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The growing share of fluctuating renewable electricity production within the German energy system causes the increasing necessity for flexible consumers, producers, and storage technologies to balance supply and demand. District heating networks with combined heat and power units, Power-to-Heat applications, and thermal energy storage capacities can serve as one of these flexible options. In this context, a simulation model of the district heating network of the rural community Dollnstein, Germany, was built. With the residual load of different regional areas (Germany, Bavaria, Eichstätt, Dollnstein) it is investigated, how the heat generators can operate in an electricity market beneficial way. Two different control algorithms were evaluated: Due to a correlation between the residual loads and the CO2 emissions of the electricity mix, the CO2 savings achieved by this control algorithm are determined. Another way to operate electricity market beneficial is to consider the current CO2 emissions of each region. The main outcomes of this paper are, that there is a high potential for sector coupling by shifting the operation times of a CHP and a heat pump according to the residual load. The electricity demand of the heat pump can be met in terms of low CO2 emissions of the electricity mix, while the CHP can replace electricity with high CO2 emissions. These results can be improved, by considering not the residual load but the current CO2 emissions in the control algorithm. [ABSTRACT FROM AUTHOR]

In this article, monthly and yearly electricity consumption predictions for the German power market were calculated using the multiple variable regression model. This model accounts for several factors that are often neglected when forecasting electricity demand in practice, in particular the role of the higher efficiency of electricity usage from year to year. The analysis performed in this paper helps to explain why no growth in power consumption has been observed in Germany during the last decade. It shows that the electricity efficiency usage dataset is a relevant input for the model, which mitigates the combined impact of other factors on the final electricity consumption. The electricity demand forecasting model presented in this article was built in the year 2013 with forecasts for the future years' electricity demand in Germany provided until 2020. These forecasts and related findings are also evaluated in this article. [ABSTRACT FROM AUTHOR]

In the context of the German Energiewende, the current government intends to install six million heat pumps by 2030. Replacing gas heating by power has significant implications on the infrastructure. One of the biggest advantages of using gas is the existing storage portfolio. It has not been clarified yet how power demand should be structured on an annual level—especially since power storage is already a problem and solar power is widely promoted to fuel heat pumps, despite having an inverse profile. In this article, three different solutions, namely, hydrogen, batteries, and carbon capture and storage, are discussed with respect to resources, energy, and financial demand. It shows that relying solely on batteries or hydrogen is not solving the structuring problem. A combination of all existing technologies (including fossil fuels) is required to structure the newly generated electricity demand. [ABSTRACT FROM AUTHOR]

Long-term transition pathways to a low-carbon energy system are analysed by applying the energy system model REMod. All in all, the paper contributes to the current research through an innovative scenario approach, using assumptions for societal trends and quantitative results for scenarios, analysing the paths towards climate neutrality and defossilization in 2050. In the case study of Germany, these trends and drivers influence the results and the technology composition in each consumption sector (buildings, transport, and industry). Across all scenarios, it can be observed that the electrification of all sectors is important for the defossilization of the energy system, as the direct use of electricity from renewable energy is more efficient than the consumption of carbon-neutral synthetic energy carriers. However, different consumer behavior (e.g., non-acceptance or resistance against specific technologies) influences not only the efficient use of (green) electricity, it also changes the optimal pathways of the transition to paths with greater efforts. One potential societal trend—sufficiency—could be an important cornerstone for reaching the targets, as the required expansion and exchange of technologies are lower and thus facilitate the transition. [ABSTRACT FROM AUTHOR]

The integration of photovoltaic (PV) generators in the envelope of a building by means of building-integrated photovoltaics (BIPV) offers an immense potential, both in market development and the production of renewable electric energy that is close to the point of electricity consumption. In Germany, for example, by integrating photovoltaics in buildings up to 50% of the electricity demand can be covered. The political support of BIPV would contribute to the development and installation of BIPV components and therefore also promote the development of new business areas for industries dealing with components used in building envelopes and photovoltaic generators. BIPV can be separated into three different integration types: "technical", "formal" and "technical & formal". Political instruments for the support of PV-installations, particularly BIPV are discussed in this paper using Germany and France as examples. Due to successful financial support policies, PV became the most powerful electricity production technology in Germany. In France, the unique financial support of BIPV is resulting in an exemplary development and growth of certified BIPV components available on the market and, from a technical, aesthetic architectural and legal certainty point of view, facilitating the easy and widespread integration of photovoltaic generators in buildings. [ABSTRACT FROM AUTHOR]

