Stochastic Optimization of Renewable-Based Microgrid Operation Incorporating Battery Operating Cost

Integration of renewable energy resources in microgrids has been increasing in recent decades. Due to the randomness in renewable resources such as solar and wind, the power generated can deviate from forecasted values. This variation may cause increased operating costs for committing costly reserve units or penalty costs for shedding load. In addition, it is often desired to charge/discharge and coordinate the energy storage units in an efficient and economical way. To address these problems, a novel battery operation cost model is proposed which considers a battery as an equivalent fuel-run generator to enable it to be incorporated into a unit commitment problem. A probabilistic constrained approach is used to incorporate the uncertainties of the renewable sources and load demands into the unit commitment (UC) and economic dispatch problems.

[1]  Dick Duffey,et al.  Power Generation , 1932, Transactions of the American Institute of Electrical Engineers.

[2]  Philip G. Hill,et al.  Power generation , 1927, Journal of the A.I.E.E..

[3]  R. Stephenson A and V , 1962, The British journal of ophthalmology.

[4]  Dimitri P. Bertsekas,et al.  Dynamic Programming: Deterministic and Stochastic Models , 1987 .

[5]  H Kiehne,et al.  Battery Technology Handbook , 1989 .

[6]  M. O'Malley,et al.  A new approach to quantify reserve demand in systems with significant installed wind capacity , 2005, IEEE Transactions on Power Systems.

[7]  G. Kariniotakis,et al.  A Stochastic Dynamic Programming Model for Optimal Use of Local Energy Resources in a Market Environment , 2007, 2007 IEEE Lausanne Power Tech.

[8]  Tony Markel,et al.  Improving Petroleum Displacement Potential of PHEVs Using Enhanced Charging Scenarios , 2009 .

[9]  Olivier Tremblay,et al.  Experimental validation of a battery dynamic model for EV applications , 2009 .

[10]  P. Van den Bossche,et al.  The Cell versus the System: Standardization challenges for electricity storage devices , 2009 .

[11]  Panida Jirutitijaroen,et al.  A probabilistic unit commitment problem with photovoltaic generation system , 2009, TENCON 2009 - 2009 IEEE Region 10 Conference.

[12]  Bri-Mathias Hodge,et al.  Wind power forecasting error distributions over multiple timescales , 2011, 2011 IEEE Power and Energy Society General Meeting.

[13]  Deepak Divan,et al.  Evaluating the application of energy storage and day-ahead solar forecasting to firm the output of a photovoltaic plant , 2011, 2011 IEEE Energy Conversion Congress and Exposition.

[14]  Victor M. Zavala,et al.  Scalable stochastic optimization of complex energy systems , 2011, 2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC).

[15]  W. Marsden I and J , 2012 .

[16]  Taher Niknam,et al.  An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation , 2012 .

[17]  Gareth Kear,et al.  Development of the all‐vanadium redox flow battery for energy storage: a review of technological, financial and policy aspects , 2012 .

[18]  Taher Niknam,et al.  Multi-operation management of a typical micro-grids using Particle Swarm Optimization: A comparative study , 2012 .

[19]  Georgios B. Giannakis,et al.  Stochastic programming for energy planning in microgrids with renewables , 2013, 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP).

[20]  Ning Lu,et al.  A comparison of forecast error generators for modeling wind and load uncertainty , 2013, 2013 IEEE Power & Energy Society General Meeting.

[21]  Azza Ahmed ElDesouky,et al.  Security and Stochastic Economic Dispatch of Power System Including Wind and Solar Resources with Environmental Consideration , 2013 .

[22]  Shahram Jadid,et al.  Energy and reserve scheduling of microgrid using multi-objective optimization , 2013 .

[23]  Georgios B. Giannakis,et al.  Risk-constrained energy management with multiple wind farms , 2013, 2013 IEEE PES Innovative Smart Grid Technologies Conference (ISGT).

[24]  Hang Li,et al.  Statistical distribution for wind power forecast error and its application to determine optimal size of energy storage system , 2014 .

[25]  Mariesa L. Crow,et al.  Performance Characterization for Photovoltaic-Vanadium Redox Battery Microgrid Systems , 2014, IEEE Transactions on Sustainable Energy.

[26]  Kwang Y. Lee,et al.  Determining PV Penetration for Distribution Systems With Time-Varying Load Models , 2014, IEEE Transactions on Power Systems.

[27]  Mohamed Maaroufi,et al.  Probabilistic Economic Emission Dispatch Optimization of Multi-sources Power System☆ , 2014 .

[28]  Nikos D. Hatziargyriou,et al.  Microgrids : architectures and control , 2014 .

[29]  Joao P. S. Catalao,et al.  A probabilistic approach to solve economic dispatch problem in systems with intermittent power sources , 2014, 2014 IEEE PES T&D Conference and Exposition.

[30]  Shahram Jadid,et al.  Integrated scheduling of renewable generation and demand response programs in a microgrid , 2014 .

[31]  Weihua Zhuang,et al.  Stochastic Modeling and Optimization in a Microgrid: A Survey , 2014 .

[32]  Wencong Su,et al.  Stochastic Energy Scheduling in Microgrids With Intermittent Renewable Energy Resources , 2014, IEEE Transactions on Smart Grid.

[33]  Soodabeh Soleymani,et al.  Scenario-based stochastic operation management of MicroGrid including Wind, Photovoltaic, Micro-Turbine, Fuel Cell and Energy Storage Devices , 2014 .

[34]  Canbing Li,et al.  Microgrid stochastic economic load dispatch based on two‐point estimate method and improved particle swarm optimization , 2015 .