Sustainable Residential Energy Supply: A Literature Review-Based Morphological Analysis

Abstract

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.

1. Introduction

The decarbonization of the energy system is promoted in many regions of the world. For example, the EU Energy Roadmap describes a scenario in which 75% of energy is supplied from renewable sources [1]. In research, even more ambitious targets are defined, aiming at an energy system which is 100% based on renewables [2,3,4]. First ideas for a 100% sustainable energy supply came up in the late 1970s. After 2010, they have become part of public debate [5]. To achieve a sustainable energy system, far-reaching transformations are necessary [6]. Previously, the electricity sector was primarily viewed in isolation. More recent studies also include the transport and heating sector because fossil fuels are the primary energy source in these sectors. Energy systems, comprising of the mobility, heat, and transport sectors, are called integrated [7], multi-energy [8], smart [9], or sector-coupled energy systems [10].

The transformation to a decarbonized energy system is often investigated with complex energy system models. For example, the energy system model renewable energy mix (REMix) optimizes the hourly dispatch and determines the least cost energy system [11]. Integrated assessment models (IAMs), such as the Regional Model of Investments and Development (REMIND), combine knowledge from several domains, for example, climate change, macro-economics, and the energy system with a temporal resolution in the order of years [12]. Energy system models or IAMs are usually based on nation-scale spatial resolution. In some cases, individual regions of nations are analyzed for their decarbonization pathways [13]. One key aspect of all these decarbonization scenarios is energy system technology. The integration of technology to the grid generates costs that can only be estimated in large-scale transformation models and a significant part of these costs arise from the integration at the distribution grid level [14].

The cost of integration of renewable technologies can be decreased by planning of shiftable loads and storage, i.e. load and energy management. These management approaches optimally schedule a device pool, whereby technical restrictions have to be considered [15,16,17,18]. To develop future energy management systems, it is necessary to define the expected equipment and its requirements. This work aims to assist the deduction of future energy technologies for districts and projected demand which have to be handled by these technologies.

In the EU, around 25% of final energy (final energy describes the energy consumed by end users and neglects delivery and transformation) is consumed by residents, meaning that this sector represents a significant share of the overall energy demand [19]. Furthermore, decarbonization of the energy system has visible effects on residents and thus influences the acceptance of renewable technologies [20]. Therefore, this contribution analyzes the developments that are relevant at a district system scale.

In this publication, the developments are brought together on the basis of a literature review, followed by a morphological analysis, taking a residential perspective. Reviews for individual technologies are widely available in the literature (e.g., [21,22,23,24]). This contribution differs from others in that it focuses on a narrowed part of the energy system (i.e., residential grids) and looks at several sectors whose future shape is highly uncertain. This enables a broader understanding of the energy system from the perspective of residents and the identification of particular challenges. An interdisciplinary approach is pursued. The core of this paper is on technologies which are suitable to be deployed at residential sites. These are placed in the context of their utilization and potential, whereby sociological or techno-economic findings are relevant. This enables a better understanding of the interactions of individual aspects of the energy supply for residents. Due to the wide distribution of this field, this article does not claim to be exhaustive. It conveys the authors findings in relation to the question of: What will future residential energy systems, based on renewable technologies, look like?

This article presents current developments in terms of traditional energy sectors, the mobility (Section 2), heat (Section 3), and electricity sectors (Section 4). Section 5 also examines challenges in integrating these sectors and potential synergy effects. Section 6 shows a morphological analysis of the energy system, based on the findings of the previous sections. Section 7 provides a conclusion.

2. Mobility Sector

The mobility sector represents an essential part of the energy demand, which is currently largely supplied by fossil fuels. Climate-neutral energy supply can, therefore, make a significant contribution to overall decarbonization [25]. The mobility sector is particularly difficult to decarbonize. For example, it is stated that 85% of future transport demand will rely on fossil fuel until 2050 [26].

In Figure 1, it is shown that this is similar to the current situation. Moreover, Figure 1 shows that road transport is currently the prevailing mode.

In this section, the transport sector is described in terms of technologies, demand, and future perspective. An overview of this section can be gained in Figure 2.

2.1. Technology Portfolio

For this study, technologies are considered which can be fueled in sustainable manner. Battery electric vehicles (BEV) can be charged by wind or PV power, bio-based fuels are based on the agricultural cycle, and hydrogen can be produced with electricity from renewables. Thus, theses three technologies are considered in this study.

A battery-based electric vehicle (BEV) stores electric energy in a battery to propel an electric engine, thus providing mobility to its passengers. However, a BEV is less suitable for distances longer than 300 miles because the required battery capacity causes significant cost and weight addition, even considering future battery prices [27]. A BEV requires charging infrastructure for operation. This can be provided by home charging or public fast-charging infrastructure. Home charging provides significantly less power than fast-charging infrastructure. However, fast charging can challenge the electricity grid and additional components, such as cooling or buffer batteries, reduce the efficiency [28].

Biofuels are, in principle, suitable fuels for internal combustion engines (ICE) or fuel cells. Challenges for the application of biofuels are the potential requirement of additional processing to enable the usage in current engines [29] and resource constraints due to land use [30]. For example, in the US, 21 gigalitres of ethanol could be produced from marginal land feedstock, which does not compete with food production. This corresponds to around 7% of the expected US liquid fuel demand in 2050 [31]. There is the distinction between first, second, and third generation biofuels [29]. These fuels differ significantly when it comes to feedstock and final chemical structure. First generation biofuels are produced primarily from food crops. Consequently, the competition of fuel and food was attached to first generation biofuel [32]. Second generation biofuel is produced by means of by-products, waste, or dedicated feedstock. The raw material is gained from non-food biomass, although, especially dedicated feedstock, still competes with food production in terms of land use [33]. Third generation biofuel is produced from algal biomass [34]. Algal biomass requires less land for growth but more energy compared to terrestrial biomass [35,36]. Further developments are so-called advanced biofuels, solar fuels, or fourth generation biofuels. These comprise the utilization of synthetic biology to produce biofuel from sunlight and environmental carbon dioxide with a high photon to fuel efficiency, although they are at the research stage [37].

