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Multi-actor modelling for MILP energy systems optimisation: application to collective self-consumption
Lou Morriet, Gilles Debizet, Frédéric Wurtz
To cite this version:
Lou Morriet, Gilles Debizet, Frédéric Wurtz. Multi-actor modelling for MILP energy systems optimi- sation: application to collective self-consumption. Building Simulation 2019, Sep 2019, Rome, Italy.
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2 – From Social Science concepts to Object Oriented Actor Modelling with constraints and objectives
4 – Application to Collective self consumption
In France, the collective self-consumption act enables prosumers to share their electricity production between various consumers connected to the public network Study cases available at: https://omegalpes-examples.readthedocs.io/en/latest/article_study_case.html
Multi-actor modelling for MILP energy systems optimisation:
Application to collective self-consumption
Lou MORRIET, G2Elab, Pacte; Gilles DEBIZET, Pacte; Frédéric WURTZ, G2Elab
Univ. Grenoble Alpes, CNRS, Grenoble INP*, G2Elab, F-38000 Grenoble, France Univ. Grenoble Alpes, CNRS, Sciences Po Grenoble**, Pacte, 38000 Grenoble, France [email protected]
# 210472
1 – Problematic: Reduce the Socio-technical Optimality Gap
3 – “Problem setting” step & stakeholder negotiations
References Acknowledgement
• Akrich, M. (1989). La construction d’un système sociotechnique. Anthropologie et sociétés 13(2), 31-54.
• Debizet, G., Tabourdeau, A., Gauthier, C., & Menanteau, P. (2016). Spatial processes in urban energy transitions: considering an assemblage of Socio-Energetic Nodes. Journal of Cleaner Production, Special Volume: Transitions to Sustainable Consumption and Production in Cities, 134 (October), 330-41.
• Hinker, J., Hemkendreis, C., Drewing, E., März, S., Hidalgo Rodríguez, D.I., & Myrzik, J.M.A. (2017). A novel conceptual model facilitating the derivation of agent-based models for analyzing socio-technical optimality gaps in the energy domain. Energy 137 (October), 1219-30.
• Moss, T. (2009). Intermediaries and the Governance of Sociotechnical Networks in Transition. Environment and Planning A 41 (6), 1480-95.
• North, D.C. (1990). Institutions, Institutional Change and Economic Performance. Cambridge University Press. Cambridge (UK).
• Soshinskaya, M., Crijns-Graus, W.H.J., Guerrero, J.M., & Vasquez, J.C. (2014). Microgrids: Experiences, barriers and success factors. Renewable and Sustainable Energy Reviews 40 (december), 659-72.
• Pajot et al. (2019). OMEGAlpes: An Optimization Modeler as an Efficient Tool for Design and Operation for City Energy Stakeholders and Decision Makers. Building Simulation Conference. Rome, 1-4 September 2019.
This work has been partially supported by the CDP Eco-SESA receiving fund from the French National Research Agency in the framework of the "Investissements d’avenir” program (ANR- 15-IDEX-02) and by the ADEME the French Agency for Environment and Energy Management with the RETHINE project.
Fig 3. Proposition for a multi-stakeholder energy project design step Fig 4. Impact of constraints negotiation on the solution space
Fig 5. Simple collective self-consumption study case representation
Pareto frontier for the PV panel sizing Pareto front objective equation α * supplier.obj1 + (1-α) * prosumer.obj2 The problem is
unfeasible Over constrained
Stakeholder Negotiation Cst 1 Obj 1 Cst 2 & 4 relaxed
Study 1 Study 2
ABSTRACT
We aim to propose a multi-actor modelling based on stakeholders’ objectives and constraints and to apply it on the optimisation model generation tool OMEGAlpes. Based on social science literature, this modelling aims to help stakeholders to formalise their constraints and objectives and to negotiate them in a multi-stakeholders problem setting process. This modelling has been applied to a simplified collective self-consumption project.
o Social science literature highlights that energy project should be considered as sociotechnical as it depends on (Akrich, 1989) :
o Decision support tools only focus on technical optima creating a socio-technical optimality gap defined by Hinker et al. (2017) as:
A socio-technical optimality gap is said to be existing for an optimization problem if the solution found is non-optimal because of an imprecise problem formulation, or if the optimum found is rejected due to non-feasibility in practice.
