An energy management system (EMS) for a household energy system is proposed in this paper, which is composed of a photovoltaic (PV) generator , a home energy storage (HES), an electric vehicle (EV), an electrical household load and a grid connection, with 24 h operation horizon. The EMS objective is to reduce the electricity cost of the household by using a linear optimization algorithm. Two different EV schedules are utilized for simulations. One mainly describes rides to work and the other describes rides in a domestic context, such as rides to a supermarket. A forecast algorithm for the electrical load of the household, based on k-means clustering and an artificial neural network, is evaluated and integrated into the EMS to realistically represent the household’s load profile. It is shown that the developed forecast algorithm performs better than two of the benchmarks. Another finding is that the more storage is available at PV-production intervals, the higher the effect of forecast uncertainties and the lower the electricity cost of the household, disregarding the investment cost.

Decarbonization requires new energy systems components to mitigate fossil fuel dependency, for instance electric vehicles and heat pumps, forming a sector integrated energy system. Energy management is a promising approach to integrate these devices more efficiently by orchestrating the respective consumption and generation. This study investigates the advantage of an advanced energy management algorithm that is applied to varying energy system scenarios. The energy management algorithm is based on economic principles and the system topology is represented by a rooted tree. Grid elements form parents, which act as auctioneers and devices act according to type specific demand and supply functions. This algorithm is compared to an approach where devices are not coordinated, at a system scale of six households. In order to account for different characteristics of the energy system, the different scenarios are defined according to a morphological analysis and are analysed by means of Monte-Carlo simulation. These scenarios vary the PV generation, heating technology, and building insulation. It is shown that the algorithm reduces peak loads across all scenarios by around 15kW. Other key performance indicators, such as own consumption and self-sufficiency show a dependency on the scenarios, although the algorithm outperforms the reference in each one, achieving an increase in own consumption of at least 13 p.p. and 22 p.p. in terms of self-sufficiency.

The decarbonization of the energy system will bring substantial changes, from supranational regions to residential sites. This review investigates sustainable energy supply, applying a multi-sectoral approach from a residential site perspective, especially with focus on identifying crucial, plausible factors and their influence on the operation of the system. The traditionally separated mobility, heat, and electricity sectors are examined in more detail with regard to their decarbonization approaches. For every sector, available technologies, demand, and future perspectives are described. Furthermore, the benefits of cross-sectoral integration and technology coupling are examined, besides challenges to the electricity grid due to upcoming technologies, such as electric vehicles and heat pumps. Measures such as transport mode shift and improving building insulation can reduce the demand in their respective sector, although their impact remains uncertain. Moreover, flexibility measures such as Power to X or vehicle to grid couple the electricity sector to other sectors such as the mobility and heat sectors. Based on these findings, a morphological analysis is conducted. A morphological box is presented to summarize the major characteristics of the future residential energy system and investigate mutually incompatible pairs of factors. Lastly, the scenario space is further analyzed in terms of annual energy demand for a district.

Battery‐ and hydrogen‐powered trains are emerging technologies that have the potential to play a key role in the decarbonization of railway lines for which full trackside electrification is not feasible. In this study, we examine Pareto‐optimal energy supply concepts for a specific location along the Cologne–Gerolstein railway line. We investigate two supply concepts, one for battery trains making use of overhead line islands (OHLIs), referred to as the OHLI supply concept, and another for hydrogen trains that make use of hydrogen refueling station (HRSs), referred to as the HRS supply concept. The public grid, as well as renewable energy sources such as wind and PV energy, are considered sources of electrical energy supply. The sizing of these takes into account storage technologies and load time series specific to each supply concept. Simulation models are defined to evaluate the characteristics of an OHLI and HRS supply concept located in a small town (Gerolstein, Germany). Our findings indicate that the HRS supply concept results in more than twice the cost per MWh (111%/MWh higher) compared to the OHLI supply concept. However, the HRS supply concept achieves a 24.7% higher degree of self‐sufficiency. Furthermore, the HRS supply concept requires a larger energy system in terms of installed renewable power and storage capacity. This enables the HRS to supply the entire line with energy, whereas the OHLI supply concept covers only a share of the overall energy demand of battery trains at the location under consideration. The remaining energy demand is covered by existing overhead lines or OHLI at another location.