Hydrogen can be produced from fossil resources, biomass, and water [38]. Hydrogen is able to power a vehicle by means of fuel cells and an electric engine. These vehicles are called fuel cell electric vehicle (FCEV). Current FCEVs, such as the Toyota MIRAI, exhibit a cruising range of 500 km, whereas the refilling time is 3 min [39]. Hydrogen can also power combustion engines. These vehicles are called hydrogen-fueled internal combustion engine (H2ICE). However, the advantages of FCEV, such as higher efficiencies and lower operation noise, make this technology more attractive than H2ICE [40]. Hydrogen-based mobility is well suited for more energy-intensive applications such as public transport and light-duty trucks [41]. Furthermore, hydrogen can serve as a fuel for aviation, although hydrogen differs from current aviation fuel in terms of combustion properties, e.g., the linear flame speed. Thus, turbine technology adaptations would be necessary [42].

2.2. Demand

From 1960 to 1990, total worldwide traveling mileage per capita doubled [43]. People spend, on average, 1.1 h per day on traveling and expend 10 to 15% of their income on mobility. These numbers describe the travel time budget and the travel money budget [44]. Combined with economic progression, it is assumed that people will increase their traveled distances and, to keep within the time budget, faster transport technologies are used. Global motorized travel decoupled from economic growth at the beginning of the 21st century. Vehicle ownership stopped increasing at 450–750 vehicles per 1000 persons, depending on the region, whereas air travel increased [45]. In addition, structural differences are visible. The car share is higher in suburbs than in the city core [46].

2.3. Perspectives and Developments

BEV and direct electricity usage are the most cost-effective transportation technology, compared to technologies using synthetic fuels [47]. BEVs exhibit higher efficiencies, compared to hydrogen-based vehicles [48]. However, it is assumed low energy densities of batteries make BEVs not suitable for freight transport or even aviation [49].

Several studies investigate expected future prices of batteries, which are a key component for electric mobility. Learning rates, based on cumulative demand, of 6 to 9% were observed [50]. Battery packs could reach $175/$\mathrm{k}$$\mathrm{Wh}$ between 2027 and 2040 [51]. Another study even estimates $124/$\mathrm{k}$$\mathrm{Wh}$ in 2020. Their model takes, besides the cumulative capacity, an innovational aspect into account, which is shown to have a stronger impact than cumulative production [52].

Bio-based fuels rely heavily on feedstock cost to be competitive, especially for first to third generation biofuel [53,54]. As with other technologies, increasing cumulative production is assumed to decrease total cost. However, in the case of biomass, the feedstock prices increase as the production increases. If the installed production capacity is low, the technology learning effect outweighs the increase in feedstock cost. Conversely, at high production volumes, the feedstock price increase can outweigh the learning effect [55]. Biodiesel, a first generation biofuel, is at a mature status, indicating no significant technological improvement potential, whereas some second generation biofuel is at the growth state [56]. Furthermore, second generation biofuels are cost superior to first generation biofuels and some could even compete with fossil fuels in 2020 if the crude oil price reaches €100/barrel [53]. Third generation biofuels require large scale facilities to be effective. These comprise cultivation, pretreatment, and downstream processing [57]. Bio-based-fuels are considered to be cheap during a transition phase to renewable transport [49].

For hydrogen-based mobility, additional infrastructure is required for production, storage, delivery, and filling [58]. The most cost-efficient infrastructure depends on the distance between production and consumption site and demand. Distances below 200 km favor road transport, bigger distances and high demand favor pipeline infrastructure [59]. Hydrogen is more cost-effective than biofuels in 2050 if the additional infrastructure requirements are disregarded. However, the necessary investments in new infrastructure for hydrogen utilization are expensive, and renewably produced hydrogen is estimated to cost $$9.7$/$\mathrm{k}\mathrm{g}$ ($$0.29$/$\mathrm{k}$$\mathrm{Wh}$, considering the lower heating value) in 2030 [38]. For around ten years, permissible hydrogen prices will not cover the infrastructure cost, resulting in negative cash flow. Amortization is expected after 15 years, thus making investments in hydrogen infrastructure risky [41].

Besides carbon-free fuels, lifestyle changes could also contribute to emission reduction and energy efficiency improvement [44]. Some bigger cities have started to shift the primary transportation mode. These concepts comprise reducing mobility demand and changing its structure by shifting car-based mobility to public transport-, bicycle-, and footpath-based mobility [46]. Another concept combines bicycles with electric mobility by assisting the propulsion with an electric engine. Thus, application scenarios of bicycles are extended to light cargo duties. The assisted propulsion also enables the usage of bicycles on more demanding routes and makes them more attractive for people who are worried about their physical constitution [60]. Different transport modes can also be combined within a single journey, which is called intermodal transport [61]. A shift of transport mode is difficult for rural areas because of lower population densities and bigger distances, compared to urban areas [62]. In rural areas, dial-a-ride services can offer mobility and bundle demands [63].

Energy systems scenarios show a broad variety of possible feature developments [64]. In [62], a drastically changed structure is assumed. Urban mobility is based on cycling and walking. Freight transport utilizes cargo bikes. In [3], BEVs are assumed to cover 50% of the private car transport demand. On the other hand, high estimates assume bio-based fuels to supply 50% of countries transport energy demand [2]. Even significant technological advancement is assumed in future scenarios. For example, in [4], batteries are assumed to be capable of powering air travel for distances up to 1500 km.

3. Heating Sector

The room temperature is an important aspect to achieve the desired comfort level of residents inside buildings. Heating and cooling is required for temperature control, although the energy demand for heating is significantly higher than for cooling [65]. This section only deals with heating, which is currently primarily based on oil and gas heaters. The possibilities for sustainable heat supply, heat demand, and perspectives are investigated. Figure 3 shows the contents of this section.

3.1. Technology Portfolio

Considered technologies are solar collectors, combined heat and power (CHP) plants, electrical immersion heaters, and heat pumps. Solar collectors convert radiation directly to heat, CHP plants exploit waste heat of a thermodynamic cycle or a electrochemical process, electrical immersion heaters convert electricity directly to heat, and heat pumps convert electricity to heat but additionally draw energy from the environment [3,66,67].

Flat and evacuated tube solar collectors convert solar radiation directly to heat. These collectors are suitable to provide thermal energy for low-temperature applications, such as domestic hot water and space heating [68]. Improved insulation enables the solar collector to provide higher temperature heat by preventing convective heat losses [21,69]. Transparent faced elements, so-called transpired solar collectors, are suitable for preheating environmental air before entering a building [70]. Heat pumps gain a major fraction of their heat output from the environment; thus, they convert electricity to heat more efficiently than immersion heaters [71]. From a climate change mitigation perspective, heat pumps are the most suitable technology to make use of excess electricity [72]. Heat pumps with flexible electricity demand, and thus a flexible heat output, can improve power quality [22]. However, immersion heaters have better grid supporting capabilities, due to the lack of startup time and no ramping constraints [71].

