Regional Water Supply System Management Under Demand

Regional Water Supply System Management Under Demand Uncertainty: Using Aggregation Rules to Derive an Operation Policy

from Implicit Stochastic Programming Models Noa Avni1,2 Barak Fishbain1,2, Mashor Housh3, Uri Shamir2

World Environmental & Water Resources Congress 2017

Sacramento, CaliforniaMay 22, 2017

1 Technion Enviromatics Lab - TechEL, Dept. of Environmental, Water and Agriculture Engineering, Faculty of Civil and Environmental2 Dept. of Environmental, Water and Agriculture Engineering, Faculty of Civil and Environmental Engineering, Technion - Israel Institute of Technology

3 Faculty of Management, Dept. of Natural Resources and Environmental Management, University of Haifa

Regional Water Supply System Management Under Demand Uncertainty, World Environmental & Water Resources Congress 2017

Background

❖ Regionalwatersupplysystems(WWSs)are compexsystems,

requiredtofulfilthedemandsofseveralconsumertypes:

municipal,industrial,andagricultural

❖ DemanduncertaintyisoneofthemaincausesofregionalWSS

managementuncertainty

2

RWSsarecharacterizedbyhighspatial

andtemporaldemandvariability


Researchgoal

3

Developandapplyamethodologyfor

derivingimplementabledecisionsfrom

ImplicitStochasticProgramming

Models


StochasticProgrammingforregionalWSSmanagement

4

❖ ImplicitStochasticProgramming(ISP):Adeterministicmodelissolvedforasetofpossiblerealizationsoftheuncertainparameter(monthlywaterdemands)

❖ ISPholdstheadvantageofshortrunningtimeandspanningthespaceofpossibleoperationpolicies

❖ ISP’sdisadvantage:doesnotprovideasingleoperationpolicy

Highqualityconsumptiondatamade

stochasticprogrammingmethodsfavorable


Example: ISP results

5

❖ 12‐monthoperationplan forasingledesalinationplan(Avnietal.,EWRI2017)

❖ Eachhistogrampresentsthesetofoptimalplantoperationsforeachmonth

HowtouseISPsolutionstoderiveasingle,

implementablepolicy?


DerivingimplementabledecisionsfromISPresults

6

❖ Theaggregationrulesareappliedtothesetofoperationpolicies forthenexttimeperiod

❖ Theaggregationrulesareappliedtoasubsetofthedecisionvariables:• Here&Now• Complete-recourse

Methodology:ApplyaggregationrulestothesetofoptimalISP

solution


Applyingtheaggregationrulesmethodology:ISP‐MWoptimization‐simulationmodel

7

❖ Applyingtheaggregationrulesina”Folding‐horizon”mode

❖ Ineachtimeperiod(month)adecisionistakenforth nextmonthoperations(desalinationproduction)

❖ Thedecisionsonaquiferwithdrawalsandpipeconveyanceresultfromthedesalinationdecisions(viaanLPmodel)

QP-optimization LP-optimization

Output for time :, , ,

ISP-MW modelISP

Update state variables:

= +1 ( T)

m


LP‐optimizationintheISP‐MWmodel• Objective function (cost/month )

LP-optimization

Output for time

Update state variables

- Flow balance

- Lower and upper bounds on the decision variables

- State variables constraints

• Constraints


Experimentalsetting

9

❖ Small WSS: 2 consumers, an aquifer, and a desalination plant (right)

❖ Large WSS: 9 consumers, 3 aquifers, and 5 desalination plants (part of the Israeli National WSS)

Large WSS model

(Housh, 2011)

Small WSS model

(Housh, 2011)


LargeWSS

10(Housh, 2011)

InthecaseoftheIsraeliNationalWSS,theaggregation

ruleswereappliedtothe p‐percentilesofdesalination

production(meetthecriteriaofhere&now and

completerecoursedecisions)


Small WSS experiment

11

Comparing planned and actual operations over three demand configurations:

1. 2 Urban consumers

2. 2 Homogeneous agricultural

3. 2 Heterogeneous agricultural

(Housh, 2011)

❖ Motivation for using various demand : analyze how different demand variability affects the WSS operations and selection of aggregation rules


ExampleforasingleISP‐MWrun‐ smallWSSresults(partial)

❖ Homogeneous agricultural demands)❖ Planning vs. actual (simulated) operations

(Housh, 2011)


Sensitivityanalysis:TheeffectofdifferentaggregationrulesonregionalWSSoperations

❖ Repeated runs (over varying demand realizations) are required in order to choose among different aggregation rules

Month ̂ operations *based on

ISP-MW

❖ ISP-MW is applied to a single demand realization❖ A single demand realization is not necessarily representative –

given a different realization, a different aggregation rule might be favorable


Choosingbetweendifferentaggregationrules(sensitivityanalysiscont’d)

Prioritizingdifferentaggregationrules:❖ Gapsbetweenplannedandactual(simulated)operation❖ Plannedvs.actualdesalination/unuseddesalinationwater❖ Operationalcost(simulated)


Sensitivityanalysis:Aggregationrules‐ smallWSSresults(partial)

❖ Each aggregation rule (30, 50, 70, and 90%) was applied to 30scenarios of demand realizations

❖ Gaps between planned and simulated desalination production (histograms)

2 Urban consumers 2 agricultural consumers (homogeneous)


Sensitivityanalysis:Aggregationrules(SmallWSS) –cont’d

2agriculturalconsumers(homogeneous)


❖ Given low variability, the gap mean remains low/increases slightly with P❖ Given high variability, small percentiles are favorable❖ Trend of Planning > actual operation❖ Choosing between different rules depends on actual operations cost and

other constraints/decision maker prefenrences

2 Urban consumers 2 agricultural consumers (homogeneous)

Sensitivityanalysis:Aggregationrules(smallWSSresults) – cont’d


LargeWSSexperiment

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1. Comparing planned and actual operations over three demand configurations: urban, agricultural, and heterogeneous

2. Comparison of different aggregation rules via analysis of the WSS operations given representative scenarios: min, med, max demands (an alternative for repetitive runs)


Aggregationrulessensitivityanalysis:planning‐simulationgaps(largeWSS)

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❖ The gaps are inversely correlated to the demand variability❖ Given low variability, increasing p decreases the planning-simulation gaps❖ Given high variability (agricultural configuration) , increasing p increases the planning-

simulation gaps


Aggregationrulessensitivityanalysis:Simulatedcost(largeWSS)

20

Regional Water Supply System Management Under Demand Uncertainty, World Environmental & Water Resources Congress 2017 21

Unused desalinated water

❖ The percentile (P) reflects (implicitly) the WSS reliability❖ High percentiles increase the amounts of unused desalinated water

Aggregationrulessensitivityanalysis:Overproductionofdesalinatedwater(largeWSS)

Regional Water Supply System Management Under Demand Uncertainty, World Environmental & Water Resources Congress 2017 22

❖ The methodology enables to derive implementable operation policy for regional WSS from ISP results

❖ ISP-MW is a simulation-optimization model that allows to analyze the WSS operations policy under different aggregation rules (using folding horizon)

❖ The developed methodology is flexible enough to accommodate for other regional (or smaller) WSS, with different management considerations (e.g., a different set of here & now and complete recourse decision variables or consumers with different characteristics)

Summaryandconclusions:DerivingimplementabledecisionsfromISPresults

Thank you

Contact Info:

• Noa Avni, [email protected]

• Assist. Prof. Barak Fishbain, [email protected]

Environmatics Lab,The Faculty of Civil and Environmental Eng.,Technion – Israel Inst. Of Technology

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Regional Water Supply System Management Under Demand