The invention relates to an energy management device for controlling a charging control appliance in an electric vehicle, the energy management device being designed to adjust a charging profile in the charging control appliance for the charging of a battery of the electric vehicle on a charging station or in the electric vehicle itself, the adjustment of the charging profile at least taking into account a planned time of use of the electric vehicle and a desired charging state of the battery as well as a pre-defined value for the maximum charging capacity of the electric vehicle, the energy management device further being coupled to a measuring unit which is designed to measure an actual charging capacity provided for the electric vehicle by the charging station and, independently or as a result of a request from the energy management device, transmit same to the energy management device, the energy management device being designed to use the value of the actual charging capacity for a corresponding new adjustment of the charging profile.

The invention relates to a method (400) for determining an operating strategy for an electrical energy store (111). The method (400) comprises the step of dividing (401) an operating time interval for which the operating strategy is to be determined into a sequence of time segments (223) in order for unvarying performance conditions to be achieved in each of the time segments (223) of the sequence of time segments (223). The method (400) also comprises the step of determining (402), for each time segment (223) of the sequence of time segments (223), a limited number of possible operating performances (221) with which the energy store (111) may be charged and run down in the respective time segment (223). The method (400) also comprises the step of determining (403) a plurality of sequences of operating points (310); wherein an operating point (310) for a time segment (223) indicates an operating performance from the limited number of possible operating performances for this time segment (223); and wherein a sequence of operating points (310) indicates a sequence of operating performances for the sequence of time segments (223). The method (400) also comprises the step of selecting (404) a sequence of operating points (310) from the plurality of sequences of operating points (310), as an operating strategy.

Multivariate descriptions of ocean parameters are quite important for the design and risk assessment of offshore engineering applications. A reliable and realistic statistical multivariate model is essential to produce a representative estimate of the sea state for understanding the ocean conditions. Therefore, an advanced modeling of ocean parameters helps towards improving ocean and coastal engineering practices. In this paper, we introduce the concepts of asymmetric copulas for the modeling of multivariate ocean data. In contrast to extensive previous research on the modeling of symmetric ocean data, this study is focused on capturing asymmetric dependencies among the environmental parameters, which are critical for a realistic description of ocean conditions. This involves particular attention to both nonlinear and asymmetrically dependent variates, which are quite common for the ocean variables. Several asymmetric copula functions, capable of modeling both linear and nonlinear asymmetric dependence structures, are examined in detail. Information on tail dependencies and measures of asymmetric dependencies are exploited. To demonstrate the advantages of asymmetric copulas, the asymmetric copula concept is compared with the traditional copula approaches from the literature using actual environmental data. Each of the introduced copula models is fitted to a set of ocean data collected from a buoy at the US coast. The performance of these asymmetric copulas is discussed and compared based on data fitting and tail dependency characterizations. The accuracy of asymmetric copulas in predicting the extreme value contours is discussed.

The success of public-private partnership (PPP)-build-operate-transfer (BOT) projects largely depends on effectively mitigating the impact of a variety of risks and uncertainties, especially those influencing the revenue over time. Revenue instability is one of the main obstacles of PPP form of procurement. Government support, which is established as a clause in the concession agreement, should be carefully designed and well formulated. Options which arise from certain clauses in the contract are more valuable for risky projects. The purpose of this paper's proposed model is to evaluate early fund generation options and also to calculate equitable bounds for a guaranteed revenue for the project sponsor under uncertainty and risk. The model is specially designed to alleviate the concern of revenue risk. To illustrate its applicability the methodology is then applied to a freeway PPP project and a power plant PPP project in Iran. The results show that the value of these options can indeed be significant and by applying the proposed systematic negotiation mechanism both public and private sectors can take advantage of its flexibility at the negotiation table. The proposed mechanisms can facilitate negotiations on the verge of a break down as well as accelerating ongoing negotiations that have become moribund.

Natural hazards have the capability to affect technological installations, triggering multiple failures and putting the population and the surrounding environment at risk. Global climate change introduces an additional and not negligible element of uncertainty to the vulnerability quantification, threatening to intensify (both in terms of frequency and severity) the occurrence of extreme climate events. Sea level extremes and extreme coastal high waters are expected to change in the future as a result of both changes in atmospheric storminess and mean sea level rise, as well as extreme precipitation events. These trends clearly suggest a parallel increase in the risks affecting technological installations and the subsequent need for mitigation measures to enhance the reliability of existing systems and to improve the design standards of new facilities. In spite of this situation, the scientific research in this field lacks robust and reliable tools for this kind of assessment, often relying on the adoption of oversimplified models or strong assumptions, which affect the credibility of the results. The main purpose of this study is to provide a novel and general model for the evaluation of the risk of exposure of spent nuclear fuel stored in a facility subject to flood hazard, investigating the potential and limitations of Bayesian networks (BNs) in this field. The network aims to model the interaction between extreme weather conditions and the technological installation, as well as the propagation of failures within the system itself, taking into account the dependencies among the different components and the occurrence of human error. A real-world application concerning the nuclear power station of Sizewell B in East Anglia, in the United Kingdom, is extensively described, together with the models and data set used. Results are presented for three different time scenarios in which climate change projections have been adopted to estimate future risks. (C) 2016 American Society of Civil Engineers.

\0