Optimisation of Urban Water Management. Prof Graeme Dandy University of Adelaide

Transcription

1 Optimisation of Urban Water Management Prof Graeme Dandy University of Adelaide

2 Outline Framework for Optimisation Models Multi-Objective Optimisation Case Study #1: Multi-Objective Optimisation of Canberra s Water Supply System Case Study #2: Melbourne s Water Supply System Case Study #3: Water Supply Options for Adelaide: Cost vs Greenhouse Gas Tradeoffs Case Study #4: Urban Water Management at the Cluster Scale Conclusions

3 Types of Models Descriptive How does the system behave? What will be the consequences of certain actions? Simulation Models Prescriptive What are the best actions to achieve a particular objective or set of objectives? Optimisation models

4 Informed actions Stakeholder preferences Support for good decisions Multi criteria analysis Controls Multi-objective optimisation Data & uncertainty P1 P2 Pn R1 R2 Rn Performance prediction models Risk assessment P = performance R = risk Uncertainty & likelihood (Ref: Blackmore et al., 2009) Additional data & Knowledge

5 Optimisation Methodology Systems approach Selection of Objectives Selection of Alternative Simulation of Alternative Optimisation Module Evaluation of Alternative Results

6 Form of an Optimisation Model Choose values for a set of decision variables so as to maximise (or minimise) a particular set of objectives Subject to a set of constraints

7 Genetic Algorithm Optimisation

8 What Are Genetic Algorithms? Guided search procedures that work by analogy to natural selection Include embedded computer simulation Each solution is represented by a string of numbers Work with a population of solutions Algorithm can run for any length of time Can t prove that you have reached the optimum solution

9 Typical GA string Distribution Network Pipe Material Distribution Network Pipe Diameters Pump Size Collection Network Pipe Material

10 Repeat Towards Convergence 100 Solution Cost ($ million) The GA conducts a directed search for optimal solutions , , , ,000 Number of Solution Evaluations

11 Multi-Objective Optimisation

12 Multi-Objective Optimisation Pareto Optimal Front

13 Multi-Objective Optimisation: Articulation of Preferences (MCA) Pareto Optimal Front

14 Advantages of Genetic Algorithms for Multi-Objective Optimisation GAs deal with a population of solutions that are spread over the solution space In one GA run, these solutions can be spread along the Pareto front by using various techniques such as the Dominance Rank Sorting Algorithm Thus the population can be made to approximate the Pareto optimal front upon convergence

15 Case Study #1: Multi- Objective Optimisation Of Canberra s Water Supply System

16 The Canberra Water Supply System Canberra and Queanbeyan

17 Canberra Network in WATHNET

18 Data/Model Used Monthly rainfall and flow data for period 1871 to 2008 Current demands increased by 75% Modified NSGA II with WATHNET

19 Scenario #1: Objectives Objective 1: Average number of months per year of restrictions (reliability measure) Objective 2: Cost

20 Scenario #1: Decision Variables Decision Variable 1: Googong base reservoir gain (BG) Cost( j) BG ( j 1) * IG j 1,, n Decision Variable 2: Googong incremental reservoir gain (IG)

21 cost ($/month average) Pareto Optimal Front (Scenario 1) 7.82 x 105 case 1-175% demand Googong base gain, Googong incremental gain restrictions (months/year)

22 Scenario #2: Objectives Objective 1: Number of months per year of restrictions (reliability measure) Objective 2: Cost Objective 3: Months/year with less than 20% storage (vulnerability measure)

23 Scenario #2: Decision Variables Decision Variable 1: Googong base reservoir gain (BG) Cost( j) BG ( j 1) * IG j 1,, n Decision Variable 2: Googong incremental reservoir gain (IG) Decision Variable 3: Canberra level 1 trigger (for restrictions)

24 Canberra Case Study Pareto Front

25 Case Study #2: Melbourne s Water Supply System (Ref: Optimatics, 2010)

26 Melbourne Water System (Ref: Kularathne et al., 2011)

27 Simplified REALM Model

28 Melbourne Water System 30-year Operations Optimisation Problem Operating Decision Decisions Options Annual desal order 30 5 Yarra pumping rate Tarago output rate Transfers Total possible combinations: 5 30 * * * =

