Method for Designing an Energy-agile Energy System ... - CyberLeninka

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ScienceDirect Procedia CIRP 29 (2015) 179 – 184

The 22nd CIRP conference on Life Cycle Engineering

Method for designing an energy-agile energy system for industrial manufacturing Timm Kuhlmann*, Thomas Bauernhansl Fraunhofer Institute for Manufacturing Engineering and Automation, Nobelstraße 12, 70569 Stuttgart, Germany * Corresponding author. Tel.: +49 711 970 1903; fax: +49-711-970-1009. E-mail address: [email protected]

Abstract A volatile energy supply, where increasing energy costs take a high toll, as well as rapidly changing energy-related technologies are posing new challenges to sustainable manufacturing. The complex interactions between energy-related issues – such as renewable energy or energy efficiency – and a factory’s economic goals prevent the consistent consideration of these topics at the stage of strategic factory planning. There is a lack of methodical support for planning an agile energy system. This paper presents the ongoing research work concerned with the development of a method to be applied during the early phase of factory planning for designing an agile energy system for industrial manufacturing. This method is based on a system dynamics approach considering the interactions between a factory and the volatile energy market.

© © 2015 2015 The The Authors. Authors. Published Published by by Elsevier Elsevier B.V. B.V.This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). 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: production; agile energy system; renewable energy; energy efficiency

1. Introduction The intensification of global competition, particularly in consideration of the growth of emerging countries and their development into industrialized nations, is one of the challenges forcing companies to increase their efforts to reduce production costs [1]. These efforts are hampered by a rise in energy costs [2]. In a 2014 industry survey among 436 companies in the plastics manufacturing industry from European German speaking countries, for example, companies rank the challenge of ‘coping with the rising energy costs‘ as the third biggest one [3]. A measure to tackle this challenge, as seen by the plastics industry, is to increase energy efficiency. They even prefer an increase of operational energy efficiency to a rise in machine and process energy efficiency [4]. If current trends in energy supply, such as the phasing out of nuclear power in Germany, the development of power-to-gas technologies, and the expansion of shale gas fracking in North America, are included in managerial planning of production systems, this will lead to uncertainty

about the future development of the energy market. This turbulence is reflected in the energy market price and contrasts with turbulence related to technological trends in onsite energy generation or energy storage. This makes it difficult for companies to consider the more and more important factor of energy with its increasing turbulence during factory planning. This paper presents the foundations for as well as the first steps taken to develop a scalable method for the design and assessment of an energy-agile production during the early phases of factory planning. 2. Motivation The goal of factory planning is to plan the systems within a factory in such a way that products can be produced as costefficient as possible [5]. Not only static requirements but also dynamic requirements have to be complied with in a factory. The flexibility of a production system makes it possible to meet these dynamic requirements. This helps to continue

2212-8271 © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of The 22nd CIRP conference on Life Cycle Engineering doi:10.1016/j.procir.2015.02.005

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Timm Kuhlmann and Thomas Bauernhansl / Procedia CIRP 29 (2015) 179 – 184

developing the factory while maintaining the option of reconfiguration. Over the past years, the aspect of agility has been added to the concept of flexibility [6-8]. This expanded notion of agility allows factories not only to rapidly adjust the system within pre-defined flexibility margins but also to push the flexibility margins through agile measures. These agile measures can be grouped into different categories of agility enablers [9], aiming at a technical or organizational change to adapt the structure of the factory. Previously the focus in factory planning was on customer market changes or on product and production technology developments as agility triggering turbulence factors. Now the challenge of increasing energy costs and the rapid rate of innovation in energy efficiency, on-site energy production and storage including the turbulence these causes, is getting more important. Schenk points out that the energy efficiency of a factory is determined by the processing sequence and layout [10]. Thus, it is an objective of factory planning to ensure an agile alignment in the early planning phase. Turbulence is complicated by the considerably longer life cycle of a factory that hinders planning. Its life cycle is longer than the life cycle of energyrelated technologies, for instance technologies for on-site energy production, energy recovery or storage. Accordingly, future developments in energy-related technologies must be anticipated in factory planning. The factory structure has to be designed in such a way that technical developments can be rapidly adopted in production system with a minimum of cost. This finally leads to conflicts between energy and traditional manufacturing objectives during factory planning, for example: x Room for manufacturing resources in conflict to room for energy related technologies like on-site energy production resources or energy storages x Positioning resources alongside the production process in conflict to positioning the resources in energy related clusters x Choice of process technology and resources in conflict to product requirements, production costs and energy usage x Investment and operational costs in conflict to savings in energy costs and increasing the stability of the energy supply These conflicts can be identified through modelling the dependencies of the factory objects and result finally in a cost decision. If the dependencies in the planned system are considered, a particularly tangled web of relations and dependencies emerges. Depending on their specific nature, these dependencies can positively or negatively impact costeffectiveness. Causal relationships with regard to production costs evolve with a nonlinear behavior. The limiting factor of ‘costs’ – here energy costs and investment – will lead to a limitation that is affected by external turbulence, which the factory cannot control by itself. This uncertainty leads to an imprecise economic assessment of agility and, in the end, prevents investment in agility [11]. In addition to the pure economic assessment of investing in energy agile factory structure and technology the ecological assessment of such a decision will also take place in this method.

