scholarly journals Demand Uncertainty and Distribution Systems:

1992 ◽  
Vol 23 (1) ◽  
pp. 131-142
Author(s):  
Yasuhiro SAKAI ◽  
Keisuke SASAKI
Keyword(s):  
Distribution Systems ◽  
Demand Uncertainty

scholarly journals Least-cost design of water distribution systems under demand uncertainty: the robust counterpart approach

2013 ◽  
Vol 15 (3) ◽  
pp. 737-750 ◽  
Author(s):  
Lina Perelman ◽  
Mashor Housh ◽  
Avi Ostfeld
Keyword(s):  
Distribution Systems ◽  
Water Distribution ◽  
Demand Uncertainty ◽  
Optimization Technique ◽  
Water Distribution Systems ◽  
Extended Period ◽  
Deterministic Equivalent ◽  
Robust Counterpart ◽  
Uncertainty Set ◽  
Ellipsoidal Uncertainty

In this study, a non-probabilistic robust counterpart (RC) approach is demonstrated and applied to the least-cost design/rehabilitation problem of water distribution systems (WDSs). The uncertainty of the information is described by a deterministic user-defined ellipsoidal uncertainty set that implies the level of risk. The advantages of the RC approach on previous modelling attempts to include uncertainty are in making no assumptions about the probability density functions of the uncertain parameters and their interdependencies, having no requirements on the construction of a representative sample of scenarios, and the deterministic equivalent problem preserves the same size (i.e. computational complexity) as the original problem. The RC is coupled with the cross-entropy heuristic optimization technique for seeking robust solutions. The methodology is demonstrated on an illustrative example and on the Hanoi network. The results show considerable promise of the proposed approach to incorporate uncertainty in the least-cost design problem of WDSs. Further research is warranted to extend the model for more complex WDSs, incorporate extended period simulations, and develop RC schemes for other WDSs related management problems.

Towards more resilient and adaptable water distribution systems under future demand uncertainty

2013 ◽  
Vol 13 (6) ◽  
pp. 1495-1506 ◽  
Author(s):  
Raziyeh Farmani ◽  
David Butler
Keyword(s):  
Distribution Systems ◽  
Water Distribution ◽  
Demand Uncertainty ◽  
Water Distribution Systems ◽  
Evaluation Methodology ◽  
Network Building ◽  
All Solutions ◽  
Multistage Design ◽  
Major Reduction

The focus of this paper is on how water distribution systems can be made more resilient and adaptable, thus reducing their vulnerability to future changes. A performance evaluation methodology is outlined and used to assess the resilience of today's water infrastructure and its vulnerability to future changes, based on adopting four future scenarios, suitably adapted to represent future water demand states. The results highlight the sensitivity of key performance indicators to a range of future conditions relative to current conditions. The concept of future proofing is introduced and three strategies compared to design/re-design and operate the network, building in varying degrees of adaptive capacity to deliver solutions that are feasible under both today's and tomorrow's conditions. The key findings are that, without any intervention, all solutions are feasible when demand is equal to or less than the design case while resilience of the system improves for small decrease in demand, major reduction in demand shows a big improvement in water quality. Three future proofing strategies, namely operation, designed in operation and multistage design and operation show great potential to create flexibility that allows for operational diversity in the short term while trying to achieve long-term goals. The multistage design and operation strategy is able to outperform the other two strategies considering reduction in cost and improvement in performance of the system.

Two new approaches for the stochastic least cost design of water distribution systems

2004 ◽  
Vol 4 (5-6) ◽  
pp. 355-363 ◽  
Author(s):  
Z. Kapelan ◽  
A.V. Babayan ◽  
D.A. Savic ◽  
G.A. Walters ◽  
S.T. Khu
Keyword(s):  
New York ◽  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Demand Uncertainty ◽  
Integration Method ◽  
Pressure Head ◽  
Problem Formulation ◽  
Independent Random Variables ◽  
System Robustness

The problem of stochastic (i.e. robust) water distribution system (WDS) design is formulated and solved here as an optimisation problem under uncertainty. The objective is to minimise total design costs subject to a target level of system robustness. System robustness is defined as the probability of simultaneously satisfying minimum pressure head constraints at all nodes in the network. The decision variables are the alternative design options available for each pipe in the WDS. The only source of uncertainty analysed is the future water consumption uncertainty. Uncertain nodal demands are assumed to be independent random variables following some pre-specified probability density function (PDF). Two new methods are developed to solve the aforementioned problem. In the Integration method, the stochastic problem formulation is replaced with a deterministic one. After some simplifications, a fast numerical integration method is used to quantify the uncertainties. The optimisation problem is solved using the standard genetic algorithm (GA). The Sampling method solves the stochastic optimisation problem directly by using the newly developed robust chance constrained GA. In this approach, a small number of Latin Hypercube (LH) samples are used to evaluate each solution's fitness. The fitness values obtained this way are then averaged over the chromosome age. Both robust design methods are applied to a New York Tunnels rehabilitation case study. The optimal solutions are identified for different levels of robustness. The best solutions obtained are also compared to the previously identified optimal deterministic solution. The results obtained lead to the following conclusions: (1) Neglecting demand uncertainty in WDS design may lead to serious under-design of such systems; (2) Both methods shown here are capable of identifying (near) optimal robust least cost designs achieving significant computational savings.

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