 The availability of cheap energy has led to rapid technological development and the prospering of our societies. Our energy consumption has increased tremendously over the past decades, but most of that is based on fossil fuels. So, as Al Gore once said, what is at risk of being destroyed is not the planet itself, but the conditions that make it hospitable for human beings. 18.5% of our energy consumption is consumed in the form of electricity. With the electrification of the transportation sector, that will further increase. 66% of our electric power generation is coming from fossil fuel-based plants and 10% from nuclear plants. So, from large-scale power plants, and then it is transmitted over high voltage transmission lines. If we want to keep the planet hospitable, then we need to rapidly increase the contribution from renewable generation. But as their penetration increases, also the challenge of integrating the variability and uncertainty increases. Storage and hydro are perfect balancing resources, but also the load is going to play a role. Because not just the grid is changing, also how the consumer interacts with the grid. New technologies allow the consumer to use energy more efficiently and smarter and participate in the generation load balancing process. So, how cool would it be if each of us would contribute to enabling a sustainable energy future? The future electric power grid will be much more distributed, with distributed generation, storage, flexible demand spread throughout the grid. We of course want to use all of their capabilities as much as we can, but that means coordinating millions and millions of components across the entire grid and across system levels. So, one particular project that we do at ETH is the develop method that allow for local close to optimal decision making. How it works is that we simulate the overall grid and determine what optimal decisions would be. We then map these optimal decisions to local measurements to create a local decision making curve. We then use this curve in online operation to determine what the optimal decisions would be. So what you can see here is in blue what we would get with our local scheme and in green a centralized optimizer and in brown current standards. So you can see that with our local scheme just using local measurements we get very close to the centralized optimizer. A slightly different concept is distributed optimization in which we're using communication to coordinate different devices. In my group we have developed approaches that allow for a completely distributed solution process of the generation load balancing optimization problem. I strongly believe that centralized and centralized and distributed decision making however have to coexist in the future electric power grid. So as we move forward our research focuses on developing the optimal control structure using these concepts wherever suitable and also interlinking them. Another big topic in electric power system is deployment of sensing technology. We hope that with a large amount of data we can better monitor and operate the system, but high granularity data and the usage of communication also increases cyber risk and the concerns for privacy. So with colleagues at Imperial College, INRIA and KTH are working on algorithms that use storage device to put some noise over the actual consumption of the consumer, thereby protecting its privacy. So how it works is that we're minimizing privacy leakage, thereby removing the correlation between what's actually measured and what the consumer is consuming. The extensive distributiveness of the future electric power grid also makes it really hard to accurately model and simulate all of the interdependencies, including also the economic part. Together with a range of researchers from different fields, we develop at ETH a simulation platform that can model all of these interdependencies. Many pieces actually need to fit together so that we can address all of the challenges ahead of us. Electric power system has clearly become a multidisciplinary research field. That is why I as a power system engineer work with control theorists, with policy experts, with communication engineers and so on. The overall goal is to use all of the capabilities that we have in the entire grid and also leverage the massive amount of data that we will have. This can be achieved by embedding intelligence along the entire supply chain from generation over transmission to distribution down to the load. But optimizing the electric power grid is a very difficult task because the state is constantly changing and the integration of renewable generation adds uncertainty and variability to the system. But as Stephen Hawkins once said, intelligence is the ability to adapt to change. So it is up to us to develop the methods that give us this intelligence. Thank you.