The energy aggregator problem – A holistic mixed-integer linear programming approach

In this paper, a new MILP model for the day-ahead operation of energy aggregators (EA) is developed. Synergies between the different types of flexibility and energy trading options enable EAs in decentralized and renewable energy systems to provide economic benefits to participating households but require a detailed consideration of technological properties and constraints of the respective types of resources. Therefore, the main contribution of this work is the development of a new EA model (EAM), which combines a holistic perspective with a high level of technical detail to better address the complexity of the EA decision. Most importantly, power-to-heat systems are integrated with their inherent thermal relations between heat pumps, heater rods and heat storages. In combination with other energy resources such as photovoltaic systems, electric vehicles, household battery storages and time-shiftable loads, households are modeled as systems with interdependent electrical power and heat flows. Moreover, three different trading levels (wholesale, local markets and internal trading) are taken into account. The model application to a case study with up to 111 individually modeled prosumer households in a summer and a winter scenario reveals high synergetic potential of EAs resulting from the flexibility of multiple trading options in combination with the flexibility of various energy resources. The results validate the efficacy of the model, as significant economic benefits for households are realized in comparison to a base case of non-aggregated households, showing that the three trading levels significantly contribute to these benefits. Further analyses give insights into the interdependent synergetic relations between different flexible resources, underlining the importance of a holistic optimization approach that explicitly takes these relations into account. For future research, the EAM is proposed as a base model to depict the behavior of EAs.