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Investment decisions in competitive power markets are based upon thorough profitability assessments. Thereby, investors typically show a high degree of risk aversion, which is the main argument for capacity mechanisms being implemented around the world. In order to investigate the interdependencies between investors’ risk aversion and market design, we extend the agent-based electricity market model PowerACE to account for long-term uncertainties. This allows us to model capacity expansion planning from an agent perspective and with different risk preferences. The enhanced model is then applied in a multi-country case study of the European electricity market. Our results show that assuming risk-averse rather than risk-neutral investors leads to slightly reduced investments in dispatchable capacity, higher wholesale electricity prices, and reduced levels of resource adequacy. These effects are more pronounced in an energy-only market than under a capacity mechanism. Moreover, uncoordinated changes in market design may also lead to negative cross-border effects.

Quantity and price risks are key uncertainties market participants face in electricity markets with increased volatility, for instance, due to high shares of renewables. From day ahead until real-time, there is a large variation in the best available information, leading to price changes that flexible assets, such as battery storage, can exploit economically. This study contributes to understanding how coordinated bidding strategies can enhance multi-market trading and large-scale energy storage integration. Our findings shed light on the complexities arising from interdependencies and the high-dimensional nature of the problem. We show how stochastic dual dynamic programming is a suitable solution technique for such an environment. We include the three markets of the frequency containment reserve, day-ahead, and intraday in stochastic modelling and develop a multi-stage stochastic program. Prices are represented in a multidimensional Markov Chain, following the scheduling of the markets and allowing for time-dependent randomness. Using the example of a battery storage in the German energy sector, we provide valuable insights into the technical aspects of our method and the economic feasibility of battery storage operation. We find that capacity reservation in the frequency containment reserve dominates over the battery's cycling in spot markets at the given resolution on prices in 2022. In an adjusted price environment, we find that coordination can yield an additional value of up to 12.5%.

Electricity retailers face increasing uncertainty due to the ongoing expansion of unpredictable, distributed generation in the residential sector. We analyze how increasing levels of households' solar PV self-generation affect the short-term decisionmaking and associated risk exposure of electricity retailers in day-ahead and intraday markets. First, we develop a stochastic model accounting for correlations between solar load, residual load and price in sequentially nested wholesale spot markets across seasons and type of day. Second, we develop a computationally tractable twostage stochastic mixed-integer optimization model to investigate the trading portfolio and risk optimization problem faced by retailers. Through conditional value-at-risk we assess retailers' profitability and risk exposure to different levels of PV self-generation by assuming different retail tariff schemes. We find risk-hedging trading strategies and tariffs to have greater impact in Summer and with low levels of residual load in the system, i.e. when the solar generation uncertainty affect more the households demand to be served and the wholesale spot prices. The study is innovative in unveiling the potential of dynamic electricity tariffs, which are indexed to spot prices, to sustain a high penetration of renewable energy source while promoting risk sharing between customer and retailer. Our findings have implications for electricity retailers facing load and revenue risks in wholesale spot markets, likewise for regulators and policy-makers interested in electricity market design.

Quantity and price risks determine key uncertainties market participants face in electricity markets with increased volatility, for instance due to high shares of renewables. In the time from day-ahead until real-time, there lies a large variation in best available information, such as between forecasts and realizations of uncertain parameters like renewable feed-in and electricity prices. This uncertainty reflects on both the market outcomes and the quantity of renewable generation, making the determination of sound trading strategies across different market segments a complex task. The scope of the paper is to optimize day-ahead and intraday trading decisions jointly for a portfolio with controllable and volatile renewable generation under consideration of risk. We include a reserve market, a day-ahead market and an intraday market in stochastic modeling and develop a multi-stage stochastic Mixed Integer Linear Program. We assess the profitability as well as the risk exposure, quantified by the conditional value at risk metric, of trading strategies following different risk preferences. We conclude that a risk-neutral trader mainly relies on the opportunity of higher expected profits in intraday trading, whereas risk can be hedged effectively by trading on the day-ahead. Finally, we show that reserve market participation implies various rationales, including the relation of expected reserve prices among each other, the relation of expected reserve prices to spot market prices, as well as the relation of the spot market prices among each other.

Machine learning and agent-based modeling are two popular tools in energy research. In this article, we propose an innovative methodology that combines these methods. For this purpose, we develop an electricity price forecasting technique using artificial neural networks and integrate the novel approach into the established agent-based electricity market simulation model PowerACE. In a case study covering ten interconnected European countries and a time horizon from 2020 until 2050 at hourly resolution, we benchmark the new forecasting approach against a simpler linear regression model as well as a naive forecast. Contrary to most of the related literature, we also evaluate the statistical significance of the superiority of one approach over another by conducting Diebold-Mariano hypothesis tests. Our major results can be summarized as follows. Firstly, in contrast to real-world electricity price forecasts, we find the naive approach to perform very poorly when deployed model-endogenously. Secondly, although the linear regression performs reasonably well, it is outperformed by the neural network approach. Thirdly, the use of an additional classifier for outlier handling substantially improves the forecasting accuracy, particularly for the linear regression approach. Finally, the choice of the model-endogenous forecasting method has a clear impact on simulated electricity prices. This latter finding is particularly crucial since these prices are a major results of electricity market models.

