Periodic Reporting for period 4 - CONENE (Control of Large-scale Stochastic Hybrid Systems for Stability of Power Grid with Renewable Energy) Reporting period: 2020-10-01 to 2021-03-31 Summary of the context and overall objectives of the project Issue: The problem being addressed is safe and efficient integration of renewable sources of energy in the power grid. This problem is important for the society because we are increasingly introducing renewable sources of energy, such as wind and solar. The power output of these renewables is unpredictable. In order to have a stable power system, the production demand and supply has to be balanced at all times. The unpredictability makes it difficult to ensure this balance. The project develops the theory and algorithms to address this issue. Challenge: In particular, increasing penetration of renewable energy sources and liberalization of electricity markets are significantly changing power system operations. Analysis and control of this system is highly challenging because of (a) large dimensions arising from a complex network of transmission and distribution lines and generators, (b) uncertainties, such as fluctuating power output from weather-based renewable generation, and (c) multi-agent complex interactions due to the participation of a large number of producers and consumers with individual objectives and coupling constraints. Overall objective: My research agenda is focused on developing fundamental theory and practical algorithms for control of large-scale stochastic multi-agent systems in order to guarantee the stability and efficiency of the power grid. Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far The work performed has been perfectly aligned with the goals of my ERC project. In particular, I have significantly advanced fundamental understanding of multi-agent decision making under uncertainty, developed novel scalable and provably safe algorithms for these systems and have verified the methods on realistic power system simulation testbeds in collaborations with power system researchers. The work can be discussed in two main categories.On the electricity market aspect, the novelty in our work was to derive new market mechanisms, motivated by advances in auction theory from the fields of computer science, game theory and economics. These mechanisms clarified the potential of jeopardizing the current electricity markets through collusion and shill bidding and offered alternatives to address these shortcomings. Furthermore, using the tools from coalition game theory, we developed a novel approach to coordinate the reserve markets across Europe. This will significantly enhance the stability of the power system and improve its efficiency. The reason is that the increased use of renewable sources of energy will require an increased need for reserve markets, which help in balance of supply and demand of electricity in real-time. We quantified the benefits of pooling together all European reserve markets both in terms of grid stability and in terms of reduction in cost of achieving the grid stability. On distributed control algorithms for large-scale systems, we have also achieved two main novel methodologies. First, in the case in which the subsystems in the large-scale system share a global objective (for example, stability in an interconnected power system), we developed a new approach to distributed control, which would ensure the optimality of the control laws derived, despite the non-convexity of the problem. The approach was based on a novel insight we developed that we can come up with various parameterizations of a controller, some of which lend themselves to tractable distributed design approaches. Furthermore, we developed a novel data-driven approach to learning distributed control problems, motivated by advances in black-box learning and optimization. This approach was not proposed in past works, and at the same time is highly relevant. The reason is that we are increasingly controlling complex systems, which are hard to model. And at the same time, we are increasingly probing our systems and obtaining data from them at an unprecedented way. Second, in terms of systems that might have individual objectives and not share a single global objective, we developed game theoretic algorithms to learn optimal local decision rules. These algorithms enable the subsystems to learn the optimal actions only using local information and hence, do not jeopardize privacy of the subsystems. We showed the applicability of our result to several case studies and examples arising in real-time electricity markets. Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far) On the electricity market aspect, the novelty in our work was to derive new market mechanisms, motivated by advances in auction theory from the fields of computer science, game theory and economics. These mechanisms clarified the potential of jeopardizing the current electricity markets through collusion and shill bidding and offered alternatives to address these shortcomings. Furthermore, using the tools from coalition game theory, we developed a novel approach to coordinate the reserve markets across Europe. This will significantly enhance the stability of the power system and improve its efficiency. The reason is that the increased use of renewable sources of energy will require an increased need for reserve markets, which help in balance of supply and demand of electricity in real-time. We quantified the benefits of pooling together all European reserve markets both in terms of grid stability and in terms of reduction in cost of achieving the grid stability. On distributed control algorithms for large-scale systems, we have also achieved two main novel methodologies. First, in the case in which the subsystems in the large-scale system share a global objective (for example, stability in an interconnected power system), we developed a new approach to distributed control, which would ensure the optimality of the control laws derived, despite the non-convexity of the problem. The approach was based on a novel insight we developed that we can come up with various parameterizations of a controller, some of which lend themselves to tractable distributed design approaches. Furthermore, we developed a novel data-driven approach to learning distributed control problems, motivated by advances in black-box learning and optimization. This approach was not proposed in past works, and at the same time is highly relevant. The reason is that we are increasingly controlling complex systems, which are hard to model. And at the same time, we are increasingly probing our systems and obtaining data from them at an unprecedented way. Second, in terms of systems that might have individual objectives and not share a single global objective, we developed game theoretic algorithms to learn optimal local decision rules. These algorithms enable the subsystems to learn the optimal actions only using local information and hence, do not jeopardize privacy of the subsystems. We showed the applicability of our result to several case studies and examples arising in real-time electricity markets. frequency.png
