A Novel Energy Management Scheme using ANFIS for Independent Microgrid

In this paper an energy management technique for Hybrid Renewable Energy System (HRES) connected with AC load using Adaptive Neuro Fuzzy Interference System (ANFIS) is proposed. This algorithm is developed with an aim of increasing the power transfer capability between the source side and load side and it offers several benefits like the enhanced predicting capability, degradation in complexity as well as the randomization and so forth. In this work, photovoltaic (PV) system, Wind Generating System (WGS), Fuel Cell (FC), Ultra Capacitor (UC) and the battery are considered as the energy sources. The ANFIS technique is trained with the inputs such as the previous instant energy of the available sources and the required load demand of the current time and the corresponding target reference power of the sources. According to the load variation, the proposed method makes the appropriate control signals at the testing time to manage the energy of the HRES. A STATCOM based voltage regulation and harmonic mitigation is introduced. The implementation of the system elements and control method has been done in MATLAB/Simulink and the performance of the proposed method is analysed by using different environmental and load test conditions. The results of the test cases confirms that the control technique proposed is effectual in prediction of energy required for the next instant and manages the energy flow among HRES power sources and energy storage devices. In this paper an energy management technique for Hybrid Renewable Energy System (HRES) connected with AC load using Adaptive Neuro Fuzzy Interference System (ANFIS) is proposed. This algorithm is developed with an aim of increasing the power transfer capability between the source side and load side and it offers several benefits like the enhanced predicting capability, degradation in complexity as well as the randomization and so forth. In this work, photovoltaic (PV) system, Wind Generating System (WGS), Fuel Cell (FC), Ultra Capacitor (UC) and the battery are considered as the energy sources. The ANFIS technique is trained with the inputs such as the previous instant energy of the available sources and the required load demand of the current time and the corresponding target reference power of the sources. According to the load variation, the proposed method makes the appropriate control signals at the testing time to manage the energy of the HRES. A STATCOM based voltage regulation and harmonic mitigation is introduced. The implementation of the system elements and control method has been done in MATLAB/Simulink and the performance of the proposed method is analysed by using different environmental and load test conditions. The results of the test cases confirms that the control technique proposed is effectual in prediction of energy required for the next instant and manages the energy flow among HRES power sources and energy storage devices.

[1]  Sunil Kumar Sinha,et al.  Load Frequency Control of Multi-area Power Systems Using Neuro-Fuzzy Hybrid Intelligent Controllers , 2015 .

[2]  Yu-Kai Chen,et al.  Design and Implementation of Energy Management System With Fuzzy Control for DC Microgrid Systems , 2013, IEEE Transactions on Power Electronics.

[3]  Prasenjit Basak,et al.  A literature review on integration of distributed energy resources in the perspective of control, protection and stability of microgrid , 2012 .

[4]  Whei-Min Lin,et al.  Intelligent approach to maximum power point tracking control strategy for variable-speed wind turbine generation system , 2010 .

[5]  Yu Zhang,et al.  Robust Energy Management for Microgrids With High-Penetration Renewables , 2012, IEEE Transactions on Sustainable Energy.

[6]  M. Omizo,et al.  Modeling , 1983, Encyclopedic Dictionary of Archaeology.

[7]  P. P. J. van den Bosch,et al.  Energy Management for the Electric Powernet in Vehicles With a Conventional Drivetrain , 2007, IEEE Transactions on Control Systems Technology.

[8]  Taher Niknam,et al.  Probabilistic energy and operation management of a microgrid containing wind/photovoltaic/fuel cell generation and energy storage devices based on point estimate method and self-adaptive gravitational search algorithm , 2012 .

[9]  Prabodh Bajpai,et al.  Hybrid renewable energy systems for power generation in stand-alone applications: A review , 2012 .

[10]  Francisco Jurado,et al.  Power control based on particle swarm optimization of grid-connected inverter for hybrid renewable energy system , 2015 .

