Metaheuristic Approaches for Hydropower System Scheduling

Abstract

This paper deals with the short-term scheduling problem of hydropower systems. The objective is to meet the daily energy demand in an economic and safe way. The individuality of the generating units and the nonlinearity of their efficiency curves are taken into account. The mathematical model is formulated as a dynamic, mixed integer, nonlinear, nonconvex, combinatorial, and multiobjective optimization problem. We propose two solution methods using metaheuristic approaches. They combine Genetic Algorithm with Strength Pareto Evolutionary Algorithm and Ant Colony Optimization. Both approaches are divided into two phases. In the first one, to maximize the plant’s net generation, the problem is solved for each hour of the day (static dispatch). In the second phase, to minimize the units’ switching on-off, the day is considered as a whole (dynamic dispatch). The proposed methodology is applied to two Brazilian hydroelectric plants, in cascade, that belong to the national interconnected system. The nondominated solutions from both approaches are presented. All of them meet demand respecting the physical, electrical, and hydraulic constraints.

Assessment and implementation of evolutionary algorithms for optimal management rules design in water resources systems

“…Elferchichi et al (2009) also applied a real-coded EA to optimize the operation of reservoirs for an on-demand irrigation system, and this was later applied to the Sinista Ofanto irrigation scheme (Foggia, Italy). In flood problems, EAs have been applied to optimize a flood control reservoir (Chang and Chen, 1998), also for real-time flood management in river-reservoir systems (Malekmohammadi et al, 2010), or to obtain a daily energy demand in an economic and safe way (Hidalgo et al, 2015).…”

“…An alternative way to conduct a search in a diverse range of optimization problems is successfully provided by genetic algorithms, even in a complex system and in the absence of domain knowledge (Chang and Chen, 1998). In the same field, evolutionary algorithms (EAs) have been applied to solve an optimal multi-objective dispatch of hydroelectric generating units (Villasanti et al, 2004), to optimize a flood control reservoir (Chang and Chen, 1998), to obtain a daily energy demand in an economic and safe way (Hidalgo et al, 2015) or for real-time flood management in river-reservoir system (Malekmohammadi et al, 2010). EAs are based on the theory of evolution making a population of individuals evolves by subjecting it to similar processes than the biological evolution (crosses and mutations).…”