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Exploring Opportunities: Optimizing Production Planning by Factoring in Energy Procurement and Trading Options


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Motivated by the increasing share of renewable energy in the markets for energy commodities, this study has evaluated the potential for optimizing production planning by taking into account disposable options for procuring energy, in this case electricity. For this purpose, a material flow simulation study extended by an electricity price simulation has been executed to examine possible cost scenarios. Our findings support the notion of a potential for further research in new optimization models involving energy procurement as well as energy trading options.
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SNE 27(2) – 6/2017 97
Exploring Opportunities: Optimizing Production
Planning by Factoring in Energy Procurement
and Trading Options
Maximilian Selmair1*, Marc Hanfeld2, Thorsten Claus1, Frank Herrmann3
1 International Institute Zittau, Central Academic Unit of Dresden Technical University, 02763 Zittau, Germany
2 Wintershall Holding GmbH, 34119 Kassel, Germany
3 Ostbayerische Technische Hochschule, 93025 Regensburg, Germany
Abstract. Motivated by the increasing share of renewable
energy in the markets for energy commodities, this study
has evaluated the potential for optimizing production plan-
ning by taking into account disposable options for procuring
energy, in this case electricity. For this purpose, a material
flow simulation study extended by an electricity price simu-
lation has been executed to examine possible cost scenari-
os. Our findings support the notion of a potential for further
research in new optimization models involving energy pro-
curement as well as energy trading options.
1 Introduction
The nuclear phase-out, planned to have been accom-
plished by 2022, leads Germany to its pioneering role in
expanding renewable energies. Along with the liberali-
zation of the European energy markets, new opportuni-
ties of energy procurement have been established. Con-
sidering the remarkable volatility of the electricity mar-
ket due to the increasing solar- and wind power feed-in
along with individual pricing structures, the application
of such opportunities imposes a flexibilization of pro-
duction [1]. Thus, the authors saw the need to develop
new approaches for production planning.
Based on the executed simulation study, the depend-
encies between production planning and energy costs
are demonstrated. The results suggest that the integra-
tion of energy trading and production planning is likely
to result in a monetary advantage for the manufacturing
In the following sections, an investigation by means
of simulation, a detailed discussion of the associated
results and perspectives for future research are provided.
2 Related Literature
Energy-efficient production planning has become an
increasingly important issue in recent years. For Ger-
many in particular, the scheduled shutdown of nuclear
power plants has raised awareness regarding resource
efficient production.
Research in the field of energy efficiency and energy
oriented production planning has become increasingly
important in the past decade. Motivated by scarce re-
sources, flexible energy prices and the fluctuating sup-
ply of renewable energy, there are several contributions
for energy-oriented production control such as [1-13].
As not every production facility is suitable for this
kind of energy orientated production planning, Kabe-
litz et al. [14] developed a method to evaluate the ener-
getic flexibility of production systems.
Another way to exploit the fluctuating supply of re-
newable energy is the integration of energy storages.
Atabay et al. [15] provided a mathematical calculation
for determining the size of energy storages required,
depending on the energy demand and the expected en-
ergy tariff. A case study performed in two very different
companies demonstrates the application of this method.
One outstanding example for knowledge transfer be-
tween theory and practice is a project named Green
Factory Bavaria which is co-operated by the Fraunho-
fer Society [16].
SNE 27(2), 2017, 97 - 103, DOI: 10.11128/
Received: May 25, 2017,
Accepted: June 10, 2017 (Special Issue Review)
SNE - Simulation Notes Europe, ARGESIM Publisher Vienna
ISSN Print 2305-9974, Online 2306-0271,
et al.
Optimizing Production Planning by Factoring in Energy Procurement
98 SNE 27(2) – 6/2017
By providing several research, demonstration and
learning platforms, the project assists the manufacturing
industry in increasing their level of resource efficiency.
Technical solutions as well as methodical approaches
are part of the knowledge transfer from applied research
to the manufacturing industry.
However, approaches taking into account financial
possibilities in energy procurement for the purpose of
optimizing production planning have not been devel-
oped yet. The following section describes the authors’
proposal of such an approach.
3 Problem Definition
In recent years, the energy market’s structure has been
changed by liberalization, energy transition and digitali-
zation. The formation of wholesale markets for energy
and the developing competition provides opportunities
to trade amounts of energy among market participants.
This also enables non-energy companies to benefit from
the energy trading opportunities. The trading of energy
can be organized by institutional exchanges (e.g. Euro-
pean Energy Exchange EEX or Intercontinental Ex-
change ICE) or may be based on bilateral negotiations.