[11]  Abdulaziz Mohamed Saeed Aldobhani,et al.  Maximum power point tracking of PV system using ANFIS prediction and fuzzy logic tracking , 2008 .

[12]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[13]  Ashwani Kumar,et al.  Reactive power compensation of an isolated hybrid power system with load interaction using ANFIS tuned STATCOM , 2014 .

[14]  Recep Yumurtaci.  Role of energy management in hybrid renewable energy systems: case study-based analysis considering varying seasonal conditions , 2013 .

[16]  Mehmet Uzunoglu,et al.  Modeling, control and simulation of an autonomous wind turbine/photovoltaic/fuel cell/ultra-capacitor hybrid power system , 2008 .

[17]  Chih-Ming Hong,et al.  Intelligent speed sensorless maximum power point tracking control for wind generation system , 2012 .

[18]  Djamel Boukhetala,et al.  A local energy management of a hybrid PV-storage based distributed generation for microgrids , 2015 .

[19]  Di Lu,et al.  Application of Petri nets for the energy management of a photovoltaic based power station including storage units , 2010 .

[20]  Bruno Francois,et al.  Power Control Design of a Battery Charger in a Hybrid Active PV Generator for Load-Following Applications , 2011, IEEE Transactions on Industrial Electronics.

[21]  Kuo-Hao Chang,et al.  Optimal design of hybrid renewable energy systems using simulation optimization , 2015, Simul. Model. Pract. Theory.

[22]  Osama A. Mohammed,et al.  Real-time energy management scheme for hybrid renewable energy systems in smart grid applications , 2013 .

[23]  Zaijun Wu,et al.  Modeling, planning and optimal energy management of combined cooling, heating and power microgrid: A review , 2014 .

[24]  Luis M. Fernández,et al.  Sizing optimization, dynamic modeling and energy management strategies of a stand-alone PV/hydrogen/battery-based hybrid system , 2013 .

[25]  Alhussein Albarbar,et al.  Hybrid Power Systems Energy Controller Based on Neural Network and Fuzzy Logic , 2013 .

[26]  Sarsing Gao,et al.  Power quality analysis of hybrid renewable energy system , 2015 .

[27]  Chitra Natesan,et al.  Survey on Microgrid: Power Quality Improvement Techniques , 2014 .

[28]  Ozan Erdinc,et al.  Optimum design of hybrid renewable energy systems: Overview of different approaches , 2012 .

[29]  Rodrigo Palma-Behnke,et al.  A Microgrid Energy Management System Based on the Rolling Horizon Strategy , 2013, IEEE Transactions on Smart Grid.

[30]  Erkan Dursun,et al.  Comparative evaluation of different power management strategies of a stand-alone PV/Wind/PEMFC hybrid power system , 2012 .

[31]  Mohammed Yekini Suberu,et al.  Renewable energy distributed electricity generation and microgrid implementation in rural villages: A review , 2013 .

[32]  Riccardo Minciardi,et al.  Modeling and optimization of a hybrid system for the energy supply of a “Green” building , 2012 .

[33]  Mohd Wazir Mustafa,et al.  Power Quality Improvement in Autonomous Microgrids Using Multi-functional Voltage Source Inverters: A Comprehensive Review , 2015 .

[34]  K. Strunz,et al.  A review of hybrid renewable/alternative energy systems for electric power generation: Configurations, control and applications , 2011, 2012 IEEE Power and Energy Society General Meeting.

[35]  Luis M. Fernández,et al.  ANFIS-Based Control of a Grid-Connected Hybrid System Integrating Renewable Energies, Hydrogen and Batteries , 2014, IEEE Transactions on Industrial Informatics.

[36]  Suryanarayana Doolla,et al.  Multiagent-Based Distributed-Energy-Resource Management for Intelligent Microgrids , 2013, IEEE Transactions on Industrial Electronics.