Energies 14 05928 v2
Energies 14 05928 v2
Energies 14 05928 v2
Article
OMEGAlpes, an Open-Source Optimisation Model Generation
Tool to Support Energy Stakeholders at District Scale
Sacha Hodencq 1, * , Mathieu Brugeron 1 , Jaume Fitó 2 , Lou Morriet 1 , Benoit Delinchant 1
and Frédéric Wurtz 1
1 University Grenoble Alpes, CNRS, Grenoble INP, G2Elab, F-38000 Grenoble, France;
mathieu.brugeron@g2elab.grenoble-inp.fr (M.B.); lou.morriet@g2elab.grenoble-inp.fr (L.M.);
benoit.delinchant@g2elab.grenoble-inp.fr (B.D.); frederic.wurtz@g2elab.grenoble-inp.fr (F.W.)
2 Laboratoire Optimisation de la Conception et Ingénierie de l’Environnement (LOCIE), CNRS UMR
5271—Université Savoie Mont Blanc, Polytech Annecy-Chambéry, Campus Scientifique, Savoie Technolac,
CEDEX, 73376 Le Bourget-Du-Lac, France; eng.fito@gmail.com
* Correspondence: sacha.hodencq@g2elab.grenoble-inp.fr
Abstract: Energy modelling is key in order to face the challenges of energy transition. There is a
wide variety of modelling tools, depending on their purpose or study phase. This article summarises
their main characteristics and highlights ones that are relevant when it comes to the preliminary
design of energy studies at district scale. It introduces OMEGAlpes, a multi-carrier energy modelling
tool to support stakeholders in the preliminary design of district-scale energy systems. OMEGAlpes
is a Mixed-Integer Linear Programming (MILP) model generation tool for optimisation. It aims at
making energy models accessible and understandable through its open-source development and the
integration of energy stakeholders and their areas of responsibility into the models. A library of use
cases developed with OMEGAlpes is presented and enables the presentation of past, current, and
Citation: Hodencq, S.; Brugeron, M.; future development with the tool, opening the way for future developments and collaborations.
Fitó, J.; Morriet, L.; Delinchant, B.;
Wurtz, F. OMEGAlpes, an Keywords: energy modelling; MILP optimisation; district scale; open-source; stakeholders; use cases
Open-Source Optimisation Model
Generation Tool to Support Energy
Stakeholders at District Scale.
Energies 2021, 14, 5928. https:// 1. Introduction
doi.org/10.3390/en14185928
The current reality of global warming invites us to rethink and reshape our human
activities. Energy consumption of human activities accounts for more than 70% of green-
Academic Editor: Patrick Phelan
house gases (GHG) emissions worldwide [1]. Energy policies are now recommended with
pillars such as energy sufficiency, efficiency, and the development of renewable energy
Received: 30 July 2021
Accepted: 10 September 2021
sources to replace fossil fuels [2]. These worldwide challenges manifest themselves at local
Published: 18 September 2021
scales, especially in urban districts. Districts consume more than half of the global primary
energy, on one hand. On the other hand, they could be the new place for energy production,
Publisher’s Note: MDPI stays neutral
with the development of low-carbon and decentralised renewable energies, as well as the
with regard to jurisdictional claims in
increase in energy recovery potential. At such scale, energy consumption is cross-sectoral,
published maps and institutional affil- with energy needs such as heating that can be met by electricity, gas, or direct heat use.
iations. This multi-energy contribution can come from various sources and networks. Moreover,
the development of renewable energy sources brings about new challenges, such as inter-
mittency. The design and management of multi-energy systems at district scales becomes
crucial to face these challenges. Work at district scale also requires taking into account
Copyright: © 2021 by the authors.
historical and new energy projects stakeholders in the transition process; supporting these
Licensee MDPI, Basel, Switzerland.
actors in the process of designing energy systems that include more variable and diffuse
This article is an open access article
sources is also of prime importance [3,4].
distributed under the terms and Energy modelling tools can play the role of decision support for energy stakeholders
conditions of the Creative Commons by accompanying them in learning about, sizing, and operating local energy systems. There
Attribution (CC BY) license (https:// exists a wide range of energy modelling tools with various modelling capabilities. The
creativecommons.org/licenses/by/ accessibility of these tools for energy stakeholders is dependent on their openness, their
4.0/). user friendliness, and their consideration of social aspects.
