Optimal Dynamic Dispatch of Wind Integrated Thermal Generators with Compressed Air Energy Storage

Yıl 2014, Cilt: 4 Sayı: 3, 689 - 697, 01.09.2014

Akanksha Bhutt Manjaree Pandit Ayush Shrivastava Hari Mohan Dubey

Öz

Fulfilling the growing demand for electricity at minimum cost while reducing the harmful gaseous emissions released by conventional power plants is a very challenging task. After the Kyoto protocol on climate change there is global focus on limiting emissions from fossil fuels. Increasing number of renewable energy resources are being integrated with existing power grids. Their intermittent and uncertain nature however creates difficulty in maintaining reliability particularly when large scale integration of these resources is planned. Efficient energy storage systems are therefore essential to store surplus power when renewable generation is in abundance and to release it during periods when renewable generation is insufficient. This paper explores the viability of operating wind farm coupled with compressed air energy storage (CAES) system to meet the demand and control the electricity prices during peak loads. The optimal dispatch of thermal units is computed using an improved particle swarm optimization (PSO) such that all thermal, wind generator and CAES system constraints are satisfied. A 24-hour dispatch period is considered by applying thermal generator ramp-rate limits between consecutive time periods. Two separate models are employed for maximizing cost and profit. The proposed method is tested on a test power system consisting of six thermal generating units integrated with 50 wind turbines.

Anahtar Kelimeler

Compressed air energy storage (CAES); Profit maximization; Cost minimization; wind energy integration; Particle swarm optimization; Optimal dispatch

