Extreme events in time series aggregation: A case study for optimal residential energy supply systems

To account for volatile renewable energy supply, energy systems optimization problems require high temporal resolution. Many models use time-series clustering to find representative periods to reduce the amount of time-series input data and make the optimization problem computationally tractable. However, clustering methods remove peaks and other extreme events, which are important to achieve robust system designs. We present a general decision framework to include extreme events in a set of representative periods. We introduce a method to find extreme periods based on the slack variables of the optimization problem itself. Our method is evaluated and benchmarked with other extreme period inclusion methods from the literature for a design and operations optimization problem: a residential energy supply system. Our method ensures feasibility over the full input data of the residential energy supply system although the design optimization is performed on the reduced data set. We show that using extreme periods as part of representative periods improves the accuracy of the optimization results by 3% to more than 75% depending on system constraints compared to results with clustering only, and thus reduces system cost and enhances system reliability

Optimization of low-carbon energy systems from industrial to national scale

Climate change mitigation requires a reduction of greenhouse gas (GHG) emissions. The main emitter of GHG emissions is the energy sector, which today is based on fossil fuels. To mitigate climate change, we need to transform the energy systems to low-carbon technologies. For this purpose, new energy system designs are required along with appropriate operational strategies. In principle, these new designs and operational strategies can be identified best using mathematical optimization. However, low-carbon technologies impose challenges in solving and assessing the resulting optimization problems. Low-carbon technologies are volatile, which increase the complexity of optimal synthesis and operation. To cope with the complexity of operational optimization, we develop a time-series decomposition method. The method decomposes the complex, time-coupled operational problem into smaller subproblems, while still providing feasible, near-optimal solutions. For the increased complexity in synthesis problems, we propose a method based on time-series aggregation. The method divides the original synthesis problem into two separate problems: one aggregated relaxed problem and another aggregated restricted problem, leading to feasible, near-optimal solutions. In addition, the transformation process requires a rigorous assessment of greenhouse gas emissions and potential burden-shifting. In particular, the assessment of emissions due to electricity usage on the industrial scale is difficult, as the underlying national electricity system is not modeled. Therefore, we propose methods to compute industrial greenhouse gas emission factors for electricity. By exploiting these emission factors, industrial energy systems can significantly reduce their emissions. On the national scale, burden-shifting towards environmental impacts besides climate change needs to be prevented in the transformation. Hence, we develop a national energy system model and extend the optimization with life-cycle assessment considering 15 further environmental impacts. With the model, we compute a cost-optimal transformation pathway to a low-carbon energy system. The transformation leads to many co-benefits, but also to severe burden-shifting, which needs to be considered during the transformation process and in the development of new low-carbon technologies. Overall, the methods and models in this thesis facilitate the integration of low-carbon technologies in energy systems

This is SpArta: Rigorous Optimization of Regionally Resolved Energy Systems by Spatial Aggregation and Decomposition

Energy systems with high shares of renewable energy are characterized by local variability and grid limitations. The synthesis of such energy systems, therefore, requires models with high spatial resolution. However, high spatial resolution increases the computational effort. Here, we present the SpArta method for rigorous optimization of regionally resolved energy systems by Spatial Aggregation and decomposition. SpArta significantly reduces computational effort while maintaining the full spatial resolution of sector-coupled energy systems. SpArta first reduces problem size by spatially aggregating the energy system using clustering. The aggregated problem is then relaxed and restricted to obtain a lower and an upper bound. The spatial resolution is iteratively increased until the difference between upper and lower bound satisfies a predefined optimality gap. Finally, each cluster of the aggregated problem is redesigned at full resolution. For this purpose, SpArta decomposes the original synthesis problem into subproblems for each cluster. Combining the redesigned cluster solutions yields an optimal feasible solution of the full-scale problem within a predefined optimality gap. SpArta thus optimizes large-scale energy systems rigorously with significant reductions in computational effort. We apply SpArta to a case study of the sector-coupled German energy system, reducing the computational time by a factor of 7.5, compared to the optimization of the same problem at full spatial resolution. As SpArta shows a linear increase in computational time with problem size, SpArta enables computing larger problems allowing to resolve energy system designs with improved accuracy

Integrated scheduling of batch production and utility systems for provision of control reserve