The aim of this article is to underline the challenges of energy modelling at district scale
and to present the tool OMEGAlpes as well as its associated use cases library. OMEGAlpes
is an optimisation tool developed to easily generate multi-carrier energy system models
in order to support stakeholders in the first steps of system studies. The tool includes
well-suited characteristics for preliminary energy system design phases, with a compromise
between computational time and degree of modelling detail, as well as a semantics close
to human understanding that enables optimisation based on energy, exergy, or specific
stakeholders’ criteria. Moreover, open-source principles have been applied in the tool
development, and energy stakeholders can be integrated in the modelling. Finally, all
the use cases developed with OMEGAlpes are gathered in a library and can be accessed,
modified, and reused online. This allows both the models to be brought to society and
society to be considered in the models.
This paper is structured as follows. First, we explore the energy modelling tools’
characteristics based on literature reviews, beginning with the importance of energy system
preliminary design. Then, we introduce the OMEGAlpes framework characteristics and
structure. We finally present the use cases library associated with the tool that allows for
the understanding, accessibility, and reproducibility of the studies.
of architecture [19], aeronautics [16], and power electronics [20]. It is therefore important
to explore the widest range of possibilities with relevant modelling and decision support
tools. This exploration will allow for making the right choices in the initial phases. It
becomes even more relevant if integrating as early as possible aspects usually decided very
late in the design cycle, such as the optimal management strategy. This is of particular
importance when considering both environmental and financial costs at stake for district-
scale energy systems. Thus, preliminary design will be one of the focuses of the rest of this
literature review.
Figure
Figure 1.
1.Model
Modeluncertainty
uncertaintyvs.
vs.complexity in in
complexity design phases.
design Source:
phases. authors
Source: fromfrom
authors Trčka et al.et[26].
Trčka al. [26].
Simulation is
Simulation is by
bynature
naturebased
basedon onthe thedefinition
definition of of study
study scenarios.
scenarios. ThisThis implies
implies po-
potentially
tentially time-consuming
time-consuming negotiations
negotiations between
between the the different
different solutions
solutions studied
studied andanda de- a
dependency on a priori determined management instructions
pendency on a priori determined management instructions without any degree of free- without any degree of
freedom.
dom. Simulation
Simulation often
often forces
forces repeated
repeated trial
trial andanderror
errorresolution
resolution[27],
[27],while
while optimisation
optimisation
enables exploring a wide range of decision variables at the same
enables exploring a wide range of decision variables at the same time. Optimisation, then,time. Optimisation,
then, seems
seems more more
suitablesuitable for goal-seeking
for goal-seeking withinwithin
complexcomplex
systemssystems [11,28],
[11,28], especially
especially in earlyin
early design
design phasesphases
wherewhere a lot ofa studied
lot of studied
scenariosscenarios
are stillare still possible.
possible. Special Special
attentionattention
should
should be paid to ensuring democratic process with optimisation models:
be paid to ensuring democratic process with optimisation models: Lund et al. [29] under- Lund et al. [29]
underline that if optimisation and simulation modelling approaches
line that if optimisation and simulation modelling approaches can actually be used can actually be usedto-
together, simulation is well-suited for democratic decision-making,
gether, simulation is well-suited for democratic decision-making, while optimisation risks while optimisation
risks imposing
imposing a unique
a unique expertexpert solution.
solution.
