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Model-Predictive Energy Management for the Integration of Plug-In-Hybrid Electric Vehicles into Building Energy Systems

机译:模型预测能源管理,用于将插件混合动力电动车集成到建筑能量系统中

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In current research projects such as "Vehicle to Grid" (V2G), "Vehicle to Building" (V2B) or "Vehicle to Home" (V2H), plug-in vehicles are integrated into stationary energy systems. V2B or V2H therefore stands for intelligent networking between vehicles and buildings. However, in these projects the objective is mostly from a pure electric point of view, to smooth the load profile on a household level by optimized charging and discharging of electric vehicles. In the present paper a small energy system of this kind, consisting of a building and a vehicle, is investigated from a holistic point of view. Thermal as well as electrical system components are taken into account and there is a focus on reduction of overall energy consumption and CO_2 emissions. A predictive energy management is presented that coordinates the integration of a plug-in hybrid electric vehicle into the energy systems of a building. System operation is optimized in terms of energy consumption and CO_2 emissions. A model predictive approach is applied to the charging phases of a plug-in hybrid electric vehicle as well as on the energy system of a building with integrated energy generation by a cogeneration unit and a photovoltaic system. In the present paper the energy-saving potential for different mobility scenarios that can be achieved through a holistic, integrative energy management is shown. The overall primary energy demand of the energy system as described is examined with a simulation model. The energy management contains, in a similar way to an MPC (Model Predictive Control) system, a model of the system dynamics. With this model, prediction of the energy process is conducted, based on a weather forecast and future mobility patterns. The future development of all relevant variables is thus predicted. Based on this, optimization of the operational management takes place. As part of this prediction process the best operation strategy for the manipulation of flexible system components is determined and selected. The energy management system optimizes and coordinates the use of components and the energy flows within the coupled energy system and involves the entire "well-to-wheel" chain for an ecological system operation.
机译:在当前研究项目中,例如“车辆到网格”(V2G),“将车辆建造”(V2B)或“车辆到家用”(V2H),插入式车辆集成到固定能量系统中。因此,V2B或V2H代表车辆和建筑物之间的智能网络。然而,在这些项目中,目标主要来自纯粹的电动角度,通过优化的电动汽车充电和放电来平滑家庭水平的负载轮廓。在本文中,从整体的角度来研究由建筑物和车辆组成的这种小型能量系统。考虑热量以及电气系统组件,重点关注整体能耗和CO_2排放的降低。提出了一种预测能量管理,其协调插入式混合电动车辆进入建筑物的能量系统中的集成。在能耗和CO_2排放方面进行了优化了系统操作。模型预测方法应用于插入式混合动力电动车辆的充电阶段以及通过热电单元和光伏系统的集成能量产生的建筑物的能量系统。在本文中,示出了可以通过整体,整合能源管理实现的不同移动性方案的节能潜力。用模拟模型检查如上所述的能量系统的总机需求。能量管理以与MPC(模型预测控制)系统类似的方式包含系统动态的模型。利用该模型,基于天气预报和未来的移动模式,进行了能量过程的预测。因此预测了所有相关变量的未来发展。基于此,对运营管理的优化发生。作为该预测过程的一部分,确定并选择用于操纵灵活系统组件的最佳操作策略。能量管理系统优化和坐标使用组件和耦合能量系统内的能量流动,并涉及整个“井轮”链进行生态系统操作。

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