District heating systems are another potent heating technology because they can directly utilize industrial waste heat. This heat usually has a sufficient temperature level for space heating and domestic hot water [73,74]. Beside waste heat, required heat can be supplied by biomass or a central, large scale heat pump. Large scale heat pumps draw energy from sources such as sewage or ambient water, e.g., from a lake or river [75]. Waste heat provides the lowest cost, whereas a central heat pump emits significantly less CO₂ (given electricity with low CO₂ emissions is used) [76].

Combined heat and power (CHP) technologies are suitable to provide heat and, furthermore, electricity. For residential application, these are called micro-CHP and generate up to 50 kW of electrical power. Different technologies provide heat and power cogeneration capabilities, which are internal combustion engines, micro-gas turbines, Stirling-engines, organic Rankine-cycles, and fuel cells [77]. In the past, CHP units were primarily powered by fossil fuels. Other possible energy carriers are synthetic or biological natural gas, methanol, and hydrogen [78]. Another energy carrier is biomass, which can be burned directly in CHP systems [66]. A challenge for the operation of CHP units is the dual dependency of heat and power. This means heat and power cannot be generated in arbitrary ratios. The scheduling of CHP units in an optimal manner is called CHP economic dispatch problem [79,80].

Heat storage allows the flexibilization of heat supply, especially if an intermittent source, such as a solar collector, is installed. Heat can be stored using different principles: sensible, latent, or thermochemical heat storage [81]. Sensible heat storage is based on the heat capacity of a material. The storage media is heated up or cooled down, depending on the desired process direction. Latent heat storage relies on the phase change enthalpy of materials, such as organic material, salt hydrates, eutectic materials (eutectic means the lowest possible melting temperature of mixtures; at the eutectic temperature, the liquid can change completely to the solid phase and vice versa), and ice [81,82]. Thermochemical heat storage is based on reaction enthalpy. An endothermic reaction charges the storage and an exothermic reaction discharges the storage [83]. In domestic application, sensible heat storage by hot water tanks is common [69]. For well-insulated buildings, the building inertial mass can act as sufficient heat storage, thus avoiding the necessity of other technologies [22]. Using this passive heat storage is more cost-effective than storage by dedicated accumulation tanks [84]. Heat storage can also work in seasonal timescales. It stores energy in big water reservoirs (hot water or aquifer thermal energy storage), the soil (borehole thermal energy storage), or combination of soil and water (gravel water thermal energy storage) [85].

3.2. Demand

Energy consumption of buildings accounts for a significant share of total energy usage. Key factors for the energy consumption of buildings are the location and size [86]. Furthermore, the number of inhabitants in a household, floor space, specific energy consumption, and economic situation affect the heating demand [65]. Considering building location, the living space per inhabitant is bigger in rural areas than in urban areas [87]. The major technical parameter for determining the heat demand is the building envelope (foundation, roof doors, and windows). Other parameter are shape, orientation, and remaining solar passive heating due to a shading system [23]. Specific building heat demand, also called heating energy intensity, has decreased significantly in the last decades. Around 1975, residential buildings built in Germany required 200 $\mathrm{k}$$\mathrm{Wh}$/($\mathrm{m}$${}^2$$\mathrm{a}$). Due to building codes, single-family residential buildings heating demand has declined to 50 $\mathrm{k}$$\mathrm{Wh}$/($\mathrm{m}$${}^2$ $\mathrm{a}$) for German houses built in 2015. The voluntary passive house standard even reaches heating energy demands below 15 $\mathrm{k}$$\mathrm{Wh}$/($\mathrm{m}$${}^2$ $\mathrm{a}$) [88]. To reach the low heating demand, the passive house standard requires a ventilation system that includes additional heat recovery, especially for low-temperature countries, such as Germany [89]. The refurbishment of buildings and construction of new buildings with improved insolation can decrease future heating demand [1]. The demolition and replacement rate of buildings is below 1% [88] and buildings have a lifetime of 40 to 120 $\mathrm{a}$ [90]. Refurbishing of existing dwelling facades and windows reduce the heating demand [91]. However, low refurbishing rates were found in the past, for example around 1% /$\mathrm{a}$ in Germany from 2005 to 2008 [92].

Besides space heating demand, domestic hot water, used for showering, dish washing, etc., requires energy to ensure an adequate temperature level. For example, in Finland, the average domestic hot water temperature is 55 to 65 ${}^{°}\mathrm{C}$ and the drawing per person is 43 $\mathrm{L}$/d [93]. Moreover, domestic hot water profiles exhibit significant peaks in the morning and in the evening due to occupant behavior [94].

3.3. Perspectives and Developments

Immersion boilers are a mature technology, for which no future cost reduction is expected. Contrarily, heat pumps are assumed to decrease in cost by economies of scale and technological improvement [95]. However, immersion heaters are identified to enable the biggest renewable energy integration, despite heat pumps and district heating being more efficient [66]. CHP that utilize biomass is a well-established technology, although fuel cost can seldom be settled by electricity revenues [96] and biomass is more valuable in the transport sector [66]. Due to increasing numbers of deployed units, fuel cells exhibited a learning rate of 13% from 2010 to 2013 [78]. Another study found a similar learning rate of 16%. Moreover, a significant fraction of the system components is already mature, thus major further cost reduction would require system simplifications [97]. Current research deals with new electrolyzer technology, which provides fuel cell and electrolyzer functionality, thus extending the functionality to hydrogen storage [98].

From a cost perspective, district heating outperforms decentralized technologies, such as heat pumps, electrical immersion heaters, and gas boilers, in areas with high heating demand, such as urban areas [14]. The lower the heat demand per area is, the higher the investment cost in district heating becomes [73]. Current development describes a so-called fourth generation district heating system, replacing older generations which used steam and relatively hot water as heat carriers. The utilization of water at around 50 ${}^{°}\mathrm{C}$ as a heat carrier reduces system losses and increases heat generation efficiency [74].

If only minor specific heat demand reduction is assumed, heating demand in western countries is supposed to stagnate. Moreover, domestic hot water demand consumption is assumed to remain almost constant from 2000 to 2050 [65]. Other studies assume that due to insulation the specific heat demand of building will decrease. For example, building heating demand could be estimated to be 63 $\mathrm{k}$$\mathrm{Wh}$/($\mathrm{m}$${}^2$ $\mathrm{a}$), which requires retrofitting of nearly the entire building stock [99].

