SCALABLE OPTIMIZATION OF POWER SYSTEMS WITH FLEXIBLE DEMAND AND RENEWABLE SUPPLY
The large-scale integration of renewable resources in electric power systems requires the mobilization of flexible consumers who can adapt their consumption to the variable and uncertain fluctuation of renewable supply. The mobilization of demand-side flexibility remains an elusive goal in electric power systems, while the majority of flexible consumers are connected to distribution systems that are currently operated passively. The major obstacles towards the optimal management of demand-side flexibility include the enormous number of flexible consumers (with ensuing challenges for scalable optimization), the presence of uncertainty at all layers of the power grid, and the physical complexity of distribution system power flow.
In this context, the ICEBERG project proposes a novel approach towards the proactive utilization of transmission and distribution system resources in a coordinated fashion.
The approach of ICEBERG to achieving this ambitious goal is based on three key ingredients:
- The first ingredient is a novel approach for planning and simulating the dispatch of the system which exploits the structure of distribution networks and can scale to systems of arbitrary size.
- The second ingredient is an original optimization framework for tackling uncertainty and non-convexity at every layer of the system.
- The third ingredient is a novel implementation of this optimization framework in parallel and distributed computing infrastructure, which will enable the optimal short-term planning and real-time coordination of resources at all layers of the system.
The vision of ICEBERG is to break down the current barriers to renewable energy integration by mobilizing the as yet untapped flexibility that is present at all layers of the network. This will enable the achieving of ambitious sustainability targets with acceptable infrastructure upgrades and without any deterioration in the quality of electric power service, which consumers currently enjoy.
This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme under the grant agreement number 850540.