Various optimisation methods can be used in energy modelling. These include heuris-
Various optimisation methods can be used in energy modelling. These include heu-
tic methods with genetic algorithms and particle swarm optimisation; stochastic optimisa-
ristic methods with genetic algorithms and particle swarm optimisation; stochastic opti-
tion; and distributed optimisation for consensus problem using game theory. Linear and
misation; and distributed optimisation for consensus problem using game theory. Linear
quadratic programming can also be employed, as well as Mixed-Integer Linear Program-
and quadratic programming can also be employed, as well as Mixed-Integer Linear Pro-
ming (MILP) [30,31]. MILP combines several interests compared to other optimisation
gramming (MILP) [30,31]. MILP combines several interests compared to other optimisa-
methods. First, many systems, such as storages, present finite states that binaries better
tion methods. First, many systems, such as storages, present finite states that binaries bet-
enable expressing compared to Linear Programming (LP) [6,8]. During the preliminary de-
ter enable expressing compared to Linear Programming (LP) [6,8]. During the preliminary
sign, a lot of variables are not set yet, which entails a lot of optimisation variables especially
design, a lot of variables are not set yet, which entails a lot of optimisation variables espe-
at high temporal resolution. At the same time, the modelling includes linear flows, such as
cially at high temporal resolution. At the same time, the modelling includes linear flows,
energy and finance. MILP optimisation can lead to the global optimum more quickly than
such as energy and finance. MILP optimisation can lead to the global optimum more
other methods for such energy optimisation problems [9,31]. However, MILP may require
quickly than other methods
data pre-processing such as for such energy
piecewise optimisation
linearisation, problems
depending [9,31].
on the However,
modelling MILP
detail of
may require data pre-processing such as piecewise linearisation, depending
the technical system representation [32]. LP and MILP are used in a variety of frameworks. on the mod-
elling detailexamples
Respective of the technical
includesystem representation
URBS [33], MODEST [34], [32].and
LP and MILP are
MESSAGE [35]used in a and
for LP; variety
the
of frameworks. Respective examples
Ehub Modeling Tool [36] and DER Cam [37] for MILP. include URBS [33], MODEST [34], and MESSAGE
[35] for LP; and the Ehub Modeling Tool [36] and DER Cam [37] for MILP.
2.3. Space and Time Scale and Resolution
2.3. Space
2.3.1. and Time Scale
Geographical Scaleand Resolution
2.3.1.Most
Geographical Scale
of existing energy modelling tools focus on large energy systems [6], allowing
for wide-scale energyenergy
Most of existing scenarios. This includes
modelling toolson
tools focus such as Balmorel
large [38], TIMES
energy systems [39,40],
[6], allowing
and PyPSA [41]. At such regional or continental scales, technical specifications are
for wide-scale energy scenarios. This includes tools such as Balmorel [38], TIMES [39,40],often
aggregated and do not represent individual plants or energy system components [7].
and PyPSA [41]. At such regional or continental scales, technical specifications are often In
Energies 2021, 14, 5928 5 of 30
contrast, district-scale energy systems bring about new challenges with the development of
local energy sources, such as energy systems stability and flexibility strategies on both the
production and demand sides [8]. Platforms such as Reopt [42] or Artelys Crystal Energy
Planner [43] offer MILP optimisation modelling at local scales.
of knowledge and eased peer-review, thus limiting errors [56], biases [52], or even fraud.
The open energy modelling tools are highly maintainable [55] as well as continuously
improved [5]. With respect to proprietary software, their source code remains available
over time, and they can meet high standards [9]. As Oberle et al. point out, such tools are
“on the right road to achieving a competing level of accuracy, while also providing a much
higher level of transparency” [12].
Open energy modelling faces challenges such as lack of awareness and practical
knowledge on these issues, lock-in to proprietary software, and institutions’ inertia [48].
In the academic world, open-source principles of early and regular release can go against
usual practices. Some may fear for their reputation or for the time they spend on support,
even if experience suggests collaboration interests outweigh this issue [52]. Organisations
can also tend to withhold information and ideas, in order to create their own unique and
closed models and to receive certain funding. Open energy modelling would need further
evolution in policy and scientific practices in order to develop and become the norm.
user-friendliness of the energy modelling tool. The terms actors and stakeholders will be
indifferently used in the rest of the article.
These practices are of particular interest for MILP problems formulation. However,
this facilitation of formalisation can lead to a biased understanding of the modelling by the
user, who will not necessarily be aware of the content of the models. Moreover, the higher
the semantic level of formulation, the more transcriptions will be necessary to present the
problem to the machine solver.
