Available online at ScienceDirect. Procedia CIRP 29 (2015 ) The 22nd CIRP conference on Life Cycle Engineering

Transcription

1 Available online at ScienceDirect Procedia CIRP 29 (2015 ) The 22nd CIRP conference on Life Cycle Engineering An approach for energy-oriented production control using energy flexibility Cedric Schultz a *, Peter Sellmaier a, Gunther Reinhart a a Institute for Machine Tools and Industrial Management of Technische Universitaet Muenchen, Beim Glaspalast 5, Augsburg, Germany * Corresponding author. Tel.: ; fax: address: cedric.schultz@iwb.tum.de Abstract Due to rising energy costs and growing awareness for green production, many companies expand their energy self-supply by wind or solar energy. To use this self-supply efficiently, producing companies aim to synchronize their energy demand with a limited energy supply. This has to be reflected in the companies production control strategies. This paper presents a concept for a short-term production control, which treats electric energy as a limited production capacity. The approach makes use of energy flexibility to align energy demand in production with energy supply while maintaining logistic goals The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( Peer-review under responsibility of the International Scientific Committee of the Conference 22nd CIRP conference on Life Cycle Peer-review Engineering. under responsibility of the scientific committee of The 22nd CIRP conference on Life Cycle Engineering Keywords: Energy flexibility; energy efficiency; renewable energies; production control; disruption management 1. Introduction In the recent past, rising energy prices as well as growing awareness in society for environmental issues and sustainability have led to new challenges in manufacturing. This is especially true in Germany where baseload plants are substituted by renewable energy sources because of the nuclear phase-out. Due to this increase in renewable energy sources, e.g. wind and solar power, energy supply becomes more volatile. Since the electric grid has to be in balance at all times and the options to store electric energy on a large scale are limited, energy providers have to increasingly rely on other measures such as demand response to guarantee energy supply [1, 2, 3]. Depending on the current ratio between power generation and demand, energy prices may fluctuate significantly [1, 4]. Due to these challenges, producing companies are forced to rethink their energy policy. Not only do they have to be energy efficient to minimize their energy demand but they also synchronize their energy consumption with the available energy supply. Many companies also increase their share in energy self-supply generating their power in block heating works or solar plants to become more independent from power authorities. In any case, electric energy can no longer be considered a limitless resource. Instead, electric energy has to be treated as a limited production capacity, whose usage has to be planned and controlled during production. The ability to do so and to synchronize energy demand with an available energy supply is determined by a production system s energy flexibility. Energy flexibility defines a production system s ability to adapt itself fast and without great expenses to changes in the energy market [5]. Therefore, a production system s energy flexibility has be reflected in a company s methods for production planning and control (PPC). This paper presents an approach for an energy-oriented production control by using the energy flexibility inherent in a production system. This will allow companies to synchronize their energy demand with a limited energy supply in order to adapt to volatile energy prices or to increase their share of energy self-supply. 2. Energy-aware production planning and control The potential of energy flexibility is that companies may curtail their energy demand during periods of low energy supply and vice versa. In order to realize this potential, it is necessary to consider energy consumption during production The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( Peer-review under responsibility of the scientific committee of The 22nd CIRP conference on Life Cycle Engineering doi: /j.procir

2 198 Cedric Schultz et al. / Procedia CIRP 29 ( 2015 ) planning and control and allot electric energy as a limited resource. As Fig. 1 indicates PPC can be represented as a closedloop circuit. In production planning the production program is planned for a number of periods according to a potential sales program. During the planning process, demands considering the required materials and resources are derived and production targets are set [6]. Based on these planned values, it is the objective of production control to execute these targets even when disruptions occur in the manufacturing process, e.g. malfunctions or delays in material supply [6]. In order to detect disruptions and react accordingly, the actual values, e.g. resource availability or progress of production, are fed back into production control. Considering energy flexibility, this concept of PPC can be extended by energy-related objectives. Since a company s energy supply can be volatile, energy costs have to be considered when setting target values during the planning process. In energy-aware production planning fluctuating energy prices are considered and production orders are generated so as to minimize cost for electric energy while maintaining scheduled delivery dates. As a result of the planning process an energy cost-efficient production plan is generated as well as an energy schedule. While the production plan defines manufacturing jobs and their respective deadlines for a given period, the energy schedule specifies how much electric energy is allotted for this specific production plan. Both serve as input data for production control. In energy-oriented production control electric energy is treated as a quasi-limited production capacity. Production is to be carried according to the production plan, but only the amount of energy specified in the energy schedule should be consumed. While the amount of available energy itself is not limited, deviations from the planned energy consumption result in severe penalty costs. Therefore, electric energy is quasi-limited. The objective of energy-oriented