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In many industries, low-carbon hydrogen will substitute fossil fuels in the course of the transformation to climate neutrality. This paper contributes to understanding this transformation. This paper provides an overview of energy- and emission-intensive industry sectors with great potential to defossilize their production processes with hydrogen. An assessment of future hydrogen demand for various defossilization strategies in Germany that rely on hydrogen as a feedstock or as an energy carrier to a different extent in the sectors steel, chemicals, cement, lime, glass, as well as pulp and paper is carried out. Results indicate that aggregate industrial hydrogen demand in those industries would range between 197 TWh and 298 TWh if production did not relocate abroad for any industry sector. The range for hydrogen demand is mainly due to differences in the extent of hydrogen utilization as compared to alternative transformation paths for example based on electrification. The attractiveness of production abroad is then assessed based on the prospective comparative cost advantage of relocating parts of the value chain to excellent production sites for low-carbon hydrogen. Case studies are provided for the steel industry, as well as the chemical industry with ethylene production through methanol and the production of urea on the basis of ammonia. The energy cost of the respective value chains in Germany is then compared to the case of value chains partly located in regions with excellent conditions for renewable energies and hydrogen production. The results illustrate, that at least for some processes – as ammonia production – relocation to those favorable regions may occur due to substantial comparative cost advantages. • Industry transformation: fossil fuels to hydrogen as feedstock and energy carrier. • Case study on Germany with hydrogen demand for industry. • Value chains: steel production, ethylene with methanol, and urea with ammonia. • Comparative cost advantage in value chains for locations with different renewable costs. [ABSTRACT FROM AUTHOR]

This study assesses alternative energy technologies (i.e., PV and battery systems, electric heat pumps, hybrid gas heating with solar thermal energy) in terms of profitability and CO 2 emissions, for the case of two simulated typical households living in detached houses in Germany. Under the status-quo regulatory framework, the energy transition in the heating sector is fostered through grants for replacing old heating systems, whereas PV generation is fostered by feed-in tariffs and indirect subsidies for self-consumption. This study considers an alternative regulatory scenario with a more market-oriented approach, finding that a CO 2 -oriented reform of energy surcharges and taxes, as well as a reform of network charges, can support a more cost-efficient energy transition in the residential sector. The paper concludes with a discussion of the consistency, cost efficiency and effectiveness of past and current policies underpinning the energy transition in the residential sector. • Assessment of home energy systems across 2 households and 2 regulatory scenarios. • Consideration of PV, batteries, heat pumps and hybrid gas-solar heating systems. • Comparison of subsidies and flat energy tariffs vs. cost-reflective, dynamic tariffs. • Market-oriented approach incentivizes far more cost-efficient CO 2 emission reduction. • The German residential energy sector needs a consistent regulatory framework. [ABSTRACT FROM AUTHOR]

The increase of electric vehicles (EVs) in the power system, in addition to environmental benefits, also has economic advantages. Charging parking lots can perform better economically through aggregators the benefits of participating in energy markets and developing different charging programs. The main challenge for participating in the electricity market for parking lots is the uncertainties in the behavior of EV owners and the day-ahead market (DAM) price. Therefore, this paper proposes a deep learning-based framework for day-ahead optimal charging scheduling, which is in three stages. In the first stage, the parameters of charging sessions are modeled using the proposed Copula generative adversarial network (CopulaGAN). In the second stage, the DAM price is forecasted using the time series multi-input nonlinear autoregressive neural network for 24 h ahead. In addition, in the proposed framework, the balancing energy market (BEM) is used to keep the charging power balance on the implementation day of the optimal charging program. In the third stage, optimal day-ahead EV scheduling is performed. The simulation is carried out using Grey Wolf Optimization (GWO) based on the Caltech charging sessions dataset and the Germany energy market dataset. The simulation results show that using the proposed deep learning framework reduces the charging cost for the parking lots compared to the fixed price approaches, the use of the BEM, the time of use (TOU) program, and using normal distribution modeling about 19.94 €, 159.97 €, 14.27 €, and 114.03 € respectively. • Presenting a deep learning framework for modeling day-ahead scheduling. • The proposed framework is applied to Caltech and the German energy market dataset. • Results show a 15% reduction in charging costs compared to conventional methods. [ABSTRACT FROM AUTHOR]

• Economic efficiency of various imported e-fuels for heavy-duty transport in different scenarios for the year 2035 has been evaluated. • E-fuels considered are Fischer-Tropsch diesel, methanol, and hydrogen transported as cryogenic liquid or in form of liquid organic hydrogen carriers. • We develop a mathematical model that covers the entire process chain. • The choice of production site has a major impact on the mobility cost using the respective fuels. The transformation of economies worldwide towards climate neutrality will require not only renewable electricity but also climate-neutral energy carriers such as hydrogen and its derivatives. The production cost of electric fuels (e-fuels) are to a large extent driven by the energy-intensive electrolytic water splitting. The option of producing e-fuels in highly industrialized countries, as Germany, competes with production at international locations, with excellent conditions for renewable energies and thus very low levelized cost of green hydrogen and its derivatives. In this paper, we examine the economic efficiency of various imported e-fuels that are under consideration in different scenarios for the year 2035: Fischer-Tropsch diesel, methanol, and hydrogen transported as cryogenic liquid or in form of liquid organic hydrogen carriers. We develop a mathematical model that covers the entire process chain, from the production of the e-fuels at various excellent locations worldwide to the energetic utilization of the fuels in a heavy duty vehicle. We find that the choice of production site has a major impact on the mobility cost using the respective fuels. Under the selected boundary conditions and assumptions, methanol, cryogenic hydrogen, and liquid organic hydrogen carriers are the most favourable options for all seven locations examined. Which e-fuel is cheapest varies with the production site. Especially in case of diesel, the levelized cost of electricity determined by the full load hours of the applied renewable energy source have a huge impact. An LOHC-based system is shown to be less dependent on electricity source compared to other technologies due to its comparatively low electricity consumption. The length of the transportation route and the price of the filling station infrastructure, however, increase mobility cost for LOHC and LH 2. [ABSTRACT FROM AUTHOR]

• Imported green e-H 2 is more expensive than e-H 2 produced in Germany and Finland. • Local supply of e-H 2 found to be more economical across all scenarios and years. • Transportation costs constitute 29–63% of the total imported e-H 2 cost. • Pipeline transmission may be cheaper than shipping for distances of 2000–2500 km. Widely available and low-cost solar photovoltaics and wind power can enable production of renewable electricity-based hydrogen at many locations throughout the world. Hydrogen is expected to emerge as an important energy carrier constituting some of the final energy demand; however, its most important role will be as feedstock for further processing to e-fuels, e-chemicals, and e-steel. Apart from meeting their own hydrogen demand, countries may have opportunities to export hydrogen to countries with area limitations or higher production costs. This paper assesses the feasibility of e-hydrogen imports to Germany and Finland from two case regions with a high availability of low-cost renewable electricity, Chile and Morocco, in comparison to domestic supply. Special attention is paid to the transport infrastructure, which has a crucial impact on the economic viability of imports via two routes, shipping and pipelines. This study has found that despite lower e-hydrogen production costs in Morocco and Chile compared to Germany and Finland, additional transportation costs make imports of e-hydrogen economically unattractive. In early 2020s, imported fuel costs are 39–79% and 34–100% higher than e-hydrogen produced in Germany and Finland, respectively. In 2050, imported e-hydrogen is projected to be 39–70% more expensive than locally produced e-hydrogen in Germany and 43–54% in the case of Finland. e-Hydrogen may become a fuel that is mostly produced domestically and may be feasible for imports only in specific locations. Local e-hydrogen production may also lower dependence on imports, enhance energy security, and add jobs. [ABSTRACT FROM AUTHOR]

