, transmission, distribution and consumption of energy. The increasing complexity of energy systems requires finding new ways to use engineering experience and data collection to improve decision-making.
With the expansion and interconnection of energy networks, the energy market is becoming more and more expansive. We are now facing the era of the Internet of Things and the Internet of Energy. Against this backdrop, big data in the energy industry, energy systems and applications is emerging as a crucial new frontier.
Operational data on monitoring and data acquisition, energy management, distribution management, distributed energy management, and many more applications are now too complex to be processed with traditional methods and methods.
Research efforts and many applications have proven that advancing bid data analysis is critical for improving the design, operation, and maintenance of energy systems, and has led to new advanced energy applications.
- From Big Data To Big Insights
Big data is a key element in solving critical business problems for utility companies. It can translate information from smart meters and smart grid projects into meaningful operational insights and understanding of customer behavior.
As smart grids and smart meters become critical to the industry, they may start generating hundreds of terabytes of data each year -- or unstructured text data compiled from maintenance records and Twitter feeds. The accuracy, breadth and depth of these new data points opens up new opportunities for utilities poised to take advantage of them.
- [University of Washington]
- Big Data Analytics is Disrupting the Energy Industry
Digital data and analytics can reduce operational costs by enabling predictive maintenance, thereby lowering electricity prices for end users. Digital data and analytics can help achieve greater efficiency through improved planning, increased power plant combustion efficiency and reduced network loss rates, and better project design across the power system.
In the network, efficiency can be improved by reducing the rate of loss in delivering electricity to consumers, for example through remote monitoring, enabling equipment to operate closer to its optimum conditions and grid operators to better manage flow and Bottlenecks.
Digital data and analytics can also reduce the frequency of unplanned downtime through better monitoring and predictive maintenance, and limit downtime by quickly identifying points of failure. This reduces costs and increases the elasticity and reliability of supply.
- How AI and IoT Fit into the Future of Energy
Artificial intelligence (AI) is entering all types of industries, including the energy industry, and the use of AI to harness big data and infer from very large data sets has grown significantly. AI is the application of ML to automate and computationally support decision-making in complex systems. AI has great potential for coordinating and optimizing the use of distributed energy, electric vehicles, and the Internet of Things (IoT).
The use of AI aligns well with the current pace of change expected by utilities, regulators, and customers to improve utility operations, including:
- Reliability (e.g., self-healing grids, operational improvements, and efficient use of renewable resources and energy storage) utilization);
- Security (eg, outage prediction and outage response); cybersecurity of the system (eg, threat detection and response);
- Optimization (eg, asset, maintenance, workflow, and portfolio management);
- Enhanced customer experience (eg, faster, more intuitive interactive voice responses, personalization, product and service matching); etc..
The use of energy storage and the Internet of Things is expected to increase significantly in the coming years, with the development of distributed energy sources for bidirectional power flow in the distribution grid and the new roles of energy service providers, utilities and consumers to produce energy or prosumers.
This evolution of the power grid is known as the "energy cloud", given the increase in the number of control points in the power grid from tens of thousands to hundreds of millions, or even billions. In the future, it will be a requirement for effective grid engagement, compared to now that AI is a tool that is exploring optimization opportunities.
[More to come ...]
