Abstract
This paper presents a micro grid generation scheduling model using Non-linear Decreasing Inertia Weight Particle Swarm Optimization (NDIW-PSO) and Time Varying Acceleration Co-efficient Particle Swarm Optimization (TVAC-PSO) techniques. Here energy management in micro grid is done in presence of renewable energy sources such as wind and solar power. In this research work, implementation of Demand Response (DR) schedules are carried out as incentive based payment i.e., on offered price packages. In the typical microgrid, different power components including Wind Turbine (WT), Photovoltaic (PV) cell, Micro-Turbine (MT), Fuel Cell (FC), battery hybrid power source and responsive loads are used. Analytical approaches and case studies are conducted for obtaining minimum operating costs and comparative studies are carried out without demand response participation and with demand response participation respectively. The results obtained represent the superiority of the proposed approach for effective generation scheduling in micro grids.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Jiayi, H., Chuanwen, J., Rong, X.: A review on distributed energy resources and micro grid. Int. J. Renew. Sustain. Energy Rev. 12(9), 2472–2483 (2008)
Chowdhury, S., Crossley, P.: Microgrids and active distribution networks. The Institution of Engineering and Technology (2009)
Rouholamini, M., Mohammadian, M.: Energy management of a grid-tied residential-scale hybrid renewable generation system incorporating fuel cell and electrolyzer. J. Energy Build. 102, 406–16 (2015)
Pandit, M., Srivastava, L., Sharma, M.: Environmental economic dispatch in multi area power system employing improved differential evolution with fuzzy selection. Appl. Soft Comput. 28, 498–510 (2015)
Aghajani, R.G., Shayanfar, A.H., Shayeghi, H.: Presenting a multi-objective generation scheduling model for pricing demand response rate in micro-grid energy management. Energy Convers. Manag. 106, 308–321 (2015)
Jabbari-Sabet, R., Moghaddas-Tafreshi, S.M., Mirhoseini, S.S.: Microgrid operation and management using probabilistic reconfiguration and unit commitment. Electr. Power Energy Syst. 75, 328–336 (2016)
Abido, M.A.: Optimal power flow using particle swarm optimization. Electr. Power Energy Syst. 24, 563–571 (2002)
Chen, C., Duan, S., Cai, T., Liu, B., Hu, G.: Smart energy management system for optimal micro grid economic operation. IET Renew. Power Gener. 5(3), 258–267 (2011)
Chongpeng, H., Yuling, Z., Dingguo, J., Baoguo, X.: On some non-linear decreasing inertia weight strategies in particle swarm optimization. In: Proceedings of the 26th Chinese Control Conference, Hunan, China, Zhangjiajie, pp. 570–753 (2007)
Imran, M., Hashima, R., Khalidb, Noor Elaiza Abd: An overview of particle swarm optimization variants. Procedia Eng. 53, 491–496 (2013)
Ratnaweera, A., Halgamuge, S.K., Watson, H.C.: Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans. Evol. Comput. 8(3), 240–255 (2004)
Acknowledgements
The authors express gratitude towards the Department of Power Engineering, Jadavpur University for providing facilities for carrying this research work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
De, M., Das, G., Mandal, K.K. (2020). Cost Driven Optimization of Microgrid Under Environmental Uncertainties Using Different Improved PSO Models. In: Roy, P., Cao, X., Li, XZ., Das, P., Deo, S. (eds) Mathematical Analysis and Applications in Modeling. ICMAAM 2018. Springer Proceedings in Mathematics & Statistics, vol 302. Springer, Singapore. https://doi.org/10.1007/978-981-15-0422-8_16
Download citation
DOI: https://doi.org/10.1007/978-981-15-0422-8_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0421-1
Online ISBN: 978-981-15-0422-8
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)