politecnico di milano

123 Pages·2015·4.45 MB·English

by  Simone Losa

POLITECNICO DI MILANO
Scuola di Ingegneria Industriale e dell’Informazione
Corso di Laurea Magistrale in
Ingegneria Energetica

Estimating Load Profile for Off-Grid Power System Design:
Model Improvement and Applications

Relatore: Prof. Emanuela Colombo
Correlatore: Ing. Stefano Mandelli

Tesi di Laurea Magistrale di:
Simone Losa Matr. 801574

Anno Accademico 2013-2014

Ringraziamenti
Molte persone hanno accompagnato questo mio percorso universitario.
Un ringraziamento dal profondo del cuore è però per le anime pazienti che mi sono sempre state
vicine nella buona e nella cattiva sorte. Ai miei genitori, che mi hanno permesso di intraprendere
questo percorso, e dai quali ho sempre ricevuto appoggio incondizionato e serenità nel momento
delle scelte. A Sara, per essere sempre stata al mio fianco, motivandomi e sostenendomi nei
momenti di difficoltà.
Un ringraziamento doveroso alla prof.ssa Emanuela Colombo, per avermi dato la possibilità di
concludere il mio percorso di studi come avrei desiderato ed aver costituito il punto di riferimento
per lo sviluppo di tutto l’elaborato.
Un ringraziamento particolare a Stefano, per tutto il tempo che mi ha saputo dedicare nello
sviluppare e valorizzare al meglio quanto racchiuso in queste pagine.
Ringrazio i miei colleghi ed amici Alessandro, Gianluca, Lorenzo, Luca, Matteo, Michele e Walter che
mi hanno accompagnato in questi 5 anni e che hanno reso più leggero il cammino.
Ringrazio tutti i miei amici e famigliari che, fuori dal Politecnico, mi hanno formato e fatto crescere
con mille esperienze diverse.

Index
Abstract ............................................................................................................................ ix
Sommario ........................................................................................................................ xii
1 Introduction, motivations and problem formulation ................................................ 1
1.1 Rural electrification and off-grid systems ......................................................... 1
1.2 Motivation: estimating load profiles ................................................................. 5
1.3 Problem formulation: the LoadProGen tool ..................................................... 8
1.4 Thesis objective and structure .......................................................................... 9
2 Characteristics of consumer loads .......................................................................... 11
2.1 Electrical loads ................................................................................................. 11
2.2 Load profiles: fundamental concepts and definitions ..................................... 12
2.3 Measuring load profile data ............................................................................ 21
2.4 Coincidence factor relationships ..................................................................... 25
3 Approaches for load profile estimate in off-grid systems: state of the art ............. 33
3.1 Load Forecasting ............................................................................................. 33
3.2 Residential Energy Consumption Modeling .................................................... 36
3.3 Load estimation for off-grid power design ..................................................... 39
4 LoadProGen: a tool to perform load profile estimates for off-grid systems .......... 47
4.1 General features .............................................................................................. 47
4.2 Input data ........................................................................................................ 48
4.3 Mathematical formulation .............................................................................. 50
5 LoadProGen improvement: developments and implementation ........................... 53
5.1 Improvements identification ........................................................................... 53
5.2 Development of the improvements ................................................................ 58
5.3 Computational framework .............................................................................. 64
5.4 LoadProGen Vs state-of-the art approaches ................................................... 71
6 Applications of the software tool ............................................................................ 75
6.1 Application for sizing of an off-grid PV system ............................................... 75
6.2 Load profile forecast application ..................................................................... 81
7 Conclusions and future work .................................................................................. 89
7.1 Conclusions...................................................................................................... 89
7.2 Future works ................................................................................................... 91
Appendix A .........................................................................................................................
Appendix B .........................................................................................................................
Appendix C..........................................................................................................................
References ..........................................................................................................................

Abstract
The problem of access to energy, in recent years, has become increasingly important in
the eyes of the international community. The link between access to energy, particularly
electricity, and human development is clear, but the problem is far to be solved. This is especially
true for the poorest countries, where the lack of access to electricity is often one of the main
obstacles to development. The rate of electrification in developing countries is even lower when
looking at rural areas: in sub-Saharan Africa, for example, only 14.1% of the rural population
have access to electricity. For this reason, the electrification of rural areas in developing
countries is an actual issue for NGOs.
There are two main possible solutions to provide access to electric energy in rural areas: (i) the
traditional approach is to extend the national electricity grid to the rural areas, nevertheless
large parts of these areas have low accessibility, low values of load demand and load factor. For
these reasons, grid extension often results to be economically unfeasible; (ii) the alternative
solution is to rely on decentralized and distributed generation, which often results to be the
most appropriate technology option since power plants are installed close to the load, they can
be sized in order to best fit with local load demand, and they can be fuelled by local resources
(i.e. renewables sources). In recent years, for a variety of reasons, the second solution has been
often preferred for rural electrification.
In order to dimension these off-grid systems, there are many advanced software based on
numerical and analytical methods; however, all require as input the electrical load to be met, in
the form of load profile (usually daily load profiles). A research on articles about projects on
system sizing for off-grid electrification of rural areas has shown that often it is not given enough
importance to the estimation phase of the load, and that there is not a clear and definite
procedure to be followed for this phase.
At the light of the issue about lack of appropriate methods to estimate load profiles for
supporting the off-grid system design process, in my thesis I worked for the improvement and
application of an existing model, coded in MATLAB, for the estimation of load profiles. The aim
of this model is to provide an appropriate method of estimation of the load to support the
optimization and sizing process of off-grid systems for rural electrification. The algorithm is
designed to work with few input data commonly required by even the simplest approaches for
the estimation of energy requirements in rural areas currently used (installed equipment, times
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of use, etc.). The main features of the model are: (i) the bottom-up approach; (ii) stochastic
sampling of switching-on instants of electrical appliances; (iii) the implementation of parameters
related to the load profiles, and of empirical correlations between these parameters; (iv) the
possibility of considering the uncertainty of the input data by introducing randomization
parameters.
The model has been finally applied in two cases. In the first application, it has been used to
estimate load profiles of a un-electrified peripheral-urban area of Uganda; the estimated load
profiles has been used as input data for a specific software which carried out the sizing of a
photovoltaic system associated with batteries. In the second application, it has been possible to
test the model, comparing the metered load profiles of an electrical load of a college in Bali,
Cameroon, and the load profiles estimated by the algorithm for the same context.
Keywords: Access to energy, Rural electrification, Off-grid power systems, Rural power systems,
Electrical Load profiles, Load profile estimation, Stochastic Model for Load profile estimation,
Bottom-up Model for Load profile estimation, Power systems sizing

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