production control is to minimize deviations from the energy schedule while maintaining logistic goals such as throughput time and production deadlines. The proposed concept was developed with respect to the constraints of the German energy markets. Therefore, the energy schedule is generated with an hourly resolution, i.e. for every hour of the planning period a certain amount of electric energy is specified. Penalty costs occur if the actual energy consumption overruns or underruns the scheduled values by more than 5 %. Nonetheless, these assumptions may be changed to transfer the concept to other energy markets Related work In the recent past, several authors have addressed the integration of energy-related objectives in PPC. Due to the nuclear phase-out, considerable research in this area is carried out in Germany. Bonneschky [7] presents a set of energy-related key performance indicators (KPI) in order to complement manufacturing master data such as energy demand per part. This way energy-related objectives such as procurement of electric power can be considered in manufacturing resource planning. Fig. 1. PPC as closed-loop control circuit Haag et al. [8] also developed a model-based approach to reflect energy consumption of resources as well as peripheral systems, e.g. compressed air supply. They derive a KPI system to optimize energy efficiency in production planning and control. Weinert et al. [9] propose a methodology called EnergyBlocks, which can be used to integrate energyefficiency criteria with production planning and scheduling. An EnergyBlock represents the energy consumption of one individual operating state. A series of EnergyBlocks can therefore be used to model energy consumption during operation in planning and scheduling. A significant amount of work has addressed energy constrains in job scheduling as part of production planning. Junge [10] proposes a scheduling approach for plastics industries which considers interdependencies between outdoor temperature, heat transfer and ventilation. While energy demand for process heat could be reduced, significant penalties in throughput time and inventory resulted from this approach. Rager [11] develops a hybrid evolutionary algorithm to minimize energy consumption of a job shop containing parallel identical work stations. This approach is able to minimize to the number of machines working and to even their energy consumption, which resulted in a reduction of energy costs as well as carbon dioxide emissions. Pechmann et al. [12] present a PPC software which incorporates an energy planning module. During scheduling, power load profiles are considered for every production step and overall energy consumption is reduced by timely adjustment as well as routing alternatives for production steps. Finally, a 24-hour power load forecast is calculated which can be passed on to energy providers. Bruzzone et al. [13] present an energy-aware scheduling algorithm for flexible flow shops based on mixed integer programming. Taking jobs from an advanced planning and scheduling system, the solver modifies the schedule to

3 Cedric Schultz et al. / Procedia CIRP 29 ( 2015 ) minimize peak power consumption at the cost of increased tardiness and throughput time. Artigues et al. [14] discuss an energy scheduling problem representing electric power limitations in parallel machine scheduling. Their goal is to limit power consumption to a given value and thereby minimize energy costs for power overruns. Several authors also address methods for energy-related production control. Eberspächer et al. [15] developed a graphbased algorithm to minimize energy consumption during nonproductive periods. Their method can be implemented into a machine tool to enhance its energy efficiency. Langer et al. [16] present the energy-sensitive Manufacturing Exectution System enimes. Several manufacturing control strategies were integrated with enimes to improve overall energy-efficiency. These include energyrelated Kanban and ConWIP algorithms, which are suitable mostly for series production. Li et al. [3] discuss the relevance of real-time production control for demand response in manufacturing. Based on this, Zhou et al. [17] present shutdown strategies considering bottlenecks in manufacturing to meet demand response requirement. Fernandez et al. [18] propose the usage of buffer inventory to reduce energy demand during peak periods. Since buffers are built up during off-peak periods this approach results in reduced energy costs. The concept is studied using a model of an automotive assembly line and nonlineari programming. It can be observed that while extensive research is carried out in the field of energy-related PPC, limitations in availability of electric energy are only considered in scheduling. However, since disruption in manufacturing are inevitable, methods for energy-oriented production control are needed in order to execute schedules according to plan. shortages in staff or material [6]. Since disruptions in production are inevitable, the most important aspect of production control is to minimize their impact on target values. As the energy consumption of a production system is directly dependent on the operating states of its work stations, most disruptions have an immediate effect on energy consumption. Therefore, disruptions have to be analyzed with respect to their cause and their impact on energy consumption. In production planning target values for energy demand as well as energy supply are calculated based on mean values and on forecasts. For example, the energy consumption of a specific operation might be planned using the mean consumption of the last times this operation was performed. The planned energy supply by a solar plant might be based on a weather forecast for the next day. Hence, looking at energy consumption four different kinds of impacts may occur from disruptions. As can be seen in Fig. 2 energy demand as well as energy supply can run over or run short of their target values. If either energy supply is decreased or energy demand increased, the actual energy consumption exceeds the target value, while if energy supply is increased or energy demand decreased the actual consumption falls behind its target. 