Optimierung der Investitions- und Einsatzplanung dezentraler Energiesysteme unter Unsicherheit

Es wird ein ganzheitliches, modulbasiertes Framework für die Investitions- und Einsatzplanungsoptimierung dezentraler Energiesysteme entwickelt. Mittels stochastischem Programm und Regret-Minimierung werden risikobehaftete und nicht probabilistische Unsicherheiten berücksichtigt. Neu ist auch die parallele Berechnung auf High-Performance-Computing-Systemen einschließlich der eingesetzten automatischen Algorithmuskonfiguration des verwendeten Solvers zur Rechenzeitreduzierung

Optimierung der Investitions- und Einsatzplanung dezentraler Energiesysteme unter Unsicherheit

Die Energieversorgung verschiebt sich kontinuierlich von einer zentralisierten zu einer dezentralen Versorgung mit deutlichem Ausbau fluktuierender erneuerbarer Energien. Bei der grundlegenden, strukturellen Neuordnung der Energieversorgung unterliegen die Investitions- und Einsatzplanung dezentraler Energiesysteme vielfältigen Unsicherheiten. Motiviert durch diese Entwicklung, wurde für die Investitions- und Einsatzplanungsoptimierung ein ganzheitliches, modulbasiertes Framework entwickelt, um diese Unsicherheiten und deren gegenseitige Abhängigkeiten konsistent modellieren und berücksichtigen zu können. Zunächst werden meteorologische Eingangsdaten über Markov-Prozesse bereitgestellt und dann in die erforderlichen Daten für die Optimierung umgewandelt. Schließlich werden Investitions- und Einsatzplanung des dezentralen Energiesystems durch ein zweistufiges stochastisches gemischt-ganzzahliges lineares Programm optimiert. Zu diesem Zweck wird dieses komplexe Programm durch Fixierung verknüpfender Variablen in Subprogramme entkoppelt und auf High-Performance-Computing-Systemen parallel ausgeführt. Der Rechenaufwand wird durch eine vorgelagerte Szenarioreduktion und eine optimierte Solver-Einstellung mittels automatischer Algorithmuskonfiguration um bis zu 75 % reduziert. Die fixierten Variablen werden im Masterprogramm durch einen ableitungsfreien Bergsteigeralgorithmus optimiert, der robust und zuverlässig das (lokale) Optimum in wenigen Iterationen findet. Mit einer übergeordneten Regret-Minimierung wird für den privaten Entscheider die Investition bestimmt, die er im schlimmsten Fall am wenigsten bereuen würde. In einer Fallstudie wird endogen die kostenminimale Investitions- und Einsatzplanung des Energiesystems eines Wohnquartiers in Karlsruhe ermittelt. Zu den möglichen Systemkomponenten gehören eine PV-Anlage, Wärmepumpen als Power-to-Heat-Anwendung sowie thermische und elektrische Speicher. Die Ergebnisse zeigen, dass die PV-Anlage als dezentrales Energieangebot in solch einem System generell wirtschaftlich ist. Wärmespeicher sind in der Regel größer, wenn Unsicherheiten berücksichtigt werden. Stochastische Programmierung kann mithin helfen, insuffiziente Investitionsplanung zu vermeiden. Bei getrenntem Heizsystem sind Speicher für Warmwasser rentabler als für Raumwärme, deren Wert mehr in der Minimierung des Risikos liegt, den Heizbedarf in kalten Wintern nicht decken zu können. Elektrische Speicher spielen aufgrund ihres höheren Investitionsbedarfs im Vergleich zu thermischen Speichern kaum eine Rolle und werden nur zwingend benötigt, um vollständige Autarkie zur Überbrückung bestimmter Wochen im Jahr zu erreichen. Die Analyse der Skalierbarkeit und der Vergleich mit einer modernen dualen Dekompositionsmethode mittels angepasster Lagrange-Relaxierung zeigen eine gute Performance des entwickelten Ansatzes für den betrachteten Problemtyp. Der Mehrwert, der sich aus der Berücksichtigung der Unsicherheiten in der Optimierung einschließlich des daraus resultierenden Rechenaufwands ergibt, wird aufgezeigt und rechtfertigt den gewählten Ansatz

Stochastic simulation of photovoltaic electricity feed-in considering spatial correlation

