Abstract

An information policy is a concerted action of various agents with information as the object, having as a purpose certain goals. The crisis we face is not only economic but also technological and due to changes in the organizational model, and we are already seeing some effects of deregulation and no planning. We can only establish solid information policies if we find a set of actions around which a wide spectrum of agents can be aligned without friction. 1. In the print world, policies should allow us to determine what exists and make that easily accessible, and know what is little used and will tend to be used less and ensure that it is preserved. 2. Research funding institutions can and must agree to facilitate open access to information and data. 3. Finally, the library-organization must be able to provide digital books, offer digital information that anybody can use, and ensure that born-digital materials are passed on to future generations. To this we must add the need to plan for information literacy training in schools. In these times of change, surrounded by uncertainty and some notable weaknesses, part of our professional duties should be devoted to elaborate agreements enabling us to take coordinated action in certain directions.

Abstract

In literature, several mechanisms are proposed to prevent Plug-in Electric Vehicles (PEVs) from overloading the distribution grid. However, it is unclear how such technical mechanisms influence the market level control strategies of a PEV aggregator. Moreover, the presence of multiple aggregators in the same distribution grid further complicates the problem. Often, grid congestion management mechanisms are proposed to solve the potential interference between the technical and market objectives. Such methods come at the expense of additional complexity and costs, which is not beneficial for the large scale application of demand response. In our work, we investigate this problem by combining a simple low level voltage droop controller with an event driven control strategy for the coordination of charging PEVs. The approach is evaluated in different distribution grid settings, using two different market objectives for the aggregator. Document type: Part of book or chapter of book

Abstract

The limited range of electric vehicles (EVs) in combination with the limited capacity of current fast charging infrastructure are both causes for a limited adoption of EVs. In order to reduce the general inconvenience that EV users experience when having to wait for available fast charging stations and to lessen the danger of damaging the infra- structure by overloading it, an efficient coordination strategy is needed. This paper proposes an anticipatory, decentralised coordination strategy for on-route charging of EVs during lengthy trips in a fast-charging infra- structure. This strategy is compared to a reference strategy that uses global real-time knowledge of charging station occupation. Simulation results using a realistic scenario with real-world traffic data demonstrate that the anticipatory strategy is able to reduce the waiting times for EV users by up to 50% while at the same time decreasing the peak loads of the electricity grid caused by charging EVs by 21%. The limited range of electric vehicles (EVs) in combination with the limited capacity of current fast charging infrastructure are both causes for a limited adoption of EVs. In order to reduce the general inconvenience that EV users experience when having to wait for available fast charging stations and to lessen the danger of damaging the infra- structure by overloading it, an efficient coordination strategy is needed. This paper proposes an anticipatory, decentralised coordination strategy for on-route charging of EVs during lengthy trips in a fast-charging infra- structure. This strategy is compared to a reference strategy that uses global real-time knowledge of charging station occupation. Simulation results using a realistic scenario with real-world traffic data demonstrate that the anticipatory strategy is able to reduce the waiting times for EV users by up to 50% while at the same time decreasing the peak loads of the electricity grid caused by charging EVs by 21%. ispartof: pages:74-85 ispartof: Advances in Practical Applications of Heterogeneous Multi-Agent Systems. The PAAMS Collection vol:8473 pages:74-85 ispartof: PAAMS'14 location:Salamanca (Spain) date:4 Jun - 6 Jun 2014 status: published

Abstract

This text is a follow-up to the previous ThinkEPI note on this subject published back in 2019 (De-Castro, 2019). The piece reflects on the two main functions that Current Research Information Systems (CRIS) are aimed to fulfill. These to some extent conflicting functions are in the first place to showcase the research activity conducted within an organization as a practical implementation of Open Science, and then to collect and store institutional research information for internal use in evidence-based decision-making processes. The text argues that both functions are in fact complementary. Moreover, simultaneously pursuing both of them requires an efficient collaboration across systems (technical interoperability) and across the various institutional units involved in the management and administration of CRIS systems (human interoperability). The text explores the present CRIS landscape in Spain under this double-function perspective and highlights some best-practice case studies in terms of this collaboration.

Abstract

Future-generations cyber-infrastructure must enable the dynamic coordinated composition of computing and information services. Resulting applications need to provide high-performance distributed computing to end users in a scalable, reliable and secure manner. This pervasive and ubiquitous grid computing infrastructure will view physical devices and computational agents uniformly, enabling radical improvement of existing applications, and opening the door for applications in entirely new domains. For example, wireless sensoractuator networks deployed in buildings and bridges can semi-automatically and cooperatively react to natural or man-made disasters in order to prevent human losses. Temperature, pressure, and stress-sensing devices can inform authorities about the probability of structural damage leading to collapse, can automatically activate fire extinguishing equipment, can help locate survivors and devise exit strategies, and can deviate traffic on emergency situations in semi-automated ways. Another example is virtual surgical planning, in which a surgeon simulates several surgery plans on a detailed computational model of a patient’s body. The surgeon can interactively analyze the effect of different plans on the patient’s body fluid dynamics. Another example is a distributed camera network coordinated by a real-time data mining component for airport surveillance and security. Unusual irregular patterns of human behavior can be detected and communicated promptly to authorities. A final example is a manned and unmanned aerial vehicle network that can exchange sensed information and plans of action, and combine them with terrain and map databases for decentralized coordinated air traffic control. These applications will directly benefit from coordinated computational resources offered by the future pervasive grid. To realize this pervasive grid vision, it is imperative to develop programming technology and modular middleware to facilitate systems development on highly heterogeneous and dynamic cyber-infrastructure. In closed grid environments, a centralized coordination module often needs to reserve dedicated network and computing resources for specific tasks. Furthermore, users are often expected to manually allocate resources and install any needed software on target computing environments. In contrast, open dynamic grid environments require coordination and resource management protocols to be automated. Resources in open grid environments can be organized into peer-to-peer networks. These networks are scalable because information lives in individual nodes and communication is highly, if not completely, decentralized. In an envisioned grid computing scenario, a process or data item will be created by a human user in a single node. The process or data item will get replicated and propagated by middleware Document type: Article