# Real-Time Allocation of Volatile Energy Related Emissions in Manufacturing

Dominik Leherbauer, Peter Hehenberger

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Dominik Leherbauer, Peter Hehenberger. Real-Time Allocation of Volatile Energy Related Emissions in Manufacturing. 19th IFIP International Conference on Product Lifecycle Management (PLM), Jul 2022, Grenoble, France. pp.599-608, 10.1007/978-3-031-25182-5_58. hal-05420895

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# Real-Time Allocation of Volatile Energy Related Emissions in Manufacturing

Dominik Leherbauer(✉) and Peter Hehenberger

Smart Mechatronics Engineering, University of Applied Sciences Upper Austria,
Stelzhamerstraße 23, 4600 Wels, Austria
{dominik.leherbauer,peter.hehenberger}@fh-wels.at

**Abstract.** International agreements target a reduction in greenhouse gas emissions. A major contributor to these greenhouse gas emissions is the generation and consumption of energy. By a varying supply and demand of different energy sources including renewables a varying energy mix results. A difficulty poses the determination of CO₂ equivalent emissions for volatile energy types, because different energy sources have type-specific emission amounts. Within the manufacturing environment, the challenge is to allocate the resulting energy flows, respectively emission flows through the existing hierarchical structure.

State of the art provides methods for the calculation of embodied energy. Life cycle assessment methods can be used to determine environmental impact, but are carried out mostly as static analysis. Within this publication an approach to determine the embodied emissions with the consideration of volatile CO₂ emissions is presented. The goal of the approach is to provide a path to map the resulting CO₂ equivalent emissions to produced goods in a manufacturing context.

Used methods include the analysis of existing methods for energy allocation in products and life cycle assessment methods. An analysis of the electricity grid has been conducted and a mathematical model for the calculation of inherent CO₂ equivalent emissions has been formulated. The paper provides a conceptual approach to map volatile equivalent CO₂ emissions to produced goods and can be used to minimize embodied energy in further applications.

**Keywords:** Embodied energy · Volatile emission allocation · Dynamic life cycle assessment · Sustainable manufacturing · CO₂ emissions

## 1 Introduction

International agreements e.g., the Paris Agreement target a reduction in emitted greenhouse gases [15]. Considering greenhouse gases, a distinction can be made between CO₂ and other gases. However, the indication of CO₂ equivalency converts the resulting effects of other gases to those of CO₂ [6]. Considering the share of energy consumed within the European Union, data shows approximately 26
<figure><img src="image_3.png" /><figcaption>Fig. 1. Source to sink sequence for the mapping of volatile CO₂ equivalent emissions</figcaption></figure>

percent of consumed final energy can be allocated in the manufacturing sector in 2020 [5].

A difficulty herein poses the temporal changes of composed energy sources e.g., electricity. While energy sources like oil or natural gas can be stored and vary only by small amounts in terms of inherent CO₂ equivalent emissions, through its non-storability the mixture of electricity in the electricity grid cannot be determined statically. By the usage of sustainable electricity sources as photovoltaic panels or wind, which vary in availability, a volatility in the electricity mix is introduced. This volatility is mainly caused by the condition, that the power fed in the energy grid must also be withdrawn at the same time. Missing energy must then be provided from control reserves or other power plants. However, these power plants may be less sustainable in terms of associated CO₂ equivalent. This publication aims to model this behavior by mapping the resulting CO₂ equivalent emissions to produced goods in the manufacturing context as visualized in Fig. 1. The publication highlights the need for a systematic approach to breakdown emissions and assess intense products and processes. A proposal is derived from the current state-of-the-art and shows a conceptual approach to carry out the respective mapping.

## 2 State of the Art

For the complete flow visualized in Fig. 1 multiple components are needed. For the individual electricity sources the variable CO₂ equivalent within the trans-
mission network must be obtained. In the manufacturing environment an alloca-
tion algorithm is sought to distribute the resulting emissions. Though, no over-
all methodology is known, existing references indicates methodical approaches.
Thus, in this section these existing approaches are presented.

