Distributed-user scenario

User access (concentrated, distributed). In the concentrated users scenario ( CON ), approximately 25% of the population have access to hydrogen by 2020, 50% by 2025 and 85% by 2030. In contrast, in the distributed users scenario ( DIS ), 50% of the population already have access to hydrogen by 2020, 90% by 2025, and the whole population by 2030. The scenarios do not differ in the period until 2015, when only the early-user centres are covered. 

The Reference scenario ( REF ) is, among others, based on the distributed users scenario for the geographical distribution of hydrogen demand, a fuelling-station strategy tailored to meeting demand only ( FS demand ) and a late road network it is further characterised by high natural gas prices and a stabilisation objective for C02... 

Figure 14.6(b). Geographical allocation of hydrogen demand in Germany until 2030 distributed users scenario . 

Figure 14.7(a). Fuelling stations in Germany for the distributed users scenario all hydrogen stations until 2030. 

The roll-out strategy for hydrogen from 2015 to 2030 ( concentrated users scenario or distributed users scenario) substantially shapes the development of the hydrogen... 

Figure 11.6 Fueling stations in the last phase (T4) - distributed- (left) and concentrated- (right) user scenarios...

From the understanding of virtual reality as a virtual place of work -where the user can carry out all steps of development - interactive planning seems feasible within this environment. Prerequisite to this scenario is a real time simulation environment for the simulation of technological systems, particularly for distributed networks. One part of simulation model is based on a vertical flow of information, whereas another part of the model is based on the material flow (Figure 6). 

The disadvantage of this technique is the large volume of screen data which is to be transferred to all partners to display the current state simultaneously for all users. The scenario in question was the cooperative work of a distributed team regarding 3D-animations using BEMView in plastics processing (see Subsect. 5.4.6). The amount of data to be transferred for shared working with an animation sequence is intolerably high. 

Transport distances to warehouses and distribution centres were based on primary data collected at the production sites. Inputs and outputs were only available for the whole site and were allocated based on the total mass of apparel stored in the warehouse. The following two distribution scenarios to the end user were included in the study ... 

Abstract. The trend to compose real time systems with standard IT known from conventional office domains results in heterogeneous technical environments. Examples are modern industrial process automation networks. It is a challenging task, because of potential impacts of security incidents to the system safety. For example, robot control rmits could be manipulated by malicious codes. The term risk communication is introduced, to describe alarm communication in hmnan-machine interaction scenarios. User adapted risk communication between humans and industrial automation systems, including home robotics, can prevent hazards and/or threats to the entire system safety and security. Current safety and security risk communication standards are compared to examine the adequacy for our uniform approach. This paper focuses on alarm system standards in the industrial process automation domain and intrusion detection systems from the conventional desktop IT domain. A uniform model based approach for risk communication in distributed IT enviromnents is introduced. 

In batch processing, uncertainty can emerge in operational or market-related parameters. Processing rates, duration of activities, material purchasing and product selling prices are common uncertain variables. Such uncertainty affects the throughput of a plant, the profitability and other key performance indicators (KPIs). In Monte Carlo simulation, numerous scenarios of a model are simulated by repeatedly picking values from a user-defined probability distribution for the uncertain variables. Those values are used in the model to calculate and analyze the outputs in a... 

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