District heating and cooling networks can pose the possibility of including a variety of renewable energy sources as well as waste heat into a district’s heat supply concept. Unfortunately, low demand densities as they increasingly occur through higher building energy standards and in rural areas render conventional heating and cooling networks inefficient. At the same time, power-to-heat is becoming more and more important to make use of a larger amount of renewable energy sources on the electrical side by providing more flexibility by means of demand response and demand-side management. Within this work, a rural Plus-Energy settlement is presented addressing those topics by a low-temperature district heating and cooling network connected to a novel agrothermal collector supplying 23 residential buildings with decentralized heat pumps and PV systems. The collector, the network, and six of the buildings are equipped with comprehensive monitoring equipment. Within those buildings, forecast and optimization algorithms are implemented to adapt their heat pump operation to enable an increase of self-consumption, to include flexible electricity tariffs, and also to participate in power markets. Thereby, for the low-temperature district heating and cooling network, it has been shown that the concept can operate in the future at competitive heat costs. On the building level, up to 50% of cost savings could be achieved under ideal conditions with the optimization of the self-consumption of PV electricity. However, to ensure optimal results, the individual system components have to be dimensioned for this task.

The present paper introduces an iterative methodology to progressively reduce building simulation model complexity with the aim of identifying potential trade-offs between computational requirements (i.e., model complexity) and energy estimation accuracy. Different levels of model complexity are analysed, from commercial building energy simulation tools to low order calibrated thermal networks models. Experimental data from a residential building in Germany were collected and used to validate two detailed white-box models and a simplified white-box model. The validation process was performed in terms of internal temperature profiles and building thermal energy demand predictions. Synthetic profiles were generated from the validated models and used for calibrating high order models. A reduction (trimming) procedure was applied to reduce the model complexity using an energy performance criterion prior to model trimming. The proposed methodology has the advantage of keeping the physical structure of the original RC model, thus enabling the use of the trimmed lumped parameter building model for other applications. The analysis showed that it is possible to reduce the model complexity by half, while keeping the accuracy above 90% for the targeted building.

The volatility of renewable energy sources (RES) poses a growing problem for operation of electricity grids. In contrary, the necessary decarbonisation of sectors such as heat supply and transport requires a rapid expansion of RES. Load management in the context of power-to-heat systems can help to simultaneously couple the electricity and heat sectors and stabilise the electricity grid, thus enabling a higher share of RES. In addition power-to-hydrogen offers the possibility of long-term energy storage options. Within this work, we present a novel optimization approach for heat pump operation with the aim to counteract the volatility and enable a higher usage of RES. For this purpose, a detailed simulation model of buildings and their energy supply systems is created, calibrated and validated based on a plus energy settlement. Subsequently, the potential of optimized operation is determined with regard to PV and small wind turbine self-consumption. In addition, the potential of seasonal hydrogen storage is examined. The results show, that on a daily basis a 33% reduction of electricity demand from grid is possible. However, the average optimization potential is reduced significantly by prediction inaccuracy. The addition of a hydrogen system for seasonal energy storage basically eliminates the carbon dioxide emissions of the cluster. However, this comes at high carbon dioxide prevention costs of $1.76$€$\mathrm{k}\mathrm{g}$⁻¹.

The transition of the transport sector to e-mobility poses various challenges but also provides great flexible load and supply potential and thus enables a stronger coupling of the transport sector with other sectors. If emerging opportunities such as bidirectional charging in the context of Vehicle-to-Home and Vehicle-to-Grid applications are utilised, a previously unimagined load management and storage potential can be tapped. This can transform e-mobility from an additional burden to the grid to a grid-supporting factor that enables greater integration of renewable energies and reduces additional investments in infrastructure like grid expansion and stationary storage systems. In order to investigate this potential, within this work we examine simulation based various Vehicle-to-Home (PV self-consumption, load shifting due to flexible electricity tariff) and Vehicle-to-Grid (secondary reserve) scenarios for different driving profiles for a residential building with heat pump, PV system and optionally a small wind turbine. In addition, a charge load optimisation is carried out using a genetic algorithm. The energy quantities, saving potential and additional number of battery cycles are quantified. The results show that, despite additional battery degradation, significant financial incentives can be achieved.

In Germany, wind and photovoltaic (PV) systems dominate renewable electricity generation, with large wind turbines contributing 24.1% and PV systems contributing 10.6% in 2022. In contrast, electricity production from small wind turbines remains marginal (<0.01%). While currently only viable in high-wind locations, factors like rising electricity prices, cheaper battery storage, and increasing electrification could boost their future role. Within this work, a residential energy supply system consisting of a small wind turbine, PV system, heat pump, battery storage, and electric vehicle was dimensioned for different sites in Germany and Canada based on detailed simulation models and genetic algorithms in order to analyze the effect of bidirectional charging on optimal system dimensions and economic feasibility. This was carried out for various electricity pricing conditions. The results indicate that, with electricity purchase costs above 0.42 EUR/kWh, combined with a 25% reduction in small wind turbine and battery storage investment expenses, economic viability could be significantly enhanced. This might expand the applicability of small wind power to diverse sites.