Towards cost-optimal energy communities: realizing privacy aware and data-driven energy efficiency, flexibility and climate resilience

Energy communities are a key enabling technology to prevent climate change by facilitating new energy market designs and promoting renewable energy sources (RES). This pivot towards energy communities follows directly from the grid issues created by increasing electrification of demand and proliferation of RES. Pilots have shown that it is possible to address these issues by leveraging the flexibility in energy communities, e.g. in thermal loads or in electric batteries etc. However, there are fundamental limitations to the way flexibility is being considered at the moment. These include a myopic cost-optimality calculation for the design of the built environment which emphasizes efficiency over flexibility and climate resilience, as well as enormous data requirements to enable automation and lack of clarity on how to address the ensuing data privacy concerns. This project proposes a novel reformulation of the cost-optimality criterion which breaks the silo that separates the building side from the grid side, and co-optimizes both design and operation to minimize lifetime costs while valuing flexibility and resilience. At the same time, it uses publicly available data to train transferrable models of archetypes of energy flexible resources to accelerate data-driven automation and employs a data market to address the privacy aspect of energy communities. In doing so, it will help energy communities meet their potential of enabling the sustainable energy transition.