Optimization method for multiple heat source operation including ground source heat pump considering dynamic variation in ground temperature
Optimization method for multiple heat source operation including ground source heat pump considering dynamic variation in ground temperature
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DOI：
10.1016/j.apenergy.2017.02.047
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发表时间：
2017-05
期刊：
影响因子：
11.2
通讯作者：
S. Ikeda;W. Choi;R. Ooka
中科院分区：
文献类型：
--
作者：
S. Ikeda;W. Choi;R. Ooka
近年来，高效能源系统的广泛使用不仅是降低能源消耗的重要措施，也是降低运营成本的重要措施。土壤源热泵系统由于其高效性而受到广泛关注。虽然许多研究已经进行了调查和评估的土壤源热泵的性能，他们没有充分研究其优化运行考虑动态的土壤温度变化引起的高的热容量的地面。同时考虑地面热历史和最优负荷分配的计算是复杂的，因此需要高的计算成本。本文提出了一种有效的优化方法，以确定用于处理冷负荷和热水需求的混合式地源热泵系统的最佳操作。所提出的方法，即ε约束差分进化随机跳跃，可以解决几乎所有可能的配置，是一个合适的方法，这里使用的非线性配置，因为土壤源热泵具有高度非线性特性和地面温度的计算不能简化为一个线性公式。与经验操作相比，所提出的方法实现的最优操作可以降低操作成本至少3.78%，最多12.56%。此外，该方法推导的解决方案迅速，同时保持高的计算精度。因此，它可以用于在实际情况下，以确定一个最佳的操作计划，作为一个日前优化。
Recent years have witnessed the widespread use of highly efficient energy systems as an important measure to reduce not only energy consumption but also operating costs. A ground source heat pump system has been attracting considerable attention because of its high efficiency. Although many studies have been conducted to investigate and evaluate the ground source heat pump’s performance, they have not sufficiently studied its optimal operation considering dynamic ground temperature variation caused by the high thermal capacity of the ground. Calculations considering both thermal history of the ground and optimal load dispatch are complicated and thus entail high computation costs. In this paper, an efficient optimization method is proposed to determine optimal operations of a hybrid ground source heat pump system that is used to handle the cooling load and hot water demand. The proposed method, namely epsilon-constrained differential evolution with random jumping, can solve nearly all possible configurations and is a suitable method for the nonlinear configuration used herein because the ground source heat pump has highly nonlinear characteristics and the ground temperature calculation cannot be simplified to a linear formulation. The optimal operations achieved by the proposed method can reduce operating costs by at least 3.78% and at most 12.56% compared to empirical operations. In addition, the proposed method derives the solution rapidly while maintaining high computation accuracy. Therefore, it can be used in practical situations to determine an optimal operating schedule as a day-ahead optimization.