A Comparative Study of Energy Control Strategies for a Standalone PV/WT/FC Hybrid Renewable System

This paper aims to design and control a standalone hybrid renewable system, comprising PV panels and wind turbines as the main energy sources along with a fuel-cell stack as a support system. In an attempt to improve the stability and security of the hybrid renewable system, a battery bank, a supercapacitor pack, and an electrolyzer are integrated as storage units, due to the intermittent and fluctuant primary energy sources contribution. In this paper several energy management strategies is designed and simulated and their performance is compared. The energy management strategies taken into account for comparative investigation in the addressed hybrid renewable system are the most commonly used ones, as follows: the state machine control, the rule-based fuzzy logic control, the ANFIS-based control strategy, the equivalent consumption minimization strategy (ECMS) and the external energy maximization strategy (EEMS). The Mandeni’s fuzzy interface system is taken into consideration, in the case of the fuzzy logic control. The ANFIS-based control strategy data requirements (training, checking and testing data set) are prepared via the state machine control, which determines the operation of the backup system and the storage units based on the battery state of charge and the energy demand shortage. The main contribution of the state machine control and the rule based fuzzy logic design approach, in addition to the demanded energy provision, is protecting the battery bank against deep discharge and overcharge. Moreover, the ECMS introduces a cost function based energy management strategy that minimizes the hydrogen consumption of the fuel-cell and the equivalent fuel consumption of the battery bank. In the following, the EEMS maximizes the energy of the storage banks, which results in the total fuel consumption minimization. Three scenarios are taken into account: Pulsed and Constant loads, for short term analysis, and Random loads, for long term analysis. The simulation study demonstrates successful operation of the energy management strategies for different initial battery SOCs, which are selected in a way to cover the operation of the controller in three battery SOC rangers. In addition, design requirements such as the hydrogen consumption, the fuel efficiency, and the fuel-cell stack efficiency are evaluated in the case of all the energy control strategies.

[1]  Luis M. Fernández,et al.  Viability study of a FC-battery-SC tramway controlled by equivalent consumption minimization strategy , 2012 .

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

[3]  Hak-Man Kim,et al.  A microgrid energy management system for inducing optimal demand response , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[4]  Kamal Al-Haddad,et al.  A Comparative Study of Energy Management Schemes for a Fuel-Cell Hybrid Emergency Power System of More-Electric Aircraft , 2014, IEEE Transactions on Industrial Electronics.

[5]  P. Seferlis,et al.  Power management strategies for a stand-alone power system using renewable energy sources and hydrogen storage , 2009 .

[6]  P. Siano,et al.  Combined Operations of Renewable Energy Systems and Responsive Demand in a Smart Grid , 2011, IEEE Transactions on Sustainable Energy.

[7]  Jérôme Bosche,et al.  Power Management Strategy Based on Weather Prediction for Hybrid Stand-alone System☆ , 2015 .

[8]  Rudi Kaiser,et al.  Optimized battery-management system to improve storage lifetime in renewable energy systems , 2007 .

[9]  Mihai Oproescu,et al.  Efficient energy control strategies for a Standalone Renewable/Fuel Cell Hybrid Power Source , 2015 .

[10]  Sandip Deshmukh,et al.  Modeling of hybrid renewable energy systems , 2008 .

[11]  N. Kumarappan,et al.  Autonomous operation and control of photovoltaic/solid oxide fuel cell/battery energy storage based microgrid using fuzzy logic controller , 2016 .

[12]  Temitope Raphael Ayodele,et al.  Optimal allocation and sizing of PV/Wind/Split-diesel/Battery hybrid energy system for minimizing life cycle cost, carbon emission and dump energy of remote residential building , 2016 .

[13]  Navneet Walia,et al.  ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey , 2015 .

[14]  Suk Won Cha,et al.  Optimal control in the power management of fuel cell hybrid vehicles , 2012 .

[15]  Rachid Chenni,et al.  Design and control of a stand-alone hybrid power system , 2016 .

[16]  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.

[17]  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.

[18]  Maria Erman,et al.  ANFIS based opportunistic power control for cognitive radio in spectrum sharing , 2014, 2013 International Conference on Electrical Information and Communication Technology (EICT).

[19]  Enrico Zio,et al.  A model predictive control framework for reliable microgrid energy management , 2014 .

[20]  Dirk Söffker,et al.  Review and Comparison of Power Management Approaches for Hybrid Vehicles with Focus on Hydraulic Drives , 2014 .

[21]  Xiaorong Xie,et al.  Distributed Optimal Energy Management in Microgrids , 2015, IEEE Transactions on Smart Grid.