4. Electricity Sector

This section deals with the electricity sector. It describes sustainable electricity generation technologies and implications for the residential electricity supply. Moreover, the electricity demand is characterized, whereas the description is based on current electricity consuming devices. The section concludes by discussing future perspectives. In Figure 4, the content of the section is shown.

4.1. Technology Portfolio

A broad range of technologies is considered for future energy generation, such as solar energy systems, wind energy converters, fossil fuel based power plants, nuclear power, CHP, hydro, tidal, and run on the river plants. Solar and wind energy systems have the greatest energetic potential amongst the renewable technologies [96]. On the low voltage distribution grid, PV-systems, small-scale CHP, and small wind energy converters are already viable [100,101,102].

For a residential application, a PV system is especially attractive. This has been supported by financial incentives [103,104]. In Germany, PV-systems are primarily mounted in the open field or on rooftops [105]. Besides that, a vertically mounted system configuration is attractive for high-rise buildings [106]. Crystalline, silicon-based PV-generators are predominant on the market. Thin film PV-generators are available on the market, although their market-share lags behind crystalline silicon generators. Multi-junction PV-systems exhibit improved efficiencies, compared to crystalline or thin film technology; however, they are mostly at the R&D stage [24].

Small-scale wind turbines have a rotor diameter of 3 to 10 $\mathrm{m}$ and a rated power of 1.4 to 16 kW [107]. These wind turbines can supply a household with sufficient electrical energy [100]. Drawbacks, such as high initial investment cost and aeroacoustic noise, hinder the emergence of smalls scale wind turbines [108]. Moreover, the bigger is the wind turbine, the better is the economic potential, considering equal wind resources [109]. However, large wind turbines emit noise, which is audible several hundred meters away from the turbine. Thus, they have to be built several hundred meters away from residential areas [110,111]. For this paper, due to the economic disadvantage of small-scale wind energy systems and restrictions to build larger ones close to a residential site, wind energy technology is assumed not to be built at a residential site.

Previously mentioned CHP units are also capable of producing electrical energy at a residential level. A more detailed description can be found in the previous heating section. Renewable energy systems, especially based on PV and wind energy, are weather dependent and thus exhibit an intermittent electricity generation profile, which does not match the load at all points in time. The temporal difference between load and generation can be compensated by storage devices. Battery storage, flywheels, supercapacitors, and small-scale compressed air energy storage is applicable in sub-MW applications [112]. Moreover, the term hydrogen storage is often found. It describes a system composed of an electrolyzer, a fuel cell stack, and a hydrogen storage device [113]. However, it has to be noted that the overall efficiency is quite low. Even though electrolyzers reach efficiencies up to 90%, the reverse process, by means of a polymer electrolyte membrane fuel cell, only reach 40% [48]. Application wise, battery storage is well suited for PV-systems. For example, battery storage can increase self-sufficiency and own consumption of a commercial building [114] or a household [102] by storing energy on a diurnal scale. The combination hydrogen storage and battery storage can decrease energy cost and CO₂ emissions if installed at a district-level [115].

4.2. Demand

The future total electricity demand is highly uncertain. Some models assume a decreasing electricity demand due to efficiency improvements of appliances while other models show increasing electricity demand due to further electrification [116]. In general, electricity demand depends on income, dwelling area and employment status [117,118]. According to data from [119], the electricity demand of an EU citizen for lighting and appliances lies at 1135 $\mathrm{k}$$\mathrm{Wh}$/$\mathrm{a}$.

Reducing electricity demand is identified as one of the key challenges for the future energy system [120]. Technology, such as efficient light bulbs, can reduce electricity demand. However, other appliances consume significantly more energy, as can be seen in Figure 5. Furthermore, it is shown that devices with high power consumption do not necessarily result in high annual energy consumption. Efficiency standards indicate the energy consumption of devices, assisting the consumer to purchase more efficient devices. It is found that these efficiency standards do not increase the purchase price. Moreover, the lifecycle cost show an accelerated decrease after efficiency standards were introduced, which have increased consumer benefit [121].

Energy savings can cause a rebound effect. This means that, due to saved money, households can afford additional appliances, which induce additional energy demand for operation. The rebound effect can be estimated to be 11 to 16%, which means that this percentage of the originally saved energy is consumed by other activities that become possible due to the energy savings [124,125].

4.3. Perspectives and Developments

From an ecological point of view, PV and wind supplied energy systems can mitigate greenhouse gas emissions [66,116,126,127]. For Europe, wind-dominated electricity systems result in lower generation cost than PV-dominated systems. However, the impact of the supply technology on the overall cost is small [127]. From a district point of view, these two technologies result in either significant local energy generation for solar dominated systems or a centralized/external energy generation for wind dominated systems.

In the past, a high learning rate and thus a cost reduction were observed for PV-systems. According to a projection from 2008, PV-systems were assumed to reach a learning rate of 20%. In 2018, PV-systems exhibited a learning rate of 25% [128]. These values show that high learning rates are realistic for renewable energy systems. A characteristic of high share renewable, solar dominated systems is the requirement of additional battery storage [116,126]. A PV-battery system can become cost-competitive in 2020 [52]. They could become increasingly important because countries have been decreasing their feed-in tariff. Therefore, own consumption of electricity becomes more beneficial. In general, the effectiveness of feed-in subsidies is part of the public debate, especially if the cost of PV-electricity reaches grid parity [129].

Most demand forecasts refer to total electricity demand. The demand depends on technological, economic, behavioral, and regional aspects [4,130]. In [131], it is expected that energy demand in Germany will decrease and in the UK energy demand will increase, although, on a per capita basis, both counties increase their total electricity demand by around $0.5$%. This shows that, from an overall perspective, the demographic development is significant. Appliance efficiency improvement can decrease the energy consumption of a household by 13% in 2020, compared to 2005 [132]. It is assumed that further reduction is difficult to achieve because major electricity consumption is caused by water heating, which shows little improvement potential [132,133]. Increasing appliance efficiency requires the exchange of the existing stock by newly produced apparatus with improved technology. However, this leads to additional energy required for production. This energy has to be saved during operation time; otherwise, reusing a product might be preferable [134]. The introduction of information and communication technology (ICT) enables advanced energy efficiency measures. However, these devices consume energy during operation, partially counteracting the efficiency improvement measures [130]. Another source of additional future electricity demand is caused by the requirement of cooling due to building insulation [135,136]. In [137], energy efficiency measures are assumed to decrease electricity consumption by 5 to 10%. For a region in northern Germany, electricity demand for devices is reduced by around 6% to account for improved efficiency [13].