This suggests a need for developing a methodical approach to assist the design of energy-agile production systems based on an in-depth assessment. This approach must meet the following requirements: a. Consideration of energy-agility at the early stages of factory planning b. Focus on the resource level of a production system including the technical facilities of the energy system like storage, on-site energy production and energy recovering c. Consideration of interaction between energy-related topics and turbulence beside the energy system. d. Enabling an economic and energy-related assessment of agility investments e. Consideration of the uncertainty of agility in the assessment of agility-enabling measures 3. State of the art To develop a suitable method, the first step is to analyze methods used in the general context of designing agile production systems for their suitability to design energy agility. Hernandez, Heinecker and Löffler provide different approaches to design an agile production system [9, 12, 13] without a monetary assessment of their solutions. They focus on utility benefit assessments of their planning solutions which can respond to turbulence related to product, customers and competitors. An immediate consideration of the risky and uncertain development of the energy sector, with its impact on the energy system of a factory and the repercussions on the production system, is missing. Despite the methodic deficiencies regarding the necessary requirements, the existing agility planning methods provide a good basis for a method to plan an energy-agile factory. The different approaches serve as a scientific foundation. A crucial aspect in agility assessment is the consideration of uncertainty with a view to the occurrence probability of change and its concrete manifestation. If, for example, energy costs are identified as driving forces, an estimation of future cost development is necessary. As this development is subject to uncertainty, the occurrence probability has to be taken into account. To this end, approaches for scenario analysis are used [9, 12, 13]. To reflect the effect of changeable input values on the relevant output values, a model has to be build. This model represents the relations and dependencies between the different values. Influences beyond the factory enter the relationship as a vector of attributes [14, 15]. Because of the dynamic and stochastic impact of influences to be considered within the production system, the relation between the input factors and the relevant output factors cannot be analyzed as a closed function. In these cases, simulation is a suitable means to examine the system reaction to a given system input [16]. In addition to the agility design methods, methods for analyzing and increasing the energy efficiency in manufacturing are valuable for the intended approach. Joost et al [17] give a wide overview of the state of the art in energy and resource efficiency increasing methods in the domain of discrete part manufacturing. The analyzed and reviewed


