Renewable origin, additionality, temporal and geographical correlation – eFuels production in Germany under the RED II regime

E-fuels are a promising technological option to reduce the carbon footprint in the transportation sector. To ensure the renewable origin of electricity-based fuels and minimize the impact of power-to-liquid facilities on the electricity grid, the European Union implemented electricity purchase conditions within the Renewable Energy Directive II. In this work, we analyze the impact of these electricity purchase conditions on the optimal placement, dimensioning and operation of facilities and the German electricity system. The results show that implementing the proposed electricity purchase conditions increases electrolysis capacity by 15.8% and reduces utilization by 672 h in 2030. With the constrained electricity supply, the power-to-liquid facilities concentrate on network nodes with high renewable potential, while the carbon dioxide supply loses importance. Overall, the German electricity system is not heavily affected by the proposed purchase conditions as the required renewable generation capacities only increase slightly. At the same time, carbon dioxide abatement costs rise by 14.3% by introducing the electricity purchase conditions

Charging Behavior of Electric Vehicles: Temporal Clustering Based on Real-World Data

The increasing adoption of battery electric vehicles (BEVs) is leading to rising demand for electricity and, thus, leading to new challenges for the energy system and, particularly, the electricity grid. However, there is a broad consensus that the critical factor is not the additional energy demand, but the possible load peaks occurring from many simultaneous charging processes. Hence, sound knowledge about the charging behavior of BEVs and the resulting load profiles is required for a successful and smart integration of BEVs into the energy system. This requires a large amount of empirical data on charging processes and plug‐in times, which is still lacking in literature. This paper is based on a comprehensive data set of 2.6 million empirical charging processes and investigates the possibility of identifying different groups of charging processes. For this, a Gaussian mixture model, as well as a k‐means clustering approach, are applied and the results validated against synthetic load profiles and the original data. The identified load profiles, the flexibility potential and the charging locations of the clusters are of high relevance for energy system modelers, grid operators, utilities and many more. We identified, in this early market phase of BEVs, a surprisingly high number of opportunity chargers during daytime, as well as switching of users between charging clusters

Charging Behavior of Electric Vehicles: Temporal Clustering Based on Real‐World Data

The increasing adoption of battery electric vehicles (BEVs) is leading to rising demand for electricity and, thus, leading to new challenges for the energy system and, particularly, the electricity grid. However, there is a broad consensus that the critical factor is not the additional energy demand, but the possible load peaks occurring from many simultaneous charging processes. Hence, sound knowledge about the charging behavior of BEVs and the resulting load profiles is required for a successful and smart integration of BEVs into the energy system. This requires a large amount of empirical data on charging processes and plug‐in times, which is still lacking in literature. This paper is based on a comprehensive data set of 2.6 million empirical charging processes and investigates the possibility of identifying different groups of charging processes. For this, a Gaussian mixture model, as well as a k‐means clustering approach, are applied and the results validated against synthetic load profiles and the original data. The identified load profiles, the flexibility potential and the charging locations of the clusters are of high relevance for energy system modelers, grid operators, utilities and many more. We identified, in this early market phase of BEVs, a surprisingly high number of opportunity chargers during daytime, as well as switching of users between charging clusters

Techno-ökonomische Bewertung der Produktion regenerativer synthetischer Kraftstoffe