• Multi-modal energy system planning approach to support political decision makers. • Energy sector coupling enables efficient transition pathway to achieve climate goals. • Main drivers: electrification of heat and mobility sectors and use of synthetic fuels. • High impact of the electricity exchange on the operation and costs of the system. In order to reach the goals of the United Nations Framework Convention on Climate Change, a stepwise reduction of energy related greenhouse gas emissions as well as an increase in the share of renewable energies is necessary. For a successful realization of these changes in energy supply, an integrated view of multiple energy sectors is necessary. The coupling of different energy sectors is seen as an option to achieve the climate goals in a cost-effective way. In this paper, a methodical approach for multi-modal energy system planning and technology impact evaluation is presented. A key feature of the model is a coupled consideration of the sectors electricity, heat, fuel and mobility. The modeling framework enables system planners to optimally plan future investments in a detailed transition pathway of the energy system of a country, considering politically defined climate goals. Based on these calculations, in-depth analyses of energy markets as well as electrical transmission and distribution grids can be performed using the presented optimization models. Energy demands, conversion and storage technologies in households, the Commerce, Trade and Services (CTS) area and the industry are modeled employing a bottom-up modeling approach. The results for the optimal planning of the German energy system until 2050 show that the combination of an increased share of renewable energies and the direct electrification of heat and mobility sectors together with the use of synthetic fuels are the main drivers to achieve the climate goals in a cost-efficient way. [ABSTRACT FROM AUTHOR]

• Off-grid Power-to-Fuel systems fit demands for implementing at early market stages. • Excellent wind park sites yield methanol costs of 1.28–1.73 €/l Gasoline Equivalent. • Future electrolyzer costs have high impact on costs, but are still highly uncertain. • Suitable regulatory framework can promote implementing Power-to-Fuel technology. In this paper the integration of systems for the production of electrofuels for a market entrance scenario from a short-term perspective is examined. A model was built to optimize the design parameters and simulate the yearlong operation of an off-grid power-to-fuel system, consisting of a wind park, electrolyzer, hydrogen storage, a CO 2 source and synthesis plant. In this manuscript, the regional focus is Germany; however, the results as well as the methodology can be applied to other regions. Successively, the production costs for the designed system were calculated on the basis of the model results. Various cases for different operational modes and sites have been analyzed. In addition, a sensitivity analysis was made to test the influence of single economic assumptions. Calculations demonstrate that the proposed off-grid systems are a viable option for implementation in a short-term scenario. The results include net production costs of 1.73 €/l GE (GE = gasoline equivalent) for methanol production in the reference case. For optimal wind farm sites, the calculated production costs can drop to 1.32 €/l GE. [ABSTRACT FROM AUTHOR]

The rapid electrification of residential heating and mobility sectors is expected to drive the existing distribution grid assets beyond their planned operating conditions This change will also reveal new potentials through sector coupling, flexibilities, and the local exchange of decentralized generation. This paper thus presents an optimization framework for multi-modal energy systems at the low voltage (LV) distribution grid level. In this, we focus on the reinforcement requirements of the grid and the techno-economic assessment of flexibility components and coordination between agents. By employing a multi-level approach, computational complexity is reduced, and various levels of coordination and flexibilities are implemented. We conclude the work with a case study for a representative rural grid in Germany, in which we observe high economic potential in the flexible operation of buildings, majorly thanks to better integration of photovoltaics. Across all paradigms barring a best-case benchmark, grid reinforcement based on a mix of passive and active measures was necessary. A synergy effect is observed between flexibilities and coordination, as their combination reduces the peaks to the extent of completely avoiding grid reinforcement. The presented framework can be applied with a wide range of grid and component types to outline the broad landscape of future LV distribution grids. • Optimization framework for holistic planning of multi-modal LV distribution system. • Framework investigates grid reinforcement needs under flexibilities and coordination. • Framework is demonstrated with an exemplary rural distribution grid in Germany. • Synergy effects between flexibilities and coordination to integrate PV excess exist. • Flexibilities and coordination together reduce system costs by %25, and grid costs by 65%. [ABSTRACT FROM AUTHOR]

• Earlier phase-out of coal is not the best answer to achieve energy transition. • The 2050 plan and historical RES investments maxima are not enough to reach climate neutrality. • What is the effect of regional RES limits over Nationwide RES limits. • Where will be the states in Germany having major RES deployment. • Myopic optimization reveals new challenges for energy policy. This paper will introduce the open-source model MyPyPSA-Ger, a myopic optimization model developed to represent the German energy system with a detailed mapping of the electricity sector, on a highly disaggregated level, spatially and temporally, with regional differences and investment limitations. Furthermore, this paper will give new outlooks on the German federal government 2050 emissions goals of the electricity sector to become greenhouse gas neutral by proposing new CO 2 allowance strategies. Moreover, the regional differences in Germany will be discussed, their role and impact on the energy transition, and which regions and states will drive the renewable energy utilization forward. Following a scenario-based analysis, the results point out the major keystones of the energy transition path from 2020 to 2050. Solar, onshore wind, and gas-fired power plants will play a fundamental role in the future electricity systems. Biomass, run of river, and offshore wind technologies will be utilized in the system as base-load generation technologies. Solar and onshore wind will be installed almost everywhere in Germany. However, due to the nature of Germany's weather and geographical features, the southern and northern regions will play a more important role in the energy transition. Higher CO 2 allowance costs will help achieve the 1.5-degree-target of the electricity system and will allow for a rapid transition. Moreover, the more expensive, and the earlier the CO 2 tax is applied to the system, the less it will cost for the energy transition, and the more emissions will be saved throughout the transition period. An earlier phase-out of coal power plants is not necessary with high CO 2 taxes, due to the change in power plant's unit commitment, as they prioritize gas before coal power plants. Having moderate to low CO 2 allowance cost or no clear transition policy will be more expensive and the CO 2 budget will be exceeded. Nonetheless, even with no policy, renewables still dominate the energy mix of the future. However, maintaining the maximum historical installation rates of both national and regional levels, with the current emissions reduction strategy, will not be enough to reach the level of climate-neutral electricity system. Therefore, national and regional installation requirements to achieve the federal government emission reduction goals are determined. Energy strategies and decision makers will have to resolve great challenges in order to stay in line with the 1.5-degree-target. [ABSTRACT FROM AUTHOR]

