Streamlining Energy Transition Scenarios to Key Policy Decisions

Uncertainties surrounding the energy transition often lead modelers to present large sets of scenarios that are challenging for policymakers to interpret and act upon. An alternative approach is to define a few qualitative storylines from stakeholder discussions, which can be affected by biases and infeasibilities. Leveraging decision trees, a popular machine-learning technique, we derive interpretable storylines from many quantitative scenarios and show how the key decisions in the energy transition are interlinked. Specifically, our results demonstrate that choosing a high deployment of renewables and sector coupling makes global decarbonization scenarios robust against uncertainties in climate sensitivity and demand. Also, the energy transition to a fossil-free Europe is primarily determined by choices on the roles of bioenergy, storage, and heat electrification. Our transferrable approach translates vast energy model results into a small set of critical decisions, guiding decision-makers in prioritizing the key factors that will shape the energy transition

Knowledge-Based Synthesis of Distributed Systems Using Event Structures

To produce a program guaranteed to satisfy a given specification one can synthesize it from a formal constructive proof that a computation satisfying that specification exists. This process is particularly effective if the specifications are written in a high-level language that makes it easy for designers to specify their goals. We consider a high-level specification language that results from adding knowledge to a fragment of Nuprl specifically tailored for specifying distributed protocols, called event theory. We then show how high-level knowledge-based programs can be synthesized from the knowledge-based specifications using a proof development system such as Nuprl. Methods of Halpern and Zuck then apply to convert these knowledge-based protocols to ordinary protocols. These methods can be expressed as heuristic transformation tactics in Nuprl.Comment: A preliminary version of this paper appeared in Proceedings of the 11th International Conference on Logic for Programming, Artificial Intelligence, and Reasoning LPAR 2004, pp. 449-46

Beiträge zur Kunstgeschichte Nürnbergs /

Mode of access: Internet

Simultaneous real-time scheduling of multi-energy systems and dynamic production processes

Volatile renewable electricity production causes a temporal mismatch of supply and demand, which can be reduced by consumers using optimal production scheduling to shift their demand in time. However, if production processes with nonlinear dynamics must be scheduled simultaneously with multi-energy systems (MESs) introducing discrete on/off decisions, the resulting optimization problems are typically not solvable in real-time today. This thesis reformulates simultaneous dynamic scheduling (SDS) problems of MESs and nonlinear production processes to mixed-integer linear programs (MILPs), which can be solved in real-time. These MILP reformulations rely on tailored scheduling models consisting of three piece-wise affine (PWA) parts: (1) process output models, (2) data-driven process energy demand models, and (3) MILP energy system models. For part 1, we present two alternatives: First, we use a scale-bridging model (SBM), which is easy-to-apply but requires heuristic tuning. We use two cooled continuous stirred tank reactor (CSTR) case studies to show that our approach can capture the major part of the nonlinear potential in real time, and a heated distillation column case study to show that our approach can reduce the number of states substantially. Second, as a rigorous alternative to the heuristically tuned SBM, we derive dynamic ramping constraints (DRCs), which are first restricted to flat processes with only one scheduling relevant variable. These DRCs consider linear dynamics of high order with PWA constraints. We use that, for flat processes, a nonlinear model can be transferred to a linear model by coordinate transformation. Again, we use a CSTR case study to show that our approach can capture the major part of the nonlinear potential. For non-flat processes, we develop heuristic DRCs, based on simulation experiments. For the heated distillation column, these heuristic DRCs perform similarly to SBMs regarding both optimization runtime and operational costs. Lastly, we show that DRCs can also consider multiple scheduling-relevant variables by applying DRCs to an electrolyzer with slow temperature dynamics. We outperform a quasi-steady-state scheduling through optimized temperature dynamics. This thesis thus offers reformulations that strike reasonable compromises between optimization runtime and solution quality for SDS of production processes and MESs. While SBMs can be applied with less effort and knowledge, DRCs offer more dynamic flexibility for flat processes

Wallenstein als student an der universität Altdorf. Ein beitrag zu seiner jugendgeschichte.

32 p