“…Proposed hybrid layout described in Figure 1 can be treated as an hybrid series system in which the primary source of power is represented by fuel cells while batteries are substantially used as power buffers. For hybrid series vehicles adopted energy management policies are typically treated as weighted combination of two different approaches 39,40 that are briefly described in equation ( 20):…”
Section: Energy Management Systemmentioning
confidence: 99%
“…This approach is also the starting base of recent works 38,39 considering optimization of the rail power management systems respect to fuel cell ageing (Figure 5). Figure 5.adopted range extender logic to manage fuel cells.…”
Section: Simplified Model Of Hybrid Storage Systemmentioning
Diesel multiple units (DMU) offer an affordable alternative to electric trains on lines in which infrastructural and maintenance costs of electrification are not justified. A further improvement of sustainability involves the abandonment of internal combustion engines in favour of an electric power train sustained by an on-board storage system. In this work, the authors propose a model to perform a rapid simulation of hybrid storage systems based on hydrogen fuel cells,a technology that should be complementary with respect to battery-operated trains. The proposed tool is applied, as a benchmark test case, to a modular passenger train devoted to regional service. Obtained results clearly demonstrate the capabilities of proposed tools and give useful indications for a feasible optimization and integration of fuel cells for railway applications.
“…Proposed hybrid layout described in Figure 1 can be treated as an hybrid series system in which the primary source of power is represented by fuel cells while batteries are substantially used as power buffers. For hybrid series vehicles adopted energy management policies are typically treated as weighted combination of two different approaches 39,40 that are briefly described in equation ( 20):…”
Section: Energy Management Systemmentioning
confidence: 99%
“…This approach is also the starting base of recent works 38,39 considering optimization of the rail power management systems respect to fuel cell ageing (Figure 5). Figure 5.adopted range extender logic to manage fuel cells.…”
Section: Simplified Model Of Hybrid Storage Systemmentioning
Diesel multiple units (DMU) offer an affordable alternative to electric trains on lines in which infrastructural and maintenance costs of electrification are not justified. A further improvement of sustainability involves the abandonment of internal combustion engines in favour of an electric power train sustained by an on-board storage system. In this work, the authors propose a model to perform a rapid simulation of hybrid storage systems based on hydrogen fuel cells,a technology that should be complementary with respect to battery-operated trains. The proposed tool is applied, as a benchmark test case, to a modular passenger train devoted to regional service. Obtained results clearly demonstrate the capabilities of proposed tools and give useful indications for a feasible optimization and integration of fuel cells for railway applications.
“…Some types of fuel cells, such as SOFC and MCFC, have a time to reach high operating temperature and as a result, a lower response speed, so they are suitable for powering building units and large transportation vehicles such as ships and locomotives. On the other hand, the PEMFC type fuel cell has a low operating temperature and a high response speed, and is suitable for providing the energy needed by cars that have instantaneous operation [20].…”
Section: Fc Overviewmentioning
confidence: 99%
“…A V-P curve is quite valuable for assessing transmission or loading constraints. Furthermore, V-P curves cannot as effectively identify regions where reactive shortages occur for generator or line outages [20].…”
In this paper, the use of a FC system connected to the network is proposed as a source of DG with high reliability, and for this purpose, the dynamic model of the fuel cell has been simulated. A hybrid system of fuel cell distributed generation (FCDG) is presented to provide electrical energy for a small isolated area. Boost converter (DC/DC), in order to increase the voltage level of the system and stabilize the DC link voltage has been used, which provides the possibility of connecting several different scattered production sources in parallel. Voltage stability is concerned with the ability of a power system to maintain acceptable bus voltages under normal conditions and after being subjected to a disturbance. The use of DG sources has many advantages, including meeting peak load needs, reducing network losses, providing reactive power locally, and regulating network voltage. Among all sources of distributed production, fuel cells are of special importance due to their high efficiency, high energy density, the ability to simultaneously produce heat and electric power, and low emission of pollutants. Using fuel cells (FCs) have several advantages and in this paper we investigate the effects of FCs on power systems via simulation a single machine (DG as small gas turbine coupled with a FC) in the Dig Silent area for different PF of FC. Different PF for FC obtained with control the DC to AC inverter. We found that by control the PF of FCs, we can increase the limitation of reactive generation of overall system and improve the V-P V-Q characteristics of overall system. With the grid-connected inverter's switching control, the active and reactive power injected into the grid is controlled independently.
“…Deep reinforcement learning (DRL) has attracted much research attention in recent years for its high calculation efficiency and satisfactory real-time performance. The DRL algorithm-based energy management model and the derived battery anti-aging strategy can be used to define the optimal battery utilization strategy in Energy-Transportation Nexus, such as hybrid electric vehicle [21], rail transportation system [22], and GEVs charging scheduling [23]. In [24], DRL algorithm is used to realize online energy management for plugin hybrid electric buses.…”
Grid-connected electric vehicles (GEVs) and Energy-Transportation Nexus bring a bright prospect to improve the penetration of renewable energy and the economy of microgrids. However, it is challenging to determine optimal vehicle-to-grid (V2G) strategies due to the complex battery aging mechanism and volatile microgrid states. This paper develops a novel online battery anti-aging energy management method for Energy-Transportation Nexus by using a novel deep reinforcement learning framework. Based on battery aging characteristic analysis and rain-flow cycle counting technology, the quantification of aging cost in V2G strategies is realized by modelling the impact of number of cycles, depth of discharge, and charge and discharge rate. The established life loss model is used to evaluate battery anti-aging effectiveness of agent actions. The coordination of GEVs charging is modelled as multi-objective learning by using a deep reinforcement learning algorithm. The training objective is to maximize renewable penetration while reducing microgrid power fluctuations and vehicle battery aging costs. The developed Energy-Transportation Nexus energy management method is verified to be effective in optimal power balancing and battery anti-aging control on a microgrid in the UK. This research provides an efficient and economical tool for microgrid power balancing by optimally coordinating GEVs charging and renewable energy, thus helping promote a low-cost decarbonization transition.
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