• Techno-economic analysis of battery storage systems providing frequency control. • A stochastic, data-driven optimisation algorithm to optimise the battery controller. • Use of frequency data and detailed, yet computationally efficient battery models. • Case study of Germany shows the highest margins for a battery rated at 1.6 MW/1.6 MWh. • Calendar ageing drives battery degradation, while cycle ageing has less impact. Optimal investment in battery energy storage systems, taking into account degradation, sizing and control, is crucial for the deployment of battery storage, of which providing frequency control is one of the major applications. In this paper, we present a holistic, data-driven framework to determine the optimal investment, size and controller of a battery storage system providing frequency control. We optimised the controller towards minimum degradation and electricity costs over its lifetime, while ensuring the delivery of frequency control services compliant with regulatory requirements. We adopted a detailed battery model, considering the dynamics and degradation when exposed to actual frequency data. Further, we used a stochastic optimisation objective while constraining the probability on unavailability to deliver the frequency control service. Through a thorough analysis, we were able to decrease the amount of data needed and thereby decrease the execution time while keeping the approximation error within limits. Using the proposed framework, we performed a techno-economic analysis of a battery providing 1 MW capacity in the German primary frequency control market. Results showed that a battery rated at 1.6 MW, 1.6 MWh has the highest net present value, yet this configuration is only profitable if costs are low enough or in case future frequency control prices do not decline too much. It transpires that calendar ageing drives battery degradation, whereas cycle ageing has less impact. [ABSTRACT FROM AUTHOR]

Highlights • A new approach for the calculation of quarter-hourly CO 2eq intensity is presented. • Effects of charging strategies on the environmental impact of BEVs are derived. • Large amounts of CO 2eq emissions are saved by optimizing the charging time. • The temporal variation of CO 2eq intensity in winter is small compared to summer. • The CO 2eq savings potential is significantly greater during the day than at night. Abstract In many countries, the replacement of internal combustion engine vehicles (ICEVs) with battery electric vehicles (BEVs) offers an opportunity to reduce CO 2 equivalent (CO 2eq) emissions in traffic. BEVs have significantly greater energy conversion efficiency from energy storage to wheel (tank-to-wheel [TTW]) and zero tailpipe emissions; these features are in stark contrast to the environmental burdens posed by vehicle production and electricity generation (well-to-tank [WTT]). Since CO 2eq emissions from vehicle use of BEVs account for the majority of the entire life cycle emissions, it is important to closely examine the CO 2eq intensity of electricity generation, which varies based on the sources of energy in the electricity mix of each country and time of day. This paper compares the environmental impact of diesel and electric buses in the city of Aachen throughout their life cycles and analyzes the effects of electricity generation with reference to variation in charging time. Based on recorded driving profiles, the vehicle dynamics of the longitudinal motion are simulated, and the energy demand as a function of the altitude profile and ambient temperature is calculated. The calculation of the quarter-hourly CO 2eq intensity is combined with the vehicle life cycle model in order to examine the effects of electricity generation on BEV life cycle emissions. To analyze the correlation between charging time and BEV environmental impact, different charging scenarios are defined, and CO 2eq savings from replacing ICEVs with BEVs are estimated. Scenario analyses are conducted over both short- and long-term time scales to predict the future environmental and economic benefits and costs of BEVs compared to ICEVs. The results demonstrate a significant dependency of BEV environmental impact on charging time, which varies by time of day and season. At night, the contribution of renewable energies to the electricity mix is low, and the variation in CO 2eq intensity is limited; therefore, the environmental impact of charging time is lower compared to charging at noon. The larger CO 2eq emissions of BEV production are compensated for all scenarios within the first year of operation. However, if BEVs are charged with electricity generated exclusively by lignite-fired power plants, more CO 2eq emissions are released over their lifetimes compared to ICEVs; this scenario also holds when ICEVs are fueled with biodiesel. Overall, the positive environmental balance of BEVs is not based on a low CO 2eq intensity, but on the greater energy conversion efficiency and the possibility of recuperating braking energy. These properties are particularly advantageous in urban traffic. [ABSTRACT FROM AUTHOR]

• Low reservoir amplitudes in run-of-river hydropower enable extensive grid balancing. • 1 MW of hydropower can facilitate grid integration of up to 11.6 MW of wind or PV. • Potentials for negative balancing services significantly higher than for positive. • Providing balancing services reduces overall energy production. • Nevertheless balancing services can increase the total incomes. The increasing shares of wind energy and photovoltaics in many countries challenge the energy systems in a twofold way. On one hand, there is an increased need for balancing energy in order to compensate abrupt changes in load or power generation. On the other hand, fossil plants being important providers of balancing services are gradually driven out of the system creating a need for renewable substitutes. Due to strict requirements on the upstream water level regime German run-of-river (ROR)-hydropower has until now remained largely unused for providing positive balancing services. However, in this paper we present a novel optimized operation scheme allowing ROR-plants to exactly keep their production schedule and, in addition to that, provide positive and negative balancing services even under these constraints. Our simulations on an exemplary power plant with a very low head show that huge unused balancing power potentials can be expected for ROR-hydropower in Germany enabling a multiplication of fluctuating renewable energy sources. Next to these potentials under current market conditions, this paper elaborates the economic and ecologic aspects of balancing operation. [ABSTRACT FROM AUTHOR]

This paper analyses the potential of the Power-to-Gas (PtG) concept in Baden-Württemberg (BW), south west Germany. A macroeconomic analysis shows that a cost-covering operation of PtG for hydrogen production is first possible under our assumptions in 2030. Previous model-based analyses for Germany identified locations, mainly in north-west Germany, where these plants could achieve these full load hours and thus be economical in the future energy system by 2040. Importantly, although some short-term storage devices (batteries) are installed in BW in this scenario, no PtG plants are seen at the level of the transport network. A more detailed analysis for BW at the municipality level develops residual load profiles for individual 110 kV transformers and municipalities. A very large increase in the residual load profiles in the north-east of Baden-Württemberg by 2040 is encountered, suggesting a requirement for network strengthening and local storage, including PtG, in this area. Four very different and representative model regions are further analysed, whereby only Aalen, a region with large wind potentials in the north east of BW, is identified as having significant potentials for PtG by 2040 (between 69 and 155 MWel). The current restrictions for injecting hydrogen into the gas network (2–10% by volume) mean that these PtG plants would have to incorporate a methanation step in order to upgrade and feed in SNG. The generation of SNG on a local level is therefore expected to be an option by about 2040, if the development of renewable energy generation proceeds as quickly as expected in the current energy-political scenario explored here. The existing CO 2 sources for methanation are not located in the vicinity of the expected PtG plants, so that a CO 2 separation from the air and/or a liquefied transport could be most economical. Further work is required to consider the local energy infrastructure, especially electrical and gas distribution networks. [ABSTRACT FROM AUTHOR]

