This paper is concerned with tuning the Stochastic Dual Dynamic Programming algorithm to make it more computationally efficient. We report the results of some computational experiments on a largescale hydrothermal scheduling model developed for Brazil. We find that the best improvements in computation time are obtained from an implementation that increases the number of scenarios in the forward...

Short term hydrothermal scheduling (STHTS) is a very complex, dynamic large-scale non-linear optimization problem. There are many algorithms and powerful optimization methods used to address this issue. Evolutionary algorithms have been effectively employed to obtain a global optimized solution of non linear problems like STHTS. Particle Swarm Optimization (PSO) is an evolutionary method. It ca...

The objective of this work is to investigate market power issues in bid-based hydrothermal scheduling. Initially, market power was simulated with a single stage Cournot–Nash equilibrium model. In this static model the equilibrium was calculated analytically. It was shown that the total production of N strategic agents is smaller than the least-cost solution by a factor of (N/(N +1)). Market pow...

Particle swarm optimization (PSO) has received increasing attention in solving multi-objective economic dispatch (ED) problems in power systems because of parallel computation, faster convergence, and easier implementation. This paper presents a detailed overview of multi-objective particle swarm optimization (MOPSO) and provides a comprehensive survey on its applications in power system econom...

The key criteria of the short-term hydrothermal scheduling (StHS) problem is to minimize gross fuel cost for electricity production by power generators considering constraints related balance; release water, and storage limitations reservoir, operating thermal hydropower plants. For addressing same problem, numerous algorithms were being used, studies exist in literature; however, they possess ...