A year ago we acquired Promaps Technology, a pioneering software tool which ensures the security of energy supply and mitigates risk in power systems. It’s a unique application that has recently gained more and more attention. Little did I know a year ago how complex our power grids would become in the modern world. Actually, electrical power grids are one of the most critical infrastructures in the world. Different approaches can be taken to prevent or minimize the consequences of disrupting power supplies, thus improving resilience. The necessity for electrical power systems to become resilient to such events is becoming compelling; indeed, it is important to understand the origins and consequences of faults.
So what can we do? If we know the kind of disturbances to expect, or the accompanying phenomena during a disturbance, we can design control systems to quickly mitigate the disturbance, and bring the system to a new stable point, ensuring stability and reliability.
We can ensure a high reliability and security of supply in a grid using innovative control strategies and systems. A robust methodology to avoid such situations is to use probabilistic modelling techniques and avoid insufficiently grounded conservative assumptions. In countries that consider the contribution of interconnectors as adequate , there is significant room for improvement in the methodologies used to quantify the resilience in power of supply.
In this regard, several countries have started to, or intend to use similar probabilistic techniques, but they typically use Monte Carlo methods in their national adequacy assessments. However, the underlying assumptions used in these assessments are often more conservative. The other problem is that Monte Carlo simulations do take a lot of time and the results are outdated, therefore the event may have already happened before operators can take mitigating measures.
A well-designed power system has the following characteristics:
- Provides all consumption regardless of geographical location
- Provides consumption at all times
- Handles variability in consumption and production
- Delivers a good quality of supply
- Meets defined quality requirements
- Is based on economic ‘optimal’ principle
- Meets required and defined security goals
The power system is rapidly changing towards the digital power system by using advanced ICT solutions, big data, smart grid, AI, machine learning and other advanced instruments. The digitalization of the power business in Norway, Europe and other parts of the world, is leading to an increase in numerous new possibilities, but also challenges. To gain trust in the machine learning technology being introduced to power systems, and to avoid similar problems in power systems as experienced by financial markets with the ‘flash crash’ in 2014, new insights are needed. The need therefore, for near real-time understanding and tracking of the power systems’ inherent dynamics and risk level properties, becomes evident.
The main question is: can the power business and the introduction of new system tools manage without probabilistic risk calculation for making use of the digitalization and the corresponding big data?
The power system is rapidly changing towards the digital power system by using advanced ICT solutions, big data, smart grid, AI, machine learning and other advanced instruments.
Graphical relationship of power faults and causes