Electrification offers an opportunity to decarbonize the transport sector, but it might also increase the need for flexibility options in the energy system, as the uncoordinated charging process of battery electric vehicles (BEV) can lead to a demand with high simultaneity. However, coordinating BEV charging by means of smart charging control can also offer substantial flexibility potential. This potential is limited by restrictions resulting from individual mobility behavior and preferences. It cannot be assumed that storage capacity will be available at times when the impact of additional flexibility potential is highest from a systemic point of view. Hence, it is important to determine the flexibility available per vehicle in high temporal (and spatial) resolution. Therefore, in this paper a Markov-Chain Monte Carlo simulation is carried out based on a vast empirical data set to quantify mobility profiles as accurately as possible and to subsequently derive charging load profiles. An hourly flexibility potential is derived and integrated as load shift potential into a linear optimization model for the simultaneous cost-optimal calculation of the dispatch of technology options and long-term capacity planning to meet a given electricity demand. It is shown that the costs induced by BEV charging are largely determined by the profile costs from the combination of the profiles of charging load and renewable generation, and not only by the additional energy and capacity demand. If the charging process can be flexibly controlled, the storage requirement can be reduced and generation from renewable energies can be better integrated. [ABSTRACT FROM AUTHOR]

This study analyses the expected utilization of an urban distribution grid under high penetration of photovoltaic and e-mobility with charging infrastructure on a residential level. The grid utilization and the corresponding power flow are evaluated, while varying the control strategies and photovoltaic installed capacity in different scenarios. Four scenarios are used to analyze the impact of e-mobility. The individual mobility demand is modelled based on the largest German studies on mobility "Mobilität in Deutschland", which is carried out every 5 years. To estimate the ramp-up of photovoltaic generation, a potential analysis of the roof surfaces in the supply area is carried out via an evaluation of an open solar potential study. The photovoltaic feed-in time series is derived individually for each installed system in a resolution of 15 min. The residential consumption is estimated using historical smart meter data, which are collected in London between 2012 and 2014. For a realistic charging demand, each residential household decides daily on the state of charge if their vehicle requires to be charged. The resulting charging time series depends on the underlying behavior scenario. Market prices and mobility demand are therefore used as scenario input parameters for a utility function based on the current state of charge to model individual behavior. The aggregated electricity demand is the starting point of the power flow calculation. The evaluation is carried out for an urban region with approximately 3100 residents. The analysis shows that increased penetration of photovoltaics combined with a flexible and adaptive charging strategy can maximize PV usage and reduce the need for congestion-related intervention by the grid operator by reducing the amount of kWh charged from the grid by 30% which reduces the average price of a charged kWh by 35% to 14 ct/kWh from 21.8 ct/kWh without PV optimization. The resulting grid congestions are managed by implementing an intelligent price or control signal. The analysis took place using data from a real German grid with 10 subgrids. The entire software can be adapted for the analysis of different distribution grids and is publicly available as an open-source software library on GitHub. [ABSTRACT FROM AUTHOR]

Fast-growing energy demand of the world makes the researchers focus on finding new energy sources or optimizing already-developed approaches. For an efficient use of solar and wind energy in an energy system, correct design and sizing of a power system is of high importance and improving or optimizing the process of data obtaining for this purpose leads to higher performance and lower cost per unit of energy. It is essential to have the most precise possible estimation of solar and wind energy potential and other local weather parameters in order to fully feed the demand and avoid extra costs. There are various methods for obtaining local data, such as local measurements, official organizational data, satellite obtained, and reanalysis data. In this paper, the Modern-Era Retrospective analysis for Research and Applications dataset version 2 (MERRA-2) dataset provided by NASA is introduced and its performance is evaluated by comparison to various locally measured datasets offered by meteorological institutions such as Meteonorm and Deutscher Wetterdienst (DWD, or Germany's National Meteorological Service) around the world. After comparison, correlation coefficients from 0.95 to 0.99 are observed for monthly global horizontal irradiance values. In the case of air temperature, correlation coefficients of 0.99 and for wind speed from 0.81 to 0.99 are observed. High correlation with ground measurements and relatively low errors are confirmed, especially for irradiance and temperature values, that makes MERRA-2 a valuable dataset, considering its world coverage and availability. [ABSTRACT FROM AUTHOR]