## 2.1 Life Cycle Inventory Approach

A widespread calculation approach for the environmental impact of manufactur-
ing goods is the life cycle assessment (LCA) approach. Following the definition of
ISO14044:2006 LCA represents a 'compilation and evaluation of the inputs, out-
puts and the potential environmental impacts of a product system throughout
its life cycle' [8]. As the name suggests LCA is applicable for the whole product
life cycle and therefore, deals with unknown quality of input data. The gathering
of the most applicable input data is covered by the life cycle inventory. In this
approach within a manufacturing context, each individual manufacturing pro-
cess is seen as an enclosed system. Each system features inputs for material and
energy and outputs for resulting materials, emissions and other outcomes. For
the usage of external resources LCA databases provide an indication of ecologic
consequences per consumed unit. Though, a region specification should be taken
into account [11], the provided values are static and averaged. Hence, inconsis-
tencies can arise in case of a variable resource consumption for resources with
a volatile composition. In general LCA is carried out as a static analysis and
features known limitations [9]. An alternative would be a dynamic analysis i.e.
taking into account the temporal behavior. In particular, a dynamic analysis
should be considered if source and sink both feature a variable behavior. Within
a manufacturing energy provision context, this may be caused by

- On-site energy generation

- Shift working times

- Variable energy consumption of products

- Variable CO₂ equivalent of energy sources

In this case a dynamic LCA (DLCA) as proposed by Collinge et al. may be used
to better account for these characteristics [2]. In the given context considering
emissions and the manufacturing phase the existing LCA respectively DLCA
approaches can be considered as too exhausting. Instead, the proposed approach
aims to extend the methodology of the DLCA for the manufacturing phase.

## 2.2 Electricity Generation and Characteristics

Considering the supply with electricity throughout the day, the CO₂ equivalent emissions vary. This is mainly caused by a varying composition of electricity generation as well as a continuous energy exchange between control zones. These can be distinguished by the representation of energy exchanges and are either based on trades or physical flow [7]. A distinction can be made between financial and ecologic costs. While plants aim to maximize financial profits, the ecologic costs
result. However, due a correlation of these costs is implied by the fundamental characteristics of different generation types. While environmentally-dependant energy generation plants, e.g. photovoltaic panels or wind power plants create a low amount of operational costs, they are active most of the time. Commodity-dependant power plants aim to maximize profits based on the market price and operational costs and thus act differently to maximize profits. These aspects are covered by merit order models, which could be validated in various publications [10, 13]. Likewise, the ecologic costs consist of initial costs embodied in infrastructure and running costs bound in operations [14]. The provision of data for these costs is covered by life cycle assessment (LCA) calculations as well as calculations by the Intergovernmental Panel on Climate Change (IPCC). A difference in these calculations exists in the granularity. While the IPCC provides calculation values per generation type, the LCA calculations can also be country specific.

A difficulty in the calculation of inherent emissions in the electricity grid is the network architecture allowing exchanges between the individual control areas. By the exchange of electricity between these areas, the emissions are also exchanged, surfacing a mathematical problem to solve. Hence, a differentiation must be made between occurring emissions by generation and consumption. To calculate the specific CO₂ equivalent emissions for the electricity grid network the calculation is based on the absolute released emissions. The mathematical relationship is trivial given the specific CO₂ equivalent emissions c, the amount of energy E and absolute released emissions C

$$C = c \cdot E \qquad (1)$$

To calculate the absolute released emissions per control area by consumption, several steps are needed. In a first step, the released emissions per generation type must be calculated. The generated energy data per type and specific emissions for each generation type are needed as a basis. Thus, the control area specific emissions can be calculated by the sum of Eq. 1 for each generation type. To solve for emissions by consumption a linear set of equations is sought. Exemplary for control area A the absolute emissions by consumption $C_{A_{con}}$ result in consideration of electricity imports $C_{A_{imp}}$ and exports $C_{A_{exp}}$ with:

$$C_{A_{con}} = \sum C_{A_{gen}} + \sum C_{A_{imp}} - \sum C_{A_{exp}} \qquad (2)$$

Expanding Eq. 2 by Eq. 1 and rewriting imports and exports per control area result in a new equation. Notably, control areas can simultaneously import and export electricity with each neighboring control zone. Declaring energy imports from B to A as $E_{BA}$ and imports from C to A as $E_{CA}$ yields:

$$(E_{A_{con}} + \sum E_{A_{exp}}) c_{A_{con}} = E_{A_{gen}} \cdot c_{A_{gen}} + E_{BA} \cdot c_{B_{con}} + E_{CA} \dots \qquad (3)$$

With the expansion and known parameters, a set of equations emerges and enables solving for the control area specific CO₂ emissions. The calculation of
<figure><img src="image_4.png" /><figcaption>Fig. 2. Energy allocation in products by Rahimifard et al. [12]</figcaption></figure>

the unknown specific CO₂ equivalent intensities *c* can be carried out by setting up Eq. 3 for each considered control area network node. By writing the equation set in matrix form and solving by matrix inversion the specific intensities can be obtained.

A limitation of this approach is the mathematical complexity and the data availability. Solving for the whole electricity grid network can be computationally expensive, in particular if *c* is only sought for a specific control area. An approximation is possible in this case by the assumption, that distant energy exchanges impact the considered control area less by more separating control areas. Hence, the approximation can be made by cutting off the calculation after a defined degree of neighboring control areas. By reaching the cutoff control area all imports are simply neglected, and the calculation complexity can be reduced. In practice, the integration of data sources can pose a problem. In case of unavailable data, the specific CO₂ equivalent can only be approximated by substitution of imported emissions with static LCA data.