3. Energy-oriented production control From the review of related work, a number of requirements is derived which need to be addressed in order to realize the concept of energy-aware PPC described in section 2. As stated above, in the context of this work the overall objective for energy-oriented production control is to meet production deadlines while minimizing deviations in energy consumption from an energy schedule. Such deviations are caused by disruptions in the production process. Therefore, actual values of energy consumption must be monitored online and fed back into production control. Based on this data, energy-oriented production control must be capable of identifying disruptions and evaluate their impact on energy consumption according to the energy schedule. Last but not least, energy-oriented production control must be able to handle disruptions and react in a way that the deviations mentioned are minimized during manufacturing Disruptions Disruptions in production processes are defined as any deviation from planned target values. Typical examples are delays during the process, malfunctions of work stations or Fig. 2. Impact of disruptions on energy consumption Furthermore, disruptions can be categorized according to their causes. For example, disruptions may be caused by material if the proper material is missing or a wrong kind of material is being processed. They may be caused by information if values used for planning were wrong or by work stations if they malfunction. Table 1 shows a scheme where disruptions are categorized by their cause and their impact on energy consumption as well as an example for every category. Energy-oriented production control has to be able to react to these disruptions and minimize their impact on energy consumption. 4. Energy-oriented order release To describe the mechanism of production control Lödding [6] developed a model, which is composed of the four tasks: generating orders, releasing orders, controlling capacities and sequencing.

4 200 Cedric Schultz et al. / Procedia CIRP 29 ( 2015 ) In order generation planned values for production, such as input dates and the planned order sequence, are determined. Often this task is a component of production planning. During order release the actual dates are set at which orders enter production. While capacity control regulates the short-term availability of single capacities, i.e. work stations. Finally, sequencing determines the actual sequence, in which orders are processed on a single work station. As a first step of energy-oriented production control, an energy-oriented order release is presented in this paper, which will release jobs for production due to the amount of energy available. At a point in time, the availability of electric energy may differ significantly from the scheduled value as a consequence of the disruptions described earlier. Therefore, the energy-oriented order release is one way to handle such disruptions. However, if sufficient slack remains, energy-related objectives can be considered. According to manufacturing master data the expected energy consumption of the job is calculated and compared to the energy actually available. In case the order release would result in a deviation, which violates the upper or lower boundary of energy consumption, the next job in the queue is checked for its energy consumption (Fig. 4). Table 1. Categorization of disruptions Disruption caused by personnel Energy consumption exceeds target value Mistakes by personnel Energy consumption falls behind target value Missing personnel material Material too hard Material too soft work stations Rejects Malfunction orders Priority order Order cancellation information Incorrect master data Incorrect forecast energy supply Demand response Short term order of energy Fig. 3. Concept of the two-step order release This process is iterated until a job is found which will not exceed the boundaries. If no such job is found, it is checked whether a job can be carried out at a different work station or whether its start date can be delayed for a better fit in energy consumption beginning with the first job in the queue. Finally, the best-fitting job is released for production. Three prerequisites were considered developing the energy-oriented order release: Firstly, meeting the delivery date of an order should be the main priority [3]. Secondly, electric energy is treated as a quasi-limited capacity specified in the energy schedule, i.e. the amount of energy may differ from the target value, but deviations should be minimized since they are associated with additional cost. Thirdly, work in progress and inventory are set to be variable. Based on these prerequisites order release is realized as a two-step process (Fig. 3). In the first step, orders are released centrally due to their planned start dates. If the planned start date for an order is reached, the order is released immediately and becomes part of the job queue in front of a work station. Jobs in the queue are sorted in order of their planned delivery date. However, before a job is actually released at the respective work station, it must pass through the second step of order release. The second order release step is conducted de-centrally in front of a work station and follows a set of rules. Whenever a job is finished at a work station, the next job in the queue is checked for its remaining slack time. If the remaining slack falls below a lower boundary, the job is released immediately to minimize tardiness. Fig. 4. Comparison of expected energy consumption with energy supply 5. Preliminary Results 5.1. Simulation study In order to assess the effectiveness and the performance of the proposed energy-oriented order release method, a simulation study was carried out using material flow simulation. The event-based simulation software Siemens Technomatix Plant Simulation 11.0 was chosen because of its capability to simulate energy consumption of work stations. Energy consumption is calculated by assigning mean values of power consumption for each state of the work stations, e.g. working, stand-by, failure mode. These mean values may differ according to the part being processed on a particular work station. The simulation model is set up as a job shop producing several variants of a gear box in a multi-stage manufacturing process. These variants differ in their machining time on the one hand and their energy consumption on the other hand.