2.8. Energy Modelling Tool Choice for Preliminary Design at District Scale
Considering this literature overview regarding energy modelling tools’ characteristics,
several points of interest can be underlined when it comes to the preliminary design of an
energy system at district scale. Macroscopic optimisation models should be opted for in
order to assess a variety of variables while keeping uncertainties low. MILP optimisation
model generation tools seem relevant for such a task. High temporal resolution and
multi-carrier energy systems should be features of the tool. The energy modelling tool
should abide by open-source principles, thus benefitting from their numerous advantages
as well as making the tool accessible to energy stakeholders. The involvement of these
Energies 2021, 14, 5928 8 of 30
stakeholders can be fostered by the tool’s user-friendliness, with an abstraction level close
to human understanding, as well as by its documentation, graphical interface, and support.
Finally, the modelling of social constraints and objectives represents a relevant additional
feature in order to bridge the gap between energy modelling and policy making. These
characteristics are presented in Figure 3 in a significance order: each choice at one level will
close possibilities afterwards and will allow the tools to be differentiated from one another.
The levels in this figure correspond to the previous sub-sections.
Figure 3. Energy modelling tool characteristics, with OMEGAlpes characteristics in bold blue. Source: authors.
actors’ understanding, with a range of functionalities adapted to the field technical reality.
These functionalities are fully accessible and usable thanks to open-source development
features, as well as a capitalisation process that is continuously improved and completed
based on studies. Moreover, it enables considering the actors’ constraints and areas of
responsibility. Thus, models can be used as negotiation supports between stakeholder, users
and developers. A presentation of OMEGAlpes general structure is showed in Figure 4. A
complete OMEGAlpes UML (Unified Modelling Language) diagram describing the tool
structure is available in Appendix A. Details regarding the energy package, including the
exergy module as well as the actor package, are provided in the sections hereinafter.
Figure 4. OMEGAlpes principle diagram including public and field. Source: authors.
• Regulators do not operate any particular energy unit but influence the energy sys-
tem with regard to grid and/or resource regulation. Their decisions can affect all
energy units.
• Operators can only influence—with respect to constraints and objectives—the units
within their area of responsibility, as defined in the following sub-section. Based on
a typology of operator actors, we have developed the following classes: Consumer,
Producer, Prosumer, and Supplier.
• Developers are modelled by the ProjectDeveloper class. It is derived from the Actor
class in order to add objectives and constraints that are specific to the actors carrying
out the project.
3.4. OMEGAlpes Features for Meta-Analysis of the Energy System Optimisation Problem
3.4.1. Algorithms for Identifying Incompatible Constraints: lpfics
A method for identifying constraints when the problem is infeasible has been de-
veloped. This method is implemented in the lpfics package (i.e., linear problems: find
infeasible constraint sets [81]). This package relies on constraints typing in two ways. The
first one directly gives the infeasible constraint set and thus offers an additional degree of
negotiation according to the types of the identified constraints, as the constraints’ names,
types, and formulations are provided. The other approach is to identify the constraints
according to their typology. For this purpose, the constraints are separated upstream into
sub-groups according to their type.
Table 1. Presentation of the energy units in the use case and their related zones.
In the industrial zone, heat production represents the waste heat dissipated in order to
maintain its industrial processes, and electricity consumption represents the EII’s electricity
consumption resulting from its industrial activities. Then, heat dissipation represents the
waste heat dissipated during the industrial processes that is not recovered and therefore is
rejected into a river.
In the district heating network zone, heat production represents all the thermal power
plants connected to the district heating network, and heat consumption represents the
district heat consumption.
Energies 2021, 14, 5928 15 of 30
In the electricity supplier zone, electricity production represents the electricity imports
coming from the power grid, and electricity consumption represents the electricity exports
provided to the EII and the heat pump from the grid.
The waste heat recovery system is constituted by two energy units. The heat pump
enables supplying heat from the industrial waste heat to the district heating network by
increasing the waste heat temperature to the network one. The thermal storage compensates
for the temporal and thermal power mismatches between the waste heat and the district
heat consumption.
Figure 6 presents a scheme of the use case.
Heat Power
Plants
----------------r-------------------------------------------1
river I
I
I
I
storage
Industrial Process
Recovery System 1
Electric Industrial river
I
Supplier Zone Zone Recovery Zone ! Thermal Supplier Zone
------------------- _________________._________________________________________________________________________________
Figure 6. Thermal scheme of the waste heat recovery use case.
