A dynamic power management strategy of a grid connected hybrid generation system using wind, photovoltaic and Flywheel Energy Storage System in residential applications

A global supervisory strategy for a micro-grid power generation system that comprises wind and photovoltaic generation subsystems, a flywheel storage system, and domestic loads connected both to the hybrid power generators and to the grid, is developed in this paper. The objectives of the supervisor control are, firstly, to satisfy in most cases the load power demand and, secondly, to check storage and grid constraints to prevent blackout, to reduce energy costs and greenhouse gas emissions, and to extend the life of the flywheel. For these purposes, the supervisor determines online the operation mode of the different generation subsystems, switching from maximum power conversion to power regulation. Decision criteria for the supervisor based on actual variables are presented. Finally, the performance of the supervisor is extensively assessed through computer simulation using a comprehensive nonlinear model of the studied system.

[1]  Abderrazak Ouali,et al.  A fuzzy logic supervisor for active and reactive power control of a variable speed wind energy conversion system associated to a flywheel storage system , 2009 .

[2]  Tomonobu Senjyu,et al.  Gain scheduling control of variable speed WTG under widely varying turbulence loading , 2007 .

[3]  P. Ferrao,et al.  The impact of demand side management strategies in the penetration of renewable electricity , 2012 .

[4]  Caisheng Wang,et al.  Power Management of a Stand-Alone Wind/Photovoltaic/Fuel Cell Energy System , 2008, IEEE Transactions on Energy Conversion.

[5]  Abderrazak Ouali,et al.  A comparative study of three different sensorless vector control strategies for a Flywheel Energy Storage System , 2010 .

[6]  Belgin Emre Turkay,et al.  Economic analysis of standalone and grid connected hybrid energy systems , 2011 .

[7]  Anastasios I. Dounis,et al.  Intelligent demand side energy management system for autonomous polygeneration microgrids , 2013 .

[8]  Abdel-Karim Daud,et al.  Design of isolated hybrid systems minimizing costs and pollutant emissions , 2012 .

[9]  Mohamed Abid,et al.  A Fuzzy-PI control to extract an optimal power from wind turbine , 2013 .

[10]  Enrico Zio,et al.  Reinforcement learning for microgrid energy management , 2013 .

[11]  Tsutomu Hoshino,et al.  Maximum photovoltaic power tracking: an algorithm for rapidly changing atmospheric conditions , 1995 .

[12]  Lotfi Krichen,et al.  Experimental investigation on the performance of an autonomous wind energy conversion system , 2013 .

[13]  Z. Vale,et al.  Demand response in electrical energy supply: An optimal real time pricing approach , 2011 .

[14]  Demetrios P. Papadopoulos,et al.  Efficient design and simulation of an expandable hybrid (wind–photovoltaic) power system with MPPT and inverter input voltage regulation features in compliance with electric grid requirements , 2009 .

[15]  Jacob Brouwer,et al.  Dynamic modeling of hybrid energy storage systems coupled to photovoltaic generation in residential applications , 2007 .

[16]  Johan Enslin,et al.  Simplified maximum power point controller for PV installations , 1993, Conference Record of the Twenty Third IEEE Photovoltaic Specialists Conference - 1993 (Cat. No.93CH3283-9).

[17]  Andreas Sumper,et al.  Experimental evaluation of a real time energy management system for stand-alone microgrids in day-ahead markets , 2013 .

[18]  Seth Blumsack,et al.  Ready or not, here comes the smart grid! , 2012 .

[19]  Abdollah Kavousi-Fard,et al.  Considering uncertainty in the optimal energy management of renewable micro-grids including storage devices , 2013 .

[20]  Pravin Varaiya,et al.  Smart Operation of Smart Grid: Risk-Limiting Dispatch , 2011, Proceedings of the IEEE.

[21]  Manuel Welsch,et al.  Modelling elements of Smart Grids – Enhancing the OSeMOSYS (Open Source Energy Modelling System) code , 2012 .

[22]  Roberto Cárdenas,et al.  Control strategies for power smoothing using a flywheel driven by a sensorless vector-controlled induction machine operating in a wide speed range , 2004, IEEE Transactions on Industrial Electronics.

[23]  Andreas Sumper,et al.  Energy management of flywheel-based energy storage device for wind power smoothing , 2013 .

[24]  Amin Hajizadeh,et al.  Intelligent power management strategy of hybrid distributed generation system , 2007 .

[25]  M.R. Iravani,et al.  Hybrid Control of a Grid-Interactive Wind Energy Conversion System , 2008, IEEE Transactions on Energy Conversion.

[26]  Abdulkerim Karabiber,et al.  A user-mode distributed energy management architecture for smart grid applications , 2012 .

[27]  Guangyi Cao,et al.  Dynamic modeling and sizing optimization of stand-alone photovoltaic power systems using hybrid energy storage technology , 2009 .

[28]  Kenji Kobayashi,et al.  A study on a two stage maximum power point tracking control of a photovoltaic system under partially shaded insolation conditions , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[29]  Gaetano Zizzo,et al.  Multi-objective optimized management of electrical energy storage systems in an islanded network with renewable energy sources under different design scenarios , 2014 .

[30]  Gianfranco Rizzo,et al.  The interaction between intermittent renewable energy and the electricity, heating and transport sectors. , 2012 .

[31]  Brian Norton,et al.  Optimising the economic viability of grid-connected photovoltaic systems , 2009 .

[32]  Henrik Lund,et al.  The implementation of renewable energy systems. Lessons learned from the Danish case , 2010 .

[33]  Shin-Yeu Lin,et al.  Distributed optimal power flow for smart grid transmission system with renewable energy sources , 2013 .

[34]  Lotfi Krichen,et al.  Electric power generation based on variable speed wind turbine under load disturbance , 2011 .

[35]  Weidong Xiao,et al.  A novel modeling method for photovoltaic cells , 2004, 2004 IEEE 35th Annual Power Electronics Specialists Conference (IEEE Cat. No.04CH37551).

[36]  Meysam Doostizadeh,et al.  A day-ahead electricity pricing model based on smart metering and demand-side management , 2012 .

[37]  Pierluigi Siano,et al.  Exploiting maximum energy from variable speed wind power generation systems by using an adaptive Takagi-Sugeno-Kang fuzzy model , 2009 .

[38]  Ran Dai,et al.  Optimal power generation and load management for off-grid hybrid power systems with renewable sources via mixed-integer programming , 2013 .

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

[40]  M. P. Moghaddam,et al.  Optimal real time pricing in an agent-based retail market using a comprehensive demand response model , 2011 .

[41]  Lars Norum,et al.  Design and implementation of a digitally controlled stand-alone photovoltaic power supply , 2002 .

[42]  Adriano Carvalho,et al.  Controllable hybrid power system based on renewable energy sources for modern electrical grids , 2013 .

[43]  F. Valenciaga,et al.  Supervisor control for a stand-alone hybrid generation system using wind and photovoltaic energy , 2005, IEEE Transactions on Energy Conversion.

[44]  Chrysovalantou Ziogou,et al.  Optimal production of renewable hydrogen based on an efficient energy management strategy , 2013 .

[45]  Lorraine Whitmarsh,et al.  The development of smart homes market in the UK , 2013 .

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

[47]  Celal Yaşar,et al.  A new hybrid approach for nonconvex economic dispatch problem with valve-point effect , 2011 .