Manufacturing companies, whose production processes
are very energy intensive, can obtain their energy re-
quirements directly or indirectly via an upstream suppli-
er by trading standardized products in the markets for
electricity and gas.
These standardized products that can be traded on
the spot and futures markets include baseload volumes
of various maturities. The following contract types can
be differentiated:
hourly contracts
daily contracts
monthly contracts
quarterly contracts
seasonal contracts
yearly contracts
For instance, within an hourly contract a constant
load will be delivered for a fixed price (e.g. 1 MWh/h
for 25 €/MWh). The commodity electricity can be di-
vided into baseload- and peak load contracts. The same
is applicable for monthly contracts, where a constant
load is supplied for all hours of a month – just as for the
quarters, seasons and years. The prices for the different
contract types are subjected to price variations at the
respective trading times.
The different products are being traded in different
maturities. For example, on the electricity market of the
EEX it is only possible to trade contracts with delivery
of single hours of a given day on that particular day
(intraday trading); the trading of daily deliveries is only
possible on the day prior. Monthly contracts are traded a
few months prior to their delivery.
Yea rly C o nt ra c ts
Quarterly Contracts
Monthly Contracts
Daily Contracts
Figure 1: Structuring of a load profile using standardized
energy trading products.
The same applies respectively for quarterly, seasonal
and yearly contracts. In practice, the scheduled purchase
of energy is based on historical load profiles. The prin-
ciple that underlies the structure of an exemplary load
profile with the above mentioned standardized products
is illustrated in Figure 1.
Interdependencies between production planning and
energy trading can be identified [15, 17]. One possibil-
ity to influence the energy costs is an advantageous
combination of the standard trading products and the
best moment to buy commodity products. In other
words: When the prices are high, a low demand is ad-
visable and vice versa. Therefore, it will be an ad-
vantage to place high-demand-periods in low-price-
Nevertheless, manufacturing companies that partici-
pate in energy trading markets are faced with cost asso-
ciated risks. These risks result from the markets’ price
volatility and have to be supervised. In this discussion,
we define the cost risk as the deviation between the
planned budget and actual costs. Therefore, a high vari-
ance of the energy demand would lead to greater cost
risks. If a high degree of capacity utilization of the pro-
duction is achieved in the early stages of planning, the
resulting load profile can almost completely be struc-
tured by forward based contracts.
et al.
Optimizing Production Planning by Factoring in Energy Procurement
SNE 27(2) – 6/2017 99
Thus, it is possible to secure energy prices in ad-
vance and – due to the absence of (unplanned) short
term load variations – to avoid the selling / buying of
short term (hourly) contracts and to reduce the cost risk.
The following figure illustrates these relationships.
Figure 2: Dependence of load profile variance and
energy purchasing.
T Scen 1
Scen 2
Scen 3
0 30.00 30.00 30.00
1 31.49 29.64 41.84
2 24.51 24.96 46.33
3 29.26 24.97 49.02
4 25.60 29.93 41.68
5 16.50 28.72 50.82
6 16.25 21.56 50.88
Table 1: Price scenarios.
The first chart of Figure 2 depicts the fictitious case
of a production plan with a high variance load profile.
This case results in high load fluctuations. If a forward
contract was to be purchased at t=0, time periods with
shortages and with surplus quantities would result. At
the beginning of the planning period, the exact prices
for selling surplus or buying shortfall quantities are
unknown. Consequently, a cost risk results.
The second chart describes low variance. If the en-
ergy procurement is planned on the basis of this load
profile, only small deviations remain, which can be
evened out by means of short term trading products.
This results in a lower cost risk in comparison to the
scenario in the first chart.
The numerical example below illustrates the prob-
lem regarding the dependency of costs for energy and
production planning. The above-mentioned cases are the
basis for the following: (1) The production plan results
in high variance of the energy load profile and (2) low
variance of the energy load profile. The planning period
amounts to 6 TU (time units). At the beginning of the
planning period (t=0), a baseload contract for these
6 TU is worth 30 €/UoM (Units of Measurement) (de-
livery across all 6 TU). Purchasing a baseload contract
for this period is only possible in t=0. Table 1 pro-
vides three price scenarios that represent possible price
trends when purchasing short-term contracts.