Control reserve is becoming increasingly important due to the increase of fluctuating renewable energy. Today, control reserve is mostly provided by large fossil-based power plants. In the future, control reserve has to be provided increasingly by decentralized utility systems. The main purpose of these utility systems is to supply energy to production systems. However, production systems are commonly scheduled without considering control reserve. Only subsequently, the utility system is scheduled to supply the production system with energy and for potential provision of control reserve. This sequential approach misses synergistic opportunities between production and utility systems. In this contribution, we propose a method for integrated scheduling of utility and production systems with provision of control reserve. The integrated scheduling identifies production schedules offering potential to provide control reserve by the utility system. Uncertainty of control-reserve request is modeled by stochastic programming. The production schedule is fixed after the integrated scheduling and also not changed if control reserve is requested. In this case, only the utility system changes its operation. Our method is applied to a case study considering a batch production system and the utility system, which are scheduled for one day of operation. Compared to the sequential scheduling with consideration of control-reserve provision, our method saves additional 3.3% of operational expenditures. Thus, integrated scheduling of production and utility system for control-reserve provision is highly beneficial and the presented method identifies this potential in practice

Design of low-carbon utility systems : Exploiting time-dependent grid emissions for climate-friendly demand-side management

Efficient energy supply is key to reduce industrial greenhouse gas emissions. In the industry, energy is often supplied by on-site utility systems with electricity grid connection. Usually, electricity from the grid is assumed to have annually averaged emission factors when greenhouse gas emissions are calculated. However, emissions from electricity production are in fact time-dependent to match continuously varying demand and supply. These time dependent emissions offer the potential to reduce emissions by temporal shifting of electricity demand in demand-side management.Here, we investigate the impact of time-dependent grid mix emissions for low-carbon utility systems. For this purpose, we present a detailed mixed-integer linear programming model for a low-carbon utility system. Subsequently, we compute time-dependent grid emission factors based on the current mix and based on the marginal technologies. These grid emission factors serve as input to determine and economic and environmental trade-off curves. We show that emissions can be reduced by up to 6% at the same costs by considering time-dependent grid mix emissions, instead of annual average grid emissions. Marginal time-dependent emission factors even allow to reduce emissions by up to 60%. Our work shows that time-dependent grid emissions factors could enable climate-friendly demand-side management leading to significant emission reductions

Where to Market Flexibility? Optimal Participation of Industrial Energy Systems in Balancing-Power, Day-Ahead, and Continuous Intraday Electricity Markets

The rising share of volatile renewable generation increases the demand for flexibility in the electricity grid. Flexible capacity can be offered by industrial energy systems through participation on either the continuous intraday, day-ahead, or balancing-power markets. Thus, industrial energy systems face the problem of where to market their flexible capacity. Here, we propose a method to integrate trading on the continuous intraday market into a multi-market optimization for flexible industrial energy systems. To estimate the intraday market revenues, we employ option-price theory. Subsequently, a multi-stage stochastic optimization determines an optimized bidding strategy and allocates the flexible capacity. The method is applied to a case study of a multi-energy system showing that coordinated bidding in all three considered markets reduces cost most. A sensitivity analysis for the intraday market volatility reveals changing market preferences, thus emphasizing the need for multi-market optimization. The proposed method provides a practical decision-support tool in short-term electricity and balancing-power markets

Incorporating Relevance Feedback for Information-Seeking Retrieval using Few-Shot Document Re-Ranking

Pairing a lexical retriever with a neural re-ranking model has set state-of-the-art performance on large-scale information retrieval datasets. This pipeline covers scenarios like question answering or navigational queries, however, for information-seeking scenarios, users often provide information on whether a document is relevant to their query in form of clicks or explicit feedback. Therefore, in this work, we explore how relevance feedback can be directly integrated into neural re-ranking models by adopting few-shot and parameter-efficient learning techniques. Specifically, we introduce a kNN approach that re-ranks documents based on their similarity with the query and the documents the user considers relevant. Further, we explore Cross-Encoder models that we pre-train using meta-learning and subsequently fine-tune for each query, training only on the feedback documents. To evaluate our different integration strategies, we transform four existing information retrieval datasets into the relevance feedback scenario. Extensive experiments demonstrate that integrating relevance feedback directly in neural re-ranking models improves their performance, and fusing lexical ranking with our best performing neural re-ranker outperforms all other methods by 5.2% nDCG@20