5 Cedric Schultz et al. / Procedia CIRP 29 ( 2015 ) According to random customer orders, manufacturing jobs are generated weekly using backwards scheduling considering limited capacities. For each job the planned energy consumption is calculated by assigning mean values of power consumption for each individual operation. Based on this data, an hourly energy schedule is calculated as input data for the energy-oriented order release. Disruptions in the manufacturing process are modeled by statistical distributions. The mean availability of work stations, which is set to %, is represented by a negative exponential distribution, while the Mean time to repair (MTTR) is modeled by an Erlang distribution with a mean value of 2 hours Scenarios In total, a period of 25 work days with two shifts each was simulated. During this period, 736 orders were generated containing individual operations. For the analysis three different scenarios were simulated. The first scenario is the reference scenario (Scenario 1), in which the energy-oriented order release is deactivated. Order release is carried out according to planned start dates only. Since the production process is affected by disruptions, deviations in energy consumption as well as delivery reliability can be observed in this scenario. In the second scenario a simplified version of energyoriented order release is implemented (Scenario 2). In this case, the order release method checks the availability of energy for the next job released and chooses the best-fitting job from the queue in front of a work station. However, jobs are neither transferred to a different work station nor is their start date shifted. The third scenario uses the complete method of energyoriented order release (Scenario 3). This method searches for the best-fitting job to release and also considers relocating jobs to other work stations or shifting their start date. This way the maximum number of degrees of freedom is achieved. several jobs were delayed, 4 % less orders could be completed in the same amount of time. Furthermore, delivery reliability decreases when using energy-oriented order release. In the reference scenario % of orders are finished on time for delivery, while in scenario 3 only % were finished before their delivery date was reached. Comparing scenario 1 and 3 throughput times are only slightly increased by 2 %. Table 2. Summary of simulation results Scenario Scenario Scenario Jobs finished Operations carried out Delivery reliability [%] Total deviation from planned consumption [kwh] All in all, scenario 2 yields only minor improvement compared to the reference scenario. This may be due to the fact that the variance in energy consumption between jobs on the same work station is limited in this particular simulation model. Therefore, the effect of changing the job sequence at a work station is limited as well. The highest impact is observed when shifting the start dates of jobs Results The simulation study is used to evaluate several key performance indicators to investigate the performance of energy-oriented production control. The main objective of energy-oriented production control is to minimize deviations between planned and actual energy consumption. Hence, the absolute value of total energy deviation is analyzed. Delivery reliability defined as the percentage of orders which are shipped on time is also considered, since delays in order delivery usually lead to additional cost and detriment customer loyalty. As a secondary indicator throughput times are also analyzed. The results are shown in Fig. 5, Fig. 6 and Table 2. In this study we find several major results. It can be observed that deviations from planned energy consumption due to disruptions in the production process are as high as 18 % of the total energy consumption in scenario 1. These deviations are lowered significantly by almost 30 % in scenario 3 using the proposed order release method. Fig. 6 shows these results for a single work day. However, since the start dates for Fig. 5. Results of the simulation study While the comparison of scenario 1 and 3 yield little differences in delivery date deviation, it should be noted that order release in the reference scenario is realized based only on planned start dates. Since backlog control or other methods which might minimize delays in manufacturing are not implemented, the variance in actual delivery dates is less than optimal even in the reference scenario. Therefore, delivery reliability must be further improved in the proposed concept for energy-oriented production control.

