A dynamic economic emission dispatch considering wind power uncertainty incorporating energy storage system and demand side management

Reducing carbon emissions is an important goal for the whole world; a high penetration of wind energy can help in reducing emissions. However, great increase in wind energy usage raises some issues concerning its variability and stochastic nature. These issues increase the importance of studying methods of wind energy representation, and in the same time studying the effect of using some flexible resources in decreasing those issues. This paper proposes a dynamic economic emission dispatch (DEED) model incorporating high wind penetration considering its intermittency and uncertainty. Energy storage system (ESS) and demand side management (DSM) are implemented in order to study their effect on the cost, emission, and wind energy utilization. The GAMS software has been utilized to solve this DEED problem. The achieved results show the importance of using ESS and DSM in decreasing both cost and emission, and increasing the wind energy utilization.

[1]  Zhongfu Tan,et al.  A two-stage scheduling optimization model and solution algorithm for wind power and energy storage system considering uncertainty and demand response , 2014 .

[2]  Hui Sun,et al.  Optimal wind–thermal coordination dispatch based on risk reserve constraints , 2011 .

[3]  Haifeng Zhang,et al.  A Dynamic Economic Dispatch Model Incorporating Wind Power Based on Chance Constrained Programming , 2014 .

[4]  X. Liu,et al.  Emission minimisation dispatch constrained by cost and wind power , 2011 .

[5]  Wilsun Xu,et al.  Economic Load Dispatch Constrained by Wind Power Availability: A Here-and-Now Approach , 2010, IEEE Transactions on Sustainable Energy.

[6]  C.S. Ozveren,et al.  Economic Load Dispatch Optimization of Renewable Energy in Power System Using Genetic Algorithm , 2007, 2007 IEEE Lausanne Power Tech.

[7]  L. Olmos,et al.  Demand Response in an Isolated System With High Wind Integration , 2012, IEEE Transactions on Power Systems.

[8]  Mohammad Shahidehpour,et al.  Hourly demand response in day-ahead scheduling for managing the variability of renewable energy , 2013 .

[9]  Mahmoud-Reza Haghifam,et al.  Unit Commitment in Presence of Wind Power Plants and Energy Storage , 2013 .

[10]  Taher Niknam,et al.  Probabilistic multiobjective wind-thermal economic emission dispatch based on point estimated method , 2012 .

[11]  Jie Wang,et al.  Multi-objective economic emission dispatch considering wind power using evolutionary algorithm based on decomposition , 2014 .

[12]  Hieu Trinh,et al.  An Approach for Wind Power Integration Using Demand Side Resources , 2013, IEEE Transactions on Sustainable Energy.

[13]  Richard D. Christie,et al.  Energy storage effects on day-ahead operation of power systems with high wind penetration , 2011, 2011 North American Power Symposium.

[14]  Jun Hong,et al.  The development, implementation, and application of Demand Side Management and control (DSM+c) algorithm for integrating micro-generation system within built environment , 2009 .

[15]  W. Short,et al.  Evaluating the Impacts of Real-Time Pricing on the Usage of Wind Generation , 2009, IEEE Transactions on Power Systems.

[16]  R. Sioshansi.  Evaluating the Impacts of Real-Time Pricing on the Cost and Value of Wind Generation , 2010, IEEE Transactions on Power Systems.

[17]  Zhongfu Tan,et al.  An Optimization Model for Large–Scale Wind Power Grid Connection Considering Demand Response and Energy Storage Systems , 2014 .

[18]  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.

[19]  Mimoun Younes,et al.  Multi-objective economic emission dispatch solution using hybrid FFA (firefly algorithm) and considering wind power penetration , 2014 .

[20]  Taher Niknam,et al.  Scenario-based dynamic economic emission dispatch considering load and wind power uncertainties , 2013 .

[21]  M. O'Malley,et al.  System operation with a significant wind power penetration , 2004, IEEE Power Engineering Society General Meeting, 2004..

[22]  Ping Ju,et al.  Determination of economic dispatch of wind farm‐battery energy storage system using Genetic Algorithm , 2014 .