This paper analyses the effects of regional renewable electricity self-sufficiency targets on the power system in Germany. For this purpose, an interdisciplinary approach from social sciences and energy system modelling was chosen, which allows considering qualitative factors such as public acceptance or political stability. Following the concept of context scenarios, consistent raw scenarios are generated by a cross-impact balance analysis (CIB), and the scenarios are quantified by the unit commitment and expansion cost minimisation model ENTIGRIS considering power plants, storages, and the electricity grid. This approach enables an understanding of the system framework conditions and their relationships and allows the combination of qualitative and quantitative scenario descriptors. The most important factors for setting regional self-sufficiency targets were identified through interviews. The main system effects identified are: The regional distribution of generation capacities is strongly influenced by a more demand-oriented installation of generation capacities. This leads to less grid reinforcement, but higher rates of curtailment. In all scenarios, higher utilization of the PV roof potential instead of ground mounted could be observed. The total system costs are increasing only slightly with regional self-supply targets. In general, it was found that the influence of regional self-sufficiency targets is less pronounced in scenarios that already achieve high national RES shares than in scenarios that achieve lower shares, since technology, storage and grid expansion measures are necessary anyway to achieve high RES shares. Overall, the effects here are rather small and the regional objective is not associated with major disadvantages for the system. In a future characterised by stagnation, the system can benefit from regional targeting, as higher renewable shares and lower costs can result. The main conclusion therefore is that regional target setting seem to be beneficial for the overall power system, in terms of system cost, national RE share, acceptance and CO2-emissions. [ABSTRACT FROM AUTHOR]

Several studies show that heat pumps need to play a major role for space heating and hot water supply in highly decarbonised energy systems. The degree of elasticity of this additional electricity demand can have a significant impact on the electricity system. This paper investigates the effect of decentral heat pump flexibilisation through thermal energy storage units on electricity storage investment. The analysis is carried using an open source model for the German electricity system based on the Open Energy Modelling Framework (oemof). Results highlight the importance of flexible heat pump operation in 100% renewable energy systems and relate well to findings of other existing studies. Flexibilisation of heat pumps in the German energy system can reduce the need for electricity storage units significantly. While no impact was found for systems with a share below 80% renewable energy, investment in short term storage units is reduced by up to 42–62% in systems with shares of more than 80% renewable energy. In contrast, the impact on long term electricity storage investment was comparatively low in all modelled scenarios. Conducted sensitivity analyses show that both findings are rather insensitive with regard to the available biomass for electricity supply as well as to changes in the heat demand covered by heat pumps. Economically flexible heat pump operation has only a minor effect on system costs. However, the indirect replacement of battery with thermal energy storage units is environmentally beneficial due a lower resource consumption of minerals. [ABSTRACT FROM AUTHOR]

The most important price for short-term electricity trading in Germany is the day-ahead auction price, which is provided by EPEX SPOT. Basically, short-term fluctuating electricity prices allow cost-optimized production planning by shifting electricity-intensive processes to times of favorable electricity prices. However, the day-ahead price as the outcome of an auction is not directly tradeable afterwards. We propose short-term flexible electricity certificates that pass on the day-ahead auction prices plus a premium for the supplier, enabling users to plan electricity consumption based on realized day-ahead auction prices. We analyze the supplier's problem of delivering electricity based on such certificates. The supplier can adjust the required electricity volume after the close of the day-ahead auction on the continuous intraday market. We analyze the price fluctuations in this market in relation to the day-ahead price and propose different trading strategies. Using the order book history of EPEX SPOT, we analyze the trading success and risk of these strategies. Furthermore, we investigate to what extent trading success can be explained by changes in market conditions, and, in particular, we identify renewable forecast errors as a driver. [ABSTRACT FROM AUTHOR]

Shallow Geothermal Energy (SGE) extracted by Ground Source Heat Pump (GSHP) is a proven clean and profitable technology. Although it is available almost everywhere, its market enjoys different maturity levels along with the other EU Members and even those within the same country. In the Murcia region, in Southern Spain, the presence of GSHP is almost nonexistent. Germany, in contrast, has an extensive tradition of exploiting its SGE resources and is an example of a mature GSHP market. In this work, the technical and non-technical barriers were assessed in both countries to identify the site-specific parameters preventing a better deployment of SGE in Southern Spain. In addition, a SWOT analysis was conducted to highlight the parameters positively and negatively influencing the geothermal resource extraction. Results showed that both study cases showed similar and good technical conditions, such as sufficient resource 80 W/m approx. or a similar impact on the environment mainly due to the use of electricity consumed. However, the regulation and legal framework greatly varied from one area to another. In conclusion, the main factors causing a poor deployment are the lack of specific regulation or regional administration support. [ABSTRACT FROM AUTHOR]