A variety of novel, recyclable and reusable, construction materials has already been studied within literature during the past years, aiming at improving the overall energy efficiency ranking of the building envelope. However, several studies show that a delicate control of indoor climating elements can lead to a significant performance improvement by exploiting the building’s savings potential via smart adaptive HVAC regulation to exogenous uncertain disturbances (e.g. weather, occupancy). Building Optimization and Control (BOC) systems can be categorized into two different groups: centralized (requiring high data transmission rates at a central node from every corner of the overall system) and decentralized 1 The terms “decentralized”, “distributed” and “agent-based” are considered to have similar meanings herein. 1 (assuming an intercommunication among neighboring constituent systems). Moreover, both approaches can be further divided into two subcategories, respectively: model-assisted (usually introducing modeling oversimplifications) and model-free (typically presenting poor stability and very slow convergence rates). This paper presents the application of a novel, decentralized, agent-based , model-free BOC methodology (abbreviated as L4GPCAO) to a modern non-residential building (E.ON. Energy Research Center’s main building), equipped with controllable HVAC systems and renewable energy sources by utilizing the existing Building Management System (BES). The building testbed is located inside the RWTH Aachen University campus in Aachen, Germany. A combined rule criterion composed of the non-renewable energy consumption (NREC) and the thermal comfort index – aligned to international comfort standards – was adopted in all cases presented herein. Besides the limited availability of the specified building testbed, real-life experiments demonstrated operational effectiveness of the proposed approach in BOC applications with complex, emerging dynamics arising from the building’s occupancy and thermal characteristics. L4GPCAO outperformed the control strategy that was designed by the planers and system provider, in a conventional manner, requiring no more than five test days. [ABSTRACT FROM AUTHOR]

Active load shifting of the electricity demand unlocks a variety of benefits. Examples of such advantages include the increased stability of energy systems, reduced electricity costs and financial savings in the transmission as well as generation infrastructure. Although the technology necessary for demand response has been extensively studied for individual appliances or at the micro-grid level, evaluations of its nationwide impact are scarce. Yet governments and policy-makers require quantitative assessments in order to understand the underlying value and derive appropriate policies. For this purpose, this paper utilizes real-world data from the German-Austrian electricity market in order to calculate ex post the impact of demand response on electricity spot prices and load. As a result, we find that a 25% adoption rate of the available potential for load shifting could have decreased nationwide electricity expenses by approximately €500 million, or 6%, in 2014. At the same time, we observe that the price volatility rises under this scheme and thus impairs non-flexible electricity customers. This observation entails significant implications in terms of designing effective policies. [ABSTRACT FROM AUTHOR]

Increasing the share of renewable energies requires an extension of grid capacity and additional storage possibilities. Although load shifting has been identified as a key instrument to relieve an overloaded grid, technologies that enable the decentralization of load shifting have hardly penetrated the electricity market. In this paper, a discrete choice experiment is applied to investigate preferences and willingness to pay values for micro-cogeneration, a technology that has huge potential to enhance load shifting, and at the same time reduce costs and CO 2 emissions for heating. Our study includes homeowners as well as tenants to capture the overall market potential. Drawing from a sample of 412 adult Germans, several drivers of willingness to pay for micro-cogeneration are identified such as cost and CO 2 saving potential, contract specifics and a feed-in tariff. The results show that most people would be willing to invest in micro-cogeneration technologies but non-monetary obstacles, such as limited institutional support, hinder investment on a larger scale. Several sources of preference heterogeneity are identified, giving rise to the development of a large variety of products and incentive structures. [ABSTRACT FROM AUTHOR]

• Optimization model for the integrated assessment of the green ammonia value chain. • Case study for ammonia trade between Australia and Germany in 2030. • Sensitivity analysis on ammonia prices for technical and economic parameters. • Cost comparison of conventional and green ammonia production. This paper contributes to understanding the transformation of global energy trade to green energy carriers, focusing on green ammonia as the foreseeable first green hydrogen carrier. We provide a comprehensive overview of today's ammonia trade and assess scaling options for the trade of green ammonia. To that aim, we develop an optimization model for the integrated assessment of the green ammonia value chain that covers all steps from green ammonia production in an exporting country, up to delivery to a harbor in an importing country. The model endogenously chooses among different technology options and determines cost minimal operation. In a case study, we apply the model to the large-scale import of ammonia from Australia to Germany in a scenario for 2030. The results show that green ammonia can reach cost parity with gray ammonia even for moderate gas prices (but not necessarily with blue ammonia) if CO 2 prices are high enough. We also provide a sensitivity analysis with respect to the interest rate and other key technical and economic parameters and show that cracking ammonia to provide pure hydrogen comes at a 45 % cost markup per MWh at the destination. [ABSTRACT FROM AUTHOR]

The electrification of the heating and transportation sectors is a major building block in Germany's effort to reduce carbon dioxide emissions. The additional loads and flexibilities resulting from the anticipated large-scale integration of the heating and transportation sectors necessitate an adequate representation in electricity market modeling. First, the paper presents a comprehensive modeling approach of the heating sector in a pan-European electricity market simulation considering both small-scale building heat pumps and large-scale conversion units with thermal energy storages in district heating networks. The operation of small-scale building heat pumps is emulated using an electro-thermal 1R1C building model integrated into the economic dispatch formulation while the operation of units in district heating networks is restricted by additional constraints resulting from the combined heat and power output, local heat load coverage and possible heat power transfer between heating networks. The transportation sector is mapped using previously published models by the authors including both controlled and fast charging processes. Subsequently, the load shift potentials resulting from the market-integration of the heating and transportation sectors and their impact on the pan-European electricity market with focus on Germany based on the three target years 2030, 2040 and 2045 are examined. Furthermore, by applying a Monte Carlo simulation to map possible future primary energy source prices a major uncertainty dimension is addressed. Considering the current German electricity market design, the integration of the two sectors lead to a moderate increase in future annual electricity demands and a substantial increase in market-based peak load. The resulting annual peak load from power-to-heat devices account to 22 GW (2030), 53 GW (2040) and 102 GW (2045) and from electric vehicles to 12 GW (2030), 36 GW (2040) and 96 GW (2045) for the target years. Further results demonstrate the larger load-shift potentials of electric vehicles compared to building heat pumps by comparing the weighted annual average market price of the flexible loads. The primary fuel price uncertainty is examined based on the 0.25, 0.50 and 0.75 quantiles for the target years which result in annual average electricity market price spreads of over 2.16 (2030), 2.97 (2040) and 3.44 (2045) between the 0.25 and 0.75 quantile scenarios. • Electricity market modeling considering the heating and transportation sectors. • Thermal building model to emulate operation of small-scale heat pumps. • Local heat load coverage and heat power transfer between heating networks. • Pan-European electricity market simulation based on the years 2030, 2040 and 2045. • Monte Carlo simulation to map future primary energy source prices. [ABSTRACT FROM AUTHOR]

