Multireservoir Operating Rules Research Articles

Simulation of Multi Reservoir Operation Rules with Interconnected Tunnel and Water Transfer

The multi-reservoir operation rules require accuracy in developing its technical parameters. This is done to prevent operational failure in one of the reservoirs. The water transfer concept is to manage the water resources distribution between the receiving watershed and the donor watershed. The availability of transferable water must be prioritized, meeting the water demand of the donor reservoir. Storage capacity in both reservoirs aims to meet water demand, especially in the recipient. The elevation of the interconnecting tunnel is the minimum limit for water use in simulation. The interconnected tunnels' location and capacity will determine the multi-reservoir's operation rule. The interconnected tunnel in the Rukoh Tiro reservoir transfers water in the operation of the two reservoirs. The simulation is carried out in three seasons, considering the inflow of each watershed, the reservoir's downstream water demand, and the reservoir's technical conditions. The simulation results of the Rukoh Tiro reservoir operated simultaneously in all three seasons show that the fulfillment of irrigation water demand can reach 100% as needed. The water transfer process through interconnected tunnels occurs throughout the year. The reservoir operating rule is expected to be a reference in the multi-reservoir operation to obtain an optimal reservoir operating rule.

Improved multi-reservoir operation rules of water supply system based on target storage curves

Abstract This paper proposes a multi-reservoir operating policy for water supply by combining target storage curves with the hedging rule. The curves are derived to solve the allocation problem of joint demand. There are three steps in the proposed methodology: Firstly, the optimal release schedule using dynamic programming to solve a long-term operation model is established, from which the suitable forms of target storage curves are identified. Secondly, a simulation and optimization model is built to identify the key points of the curves with the hedging rule based on aggregated reservoir using improved particle swarm optimization (IPSO). Finally, synthetic inflow series is used to test and verify the efficiency of the proposed rule. The water-supply multi-reservoir system located in the Liaoning province of China is employed as a case study to verify the effect of the proposed operating policy and the efficiency of target storage curves. The results indicate that the proposed operating policy is suitable to handle the multi-reservoir operation problem, especially for periods of drought. The target storage curves also show improved performance for distribution of system storages.

Performance of Implicit Stochastic Approaches to the Synthesis of Multireservoir Operating Rules

AbstractWith increasing pressure on water resources availability and dependability and constraints due to environmental concerns, the traditional approaches for defining reservoir management rules ...

Optimization of Multi-Reservoir Operating Rules for a Water Supply System

To obtain the optimal releases of the multi-reservoir system, two sets of joint operating rules (JOR-I and JOR-II) are presented based on the aggregation-disaggregation approach and multi-reservoir approach respectively. In JOR-I, all reservoirs are aggregated to an equivalent reservoir, the operating rules of which, the release rule of the system is optimized following operating rule curves coupled with hedging rules. Then the system release is disaggregated into each reservoir according to water supply priorities and the dynamic demand partition approach. In JOR-II, a two-stage demand partition approach is applied to allocate the different demand priorities to determine the release from each reservoir. To assess the reliability and effectiveness of the joint operating rules, the proposed rules are applied to a multi-reservoir system in Liaoning province of China. Results demonstrate that JOR-I is suitable for high-dimensional multi-reservoir operation problems with large-scale inflow data, while JOR-II is suitable for low-dimensional multi-reservoir operation problems with small-scale inflow data, and JOR-II performs better than JOR-I but requires more computation time. The research provides guidelines for the management of multi-reservoir system.

Sustainable Basin-Scale Water Allocation with Hydrologic State-Dependent Multi-Reservoir Operation Rules

This study extends the PSO-MODSIM model, integrating particle swarm optimization (PSO) algorithm and MODISM river basin decision support system (DSS) to determine optimal basin-scale water allocation, in two aspects. The first is deriving hydrologic state-dependent (conditional) operating rules to better account for drought and high-flow periods, and the second is direct, explicit consideration of sustainability criteria in the model’s formulation to have a better efficiency in basin-scale water allocation. Under conditional operating rules, the operational parameters of reservoir target storage levels and their priority rankings were conditioned on the hydrologic state of the system in a priority-based water allocation scheme. The role of conditional operating rules and policies were evaluated by comparing water shortages associated with objective function values under unconditional and conditional operating rules. Optimal basin-scale water allocation was then evaluated by incorporating reliability, vulnerability, reversibility and equity sustainability indices into the PSO objective function. The extended model was applied for water allocation in the Atrak River Basin, Iran. Results indicated improved distribution of water shortages by about 7.5% using conditional operating rules distinguishing dry, normal and wet hydrologic states. Alternative solutions with nearly identical objective function values were found with sustainability indices included in the model.

