Comparison of emerging metaheuristic algorithms for optimal hydrothermal system operation

Abstract Optimal hydrothermal system operation (OHSO) is one of the complex and hard-to-solve problems in power system field due to its nonlinear, dynamic, stochastic, non-separable and non-convex nature. Traditionally, this problem has been solved through classical optimization algorithms, which require some approximations to tackle a more tractable variant of the original problem formulation. Metaheuristic optimization has undergone a significant development in recent years, thus, there is a variety of tools with different conceptual differences, which offer a great potential for solving OHSO without extensive simplifications. This paper provides a comparative study on the application of six emerging metaheuristic algorithms to OHSO, namely, the Comprehensive Learning Particle Swarm Optimizer (CLPSO), Genetic algorithm with Multi-Parent Crossover (GA-MPC), Differential Evolution with Adaptive Crossover Operator (DE-ACO), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Linearized Biogeography-based Optimization (LBBO), and the Hybrid Median-Variance Mapping Optimization (MVMO-SH). Since these tools have been successfully applied to other hard-to-solve optimization problems, the goal is to ascertain their effectiveness when adapted to tackle the OHSO problem by evaluating their performance in terms of convergence speed, achieved optimum solutions, and computing effort. Numerical experiments, performed on a test system composed by four cascaded hydro plants and an equivalent thermal plant, highlight the relevance of the adopted global search mechanisms, especially for LBBO and MVMO-SH. A nonlinear programming (NLP) algorithm is used as reference to validate the results.

[1]  Nikolaus Hansen,et al.  Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[2]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[3]  Nikolaus Hansen,et al.  The CMA Evolution Strategy: A Tutorial , 2016, ArXiv.

[4]  C. Lyra,et al.  Optimal Generation Scheduling of Hydrothermal Power Systems , 1980, IEEE Transactions on Power Apparatus and Systems.

[5]  István Erlich,et al.  Hybrid Mean-Variance Mapping Optimization for solving the IEEE-CEC 2013 competition problems , 2013, 2013 IEEE Congress on Evolutionary Computation.

[6]  Xavier Blasco Ferragud,et al.  Hybrid DE algorithm with adaptive crossover operator for solving real-world numerical optimization problems , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[7]  Dan Simon,et al.  Biogeography-Based Optimization , 2022 .

[8]  Mohamed A. El-Sharkawi,et al.  Modern heuristic optimization techniques :: theory and applications to power systems , 2008 .

[9]  István Erlich,et al.  A Mean-Variance Optimization algorithm , 2010, IEEE Congress on Evolutionary Computation.

[10]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[11]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[12]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[13]  I. A. Farhat,et al.  Optimization methods applied for solving the short-term hydrothermal coordination problem , 2009 .

[14]  John W. Labadie,et al.  Optimal Operation of Multireservoir Systems: State-of-the-Art Review , 2004 .

[15]  Dan Simon,et al.  Linearized biogeography-based optimization with re-initialization and local search , 2014, Inf. Sci..

[16]  Ruhul A. Sarker,et al.  GA with a new multi-parent crossover for solving IEEE-CEC2011 competition problems , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[17]  Yongqiang Wang,et al.  An improved self-adaptive PSO technique for short-term hydrothermal scheduling , 2012, Expert Syst. Appl..

[18]  Hans-Paul Schwefel,et al.  Evolution strategies – A comprehensive introduction , 2002, Natural Computing.