The increasing share of variable renewable energies in the power grid is an incentive to explore demand response strategies. Chlor-alkali processes are high potential candidates, according to previous publications. Within Germany's chemical industry, chlorine production accounts for approximately 20% of electricity use and could play a significant role in power grid stabilisation on the consumer end. This study focuses on the feasibility of load flexibilisation in epichlorohydrin plants, with the second biggest estimated demand response potential for chlorine-based products in Germany. A plant model with allyl chloride storage was created based on real data and literature values. Results from this model, spot market and balancing power prices, and future electricity market scenarios were used in a mixed-integer linear optimisation. We find that benefits from demand response can be generated as soon as additional power and storage volume is provided. The composition of provided types of balancing power bids follows the price trend on the market. Additionally, the computation time could be lowered significantly by running the scenarios in parallel. The results encourage a practical validation of the flexibility of epichlorohydrin production. [ABSTRACT FROM AUTHOR]

The expansion of photovoltaics in German cities has so far fallen short of expectations. The concept of 'tenant electricity' ('Mieterstrom' in German), in which tenants of a building are supplied with solar power produced on site, offers great potential here. A study on behalf of the German Federal Ministry for Economic Affairs and Energy estimated the number of tenant households with good conditions for solar tenant electricity at 3.8 million. At the same time, the federal tenant electricity promotion scheme has been in place since 2017, but only about 1% of the annual budget has been claimed. The aim of this study is to identify the barriers for and drivers of diffusion of the tenant electricity model. To this end, a qualitative document analysis and a range of semi-structured expert interviews have been conducted. The theoretical framework used to guide the analysis is the multi-level perspective. The main barrier found for tenant electricity diffusion is the legal framework on the regime level, which also leads to high transaction costs of implementing tenant electricity. A social barrier is the inertia of some residents to actively concern themselves with their electricity supply and switch to a tenant electricity contract. Among its drivers are long-term trends such as the increasing electricity demand in urban areas, technical developments like blockchain technology and the increasing deployment of smart meters, and the EU Renewable Energy Directive. As long as the restrictive legal framework prevails, the further diffusion of tenant electricity will remain limited. [ABSTRACT FROM AUTHOR]

In Germany, the political decision to phase out nuclear and coal-fired power as well as delays in the planned grid extension are expected to intensify the current issue of high grid congestion volumes. In this article, we investigate two instruments which may help to cope with these challenges: market splitting and the introduction of a capacity mechanism. For this purpose, we carry out a comprehensive system analysis by jointly applying the demand side models FORECAST and eLOAD, the electricity market model PowerACE and the optimal power flow model ELMOD. While a German market splitting has a positive short-term impact on the congestion volumes, we find the optimal zonal delimination determined for 2020 to become outdated by 2035 resulting in new grid bottlenecks. Yet, readjusting the zonal configuration would lower the ability of the market split to provide regional investment incentives. Introducing a capacity mechanism with a congestion indicator allows allocating new power plants in regions with higher electricity demand. Consequently, we find the required congestion management to be substantially reduced in this setting. However, given the large amount of design parameters, any capacity mechanism needs to be carefully planned before its introduction to avoid new inefficiences on the market side. [ABSTRACT FROM AUTHOR]

Time-of-use (TOU) electricity tariffs represent an instrument for demand side management. By reducing energy demand during peak times, less investments in otherwise necessary, costly, and CO2 intensive redispatch would be required. We use a choice experiment (CE) to analyze private consumers' acceptance of TOU tariffs in Germany. In our CE, respondents choose between a fixed rate tariff and two TOU tariffs that differ by peak time scheme and by a control of appliances' electricity consumption during that time. We use a mixed logit model to account for taste heterogeneity. Moreover, investigating decision strategies, we identify three different strategies that shed light on drivers of unobserved taste heterogeneity: (1) Always choosing the status quo, (2) always choosing the maximum discount, and (3) choosing a TOU tariff but not always going for the maximum discount. Overall, about 70% of our 1398 respondents would choose a TOU tariff and shift their electricity demand, leading to a decline in energy demand during peak times. Rough estimates indicate that this would lead to significant savings in electricity generation, avoiding up to a mid to large-sized fossil-fuel power plant. [ABSTRACT FROM AUTHOR]