6 202 Cedric Schultz et al. / Procedia CIRP 29 ( 2015 ) Fig. 6: Simulation results for a single work day 6. Summary In this article we first discussed the relevance of energyrelated objectives in production planning and control. Following a literature review on this topic an approach for an energy-oriented production control was presented. In this approach electric energy is treated as a quasi-limited production capacity and special focus is set on disruptions which may lead to deviations in energy consumption. Therefore, we discussed and categorized several disruptions which may occur during the production processes with respect to their impact on energy consumption. On this basis, the concept of an energy-oriented order release is proposed and evaluated using material flow simulation. Using this approach, deviations from energy consumption specified in production planning could be reduced with only minor penalties to delivery date deviation. Further research activities will focus on combining the proposed order release method with load management in manufacturing. By including measures of load management, e.g. deferrable loads and energy storages, we expect to minimize delivery date deviation and penalties in throughput time, since relocation and time shifting of jobs may be minimized. Acknowledgements The authors would like to thank the Bavarian Research Foundation for funding our research project FOREnergy. References [1] Paulus M, Borggrefe F. The potential of demand-side management in energy-sensitve industries for electricity markets in Germany. Applied Energy 2011;88: [2] Department of Energy. Benefits of demand response in electricity markets and recommendations for achieving them. Washington: U.S. Department of Energy [3] Li L, Sun Z, Tang Z. Real time demand response for sustainable manufacturing systems: Challenges and a case study. In: Proceedings of the 8 th International Conference on Automation Science and Engineering, Seoul, August 20 th -24 th p [4] Graßl M, Reinhart G. Evaluationg measures for adapting the energy demand of a production system to volatle energy prices. Procedia CIRP 2014;15: [5] Graßl M, Vikdahl E, Reinhart G. A Petri-net based approach for evaluating energy flexibility of production machines. In: Proceeding of the 5 th International Conference on Changeable, Agile, Reconfigurable and Virtual Production, Munich, October 6 th -9 th p [6] Lödding H. Handbook of Manufacturing Control Fundamentals, Description, Configuration. 1st ed. Heidelberg New York Dordrecht London: Springer; [7] Bonneschky A. Intergration energiewirtschaftlicher Aspekte in Systeme der Produktionsplanung und -steuerung. Ph.D. Thesis. Berlin: dissertation.de Verlag im Internet GmbH, [8] Haag H, Siegert J, Bauernhansl, Westkämper E. An approach for the planning and optimization of energy consumption in factories considering the peripherl systems. In: Proceedings of the 19 th CIRP Conference on Life Cycle Engineering, Berkeley, May 23 rd -25 th p [9] Weinert N, Chiotellis S, Seliger G. Methodology for planning and operating energy-efficient production systems. CIRP Annals Manufacturing Technology 2011;60: [10] Rager M. Energieorientierte Produktionsplanung. Ph.D. Thesis. Wiesbaden: Betriebswirtschaftlicher Verlag Dr. Th. Gabler, [11] Junge M. Simulationsgestützte Entwicklung und Optimierung einer energieeffizienten Produktionssteuerung. Ph.D. Thesis. Kassel: kassel university press GmbH, [12] Pechmann A, Schöler I, Hackmann R: Energy efficient and intelligent production scheduling: Evaluation of new production planning and scheduling software. In: Proceedings of the 19 th CIRP Conference on Life Cycle Engineering, Berkeley, May 23 rd -25 th p [13] Bruzzone A.A.G, Anghinolfi D, Paolucci M, Tonelli F. Energy-aware scheduling for improving manufacturing process sustainability: A mathematical model for flexible flow shops. CIRP Annals Manufacturing Technology 2012;61: [14] Artigues C, Lopez P, Hait A. The energy scheduling problem: Industrial case-study and contraint propagation techniques. Int. J. Production Economics 2013; 143: [15] Eberspärcher P, Verl A. Realizing energy reduction of machine tools through a control-integrated consumption graph-based optimization method. Procedia CIRP 2013; 7: [16] Langer T, Schlegel A, Stoldt J, Putz M. A model-based approach to energy-saving manufacturing control strategies. Procedia CIRP 2014;15: [17] Zhou Z, Li L. Real time electricity demand response for sustainable manufacturing systems considering throughput bottleneck detection. In: Proceedings of the IEEE International Conference on Automation Science and Engineering, Madison, August 17 th -20 th p [18] Fernandez M, Li L, Sun Z. Just-for-Peak buffer inventory for peak electricity demnad reduction of manufacturing systems. Int. J. Production Economics 2013;146: