4 research outputs found
New methods and results in the optimisation of solar power tower plants
Renewable energy technology has seen great advances in recent decades,
combined with an ever increasing interest in the literature. Solar Power Tower
(SPT) plants are a form of Concentrating Solar Power (CSP) technology which
continue to be developed around the world, and are formed of subsystems that
are open to optimisation.
This thesis is concerned with the development of new methods and results
in the optimisation of SPT plants, with particular focus on operational optimi-
sation.
Chapter 1 provides background information on the energy sector, before
describing the design and modelling of an SPT plant. Here, the optical theory
behind the transfer of incident radiation in the system is developed and the
relevant equations presented.
In Chapter 2, the cleaning operations of the heliostat eld are optimised
for a xed schedule length using Binary Integer Linear Programming (BILP).
Problem dimensionality is addressed by a clustering algorithm, before an ini-
tial solution is found for the allocation problem. Finally, a novel local search
heuristic is presented that treats the so-called route \attractiveness" through the
use of a sequential pair-wise optimisation procedure that minimises a weighted
attractiveness measure whilst penalising for overall energy loss.
Chapters 3-6 investigate the aiming strategy utilised by the heliostat eld
when considering a desired
ux distribution pro le and operational constraints.
In Chapter 3, a BILP model was developed, where a pre-de ned set of aim-
ing points on the receiver surface was chosen. The linear objective function was
constrained with linear equalities that related to distribution smoothing (to pro-
tect receiver components from abnormal
ux loads) via the use of penalisation.
Chapter 4 extended this model by instead considering continuous variables with
no xed grid of aiming points. This led to an optimisation problem with a non-
linear, non-convex objective function, with non-linear constraints. In this case,
a gradient ascent algorithm was developed, utilising a non-standard step-size
selection technique. Chapter 5 further extended the aiming point optimisation
topic to consider the dynamic case. In this sense, the aiming strategy across a
period of time could be optimised, taking into account SPT plant technologi-
cal limitations. Two algorithms were considered, Penalisation and Augmented
Lagrangian, where theoretical properties for optimality and solution existence
were presented. Finally Chapter 6 considered the efects of inclement weather on the optimisation model presented in Chapter 3. Stochastic processes were in-
vestigated to determine optimal aiming strategies at a xed point in time when
weather data could not be known for certain.
All research presented in this thesis is illustrated using real-world data for an
SPT plant, and conclusions and recommendations for future work are presented