• Energy Research

Abstract The residential building sector has an important role to play in the energy transition due to a high share of final energy consumed and a considerable amount of CO 2 emitted. Ambitious targets in Germany relate amongst other things to a primary energy reduction of 80% and an increased usage of renewable energy sources in heat supply to 60% in 2050. Existing research in this area both lacks detail in modelling decentralised heat supply in residential buildings and fails to adequately quantitatively analyse this target achievement for Germany. In order to overcome these limitations, a novel model-based approach is presented in which the developed TIMES-HEAT-POWER optimisation model is coupled with a decentralised energy system optimisation model to determine optimal and realistic technology configurations, and a building stock simulation model to adequately and consistently project the evolution of the building stock in Germany. This novel configuration of models is then used to investigate the evolution of the electricity system and the residential heat system in Germany in the context of key energy-political targets up to 2050. The national goals related to primary energy reduction and the share of renewable energy sources in final energy demand in the residential heat sector are missed in the Reference Scenario. On the other hand, target achievement requires deep insulation measures and a supply-side technology shift away from gas and oil boilers towards heat pumps and solar thermal. The scenario analysis reveals a significant sensitivity of the deployment of micro-Combined heat and power technologies (μCHP) and heat pumps to, amongst other things, the evolution of fuel prices, renewable electricity technologies, heat and electricity demand as well as technological progress. Further model extension can be identified inter alia in broadening the system boundaries to integrate further sectors (tertiary, industrial) or incorporating different user categories and decision rationales.

<p>The decarbonisation of power production is key to achieving the Paris Agreement goal of limiting global mean surface temperature rise to well below 2°C, particularly so given the drive to electricity transport and heat. At the same time, variable renewable energy (VRE) sources such as solar photovoltaics (PV) and wind have seen rapid cost reductions in recent decades bringing them into cost parity with base load fossil generation. Therefore, recent long term planning studies, which utilise cost-optimising models, have demonstrated the important role of VREs in decarbonising power systems across the world. However, while techno-economically detailed, such studies tend to neglect key social factors that often shape the real world evolution of the energy system.</p><p>Of particular relevance to VRE deployment is their visual impact on the landscape which can act to undermine their public acceptability. Here, we use crowd-sourced scenicness data to derive spatially explicit, empirically grounded wind energy capacity potentials for three scenarios of public sensitivity to this visual impact. We augment these with a detailed analysis of Great Britain’s (GB) solar PV capacity potential. We then use these scenarios in a cost-optimising model of GB’s power system to assess their impact on the cost and design of the electricity system in 2050. Our results show that the levelised cost of the system can increase by up to 15% when public sensitivity to visual impact is high compared to low. In part this is driven by our finding that some of the most picturesque parts of GB also happen to be the most cost-effective for onshore wind, leading to large reductions in installed capacity as we move through our sensitivity scenarios. Indeed, deployment is heavily limited in Scotland and the South-West which in turn acts to limit the spatial diversity of onshore wind. We conclude that it is essential for policy makers to consider these cost implications and to find mechanisms to ameliorate the visual impact of onshore wind in local communities.</p>

The installed capacity of PV plants has increased dramatically in the past years. A common approach to determine the actual power of an ensemble of PV systems within a specific region typically employs data from measured reference plants. Obviously the precision of the power estimation depends on having representative reference plants, which are not influenced by strong individual characteristics. The goal of this contribution is to detect such apparently atypical behavior of PV systems by comparing their measured power to simulations based on a nearby weather station and clear sky irradiance. Deviations are studied in the course of each day for the year 2012 and 48 PV systems, indicating systematic characteristics independent from meteorological conditions. Additionally, an approach is presented to detect such unexpected deviations automatically. This can be the basis for a dynamic nowcasting algorithm, which selects the reference units based on their (temporal) suitability.

As one possibility to increase flexibility, battery storage systems (BSS) will play a key role in the decarbonization of the energy system. The emissions-intensity of grid electricity becomes more important as these BSSs are more widely employed. In this paper, we introduce a novel data basis for the determination of the energy system’s CO$_{2}$ emissions, which is a match between the ENTSO-E database and the EUTL databases. We further postulate four different dynamic emission factors (EF) to determine the hourly CO$_{2}$ emissions caused through a change in electricity demand: the average emission factor (AEF), the marginal power mix (MPM), the marginal system response (MSR) and an energy-model-derived marginal power plant (MPP). For generic and battery storage systems, a linear optimization on two levels optimizes the economic and environmental storage dispatch for a set of 50 small and medium enterprises in Germany. The four different emission factors have different signaling effects. The AEF leads to the lowest CO$_{2}$ reduction and allows for roughly two daily cycles. The other EFs show a higher volatility, which leads to a higher utilization of the storage system from 3.4 to 5.4 daily cycles. The minimum mean value for CO$_{2}$ abatement costs over all 50 companies is 14.13 €/t$_{CO}$$_{2}$.

