Measuring variability on electrical power demands in manufacturing operations
Introduction
Discrete manufacturing energy analysis is currently an actively researched topic, for two principle reasons: energy costs are expected to fluctuate; and energy-awareness has become more important in manufacturing. Studies of manufacturing energy can provide a tool to help manufacturers make more cost-effective and environmentally-friendly managerial decisions. While various types of energy are consumed in discrete manufacturing plants, most manufacturing machines require electricity as a main energy source. Thus, research on electrical energy consumption can cover the broader aspects of discrete manufacturing energy analysis.
When analyzing the electrical energy required by a manufacturing system, we must consider not only mean demand (MD) but also variability of demand. Energy consumption can be estimated using MD, and peak demand (PD) of electrical power can be calculated from MD and variability. MD and PD can be shown as in Fig. 1. If demand is considered a function of time (t), energy consumption is the area under the demand. MD is defined as energy consumption divided by a time period (t1), and PD is defined as the maximum demand value during t1.
While data on MD and PD of a manufacturing plant can be useful in various ways, one of the most significant applications is in estimating electricity costs for time-of-use (TOU) pricing. For example, the Energy Policy Act of 2005 passed by the U.S. Congress requires power suppliers to provide customers with TOU pricing rates upon customer request; large electricity consumers such as manufacturing plants in the U.S. can receive credits by entering into pre-established PD reduction agreements that reduce planned capacity obligations (U.S. Congress, 2005). MD and PD can also be used to calculate electricity costs directly. One U.S. power supplier (National Grid) introduced an electricity pricing plan charging not only for energy consumption but also for PD.
Research on estimating MD and PD can also provide input to other research areas. In particular, production rescheduling models for TOU electricity pricing require accurate MD and PD estimates as input parameters. Applying studies on production rescheduling, manufacturing plants can minimize electricity costs by keeping the total power demand below the allowed maximum value. Studies by Ashok (2006) and Babu and Ashok (2008) introduced relevant production rescheduling optimization models for a manufacturing plant, considering energy consumption and PD charges. Consideration of MD and PD will also be beneficial in the design of manufacturing plants, since research on MD and PD can provide analysis that is not possible using only MD. In the design stage, estimated PD can make useful contributions to the selection of both power supply and distribution equipment. Hence, in order to help manufacturing plants to reduce energy costs (and possibly energy use) and to offer crucial input to other relevant research areas, studies on electricity demands for manufacturing must consider both MD and PD.
Estimating MD and PD is, however, not straightforward, since the two demands are dependent on many variables. Any change in operation hours, worker shift schedule, types of machines, product design, raw material, or manufacturing processes may influence energy demand, creating hourly, seasonal and annual variation in MD and PD (Ashok, 2006, Thiede et al., 2013). The PD spike shown in Fig. 1 could be caused by any of a number of the operating decisions, and estimating MD and PD from operational parameters is difficult in general. In other words, a method to estimate MD and PD is urgently needed for existing and future manufacturers, but a systematic, unified method to connect managerial/operational decisions, manufacturing parameters, and MD and PD together is not currently available.
Section snippets
Background
Most of the conventional manufacturing systems are modeled and summarized by Altiok (1997). In this book, typical manufacturing systems, such as a job shop and flow shop are introduced, and probabilistic approaches to modeling the systems are discussed, with a focus on performance analysis. Jebaraj and Iniyan (2006) provided a broad review of the literature on energy models, including energy planning, energy supply-demand, and forecasting. This review deals with macro-economic types of energy
Modeling manufacturing power demand
In conventional manufacturing models, system performance, measured in terms of throughputs, machine utilization, flow time, and customer service levels, has been a major focus of research. In energy-aware manufacturing models, however, we consider additional factors, including the power demand and energy consumption of a manufacturing system. While energy-aware models depend on power and energy parameters, which have not been a part of the conventional models, we can reconcile the conventional
Illustrative examples
In Section 4, we discuss modeling of the power demands of manufacturing machines and how to apply the proposed models to estimate the system level peak demand and energy consumption. To that end, we analyze measured power profiles from real manufacturing machines, and propose methods to characterize each profile as manufacturing parameters. As discussed earlier, we discretize the time horizon; the time interval is 0.5 s, the finest sampling interval of the measuring device (Fluke 1735).
Results and discussion
Following the proposed approaches, we show the results of the case study, and discuss the significance of the results. More details on the case study results are provided in Table 15, in which we compare the simulated results (SIM) with the estimates by the proposed method (EST).
For each case, we estimate μW and σW by applying our proposed method. Among alternative methods, we use the designated method found in Subsection 4.1, including MTA (c = 1.44). After aggregating all Xi in each
Conclusions
This paper presents a systematic method to measure manufacturing power demand variability as MD and PD using manufacturing parameters at the machine state, machine, and system levels. Since the proposed model requires only readily available manufacturing parameters (process plans and machine utilization) as inputs, the proposed approach can be implemented with less effort and resources than a simulation-based method requiring machine power profiles. Our proposed method begins with models at the
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