Multi-objective Optimization Using Two-stage EMO for Renewable Energy Management in Medical Facility

In this paper, a method of EMO to be applied for the energy management of the power system in which the renewable energy is introduced in a large-scale medical facility is proposed. The subject of the case study is a multi-objective optimization problem that seeks the optimal configuration and operation method of EG. The multiple objectives of minimizing the fuel consumption in emergency and minimizing the total power cost in normal time by means of peak cut in the power supply system combining with PV and EG are required to be simultaneously satisfied. The approach for this problem by EMO is a new attempt. It has been considered that it is difficult to obtain a highly accurate solution set by using the simple EMO alone, and it is confirmed also in our preliminary experiments. Therefore, in this paper, a two-stage EMO combining the multi-start with the local search function has been proposed. The proposed method is applied into case studies to evaluate, and the effectiveness is demonstrated.

[1]  Markus Schatten,et al.  A Case Study on Renewable Energy Management in an Eco-Village Community in Croatia – An Agent Based Approach , 2016, International Journal of Renewable Energy Research.

[2]  Hisao Ishibuchi,et al.  Recombination of Similar Parents in EMO Algorithms , 2005, EMO.

[3]  Hamdy. A. Ashour,et al.  Distribution network reconfiguration in smart grid system using modified particle swarm optimization , 2016, 2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA).

[4]  Fujio Kurokawa,et al.  GA Based Optimization for Configuration and Operation of Emergency Generators in Medical Facility Using Renewable Energy , 2018, International Journal of Renewable Energy Research.

[5]  Hisao Ishibuchi,et al.  A multi-objective genetic local search algorithm and its application to flowshop scheduling , 1998, IEEE Trans. Syst. Man Cybern. Part C.

[6]  Fujio Kurokawa,et al.  A new approach of optimum energy scheduling of emergency generators using linear programing in a large hospital , 2016, 2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA).

[7]  M. Christian G. Quintero,et al.  Hybrid renewable energy system based on intelligent optimization techniques , 2016, 2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA).

[8]  Shigenobu Kobayashi,et al.  Hybridization of genetic algorithm and local search in multiobjective function optimization: recommendation of GA then LS , 2006, GECCO '06.

[9]  Fujio Kurokawa,et al.  Comparison of optimum energy scheduling of emergency generators of a large hospital with renewable energy system using mathematical programming method , 2017, 2017 IEEE 6th International Conference on Renewable Energy Research and Applications (ICRERA).

[10]  Myeong Jin Ko,et al.  Multi-Objective Optimization Design for a Hybrid Energy System Using the Genetic Algorithm , 2015 .

[11]  Fujio Kurokawa,et al.  Genetic algorithm based optimization for configuration and operation of emergency generators in medical facility , 2017, 2017 IEEE 6th International Conference on Renewable Energy Research and Applications (ICRERA).

[12]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[13]  Tomoyuki Hiroyasu,et al.  SPEA2+: Improving the Performance of the Strength Pareto Evolutionary Algorithm 2 , 2004, PPSN.

[14]  Hossein Shayeghi,et al.  Feasibility and Optimal Reliable Design of Renewable Hybrid Energy System for Rural Electrification in Iran , 2012 .

[15]  Fujio Kurokawa,et al.  Estimation of optimum capacity of battery by combined use of a renewable energy system and distributed emergency generators in a large hospital , 2017, 2017 IEEE 6th International Conference on Renewable Energy Research and Applications (ICRERA).

[16]  Bogaraj Thirumalaisamy,et al.  A Novel Energy Management Scheme using ANFIS for Independent Microgrid , 2016 .

[17]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[18]  Alok Kumar,et al.  Techno-Economic Optimization of a Stand-alone PV/PHS/Battery systems for very low load situation , 2017, International Journal of Renewable Energy Research.

[19]  Hisao Ishibuchi,et al.  Some Issues on the Implementation of Local Search in Evolutionary Multiobjective Optimization , 2004, GECCO.

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

[21]  R. Jegatheesan,et al.  Energy Management of a Microgrid Using Multi Objective Genetic Algorithm , 2017 .

[22]  Ridha Bouallegue,et al.  An Agent Based Fuzzy Control for Smart Home Energy Management in Smart Grid Environment , 2017 .