Biofuel production is not cost competitive and thus requires governmental intervention. The effect of the institutional framework on the development of the biofuel sector is not yet well understood. This paper aims to analyze how biofuel production and production capacity could have evolved in Germany in the period 1992–2014. The effects of an agricultural policy intervention (liberalization of the agricultural market) and a bioenergy policy intervention (a tax on biodiesel after an initial exemption) are explored. Elements of the Modeling Agent systems based on Institutional Analysis (MAIA) framework, complex adaptive systems (CAS) theory, and Neo Institutional Economics (NIE) theory were used to conceptualize and formalize the system in an agent-based model. It was found that an early liberalization of the agricultural market led to an under-production of biodiesel; a late liberalization led to the collapse of biodiesel production. An early introduction of the biodiesel tax led to stagnation in biodiesel production and production capacity; a late introduction led to an increase in sunk costs provided that the biofuel quota is binding. Also, a lack of agents’ adaptation mechanism to forecast prices led to a decrease in patterns of biodiesel production when an external shock was introduced in the system. In sum, we argue that system behavior is influenced by individual behavior which is shaped by institutions. [ABSTRACT FROM AUTHOR]

The effectiveness of the energy-only market (EOM) in providing sufficient incentives for investments is intensively discussed in Europe. While supporters claim that an improved EOM can guarantee generation adequacy, energy suppliers in particular favor the introduction of a capacity market to finance power plant investments. However, there is a lack of quantitative assessment of market design options taking into account individual decisions of market players. Existing studies mainly include a system view based on a central planner optimization. This paper on the other hand is based on an agent-based simulation model for the German electricity market. This method can explicitly incorporate individual investment decisions and aggregate them to present a holistic view of the system. Our results show that an EOM extended with a strategic reserve can incentivize investments, and guarantee supply security in a market with high share of renewable energies. However, the generation adequacy can be more easily achieved with a capacity market. Furthermore, the cost advantage of an EOM diminishes in the long-term, as scarcity prices in the EOM lead to similar system costs as with a capacity market. [ABSTRACT FROM AUTHOR]

[Display omitted] • Integrated design approach for energy supply systems in three industrial sites. • Multi-year optimization to integrate long-term trends. • Sequential and strategic planning approaches enable adaptive decision making. • Discussion of rising carbon prices and cooperate decarbonization targets. Cogeneration plants appear highly attractive for industrial companies as production sites typically combine demands for electricity, heating, and cooling. However, gas-fired cogeneration might become economically less attractive with further increasing carbon prices and companies' commitment to decarbonization targets. This paper proposes a methodology to determine technology pathways from an optimization model. Technology pathways describe the evolution of an energy supply system over a multi-decade planning horizon. Therefore, projected long-term trends are integrated into the decision-making process for energy supply infrastructure. Three investment strategies are compared regarding their accuracy and computational complexity. The methodology is illustrated for industrial sites from dairy, pharmaceutical and automotive industry in Southern Germany. Results highlight the advantages of technology pathways compared to optimization approaches based on a representative year. A strategic planning approach identifies optimal decarbonization pathways with economic saving potentials of up to 20 %. For most of the considered case studies, a sequential decision approach with a rolling horizon heuristic yields similar results in 89 % less solving time. [ABSTRACT FROM AUTHOR]

[Display omitted] • German Resilience Index (GRI) for measuring socio-economic-ecological resilience. • The GRI is based on the German SDGs and universal ethical values. • Development of the 'resilience space function' based on the resilience factor k. • Germany is at a level of roughly 75% (GRI total = 0.747) of its way to resilience. • With a GRI energy = 0.568, the energy sector is farthest away from resilient development. The challenges of the 21st century require resilient societies and a robust international regulatory framework [1]. The current disruptions to the global framework (most notably by the Covid-19 pandemic and the war on Ukraine) as well as the historic experiences especially of the Great Depression and the Lehman crisis (Annex II) elucidate the importance of the UN Sustainable Development Goals (SDGs) as a global regulatory framework. The SDGs provide new targets for resilient development. In this paper we set specific focus especially on SDG 17 "Partnerships for the Goals" and its aim of a free multilateral trading system (Annex I, SDG 17.10-17.12) for sustainable development [2]. Against this background the German Resilience Index was developed as a new measure for defining the resilience of the German society based on the SDGs including the explicit consideration of trade dynamics. The German Resilience Index (GRI) enables an analysis of the extent to which Germany has succeeded in building socio-economic-ecological resilience to defy the storms of globalization. The index is based on the German Sustainable Development Goals and the defined targets, considering also ethical values derived from the Universal Declaration of Human Responsibilities of the InterAction Council and trade relations as corner stones of resilient development. The analysis thus provides a first approach for systematically considering the impact of Germany's intensive participation in globalization and the ethical values of the German Sustainability Strategy and its SDGs. The results are important for the political decision-making process and the political as well as social discourse about the future course of sustainable, and thus resilient, development in Germany. [ABSTRACT FROM AUTHOR]

Wind energy is one of the most important renewable energy sources in Germany and plays a key role regarding energy and climate policy targets of the German government. However, a further diffusion of wind farms involves strong spatial implications that refer to various adverse effects on landscape (onshore wind), noise level, and wildlife. Negative environmental impacts caused by the sometimes suboptimal siting of wind farms have induced an increasing gap between the social acceptance of this technology on the global and local levels. Particularly on the local level, siting processes of wind farm projects often trigger public protest. The aim of this paper is to improve the siting assessment by providing a holistic multi-criteria decision making approach that incorporates techno-economic, socio-political, and environmental criteria, which are defined in a way that social acceptance-related issues are specifically emphasized. We apply a GIS-based Analytic Hierarchy Process approach, where a group of local experts and stakeholders is asked to pairwise compare the incorporated criteria in order to derive the relative importance of each criterion. The results obtained indicate that 9.4% of the study area is still available for wind energy development, whereas only 1.74% of the region is characterized by high suitability. In particular, the northern part of the region offers substantial unexploited wind energy potential. A comparison with the location of existing wind farms validates the reliability and accuracy of the model results. [ABSTRACT FROM AUTHOR]

In future energy systems with high shares of non-dispatchable renewable generation, the storage of electricity will play a key role. Today, a number of different storage options are discussed to cover the fast growing demand for storage. One of these options involves federal waterways for pumped hydro storage plants. The paper analyses the concept and the results of achievable storage potentials for Germany. The total storage potential for Germany sums up to approx. 400 MWh of which the majority can be attributed to four flight of locks which have been identified as the most suitable sites. [ABSTRACT FROM AUTHOR]

