Measuring variability on electrical power demands in manufacturing operations
Measuring variability on electrical power demands in manufacturing operations
复制标题
测量制造运营中电力需求的变化
DOI：
10.1016/j.jclepro.2016.03.102
复制
发表时间：
2016
影响因子：
11.1
通讯作者：
V. Prabhu
中科院分区：
文献类型：
--
作者：
H. Jeon;M. Taisch;V. Prabhu
制造业能源研究通常侧重于估计机器所需的平均电功率。然而，变异性也是制造业电力研究中需要考虑的一个重要因素，因为了解电力需求的不确定性可以支持对峰值电力需求的估计，这将影响能源成本和配电系统。例如，美国联邦法律要求电力供应商在达成预先确立的高峰需求削减协议时，向制造商等用电大户提供信用额度。制造工厂的高峰需求还可用于工厂电气系统的规划和设计，以及确定向新的大客户提供电力服务的设施和设备的投资金额，因此，本文提出了一种基于制造参数等现成信息来估计高峰需求的系统方法。更具体地说，该方法通过考虑不同的制造过程，从机器状态级的制造参数中提取功率需求均值和方差。然后，通过实现概率混合模型，使用机器状态级别的均值和方差来近似机器级别的均值和方差。将林德伯格中心极限定理应用于机器级别的信息，我们估计了制造系统级别的功率需求的均值和方差。在此基础上，提出了一种在系统层次上利用均值和方差估计峰值功率需求的方法，并以实际制造功率轮廓为例进行了验证。我们展示了如何将制造工艺参数与功率需求的均值和方差联系起来，以及如何基于均值和方差估计峰值功率需求。为了验证该方法的有效性，我们对一个假设的制造系统进行了仿真，结果表明，该方法估计平均功率需求的误差和标准差分别为2%和12%，峰值功率需求的估计即使在最坏的情况下也可以估计20%的误差。
Manufacturing energy studies have generally focused on estimating the mean electrical power demanded by machines. Variability is, however, also an important factor to consider in manufacturing power studies, since an understanding of electrical demand uncertainty can support the estimation of peak power demand, which impacts energy costs and electricity distribution systems. For example, U.S. federal law requires power suppliers to provide credits to large electricity consumers, such as manufacturers, when they enter into pre-established peak demand reduction agreements. The peak demand in a manufacturing plant can also be used to plan and design a factory electrical system, and to determine the amount of capital to be invested in facilities and equipment providing electricity services to new, large customers.Thus, this paper proposes a systematic method for estimating the peak demand based on readily available information such as manufacturing parameters. More specifically, the proposed method extracts the power demand mean and variance from manufacturing parameters at the machine state level by considering various manufacturing processes. The machine state level mean and variance are then used to approximate the mean and variance at the machine level by implementing the probability mixture model. Applying the Lindeberg central limit theorem to the machine level information, we estimate the mean and variance of power demands at the manufacturing system level. Then, a method for estimating the peak power demand using the mean and variance at the system level is proposed.In an illustrative example, we demonstrate our proposed method for real manufacturing power profiles, including milling, turning, and welding. We show how to connect manufacturing process parameters with the mean and variance of power demands and how to estimate the peak power demand based on the mean and variance. To validate the proposed method, we simulate a hypothetical manufacturing system; the results suggest that the proposed method can estimate the mean power demand with 2% error and standard deviation with 12% error, and that the peak power demand can be estimated with 20% error even in the worst case.