Table 2 displays case (1) with high variance. For
demonstration purposes, it is assumed that 3 UoM must
be produced in total and each product UoM requires
1 UoM of energy. Thus, in t=0 it is only possible to
purchase a baseload contract for the next 6 TU. In this
example, the baseload contract is determined by
0.5 UoM. In this case, the initial production plan speci-
fies the production of 1 UoM in periods t={1,4,5}
respectively. Due to the purchasing of energy with a
load of 0.5 UoM in t = 0, in t={2,3,6}, surplus quanti-
ties occur. These quantities are sold on the market at the
prices mentioned in Table 1. Consequently, shortages
arise in t={1,4,5} that need to be procured at prices
which also listed in Table 1 The distribution of costs
displayed below in Table 2 results from the scenario of
the planning point in t=0.
Based on this example, the energy costs are subject
to variations. Depending on the price development, the
costs range from € 84,05 to € 98,39.
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Optimizing Production Planning by Factoring in Energy Procurement
100 SNE 27(2) – 6/2017
t Load
+ long /
- short
Scen 1
Scen 2
Scen 3
1 1 0.5 -0.5 -30.75 -29.82 -35.92
2 0 0.5 0.5 -2.75 -2.52 8.16
3 0 0.5 0.5 -0.37 -2.51 9.51
4 1 0.5 -0.5 -27.80 -29.96 -35.84
5 1 0.5 -0.5 -23.25 -29.36 -40.41
6 0 0.5 0.5 -6.88 -4.22 10.44
Σ-91.80 -98.39 -84.06
Table 2: Cost distribution for a high-variance-load-profile.
In the following section, the scenario for case (2) is
discussed. The initial production plan results in an even
distribution of the 3 UoM over the 6 TU. This results in
a load profile of 0.5 UoM/TU, which is purchased as a
baseload contract in t =0, results (see Table 3). Thus,
the load variations in the planning period as well as the
necessity to sell/buy surplus/shortfall quantities are
reduced to a minimum.
Therefore, the load profile can be covered entirely
by purchasing the baseload contract in t=0 and the
price of 30 €/UoM can be secured. The overall costs for
purchasing energy amounts € 90 in every of the three
price scenarios (see Table 3). In this case, there are no
cost variations and the costs in every price scenario are
the same.
As illustrated in the above-mentioned example cas-
es, it is likely that an integrated view of production
planning and energy purchasing will influence energy
costs. On the one hand, the forward markets’ opportuni-
ties for securing energy prices, and on the other hand,
the trading of short-term contracts on the spot market
provides a potential for optimizing the flexibility of the
production process and for reducing the costs for energy.
The main target of this contribution is the implemen-
tation of a simulation study. Therewith, the impact of an
integrated view of production planning in combination
with the opportunities of energy trading on the expense
situations of companies can be conjectured. Finally, the
optimization potential is identified and the determinants
of the optimization problem are specified.
+ long /
- short
Scen 1
Scen 2
Scen 3
10.5 0.5 0 -15.00 -15.00 -15.00
20.5 0.5 0 -15.00 -15.00 -15.00
30.5 0.5 0 -15.00 -15.00 -15.00
40.5 0.5 0 -15.00 -15.00 -15.00
50.5 0.5 0 -15.00 -15.00 -15.00
60.5 0.5 0 -15.00 -15.00 -15.00
Σ -90.00 -90.00 -90.00
Table 3: Cost distribution for a low-variance-load-profile.
4 Simulation Study
This section describes the details of the parameters
utilized in the simulation study. Figure 3 illustrates the
resulting energy cost distribution as a result of merging
a material flow simulation and an electricity price simu-
lation. Both mentioned simulations are independent of
one another.
Material Flow Simulation. The requirement for
this part of the simulation was to determine a complete
energy consumption pattern for a fictitious production
system. For this purpose, a job shop production system
with a total number of nine machines was designed. In
order to examine different consumption patterns, it was
decided to apply different priority rules when
scheduling this production system. Such heuristic
methods are used in industrial practice to avoid time-
consuming constraint-based approaches. Although these
methods have nothing in common with energy saving
methods, they can be used to obtain different energy
Material Flow
Simulation of
Energy Prices
Energy Cost Distribution
Figure 3: Proceeding of the simulation study.
et al.
Optimizing Production Planning by Factoring in Energy Procurement
SNE 27(2) – 6/2017 101
Commonly applied rules are as follows [18]:
FIFO: First-In-First-Out
LIFO: Last-In-First-Out
SJF: Shortest Job First
LJF: Longest Job First
SRPT: Shortest Remaining Processing Time
LRPT: Longest Remaining Processing Time
EDD: Earliest Due Date
With a flow simulation model designed in Tecnomatix
Plant Simulation 13, these seven rules for the same
volume of orders were applied to generate different
consumption patterns. As the total consumption of the
production system is the point of interest, the energy
patterns of all machines are identical:
Ramp Up: 10 kW/h
Setup: 7 kW/h
Processing: 35 kW/h
Standby: 6 kW/h
Ramp Down: 7 kW/h
The case study includes a total amount of 810 jobs.
