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.
| Identifer | oai:union.ndltd.org:canterbury.ac.nz/oai:ir.canterbury.ac.nz:10092/1975 |
| Date | January 2008 |
| Creators | Newham, Nikki |
| Publisher | University of Canterbury. Electrical and Computer Engineering |
| Source Sets | University of Canterbury |
| Language | English |
| Detected Language | English |
| Type | Electronic thesis or dissertation, Text |
| Rights | Copyright Nikki Newham, http://library.canterbury.ac.nz/thesis/etheses_copyright.shtml |
| Relation | NZCU |
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