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Towards Constant Calculation in Disjunctive Inequalities Using Wound Treatment Optimization

2019 , Ponce, Hiram , Marmolejo Saucedo, José Antonio , Martinez-Villaseñor, Lourdes

When using the mixed-integer programming to model situations where the limit of the variables follows a box constraint, we find nonlinear problems. To solve this, linearization techniques of these disjunctive inequality constraints are typically used, including constants associated to the variable bounds called M-constants or big-M. Calculation of these constants is an open problem since their values affect the reliability of the optimal solution and convergence of the optimization algorithm. To solve this problem, this work proposes a new population-based metaheuristic optimization method, namely wound treatment optimization (WTO) for calculating the M-constant in a typical domain known as the fixed-charge transportation problem. WTO is inspired on the social wound treatment present in ants after raids. This method allows population diversity that allows to find near-optimal solutions. Experiments of the WTO method on the fixed-charge transportation problem validated its performance and efficiency to find tighten solutions of the M-constant that minimizes the objective function of the problem. © Springer Nature Switzerland AG 2019.

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A Genetic Algorithm to Solve Power System Expansion Planning with Renewable Energy

2018 , Martinez-Villaseñor, Lourdes , Ponce, Hiram , Marmolejo Saucedo, José Antonio , Ramírez, Juan Manuel , Hernández, Agustina

In this paper, a deterministic dynamic mixed-integer programming model for solving the generation and transmission expansion-planning problem is addressed. The proposed model integrates conventional generation with renewable energy sources and it is based on a centralized planned transmission expansion. Due a growing demand over time, it is necessary to generate expansion plans that can meet the future requirements of energy systems. Nowadays, in most systems a public entity develops both the short and long of electricity-grid expansion planning and mainly deterministic methods are employed. In this study, an heuristic optimization approach based on genetic algorithms is presented. Numerical results show the performance of the proposed algorithm. © 2018, Springer Nature Switzerland AG.

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Analysis of Constraint-Handling in Metaheuristic Approaches for the Generation and Transmission Expansion Planning Problem with Renewable Energy

2018 , Martinez-Villaseñor, Lourdes , Ponce, Hiram , Ramírez, Juan Manuel , Marmolejo Saucedo, José Antonio , Hernández, Agustina

A multiperiod generation and transmission expansion planning (G&TEP) problem is considered. This model integrates conventional generation with renewable energy sources, assuming a stochastic approach. The proposed approach is based on a centralized planned transmission expansion. Due to the worldwide recent energy guidelines, it is necessary to generate expansion plans adequate to the forecast demand over the next years. Nowadays, in most energy systems, a public entity develops both the short and long of electricity-grid expansion planning. Due to the complexity of the problem, there are different strategies to find expansion plans that satisfy the uncertainty conditions addressed. We proposed to address the G&TEP problem with a pure genetic algorithm approach. Different constraint-handling techniques were applied to deal with two complex case studies presented. Numerical results are shown to compare the strategies used in the test systems, and key factors such as a prior initialization of population and the estimated minimum number of generations are discussed. ©2018, Wiley/Hindawi.