Abstract
Water shortage and water pollution have become major problems hindering socio-economic development. Due to the scarcity of water resources, the conflict between water supply and demand is becoming more and more prominent, especially in urban areas. In order to ensure the safety of urban water supply, many cities have begun to build reservoirs. However, few previous studies have focused on the optimal allocation of water resources considering storage reservoirs. In this study, a multi-water resources and multiple users chance-constrained dynamic programming (MMCDP) model has been developed for water resources allocation in Beijing, China, which introduces reservoir and chance-constrained programming into the dynamic programming decision-making framework. The proposed model can distribute water to different departments according to their respective demands in different periods. Specifically, under the objective of maximal benefits, the water allocation planning and the amount of water stored in a reservoir for each season under different feasibility degrees (violating constraints or available water resources situations) can be obtained. At the same time, the model can be helpful for decision-makers to identify the uncertainty of water-allocation schemes and make a desired compromise between the satisfaction degree of the economic benefits and the feasibility degree of constraints.
Highlights
As an important natural resource, water is vital for supporting regional economic development and improving human-beings’ quality of life in densely populated areas
The development of effective water resources management and protection schemes is desired in urban areas under uncertainty
The water distribution problem can be formulated as a multi-water sources and multiple users dynamic programming (MMDP) model
Summary
As an important natural resource, water is vital for supporting regional economic development and improving human-beings’ quality of life in densely populated areas (i.e., urban areas). 2009, developed a two-stage fuzzy chance-constrained programming for water sources management under uncertainty, which could provide a desired water distribution plan by maximizing the system’s benefits [11]. These inexact optimization methods can effectively solve some problems, including water allocation, trade-off between economic benefits and environmental objectives, and various uncertainties that exist in water resources management [17]. How to balance the quantity of water intake from different water sources and identify the uncertainty in water-allocation planning still needs some special approaches in order to be resolved Among those proposed inexact optimization methods, chance-constraints programming can effectively address independent random variables in the constraints [18]. It is assumed that there is suchUsers a case, wherein decision-makers are Model responsible for allocating
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