Accurate hydrologic time-series prediction plays an important role in modern water resource planning, water supply management, environmental protection, and power system operation. In general, single-layer feedforward networks (SLFNs) can provide satisfactory forecasting results, but classical gradient-based learning algorithms are time consuming and easily trapped into local optimums. As a new training method for SLFNs, extreme learning machine (ELM) has faster training speed and stronger nonlinear mapping than gradient-based algorithms, and provides an effective technique for hydrologic time-series prediction. However, ELM may converge to local minimums in some cases due to the random determination of input weights and hidden biases. Thus, in order to overcome the shortcomings of ELM, this paper introduces a novel ELM–quantum-behaved particle swarm optimization (QPSO) model (ELM-QPSO) combining the advantages of ELM and QPSO. The proposed model adopts the QPSO algorithm to select the optimal input-hidden weights and hidden biases of ELM, and uses the Moore–Penrose generalized inverse to analytically determine the output weights. The proposed approach is assessed with daily runoff data of Xinfengjiang reservoir in China from January 1, 2000 to December 31, 2014. The results indicate that the ELM-QPSO can significantly improve the generalization performance of ELM for hydrologic time-series prediction, and that QPSO is an alternative training algorithm for ELM parameters selection.

With the increasingly serious energy crisis and environmental pollution, the short-term economic environmental hydrothermal scheduling (SEEHTS) problem is becoming more and more important in modern electrical power systems. In order to handle the SEEHTS problem efficiently, the parallel multi-objective genetic algorithm (PMOGA) is proposed in the paper. Based on the Fork/Join parallel framework, PMOGA divides the whole population of individuals into several subpopulations which will evolve in different cores simultaneously. In this way, PMOGA can avoid the wastage of computational resources and increase the population diversity. Moreover, the constraint handling technique is used to handle the complex constraints in SEEHTS, and a selection strategy based on constraint violation is also employed to ensure the convergence speed and solution feasibility. The results from a hydrothermal system in different cases indicate that PMOGA can make the utmost of system resources to significantly improve the computing efficiency and solution quality. Moreover, PMOGA has competitive performance in SEEHTS when compared with several other methods reported in the previous literature, providing a new approach for the operation of hydrothermal systems.

In order to satisfy the practical requirement of the power grid in China, this paper presents a multiobjective operation model for a cascaded hydropower system simultaneously considering the maximization of gross generation and firm output, as well as various complex constraints. Then, a multiobjective particle swarm optimization (MOPSO) is presented here to solve this problem. This algorithm combines the merits of chaos theory, genetic operators, adaptive adjustment strategy, and the heuristic constraint-handling method to help the population explore the search space efficiently: the logistic map is introduced to generate the initial population distributed uniformly in problem space; both the inertia weight and learning coefficients are dynamically changed as iteration goes on; an archive set is used to conserve the nondominated solutions found during evolution; the personal and global best position for each particle are determined by crowding distance and a feasibility-based dominance relationship; the classical mutation and crossover operators are introduced to enhance the population diversity; a novel constraint-handling method is proposed to address the complicated constraints. To testify to its effectiveness, the MOPSO is applied to the Wu River cascade hydropower system in southwest China. The results in different cases indicate that MOPSO is able to provide better solutions than the NSGA-II method, providing an effective technique for the operation of a hydropower system.

In recent years, growing peak pressure is posing a huge challenge for the operators of electrical power systems. As the most important clean renewable energy, hydropower is often advised as a response to the peak loads in China. Thus, a novel hybrid sine cosine algorithm (HSCA) is proposed to deal with the complex peak operation problem of cascade hydropower reservoirs. In HSCA, the elite-guide evolution strategy is embedded into the standard sine cosine algorithm to improve the convergence rate of the swarm. The Gaussian local search strategy is used to increase the diversity of the population. The random mutation operator is adopted to enhance the search capability of the individuals in the evolutionary process. The proposed method is applied to solve the complex peak operation problem of two hydropower systems. The simulations indicate that in different cases, HSCA can generate the scheduling results with higher quality than several benchmark methods. Hence, this paper provides a feasible method for the complex peak operation problem of cascade hydropower reservoirs.

The progressive optimality algorithm (POA) is commonly used to identify optimal hydropower operation schedules in China. However, POA may not converge within a reasonable time for large and complex problems because its computational burden grows exponentially with the expansion of system scale. In order to effectively alleviate the dimensionality problem of POA, an improved POA variant called orthogonal progressive optimality algorithm (OPOA) is introduced in this paper. In the OPOA, an orthogonal experimental design is used to replace the exhaustive combinatorial evaluation at each POA two-stage subproblem. The theoretical analysis shows that POA and OPOA have exponential and approximately polynomial growth in computational complexity, respectively. The proposed method is applied to a large-scale multireservoir system located on the Wu River in China. The results indicate that, compared with POA, OPOA can remarkably enhance the computing efficiency in different cases, showing its practicability and feasibility for multireservoir system operation.