Hydropower Optimization Using Split-Window, Meta-Heuristic and Genetic Algorithms

Abstract

In this paper, we try to find the most efficient optimization algorithm that can be used to resolve the hydropower optimization problem. We propose a novel optimization technique is called the Split-window method. The method is relatively simple and reduces the complexity of the optimization problem by split-ting the planning horizon (and datasets) into equal windows and assigning the same values to policies(actions) within each part. After splitting, a meta-heuristic technique is used to optimize the actions, and the dataset is split again until a split contains only one instance (timestep). The unique values to be optimized during each iteration is equal to the number of splits which makes it very fast and requires fewer computations. We also propose a novel initialization method based on ranking of price and assigning a higher value of production and hatch release for higher prices. We apply this initialization technique to most of the algorithms used in this paper. We compare the split-window technique with meta-heuristic methods such as hill climbing, simulated annealing, line search, and genetic algorithms by running simulations on the data collected from a real-world hydropower river system in southern Norway. In total, we benchmark the performance of seven different optimization algorithms for a large number of hydrological and price scenarios. The results show that the Split-window method is able to beat other techniques in terms of performance score, speed of convergence and core algorithmic complexity by a considerable margin.