In the next sub-section, the energy model of the use case will be detailed, considering
the total energy balances. Then, the use case modelling using the tool OMEGAlpes is de-
veloped. Finally, applications of the additional packages and functionalities are presented
in order to highlight the related additional options
In order to respect the energy balances of the units that have been defined in the
previous section, it is necessary to define the connection elements between the energy units.
As explained earlier, this connection is made possible through the use of energy nodes,
which connect a set of units to each other. Table 3 lists all the energy nodes necessary for
the modelling of the case study and the associated energy units.
Table 3. List of energy nodes to define the energy balances to model the case study.
Figure 7 represents the OMEGAlpes model using the OMEGAlpes graphic convention,
including energy units and nodes symbols, energy carriers colours, and the main constraints
and objective.
Figure 7. OMEGAlpes graphical representation of the waste heat recovery use case, including the main constraints
and objective.
4.2.3. Applying OMEGAlpes Pareto, lpfics, and Exergy Functionalities to the Use Case
After focusing on the technical aspects of the use case, this sub-section describes
additional features and packages. They provide more ways to study the optimisation
problem (Pareto and lpfics features) or to provide more optimisation options considering
both social and technical aspects (respectively, actor package and exergy module).
As introduced in the presentation of the OMEGAlpes tool, it is possible to carry out a
Pareto study between two objectives, in this case minimising the heat supplier production
on the network at the same time as the size of the thermal storage. The size of the storage
is directly related to its capacity and so to the heat recovery potential, but it is also directly
related to its capital expenditures. It is therefore possible to draw a Pareto front between
these two antagonistic objectives, which can be of use for compromise and negotiation on
the system design.
lpfics is a package that can be used when the optimisation problem is facing infeasibil-
ity issues. As explained in the section on lpfics (Section 3.4.1), it is possible to obtain access
to a set of constraints in which there are one or several infeasible constraints. An example
of its use for the waste heat recovery use case is detailed in the corresponding notebook.
The exergy module enriches the technical aspects of the energy model by imple-
menting exergy concepts into the energy units. In the case study, this translates into the
evaluation of the exergy destruction of each of the thermal units, which are the dissipation
(DISS), the thermal storage (TS), the heat pump (HP), the heat generation plants (HS), and
the district heat consumption (DHC). By associating a class of calculation for exergy and
exergy destruction with each of them, it becomes possible to evaluate the exergy destroyed
and thus to consider this evaluation as an additional objective of the optimisation problem
of this case study. Such an objective often offers a unique solution with respect to energy
objectives. Moreover, the economic notions that can be considered, based on the cost of
energy, can be applied in the same way to exergy, thus giving an exergo-economic evalua-
tion. This makes it possible to provide a wider range of technical assessments with energy,
economic, exergy, and exergo-economic concepts [80].
Energies 2021, 14, 5928 18 of 30
Table 4. List of actors present in the case study with their respective areas of responsibility.
In order to evaluate the two scenarios, the technical aspects of the model must be
reconsidered. We now assume that the areas dedicated to thermal storage are of different
sizes depending on the actor, with the DHNO having access to wider areas. This results in
the consideration of a maximum thermal storage capacity that differs depending on which
actor is responsible for it. Furthermore, as shown in Table 5, the placement of the storage
system may differ depending on who is responsible.
For the case study, we propose changing the location of the thermal storage from the
EII node to the DHN node. Finally, the maximum thermal storage capacity as well as the
objective will be considered via actors’ constraints and actors’ objectives and are discussed
as such.
Afterwards, we can introduce notions of regulations through a RegulatorActor. This
allows the user to define specific actor constraints, such as a maximum energy dissipation
or maximum GHG emissions threshold. This has the effect of enriching the energy model
by considering regulatory aspects from the point of view of constraints and objectives.
Finally, the actor package allows to consider constraints applied by one actor to another.
