The operation of residential energy hubs with multiple energy carriers (electricity, heat, mobility) poses a significant challenge due to different carrier dynamics, hybrid storage coordination and high-dimensional action-spaces. Energy management systems oversee their operation, deciding the set points of the primary control layer. This paper presents a novel 2-stage economic model predictive controller for electrified buildings including physics-based models of the battery degradation and thermal systems. The hierarchical control operates in the Dutch sequential energy markets. In particular common assumptions regarding intra-day markets (auction and continuous-time) are discussed as well as the coupling of the different storage systems. The best control policy it is best to follow continuous time intra-day in the summer and the intra-day auction in the winter. This sequential operation comes at the expense of increased battery degradation. Lastly, under our controller, the realized short-term flexibility of the thermal energy storage is marginal compared to the flexibility delivered by stationary battery pack and electric vehicles with bidirectional charging.

In low-voltage distribution networks, the high penetration of renewable energy generators in residential buildings has proven challenging for system operators. In response, the grid operators can reinforce the grid infrastructure or deploy battery energy storage systems throughout the network to compensate for the voltage fluctuations. Alternatively, new energy markets for ancillary services have been proposed to involve the prosumers; however, most are at medium and high voltage levels. This paper investigates, from a cost perspective, what conditions can make it attractive for individual prosumers to participate in a low-voltage ancillary service market, specifically power curtailment and peak shaving. We considered a prosumer with a 2 kWp PV system for both ancillary services, adding a 10 kWh battery for the peak shaving case. Curtailing power to comply with the maximum power exchange with the grid does not create any significant change in the LCoE of the PV system, keeping it near 0.072 €/kWh for permitted return grid powers above 1.25 kW. Scenarios closer to zero-injection increase exponentially the LCoE to 0.222 €/kWh. Using a semi-empirical ageing model, we estimated the degradation of the batteries for the cases with and without providing peak shaving, concluding that doing peak-shaving to avoid demanding more than 1 kW from the grid extends the battery life by up to 320 % while increasing its LCoS only 9.5 % when compared to a zero-consumption scenario due to the reduced depth-of-discharge and number of cycles. The results suggest that power curtailment and peak shaving can be attractive for prosumers, thus creating opportunities for ancillary services business models at the residential scale.

This paper presents a Mix Integer Linear Programming (MILP) optimization approach to reduce peak demand and maximize revenue in a grid-connected building with a PV-equipped charging station for Shared EVs. The study investigates the impact of EV availability on the effectiveness of the system by comparing the results for different connection times of a fleet of Shared EVs, a private EV used for commuting, and a stationary battery. Results from the case study conducted in The Netherlands demonstrate that not only the duration but also the timing of EV connection significantly influence system effectiveness, emphasizing the need for accurate availability estimation. The trade-off between peak reduction and Peak-to-Average Ratio (PAR) reduction is also highlighted, underscoring the importance of considering both factors for optimizing charging station usage. These findings provide valuable insights for optimizing energy management, reducing peak loads, and increasing the utilization of renewable energy sources in the context of Shared EVs and V2G technology.

In the context of building electrification the operation of distributed energy resources integrating multiple energy carriers poses a significant challenge. Such an operation calls for an energy management system that decides the set-points of the primary control layer in the best way possible. This is done by fulfilling user requirements, minimizing costs, and balancing local generation with energy storage. This last component is what enables building flexibility. This paper presents a novel aging-aware strategy for operating grid-connected buildings that combine multiple energy carriers (heat and electricity), storage devices (electric vehicles, batteries, and thermal storage), and power sources (solar photovoltaics, solar collectors). The novel energy management algorithm presented considers the aging of the batteries to enhance the operational differences between storage technologies, thus making explicit the trade-off between the services provided by the hybrid energy storage system and its degradation. This unlocks grid cost reductions between 20–45 % depending on the season when compared to state-of-the-art solutions.