Abstract
Simulation-optimization methods are often used to derive operation rules for large-scale hydropower reservoir systems. The solution of the simulation-optimization models is complex and time-consuming, for many interconnected variables need to be optimized, and the objective functions need to be computed through simulation in many periods. Since global solutions are seldom obtained, the initial solutions are important to the solution quality. In this paper, a two-stage method is proposed to derive operation rules for large-scale hydropower systems. In the first stage, the optimal operation model is simplified and solved using sampling stochastic dynamic programming (SSDP). In the second stage, the optimal operation model is solved by using a genetic algorithm, taking the SSDP solution as an individual in the initial population. The proposed method is applied to a hydropower system in Southwest China, composed of cascaded reservoir systems of Hongshui River, Lancang River, and Wu River. The numerical result shows that the two-stage method can significantly improve the solution in an acceptable solution time.
Highlights
Considering system states, inflow uncertainty, power system demands [1], and other factors, hydropower systems adopt operating rules widely to determine the power generations of each reservoir in the current period
The methods based on dynamic programming (DP), including stochastic dynamic programming (SDP) [2], sampling stochastic dynamic programming (SSDP) [3], aggregation-disaggregation approach [4,5,6,7], and stochastic dual dynamic programming (SDDP) [8,9], are among the most popular reservoir operation methods
Lund and Ferreira [14] presented the application of deterministic optimization in the reservoir system of the main stem of the Missouri River, and developed and tested the derived optimal operation rules according to the results of these models
Summary
Considering system states, inflow uncertainty, power system demands [1], and other factors, hydropower systems adopt operating rules widely to determine the power generations of each reservoir in the current period. The deterministic optimization or simulation method is widely used to derive operation rules. Jiang et al [16] used the least square principle to derive operation rules of the Lianghekou reservoir based on optimal computing results of multi-dimensional dynamic programming. Liu et al [22] developed a simulation-optimization-test framework and hybrid multi-objective genetic algorithms, aiming at maximizing the utilization efficiency under flood control safety conditions, and derived the optimal refill rules for multi-purpose reservoirs. The initial solution is used as an individual of the initial population of the genetic algorithm (GA), to obtain a better operation rule using the simulation-optimization method in the second stage. The values of H m Smt , f and Qtm vary with operation rules and can be approximately estimated by using simulation results from an individual reservoir operation rule before optimizing cascade operation rules, and in most cases, this error may not affect the sorting of Tmt ,j
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