Towards a smart community evaluation and implementation toolkit - low-cost mini-district predictive controls with flexible tariffs

The drive to decarbonise the grid through intermittent generation requires an increase in system flexibility. To achieve this all energy assets, regardless of size and location, need to be incentivised to contribute. For smaller and remote assets and microgrids, the ability to participate in the current third-party flexibility markets remains limited. A toolkit has therefore been developed to allow assessment for smaller systems to use standard flexible energy tariffs, orchestrated by a simple, independent locally-situated controller, to achieve financial benefits and assist grid balancing. The toolkit has demonstrated that significant savings are achievable for a small mini-district scheme. The integrated Python-based optimisation engine can be used on low-cost platforms, such as the Raspberry Pi, which indicates that the developed algorithms has the potential to orchestrate microgrids as part of an integrated control system

Towards detailed occupancy and demand modelling of low-carbon communities

The integration of low carbon energy systems with zero carbon housing and communities requires a detailed understanding of the scale and timing of energy demand. Household demand is closely related to occupant type and associated occupancy patterns. A high resolution occupancy modelling technique is proposed as a foundation for future demand modelling. Probability data is compiled for multiple occupant, household, and day types from Time-Use Survey data. A higher order Markov approach is then used to generate representative occupancy profiles over extended time periods. An improved method to model family interactions has also been developed