## 2.3 Energy Allocation

The allocation of needed energy in goods originates from the economic input-output analysis as well as process analysis approaches. From these approaches the hybrid LCA approaches emerged, which combines these analysis methods as presented by Crawford et al. [4]. Based on the first documented hybrid analysis approach by Bullard et al. [1] the concept of embodied energy was introduced by Costanza [3] within an economic context. Since then, multiple approaches for specific use cases and product life cycle phases emerged. Rahimifard et al. [12] used the originally provided approach to allocate energy within a manufacturing environment. The framework presented by Rahimifard et al. aims to provide an indication for energy efficiency applications and is visualized in Fig. 2.

As displayed, the energy consumption is split into a part of direct and indirect energy consumption. The direct energy consumption results by manufacturing
processes needed to manufacture a product. A distinction for energy efficiency related purposes can be made between the physical minimum to carry out the process, namely theoretical energy and all other direct consumption caused by inefficiencies or auxiliary consumption, namely auxiliary energy. The indirect energy consumption covers all other consumption needed by the manufacturing environment e.g., lighting. Caused by the missing causal relationship between fluctuations in indirect and direct consumption Rahimifard et al. suggested to average indirect consumption per hour.

# 3 Proposed Approach

Though, the discussed state-of-the-art approaches are complete in itself, integration poses several difficulties to consider. A core problem is the integration of the time intervals of the electricity with the manufacturing systems sensing. Hence, the occurring manufacturing environments' properties must be considered.

## 3.1 Hierarchical Classification

The manufacturing environment can be represented with a hierarchical model. In the hierarchy, multiple levels can be considered, depending on sensing equipment. The existing energy allocation approaches indicate the usage the need for manufacturing processes, zones, energy consumers, and products as pointed out in Fig. 2. Additional references for the hierarchy can be found in ISA-88 and IEC61512-1:2000 which, besides other, both mention manufacturing site, area and process cells. The hierarchical levels by Wiendahl et al. indicate, the manufacturing processes are the basement of the levels within a changeable factory [16]. An assignment of processes to jobs thus provides a transparent path from the manufacturing site level to individual manufacturing jobs. Extending the mentioned levels by a buffer for the averaging of indirect consumption the following hierarchy presented in Table 1 can be derived.

<table><caption>**Table 1.** Derived hierarchical levels for a multi-level allocation in a manufacturing environment</caption><thead><tr><td>Name</td><td>Function</td><td>Example</td></tr></thead><tbody><tr><td>Manufactory</td><td>Entry point of overall sensed energy times current CO<sub>2</sub> equivalent emissions</td><td>Power grid connected plant</td></tr><tr><td>Area</td><td>Distribution of indirect energy consumption and unassigned energy flows</td><td>Machining area</td></tr><tr><td>Energy consumer</td><td>Actual energy consuming entity with sensing capabilities</td><td>Machine</td></tr><tr><td>Indirect buffer</td><td>Averaging and allocation of indirect flows</td><td>Losses</td></tr><tr><td>Process</td><td>Manufacturing process caused by a product</td><td>Milling</td></tr><tr><td>Job</td><td>Container for one or more produced products</td><td>12 pcs of part no. 5</td></tr></tbody></table>
## 3.2 Energy Sensing

A requirement for the real-time allocation of emissions within the proposed hierarchy is an appropriate energy sensing system. To allocate the emissions the levels manufactory, area and energy consumers must feature a measurement system. The sampling rate of measurement must be adapted to the electricity grid and the processes. While the direct embodied energy relies on the difference of consumed energy at process start and end, the relative CO₂ equivalent by electricity composition changes in 15 min intervals. Hence, a requirement for the integration is a sampling rate of energy consumption sensing, which enables the mapping of CO₂ equivalent emissions in relation to the actual manufacturing process and changing electricity mix.

Another difficulty is the mapping of energy losses. These can occur by a multitude of reasons and are not explainable in every case. To obtain meaningful results in terms of emission allocation a representable coverage of consumption is required. The coverage of measurements can be determined by an energy transparency key performance indicator. Energy transparency $\gamma$ in this context is defined as the total consumption of all energy consuming entities per the according sensing zones' consumption. In case of no untraceable losses, this factor $\gamma$ would be 1. A factor $\gamma > 1$ implies bad matching of sampling rates or an erroneous hierarchical classification and requires adaption. A factor $\gamma < 1$ implies plausibility of the sensed values, should however, be ideally be close to 1. As a consequence of losses, an unassignable share also results. As a difference to the sensed energy consumption, the unassignable share can only be determined by calculation and shall be carried out for every sensed layer.

