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Energy Storage in Smart Homes: Grid-Convenience Versus Self-Use and Survivability

机译:智能家居的储能:网格 - 便利与自用和生存能力

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The number of local power generation units, such as photovoltaic panels (PV), increased enormously in recent years. Their production patterns are highly variable and depend on the current weather. The resulting fluctuation in production poses a major challenge to the stability of the power grid. The use of local energy storages may help to ensure that the locally produced power is fed into the grid in a grid-convenient way. It may also help clients to increase the self-use of locally generated power and to increase the so-called survivability of their homes in the presence of a power outage. This paper compares the interest of the power operator, i.e. grid-convenience with the interests of the user, i.e. self-use and survivability for different battery management strategies: i) direct loading, ii) delayed loading and iii) peak shaving. We use a Hybrid Petri Net model with one stochastic variable (HPnG) to model smart homes with local power generation, local storage and different battery management strategies in the presence of power outages. Recent algorithms for analyzing and model checking HPnGs enable the computation of the above mentioned measures of interest. We are able to show that whenever good predictions of production and demand exist, grid-convenience does not decrease the survivability and the self-use of a smart home.
机译:近年来,局部发电单元(如光伏电池板(PV))的数量增加。他们的生产模式是高度变化的,依赖目前的天气。产生的生产波动对电网的稳定性带来了重大挑战。局部能量存储器的使用可以有助于确保以网格方便的方式将本地产生的电力馈入网格中。它还可以帮助客户增加本地产生的能量的自我使用,并在存在停电时增加其房屋的所谓的生存能力。本文比较了电力运营商的兴趣,即,对用户的利益,即不同电池管理策略的自我使用和生存能力:i)直接加载,ii)延迟加载和III)峰值剃须。我们使用具有一个随机变量(HPNG)的混合培养净模型来模拟智能家庭,在停电时,在存在的情况下,本地发电,本地存储和不同的电池管理策略。用于分析和模型检查HPNG的算法使得能够计算上述感兴趣的措施。我们能够表明,只要存在对生产和需求的良好预测,栅格方便不会降低智能家居的生存能力和自我使用。

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