Finding Multiple Optimal Solutions to Optimal Load Distribution Problem in Hydropower Plant

Abstract

Optimal load distribution (OLD) among generator units of a hydropower plant is a vital task for hydropower generation scheduling and management. Traditional optimization methods for solving this problem focus on finding a single optimal solution. However, many practical constraints on hydropower plant operation are very difficult, if not impossible, to be modeled, and the optimal solution found by those models might be of limited practical uses. This motivates us to find multiple optimal solutions to the OLD problem, which can provide more flexible choices for decision-making. Based on a special dynamic programming model, we use a modified shortest path algorithm to produce multiple solutions to the problem. It is shown that multiple optimal solutions exist for the case study of China's Geheyan hydropower plant, and they are valuable for assessing the stability of generator units, showing the potential of reducing occurrence times of units across vibration areas.

Using stochastic dual dynamic programming in problems with multiple near‐optimal solutions

“…[] state that the search for MNOS offers more flexibility than that of a single optimum; besides, the search for a single optimum ought to be replaced by that of a limited number of alternatives that can later be compared, possibly using criteria that were not accounted for in the objective function to be optimized [ Loucks and van Beek , ]. For instance, the presence of MNOS in a deterministic hydropower production problem enables the exploration of the solutions to find which are best for the reliability and safety of the production units [ Liu et al ., ]. In this work, a NOS is given by a sequence of Bender's cuts that covers a year, and that lies within a desired range of any other solution, e.g., one percent.…”

Short-Term Optimal Operation of Baluchaung II Hydropower Plant in Myanmar

“…It can be noted that efficiency is higher when the power generation flow is lower. The optimal solution can save over 200 m 3 /s water in some months whereas in some other literature, the optimized solution could save, at the most, 10 m 3 /s [6]. After fitting the curve, the equal micro-increment method was used to calculate the results of optimal power generation flow and dynamic programming was used to find the optimal power generation.…”

“…There are many algorithms for mathematical models to optimize operation with correctly conceptualized theories and formulas [3]. In designing a model, there are two methods used: optimization and simulation [4][5][6]. A simulation model is a descriptive model that tries to express the actual system of operation characteristics by using the complicated interrelations between components.…”

Parallel Multi-Objective Genetic Algorithm for Short-Term Economic Environmental Hydrothermal Scheduling

“…Along with the rapid development of global economy over the past several decades, the power demand has increased continuously, and a large amount of hydro and thermal plants have been successively built to supply sufficient energy [1][2][3]. However, thermal plants inevitably produce emissions of pollutants like sulfur oxide and nitrogen oxide, which gives rise to a series of serious environmental problems and high social economic costs [4][5][6].…”