DWN Management Challenges and opportunities

Project Goals ¡ Devise algorithms and software for the

operational management of drinking water networks to control pumping and valve operations in real time in a profitable and risk-averse manner,

¡ Optimal placement of bids in the day-ahead market to complement existing bilateral contracts,

¡  Early and systematic detection of leaks for the minization of non-revenue water and

¡ Detection of contaminations.

Part I: Control

Control Module Goals ¡  Reduce energy consumption for pumping,

¡ Meet the demand requirements,

¡  Keep the storage above safety limits,

¡  Respect the technical limitations: pressure limits, overflow limits & pumping capabilities,

¡  Have foresight (predict how the water demand and energy cost will move and act accordingly).

Control Challenges The control module of a DWN should take into account:

¡  The volatility in water demand,

¡  The volatility in energy prices (€/kWh),

¡  Reconstructed online measurements (measurements often come from faulty sensors or are not accessible),

¡ Operational constraints.

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Past Data

Observed

Forecast

The Control Module

Energy Price

Water Demand

Drinking Water Network

Online Measurements

Flow Pressure Quality

Forecasting Module

History Data

Data Validation Module

Validated Measurements

Commands Model Predictive Controller

Prediction of water demand

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Forecasting of Water Demand

Future Past

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Scenario Fan: A set of possible scenarios for the evolution of upcoming water demands.

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Scenario Tree: Contains appro-ximately the same information as the scenario fan, but is of lower complexity.

How MPC works…

Prefer to pump when the price is low!

Stay above the safety storage volume

PAST FUTURE

Volume in tank (m3)

Time (h)

Do not overflow!

Time (h)

Pumping (m3/h)

Avoid pumping when the price is high!

Account for the pumping capabilities

Why MPC:

¡  Optimal: Computes the control actions by optimizing a performance criterion,

¡  Realistic: Accounts for the operational constraints,

¡  Predictive: Has foresight; acts early before the price or the demand changes.

MPC: Performance

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MPC in action •  88 demand nodes •  63 tanks •  114 pumping stations •  17 flow nodes

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Low price à Pumping

The system operator has information about the current and the predicted operation cost.

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Closed−loop MPC Simulation

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MPC: Performance

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Foresight: Tanks starts loading up before a DMA asks for water.

Clear Economic Benefit! ¡ MPC outperforms the currect control solution for

the Barcelona case study,

¡  Reduction of production and transporation costs*.

* A.K. Sampathirao, J.M. Grosso, P. Sopasakis, C. Ocampo-Martinez, A. Bemporad and V. Puig, Water demand forecasting for the optimal operation of large-scale drinking water networks: The Barcelona Case Study, 19th IFAC World Congress, Cape Town, South Africa.

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Minimum Volume

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MPC Upper Bound

MPC Lower Bound

Predicted Trajectory

Closed−loop trajectory

Modelling of the uncertain demand time series.

Hydraulic model for the DWN of Barcelona with flow and pressure dynamics. Definition of the control

architecture for a DWN using MPC and demand/price forecasters

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Definition of the technical and economic objectives for the operation of the water network

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Economic Cost (E.U.)

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Estimation of the online operating and economic costs. Formulation of the MPC

problem taking into account the associated uncertainty

EFFINET: Developments

EFFINET: Developments

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time (h)

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SimulatorReal dataUpper limitSafety level

Validation of the hydraulic model against real data

Efficient MATLAB simulator that allows a very productive in-silico simulation of a DWN in closed loop with an MPC.

Up-to-date Simulink simulator with an MPC-based control and a (sensor) fault detection module.

Implementation of numerical optimisation routines on GPUs.

Stochastic MPC

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Motivation:

¡  We may not assume that we have exact knownledge of the future water demand and electricity price,

¡  Probabilistic information is available for the future demand and price evolution,

¡  We need to optimize the expectation of the cost (with respect to the constraints),

¡  A certain risk for not satisfying the demand requirements can be allocated beforehand.

Part II: Leak & Contamination Detection

Monitoring Module Goals ¡  Independent infrastructure to detect and isolate

leakages and contamination events,

¡ Minimization of non-revenue water (most water transportation systems waste a 20% of the water)*

¡  Estimate the magnitude of a leakage or contamination

¡ Detect faulty sensors,

¡ Optimize the placement of sensors in the network.

* For the Barcelona DWN, this sums up to more than 80M€/year.

In practice, things can go wrong…

Leakage detection Combination of technologies (hardware/software):

¡ Online measurements (pressure, flow),

¡ Manual measurements,

¡  Software: algorithmic solutions. Repairment: Portable equipment for in-situ detection.

Measurements are used by the leakage detecion software. Measurements are

collected by the central system.

Monitoring architecture •  Flows/Pressures, (DMAs/AMRs) •  Quality data •  Levels, Pumps, Valves •  Consumer complaints •  Data gathered manually

Flow meters, pressure meters, level meters, state of the pumps & valves

Demand forecasts Control Actions

Alarms

Leakage detection algorithm ¡ Makes use of a hydraulic model of the network

and compares the actual and the predicted (ideal) state of the network (pressures, flows),

¡  Examines whether there is a possible leakage at some place in the network,

¡  If the leakage is confirmed, it alters the network operator,

¡  Tries to locate the leakage (using series data from the available sensors).

Demonstrable results à Controlled leaks in the networks of Barcelona and Limassol were detected by computer algorithms!

Contamination detection & isolation algorithm ¡  Uses online measurements from quality sensors,

¡  If a contamination event is confirmed, the software predicts its spread across the network and suggests the possible isolation of parts of the DWN.

¡ After in-site measurements, the network operator resolves the issue.

Thank you for your attention.