methods focus on increasing the energy efficiency during the operational phase of the factory. For factory design and operation influences Joost et al refer to commercial software which can visualize the energy flow during factory planning. Ghadimi et al [18, 19] have investigated the influence of operational strategies of CHP systems on the sizing and the economic aspects. They have thereby analyzed the economic and ecological aspects of on-site energy production. Focusing on this on-site energy production, the production system itself is modelled on factory level as single consumer with a specific energy demand. The described approach in this paper can include those results about the on-site energy production and analyze the influences of uncertainty in the production system on selection of the on-site energy production system combines with other energy related technologies like storages. Posselt et al [20] developed a method for an increased transparency of energy flows based on Sankey diagrams. Integrating a Total Cost of Ownership assessment, the method is feasible to assist factory planer by choosing different technology measures in the field of technical building equipment and on-site energy production. Germani et al [21] evaluate with their method the economic and ecological sustainability of a production line. Taking account the costs and environmental impact during the complete life cycle of a production line, the method gives the process design manager a tool that estimates the upcoming costs and impacts in the early phase of line design. The dependencies and interaction between different lines and onsite energy production or energy related technology is not taking account by this method. The above considerations show that a model to assess energy agility should be a dynamic and stochastic model. Simulation models of production systems are discrete simulation models. Continuous models are used in simulating physical or technical systems but also in highly aggregated system of economics and ecosystems. During the analysis of dependencies and relations within a production system, especially over a long term, strategic planning approaches of continuous simulation can be found [22, 23]. While system dynamics approaches are not focusing on single products, they rather represent the material flow as a continuous flow through production. Doing this, the model focuses on the dependencies between the parts of the system and the flow. To analyze the sustainability of a production system, Jain introduces a framework [24] which represents all relevant fields of influence at top level with their relations and dependencies among each other. Aligning with that framework ensures the compatibility of detailed models. Single detailed solutions can be adopted inside the framework and interact with other detailed models. During this ongoing research, it will be examined if the models to be developed can be related to and integrated into this framework. The state-of-the-art reveals the lack of a method for designing an agile energy system for manufacturing in the early phase of factory planning. From the analysis of the requirements, a simulation-based method seems to be most promising. Due to the strategic time horizon of planning with its uncertain data and different, variable causal relations

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between system elements, system dynamics appears as a promising simulation method. 4. Approach This paper describes an approach that answers the core question, how an energy system in a factory can be designed so that, in the light of tension between external turbulences and a volatile energy market, it will be agile with the longterm goal of economic and ecological sustainability. The described approach focusses on production processes and energy supplying side processes but is not limited to them. Support rooms like offices or canteens could be integrated like a single production resource. Never the less the agility of such energy using processes on a strategic perspective is seen as insignificant at this point of the development of the approach. To achieve the goals of the core question, a fivestep, iterative method is presented. 4.1 Analyzing the drivers of agility As part of the German government funded project “WPSlive” [25], a method was developed which allows identifying the drivers of agility in a concrete company environment. Experts from different departments collect, structure and rate important agility drivers from a company view point in a series of workshops. Although the energy perspective was missing in the WPSlive project, the method can be used in this approach due to the structural similarity of energy agility to common agility. 4.2 Instantiating energy-related system elements The energy-related system elements stand for the combination of designable energy properties in the single objects of a factory. As a whole, they make up the energy system. Every individual machine is a single energy-related object. Here, the individual process categories – main processes, ancillary processes and auxiliary processes – are combined within a machine and regarded as an energy-related system object (fig.1). This energy-related system object has an energy input and output. The output is distinguished into energy which can be recovered and energy which radiates in an uncontrolled way. Compared to the energy usage of the machines, the energy inside the product after completion is usually much lower and can be neglected. In addition to the energy details, every system object is furnished with information about its usage within the value-adding manufacturing process. How the processes interact with material and products is not relevant from an energy point of view. For an energy-related assessment of the system objects, it is sufficient to know how long and in which way the machine is used in the process. Interactions between the energy-related system objects take place through direct energy links while using recovered output energy from one system object as input energy for another system object.

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(1)

Fig. 1: Energy connection of resources

To instantiate the energy-related system objects of a specific factory, the first step is to turn every single machine into such a system object. To avoid modeling any energy recovery process between one system object and all others, the individual system objects are connected to a logical energy pool (fig. 2). This energy pool is responsible for the exchange of energy between the system objects. In addition, the logical energy pool also assumes the function of real energy storage systems. Moreover, the energy pool is connected with the peripheral energy system of the factory in terms of the external power supply system as well as with its on-site energy generating system, e.g. with a photovoltaic or CHP systems. System objects for energy transformation, such as air compressors, have to be switched between the energy sources and the logical energy pool.

If, in addition, the basic energy functions depend on the individual product, for example due to different process parameters of a production machine, the dimension of ‘product dependency’ is added. The energy-related system object is described by a matrix (equ. 2). This should always be observed over time. (2)

4.3 Describing the dynamics The production order indicates in what quantity and processing order production is run. It is the task of production control to plan and start the orders appropriately. Switching now from the order flow view to the resource view, and considering the order processing activities from the isolated perspective of a single resource, then the process sequence is of no consequence for the individual resource when it comes to the energy needed during setup for job change. Here, only the capacity utilization of the resource and the time required to process the product is important. Assuming a stock of orders to be processed in front of the resource, the jobs are processed individually. Since the whole production system in the early phase of factory design is subject to considerable uncertainty, it is acceptable to assume that the jobs to be executed on a resource will be sequenced in a simple manner without risking a significant error in the analysis. From a resource point of view, order processing is analog to the energy perspective in section 4.2 (fig. 3).