Investment decisions in competitive power markets are based upon thorough profitability assessments. Thereby, investors typically show a high degree of risk aversion, which is the main argument for capacity mechanisms being implemented around the world. In order to investigate the interdependencies between investors' risk aversion and market design, we extend the agent-based electricity market model PowerACE to account for long-term uncertainties. This allows us to model capacity expansion planning from an agent perspective and with different risk preferences. The enhanced model is then applied in a multi-country case study of the European electricity market. Our results show that assuming risk-averse rather than risk-neutral investors leads to slightly reduced investments in dispatchable capacity, higher wholesale electricity prices, and reduced levels of resource adequacy. These effects are more pronounced in an energy-only market than under a capacity mechanism. Moreover, uncoordinated changes in market design may also lead to negative cross-border effects.

Data related to PBM procurement competitions have led to interesting observations about PBM competitors and enhancements in the procurement process. Employers can apply careful purchasing techniques to exploit market inefficiencies and secure PBM contractual terms that benefit their bottom line, as well as the wallets of their covered employees and dependents. With a more holistic approach, employers can take advantage of the inefficient marketplace by testing the PBM competitors' business models on a fair and equitable basis, securing PBM services at the best possible price and holding the PBM accountable for achieving verifiable drug plan performance metrics. In some PBM procurement competitions, the PBM with the best pricing terms may not offer the lowest employer cost. In fact, in four studies in 2007, two of the \"lowest employer cost\" winners did not have the best pricing terms, surpassing the pricing term leader with superior drug-mix management protocols specific to the employer.

Another PBM tactic is to attempt to convert your contractual pricing to a non-AWP index, with such a change being \"cost-neutral.\" But if the \"cost-neutrality\" is calibrated to the pre-settlement state, the PBM will lock in the excess revenue it is enjoying from the current AWP inflation. When the settlement ultimately lowers AWPs, it will not lower the values of the non-AWP pricing index to which your contract has been switched. Until January 2008, it seemed imminent that the settlement would be much more far-reaching - abolishing the AWP standard itself. If the AWP standard were abolished, plan sponsors and PBMs would need a mechanism to change the AWP-based terms of their contracts. Because many contracts were negotiated during this time of uncertainty, they contain provisions giving PBMs freedom to change contractual terms in response to the settlement. These provisions are now being used to change contracts. Self-funded employers should be concerned about any language in a PBM contract that references the AWP settlement and provides PBMs the ability to change pricing terms or refers to maintaining \"cost-neutrality\" or \"pricing stability.\" The settlement should be approved in the first quarter of 2009 and its provisions implemented this year. Firms should take these steps to defend their financial interests:

Nearly all molecular oxygen (O2) on Earth is produced via oxygenic photosynthesis by plants or photosynthetically active microorganisms. Light-independent O2 production, which occurs both abiotically, e.g. through water radiolysis, or biotically, e.g. through the dismutation of nitric oxide or chlorite, has been thought to be negligible to the Earth system. However, recent work indicates that O2 is produced and consumed in dark and apparently anoxic environments at a much larger scale than assumed. Studies have shown that isotopically light O2 can accumulate in old groundwaters, that strictly aerobic microorganisms are present in many apparently anoxic habitats, and that microbes and metabolisms that can produce O2 without light are widespread and abundant in diverse ecosystems. Analysis of published metagenomic data reveals that the enzyme putatively capable of nitric oxide dismutation forms four major phylogenetic clusters and occurs in at least 16 bacterial phyla, most notably the Bacteroidota. Similarly, a re-analysis of published isotopic signatures of dissolved O2 in groundwater suggests in situ production in up to half of the studied environments. Geochemical and microbiological data support the conclusion that “dark oxygen production\" is an important and widespread yet overlooked process in apparently anoxic environments with far-reaching implications for subsurface biogeochemistry and ecology.

Despite extensive research, the pathogenesis and predispositions underlying long COVID (long-term coronavirus disease 2019) remain poorly understood. To address this, we analyzed the immunological landscapes of 44 patients with long COVID and 44 matched convalescents using single-cell RNA sequencing (scRNA-seq) of peripheral blood mononuclear cells (PBMCs) and validated the findings with plasma cytokine measurements via Luminex technology. While the immune cell compositions showed minimal quantitative differences only among natural killer (NK) cells, the transcriptome analyses identified distinct gene expression patterns, particularly in classical monocytes: patients with long COVID exhibited downregulation of the inflammation-associated genes, including and . Imputation of the transcription factor activity hinted at a reduced inflammasome activity (via ) and an impaired monocyte differentiation (via ) in long COVID. The RNA velocity data supported the presence of immature classical monocytes in these patients. These findings show that monocytes might be dysregulated and/or exhausted in patients with long COVID.