Abstract A growing number of German municipalities are striving for energy autonomy. Geothermal plants are increasingly constructed in municipalities in order to exploit the high hydrothermal potential. This paper analyses the potential contribution of simultaneous geothermal power and heat generation in German municipalities to achieving energy autonomy. A linear regression estimates the achievable hydrothermal temperatures and the required drilling depths. Technical restrictions and cost estimations for geothermal plants are implemented within an existing linear optimisation model for municipal energy systems. Novel modelling approaches, such as optimisation with variable drilling depths, are developed. The new approach is validated with data from existing geothermal plants in Germany, demonstrating a Root Mean Squared Error of about 15%. Eleven scenarios show that achieving energy autonomy is associated with at least 4% additional costs, compared to scenarios without it. The crucial role of geothermal plants in providing base load heat and power to achieve energy autonomy is demonstrated. The importance of simultaneous modelling of electricity and heat generation in geothermal plants is also evident, as district heating plants reduce the costs, especially in municipalities with high hydrothermal potential. Further work should focus on the optimal spatial scale of the system boundaries and the impact of the temporal resolution of the analysis on the costs for achieving energy autonomy.

Purpose The connection between urbanization and energy consumption in the context of cross-country and cross-sector analyses is poorly understood, especially in the Association of South East Asian (ASEAN). This paper aims to present the first extensive multi-level analysis of the relationship between urbanization and energy consumption in ASEAN countries from 1995 to 2013. Design/methodology/approach The multi-level (across country and sector) index decomposition method is used to analyze urbanization, energy mix, energy intensity and activity effects on energy demand. Urbanization is measured by two representative factors, name the urban population and the number of non-agriculture workers. Findings Despite the decreasing rate of urbanization, its effect on energy consumption has played the most important role since 2000. Since then, the effect has continued to increase at the national and sectoral levels across the whole region. The strongest urbanization impacts are encountered in the residential sector, followed by transportation and industrial sectors with much weaker effects in the commercial sector. The way in which urbanization impacts energy consumption depends strongly on the income level of the country studied. Practical implications The results provide quantitative relationships between urbanization and energy demand. For example, if the urban population and the non-agriculture workers decreased by 0.1 per cent per year, this would reduce energy demand by 1.4 per cent and 2.6 per cent per year respectively. Originality/value This contribution provides detailed quantitative insights into the relationships between urbanization and energy demand at sectoral, national and international levels, which are invaluable for policymakers in the region.

The recent rapid expansion of renewable energy capacities in Germany has been dominated by decentralised wind, photovoltaic (PV) and bioenergy plants. The spatially disperse and partly unpredictable nature of these resources necessitates an increasing exploitation of integration measures such as curtailment, supply and demand side flexibilities, network strengthening and storage capacities. Indeed, one solution to the large-scale integration of renewable energies could be decentralised autonomous municipal energy systems. The achievement of grid parity for some renewable energy technologies has strengthened the desire of some communities to become independent from central markets. Whilst many communities in Germany already strive for socalled energy autonomy, the vast majority do so only on an annual basis. Several studies have already analysed the technical and economic implications of the mainly decentralised future energy system, but most are restricted in their insights by limited temporal and spatial resolution. The large number (11,131) of German municipalities means that a national analysis at this resolution is not feasible. Hence, this study employs a cluster analysis to develop a municipality typology in order to analyse the techno-economic suitability of these municipalities for autonomous energy systems. A total of 34 socio-technical indicators are employed at the municipal level, with a particular focus on the sectors of Private Households and Transport, and the potentials for decentralised renewable energies. The first step is to scale the indicator values and reduce their number by using a factor analysis. Several alternative methods are weighed against each other, and the most suitable methods for the factor analysis are chosen. Secondly, selected quantitative cluster validation methods are employed alongside qualitative criteria to determine the optimal number of clusters. This results in a total of ten clusters, which show a large variation as well as some overlap with respect to specific indicators. For example, one cluster contains all major German cities and has a low potential for renewable energies. Another cluster, on the other hand, contains the municipalities with a higher potential for renewable energies due to their high hydrothermal potential for geothermal power. An analysis of the municipalities from three German renewable energy projects “Energy Municipalities”, ”Bioenergy Villages” and “100% Renewable Energy Regions” shows that in eight of the ten clusters municipalities are aiming for energy autonomy (in varying degrees). It is challenging to differentiate between the clusters regarding readiness for energy autonomy projects, however, especially if the degree of social acceptance and engagement for such projects is to be considered. To answer the more techno-economical part of this question, future work will employ the developed clusters in the context of an energy system optimisation. Insights gained at the municipal level will then be qualitatively transferred to the national context to assess the implications for the whole energy system.