An Agent-Based Simulation for Water Sharing Between Different Users

Water sharing has become a serious problem in France. One of the objectives of 1992 and 2000 directives proposed by the European Union was to reduce both the frequency and the extent of water conflicts through the establishment of multilateral negotiations, where different public and private interests can be represented in a structured institutional environment. In France, many negotiations take place at local level between farmers, water supplies, public services and environmental lists to allocate water resources between users. We suggest that Agent-Based Modelling (ABM) using a multi-agent approach could help negotiations between different players by showing the consequences of water allocation rules and taking in consideration the players' respective attitudes and their ability to change their behaviour.Multiagent-based simulation, user and agent modelling, conflict resolution and negotiation, irrigation application, Resource /Energy Economics and Policy,

Some space shuttle tile/strain-isolator-pad sinusoidal vibration tests

Vibration tests were performed on the tile/strain-isolator-pad system used as thermal protection for the space shuttle orbiter. Experimental data on normal and in-plane vibration response and damping properties are presented. Three test specimens exhibited shear type motion during failures that occurred in the tile near the tile/strain-isolator-pad bond-line. A dynamic instability is described which has large in-plane motion at a frequency one-half that of the nominal driving frequency. Analysis shows that this phenomenon is a parametric response

Using conditional kernel density estimation for wind power density forecasting

Of the various renewable energy resources, wind power is widely recognized as one of the most promising. The management of wind farms and electricity systems can benefit greatly from the availability of estimates of the probability distribution of wind power generation. However, most research has focused on point forecasting of wind power. In this paper, we develop an approach to producing density forecasts for the wind power generated at individual wind farms. Our interest is in intraday data and prediction from 1 to 72 hours ahead. We model wind power in terms of wind speed and wind direction. In this framework, there are two key uncertainties. First, there is the inherent uncertainty in wind speed and direction, and we model this using a bivariate VARMA-GARCH (vector autoregressive moving average-generalized autoregressive conditional heteroscedastic) model, with a Student t distribution, in the Cartesian space of wind speed and direction. Second, there is the stochastic nature of the relationship of wind power to wind speed (described by the power curve), and to wind direction. We model this using conditional kernel density (CKD) estimation, which enables a nonparametric modeling of the conditional density of wind power. Using Monte Carlo simulation of the VARMA-GARCH model and CKD estimation, density forecasts of wind speed and direction are converted to wind power density forecasts. Our work is novel in several respects: previous wind power studies have not modeled a stochastic power curve; to accommodate time evolution in the power curve, we incorporate a time decay factor within the CKD method; and the CKD method is conditional on a density, rather than a single value. The new approach is evaluated using datasets from four Greek wind farms

Cost-effective and Resilient Operation of Distribution Grids and 5G Telecommunication

5G base stations have growing importance in an integrated electric power and telecommunication system, for mobile user equipment mobile data supply and demand response in distribution grids. However, demand response through base station’s flexibility can have a growing impact on the power flow of the grid. Additionally, in extreme events, if a power outage occurs at the physical base station, data loads need to be first reconnected to the 5G network, which is essential for the grid to further recover the electricity loads. In this paper, a cost-effective and resilient operation method is proposed to optimally utilize the flexibility of renewable-based 5G base stations and the data load shedding to recover the data transmission. The flexibility from batteries equipped in the base stations and the flexible associations between user equipments and base stations are considered. The simulation results verify the proposed method can achieve lower energy costs and power losses of the grid in normal operation and a resilient operation in an extreme event

On machine learning-based techniques for future sustainable and resilient energy systems

Permanently increasing penetration of converter-interfaced generation and renewable energy sources (RESs) makes modern electrical power systems more vulnerable to low probability and high impact events, such as extreme weather, which could lead to severe contingencies, even blackouts. These contingencies can be further propagated to neighboring energy systems over coupling components/technologies and consequently negatively influence the entire multi-energy system (MES) (such as gas, heating and electricity) operation and its resilience. In recent years, machine learning-based techniques (MLBTs) have been intensively applied to solve various power system problems, including system planning, or security and reliability assessment. This paper aims to review MES resilience quantification methods and the application of MLBTs to assess the resilience level of future sustainable energy systems. The open research questions are identified and discussed, whereas the future research directions are identified

Random percolation as a gauge theory

Three-dimensional bond or site percolation theory on a lattice can be interpreted as a gauge theory in which the Wilson loops are viewed as counters of topological linking with random clusters. Beyond the percolation threshold large Wilson loops decay with an area law and show the universal shape effects due to flux tube quantum fluctuations like in ordinary confining gauge theories. Wilson loop correlators define a non-trivial spectrum of physical states of increasing mass and spin, like the glueballs of ordinary gauge theory. The crumbling of the percolating cluster when the length of one periodic direction decreases below a critical threshold accounts for the finite temperature deconfinement, which belongs to 2-D percolation universality class.Comment: 20 pages, 14 figure

Transistors

Contains reports on eight research projects.Lincoln Laboratory under Contract AF19(122)-45

A reduced-reference perceptual image and video quality metric based on edge preservation

In image and video compression and transmission, it is important to rely on an objective image/video quality metric which accurately represents the subjective quality of processed images and video sequences. In some scenarios, it is also important to evaluate the quality of the received video sequence with minimal reference to the transmitted one. For instance, for quality improvement of video transmission through closed-loop optimisation, the video quality measure can be evaluated at the receiver and provided as feedback information to the system controller. The original image/video sequence-prior to compression and transmission-is not usually available at the receiver side, and it is important to rely at the receiver side on an objective video quality metric that does not need reference or needs minimal reference to the original video sequence. The observation that the human eye is very sensitive to edge and contour information of an image underpins the proposal of our reduced reference (RR) quality metric, which compares edge information between the distorted and the original image. Results highlight that the metric correlates well with subjective observations, also in comparison with commonly used full-reference metrics and with a state-of-the-art RR metric. © 2012 Martini et al

A market for trading forecasts: A wagering mechanism

In many areas of industry and society, including energy, healthcare, and logistics, agents collect vast amounts of data that are deemed proprietary. These data owners extract predictive information of varying quality and relevance from data depending on quantity, inherent information content, and their own technical expertise. Aggregating these data and heterogeneous predictive skills, which are distributed in terms of ownership, can result in a higher collective value for a prediction task. In this paper, a platform for improving predictions via the implicit pooling of private information in return for possible remuneration is envisioned. Specifically, a wagering-based forecast elicitation market platform has been designed, in which a buyer intending to improve their forecasts posts a prediction task, and sellers respond to it with their forecast reports and wagers. This market delivers an aggregated forecast to the buyer (pre-event) and allocates a payoff to the sellers (post-event) for their contribution. A payoff mechanism is proposed and it is proven that it satisfies several desirable economic properties, including those specific to electronic platforms. Furthermore, the properties of the forecast aggregation operator and scoring rules are discussed in order to emphasize their effect on the sellers’ payoff. Finally, numerical examples are provided in order to illustrate the structure and properties of the proposed market platform