5. Integration

The integration of the heat, mobility, and electricity sector into a combined energy system significantly decrease the overall system cost [14]. On the bigger scale, these are often described as Power to X or vehicle to grid. On a smaller scale, the combination of technologies offers new conversion paths or synergies. Moreover, many upcoming technologies rely on electricity to operate, such as BEVs and heat pumps. Therefore, reliable electricity supply is crucial for the integration of these technologies.

5.1. Technology Coupling

Different integrated energy approaches are currently under investigation. Most of them are based on the electricity sector. Combinations of the electricity sector and any other sector are called Power to X. In this case, X represents the desired form of energy. Power to X substantially improves the system flexibility by adding nonelectrical storage possibilities.

Power to heat describes the utilization of electricity to generate heat. This leads to an integration of the electricity sector into the heat sector. Furthermore, heat can be easily stored, thus providing additional flexibility to the system [138]. Power to gas describes the usage of electricity to produce either hydrogen or synthetic natural gas. The first step comprises electrolysis, which requires electricity. The produced hydrogen can be further processed to synthetic natural gas, which requires an additional carbon source. If catalytic methanation is performed, the reaction is highly exothermic, which means heat has to be removed and can be utilized by another process [139]. Power to liquid is similar to power to gas, i.e., a chemical energy carrier is produced by means of electricity-based hydrogen production and a carbon source. The carbon source can be CO₂ or biomass. Produced products are suitable fuels for the transport sector, for example methanol [49]. Hydrocarbon products from power to gas or power to liquid processes are called electrofuels [66]. Power to heat has potential benefits at residential levels, such as lower operational cost due to fuel substitution [140]. Power to heat can convert PV generated electricity for heating applications by means of immersion heaters, thus reducing fossil fuel consumption [141]. Furthermore, power to heat is cheaper than power to gas if it serves only energy storage purposes [142]. Power to gas is not cost-effective, especially due to electrolyzer capital cost [139]. However, power to gas could become cost competitive if a high share of volatile renewable energy is installed and investment cost decline [143].

Contrary to Power to X, vehicle to grid can feed electricity back into the grid. It connects the electricity sector directly to the mobility sector. Fuel cell-based electric vehicles can supply neighboring houses, thus reducing the power imported from the utility grid. However, to be economically beneficial, the hydrogen price needs to decrease [144]. BEVs are capable of primary frequency control. They improve the system frequency response and reduce the required primary control reserves of conventional power plants [145].

Furthermore, technologies can be combined to achieve synergistic effects. A combination of a PV generator and a solar collector is called hybrid photovoltaic thermal (PV/T) system. This system removes heat from a PV generator by a working fluid. Thus, the efficiency of the PV generator is improved. The overall system efficiency can surpass 90% [146]. A heat pump can be combined with a solar collector. The solar collector evaporates the refrigerant of the heat pump at increased temperature, compared to ground or air-based heat pump. The system shows payback times of about two years under optimal configuration and Singaporean weather conditions [147]. A heat pump can also be combined with a PV system, for increased electricity self-consumption. However, for Germany, it is found that PV-generation is high when the heat demand is low and vice versa, leading to a moderate increase in self-sufficiency [148]. The combination of solar collectors, domestic heat storage, and an organic Rankine-cycle provides domestic hot water and electricity, taking advantage of low-cost thermal energy storage [149]. Furthermore, a solar collector can be coupled with a heat pump and a seasonal heat storage. The solar collector heats up the storage if excess heat is available for storage. Thus, the efficiency of the heat pump improves, due to higher operation temperature [150].

5.2. Grid Structure and Operational Aspects

Future energy supply, based on renewables, will cast additional challenges to the electricity grid. The emerging deployment of electricity-based mobility and heating technologies complicates grid operation, i.e., frequency stabilization and voltage control.

The electricity grid structure is characterized by its primary voltage, the network length, the number of loads or nodes and the peak load [151]. The grid cannot store energy; demand and supply must be balanced at all times. Moreover, the electricity grid is a complex system. Even local problems can cascade and become uncontrollable [152].

The challenges of grids in urban areas differ from rural areas. The reason is a high load density, which motivates the usage of heavily meshed systems. In general, these are resilient to single component failure [153]. In rural areas with low load densities, radial grids are preferred. These electricity grids ensure operation either by backup generation capacities or with connections to neighboring grids [154].

The integration of renewable energy introduces new challenges to the distribution grid. In the past, power was distributed from high voltage levels to the consumers. However, the installation of PV-Systems can cause a reverse power flow, leading to voltage limit violations [155]. Moreover, voltage band violations can also be caused by the addition of EVs, especially if they are charged in an uncontrolled manner [156]. Voltage band violations can be mitigated by local voltage controllers. The controllers are able to reduce the charging power of the EV, thus increasing the local voltage [157]. Another approach uses game-theoretic approaches to adjust the charging power of EVs in a distribution grid, also aiming to mitigate voltage band violations [158]. If heat pumps and PV-systems are added to a residential grid, voltage band requirements are barely followed and, moreover, quick voltage variations, due to volatile PV-generation, are observed [159].

Integrated energy systems can provide grid services, in order to maintain operation specification. In [160], it is shown that batteries can provide frequency regulation (primary control). Combined with power to heat technologies, such as electric immersion boilers, the overall self-consumption of a household increases. Grid-connected PV and wind systems rely on power electronics to feed electric energy into the grid. These power electronics can provide auxiliary services such as voltage control by variable reactive power output. Additionally, storage can improve the power quality further [161]. Adding battery storage to a residential grid smoothens the power characteristics by reducing the peak to average ratio. However, in [162], it is found that voltage quality is not significantly affected.

6. Morphological Analysis

In the following, a scenario is defined as a future system state. To investigate scenarios for future residential energy supply, a morphology analysis is performed. The morphological analysis is a systematic, analytical technique [163]. It is based on scientific fundamentals, i.e., analysis and synthesis and especially useful for multi-dimensional, non-quantifiable problems. The morphological analysis identifies the most important dimensions of a problem and examines the internal relationships to reduce the scenario space [164].