Fig. 2: Model of the energy relations

The energy transport within the production is another energy-related system object. This system object is located between the system object of the machine and the logical energy pool. It causes a loss in energy, which is proportional to the amount of energy transported and the distance between the system object and the source of energy generation. The energy lost by transportation is modelled by a loss factor for the energy supply of the single machines. To model the portfolio of different energy types which are used in the specific factory, the energy-related indicators are not specified as single values but as vectors (equ. 1). The individual dimensions of a vector are defined by the types of energy to be considered, e.g. as electrical energy, gas or compressed air.

Fig.3: Resource view of production order system

In the same way, the order quantity can be described as a vector over the product variants (equ. 3). (3)

To account for the energy for the setup activities, a second energy matrix (equ. 4) is introduced in addition to the energy


matrix (equ. 2). This setup energy matrix considers the amount of energy usage over time during the resource status “job change”.

(4)

In the same way, more system states such as "stand-by" or "off" can be integrated into the model. A productive resource will now begin processing an order, if the necessary order volume is available in the work-inprocess pool to form a production lot and free production capacities are available. By limiting to the resource view with the aim of analyzing the energy system, it is not necessary to determine lead times for production orders. In the work-in-process pool, every customer order is immediately converted into production orders for resources, which are then processed independently and are deleted after processing. A prerequisite of this simplification is mass production, where the same jobs are repeated for the same products. Therefore, the method described here is primarily applicable for mass production and represents an ongoing production. These limitations of the method are acceptable when used in the early stages of factory planning, as general structures and behaviors of the production should be identified and included in the further detailing of factory design. Furthermore, the method aims at energy-agility in the production system to be able to adjust the energy system to structural capabilities. These adjustments are classified in a medium to long-term planning period and are not used for short-term energy flexibility of the production system. 4.4 Analyzing and evaluating scenarios In order to analyze and evaluate scenarios, they must be planned in advance. By viewing the production system from a higher level, while reducing the level of detail of individual production processes and the extension of the time horizon, a production flow system of it is created. Considering the early planning and an analysis of the responses of the energy system to turbulence and the evaluation of strategic measures for energy-agility, such abstraction is necessary. Therefore, this approach, which is based on relations and dependencies, uses the system dynamics method for modeling and simulation of the system. System dynamics refers to the methodological developments of Forrester [26]. They have been applied to numerous economic, political and social issues. Numerous works have also been published in the fields of business management and production analysis and evaluation [22, 23]. These examples serve as a general evidence of the suitability of the method for productionrelated analysis. However, for this specific question of the behavior of the energy system within the production system, the production-related works are of no use because they entirely neglect the energy point of view.

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The abstracted view on the energy models from section 4.2 and 4.3 can be converted into an integrated system dynamics model. The energy matrices as well as order matrices form the basis for the functional evaluation of the relationships. In addition, the company-specific facilities for energy storage and energy generation from renewable sources are to be modeled and added to the model. Besides the functional relationships, the model includes a large number of parameters that must be specified according to their range of values and their probable occurrence distribution. Monte Carlo-based methods allow a randombased generating of parameter vectors which include process parameter, layout parameters, energy parameter and costs. These parameter vectors provide the model result vectors that span the expected solution space of the relevant energy indicators. These multi-criteria measures show the behavior of the energy system to the planed manufacturing system. In order to achieve a meaningful distribution of the expected solutions it must be ensured that their entry in the input parameters is independent of each other. In addition, the parameter combinations also have to correspond in their associated probability of occurrence to the real probability. The evaluation in the technical model is based on energy ratios and in terms of the robustness of the system with respect to parameter changes. A monetary perspective can be added by assessing monetarily the technical measures and the energy flows which are connected to the peripheral system. 4.5 Iterative design of agility The simulation and assessment model described in section 4.4 does not provide any direct design recommendations. Instead, it identifies bottlenecks and untapped potential within the energy system. In addition, the robustness of the energy system can be evaluated in response to turbulence. The active design of agility requires the implementation of solutions for the energy system, the effect of which can be tested in the model. Here, solutions for the design of agility are fundamentally characterized by characteristics of enablers of agility [9]. The elaboration of the respective solutions, for example the use of centralized or decentralized energy storage solutions, different types of renewable energy as well as their dimensions, is regarded as factory planning task. It requires the know-how and creativity of designers. The method is intended to support the iterative application in the evaluation of the proposed measures. So, the gradual improvement of the energy system can be accomplished. 5. Roadmap Due to the complexity of the requirements and the methodological approach, the system design and system evaluation must be intensified in the next development steps. In the first step, the relevant agility drivers are carefully analyzed and classified with respect to the influence on the energy system. In addition, it requires a performance measurement vector with target dimensions, which is used for assessing the efficiency and environmental benefits for the production system. Both the agility drivers as input variables

