Long-Term Joint Operation of Cascade Reservoirs Using Enhanced Progressive Optimality Algorithm and Dynamic Programming Hybrid Approach

Abstract

Dynamic programming (DP) is one of the most classical methods adopted for reservoir operation. It reduces the computational efforts of complex high-dimensional problems by piecewise dimensionality reduction and provides the global optimums of the problems, but it suffers the "curse of dimensionality".Progressive optimality algorithm (POA) has been used repeatedly in reservoir operation studies during last decades because it alleviates the "curse of dimensionality" of DP and has good convergence and extensive applicability.Nonetheless, POA encounters two difficulties in multi-reservoir operation applications. One is the transfer interrupt problem that makes the search procedure hard to achieve free allocation of water between two nonadjacent stages, and the latter is the dimensionality problem that leads to a low convergence rate. In order to overcome these deficiencies, this paper makes some enhancements to POA and proposes a hybrid approach combining the enhanced POA and DP (EPOA-DP) for long-term operation of cascade reservoir systems. In EPOA-DP, EPOA is employed 2 to improve the quality of the solutions and DP is used to reduce the computational effort of the two-stage problem solution. The proposed approach was tested using a real world four-reservoir cascade system and a ten-reservoir benchmark test example, and the results demonstrate that it outperforms POA both in computational time and quality of the solution.

A Comprehensive Model for Assessing Synergistic Revenue–Cost for the Joint Operation of a Complex Multistakeholder Reservoir System

“…The joint operation of reservoirs not only generates synergistic revenue to the total [6][7][8] but also incurs operation costs for some individual members [9]. The total revenue of compensated reservoirs (often downstream) can be increased, and the added value compared to the separate operation is defined as synergistic revenue.…”

Flood Control Optimization of Reservoir Group Based on Improved Sparrow Algorithm (ISSA)

“…Intelligent optimization algorithms such as genetic algorithm (GA) [12,13] have been gradually applied to solve the problem of optimal flood control scheduling of reservoirs. Subsequently, some scholars successively applied ant colony algorithm (ACO) [14,15], differential evolution algorithm (DE) [16], particle swarm optimization algorithm (PSO) [17,18], and so on, to solve the flood control optimization and dispatching problem of reservoir. Although the swarm intelligent algorithm makes up for the shortcomings of traditional optimization algorithms, the swarm intelligent algorithm often has the disadvantages of precocious convergence and poor convergence performance when solving problems, and it cannot ensure the convergence to the best advantage [19].…”

Optimal Flood-Control Operation of Cascade Reservoirs Using an Improved Particle Swarm Optimization Algorithm

“…Young [8] first applied DP to solve a single-reservoir operation problem, and then the multi-stage DP, stochastic DP, coupling parallel DP with importance sampling and successive approximation, hybrid DP and LP, and spark-based parallel DP models were put forward by Ji et al [9], Wu et al [10], He et al [10], Zhong et al [11], and Ma et al [12], respectively. Because POA can alleviate the problem of "curse of dimensionality", Zhong et al [13], Jiang et al [14], Zhou et al [15], Chen et al [16], and Ji et al [17] proposed the orthogonal POA, multi-stage POA, DP combined with POA, enhanced POA and DP hybrid approach, and nested POA to solve the optimal operation strategy. However, when faced with a complex flood-control system composed of reservoir groups, flood storage and detention areas, lakes, and other flood-control projects, conventional optimization algorithms exhibit obvious limitations, such as low convergence efficiency and dimensionality.…”