The objective of this research project is to develop new software tools capable of operational
optimisation of existing, large-scale water distribution networks. Since pumping operations
represent the main operating cost of any water supply scheme, the optimisation problem is
equivalent to providing a new sequence for pumping operations that makes better use of the
different electricity tariff structures available to the operators of distribution systems. The
minimisation of pumping costs can be achieved by using an optimal schedule that will allow
best use of gravitational flows, and restriction of pumping to low-cost power periods as far as
possible.
A secondary objective of the operational optimisation is to maintain the desired level of
disinfectant chlorine at the point of delivery to consumers. There is a steady loss of chlorine
with residence time in the system. If the level drops too low there is a risk of bacterial activity.
Re-dosage points are sometimes provided in the network. Conversely, too high a level produces
an unacceptable odour.
The combinatation of dynamic elements (reservoir volumes and chlorine concentration responses)
and discrete elements (pump stati and valve positions) makes this a challenging Model
Predictive Control (MPC) and constrained optimisation problem, which was solved using
MINLP (Mixed Integer Non-linear Programming). The MINLP algorithm was selected for its
ability to handle a large number of integer choices (valves open or shut / pumps on or off in this
particular case).
A model is defined on the basis of a standard element, viz. a vessel containing a variable volume, capable of receiving multiple inputs and delivering just two outputs. The physical properties of
an element can be defined in such a way as to allow representation of any item in the actual
network: pipes (including junctions and splits), reservoirs, and of course, valves or pumps. The
overall network is defined by the inter-linking of a number of standard elements. Once the
network has been created within the model, the model predictive control algorithm minimises a
penalty function on each time-step, over a defined time horizon from the present, with all
variables also obeying defined constraints in this horizon. This constrained non-linear
optimization requires an estimate of expected consumer demand profile, which is obtained from
historical data stored by the SCADA system monitoring the network. Electricity cost patterns,
valve positions, pump characteristics, and reservoir properties (volumes, emergency levels,
setpoints) are some of the parameters required for the operational optimisation of the system. / Thesis (M.Sc.Eng.)-University of Natal, Durban, 2004.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:ukzn/oai:http://researchspace.ukzn.ac.za:10413/9015 |
| Date | January 2004 |
| Creators | Biscos, Cedric P. G. |
| Contributors | Mulholland, Michael., Buckley, Christopher A., Brouckaert, Christopher J., Le Lann, Marie Veronique. |
| Source Sets | South African National ETD Portal |
| Language | en_ZA |
| Detected Language | English |
| Type | Thesis |
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