Kaynakça

• P. Wang, Z. Gao, L. Bertling Tjernberg, “Operational adequacy studies of power systems with wind farms and energy storages “ , IEEE Transl. on Power System 1.
• A.J.Wood and B.F.Wollenberg, Power Generation, Operation and Control, New York: Wiley, 1984. (Book)
• D.P.Kothari and I.J.Nagrath, Power system engineering, Tata McGraw- Hill, New Delhi 2008. (Book)
• Azza A. ElDesouky, “Security and stochastic economic dispatch of power system including wind and solar resources
• International journal of renewable energy research, Vol.3, No.4, 2013.
• consideration”, [5] C.X. Guo , Y.H. Bai , X. Zheng , J.P. Zhan , Q.H. Wuc, “Optimal generation dispatch with renewable energy embedded using multiple objectives” ,Electrical Power and Energy Systems, vol. 42, pp. 440–447, 2012. [6] C. Kuo, “Wind energy
• environmental and economic factors”, Renewable Energy, Vol. 35, Issue10, pp. 2217-2227, October 2010. [7] J. Lee , W. Lin , G. Liao and T. Tsao, “Quantum genetic algorithm for dynamic economic dispatch with valve- point effects and including wind power system”, Electrical Power and Energy Systems, vol. 33, pp. 189– 197, 2011.
• S. Mondal , A. Bhattacharya , S. Halder nee Dey, “Multi- objective economic emission load dispatch solution using gravitational search algorithm and considering wind power penetration”, Electrical Power and Energy Systems, vol. 44, pp. 282–292, 2013.
• H.T. Jadhav, R. Roy, “Gbest guided artificial bee colony algorithm
• considering wind power”, Expert Systems with Applications, vol. 40, pp. 6385–6399, 2013.
• dispatch [10] H.T. Jadhav, H. Bhandari, Y. Dalal and R. Roy, “Economic load dispatch including wind power using plant growth simulation algorithm”, IEEE Environment and Electrical Engineering International Conference, pp. 388-393, May 2012. (Conference Paper)
• G. Liao, “A novel evolutionary algorithm for dynamic economic dispatch with energy saving and emission reduction in power system integrated wind power”, Energy, vol. 36, pp. 1018-1029, 2011.
• J. Hetzer, D. C. Yu, K. Bhattarai, “An economic dispatch model incorporating wind power”, IEEE Transactions on Power Systems, Vol. 23, No. 2, pp. 603-611, June 2008.
• J. Aghaei , T. Niknam , R. Azizipanah-Abarghooee and J. M. Arroyo, “Scenario-based dynamic economic emission dispatch considering load and wind power uncertainties”, Electrical Power and Energy Systems, vol. 47, pp. 351–367, 2013.
• C. Lu , C. Chen , D. Hwang and Y. Cheng, “Effects of wind energy supplied by independent power producers on the generation dispatch of electric power utilities”, Electrical Power and Energy Systems, vol. 30, pp. 553–561, 2008.
• K. De Vos , A. G. Petoussis , J. Driesen and R. Belmans, “Revision of reserve requirements following wind power integration in island power systems”, Renewable Energy, vol.50, pp. 268-279, 2013.
• H. Chen, T. Ngoc Cong, W. Yang, C. Tan, Y. Li and Y. Ding, “Progress in electrical energy storage system: A critical review”, Progress in Natural Science, Vol. 19, Issue 3, pp. 291–312, 10 March 2009.
• H. Ibrahim, A. Ilinca, J. Perron, “Energy storage systems—Characteristics
• Renewable and Sustainable Energy Reviews, Vol. 12, Issue 5, pp. 1221–1250, June 2008.
• comparisons”, [18] I. Hadjipaschalis, A. Poullikkas, V. Efthimiou, “Overview of current and future energy storage technologies for electric power applications”, Renewable and Sustainable Energy Reviews, Vol. 13, Issues 6–7, pp. 1513–1522, August–September 2009.
• S. Van der Linden, “Bulk energy storage potential in the USA, current developments and future prospects”, Energy, Vol. 31, Issue 15, pp. 3446–3457, December 2006.
• D. Zafirakis , K. J. Chalvatzis, G. Baiocchi and G. Daskalakis, “Modeling of financial incentives for investments in energy storage systems that promote the large-scale integration of wind energy”, Applied Energy, vol. 105, pp. 138–154, 2013.
• B. Bahmani-Firouzi , R. Azizipanah-Abarghooee, “Optimal sizing of battery energy storage for micro- grid operation management using a new improved bat algorithm”, Electrical Power and Energy Systems, vol. 56, pp. 42–54, 2014.
• X. Tan , Q. Li , H. Wanga, “Advances and trends of energy storage technology in Microgrid”, Electrical Power and Energy Systems, vol. 44, pp. 179–191, 2013.
• P. Denholm, R. Sioshansi, “The value of compressed air energy storage with wind in transmission- constrained electric power systems”, Energy Policy, Vol. 37, Issue 8, pp. 3149–3158, August 2009.
• H. Ibrahim, R. Younès, T. Basbous, A. Ilinc and M. Dimitrova,
• performances for a hybrid wind–diesel system with compressed air energy storage”, Energy, Vol. 36, Issue 5, pp. 3079–3091, May 2011. of diesel
• engine [25] M. Abbaspour , M. Satkin , B. Mohammadi-Ivatloo , F. Hoseinzadeh Lotfi and Y. Noorollahi, “Optimal operation scheduling of wind power integrated with compressed air energy storage (CAES)”, Renewable Energy, vol. 51, pp. 53-59, 2013.
• S. Succar , D. C. Denkenberger , R. H. Williams, “Optimization of specific rating for wind turbine arrays coupled to compressed air energy storage”, Applied Energy, vol. 96, pp. 222–234, 2012.
• H. Lund, G. Salgi, “The role of compressed air energy storage (CAES) in future sustainable energy systems”, Energy Conversion and Management, Vol. 50, Issue 5, pp. 1172–1179, May 2009.
• E. Fertig, J. Apt, “Economics of compressed air energy storage to integrate wind power: A case study in ERCOT”, Energy Policy, Vol. 39, Issue 5, pp. 2330– 2342, May 2011.
• H. Lund, G. Salgi, B. Elmegaard and Anders N., “Optimal operation strategies of compressed air energy storage (CAES) on electricity spot markets with fluctuating prices”, Applied Thermal Engineering, Vol. 29, Issues 5–6, pp. 799–806, April 2009.
• A. Arabali, M. Ghofrani, M. Etezadi-Amoli, “Cost analysis of a power system using probabilistic optimal power flow with energy storage integration and wind generation”, Electrical Power and Energy Systems, vol. 53, pp. 832–841, 2013
• J.Kennedy, R. Eberhart, “Particle swarm optimization”, in proc. IEEE Conf. on Neural Networks (ICNN’95), vol. IV, Perth, Australia, 1995, pp.1942-1948.
• K. T. Chaturvedi, M. Pandit and L. Srivastava, “Self- organizing hierarchical particle swarm optimization for nonconvex economic dispatch”, IEEE Transactions on Power Systems, vol. 23, no. 3, 2008, pp. 1079-1087.
• [Online]. Available from: motor.ece.iit.edu/data/ SCUC_118test.xls, February 2011.
• [Online]. Available from: http://www.enercon.de/p/ downloads/ENProduktue bersicht0710.pdf, accessed February 2011.