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Institute
Investments in electricity transmission and generation capacity must be made despite significant uncertainty about the future developments. The sources of this uncertainty include, among others, the future levels and spatiotemporal distribution of electricity demand, fuel costs and future energy policy. In recent years, these uncertainties have increased due to the ongoing evolution of supply- and demand-side technologies and rapid policy changes designed to encourage a transition to low-carbon energy systems. Because transmission and generation investments have long lead times and are difficult to reverse, they are subject to a considerable – and arguably growing – amount of risk.
This paper is set up to answer the question: What do we know about future hydrogen import costs on the European border? Our work focuses on breaking down, structuring and analysing the body of literature on hydrogen supply chains. We provide a comprehensive analysis on (i) What ranges of costs exist in the literature for specific steps of hydrogen supply chains? (ii) Which assumptions show the highest uncertainty? (iii) Related, what assumption seems to have the most substantial effect on the resulting EUR/kgH2 import cost estimate? While we narrow our discussion focus on North Africa and Australia to Europe in 2050, we hope policymakers, research fellows and energy-sector stakeholders around the globe might be interested in our results.
In this paper, we endogenously compute worst-case weather events in a transmission system expansion planning problem using the robust optimization approach. Mathematically, we formulate a three-level mixed-integer optimization problem, which we convert to a bi-level problem via the strong duality concept. We solve the problem using a constraint-and-column generation algorithm. We use cardinality-constrained uncertainty sets to model the effects of extreme weather realizations on supply from renewable generators.
To unlock the high potential of green hydrogen in reaching the ambitious 1.5 °C goal declared by the Paris Agreement, enormous (public) investments are needed. Social acceptance of these investments is required in order to implement hydrogen technologies as fast and efficiently as possible. This study investigates which benefits associated with green hydrogen foster its social acceptance. Using a between-subject design, we implement two different treatments. Both treatments have in common that the participants experience the transformation into a hydrogen economy in a virtual reality scenario. In "Info Security of Supply", we provide the participants information about the benefits of green hydrogen regarding the security of energy supply and climate protection. In Control, we inform them only about the benefits of climate protection. Subsequently, the participants decide about the financial support of a hydrogen project. Our preliminary results show a higher support if the focus is solely on the positive impact of green hydrogen for climate protection.
Offshore wind energy is rapidly expanding, facilitated largely through auctions run by governments. We provide a detailed quantified overview of utilised auction schemes, including geographical spread, volumes, results, and design specifications. Our comprehensive global dataset reveals heterogeneous designs. Although most auction designs provide some form of revenue stabilisation, their specific instrument choices vary and include feed-in tariffs, one-sided and two-sided contracts for difference, mandated power purchase agreements, and mandated renewable energy certificates.
We review the schemes used in all eight major offshore wind jurisdictions across Europe, Asia, and North America and evaluate bids in their jurisdictional context. We analyse cost competitiveness, likelihood of timely construction, occurrence of strategic bidding, and identify jurisdictional aspects that might have influenced auction results. We find that auctions are embedded within their respective regulatory and market design context, and are remarkably diverse, though with regional similarities. Auctions in each jurisdiction have evolved and tend to become more exposed to market price risks over time. Less mature markets are more prone to make use of lower-risk designs. Still, some form of revenue stabilisation is employed for all auctioned offshore wind energy farms analysed here, regardless of the specific policy choices. Our data confirm a coincidence of declining costs and growing diffusion of auction regimes.
In this paper, 180 papers on photovoltaic (PV) output forecasting were reviewed and a database of forecast errors was extracted for statistical analysis. The paper shows that among the forecast models, hybrid models are most likely to become the primary form of PV output forecasting in the future. The use of data processing techniques is positively correlated with the forecast quality, while the lengths of the forecast horizons and out-of-sample test sets have negative effects on the forecast accuracy. The paper also found that the use of data normalization, the wavelet transform, and the inclusion of clear sky index and numerical weather prediction variables are the most effective data processing techniques. Furthermore, the paper found some evidence of “cherry picking” in the reporting of errors and we recommend that the test sets be at least one year long to avoid any distortion in the performance of the models.
Two German offshore wind auctions in 2017 and 2018 saw more than 50% of winning capacity with “zero bids”. The nature of these surprisingly low bids is, however, not yet clear. In our paper, we discuss four hypotheses for possible causes for the auction results: (i) the bids are expected to be profitable due to market development and technological progress, (ii) bids can be perceived as “options to build” that can be realized if projects are profitable, (iii) bids are adjusted to secure grid access, and (iv) other long term reasons not primarily driven by the profitability of the winning bids. Our results suggest that there is evidence for all hypotheses to influence the decision making of auction bidders. In fact, we suggest to see the four hypothesis as cumulative value components, which reveal the true value of winning the auction at “zero cost” in aggregate.
This paper develops an integrated fundamental investment model which considers both the gas and electricity sector. Furthermore, we adopt the theory of stochastic programming with recourse in the combined model to account for uncertainty in the gas market. This approach enables us to analyze how uncertain gas demand in other sectors affects decisions to invest in electricity generation capacities. We find an overall decrease and a reallocation of investments in gas-fired power plants. We also quantify the expected costs of ignoring uncertainty.