29 Utility Utility Utility Components of Water Supply Security Relative Weights: a) b) c) % 50% 100% Reliability Duration (months) Restriction Level

30 Water Supply Security Pareto Optimum Front 0.6 E 0.5 Water Supply Security A B C D Annual Annual Operating Cost ($million) Cost

31 Spider Graphs for Options D and E d) Severity Desalination Costs Other Costs e) Severity Desalination Costs Other Costs Duration Reliability Duration Reliability D: Poor value for money D E: Expensive, High Security E

32 Spider Graphs for Options A and B a) Severity Desalination Costs Other Costs b) Severity Desalination Costs Other Costs Duration Reliability Duration Reliability A: Inexpensive, Low Security A B: Balanced (less expensive) B

33 Water Supply Security Pareto Optimum Front 0.6 E 0.5 Water Supply Security A B C D Annual Annual Operating Cost ($million) Cost

34 An Example of Weights in a Multi- Criteria Analysis (Ref: Smith et al., 2012)

35 Case Study #3: Water Supply Options for Adelaide. Cost vs GHG Tradeoffs (Ref: Baulis et al, 2008)

36 TEMPORAL SCENARIOS SUPPLY TYPE ALTERNATIVES

37 Risk Based Performance Assessment ML/day ML/day ML/day 0KL Water Simulation Model (WaterCress) Run simulation model Check constraints Output results < 30GL/yr Calculate: - Reliability - Resilience - Vulnerability

38 Water Supply (GL/yr) Risk Based Performance Assessment Myponga Reservoir Happy Valley Reservoir River Murray The amount of water supplied 20 by each supply 10 type for the Southern system for Date Desalination Plant Maximum River Murray Flow Reliability Resilience (years -1 ) Vulnerability (GL) 225ML/day 0KL 85%

39 Optimisation Objectives: Minimise present value of total system cost Minimise (discounted) greenhouse gas emissions Constraint: Availability of water from the Murray (30 GL/year) Decision Variables: Capacity of desalination plant (ML/day) Size of rainwater tanks for all households (kl) Operating rules for the system

40 Optimisation Process

41 GHG emissions (Megatonnes of CO2-e) Optimisation Process trade-offs Feasible (300ML/day, 5KL) Infeasible 0KL) (225ML/day, Cost ($2007 billion)

42 GHG emissions (Megatonnes of CO2-e) Optimisation Process 2060 trade-offs Feasible (300ML/day, 5KL) Infeasible 0KL) (225ML/day, Pareto Optimal Front Cost ($2007 billion)

43 GHG emissions (Megatonnes of CO 2-e) GHG emissions (Megatonnes of CO 2-e) Optimisation Process 2060 results Cost ($2007 billion) The 2060 Pareto Front Breakpoint (250ML/day, 2KL) Cost ($2007 billion)

44 GHG emissions (Megatonnes of CO 2-e) GHG emissions (Megatonnes of CO 2-e) Optimisation Process 2060 results Cost ($2007 billion) The 2060 Pareto Front $45/tonne Breakpoint (250ML/day, 2KL) $1000/tonne Cost ($2007 billion)

45 GHG emissions (Megatonnes of CO 2-e) GHG emissions (Megatonnes of CO 2-e) Optimisation Process 2060 results Cost ($2007 billion) The 2060 Pareto Front (251ML/day, 1.8KL) $45/tonne Breakpoint (250ML/day, 2KL) $1000/tonne (248ML/day, 2.6KL) Cost ($2007 billion)

46 Optimisation Process Range depends on optimal rainwater tank size, which depends on average yearly water supply per tank: Average yearly water supply per tank 1KL 24KL $3.08/KL 0.96kgCO 2 -e/kl 20KL 48KL $3.27/KL 3.34kgCO 2 -e/kl

47 Sensitivity Analysis of the Optimisation Process Economic Discount Rate Social Discount Rate Demand River Murray Supply Constraint Climate Change Impacts Lifetime of Rainwater Tanks Lifetime of the Desalination Plant Economic Discount Rate Social Discount Rate Demand River Murray Supply Constraint Climate Change Impacts Lifetime of Rainwater Tanks Lifetime of the Desalination Plant Desalination Plant Size (ML/day) Tank Size (KL)