The activation of demand response (DR) potentials offered by electricity consumer flexibility is one promising option for providing balancing power and energy in supply systems with high share of variable renewable energy (VRE) power generation. In this paper, a model-based assessment of the economic DR potential in Germany is presented. It relies on the extension of the REMix energy system model by flexible electric loads. In a case study considering a future German power supply system with a VRE share of 70%, possible cost reductions achieved by investment in DR are quantified. The sensitivity of the results to changes in the assumed DR costs and characteristics are analyzed in additional simulations. The results show that the major benefit of employing DR is its ability to substitute peak power generation capacity, whereas the impact on the integration of VRE power generation is lower. This implies that the focus of DR is on the provision of power, not energy. Even at rather pessimistic cost DR assumptions, more than 5 GW of power plant capacity can be substituted. Consumer flexibility furthermore triggers an increase in the operation of back-up power plants, whereas it decreases the utilization of pumped storage hydro stations. In the model results, the reductions in annual power supply costs achieved by DR add up to several hundreds of millions of Euros. [ABSTRACT FROM AUTHOR]

In this paper, we present an approach on automatic energy system modeling and simulation. We develop two different methods to dimension the components of energy systems: one approach is an easy-to-use and to-adapt rule-based method, where the size of components is based e.g. on the heat demand of the buildings. The second approach is to dimension components with a genetic algorithm with a target function to reduce total annual cost. We apply and compare the methods for two different system designs to a building cluster case study in Germany. One system is a decentral heat pump system with back-up gas boiler, thermal and electrical storage and PV, the other system is a hydrogen-based central fuel cell system with electrolyzer, thermal and electrical storage and PV. We compare both systems based on current (2020) and future (2050) framework conditions. Through the application of the genetic algorithm a reduction of equivalent annual cost of up to 27% (2020 scenario) and 21% (2050 scenario) is achieved for the heat pump system. The hydrogen system with optimized dimensioning becomes economically viable under 2050 conditions due to reduced fuel prices and a higher electricity feed-in tariff compared to 2020. Depending on the use case, both approaches have their merit: rule-based dimensioning can quickly simulate a variety of different scenarios, the genetic algorithm can achieve economically ideal system designs, but with longer computation time, especially with unfamiliar framework conditions. • Set-up of modular simulation framework to enable automatic parametrization and simulation. • Development of two dimensioning methods: rule-based and optimization using genetic algorithms. • Automatic dimensioning of heat pump and fuel cell simulation models for urban scale analysis. • Comparison of cost and performance for a case study for different framework conditions in 2020 and 2050. [ABSTRACT FROM AUTHOR]

The use of Demand Side Integration (DSI) makes it possible to control electricity consumption and, at the same time, allows for different ancillary system (e.g. voltage and frequency control) or market services (e.g. load shifting) to be provided. In light of today’s power system, which is faced with a high penetration of renewable energy (more than 20%), DSI has become even more important. These new power systems make it especially necessary to shift plenty of demand to times of high feed-in from wind and solar power plants in order to avoid wasting green energy. Additionally providing ancillary services using DSI can help balancing the system. This paper is focused on the analysis of load shifting potential in the residential and commercial sectors. Therefore a scenario based procedure was developed and applied. It uses a genetic algorithm to consider the time-dependent behavior of the different loads, which are modeled as load blocks. The investigation was conducted in the scope of a VDE/ETG working group in close cooperation with industrial partners. The results determined a practical shifting potential for the investigated sectors (residential and commercial) in Germany which could reach 8 GW in 2030. [ABSTRACT FROM AUTHOR]

Over the past few years, studies have shown that the efficiency issue of biomass heating has become increasingly important to enhance their competitiveness. The integration of a compression heat pump into the system to recover heat from exhaust gases could be a promising solution. However, the different ways of integrating heat pumps lead to highly variable techno-economic results over their lifetime. To find out how to integrate compression heat pumps into a biomass heating network more effectively, a general approach for the integration is presented in this paper, where the heat pumps can be integrated either into the flue gas condenser or into the network return flow. A detailed model for implementing the integration approach is proposed and validated against the measured data from two real biomass heating networks. The evaluation results show that both integration variants improved system efficiency by more than 17% in practice. For a techno-economic comparison and analysis of both variants, the proposed integration approach is also applied to a use case in Germany. In the studied case, the flue gas-side integration is more cost-effective than the network-side integration, and both concepts improve system efficiency by 12.6%. By analyzing the technical parameters, it is noticed that both integration concepts can be achieved cost-effectively in practice by finding the optimal setpoint of exhaust gas temperature under certain conditions. The analysis of electricity and fuel prices shows that the integration concepts are sensitive to the ratio of electricity price and biomass fuel price. If the fuel prices increase sharply in the future, both concepts will take on greater significance. [Display omitted] • A general approach to integrating heat pumps into biomass heating for heat recovery. • Evaluation of operational experiences from the few real-world use cases. • Comparison and Evaluation of the different heat pump connection variants. • Selection of optimal concepts and design parameters under certain conditions. [ABSTRACT FROM AUTHOR]

With the expansion of distributed energy resources and the phaseout of the feed-in-tariff scheme in Germany, self-consumption and electricity sharing within a community of prosumers are becoming more profitable. This paper derives optimal business models for a sustainable peer-to-peer (P2P) energy trading platform (ETP) in Germany. It examines data from 1618 residential households collected from an online survey, including 1311 consumers and 307 prosumers. Our research aims to better understand under what circumstances these households would participate in a P2P ETP and how business models can support such platforms to create added value for private households. Therefore, households' beliefs concerning their attitudes, perceived behavioral control, and subjective norms are analyzed according to the Theory of Planned Behavior, and business models are designed correspondingly. In order to evaluate the developed business models' effectiveness and usefulness, we apply them to fifteen existing pioneer energy communities and platforms in Germany. We find that cost-saving and other financial benefits for households must be considered to be the primary value proposition offered by a service provider. Business models which help households to become more electricity self-sufficient and to consume less electricity from the public grid are the second-most important source of value creation from a household's point of view. By connecting the business models and the P2P prosuming market model, recommendations for companies, policy-makers, and regulatory authorities are made. • Households decide to become prosumers due to cost savings and financial benefits. • A suite of business models for future energy service providers is suggested. • Business models increase households' financial benefits and save on energy costs. • Business models proposed enable households to increase energy self-sufficiency. • Companies should focus more on creating P2P energy-sharing communities or platforms. [ABSTRACT FROM AUTHOR]

The advancing energy transition is leading to a constantly increasing number of prosumers and active participants in the energy system. Local energy markets are considered a promising approach to coordinate those active participants efficiently, increase acceptance among the population, and decrease grid expansion costs. However, energy models and markets do not consider heterogeneous user preferences which are considered crucial for the progressing energy transition. Therefore, this paper proposes new auction-based local energy market models that consider user preferences and users' willingness to pay a premium for heterogeneous energy qualities. In order to account for the unknown and stochastic user behavior in such a new market setting, we simulate and compare existing and newly developed auction-based clearing algorithms with an unbiased Monte Carlo method and evaluate whether they fulfill pre-defined key characteristics. Based on the results, we conclude and identify a clearing algorithm that verifiably satisfies user preferences, considers willingness to pay a premium, increases local coverage of electricity, maintains individual rationality, and computational tractability. The presented clearing algorithms enable new market designs that can help to increase acceptance and accelerate the expansion of renewable energies. All market models and simulations are publicly available in the open-source repository lemlab. Future research will validate the presented results in a field trial in Germany with 20 households. • New auction-based clearing algorithms for community-based local energy markets. • New bidding format for local energy markets. • Verifiable satisfaction of user preferences. • Incorporation of willingness-to-pay a premium for heterogeneous energy qualities. • Increasing local coverage of electricity supply and demand. [ABSTRACT FROM AUTHOR]