Die Emissionen des Mobilitätssektors basieren derzeit maßgeblich auf der Nutzung fossiler flüssiger Energieträger . Trotz zunehmender Elektrifizierung, spielen flüssige und gasförmige Kraftstoffe kurz und mittelfristig noch eine bedeutende Rolle und werden voraussichtlich in einigen Bereichen auch in längerfristiger Zukunft benötigt. Um dennoch eine Minderung der THG Emissionen zu erreichen, stehen mehrere Technologien und Verfahrenskonfigurationen zur Erzeugung regenerativer synthetischer Kraftstoffe zur Verfügung. Im Rahmen einer techno ökonomischen Analyse werden potenzielle Herstellungsverfahren identifiziert, sowie deren charakteristische Merkmale zusammengetragen. Für die anschließende technische Analyse wurde eine Prozesssimulation in Aspen Plus ® erstellt, woraus die Bestimmung charakteristischer technischer Kenngrößen erfolgte. Anhand der Simulationsergebnisse wurde anschließend eine ökonomische Bewertung des Verfahrens durchgeführt . Die Wahl fiel auf ein Power to Liquid Verfahren unter Nutzung von CO 2 Emissionen eines Zementwerks. Die Konversion zu Kraftstoffen erfolgt durch eine Fischer Tropsch Synthese und teilweiser Veredelung in einem Hydrocracking Verfahren in die Zielprodukte einer Diesel und einer Benzinfraktion. Im Rahmen dieser Arbeit wird das beschriebene Verfahren für drei Produktionskapazitäten untersucht. Unter den getroffenen Annahmen beträgt die Kohlenstoffeffizienz der gewählten Prozessroute 92%, der Wasserstoffwirkungsgrad 63%, sowie der energetische Wirkungsgrad 37%. Die spezifischen Herstellungskosten liegen für den Basisfall bei 3,26 €, für eine Anlage geringerer Produktionskapazität bei 3,51 € und für eine Anlage höherer Kapazität bei 3,11 € pro Liter Kraftstoff. Die Kosten der elektrischen Energie und der Investitionsbedarf der PEM Elektrolyse sind dabei die maßgeblichen Kostenfaktoren

Technical assistance to assess the potential of renewable liquid and gaseous transport fuels of non-biological origin (RFNBOs) as well as recycled carbon fuels (RCFs), to establish a methodology to determine the share of renewable energy from RFNBOs as well as to develop a framework on additionality in the transport sector

This report is a summary of the work conducted in Task 1 of the technical assistance to assess the potential of renewable fuels of non-biological origin (RFNBOs) and recycled carbon fuels (RCFs) to establish a methodology to determine the share of renewable energy from RFNBOs as well as to develop a framework on additionality in the transport sector. The goal of Task 1 within the entire project was the assessment of the deployment potential of RFNBOs and RCFs over the period from 2020 to 2050 in the EU transport sector. All relevant transport sub-sectors and modalities are considered: road transport, maritime and inland shipping, aviation, and railway. Furthermore, the competition for RFNBOs and RCFs between the transport sectors and other sectors and applications of RFNBOs is considered. A central result is the potential gross final consumption of RFNBOs and RCFs that would count towards the RES target in the transport sector. In addition, the needed resources and the arising costs for this deployment as well as the impacts on greenhouse gas emissions and local environments are analyzed. Finally, barriers to the deployment and options to overcome these are outlined

Unit Commitment of Photovoltaic-Battery Systems: An Advanced Approach Considering Uncertainties from Load, Electric Vehicles, and Photovoltaic

Increasing use of renewable energy leads to change in load flows from predictable generation and inelastic demand to more volatile and price-elastic patterns, especially on the distribution level. New applications such as electric vehicles further increase the demand of electricity. Therefore, a reliable, local control of load flexibilities is a key competence of future system operators. This paper presents a central planner–decentral operator approach to schedule local electricity flows. The central planner conducts a two-stage optimization to derive the demand limit and a corresponding battery schedule, while the decentral operator simply applies the battery schedule and heuristically reacts to unforeseen deviations between the forecasted and actual loads and power generation. Privacy concerns of the decentral planner are avoided as no private information is shared with the central planner. A relaxation factor and a reserve capacity for the battery are derived from a Monte Carlo simulation to consider the underlying uncertainties of load, photovoltaic generation, and electric vehicle charging. Our results show that the load of the decentral operator can be limited reliably for six days of the considered week and a maximum reduction of 2.6 kW (52%) of peakload has been accomplished. Furthermore, the approach is suitable for systems with limited computational resources at the place of the decentral operator, which is the common case in this field