An integrated flexibility optimizer for economic gains of local energy communities — A case study for a University campus

With a capacity-based network tariff structure, consumers are encouraged to reduce their connection capacity to avoid higher costs. However, overloading beyond the administrative grid connection capacity limit would result in an increased connection capacity, thus prosumers have to pay the increased electricity bill for the rest of the year. Therefore, it is important to optimize the energy generation and consumption profiles of local energy communities (LECs) considering the comfort level of occupants. This work aims to reduce the overloading of the grid connection and increase the utilization of local renewable energy resources (RES) thus avoids being penalized throughout the year due to casual intermittent overloading in peak hours, even once in a year. The present work proposes a novel data-driven flexibility optimizer model for day-ahead scheduling of energy profiles for LECs, considering photovoltaic (PV) generation, heat pump (HPs), and cooling loads. The proposed methodology has been developed to explore the flexibility potentials from a university campus network which includes both electrical and heating/cooling systems in an integrated way. A two-layer optimization strategy is developed, to guard the occupant’s comfort level. Simulation has been performed for complete two months, considering winter and summer scenarios. A peak demand reduction of 16% has been observed with negligible energy usage differences between the proposed and the baseline case. Two types of flexibility indicators are estimated to give a deeper insight into the performance. Economical gains of 9 % and 16 % are estimated depending on the type and voltage level of the connection.