This paper presents a sequential framework based on a two-stage stochastic programming model for the planning and expansion of distribution networks under the influence of heavy-duty electric vehicles (HDEVs) charging. In the first step, a capacity assignment model determines the spatio-temporal charging demand of HDEVs, where a congestion penalty function is introduced to produce spatially diversified charging demand, preventing vehicle clustering. In the second step, this demand is used as input to a mixed-integer second-order cone programming (MISOCP) model for the joint planning of energy sources and grid reinforcements, which is solved via Benders decomposition. The objective is to minimize total asset investment and operational costs while accounting for uncertainties in electricity demand, renewable energy generation and the arrival of HDEVs at charging stations. The framework is tested across fleet electrification rates and seasonal PV generation conditions to quantify how grid investment scales with HDEV adoption.

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.

Home Energy Management Systems (HEMS) play a key role in the electrification of the residential sector. In a future where different energy carriers must be converted and used seamlessly, HEMS are responsible for orchestrating all energy assets within a home. To determine the power flows, the HEMS must be aware of its impact on the aging of the system's batteries. On the other hand, since this is typically performed through optimization models, various variables are needed, such as ambient temperature, solar radiation, and initial conditions. These inputs are uncertain and require proper forecasting. This paper presents the operation of an aging-aware HEMS using probabilistic forecasts of exogenous information, directly relating them to the quality of the degradation control. The relationship is studied under deterministic and stochastic optimal control settings.

In the context of building electrification, the operation of distributed energy resources integrating multiple energy carriers (electricity, heat, mobility) poses a significant challenge due to the nonlinear device dynamics, uncertainty, and computational issues. As such, energy management systems seek to decide the power dispatch in the best way possible. The objective is to minimize and balance operative costs (energy bills or asset degradation) with user requirements (mobility, heating, etc.). Current energy management uses empirical battery ageing models outside of their specific fitting conditions, resulting in inaccuracies and poor performance. Moreover, the link to thermal systems is also overlooked. This paper presents an ageing-aware nonlinear economic model predictive controller for electrified buildings that incorporates physics-based battery ageing models. The models distinguish between energy storage systems (chemistry, ageing state, etc.) and make explicit the trade-off between grid cost and battery degradation. The proposed algorithm can either cut down on grid costs or extend battery lifetime (electric vehicle or stationary battery packs). Additionally, substituting NMC cells with LFP chemistries optimizes grid performance during the summer, yielding a 10% grid cost reduction and a 20% decrease in degradation. Finally, the grid cost and degradation of the presented MPC when using aged batteries are improved with respect to the state of the art by 10% and 5% respectively, in periods with high solar generation and low thermal loads like summer.

The inclusion of PV and heat pumps in residential low-voltage distribution systems is a fundamental component of the energy transition. Nevertheless, adoptions below 40% can already cause voltage conditions incompliant with the standard EN50160 during winter. Aggregated storage systems have been proposed as a solution; however, the literature generally assumes full observability and controllability of the assets, which is unrealistic in many cases. This paper evaluates the potential of aggregated single- and multi-carrier storage systems to maintain voltage stability in low voltage networks, considering separated controllers for the prosumer and the aggregator. We used a real 301-node residential distribution network in the Netherlands as case study. Our results demonstrate that aggregated multi-carrier energy storage can ensure the voltage conditions established in the standard EN50160 for energy transition adoptions up to 80%, while aggregated single-carrier storage can reach 60% and centralized storage only 40%. We concluded that aggregation of storage assets increases the utilization of the existing grid infrastructure, reducing reinforcement costs for the DSOs. However, the energy storage assets’ high investment costs lead to unattractive conditions for single- and multi-carrier storage, compared to a case with only PV and heat pumps. Considering the current market conditions, using storage for voltage support would require economic compensations. These findings provide DSOs valuable insight on alternative solutions to grid reinforcement and centralized storage to address the challenges of the energy transition.

The urge to reduce the dependence on natural gas for heating at the residential level has led to the deployment of different fossil fuel-free alternatives. In the Netherlands, two technologies are leading the transition: heat pumps, due to their high COP, and photovoltaic–thermal systems, due to their dual electric-thermal output. However, both represent a challenge for users and grid operators, aside from their stochastic behavior. Heat pumps alone can surpass a typical Dutch house's total energy and power consumption. Photovoltaic–thermal systems, as their only electric homologs, usually have a mismatch between generation and demand, causing energy injections to the grid. From the electric perspective, storage systems are a proven solution to reduce the energy exchange with the distribution network. This paper proposes four multi-carrier energy system configurations for a Dutch household, comprising different combinations of a photovoltaic–thermal system, a battery energy storage, a heat pump, and an underground water tank thermal energy system, providing analytical models for every component (including the thermal losses from the thermal storage to the ground), and the space heating and electrical demands. We determined the components’ compatibility and evaluated the combinations considering their thermal performance, electrical performance, and equivalent CO₂ emissions. The results suggest that using a heat pump combined with a photovoltaic system and a battery provides the best trade-off. The photovoltaic–thermal system alone could not supply the thermal demand required for comfortable space heating nor reach temperatures high enough to charge the thermal storage. Combining the thermal storage with the heat pump allows a certain degree of flexibility for the heat pump activation at the cost of COPs between 0.8 and 1.38 when used to charge the thermal storage, thus increasing energy consumption and equivalent emissions considerably.