Hierarchical genetic optimization of a fuzzy logic system for energy flows management in microgrids |
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| Affiliation: | 1. Department of Information Engineering, Electronics, and Telecommunications, SAPIENZA University of Rome, Via Eudossiana 18, 00184 Rome, Italy;2. Department of Computer Science, Ryerson University, 350 Victoria Street, Toronto, ON M5B 2K3, Canada;1. Department of Applied Mathematics, Chung Yuan Christian University, Chung-Li 32023, Taiwan;2. Center for Teacher Education, National Tsing Hua University, Hsin-Chu, Taiwan;1. Department of Mathematics, Harbin Institute of Technology, Harbin 150001, PR China;2. School of Mathematics and Statistics, Chongqing University of Technology, Chongqing 400054, PR China;1. University of Caen Basse Normandie, LUSAC Laboratory, Caen, France;2. Electrical and Computer Engineering, Florida International University FIU, USA;3. Faculty of Engineering, Helwan University, Egypt;1. Department of Electrical and Electronic Engineering, Public University of Navarre (UPNa), Edificio de los Pinos, Campus Arrosadía s/n, 31006 Pamplona, Spain;2. Ingeteam Power Technology S.A., Avda. Ciudad de la Innovación 13, 31621 Sarriguren, Spain;1. Department of Electrical Engineering, Indian Institute of Technology Jodhpur, Jodhpur 342037, India;2. Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi 110016, India;3. Department of Electrical Engineering, ZHCET, Aligarh Muslim University, Aligarh 202002, India |
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| Abstract: | Bio-inspired algorithms like Genetic Algorithms and Fuzzy Inference Systems (FIS) are nowadays widely adopted as hybrid techniques in improving goods and services. In this paper we present an interesting application of the fuzzy-GA paradigm to the problem of energy flows management in microgrids, concerning the design, through a data driven synthesis procedure, of an Energy Management System (EMS). The main aim consists in performing decision making for power flow management tasks in the proposed microgrid model, equipped by renewable sources and an energy storage system, aiming to maximize the accounting profit in energy trading with the main-grid. In particular this study focuses on the application of a Hierarchical Genetic Algorithm (HGA) for tuning the Rule Base (RB) of a Fuzzy Inference System (FIS), trying to discover a minimal fuzzy rules set as the core inference engine of an an EMS. The HGA rationale focuses on a particular encoding scheme, based on control genes and parametric genes, applied to the optimization of the FIS parameters, allowing to perform a reduction in the structural complexity of the RB. A performance comparison is performed with a simpler approach based on a classic fuzzy-GA scheme, where both FIS parameters and rule weights are tuned, while the number of fuzzy rules is fixed in advance. Experiments shows how the fuzzy-HGA approach adopted for the synthesis of the proposed controller outperforms the classic fuzzy-GA scheme, increasing the accounting profit by 67% in the considered energy trading problem, yielding at the same time a simpler RB. |
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| Keywords: | Microgrid Energy Management System Battery Energy Storage Power flow optimization Storage system management Fuzzy systems Evolutionary computation Hierarchical Genetic Algorithms |
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