Every job is linked to a working schedule specified by
one of four possible products that is to be produced.
This sequence can be inferred from Figure 4. All dura-
tions are stated in minutes.
Product 1
Product 2
Product 3
 
Figure 4: Sequence, setup time and operation time
for each product.
The order quantity of each job is an evenly distributed
number between 2 and 6. Depending on the applied
priority rule, processing these jobs will take between 33
and 36 days. This algorithm to create all 810 jobs is
given in the pseudocode shown next.
for iPer = 1 to 27 loop
for iCnt = 1 to 10 loop
for iPro = 1 to 3 loop
Product = "P"+iPro
Amount = Uniform(2,7)
ReleaseDate = iPer
DueDate = Uniform(iPer+1,iPer+5)
Listing 1: Algorithm for job-compiling.
The algorithm generates jobs for 27 periods (first loop).
Every period contains 10 jobs (second loop) for each
product (third loop).
Price Simulation. The Ornstein-Uhlenbeck-Process
was used to model and simulate the stochastic behavior
of electricity price developments. This approach is in
line with [19] as a base model for commodity prices:
=κμ−+ (1)
Parameters are described as follows:
S: Electricity Spot Price
κ: Mean Reversion Factor
µ: Mean (e.g. price of forward contracts)
dZ: Brownian Motion
σ: Standard deviation of the price returns
dt: Time increment
As the historical spot prices for electricity contain
negative prices, the logarithm of the prices discussed by
Schwartz [19] is not applicable. Therefore, the naive
discretized approach, mentioned in [20], was applied
when simulating the electricity spot prices:
=μ−△+ (2)
The process simulation was based on the following
parameter values:
S: 24.21
κ: 0.0736
μ: 25
σ: 4.7051
△t: 1
et al.
Optimizing Production Planning by Factoring in Energy Procurement
102 SNE 27(2) – 6/2017
Using the model mentioned above, 1,000 price paths
for a period from 01/05/2016 0:00 till 05/06/2016 20:00
was simulated. The simulated price paths are negative in
some cases. That is, in these periods a company would
receive money when obtaining energy from the supplier.
For the purpose of simplification and reproducibility
of the results, we refrained from using a more complex
model. For an extended spot price model see [17].
5 Results
In this section, closer look at the results of the above
described simulations and the resulting energy cost
distribution is presented. A different load profile was
generated for each applied priority rule, as visualized in
Figure 5. The combination of these load profiles with
1,000 random price paths leads to Figure 6. Here, the
cost distributions for every applied priority rule are
It is noticeable that all distributions are different re-
garding their expected value and spread. Thus, in this
case study, some priority rules such as LRPT or FIFO
lead to lower expected energy costs than others. Anoth-
er important indicator is the spread of the cost distribu-
tion. The wider the distribution, the higher is the uncer-
tainty and thus the resulting cost risk. Therefore, the
EDD rule provides the narrowest distribution and thus
the lowest uncertainty. Depending on the market’s ener-
gy prices, the costs for obtaining energy can deviate
more from the expected value if the distribution is wide-
spread. All cost distributions are described detailed with
estimated costs, minimum, maximum, spread and an
exemplary historical value in Table 4.
Figure 5: Load profile for applied priority rules.
The various distributions, especially their deviations,
provide a potential for using energy procurement and
trading options to minimize energy costs in industrial
manufacturing. The application of seven different pri-
ority rules for scheduling the production system of our
case study leads to different and random procurement
As can be seen in Figure 6 and read in more detail in
Table 4, the resulting costs and also the resulting cost
uncertainty differs between all cases. This implies a cost
sensitivity regarding a) the combination of contracts in
the forward market, which can be bought at the begin-
ning (t=0) and should be adjusted during the produc-
tion period depending on the price development for the
tradeable forward contract and b) the reaction of short
term price movements on the spot market.
Using this potential requires a suitable planning ap-
proach and should be object of further research.