48 Case Study #4: Urban Water Management at the Cluster Scale

49 Woden Case Study (Canberra)

50 Urban Developer Case Study Collier Street 39 Houses Measured roof areas Census data used for number of occupants

51 Urban Developer Case Study Occupants Number of Houses

52 Urban Developer Case Study Households vary between 2-5 people 5 different sizes of rainwater tank: 1kL, 2kL, 5.5kL, 9kL,10kL Water consumption data: From Domestic Water Use in the ACT (Troy et al., 2006)

53 Objectives Set in collaboration with ACT government and ACTEW Reduce potable water demand Reduce total water consumption Further objectives Cost (minimize) Energy use (minimize) Ecological objectives

54 Decision Variables Inputs into the model that can be changed in order to meet objectives: Number of houses with rainwater tanks of various sizes Uptake of water efficient appliances (WELS 1 to 5) Three Scenarios corresponding to water prices of $2, $3 and $4 per kl up to 548 kl per year (scenarios 0,1,2)

55 Average annual water use (kl/household) Multi-Objective Tradeoffs Scenario 0 Scenario 1 Scenario Average annual mains water use (kl/household) Average annual household cost ($)

56 Average annual water use (kl/household) Tradeoffs in Terms of Two Objectives Scenario 0 Scenario 1 Scenario Average annual household cost ($)

57 Average annual mains water use (kl/household) Tradeoffs in Terms of Two Objectives Scenario 0 Scenario 1 Scenario Average annual household cost ($)

58 Distribution of Rainwater Tank Sizes Scenario no tank 1kL 2kL 5.5kL 9kL 10kL

59 Distribution of Rainwater Tank Sizes Scenario no tank 1kL 2kL 5.5kL 9kL 10kL

60 Distribution of WELS Ratings Scenario star 2 star 3 star 4 star 5 star

61 Distribution of WELS Ratings Scenario star 2 star 3 star 4 star 5 star

62 Conclusions (1) Urban Water Systems are inherently complex with many objectives and many options Multi-objective optimization (MOO) enables the exploration of a wide range of options for these systems MOO can be used to produce a Pareto optimal front which provides a range of efficient options from which the decision-maker can choose

63 Conclusions (2) Multi-objective optimisation (MOO) has a key role to play in providing decision support for the planning and operations of major water supply systems Tradeoffs between cost and system security or greenhouse gas emissions can be developed The combination of MOO and MCDA enables efficient outcomes that satisfy the preferences of stakeholders

64 Acknowledgements H.Maier, J.Ravalico, F.Paton, J.Baulis, L. Lloyd, B. Staniford (University of Adelaide) G.Kuczera, L.Cui (University of Newcastle) J. Blackmore (CSIRO) D.McIver, D.Broad, H.Schultz-Byard, T.Rowan, P.Smith (Optimatics Pty Ltd) U.Kularathna, B.Rhodes, D.Flower, B.Baker (Melbourne Water)

65 References Baulis, J., Lloyd, L., Paton, F. and Staniford, B. (2008) Multi-Objective Optimisation of Urban Water Supply Systems at the Regional Scale Incorporating Sustainability Final Year Honours Presentation, School of Civil, Environmental and Mining Engineering, University of Adelaide. Blackmore, J.M., Dandy, G.C., Kuczera, G. and Rahman J. (2009) Making the most of modelling: A decision framework for the water industry. In Anderssen, R.S., R.D. Braddock and L.T.H. Newham (eds) 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation, July. Kularathna, M.D.U.P., Rowan, T.S.C., Schultz-Byard, H., Broad, D.R., McIver D., Flower, D., Baker, B., Rhodes, B.G. and Smith, P. J. (2011) Multi-Objective Optimisation using Optimizer WSS to Support Operation and Planning Decisions of Melbourne Water Supply System, Proceedings,19 th International Congress on Modelling and Simulation, Perth, Dec. Optimatics Pty Ltd (2010) Water Supply System Optimisation. Feasibility Study Report prepared for Victorian Smart SMEs Market Validation Program. Host Institution: Melbourne Water, April 2010 Smith, P. J., Kularathna, M. D. U. P., Rhodes, B., Broad, D. R., Schultz-Byard, H. (2012) Decision Support for Management of Melbourne s Water Supply System, Proceedings, Ozwater 12, Sydney, May.

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