Formal Methods for Probabilistic Energy Models

The energy consumption that arises from the utilisation of information processing systems adds a significant contribution to environmental pollution and has a big share of operation costs. This entails that we need to find ways to reduce the energy consumption of such systems. When trying to save energy it is important to ensure that the utility (e.g., user experience) of a system is not unnecessarily degraded, requiring a careful trade-off analysis between the consumed energy and the resulting utility. Therefore, research on energy efficiency has become a very active and important research topic that concerns many different scientific areas, and is as well of interest for industrial companies. The concept of quantiles is already well-known in mathematical statistics, but its benefits for the formal quantitative analysis of probabilistic systems have been noticed only recently. For instance, with the help of quantiles it is possible to reason about the minimal energy that is required to obtain a desired system behaviour in a satisfactory manner, e.g., a required user experience will be achieved with a sufficient probability. Quantiles also allow the determination of the maximal utility that can be achieved with a reasonable probability while staying within a given energy budget. As those examples illustrate important measures that are of interest when analysing energy-aware systems, it is clear that it is beneficial to extend formal analysis-methods with possibilities for the calculation of quantiles. In this monograph, we will see how we can take advantage of those quantiles as an instrument for analysing the trade-off between energy and utility in the field of probabilistic model checking. Therefore, we present algorithms for their computation over Markovian models. We will further investigate different techniques in order to improve the computational performance of implementations of those algorithms. The main feature that enables those improvements takes advantage of the specific characteristics of the linear programs that need to be solved for the computation of quantiles. Those improved algorithms have been implemented and integrated into the well-known probabilistic model checker PRISM. The performance of this implementation is then demonstrated by means of different protocols with an emphasis on the trade-off between the consumed energy and the resulting utility. Since the introduced methods are not restricted to the case of an energy-utility analysis only, the proposed framework can be used for analysing the interplay of cost and its resulting benefit in general.:1 Introduction 1.1 Related work 1.2 Contribution and outline 2 Preliminaries 3 Reward-bounded reachability properties and quantiles 3.1 Essentials 3.2 Dualities 3.3 Upper-reward bounded quantiles 3.3.1 Precomputation 3.3.2 Computation scheme 3.3.3 Qualitative quantiles 3.4 Lower-reward bounded quantiles 3.4.1 Precomputation 3.4.2 Computation scheme 3.5 Energy-utility quantiles 3.6 Quantiles under side conditions 3.6.1 Upper reward bounds 3.6.2 Lower reward bounds 3.6.2.1 Maximal reachability probabilities 3.6.2.2 Minimal reachability probabilities 3.7 Reachability quantiles and continuous time 3.7.1 Dualities 4 Expectation Quantiles 4.1 Computation scheme 4.2 Arbitrary models 4.2.1 Existential expectation quantiles 4.2.2 Universal expectation quantiles 5 Implementation 5.1 Computation optimisations 5.1.1 Back propagation 5.1.2 Reward window 5.1.3 Topological sorting of zero-reward sub-MDPs 5.1.4 Parallel computations 5.1.5 Multi-thresholds 5.1.6 Multi-state solution methods 5.1.7 Storage for integer sets 5.1.8 Elimination of zero-reward self-loops 5.2 Integration in Prism 5.2.1 Computation of reward-bounded reachability probabilities 5.2.2 Computation of quantiles in CTMCs 6 Analysed Protocols 6.1 Prism Benchmark Suite 6.1.1 Self-Stabilising Protocol 6.1.2 Leader-Election Protocol 6.1.3 Randomised Consensus Shared Coin Protocol 6.2 Energy-Aware Protocols 6.2.1 Energy-Aware Job-Scheduling Protocol 6.2.1.1 Energy-Aware Job-Scheduling Protocol with side conditions 6.2.1.2 Energy-Aware Job-Scheduling Protocol and expectation quantiles 6.2.1.3 Multiple shared resources 6.2.2 Energy-Aware Bonding Network Device (eBond) 6.2.3 HAECubie Demonstrator 6.2.3.1 Operational behaviour of the protocol 6.2.3.2 Formal analysis 7 Conclusion 7.1 Classification 7.2 Future prospects Bibliography List of Figures List of Table

Infrastructure coverage and the poor : the global perspective

The authors use the World Bank's Living Standards Measurement Study (LSMS) surveys from 15 countries (covering more than 55,500 households) to examine the relationship between infrastructure coverage and household income. The results show that throughout the world all income groups have much higher levels of coverage for electricity than for other formal infrastructure services (in-house piped water service, sewerage service, and private telephone service). In many countries most households in urban areas now have electricity service. As monthly household incomes increase from $100 to$250, coverage of all these infrastructure services rises, but at different rates. The findings confirm that the very poor rarely have these infrastructure services - with exceptions. The very poor often do have electricity if they live in urban areas. The very poor in Eastern Europe and Central Asia have much higher levels of coverage than those elsewhere in the world; they often have electricity, water, sewer, and telephone services. The results also suggest that if the poor gain access to services in their communities, many will decide to connect.Town Water Supply and Sanitation,Water Use,Housing&Human Habitats,VN-Acb Mis -- IFC-00535908,Health Economics&Finance

A Cross-Domain Approach to Analyzing the Short-Run Impact of COVID-19 on the U.S. Electricity Sector

The novel coronavirus disease (COVID-19) has rapidly spread around the globe in 2020, with the U.S. becoming the epicenter of COVID-19 cases since late March. As the U.S. begins to gradually resume economic activity, it is imperative for policymakers and power system operators to take a scientific approach to understanding and predicting the impact on the electricity sector. Here, we release a first-of-its-kind cross-domain open-access data hub, integrating data from across all existing U.S. wholesale electricity markets with COVID-19 case, weather, cellular location, and satellite imaging data. Leveraging cross-domain insights from public health and mobility data, we uncover a significant reduction in electricity consumption across that is strongly correlated with the rise in the number of COVID-19 cases, degree of social distancing, and level of commercial activity.Comment: This paper has been accepted for publication by Joule. The manuscript can also be accessed from EnerarXiv: http://www.enerarxiv.org/page/thesis.html?id=198

Time-Varying Risk Aversion and the Profitability of Carry Trades: Evidence from the Cross-Quantilogram

open access articleThis paper examines the predictive power of time-varying risk aversion over payoffs to the carry trade strategy via the cross-quantilogram methodology. Our analysis yields significant evidence of directional predictability from risk aversion to daily carry trade returns tracked by the Deutsche Bank G10 Currency Future Harvest Total Return Index. The predictive power of risk aversion is found to be stronger during periods of moderate to high risk aversion and largely concentrated on extreme fluctuations in carry trade returns. While large crashes in carry trade returns are associated with significant rises in investors’ risk aversion, we also found that booms in carry trade returns can be predicted at high quantiles of risk aversion. The results highlight the predictive role of extreme investor sentiment in currency markets and regime specific patterns in carry trade returns that can be captured via quantile-based predictive models

Information Disclosure Policies: Evidence from the Electricity Industry

A “third wave” of environmental policy has recently emerged that emphasizes information provision as an integral part of the risk mitigation strategy. While theory suggests information programs may correct market failures and improve welfare, the empirical effectiveness of these programs remains largely undetermined. We show that mandatory information disclosure programs in the electricity industry achieve stated policy goals. We that the average proportion of fossil fuels decreases and the average proportion of clean increases in response to disclosure programs. However, the programs also produce unintended consequences. Customer composition and pre-existing fuel mix significantly affect program response, suggesting that effective information disclosure policies may not be efficient.disclosure, information, fuel mix, electric utilities