Multi-stakeholder projects in pre-study phases require to help stakeholders to :
• understand the impact of their requirements – constraints and objectives – on the solution (Fig 4)
• discuss and coordinate on possible and satisfactory solutions mainly negotiating their constraints and objectives (Fig 3)
Fig 2. Pre-modelled actor classes in OMEGAlpes – UML representation
Stakeholders are one of the barriers of urban renewable energy development (Soshinskaya et al., 2014).
Stakeholders are divided in two categories (North 1990; Moss 2009)
• Regulators
who lay down rules and procedures• Operators
who operate energy unitswhile respecting the regulators’ constraintsOperators operate energy units in their area of responsibility, considered as a Socio-Energy Node (SEN) (Debizet et al., 2016)
SEN: Group of physical elements collecting, converting, and/or supplying energy, built (or operated) by the same decision-maker
Fig 1. Actor representation
Social science literature tells us
Resolution data 1014 variables
• 581 continuous
• 433 integer (432 binary) 2862 non-zeros Generation time : 0,34 s Intel bicore i5 2.7 GHz CPU.
Actor
model of a decision-maker or set of decision-makers with the availability to have a consistent influence on the final solution of the energy project.
Actors’ influence is modelled as constraints and objectives
Our modelling
o Model Generation Tool for energy projects
improving decision makers’ understanding and discussion based on various study case
o MILP Optimisation
•numerous decision variables ->
Optimisation
•continuous and discrete variables ->
MILP (Mixed-Integer Linear Programming)
o Open source
facilitating the model and tool access and modification to stakeholders and energy project designers
Python 3.6
OMEGAlpes
(Pajot et al., 2019)
Actor modelling available at:
https://gricad-gitlab.univ-grenoble-alpes.fr/omegalpes Area of responsibility
•∑
• ∑weight ∗ objective
Variable 2 (x2) Variable 1 (x1)
Cst1(x1)
Cst2(x2) Cst3(x1, x2)
Negotiable actors’
constraint Solution
space
x2max
Non negotiable physical constraint
x x x
x
x x x
Pareto front of optimal solutions x x
Constraint negotiation Legend
Energy Layer
Supplier’s area of responsibility
Prosumer’s area of responsibility Dwelling
consumption 1
Dwelling consumption 2
Local PV production Supplier
Production Supplier consumption
*nb
Legend study 1 study 2 FixedConsumptionUnit SeveralProductionUnit VariableConsumptionUnit VariableProductionUnit
*nb
•Cst 1: s_cons[t] == 0 Obj 1: minimize_consumption
•Cst 2: pv_nb <= 40
•Cst 3: pv_nb >= 1
•Cst 4: d_1[t] * pv _u[t] + d_2[t] * pv _u[t] <= pv[t]
Obj 2: maximize_cons_prod_match
pv_nb: number of PV panels pv_u: functioning binary variable d_i: dwelling consumption s_cons: supplier consumption
Technical model design Actor model design
Model solving
Solutions analysis
Technical constraints
& SEN negotiation
Agreement?
Actors’ constraints relaxation Solution?
Solution?
No Yes
No Yes
Yes Actors’ constraints &
SEN negotiation
No
Negotiate model design, constraints, objectives & SEN
Modelling step Solving step Analysis step Negotiation step
Legend
Decision step Multi-actor energy system project outline
Multi-actor energy system project launch
Social dimensions
social practice,
political and legal institutions…
Technical issues
material selection, available technology…
0 PV panel
37 PV panels 36 35 34 33 32