For example, if we refer to the notions of start-up and shutdown times of production plants,
we can assume that the DHNO actor would accept heat injections in the network only
inj
if it exceeds a duration ∆Tmin , considering the start-up times and a safety margin. This
constraint is then added at the EII’s heat injection level and can widen the scope for
negotiation. Indeed, there are cases in which this constraint leads to the non-injection
inj
of heat production whose duration is close to the duration ∆Tmin . From this point of
view, it becomes possible to bring about additional negotiation elements and to consider a
re-evaluation of the minimum injection duration.
Energies 2021, 14, 5928 19 of 30
Table 6. OMEGAlpes use cases library including article references and languages, source code, notebooks, space and time scale and resolution, developments, and research objective.
Table 6. Cont.
case study benefits from a great diversity in its implementation, with flexibility studies
on consumption, battery pack sizing, or incentives at the actor level. In the preliminary
studies, many sizing choices are preponderant and may depend on the actors. An actual
preliminary design of an autonomous energy system was an opportunity to consider
technical elements that had not been considered yet, which allowed the implementation of
new classes such as AssemblyUnit and ReversibleUnit.
6.2. Perspectives
This work includes several perspectives. First, regarding OMEGAlpes itself, improve-
ments consistent with the reviewed literature could include firstly a better modelling of
district heating, based on an actual local waste heat recovery project, and the inclusion of
exergy post-treatments in the tool. These improvements could also include an update of
the graphical interface in order to make it up to date with the tool’s latest functionalities
and enable online model solving. An economy package is also in development, in order
to go further in economics considerations than those presently considered with existing
economic parameters. Uncertainty calculation and management is an additional potential
improvement for the tool.
Current work also focuses on the development of an open and collaborative digital
platform in order to foster user interaction and collaboration based on complete open
energy modelling processes in use cases. The public of this platform would range from
fellow researchers to local authorities, engineering offices, and citizen collectives. This
platform would enable bringing the use cases and associated energy models to society,
in addition to bringing society into models through actor package development. Use
cases will be further developed, including both current use cases such as photovoltaic self-
consumption and waste heat recovery and new ones exploring maters of energy sufficiency
and autonomy. These use cases will bring new collaborations with an engineer’s office
for collective self-consumption. The tool is also going to be put to good use for teaching
materials in engineering and architecture schools.
Finally, on the topic of collaboration, an additional perspective on OMEGAlpes con-
cerns its link with other energy modelling tools. This can be established through hard or
Energies 2021, 14, 5928 24 of 30
soft model coupling [Chang2021], as well as through collaboration with external teams of
energy modellers. OMEGAlpes’ developer team tried to develop relevant functionalities
for district scale energy projects, such as actors and exergy considerations. The question
of integrating such functionalities into an existing open energy modelling framework via
collaboration and contribution, thus putting into practice open science principles, still
needs to be explored.
6.3. Conclusions
This article presented OMEGAlpes, an open-source model generation tool for district-
scale energy systems’ preliminary design using MILP optimisation. After reviewing the
main characteristics of the energy modelling tool, the article introduced the key features
of OMEGAlpes as well as their use in a simplified waste heat recovery example. The
range of use cases and associated research fields explored with OMEGAlpes was also
presented. OMEGAlpes’ open development with the source code documentation and
Gitlab, as well as the use cases library, enabled collaborations with laboratories from
various fields including electrical engineering, heat, and social sciences. Common work
is also lead with engineering schools, engineering offices, local authorities, and energy
system operators. These collaborations bring about a connection to the field reality and
OMEGAlpes’ continuous improvement.
With this article, we offer understanding of OMEGAlpes’ method and use cases
library with respect to existing energy modelling tool. From energy project actors to
energy modelling tool developers, we believe OMEGAlpes’ method, library, use, and
development can support negotiations in energy projects at district scale. Taking into
account the stakeholders in the design of energy projects could thus favour local energy
transition.
Energies 2021, 14, 5928 Acknowledgments: The authors first would like to thank Camille Pajot for all the work she carried25 of 30
out in the development of OMEGAlpes. They also want to thank the openmod community for the
rewarding online and face to face exchanges.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A. Comprehensive OMEGAlpes UML Diagrams
Appendix A. Comprehensive OMEGAlpes UML Diagrams
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