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.

An efficient reservoir operation technique plays a very important role in improving the water resources and energy efficiency of reservoirs. In order to effectively avoid or alleviate the “curse of dimensionality” of Multi-dimensional Dynamic Programming (MDP) in the application of cascade reservoirs operation optimization (CROO) and keep a global convergence at the same time, two dimension reduction methods are proposed in this paper. One is a hybrid algorithm of MDP and a Progressive Optimality Algorithm (POA), named MDP-POA, which combines the global convergence of MDP and the strong local search ability of POA. MDP-POA first takes the global optimal trajectory of MDP in a low discrete degree as the initial trajectory of the POA, and then implements further optimization to the obtained initial trajectory by the POA with a high discrete degree, so as to avoid the “curse of dimensionality” of MDP in high discrete degree and the dependency of the POA for the initial trajectory. The other is an improved MDP (IMDP), which first constructs a corridor by the optimal trajectory of MDP in a lower discrete degree, and then implements further optimization in the corridor by MDP with a relatively high discrete degree, so as to avoid a large number of unnecessary calculations, and shorten the run-time effectively. In a case study, the results of MDP-POA, IMDP, and MDP are compared and analyzed from the aspects of power generation and run-time. The analysis indicates that the proposed MDP-POA and IMDP both have a good application effect and are worthy of further promotion.

A new improved shuffled frog leaping algorithm (SFLA), the chaos catfish effect SFLA (CCESFLA) is proposed by coupling a local ‘refine search’ mechanism and a ‘global incentive adjustment’ mechanism. Chaotic technology is introduced in the local ‘refine search’ mechanism to improve local search ability, by implementing more refined local search around the optimal individuals. The catfish effect mechanism is adopted in the ‘global incentive adjustment’ mechanism to improve global convergence, by motivating the frogs to ‘jump out’ of the local steady state. The operation optimization by the CCESFLA is carried out taking the Li Xianjiang cascade reservoirs in China as an example. Compared with SFLA, Particle Swarm Optimization, immune SFLA and cloud SFLA, the average annual power generation of the CCESFLA can be increased by 6.7%, 7.5%, 3.0% and 0.8%, respectively. The convergence process of the CCESFLA is more stable, and its execution time is the least in the three improved SFLAs.

In flood control scheduling of reservoirs, the usual conventional scheduling fails to obtain the optimal solution to the problem. The dynamic programming method is applied to the field. However, the problem of ‘dimensional disaster’ restricts its application. To solve these problems, the improved DP is constructed according to the principle of period inflow and water balance, which includes a variable dispersion mechanism of the retraction space and a calculation method for the smallest discrete points of each discrete range. Taking Dongjiang Reservoir as the research object, based on the maximum peak clipping criterion and the maximum flood control safety guarantee criterion, the improved DP is used for optimization. The results find that when the former criterion is used for scheduling, the discharge flow processes of the two floods are more uniform than conventional scheduling. When the latter criterion is used for scheduling, the minimum water levels of the two floods are lower than the conventional scheduling. As such, it is found that in the two flood dispatches, the calculation time after the first flood DP dimensionality reduction processing is reduced by about 65%, and the second flood is reduced by about 59%, which greatly improves the calculation efficiency of the DP.