Multi-reservoir Operation Rules: Multi-swarm PSO-based Optimization Approach

Reservoir operation rules are intended to help an operator so that water releases and storage capacities are in the best interests of the system objectives. In multi-reservoir systems, a large number of feasible operation policies may exist. System engineering and optimization techniques can assist in identifying the most desirable of those feasible operation policies. This paper presents and tests a set of operation rules for a multi-reservoir system, employing a multi-swarm version of particle swarm optimization (MSPSO) in connection with the well-known HEC-ResPRM simulation model in a parameterization–simulation–optimization (parameterization SO) approach. To improve the performance of the standard particle swarm optimization algorithm, this paper incorporates a new strategic mechanism called multi-swarm into the algorithm. Parameters of the rule are estimated by employing a parameterization–simulation–optimization approach, in which a full-scale simulation model evaluates the objective function value for each trial set of parameter values proposed with an efficient version of the particle swarm optimization algorithm. The usefulness of the MSPSO in developing reservoir operation policies is examined by using the existing three-reservoir system of Mica, Libby, and Grand Coulee as part of the Columbia River Basin development. Results of the rule-based reservoir operation are compared with those of HEC-ResPRM. It is shown that the real-time operation of the three reservoir system with the proposed approach may significantly outperform the common implicit stochastic optimization approach.

Optimizing multi-reservoir operation rules: an improved HBMO approach

We present an improved version of Honey Bees Mating Optimization (HBMO) algorithm to develop operating rules for multi-reservoir systems. The performance of the proposed model is tested through sensitivity analysis and comparing the result with those of a real-coded Genetic Algorithm (GA) for a 60-month period single-reservoir operation problem. The improved model is subsequently employed to derive release rule and storage balancing functions which form operating policy for a multi-reservoir system along two case examples: (i) water supply and (ii) hydropower generation. The obtained operating rule curves can be used to guide the system operators in decision-making. These rule curves provide the operator with the opportunity to systematically look at the system and to make proper decisions. The obtained results showed that the optimization technique proposed in this study is capable of solving complex multi-reservoir systems operation problems. Moreover, the proposed structure properly handled the tight constraints defining the parallel reservoirs operation in such a way that all the generated solutions were feasible after a particular set of iterations. The proposed optimization algorithm of this study can be developed more in future to solve multi-modal optimization problems, and also to define operation policies for highly complex multi-reservoir systems.

GRID computing approach for multireservoir operating rules with uncertainty

Developing mathematical optimization models that define multireservoir operating rules that are worthy of being evaluated by simulation models is a challenging task. The traditional approach can easily be scaled, and GRID computing can reduce computation times for large water resource systems. The potential for GRID computing in this area of research is largely unexplored. In this paper, the migration of a mixed optimization–simulation approach and preliminary applications to a water system in Southern Italy are presented. Results indicate that GRID computing provides significant savings in computation time.

Multireservoir Modeling with Dynamic Programming and Neural Networks

For optimal multireservoir operation, a dynamic programming-based neural network model is developed in this study. In the suggested model, multireservoir operating rules are derived using a feedforward neural network from the results of three state variables' dynamic programming algorithm. The training of the neural network is done using a supervised learning approach with the back-propagation algorithm. A multireservoir system called the Parambikulam Aliyar Project system is used for this study. The performance of the new multireservoir model is compared with (1) the regression-based approach used for deriving the multireservoir operating rules from optimization results; and (2) the single-reservoir dynamic programming-neural network model approach. The multireservoir model based on the dynamic programming-neural network algorithm gives improved performance in this study.

Evaluating multireservoir operating rules

Different decision rules for operating a multireservoir system are examined. The system analyzed serves recreation as well as flow augmentation purposes. The operation rules range from the so‐called conventional policy to a policy that uses flow forecasting and mathematical programing for the optimization. Historical data are used to simulate the operation of the system under the different rules, and an assessment of the policies is made on the basis of multiobjective criteria.