The growing generation capacity of electricity from renewable energy sources (RES-E) around the globe has an increasing impact on traditional energy and electricity markets. Well-ahead planned investment decisions as well as short term management of the power plant and storage dispatch and other challenges are highly dependent on the feed-in of RES-E. Therefore a thorough research of RES-E supply and knowledge about methods to generate corresponding model input is crucial when simulating electricity markets

Self-consumption through power-to-heat and storage for enhanced PV integration in decentralised energy systems

Many countries have adopted schemes to promote investments into renewable energy sources resulting, among others, in a high penetration of solar PV energy. The system integration of the increasing amount of variable electricity generation is therefore a highly important task. This paper focuses on a residential quarter with PV systems and explores how heat pumps and thermal and electrical storages can help to integrate the PV generation through self-consumption. However, self-consumption and PV integration are not only affected by technologies but also by pricing mechanisms. This paper therefore analyses the impact of different tariffs on the investment and operation decisions in a residential quarter and its interaction with the external grid. The considered tariffs include a standard fixed per-kilowatt-hour price, a dynamic pricing scheme and a capacity pricing scheme. To account for the interdependent uncertainties of energy supply, demand and electricity prices, we use a module-based framework including a Markov process and a two-stage stochastic mixed-integer program. Analysing a residential quarter in Southern Germany as a case study, we find that the integration of a PV system is economically advantageous for all considered tariffs. The self-consumption rate varies between 58 and 75%. The largest PV system is built when dynamic prices are applied. However, the peak load from the external grid increases by a factor of two under this tariff without any incentive for reduction. In contrast, capacity pricing results in a reduction of the peak load by up to 35%

Using automated algorithm configuration to improve the optimization of decentralized energy systems modeled as large-scale, two-stage stochastic programs

The optimization of decentralized energy systems is an important practical problem that can be modeled using stochastic programs and solved via their large-scale, deterministic equivalent formulations. Unfortunately, using this approach, even when leveraging a high degree of parallelism on large high-performance computing (HPC) systems, finding close-to-optimal solutions still requires long computation. In this work, we present a procedure to reduce this computational effort substantially, using a stateof-the-art automated algorithm configuration method. We apply this procedure to a well-known example of a residential quarter with photovoltaic systems and storages, modeled as a two-stage stochastic mixed-integer linear program (MILP). We demonstrate substantially reduced computing time and costs of up to 50% achieved by our procedure. Our methodology can be applied to other, similarly-modeled energy systems

Two-stage stochastic, large-scale optimization of a decentralized energy system : a case study focusing on solar PV, heat pumps and storage in a residential quarter

The expansion of fluctuating renewable energy sources leads to an increasing impact of weather-related uncertainties on future decentralized energy systems. Stochastic modeling techniques enable an adequate consideration of the uncertainties and provide support for both investment and operating decisions in such systems. In this paper, we consider a residential quarter using photovoltaic systems in combination with multistage air-water heat pumps and heat storage units for space heating and domestic hot water. We model the investment and operating problem of the quarter’s energy system as two-stage stochastic mixed-integer linear program and optimize the thermal storage units. In order to keep the resulting stochastic, large-scale program computationally feasible, the problem is decomposed in combination with a derivative-free optimization. The subproblems are solved in parallel on high-performance computing systems. Our approach is integrated in that it comprises three subsystems: generation of consistent ensembles of the required input data by a Markov process, transformation into sets of energy demand and supply profiles and the actual stochastic optimization. An analysis of the scalability and comparison with a state-of-the-art dual-decomposition method using Lagrange relaxation and a conic bundle algorithm shows a good performance of our approach for the considered problem type. A comparison of the effective gain of modeling the quarter as stochastic program with the resulting computational expenses justifies the approach. Moreover, our results show that heat storage units in such systems are generally larger when uncertainties are considered, i.e., stochastic optimization can help to avoid insufficient setup decisions. Furthermore, we find that the storage is more profitable for domestic hot water than for space heating

Two-stage stochastic, large-scale optimization of a decentralized energy system - a residential quarter as case study