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and the evaluation indicators as output variables must be linked in a dependency network with the energy-related system objects. This dependency network is integrated into the described conceptual model in the second step. This results in the system dynamics model of the energy system which is able to simulate the impact of energy-related measures. This model must finally be validated. Validation means by following the relativist / holistic science philosophy [27] proving the usefulness of the model for analyzing the core question about how an energy system in a production can be designed agile. Therefor the validation will take place in three areas: structural test, structure-orientated behavior test and behavior pattern test. To decrease the complexity, the validation will be carried out in single steps by validating the subsets “resource”, “transformation” and “energy pool” first and then validating the whole model. To do this, empirical data as well as theoretical or already validated information will be considered. Based on company-specific model instantiations, studies will be made using the described method approach to demonstrate the usefulness of the design method. References [1] Oxford economics: Manufacturing Transformation - Achieving competitive advantage in a changing global marketplace [online], 2013, [last accessed on 01-07-2015]. available at: http://support.ptc.com/WCMS/files/155978/en/Manufacturing_Transform ation_Report.pdf [2] Bundesverband der Energie- und Wasserwirtschaft e.V.: BDEWStrompreisanalyse Mai 2013 (electricity cost analyse May 2013) [online], [last accessed on 10-03-2014]. available at: 2013, http://www.bdew.de/internet.nsf/id/123176ABDD9ECE5DC1257AA200 40E368/$file/13%2005%2027%20BDEW_Strompreisanalyse_Mai%2020 13.pdf [3] Kunststoff Information Verlagsgesellschaft mbH: Umfrage zur Kunststoffkonjunktur 2014 (survey about the economic situation in the plastic industry 2104) [online], 2014, [last accessed on 10-03-2014]. at: http://www.kiweb.de/pdf/presse/PM_Kunststoffavailable Konjunktur_0114.pdf [4] Kunststoff Information Verlagsgesellschaft mbH: Umfrage zur Kunststoffkonjunktur 2013 (survey about the economic situation in the plastic industry 2013) [online], 2014, [last accessed on 10-03-2014]. at: http://www.kiweb.de/pdf/presse/PM_Kunststoffavailable Konjunktur_0713.pdf [5] Aggteleky, B.: Werksentwicklung und Betriebsrationalisierung (Plant development and operation rationalization). Munich / Vienna: Carl Hanser Verlag, 1990 [6] Gunasekaran, A.: Agile manufacturing: A framework for research and development. In: International Journal of Production Economics (62). Elsevier; 1999. p. 87-105 [7] Zäh, Michael F.; Möller, N.; Vogl, W.: Symbiosis of Changeable and Virtual Production – The Emperor’s New Clothes or Key Factor for Future Success? In: First International Conference on Changeable, Agile, Reconfigurable and Virtual Production (CARV 2005). Munich: utz; 2005. p. 3-10 [8] Bauernhansl, T.; Mandel, J.; Diermann, S.: Evaluating Changeability Corridors for Sustainable Business Resilience. In: Proccedings of the 45th CIRP Conference on Manufacturing Systems 2012. Elsevier; 2012. p. 364–369 [9] Hernandez, R.: Systematik der Wandlungsfähigkeit in der Fabrikplanung (Systematic of agility in factory planning). Dissertation at university of Hannover. In: Fortschr.-Ber. VDI series 16 Nr. 149. Düsseldorf: VDIVerlag, 2003

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