Battery electric vehicles (BEV) provide an opportunity to balance supply and demand in future power systems with high shares of fluctuating renewable energy. Compared to other storage systems such as pumped-storage hydroelectricity, electric vehicle energy demand is highly dependent on charging and connection choices of vehicle users. We present a model framework of a utility-based stock and flow model, a utility-based microsimulation of charging decisions, and an energy system model including respective interfaces to assess how the representation of battery electric vehicle charging affects energy system optimization results. We then apply the framework to a scenario study for controlled charging of nine million electric vehicles in Germany in 2030. Assuming a respective fleet power demand of 27 TWh, we analyze the difference between power-system-based and vehicle user-based charging decisions in two respective scenarios. Our results show that taking into account vehicle users' charging and connection decisions significantly decreases the load shifting potential of controlled charging. The analysis of marginal values of equations and variables of the optimization problem yields valuable insights on the importance of specific constraints and optimization variables. Assumptions on fleet battery availability and a detailed representation of fast charging are found to have a strong impact on wind curtailment, renewable energy feed-in, and required gas power plant flexibility. A representation of fleet connection to the grid in high temporal detail is less important. Peak load can be reduced by 5% and 3% in both scenarios, respectively. Shifted load is robust across sensitivity analyses while other model results such as curtailment are more sensitive to factors such as underlying data years. Analyzing the importance of increased BEV fleet battery availability for power systems with different weather and electricity demand characteristics should be further scrutinized. [ABSTRACT FROM AUTHOR]

Independence from the power grid can be pursued by achieving total self-sufficient electricity supply. Such an energy supply model might be particularly interesting for settlements located in rural areas where enough space is available for energy generation installations. This article evaluates how and at what cost electricity demand of residential users across Germany and the Czech Republic could be covered by hybrid renewable energy generation systems consisting of photovoltaics, micro-generation wind turbines and batteries. High-resolution reanalysis data are used to calculate necessary system sizes over a large area by simultaneously accounting for the temporal variability of renewable energy. For every potential location in the research area, the hybrid system requirements for clusters of 50 self-sufficient single-family houses are calculated. The results indicate no general trend regarding the size of the respective technologies, although larger areas where PV-wind power complementarity enables lowering the total system costs and required storage capacities were determined. Assuming that the cluster of households could be constituted and depending on the location, the total installation and operation costs for the proposed systems for a lifetime of 20 years range between EUR 1.8 Million and EUR 5 Million without considering costs of financing. Regions with the lowest costs were identified mainly in the south of Germany. [ABSTRACT FROM AUTHOR]

Due to the high demands of new technologies such as social networks, e-commerce and cloud computing, more energy is being consumed in order to store all the data produced and provide the high availability required. Over the years, this increase in energy consumption has brought about a rise in both the environmental impacts and operational costs. Some companies have adopted the concept of a green data center, which is related to electricity consumption and CO2 emissions, according to the utility power source adopted. In Brazil, almost 70% of electrical power is derived from clean electricity generation, whereas in China 65% of generated electricity comes from coal. In addition, the value per kWh in the US is much lower than in other countries surveyed. In the present work, we conducted an integrated evaluation of costs and CO2 emissions of the electrical infrastructure in data centers, considering the different energy sources adopted by each country. We used a multi-layered artificial neural network, which could forecast consumption over the following months, based on the energy consumption history of the data center. All these features were supported by a tool, the applicability of which was demonstrated through a case study that computed the CO2 emissions and operational costs of a data center using the energy mix adopted in Brazil, China, Germany and the US. China presented the highest CO2 emissions, with 41,445 tons per year in 2014, followed by the US and Germany, with 37,177 and 35,883, respectively. Brazil, with 8459 tons, proved to be the cleanest. Additionally, this study also estimated the operational costs assuming that the same data center consumes energy as if it were in China, Germany and Brazil. China presented the highest kWh/year. Therefore, the best choice according to operational costs, considering the price of energy per kWh, is the US and the worst is China. Considering both operational costs and CO2 emissions, Brazil would be the best option. [ABSTRACT FROM AUTHOR]