6.1. Scenario Space

The characteristics and factors are derived from the previous literature review. Comparing the technology portfolio of the mobility, heating, and electricity sectors, some differences are indicated. In the mobility sector, some technologies that can provide sustainable mobility are available, but it is difficult to estimate which will prevail in the future or whether there will be an application-specific design, at present. In addition, there are efforts to reduce the use of these technologies, for example by changing mobility habits. In the heating sector, on the other hand, there are many technologies available, and their use, unlike in the mobility sector, depends to a certain extent on local conditions. Furthermore, the heating sector can be considered rather slow in its adaptability (long building lifetimes compared to shorter lifetimes of cars). These can also cause fragmentation, for example by building a new district with modern technologies close to an old district. In the electricity sector especially PV systems are common for residential energy systems. These systems are field-tested and economically viable applications causing less uncertainty regarding the future development compared with the other sectors.

The aim is to construct a morphological box, also known as Zwicky box [165]. The factors follow the structure of the literature review. Each sector, i.e., mobility, heat, or electricity, is divided into technology and demand, which are considered as a factor. All scenarios aim to show the load on the residential electricity grid of a decarbonized energy system to identify future challenges. In the following, the considered factor-parameters and underlying hypotheses are described.

Factor 1: Individual mobility

M1 (BEV):

Current individual mobility is completely replaced by electric mobility. Due to research and economics of scale, the battery prices decrease and BEVs become affordable to everyone.

M2 (Hydrogen (local)):

Hydrogen mobility is one of the much-researched technologies. Currently, vehicles are already available. Further development improves hydrogen production technology. Infrastructural development results in on-site production at residential sites to reduce transport infrastructure cost.

M3 (Hydrogen (central):

Hydrogen mobility is predominant. To achieve scaling effects, hydrogen will be produced in centralized, large-scale plants outside the district, resulting in no local electricity consumption for hydrogen production at the residential site. Utilizing biofuels would have the same effect due to external fuel production. They could be provided by large scale production of third generation biofuel.

Factor 2: Mode shift/Mobility demand

MD1 (Significant):

In the urban environment, local public transport and bicycle traffic is pushed severely through the conversion of the urban infrastructure. This way, automobiles are no longe used for short distance traveling.

MD2 (Minor):

In the urban environment, local public transport and bicycle traffic is promoted by infrastructure improvement. However, automobiles are still frequently used for short distance traveling.

MD3 (None):

The local infrastructure is not adjusted at all. The automobile remains the primary mode of travel.

Factor 3: Heating technology

H1 (Heat pump):

Heat pumps are the primary technology for heat generation. They are considered to be the most effective technology for using electricity to provide heat and cost are reduced by scaling effects and technological improvement.

H2 (Immersion heater):

Advanced building insulation allows the use of fast heating elements. This enables the integration of large scale renewables by providing flexibility to the system.

H3 (CHP):

Mikro-CHP plants generate heat and power with high overall efficiency. In this scenario, they are widely installed. The required fuel originates from biomass, externally produced hydrogen, or synthetic natural gas. It is assumed that the CHP unit is operated in heat guided mode.

H4 (No electrical heating):

District heating systems, which utilize waste heat, provide cost-effective heat supply. Furthermore, they do not require significant energy, thus they are considered as no electrical heating technology. Another renewable, non-electric heating approach (from a residential grid perspective) is via power to gas utilization and gas heaters at the residential site.

Factor 4: Building insulation

HD1 (Passive house):

Thermal building isolation is severely pushed. The passive house standard is applied to every building of the considered district.

HD2 (Current building code):

Thermal building isolation is improved. Every building complies with the current building code. Older buildings are refurbished.

HD3 (Partial refurbishment):

Refurbishment significantly reduces the heat demand, due to buildings according to current building code. However, many buildings with old insulation are still inhabited.

HD4 (Old building):

Thermal insulation is not improved. Older houses are still in use without refurbishment.

Factor 5: Local electricity generation

E1 (High PV-generation): 

PV systems are the primary technology for generating electricity. Their installation is strongly supported. Even non-south-orientated rooftop areas and facades are used for PV-system installation.

E2 (Medium PV-generation): 

PV-systems are common in districts. They are installed at rooftop areas with optimal properties regarding orientation and inclination.

E3 (External generation): 

Electricity is not generated locally. Due to our focus on residential aspects, the specific energy source is out of scope for this hypothesis; for example large scale wind energy could be the primary generation technology.

Factor 6: Device consumption

ED1 (Reduced): 

Electric appliances continue to decrease in energy consumption. New devices will not consume significant amounts of electricity, thus the overall electricity demand decreases.

ED2 (Steady): 

Appliances efficiency increases, although savings are invested into additional devices which mitigate the savings and, moreover, new technologies are added to the household which consume additional electricity. Overall, the electricity consumption of devices remains constant.

ED3 (Increasing): 

Appliances efficiency improvements are minor because conversion from electricity to heat is already very efficient and many devices rely on electricity. Moreover, additional appliances are added, such as air conditioning and smart home technology.

Table 1. Morphological box of the residential energy system.

	Factor 1:	Factor 2:	Factor 3:	Factor 4:	Factor 5:	Factor 6:
	Individual Mobility	Mode Shift	Heating	Heating Demand	Electricity	Appliance Demand
hypotheses	M1:	MD1:	H1:	HD1:	E1:	ED1:
	BEV	Significant	Heat pump	Passive house	High PV-generation	Reduced
	M2:	MD2:	H2:	HD2:	E2:	ED2:
	Hydrogen (local)	Minor	Immersion heater	Current building code	Medium PV-generation	Steady
	M3:	MD3:	H3:	HD3:	E3:	ED3:
	Hydrogen (central)	None	CHP	Partial refurbishment	External generation	Increasing
			H4:	HD4:
			No el. heating	Old buildings

shows the overall scenario space by means of the morphological box. Every factor is represented by one column. A scenario is created by the combination of one value for every factor. Overall, the morphological box describes 1296 possible scenarios.

6.2. Scenario Space Pruning

The scenario space pruning is based on the literature review. However, it has to be noted that excluded combinations are still based on the authors’ interpretation and current state of the art. Unlikely combinations are analyzed by means of a cross-consistency assessment. This procedure investigates the possible occurrence of binary combinations. Impossible or inconsistent combination are marked. The following combinations are disregarded:

• The immersion heater technology is assumed to be feasible only with low heat demand buildings to compensate for the low efficiency of immersion heaters. For this investigation, it is assumed that only the passive house is feasible to be combined with immersion heaters.
• The pair of device consumption improvement and passive house is not considered because additional air conditioning and ventilation due to building insulation require significant electrical energy.
• Local hydrogen production requires electricity for operation. Thus, if no generation is installed at a residential site, hydrogen production would be more effective closer to electricity producing facilities. Consequently, the combination of locally produced hydrogen for mobility and no local electricity generation is disregarded.

