A 1D linearization–based MILP–NLP method for short-term hydrothermal operation

Abstract

Abstract The scheduling and optimization of short-term hydrothermal operation utilize the water resources efficiently and magnify the benefits of hydro energy while reducing the fuel and operation cost of thermal power plants. Short-term hydrothermal operation is a complex non-linear and non-convex optimization problem, which reflects thermal valve point effects, load balances, generation bounds and water transport delay. It is highly important and significant to figure out these problems to maximize the benefits of hydrothermal joint operation for energy saving, emission reduction and efficient resources management. This article presents a 1D linearization technique to deal with the non-linear function of two variables, a combined mixed-integer linear programming (MILP) and non-linear programming (NLP) method to solve this problem. The applied method introduces integer variables to linearize the original model into a MILP one; the solution by MILP is then corrected and re-optimized to formulate a feasible solution to warm start NLP. The 1D linearization method is efficient for MILP as it avoids the coupled relationships of integer variables, and the NLP local search is used to correct the influence of linearization errors and search further. The case studies show that the result derived by the 1D linearization–based MILP–NLP method is superior to that of previous works. The method is tested with different linearization accuracy, and the results demonstrate that the method is stable. The presented technique is promising for real-world hydrothermal operation and related problems.