Authors:
Camille Pajot
1
;
Benoit Delinchant
1
;
Yves Maréchal
1
;
Frederic Wurtz
1
;
Lou Morriet
1
;
Benjamin Vincent
2
and
François Debray
2
Affiliations:
1
Univ. Grenoble Alpes, CNRS, Grenoble INP and G2Elab, France
;
2
CNRS and LNCMI, France
Keyword(s):
Energy Planning, MILP Optimization, District, Energy Efficiency, CO2 Emissions.
Related
Ontology
Subjects/Areas/Topics:
Case Studies and Innovative Applications for Smart(Er) Cities
;
Energy and Economy
;
Energy-Aware Systems and Technologies
;
Frameworks and Models for Smart City Initiatives
;
Optimization Techniques for Efficient Energy Consumption
;
Smart Cities
Abstract:
As energy transition is fundamental to have a chance to fight climate change, every stakeholder concerned
by energy should be able to get a better knowledge of the consequences of these actions. However, it could
be very complex to understand energy problematics without being an expert. This article focuses on giving
the possibility to an energy intensive consumer of a district to make decisions about its energy planning
while taking into account its specific operating constraints. A practical case has been studied in a heat
recovery project to help the experiments planning of a research laboratory according to the thermal needs of
the district. At first, the energy planning only aims to reduce its electricity consumption bill. In a second
time, we consider re-using the thermal power from processes cooling. Then, two energy planning were
realised: reducing district CO2 emissions and reducing district supply cost. Finally, trade-offs between these
two goals have been studied.
The work is based on mixed-integer linear optimization models (MILP)
gathered into a Python library to provide a modular decisions tool for energy stakeholders.
(More)