Particle Swarm Optimization applied to Campus Microgrid | BEAMS

Particle Swarm Optimization applied to Campus Microgrid

Project information
Project type: 
Master thesis
Academic year: 
2017-2018
Status: 
Attributed
Research unit: 
Electrical Energy
BEAMS supervisors
Academic promoter
Supervisor
External supervisors
Pedro Machado
Supervisor
Student(s)
François Herinckx
Jeremy Wynants
1 Introduction
The volatile feed-in of distributed generation based on renewable energy sources as well
as new and intelligent loads and storages require an appropriate consideration in the
distribution grid planning process .
Basically all investigators of Microgrid will agree that such a system will include
small-scale distributed generators, a dedicated section (even only a busbar can do) of
distribution grid, a number of end-users of electricity, maybe some central or distributed
energy storage devices, and of course an online energy/network management system
to support the Microgrid to optimize its performance under grid-connected, islanded,
as well as interim transition modes. In the context of Microgrid systems, a university
campus can be considered as good environment to test and evaluate different strategies
in this new scenario of distribution system.
Several universities involved in research projects related to ways of optimizing energy
and started building a campus Microgrid. Creating a university Microgrid has many
bene ts: it will be an optimal small scale research project and also it will enable the
creation of a test-bed that can be used to measure and optimize electricity utilization
through building Microgrids
Considering this, the present project presents the Particle Swarm Optimization
(PSO) technique to promote an planning analysis of a campus Microgrid composed
by distributed generators and storage system.
 
 
2 Methodology
 
The proposed methodology can be divided in two parts, the rst called campus Microgrid
speci cation, and the second named as Microgrid optimal planning.
 
2.1 Campus Microgrid Speci cation
This stage comprises in to investigate and formalize the campus Micrgrid architecture.
Considering this, it is necessary to de ne the demand, technologies of distributed gen-
eration, generation models, load model, storage model, and operational constraints. In
fact, this stage concerns in a survey with all necessary data to formulate the problem.
 
2.2 Microgrid Optimal Planning
The campus Microgrid needs to be well-designed in order to avoid bad investments. So,
for the proposed optimal system, it is considered a multi-objective function composed
by three distinct variables: operation cost, pollution emission, network loss.
Still considering the optimized planning, the proposed tool is the Particle Swarm
Optimization (PSO), which permits to found an optimal solution based in some grid
constraints.
 
3 Guidelines
The work will be mainly developed in MATLAB software, using its common Toolboxes
and code scripting.
The two supervisors will divide the student orientation. The rst part of the project
will be guided by Pedro Machado, and the second by Benoit Mattlet.
 
4 Requirements
{ Power System Analysis
{ Computational programming skills (suggested basic knowledge in Arti cial Intelli-
gence techniques)
{ Simulation
 
 
References
1. J. Kays, C. Rehtanz "Planning process for distribution grids based on 
exibly generated time
series considering RES, DSM and storages". IET Generation, Transmission & Distrbution,
2016.
2. L. Tao, et al. "From laboratory Microgrid to real marketsChallenges and opportunities".
Power Electronics and ECCE Asia (ICPE & ECCE), 2011 IEEE 8th International Conference
on. IEEE, 2011.
3. H. Talei, et al. "Smart campus microgrid: Advantages and the main architectural compo-
nents." Renewable and Sustainable Energy Conference (IRSEC), 2015 3rd International.
IEEE, 2015.
4. C. Dou, B. Liu, "Multi-agent based hierarchical hybrid control for Smart Microgrid", IEEE
Transactions on Smart Grid, vol. 4, no. 2, June 2013.
5. B. Mattelet, J.C. Maun, "Assessing the bene ts for the distribution system of an optimal
scheduling of 
exible residential loads". IEEE International Energy Conference (ENERGY-
CON), 2016.

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