[Display omitted] • We simulate renewables pathways under different support phase-out scenarios. • We extend an investment algorithm by preferences and calibrated returns. • Our improved algorithm incorporates more heterogeneity resulting in stronger effects. • Further auction support is necessary for most capacity targets in the case countries. • Countries should coordinate policy changes due to cross-border effects. Support schemes have been central to the expansion of renewable electricity globally and in the European Union. As technologies mature, individual member states may decide to phase out these policies. While previous research has shown that such policy changes affect investors' decisions, we investigate how they affect pathways and electricity prices by simulating investment decisions in an agent-based model in two case countries. This paper contributes and applies an adapted investment decision algorithm that incorporates empirically observed technology and return preferences and is calibrated by return observations. The new algorithm yields more refined and stronger effects compared to its predecessor. Results show that the phase-out of auctions in Germany and the Netherlands slows down their deployment of renewable capacity by up to ∼60% and ∼35%, respectively. With the exception of photovoltaics and onshore wind projects in the Netherlands, the targeted capacities can only be reached by continuing support in both countries. Furthermore, ending support in a large country like Germany leads to higher electricity prices and fosters a market-driven but insufficient capacity expansion in smaller neighbours like the Netherlands. As the electricity grids in many countries are strongly interconnected, such cross-border effects are of international relevance. Our findings suggest that continued auctions may be necessary and that countries should coordinate policy changes to stay on track for meeting their renewables targets. [ABSTRACT FROM AUTHOR]

District heating is expected to play an essential role in the cost-effective decarbonisation strategy of many countries. Resource-optimised management of district heating networks depends on a wide range of factors, including demand forecasting, operational flexibility, and increasingly volatile market conditions. However, traditional operations often still rely on static models and rather simple heuristics, while holistic optimisation requires dynamic cross-domain interoperability to allow the consideration of all these factors. This paper demonstrates a proof-of-concept for a knowledge graph based optimisation problem to minimise total heat generation cost for a district heating provider. The optimisation follows a hierarchical approach based on a merit-order principle and is embedded in a model predictive control framework to allow the system to incorporate most recent information and react to disturbances promptly. A detailed sensitivity study is conducted to identify key model parameters and assess the impact of anticipated changes in regulation and market conditions. Simulation-based optimisation is used to determine the short-term heat generation mix based on data-driven gas consumption models and day-ahead forecasts for the network's energy demand and grid temperatures. Seasonal autoregressive integrated moving average models with exogenous predictor variables are found to be sufficiently accurate and precise. The effectiveness of the approach is demonstrated for a case study of an existing heating network of a midsize town in Germany, where a reduction of approximately 20% and 40% compared to baseline operational data is obtained for operating cost and CO 2 emissions, respectively. [Display omitted] • Developed heat generation optimisation framework for district heating providers. • Identified key cost drivers and market dynamics using a detailed sensitivity analysis. • Developed high accuracy time series forecasting models for key input variables. • Assessed optimisation effectivity using a German municipal utility case study. • Improvement potential for operational cost and CO 2 emissions both exceed 20%. [ABSTRACT FROM AUTHOR]

The threat of climate change motivates the expansion of variable renewable energy sources (VRES) as well as the transition to plug-in electric vehicles (PEV). Both technologies present unique challenges to electricity systems, potentially leading to high grid expansion cost. In this paper, we present a detailed assessment on how smart-charging of PEVs can mitigate these problems and reduce cost on the distribution grid level. To this end, we propose a heuristic policy that dynamically decides on charging of PEVs, curtailment of VRES, and subsequently on infrastructure investments trading off fixed investments with variable operational costs. The main inputs are modeled as stochastic and the proposed policy is non-anticipative. We conduct a comprehensive case study for Germany in the year 2035 using detailed descriptions of existing distribution grids, realistic driving patterns, and real-world VRES feed-in data. Potential savings of €6.2 billion in investment costs lead to a reduction in total distribution grid cost of around 19%. This result is achieved by upgrading 7 million (21%) PEV chargers all over Germany. Upgrading 100% of chargers to smart-chargers as is proposed in the extant literature is clearly sub-optimal as it leads to significantly higher total cost. A closer investigation for single grids reveals that savings as well as the optimal share of smart chargers varies widely between grids. In particular, the potential of smart charging is much greater in rural areas than in urban centers. Furthermore, the results suggest that curtailment of VRES production is economically only in rare circumstances. • We consider distribution grids with renewables generation and electric vehicles. • We propose a non-anticipative policy for curtailment and charging of vehicles. • Smart charging considerably reduces overall system cost. • Optimal levels of smart charging are far below 50%. • Optimal decisions and savings fluctuate depending on grid characteristics. [ABSTRACT FROM AUTHOR]

[Display omitted] • Open model-based analysis for 100% renewable and sector-coupled energy systems. • Cost-optimization tool based on Oemof to investigate Germany's energy system in 2050. • Renewable energy is sufficient for electricity and building heat in Germany in 2050. • Investment cost (bn €/yr): 17.6–26.6 (Volatile), 23.7–28.8 (Heat), 2.7–3.9 (Storage). • Energy mix and LCOE comparison validate the developed tool's effectiveness. The ambitious energy target to achieve climate-neutrality in the European Union (EU) energy system raises the feasibility question of using only renewables across all energy sectors. As one of the EU's leading industrialized countries, Germany has adopted several climate-action plans for the realistic implementation and maximum utilization of renewable energies in its energy system. The literature review shows a clear gap in comprehensive techniques describing an open modeling approach for analyzing fully renewable and sector-coupled energy systems. This paper outlines a method for analyzing the 100% renewable-based and sector-coupled energy system's feasibility in Germany. Based on the open energy modeling framework, an hourly optimization tool 'OSeEM-DE' is developed to investigate the German energy system. The model results show that a 100% renewable-based and sector-coupled system for electricity and building heat is feasible in Germany. The investment capacities and component costs depend on the parametric variations of the developed scenarios. The annual investment costs vary between 17.6 and 26.6 bn €/yr for volatile generators and between 23.7 and 28.8 bn €/yr for heat generators. The model suggests an investment of a minimum of 2.7–3.9 bn €/yr for electricity and heat storage. Comparison of OSeEM-DE results with recent studies validates the percentage-wise energy mix composition and the calculated Levelized Cost of Electricity (LCOE) values from the model. Sensitivity analyses indicate that storage and grid expansion maximize the system's flexibility and decrease the investment cost. The study concludes by showing how the tool can analyze different energy systems in the EU context. [ABSTRACT FROM AUTHOR]

