Optimal Resources Planning of Residential Complex Energy System in a Day-ahead Market Based on Invasive Weed Optimization Algorithm

Authors

  • P. Ahmadi Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
  • M. H. Nazari Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran
  • S. H. Hosseinian Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran
Volume: 7 | Issue: 5 | Pages: 1934-1939 | October 2017 | https://doi.org/10.48084/etasr.1324

Abstract

This paper deals with optimal resources planning in a residential complex energy system, including FC (fuel cell), PV (Photovoltaic) panels and the battery. A day-ahead energy management system (EMS) based on invasive weed optimization (IWO) algorithm is defined for managing different resources to determine an optimal operation schedule for the energy resources at each time interval to minimize the operation cost of a smart residential complex energy system. Moreover, in this paper the impacts of the sell to grid and purchase from grid are also considered. All practical constraints of the each energy resources and utility policies are taken into account. Moreover, sensitivity analysis are conducted on electricity prices and sell to grid factor (SGF), in order to improve understanding the impact of key parameters on residential CHP systems economy. It is shown that proposed system can meet all electrical and thermal demands with economic point of view. Also enhancement of electricity price leads to substantial growth in utilization of proposed CHP system.

Keywords:

combined heat and power system (CHP), electricity tariff, energy management system, smart home

Downloads

Download data is not yet available.

References

H. Ren , W. Gao, Y. Ruan, “Optimal sizing for residential CHP systemâ€, Applied Thermal Engineering, Vol. 28, No. 5-6, pp. 514–523, 2008 DOI: https://doi.org/10.1016/j.applthermaleng.2007.05.001

Y. Yang, W. Pei, Z. Qi, “Optimal Sizing of Renewable Energy and CHP Hybrid Energy Microgrid Systemâ€, IEEE Innovative Smart Grid Tech. ASIA Conf., pp. 1-5, China, May 21-24, 2012

M. H. Moradi, M. Eskandari, M. Hossenian, “Operation strategy optimization in an optimal sized microgridâ€, IEEE Transactions on Smart Grid, Vol. 6, No. 3, pp. 1087–1095, 2015 DOI: https://doi.org/10.1109/TSG.2014.2349795

M. J. Sanjari, H. Karami, A. H. Yatim, G. B. Gharehpetian, “Application of Hyper-Spherical Search algorithm for optimal energy resources dispatch in residential microgridsâ€, Applied Soft Computing, Vol. 35, pp. 15-23, 2015 DOI: https://doi.org/10.1016/j.asoc.2015.08.006

H. Karami, M. J. Sanjari, S. H. Hosseinian, G. B. Gharehpetian, “An Optimal Dispatch Algorithm for Managing Residential Distributed Energy Resourcesâ€, IEEE Transactions on Smart Grid, Vol. 5, No. 5, pp. 2360-2367, 2014 DOI: https://doi.org/10.1109/TSG.2014.2325912

G. Notton, C. Cristofari, M. Mattei, P. Poggi, “Modelling of a double-glass photovoltaic module using finite differencesâ€, Applied Thermal Engineering, Vol. 25, No. 17-18, pp. 2854–2877, 2005 DOI: https://doi.org/10.1016/j.applthermaleng.2005.02.008

E. Skoplaki, J.A. Palyvos, “On the temperature dependence of photovoltaic module electrical performance: A review of efficiency/power correlationsâ€, Solar Energy, Vol. 83, No. 5, pp. 614-624, 2009 DOI: https://doi.org/10.1016/j.solener.2008.10.008

M. Y. El-Sharkh, M. Tanrioven, A. Rahman, M. S. Alam, “Cost related sensitivity analysis for optimal operation of a grid-parallel PEM fuel cell power plantâ€, Power Sources, Vol. 161, No. 2, pp. 1198–1207, 2006 DOI: https://doi.org/10.1016/j.jpowsour.2006.06.046

A. Gianfreda, L. Grossi, “Zonal price analysis of the Italian wholesale electricity marketâ€, 6th International Conference on the European Energy Market, pp. 1-6, Belgium, May 27-29, 2009 DOI: https://doi.org/10.1109/EEM.2009.5207198

S. Y. Derakhshandeh, A. S. Masoum, S. Deilami, M. A. S. Masoum,, M. E. Hamedani Golshan, “Coordination of Generation Scheduling with PEVs Charging in Industrial Microgridsâ€, IEEE Transactions on Power Systems, Vol. 28, No. 3, pp. 3451 – 3461, 2013 DOI: https://doi.org/10.1109/TPWRS.2013.2257184

H. S. Rad, C. Lucas “A recommender system based on invasive weed optimization algorithmâ€, IEEE Congress on Evolutionary Computation, Singapore, September 25-28, 2007 DOI: https://doi.org/10.1109/CEC.2007.4425032

Akbar Maleki, Alireza Askarzadeh, “Optimal sizing of a PV/wind/diesel system with battery storage for electrification to an off-grid remote region: A case study of Rafsanjan,Iranâ€, Sustainable Energy Technologies and Assessments, Vol. 7, pp. 147–153, 2014 DOI: https://doi.org/10.1016/j.seta.2014.04.005

M. Ahmadi, H. Mojallali, R. Izadi-Zamanabadi, “State estimation of nonlinear stochastic systems using a novel meta-heuristic particle filterâ€, Swarm & Evolutionary Computation, Vol. 4, pp. 44–53, 2012 DOI: https://doi.org/10.1016/j.swevo.2011.11.004

S. Karimkashi, A. A. Kishk, “Invasive Weed Optimization and its Features in Electromagneticsâ€, IEEE Transactions on Antennas and Propagation, Vol. 58, No. 4, pp. 1269–78, 2010 DOI: https://doi.org/10.1109/TAP.2010.2041163

S.G. Tichi, M. M.Ardehali, M.E.Nazari, “Examination of energy price policies in Iran for optimal configuration of CHP and CCHP systems based on particle swarm optimization algorithmâ€, Energy Policy, Vol. 38, No. 10, pp. 6240–6250, 2010 DOI: https://doi.org/10.1016/j.enpol.2010.06.012

Downloads

How to Cite

[1]
P. Ahmadi, M. H. Nazari, and S. H. Hosseinian, “Optimal Resources Planning of Residential Complex Energy System in a Day-ahead Market Based on Invasive Weed Optimization Algorithm”, Eng. Technol. Appl. Sci. Res., vol. 7, no. 5, pp. 1934–1939, Oct. 2017.

Metrics

Abstract Views: 621
PDF Downloads: 299

Metrics Information
Bookmark and Share

Most read articles by the same author(s)