POWADIMA research project

Objective
The aim of the POWADIMA research project is to establish the feasibility of introducing optimal, real-time control of water distribution networks. Since demand is fluctuating continually, it is necessary to adjust the control apparatus frequently if optimal or near-optimal control is to be achieved. The objective function is to meet the forecast demands at minimal operating cost, subject to operational constraints such as statutory minimum pressure, minimum acceptable flow to avoid stagnation etc. This implies minimizing pumping costs which are the largest component of the operating cost (hence the acronym POWADIMA).

Difficulties
In developing an optimal, real-time control system for water distribution, the difficulties that have to be overcome include:

• the size and complexity of water-distribution networks;
• existing networks have not been designed with optimal control in mind;
• an uncontrolled demand which is highly variable;
• the short time available between successive changes in control settings;
• the enormous numbers of possible combinations of valve and pump settings;
• the computational time and memory requirements for large-scale networks;
• the need to include complicated energy tariff structures.

When formulating the research programme, all of these issues have been considered, many of them for the first time.

Approach adopted
It will be appreciated that the use of a conventional hydraulic simulation model for optimal real-time control is impractical for large networks because of the excessive computational burden optimization imposes. If it were necessary to run a hydraulic simulation model to evaluate the impact of different combinations of pump/valve settings, it would not be possible to identify the optimal setting in the time available before the next update. Nevertheless, a hydraulic simulation model is required to estimate the consequences of different valve/pump settings. Therefore, the approach adopted has been to capture the knowledge-base of a complex hydraulic simulation model of the network in a far more efficient form using an artificial neural network (ANN). Thereafter, the ANN-predictor is embedded in a Genetic Algorithm (GA)- optimizer which has been specifically developed for adaptive, real-time control. The advantage of this approach is the vast improvement in computational efficiency which enables the optimal control settings for the current and all future time steps up until the operating horizon, to be completed quickly. This is repeated at the next time interval, following the latest update of the system state from the Supervisory Control and Data Acquisition (SCADA) scheme. Whilst initially the resulting control system would be advisory to experienced operational staff, this does not preclude the possibility of closed-loop control in the longer term.

POWADIMA homepage