## 3.3 Allocation of Emissions

To integrate energy allocation and real-time emissions, the information output must be defined. Following the definition of Rahimifard et al. [12] the share of direct energy consumption is split into theoretical energy and auxiliary energy consumption. If the theoretical energy efficiency provides no viable information, this split can be neglected and only the measured direct energy share considered. The determination of theoretical and auxiliary energy depends also on the available information of the consuming entity. Considering a large scale manufacturing system, a substantial modeling effort would be necessary to obtain the classification for every consumer. By neglecting this split, the respective energy sensing can be the relevant information provider.

Combining the discussed indications, a multi-level mapping can be derived. The calculation of emissions can be carried out based on the discussed approaches. Combining the energy measurement system with the derived specific CO₂ equivalent intensities, the overall accountable emission can be calculated by Eq. 1. In order to distribute these emissions, the local zone-wise metering must be taken into account and an emission flow results.

Considering the allocation of direct and indirect energy consumption, there is a difference in the difficulty of the allocation. By means direct energy consumption is caused by a manufacturing process and the emission mapping to
<figure><img src="image_5.png" /><figcaption>Fig. 3. Real-time emission flow from the manufacturing environment to products</figcaption></figure>

the process is implied. The allocation of indirect emissions lacks the implica-
tion, thus an allocation strategy must be defined. Fundamentally, two allocation
strategies are possible to split the unassignable emissions: (1) by share of allo-
cable energy or (2) by share of other metrics. Other metrics can be chosen if
they provide a better description to split the emissions. An example would be
the allocation of other media consumption by tracing, like pressurized air or
the split based on process times. In explanation the longer a process takes, the
more indirect emissions from e.g., lighting shall be allocated. Otherwise, short-
term processes with high energy respectively emission intensity would wrongly
get penalized. Thus, analogous to LCA, the availability of additional data can
significantly increase the validity of results.

Between the energy consumer and process level, an indirect buffer is located.
If the sampling rates between the sensing equipment on the individual level is
not in sync, discrepancies in the indirect consumption may happen. By averaging
and buffering the indirect consumption, these effects can be mitigated. Visual-
izing the emission flows as Sankey diagram, the buffered emissions thus can be
represented a loop The resulting approach to allocate the resulting emissions of
the electricity grid is visualized in Fig. 3.
# 4 Discussion

The proposed conceptual approach enables the real-time allocation of emissions into manufacturing jobs respectively products. Especially in the context of LCA benefits in accuracy can result by consideration of real conditions. A limitation of the approach could be given by the need for smart-metered zone consumption. The availability of these data would however also increase the energy transparency and provide further value within data-driven manufacturing environments. Another synergetic relationship with cyber-physical production systems would be the availability of high quality process data needed for the indirect allocation. This would result in a wider range of applicable indicators, but would simultaneously introduce another problem. With a rising amount of available indicators, the possibilities of incomprehensible selections also rise. To maintain comprehensibility, a policy is needed defining the selection of indirect indicators.

Another possible application of the proposed approach are the usage as key performance indicator for embodied emissions of products. By also considering the indirect emissions and external parameters it aims to be a holistic indicator for environmental impacts. Thus, further usage in optimization problems would also be possible. However, a known problem in such applications are the needed predictions of the electricity markets composition.

The presented approach is conceptual, but built upon the intensively researched energy allocation. Additional validation is needed for industrial applications with real sensors and the matching of needed sensor sampling rates. The research contribution of this publication is the application of allocation methods in a temporal context.

# 5 Conclusion

In this paper, an approach for the dynamic allocation of CO₂ equivalent emissions in products was presented. A literature research revealed there was no consistent approach to allocate the emissions of volatile energy sources in products. Therefore, several components were researched including (1) state-of-the-art approaches to tackle this problem, (2) the real-time calculation of specific emissions of electricity and (3) energy allocation. A proposal was made to integrate these components into a real-time allocation of resulting CO₂ equivalent emissions. The proposed approach allows breaking down emissions to products and processes.

Potential for further research can be found in practical applications of approach. Possible applications are optimization problems to minimize embodied CO₂ equivalent emissions. The real-time allocation approach enables optimization on a global level by considering also implications on indirect energy consumers and energy sources' conditions. Further usages of the proposed approach can be made within a practical case study for validation. A major challenge in a practical application is the availability of data from electricity grid side and the hierarchical sensing data.
**Acknowledgements.** The research has been co-financed by the European Union's Horizon 2020 Program under Grant Agreement No. 958478 entitled 'ENERgy-efficient manufacturing system MANagement'.

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