A Study the Eho-FF Algorithms for Optimal Distribution energy resource Sizing
Main Article Content
Authors
Abstract
The advent of the Smart Grid idea has presented a significant obstacle in the power industry. The smart grid now mostly consists of distributed power that integrates renewable energy resources. Demand Side Management (DSM) adapts to fluctuating demand by letting customers choose their own power generation options. By limiting demand & maximising resource allocation for power generation, DSM enables customers to access power at a reduced cost. Developing a hybrid evolutionary algorithm for DSM that accurately monitors smart grid real power loss is the main goal of this study. This method will be useful for reactive power optimisation, or RPO. Improving the power grid network's energy efficiency and voltage profile under various load situations is the goal of the proposed effort.
Downloads
Article Details
Section
References
- Calafate, C.T. 2009, ‘Qos support in MANET: a modular architecture based on the IEEE 802.11e technology’. Circuits and Systems for Video Technology, IEEE Transactions on Volume:19 , Issue: 5 pp.678-692.
- Bose, 2010, ‘ Smart transmission grid application and their supporting infrastructure’. A Smart Grid, IEEE Transactions on Volume: 1, Issue: 1. pp.11-19.
- Arif, S. M., Hussain, A., Lie, T. T., Ahsan, S. M., & Khan, H. A. (2020). Analytical hybrid particle swarm optimization algorithm for optimal siting and sizing of distributed generation in smart grid. Journal of Modern Power Systems and Clean Energy, 8(6), 1221-1230.
- Duman, S, Rivera, S, Li, J & Wu, L 2020, ‘Optimal power flow of power systems with controllable wind-photovoltaic energy systems via differential evolutionary particle swarm optimization’, International Transactions on Electrical Energy Systems, vol. 30, no. 4, pp. 1-28.
- Ghasemi, M, Ghavidel, S, Ghanbarian, MM, Gharibzadeh, M & Vahed, AA 2014c, ‘Multi-objective optimal power flow considering the cost, emission, voltage deviation and power losses using multi-objective modified imperialist competitive algorithm’, Energy, vol. 78, pp. 276-289
- Ghasemi, M, Ghavidel, S, Rahmani, S, Roosta, A & Falah, H 2014a, ‘A novel hybrid algorithm of imperialist competitive algorithm and teaching learning algorithm for optimal power flow problem with non-smooth cost functions’, Engineering Applications of Artificial Intelligence, vol. 29, pp. 54-69.
- Kannayeram, G. P., Muniraj, R., Prakash, N. B., Jarin, T., & Boselin Prabhu, S. R. (2021). An elitist control scheme for power flow management in smart grid system: a hybrid optimization scheme. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 1-22.
- Kessel, P. and Glavitsch, H. (1986). Estimating the voltage stability of a power system. IEEE Transactions on Power Delivery., 1(3):346-354.
- Kumar, AR & Premalatha, L 2015, ‘Optimal power flow for a deregulated power system using adaptive real coded biogeography-based optimization’, International Journal of Electrical Power & Energy Systems, vol. 73, pp. 393-399.
- Kumar, S, Tyagi, B, Kumar, V & Chohan, S, 2018, ‗Incremental PMU placement considering reliability of power system network using analytical hierarchical process‘, IET Generation, Transmission & Distribution, vol. 12, no. 16, pp. 3900-3909.
- Zhao, Y, Cai, Y & Cheng, D 2017, ‗A novel local exploitation scheme for conditionally breeding real-coded genetic algorithm‘, Multimedia Tools and Applications, vol. 76, no. 17, pp.17955-17969.
- Zehar, K & Sayah, S 2008, ‘Optimal power flow with environmental constraint using a fast successive linear programming algorithm: Application to the Algerian power system’, Energy Conversion and Management, vol. 49, no. 11, pp. 3362-3366.