Costs [€]
Min [€] Max [€] Spread [€] Hist.* [€]
FIFO 3.753 3.500 4.014 514 3.757
LIFO 3.863 3.662 4.061 400 3.873
SJF 3.748 3.389 4.120 731 3.671
LJF 3.841 3.709 3.977 268 3.830
SRPT 3.854 3.694 4.037 343 3.878
LRPT 3.660 3.381 3.944 563 3.608
EDD 3.823 3.690 3.984 293 3.781
* historical value from 01/05/2016
Table 4: Description of all cost distribution.
Figure 6: Energy cost distributions for applied priority rules.
0 100 200 300 400 500 600 700 800
timeline [h]
load [kWh]
3200 3400 3600 3800 4000 4200
et al.
Optimizing Production Planning by Factoring in Energy Procurement
SNE 27(2) – 6/2017 103
6 Conclusion and Future Work
In this investigation, potential for augmenting the opti-
mization of production planning by factoring in energy
procurement and trading options was identified. Provid-
ed that a company’s production offers enough flexibil-
ity, short-term reactions to changing market situations
are possible. By means of our simulation study, our
results are demonstrating different energy costs distribu-
tions for a variety of schedules generated by applying
common priority rules. Consequently, a potential for
optimization is apparent.
Therefore, an optimization model that focuses on
saving energy costs in periods in which the production
schedule is flexible will be our objective for further
research. While planning continuously, the model
should re-plan the whole planning horizon after each
period to consider short-term as well as long-term ener-
gy price changes. Thus, an inclusion to the hierarchical
production planning concept provided by Hax and Meal
[21] seems appropriate to the authors.
Finally, we want to highlight that this research project
does not focus on a higher energy efficiency and will not
save energy in particular. Rather, it should help to reduce
energy costs and decrease the cost risk for companies
without fixed energy prices by means of an integrated
consideration of energy procurement and trading options.
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The energy turnaround in Germany increases the share of renewable energies. Since the amount of renewable energy supply is immanently subject of variation, the electricity price at the European Energy Exchange EEX is highly volatile. If companies would purchase the electricity directly at the EEX instead of from a wholesale power supplier along with price fixing, companies would benefit from increasing production in times with low electricity costs and reducing production in high-cost times. This paper shows, that influencing respectively shifting the time of electricity consumption -e.g. by adjustment of process parameter, shift period, order of jobs or machine utilization, by pausing of processes or delaying of job starts - can theoretically reduce electricity expenditures. The measures are being explained and discussed, followed by a description of company in-house and external requirements for the energy flexible production. © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.
Conference Paper
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Der steigende Anteil erneuerbarer Energien im Zuge der Energiewende fordert Lösungen, die das wetterabhängige Energieangebot nutzen können. Abgesehen von Speicherlösungen sind Methoden gefragt, die Einfluss auf die Produktionsplanung nehmen, also eine angebotsorientierte Nachfrage generieren. Allerdings ist nicht jede Produktion geeignet für eine energieorientierte Produktionsplanung. In diesem Beitrag wird daher eine Methode zur Bewertung der energetischen Flexibilität von Produktionssystemen vorgestellt und wie eine energieorientierte Produktionsplanung mittels mathematischer Optimierungsmethoden umgesetzt werden kann.
In order to allow for truly holistic considerations, as intended in the Digital Factory concept, energy-related factors need to be considered. This has not been widely implemented for discrete event simulation (ie, material flow simulation) in industrial companies, yet, even though it may foster the energy efficiency within production sites considerably. A primary reason for the lack of acceptance is that previously discussed approaches did not meet the users’ requirements. This paper discusses how a suitable extension (eniBRIC), which renders energy-related considerations possible within material flow simulation, can be developed paying heed to both user requirements and the state of the art. A special focus is set on its implementation in Siemens Tecnomatix Plant Simulation. The workflow for the integration of the extension into existing and new simulation models is outlined. Opportunities for its utilisation in specific application examples, as well as the associated extra time and effort are discussed.
This optimisation approach focuses on the shop floor of a manufacturing company. It aims for an integrated lean and green assessment of a manufacturing system and the identification of a cost optimized combination of lean and green strategies with regard to green targets. For green assessment material and energy inputs as well as resulting CO2 emissions are taken into account. Lean assessment focuses on costs and throughput time. Potential lean and green strategies identified during top down analysis are integrated into a discrete event simulation model. This model is connected with optimisation heuristics which improve combined lean and green strategy deployment to the manufacturing system.
In the past two decades researchers in the field of sequencing and scheduling have analyzed several priority dispatching rules through simulation techniques. This paper presents a summary of over 100 such rules, a list of many references that analyze them, and a classification scheme.