The trend towards decentralized energy systems with an emphasis on renewable energy sources (RES) causes increased fluctuations and non-negligible weather-related uncertainties on the future supply side. Stochastic modeling techniques enable an adequate consideration of uncertainties in the investment and operation planning process of decentralized energy systems. The challenge is that modeling of real energy systems ends up in large-scale problems, already as deterministic program. In order to keep the stochastic problem feasible, we present a module-based, parallel computing approach using decomposing techniques and a hill-climbing algorithm in combination with high-performance computing (HPC) for a two-stage stochastic optimization problem. Consistent ensembles of the required input data are simulated by a Markov process and transformed into sets of energy demand and supply profiles. The approach is demonstrated for a residential quarter using photovoltaic (PV) systems in combination with heat pumps and storages. Depending on the installed technologies, the quarter is modeled either as stochastic linear program (SLP) or stochastic mixed-integer linear program (SMILP). Our results show that thermal storages in such a decentralized energy system prove beneficial and that they are more profitable for domestic hot water than for space heating. Moreover, the storage capacity for space heating is generally larger when uncertainties are considered in comparison to the deterministic optimization, i.e. stochastic optimization can help to avoid bad layout decisions

Proteomic analysis of hepatic effects of phenobarbital in mice with humanized liver

Activation of the constitutive androstane receptor (CAR) may induce adaptive but also adverse effects in rodent liver, including the induction of drug-metabolizing enzymes, transient hepatocellular proliferation, and promotion of liver tumor growth. Human relevance of CAR-related adverse hepatic effects is controversially debated. Here, we used the chimeric FRG-KO mouse model with livers largely repopulated by human hepatocytes, in order to study human hepatocytes and their response to treatment with the model CAR activator phenobarbital (PB) in vivo. Mice received an intraperitoneal injection with 50 mg/kg body weight PB or saline, and were sacrificed after 72–144 h. Non-repopulated FRG-KO mice were used as additional control. Comprehensive proteomics datasets were generated by merging data obtained by targeted as well as non-targeted proteomics approaches. For the first time, a novel proteomics workflow was established to comparatively analyze the effects of PB on human and murine proteins within one sample. Analysis of merged proteome data sets and bioinformatics data mining revealed comparable responses in murine and human hepatocytes with respect to nuclear receptor activation and induction of xenobiotic metabolism. By contrast, activation of MYC, a key regulator of proliferation, was predicted only for mouse but not human hepatocytes. Analyses of 5-bromo-2′-deoxyuridine incorporation confirmed this finding. In summary, this study for the first time presents a comprehensive proteomic analysis of CAR-dependent effects in human and mouse hepatocytes from humanized FRG-KO mice. The data support the hypothesis that PB does induce adaptive metabolic responses, but not hepatocellular proliferation in human hepatocytes in vivo.publishedVersio

The Hitchhiker\u27s Guide to Europe: the infection dynamics of an ongoing Wolbachia invasion and mitochondrial selective sweep in Rhagoletis cerasi

Wolbachia is a maternally inherited and ubiquitous endosymbiont of insects. It can hijack host reproduction by manipulations such as cytoplasmic incompatibility (CI) to enhance vertical transmission. Horizontal transmission of Wolbachia can also result in the colonization of new mitochondrial lineages. In this study, we present a 15-year-long survey of Wolbachia in the cherry fruit fly Rhagoletis cerasi across Europe and the spatiotemporal distribution of two prevalent strains, wCer1 and wCer2, and associated mitochondrial haplotypes in Germany. Across most of Europe, populations consisted of either 100% singly (wCer1) infected individuals with haplotype HT1, or 100% doubly (wCer1&2) infected individuals with haplotype HT2, differentiated only by a single nucleotide polymorphism. In central Germany, singly infected populations were surrounded by transitional populations, consisting of both singly and doubly infected individuals, sandwiched between populations fixed for wCer1&2. Populations with fixed infection status showed perfect association of infection and mitochondria, suggesting a recent CI-driven selective sweep of wCer2 linked with HT2. Spatial analysis revealed a range expansion for wCer2 and a large transition zone in which wCer2 splashes appeared to coalesce into doubly infected populations. Unexpectedly, the transition zone contained a large proportion (22%) of wCer1&2 individuals with HT1, suggesting frequent intraspecific horizontal transmission. However, this horizontal transmission did not break the strict association between infection types and haplotypes in populations outside the transition zone, suggesting that this horizontally acquired Wolbachiainfection may be transient. Our study provides new insights into the rarely studied Wolbachia invasion dynamics in field populations