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Bottom-up Markov Chain Monte Carlo approach for scenario based residential load modelling with publicly available data

机译:自底向上的马尔可夫链蒙特卡洛方法,用于基于情景的住宅负荷建模,具有公开数据

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In the residential sector, with the introduction of electric vehicles and photovoltaics, developments are taking place which have an impact on residential load curves. In order to assess the integration of these new types of technologies on both the generation and load side, as well as to develop mitigation strategies like demand side management, detailed information is required about the load curve of a household. To gain knowledge about this load curve a residential load model is developed based on publicly available data. The model utilises a Markov Chain Monte Carlo method to model the occupancy in a household based on time use surveys, which together with weather variables, neighbourhood characteristics and behavioural data are used to model the switching pattern of appliances. The modelling approach described in this paper is applied for the situation in the Netherlands. The resulting load curve probability distributions are validated with smart meter measurements for 100 Dutch households for a week. The validation shows that the model presented in this paper can be employed for further studies on demand side management approaches and integration issues of new appliances in distribution grids. (C) 2015 Elsevier B.V. All rights reserved.
机译:在住宅领域,随着电动汽车和光伏技术的引入,正在发生着对住宅负荷曲线产生影响的发展。为了评估在发电和负荷方面这些新型技术的集成,以及开发缓解策略(如需求方管理),需要有关家庭负荷曲线的详细信息。为了获得有关此负荷曲线的知识,根据公开数据开发了住宅负荷模型。该模型基于时间使用调查,利用马尔可夫链蒙特卡罗方法对家庭中的居住情况进行建模,该调查与天气变量,邻里特征和行为数据一起用于对电器的开关模式进行建模。本文描述的建模方法适用于荷兰的情况。每周用100个荷兰家庭的智能电表测量结果验证得出的负载曲线概率分布。验证表明,本文提出的模型可用于需求侧管理方法和配电网中新设备集成问题的进一步研究。 (C)2015 Elsevier B.V.保留所有权利。

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