Achieving the European Green Deal’s goals requires deep decarbonization of buildings and transport, which are major sources of emissions. Electric heat pumps and electric vehicles (EVs) support this shift through high efficiency and renewable integration. In cold-climate regions like Dresden, where heating demand is high, combining Solar-Assisted Heat Pumps (SAHPs) with bidirectional EVs offers a promising solution. SAHPs reduce grid reliance by using on-site solar energy, while Vehicle-to-Home (V2H) EVs provide flexibility during peak periods. Yet without coordination, their operation may intensify demand spikes rather than reduce them, highlighting the need for intelligent scheduling to fully harness the potential of distributed low-carbon technologies.

Coordinating residential energy systems requires balancing competing objectives with differing priorities. Traditional approaches often rely on predefined weights, which can be difficult to choose and may lack flexibility. To address this, this study adopts the Non-dominated Sorting Genetic Algorithm II (NSGA-II), a multi-objective evolutionary algorithm that generates a Pareto front: a set of non-dominated solutions representing different trade-offs. This enables flexible decision-making based on user preferences or policy goals.

The NSGA-II optimization targets three key objectives: (1) minimizing electricity costs under time-of-use pricing, (2) reducing CO₂ emissions based on time-varying emission factors, and (3) smoothing net-load profiles to reduce grid stress. These goals often diverge; for instance, the cheapest electricity may coincide with high-emission or peak-load periods. Simulations are conducted over daily cycles using real residential load and solar generation data from Dresden. Results are compared against heuristic control strategies to assess the added value of coordinated scheduling.

Simulation outcomes are evaluated across different household usage scenarios to reflect behavioral and demand variability. Findings show that coordinated EV charging/discharging significantly improves cost-efficiency and emissions performance, particularly under high-demand conditions such as winter evenings. The NSGA-II algorithm reveals clear trade-offs between conflicting goals and generates a Pareto front of optimal solutions tailored to user or policy preferences. This study demonstrates that residential flexibility when intelligently managed, can enhance energy efficiency and grid stability without large-scale infrastructure upgrades.