An Optimization Scheduling Model for Wind Power and Thermal Power with Energy Storage System considering Carbon Emission Trading

Wind power has the characteristics of randomness and intermittence, which influences power system safety and stable operation. To alleviate the effect of wind power grid connection and improve power system’s wind power consumptive capability, this paper took emission trading and energy storage system into consideration and built an optimization model for thermal-wind power system and energy storage systems collaborative scheduling. A simulation based on 10 thermal units and wind farms with 2800 MW installed capacity verified the correctness of the models put forward by this paper. According to the simulation results, the introduction of carbon emission trading can improve wind power consumptive capability and cut down the average coal consumption per unit of power. The introduction of energy storage system can smooth wind power output curve and suppress power fluctuations. The optimization effects achieve the best when both of carbon emission trading and energy storage system work at the same time.

[1]  Boon-Teck Ooi,et al.  Frequency deviation of thermal power plants due to wind farms , 2006, IEEE Transactions on Energy Conversion.

[2]  A.K. Srivastava,et al.  Generation scheduling with integration of wind power and compressed air energy storage , 2010, IEEE PES T&D 2010.

[3]  M. Trovato,et al.  Planning and Operating Combined Wind-Storage System in Electricity Market , 2012, IEEE Transactions on Sustainable Energy.

[4]  Wing Shing Wong,et al.  Hybrid event-time-triggered networked control systems: Scheduling-event-control co-design , 2015, Inf. Sci..

[5]  X. Yao,et al.  Regional energy efficiency, carbon emission performance and technology gaps in China: A meta-frontier non-radial directional distance function analysis , 2015 .

[6]  Ilias Tsiakas,et al.  Carbon Emissions and Stock Returns: Evidence from the EU Emissions Trading Scheme , 2015 .

[7]  Liwei Liu,et al.  China׳s carbon-emissions trading: Overview, challenges and future , 2015 .

[8]  Gilles Fedak,et al.  Multi-criteria and satisfaction oriented scheduling for hybrid distributed computing infrastructures , 2016, Future Gener. Comput. Syst..

[9]  Yuanxin Liu,et al.  The industrial performance of wind power industry in China , 2015 .

[10]  Corinne Le Quéré,et al.  Climate Change 2013: The Physical Science Basis , 2013 .

[11]  Tooraj Jamasb,et al.  Delivering a low carbon electricity system , 2008 .

[12]  David C. Yu,et al.  An Economic Dispatch Model Incorporating Wind Power , 2008, IEEE Transactions on Energy Conversion.

[13]  Yiping Dai,et al.  Capacity allocation of a hybrid energy storage system for power system peak shaving at high wind power penetration level , 2015 .

[14]  Lean Yu,et al.  Carbon emissions trading scheme exploration in China: A multi-agent-based model , 2015 .

[15]  A.M. Gonzalez,et al.  Stochastic Joint Optimization of Wind Generation and Pumped-Storage Units in an Electricity Market , 2008, IEEE Transactions on Power Systems.

[16]  Antonio J. Conejo,et al.  Wind power investment within a market environment , 2011 .

[17]  Zhenliang Liao,et al.  Case study on initial allocation of Shanghai carbon emission trading based on Shapley value , 2015 .

[18]  Lizhi Wang,et al.  Designing effective and efficient incentive policies for renewable energy in generation expansion planning , 2011 .