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Power System Investment Planning using Stochastic Dual Dynamic ProgrammingNewham, Nikki January 2008 (has links)
Generation and transmission investment planning in deregulated markets faces new challenges
particularly as deregulation has introduced more uncertainty to the planning problem. Tradi-
tional planning techniques and processes cannot be applied to the deregulated planning problem
as generation investments are profit driven and competitive. Transmission investments must
facilitate generation access rather than servicing generation choices. The new investment plan-
ning environment requires the development of new planning techniques and processes that can
remain flexible as uncertainty within the system is revealed.
The optimisation technique of Stochastic Dual Dynamic Programming (SDDP) has been success-
fully used to optimise continuous stochastic dynamic planning problems such as hydrothermal
scheduling. SDDP is extended in this thesis to optimise the stochastic, dynamic, mixed integer
power system investment planning problem. The extensions to SDDP allow for optimisation of
large integer variables that represent generation and transmission investment options while still
utilising the computational benefits of SDDP. The thesis also details the development of a math-
ematical representation of a general power system investment planning problem and applies it to
a case study involving investment in New Zealand’s HVDC link. The HVDC link optimisation
problem is successfully solved using the extended SDDP algorithm and the output data of the
optimisation can be used to better understand risk associated with capital investment in power
systems.
The extended SDDP algorithm offers a new planning and optimisation technique for deregulated
power systems that provides a flexible optimal solution and informs the planner about investment
risk associated with uncertainty in the power system.
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In-Stream water quality modelling and optimisation by mixed-integer programming : simulation and application in actual systemMahlathi, Christopher Dumisani January 2013 (has links)
Water scarcity has become a global problem due to diminishing water resource and
pollution of the remaining resources. The problems arising fromwater scarcity are exacerbated
rapid urbanisation and industrialisation. Water quality management systems are introduced.
Numerous water management methods exist some of which, if applied e ectively, can
remedy these problems. In South Africa, water management systems are urgently needed
to start addressing issues around the longterm sustainability of our limited water resource.
Water quality modelling is one of the tools employed to assist in validating decisions
made during the planning phase of a water quality management system. It also provides
a means of exploring viable options to be considered when these decisions are to be made.
A range of management options exist and implementing all of them may prove costly,
therefore optimisation techniques are utilised to narrow down options to the most e ective
and least costly among the available choices. Commonly, water quality models are used to
predict concentrations in the river from which constraint equations are generated. The
constraint equations are used in optimisation models to generate feasible solutions by
either maximising or minimising the objective function. In this case the objective function
is wastewater treatment cost. Constraints equations are based on the set in-stream water
quality standard at selected theoretical measuring stations (checkpoints) in the stream
and a feasible solution is one that suggests a treatment method that will ensure water
quality standards are met at the lowest regional treatment cost.
This study focused on the Upper Olifants river catchment near Witbank in Mpumalanga
province. This catchment is subjected to extensive wastewater e uents from various
mining operations and wastewater treatment plants. The aim here was to develop a
water quality model for predicting dissolved oxygen (DO) concentration in the river, and
to use a modelling approach to generate constraint equations for the system.
A Streeter-Phelps stream simulation model was employed to predict DO concentration in
the river. A mixed-integer programming technique was then used to evaluate the impact
of nine wastewater treatment facilities discharging e uent into the river. Treatment levels
were varied to test model reliability. The coupled stream simulation and optimisation model produced feasible solutions under
2 minutes, with each solution suggesting a range of treatment levels which ensured that
the critical DO concentration was above 5 mg/L and the most stringent DO concentration
the system could manage without violations anywhere else in the stream was obtained to
be 7mg/L. / Dissertation (MEng)--University of Pretoria, 2013. / gm2014 / Chemical Engineering / unrestricted
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