The first wave of the COVID-19 pandemic led to decreases in electricity demand and a rising share of Renewable Energy Sources in various countries. In Germany, the average proportion of net electricity generation via Renewable Energy Sources rose above 55 % in the first half of 2020, as compared to 47 % for the same period in 2019. Given these altered circumstances, in this paper we analyze how the German and other European electricity systems behaved during the COVID-19 pandemic. We use data visualization and descriptive statistics to evaluate common figures for electricity systems and markets, comparing developments during the COVID-19 pandemic with those of previous years. Our evaluation reveals noticeable changes in electricity consumption, generation, prices, and imports/exports. However, concerning grid stability and ancillary services, we do not observe any irregularities. Discussing the role of various flexibility options during the COVID-19 pandemic, a relatively higher grid capacity resulting from a decreased electricity consumption, in particular, may have contributed to grid stability. [ABSTRACT FROM AUTHOR]

When COVID-19 pandemic spread in Europe, governments imposed unprecedented confinement measures with mostly unknown repercussions on contemporary societies. In some cases, a considerable drop in energy consumption was observed, anticipating a scenario of sizable low-cost energy generation, from renewable sources, expected only for years later. In this paper, the impact of governmental restrictions on electrical load, generation and transmission was investigated in 16 European countries. Using the indices provided by the Oxford COVID-19 Government Response Tracker, precise restriction types were found to correlate with the load drop. Then the European grid was analysed to assess how the load drop was balanced by the change in generation and transmission patterns. The same restriction period from 2020 was compared to previous years, accounting for yearly variability with ad hoc statistical technique. As a result, generation was found to be heavily impacted in most countries with significant load drop. Overall, generation from nuclear, and fossil coal and gas sources was reduced, in favour of renewables and, in some countries, fossil gas. Moreover, intermittent renewables generation increased in most countries without indicating an exceptional amount of curtailments. Finally, the European grid helped balance those changes with an increase in both energy exports and imports, with some net exporting countries becoming net importers, notably Germany, and vice versa. Together, these findings show the far reaching implications of the COVID-19 crisis, and contribute to the understanding and planning of higher renewables share scenarios, which will become more prevalent in the battle against climate change. • During COVID-19 spread, energy load dropped significantly in most European countries. • Only some restrictions strongly correlate with load drop. • Generation mix and exchange changed in most countries. • The biggest decrease in generation was from nuclear, fossil coal and oil. • Intermittent renewable generation continued to increase also in 2020. [ABSTRACT FROM AUTHOR]

• Assessment of responsiveness' role in mechanisms for adjusting economic incentives. • Agent-based model of feed-in tariff for solar adoption in Germany, Spain, Switzerland. • More responsive designs meet deployment policy goal accurately and lower uncertainty. • Surprisingly, results show diminishing returns of responsiveness that add policy cost. • Country-specific attributes shown to influence solar power diffusion patterns. A rapid, global diffusion of clean energy technologies is central to the efforts to curb global carbon dioxide emissions. Deployment policies based on economic incentives have proved effective in accelerating the uptake of renewable technologies, such as solar photovoltaics, and could quicken the adoption of others. However, their outcomes were often uncertain and costly as many countries struggled to adjust incentives while technology prices fell rapidly. This study addresses this challenge by investigating the role of responsiveness – the policy's ability to react to changes in the context in which it operates – in the design of mechanisms for adjusting incentives over time. This paper employs an agent-based model to evaluate quantitatively six policy designs with varying degrees of responsiveness for adjusting a feed-in tariff for solar photovoltaics in three countries – Germany, Spain, and Switzerland – for over a decade. Our first finding confirms that more responsive policy designs tended to produce policies that meet their goals more accurately and certainly. Between the least and the most responsive designs, deviation from policy goals was reduced by 60% and uncertainty was reduced by more than 50%. Our results also suggest that policy responsiveness could have diminishing returns: improvements in the policy's accuracy and certainty tended to become smaller while policy costs per capita tended to become larger as design responsiveness increased. Finally, simulation results show how country-specific attributes influence the diffusion pattern of the technology. These findings have major implications for the design of future deployment policies supporting the diffusion of clean energy technologies. [ABSTRACT FROM AUTHOR]

• Linear Program for designing bidirectional low temperature networks. • Holistic sizing approach for all building energy systems in a district. • Performance evaluation with economic, thermodynamic and ecologic indicators. • Comparison with stand-alone HVAC systems. • Bidirectional low temperature network result in substantial energy and cost savings. This paper presents a novel design methodology based on Linear Programming for designing and evaluating distributed energy systems with bidirectional low temperature networks (BLTNs). The mathematical model determines the optimal selection and sizing of all energy conversion units in buildings and energy hubs connected to the BLTN while minimizing total annualized costs. The optimization superstructure of building energy systems comprises heat pumps, compression chillers, heat exchangers for direct cooling, cooling towers and thermal energy storages. The design approach is applied to a real-world use case in Germany and the BLTN performance is compared to a reference case with individual HVAC systems. The BLTN concept shows a cost reduction of 42% and causes 56% less CO 2 emissions compared to individual HVAC systems. [ABSTRACT FROM AUTHOR]

• Analysis of hydrothermal potential in German municipalities. • Optimisation of simultaneous geothermal heat and electricity generation. • Drilling depth and hydrothermal temperature are implemented endogenously. • Integration of geothermal plants in a holistic energy system optimisation. • Geothermal plants reveal a potential for cost reduction in off-grid municipalities. 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. [ABSTRACT FROM AUTHOR]

• Gas-fired power plants are often no more profitable under current market situation. • Retrofit measures that enable a more flexible operation can enhance profitability. • A real options model is proposed for the flexibility investment decision-making. • The optimal operation strategy is shown for two hourly profitability indicators. • The retrofit decision depends significantly on some of the economic parameters. The promotion of electricity from renewable energies in Germany causes difficulties in the profitable operation of many modern conventional power plants. Also, fluctuating availability of power from renewable energy sources requires flexible conventional plants, i.e. power plants which are able to react fast to these fluctuations. In this paper, we propose a model which provides some decision support tool for the operators of conventional power plants regarding investments in technical upgrades aimed at enhancing the flexible operation of these power plants. Because of the irreversible character of the investments considered we apply a real options approach to the gas-fired power plant investigated. We compare the electricity price and the spark spread as profitability indicators and sources of uncertainty, and use a simplified 3-stage definition of the power plant's operation strategy. In a case study for a combined cycle gas-fired turbine plant recently built in Germany, we find that the retrofit decisions depend strongly on economic parameters, such as the level of the operation and maintenance costs, as well as the enabled longer operation time on the maximum level. The proposed procedure can help to assess the expected future profitability role of conventional power plants, and supports the decision-making process regarding both reinvestments and optimal operation of power plants in markets with high shares of variable renewables. [ABSTRACT FROM AUTHOR]