Without these pairs, the scenario space is reduced to 840 scenarios. Figure 6 shows the cross-consistency matrix. The disregarded pairs are marked with a cross.

6.3. Scenario Space Evaluation

The scenario space is investigated by associating parameters for every factor. From that, the annual net electricity demand for a residential district is calculated. The problem is inherently complex. Especially factors such as mode shift and device efficiency are difficult to quantify. To be able to describe the scenario space, assumptions for the effect of the individual factor values are made.

Table 2. Parameters for calculation.

Variable	Symbol	Value
Number households	$N_h$	100
Persons per household	n	1.9 [166]
Living space per person	$A_N$	$46.5$ m${}^2$ [167]
Optimal PV rooftop area per person	$A_{roof,N}$	$13.1$$\mathrm{m}$${}^2$ [168]
PV-generation	$E_{PV,A}$	180 kWh/(m${}^2$ $\mathrm{a}$) [105]
Device electricity consumption per person	$E_{dev}$	1135 $\mathrm{k}$$\mathrm{Wh}$/$\mathrm{a}$ [119]
Vehicle ownership per person	$v{e}_{ownership}$	0.5 [45]
Vehicle usage	$v{e}_{usage}$	11370 km/$\mathrm{a}$ [46]
Domestic hot water demand per person	$DH{W}_{demand}$	42 $\mathrm{L}$/day [93]
Domestic hot water temperature	${\theta }_{DHW}$	60 ${}^{°}\mathrm{C}$ [94]
Local hydrogen production efficiency	${\eta }_{H_2,prod.}$	0.85
Cold water supply temperature	${\theta }_{coldwater}$	12 ${}^{°}\mathrm{C}$
Heat capacity water	$c{p}_{water}$	$1.16$$\mathrm{W}$$\mathrm{h}$/($\mathrm{k}\mathrm{g}$$\mathrm{K}$)
Density water	${\rho }_{water}$	$1.0$$\mathrm{k}\mathrm{g}$/${\mathrm{m}}^3$

shows basic demographic and technical parameters. To put less weight on unique, individual behavior of inhabitants, one hundred households are chosen to represent a district.

In

Table 3. Morphological box, supplemented with numerical assumptions (input variables).

	Factor 1:	Factor 2:	Factor 3:	Factor 4:	Factor 5:	Factor 6:
	Individual Mobility	Mode Shift	Heating	Heating Demand	Electricity	Appliance Demand
Symbol:	$e_{ve}$	$r_{shift}$	$CO{P}_h$	$I_{heat}$	$f_{PV}$	$r_{dev}$
hypotheses	$18.9$$\mathrm{k}$$\mathrm{Wh}$/100	90	4.5	15 $\mathrm{k}$$\mathrm{Wh}$/($\mathrm{m}$${}^2$ $\mathrm{a}$)	2	−30
	$30.3$$\mathrm{k}$$\mathrm{Wh}$/100	50	1	50 $\mathrm{k}$$\mathrm{Wh}$/($\mathrm{m}$${}^2$ $\mathrm{a}$)	1	0
	0	0	−0.8	125 $\mathrm{k}$$\mathrm{Wh}$/($\mathrm{m}$${}^2$ $\mathrm{a}$)	0	+30
			0	200 $\mathrm{k}$$\mathrm{Wh}$/($\mathrm{m}$${}^2$ $\mathrm{a}$)

, parameter assumptions are shown, following the structure of the morphological box. The energy consumption of a BEV is assumed to be 17 $\mathrm{k}$$\mathrm{Wh}$/100 km (tank-to-wheel) [169] with a charging efficiency of 95% (round trip efficiency of 90 % [48], assuming the charging efficiency to be equal to the discharging efficiency). The energy consumption of a hydrogen fueled vehicle is calculated with a hydrogen demand of $0.76$ $\mathrm{k}\mathrm{g}$/100 km [59] (specific energy hydrogen: 33 $\mathrm{k}$$\mathrm{Wh}$/$\mathrm{k}\mathrm{g}$), taking an electrolyzer efficiency of 90% [48] and filling efficiency of 92% (energy for compression: $2.7$ $\mathrm{k}$$\mathrm{Wh}$/$\mathrm{k}\mathrm{g}$_H2 [59]) into account. The mode shift ratio describes how much of the current car based demand is shifted to alternative concepts. From the authors’ perspective, this is a major uncertainty which is reflected by the broad value distribution.

The heating technology is described by the COP, which expresses the ratio of gained heat divided by spent electricity. The heat pump value describes a high-efficiency air-source heat pump [22]. The immersion boiler is assumed to transform heat to electricity without any losses and the CHP scenario is implemented by fuel cells. The heat demand values are taking from the reviewed values for Germany.

The electricity generation is based on optimal rooftop areas for PV installation and scaled according to the assumed factor. Device consumption is also a major uncertainty and, thus, the values describe a broad scenario space for the respective factor. The (hydrogen (central) hypothesis assumes external hydrogen generation. The centralized fuel production does not affect the local grid. This means, mobility does not contribute to local electricity demand, which is expressed by zero energy demand. For CHP, the description via COP is uncommon. It is used to be consistent with the other technologies. A thermal to electrical power ratio of 0.8 is assumed, representing a fuel cell operating at max. efficiency [80]. Because this device does not consume, but rather produces, electricity, the COP gets a negative sign.

Equations (1)–(3) describe the modeling approach for the individual sectors (parameters and input variables are listed in Table 2 and Table 3, respectively). For the mobility sector, we model the demand for electrical energy as

$$E_{mobility}:=N·v{e}_{ownership}·v{e}_{usage}·(1-r_{shift})·e_{av},$$

(1)

where

$$N:=N_h·n.$$

The heat demand is modeled by

$$Q_{heat,demand}:=N·A_N·I_{heat}+DH{W}_{demand}·N·365\mathrm{d}/\mathrm{a}·{\rho }_{water}·({\theta }_{DHW}-{\theta }_{coldwater})·c{p}_{water}$$

and thus the demand for electrical energy in this sector is modeled as

$$E_{heat}:=Q_{heat,demand}·CO{P}_h.$$

(2)

We model the energy demand in the electricity sector as

$$E_{electricity}:=-N·A_{roof,N}·E_{PV,A}·f_{PV}+E_{dev}·N·(1-r_{dev}).$$

(3)

Equations (4)–(6) model the synergy between local hydrogen production for mobility and CHP (again, parameters and input variables are listed in Table 2 and Table 3, respectively). Excess PV-production, after considering demand of mobility and devices (see Equations (1) and (3)), is modeled as

$$E_{ex.PV}:=\left\{\begin{array}{cc}{E}_{mobility}+E_{electricity}\hfill & \mathrm{if}{E}_{mobility}+E_{electricity}<0\hfill \\ 0\hfill & \mathrm{otherwise}\hfill \end{array}\right..$$

(4)

Note that the variables are defined such that generated energy is indicated by a negative sign.

