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Solving Smart Grid Operation Problems Through Variable Neighborhood Search Macedo, São Paulo State University-Ilha Solteira, Brazil Zita Vale, Institute of Engineering – Polytechnic of Porto, Portugal Sergio Rivera, Universidad Nacional de Colombia, Colombia Presentations given in the panel session at the 2017 IEEE PES General meeting: The problem definitions, implementation & submission guidelines, and Matlab codes for the test beds can be downloaded here. Lee, Baylor University, USA call for competition can be downloaded her e. István Erlich ,University of Duisburg-Essen, Germany 2: Prof. José Rueda, Delft University of Technology, Netherlands 1: Prof. The details of the evaluation process and the order of the top 3 algorithms for each test bed was announced in the 2017 IEEE PES General Meeting.Ĭhairman: Dr. María Guzmán, Universidad Nacional de Colombia (Colombia)

Sergio Contreras, Universidad Nacional de Colombia (Colombia) Sergio Rivera, Universidad Nacional de Colombia (Colombia)Ĭamilo Cortes, Universidad Nacional de Colombia (Colombia) Modified Chaotic Biogeography-based Optimisation (CBBO) with Random Sinusoidal Migration Test bed 2: Optimal scheduling of distributed energy resources Levy Differential Evolutionary Particle Swarm Optimization (LEVY DEEPSO) Ortega-Vazquez, University of Washington (USA) Rubén Romero, São Paulo State University-Ilha Solteira (Brazil) Franco, São Paulo State University-Ilha Solteira (Brazil) Macedo, São Paulo State University-Ilha Solteira (Brazil)

Variable Neighborhood Search algorithm (VNS) Vladimiro Miranda, INESC TEC and Faculty of Engineering of the University of Porto – FEUP, (Portugal)Īrmando Leite da Silva, Pontifícia Universidade Católica do Rio de Janeiro (Brazil)Ĭarolina Marcelino, Centro Federal de Educação de Minas Gerais (Brazil)Įlizabeth Wanner, School of Engineering and Applied Sciences (UK) and Centro Federal de Educação de Minas Gerais (Brazil) Test bed 1: Stochastic OPF based active-reactive power dispatchĬross entropy method and evolutionary particle swarm optimization (CEEPSO) The top three ranked algorithms (sorted lists): The evaluation process finished on 28-4-2017. The deadline for submission of results and codes was 30-03-2017. All interested participants were encouraged to send an email to by 30-01-2017, indicating their names, affiliation, and the algorithm to be used. It was indicated that the first three ranked algorithms will be selected for presentation at the panel, for which only PowerPoint presentations are required. The call for competition was made on 14-01-2017. They are only allowed to improve the methodological framework of their algorithms. Developers: Zita Vale and João Soares (Institute of Engineering – Polytechnic of Porto)īesides, the panel presented the results and a comparative evaluation concerning the performance of different modern heuristic optimization algorithms, which are developed by different researchers worldwide. Researchers are challenged to solve the benchmarks, which are treated as black-box problems.

Test bed 2: Optimal scheduling of distributed energy resources.

Lee (Baylor University), István Erlich (University Duisburg-Essen)

Test bed 1: Stochastic OPF based active-reactive power dispatch. Developers: Sergio Rivera (Universidad Nacional de Colombia), Andres Romero (Universidad Nacional de San Juan), José Rueda (Delft University of Technology), Kwang Y.

This panel and competition, which took place at the 2017 IEEE PES General Meeting, introduced two benchmark problems (also denoted as optimization test beds): non-convexity, discontinuity, multi-modality, high-dimensionality), which emphasizes the need of advanced optimization solvers in order to find optimal solutions that guarantee efficient and flexible operations. In this context, operational problems posse highly complex mathematical properties (e.g. The increasing penetration of renewable energy sources and the new and adaptive patterns of demand side response entail a higher level of variability of the operation of electrical sustainable power systems.

2017 Competition & panel: Evaluating the Performance of Modern Heuristic Optimizers on Smart Grid Operation Problems