• Energy Research

In light of offshore wind expansions in the North and Baltic Seas in Europe, further ideas on using offshore space for renewable-based energy generation have evolved. One of the concepts is that of energy islands, which entails the placement of energy conversion and storage equipment near offshore wind farms. Offshore placement of electrolysers will cause interdependence between the availability of electricity for hydrogen production and for power transmission to shore. This paper investigates the trade-offs between integrating energy islands via electricity versus hydrogen infrastructure. We set up a combined capacity expansion and electricity dispatch model to assess the role of electrolysers and electricity cables given the availability of renewable energy from the islands. We find that the electricity system benefits more from connecting close-to-shore wind farms via power cables. In turn, electrolysis is more valuable for far-away energy islands as it avoids expensive long-distance cable infrastructure. We also find that capacity investment in electrolysers is sensitive to hydrogen prices but less to carbon prices. The onshore network and congestion caused by increased activity close to shore influence the sizing and siting of electrolysers.

To phase out fossil fuels, energy systems must shift to renewable electricity as the main source of primary energy. In this paper, we analyze how electrification can support the integration of fluctuating renewables, like wind and PV, and mitigate the need for storage and thermal backup plants. Using a cost-minimizing model for system planning, we find substantial benefits of electricity demand in heating, transport, and industry adapting to supply. In Germany, flexible demand halves the residual peak load and the residual demand and reduces excess generation by 80%. Flexible operation of electrolyzers has the most significant impact accounting for 42% of the reduction in residual peak load and 59% in residual demand. District heating networks and BEVs also provide substantial flexibility, while the contribution of space and process heating is negligible. The results are robust to restrictions on the expansion of the transmission grid. Energy, 278 (Part A) ISSN:0360-5442 ISSN:1873-6785

Abstract This paper investigates the impact of uncertain photovoltaic generation on unit commitment decisions for the German rolling planning procedure employing a large-scale stochastic unit commitment electricity market model (stELMOD). A novel approach to simulate a time-adaptive intra-day photovoltaic forecast, solely based on an exponential smoothing of deviations between realized and forecast values, is presented. Generation uncertainty is then incorporated by numerous multi-stage scenario trees that account for a decreasing forecast error over time. Results show that total system costs significantly increase when uncertainty of both wind and photovoltaic generation is included by a single forecast, with more frequent starting processes of flexible plants and rather inflexible power plants mainly deployed at part-load. Including the improvement of both wind and photovoltaic forecasts by a scenario tree of possible manifestations, the scheduling costs could be significantly reduced in representative weeks for spring and summer. In general, stochastic representations increase the need for congestion management as well as more frequent use of storage in the model, leading to a more realistic depiction of the markets.

Swarm Grid Demand Tool

This reply aims to address the points raised in an analysis provided in the comment entitled “Comments on ‘Uncertainties in estimating production costs of future nuclear technologies: A model-based analysis of small modular reactors’ [Energy 281 (2023) 128204]”, specifically on the used scaling coefficients and cost assumptions.

The development of energy systems is not a technocratic process but equally shaped by societal and cultural forces. Key instruments in this process are model-based scenarios describing a future energy system. Applying the concept of fictional expectations from social economics, we show how energy scenarios are tools to channel political, economic, and academic efforts into a common direction. To impact decision-making, scenarios do not have to be accurate -- but credible and evoke coherent expectations in diverse stakeholders. To gain credibility, authors of scenarios engage with stakeholders and appeal to the authority of institutions or quantitative methods. From these insights on energy scenarios, we draw consequences for developing and applying planning models, the quantitative tool energy scenarios build on. Planning models should be open and accessible to facilitate stakeholder participation, avoid needlessly complex methods to minimize expert bias and aim for a large scope to be policy relevant. Rather than trying to simulate social preferences and convictions within engineering models, scenario development should pursue broad and active participation of all stakeholders, including citizens.

This short communication is based on a workshop on hydrogen network modeling in macro-energy system models discussing the whole hydrogen value chain: production, transmission, storage, and use, as well as the related issues of demand flexibility, alternative fuels of biological origin, and the integration with district heating. It was organized by the Danish research network ENERforsk with leading modelers from academia, industry, and transmission system operators. The workshop collected (1) lessons learned, (2) best practices, and (3) potential next steps. We conclude that no-regret pathways need to consider evolving hydrogen regulation while balancing social aspects with interests driven by green industrial policy. Important and uncertain issues include the repurposing of natural gas networks, considering hydrogen pipeline standards and existing contracts, or the disposal of brine water. As more demand sectors and carbonaceous fuels are included in the models, carbon management, sustainable biomass, and carbon networks become more important. Collecting and incorporating lessons learned, best practices, and potential next steps will help the modeling community and policymakers to develop sound policies.

Predicting future costs of technologies not yet developed is a complex exercise that includes many uncertain parameters and functional forms. In that context, small modular reactor (SMR) concepts that are in a rather early development stage claim to have cost advantages through learning effects, standardized design, modularization, co-siting economies, and other factors, such as better time-to-market even though they exhibit negative economies of scale in their construction costs due to their lower power output compared to conventional nuclear reactors. In this paper, we compare two different approaches from production theory and show that they have a theoretically equal structure. In the second step, we apply these approaches to estimate a range of potential construction costs for 15 SMR projects for which sufficient data is available. These include water cooled, high temperature, and fast neutron spectrum reactors. We then apply the Monte Carlo method to benchmark the cost projections assumed by the manufacturers by varying the investment costs, the weighted average cost of capital, the capacity factor, and the wholesale electricity price in simulations of the net present value (NPV) and the levelized cost of electricity (LCOE). We also test whether the differences between the manufacturer estimates and ours differ between technology families of SMR concepts and apply a sensitivity analysis. Here we contribute to an intensifying debate in the literature on the economics and finance of SMR concepts. The Monte Carlo analysis suggests a broad range of NPVs and LCOEs: Surprisingly, the lowest LCOE is calculated for a helium-cooled high-temperature reactor, whereas all of the light water reactors feature higher LCOEs. None of the tested concepts is able to compete economically with existing renewable technologies, not even when taking their variability and necessary system integration costs into account. The numerical results also confirm the importance of the choice of production theory and parameters. We conclude that any technology foresight has to take as much of the case specifics into account, including technological and institutional specifics; this also holds for SMR concepts.