The amount of heat that can be produced from excess PV-production by producing hydrogen via an electrolyzer and utilizing that hydrogen by CHP-technology is modeled as

$$Q_{H_2,poss}:=\frac{\left|E_{ex.PV}\right|·{\eta }_{H_2,prod.}}{1+\frac{1}{\left|CO{P}_h\right|}}.$$

(5)

It is assumed that electrolyzers are primarily built to produce hydrogen for mobility. Thus, for this investigation, the hypotheses local hydrogen production and CHP are required to locally produce hydrogen for heating. The amount of local electricity for heating via hydrogen, when no excess hydrogen is produced, is modeled as

$$E_{el,H_2,heat}:=\left\{\begin{array}{cc}{E}_{ex.PV}·\frac{Q_{H_2,poss}}{Q_{heat,demand}}\hfill & \mathrm{if}{Q}_{H_2,poss}>Q_{heat,demand}\wedge CO{P}_h=-0.8\wedge e_{av}=30.3\hfill \\ E_{ex.PV}\hfill & \mathrm{if}{Q}_{H_2,poss}\le Q_{heat,demand}\wedge CO{P}_h=-0.8\wedge e_{av}=30.3\hfill \\ 0\hfill & \mathrm{otherwise}\hfill \end{array}\right..$$

(6)

Finally, Equation (7) models the overall net energy demand, using Equations (1)–(4), for a scenario as

$$E_{total}:=E_{mobility}+E_{heat}+E_{electricity}-E_{el,H_2,heat}.$$

(7)

Figure 7 shows the net electrical energy demand of the scenario space. Negative values indicate net feeding behavior throughout the year. The majority of the scenarios result in net feeding (69%). Maximum demand for the considered districts is 914 $\mathrm{M}$$\mathrm{W}$$\mathrm{h}$/$\mathrm{a}$ and the minimum demand is $-2229$ $\mathrm{M}$$\mathrm{W}$$\mathrm{h}$/$\mathrm{a}$, indicating the maximum amount of energy fed back.

Figure 8, Figure 9 and Figure 10 investigate the scenario space in more detail, by indicating the influence of every hypothesis, according to technology and demand factors within the scenario space.

Figure 8 shows the mobility sector related factors, mobility technology, and mode shift, in terms of their respective hypotheses. It indicates that every hypothesis can be found across the scenario space. Note that, for the mobility technology, the local hydrogen hypotheses does not result in exceptionally high demanding scenarios because the combination of no local generation and local hydrogen production is disregarded by the cross-consistency assessment.

Figure 9 indicates a clear localization of the heating technology within the scenario space. CHP-technology scenarios are predominantly found on the feeding side. High demand scenarios utilize heat pumps or immersion heaters. Overall, immersion heater scenarios are low in numbers within the scenario space because it is assumed that only passive house heat isolation may be combined with immersion heaters to compensate for the high energy demand during operation. No electrical heating scenarios are centered relatively narrowly around zero energy demand, indicating a dependency on other factors. In terms of demand, it is shown that high feeding scenarios consist of old buildings or partial refurbishment insulation hypotheses. Better insulated scenarios are more closely centered around zero energy demand.

Figure 10 shows a distinct assignment of local generation scenarios. High PV generation tends to feed more energy over the course of the year, while lower generation scenarios are shifted to the demanding side. In terms of appliance efficiency, no clear mapping is shown; every hypothesis occurs throughout the scenario space.

7. Conclusions

This review has dealt with technology and perspectives of future energy supply for residents and integrated the results into a morphological analysis. The traditionally separated sectors mobility, heat, and electricity have been examined in detail, as have been the challenges of integration into the electricity grid and possible technological synergy potentials. Based on the investigated literature, these are the authors conclusions:

BEVs are the most mature technology for renewable transport today, which is reflected in high learning rates and, thus, cost-reduction. They would increase the local residential energy demand due to home charging. High-density energy carriers, such as hydrogen or bio-based fuels are pivotal for applications beyond BEV range, such as cargo duties and aviation. Currently, infrastructure and cost are the major hindrances of hydrogen, whereas land-use constrains bio-based fuel utilization. Hydrogen and bio-based fuels could be produced externally, thus not affecting the local electricity grid directly. However, hydrogen could also be produced locally. Overall, also taking the uncertainty due to mode shift into account, the effect of the transport sector on the local grid strongly depends on the decarbonization pathway.

At the moment, heat pumps are the most energy efficient heating technology. Immersion boilers require additional electricity for the same heating output, albeit they offer better grid integration capabilities and lower investment cost than heat pumps. Building heating demand decreases because of building code, refurbishment, and reconstruction. However, the modernization of the entire building stock requires generations due to the low refurbishment rates and long building lifetimes. Thus, the energy demand could vary significantly, depending on the building structure of a respective district.

Renewable energy supply, locally on residential sites, is expected to be predominantly based on PV-generators. Future appliance energy demand remains uncertain. Efficiency measures reduce the energy consumption of devices. However, not every device shows significant optimization potential. New devices will most likely be added to households in the future, such as information and communication technology.

Interconnections between the electricity sector and heat, power, or gas sector increase the system flexibility. Especially power to heat affects the residential system because it can be implemented by heat pumps or immersion heaters. It is widely assumed, but far from certain, that residential energy supply will rely primarily on electricity. Therefore, the electricity grid could be of central importance. Current research investigates measures to ensure the electricity grid stability during operation, e.g. by local voltage controllers.

The scenario space of this paper represents possible configurations of the future residential energy system, i.e., future system states. It provides a basis for future work to evaluate technical applications for the residential sector such as grid expansion, energy management systems, or local control systems. The scenarios offer a broad diversification of the energy demand, as indicated by the morphological analysis, primarily influenced by heating technology, heat insulation, and local generation.The results of this study provide researchers and practitioners with a basis for further parameterization and an estimation of the parameter impact, especially for investigations on lower temporal resolutions. Furthermore, it can serve as an overview of influencing factors for residential energy systems or as a starting point for a deeper literature investigation regarding certain subtopics.