Document Type : Research paper


Department of Electrical Engineering, University of Mohaghegh Ardabili, Ardabil, Iran


Recently due to technical, economical, and environmental reasons, penetration of renewable energy resources has increased in the power systems. On the other hand, the utilization of these resources in remote areas and capable regions as isolated microgrids has several advantages. In this paper, a hybrid microgrid, which includes photovoltaic (PV)/wind/energy storage, is investigated. It has been located in Iran-Khalkhal. The purposes of this study are optimal energy management and sizing of the microgrid. Since the magnitude of the harvested renewable energy deals severely and complexly with season and climate issues, planning of the system based on their specific values is an oversimplification. Therefore, in addition to conventional constraints such as environmental and operational ones, estimation of the wind speed at the site is considered. The Monte Carlo method is employed to model and estimate wind behavior. Also, for regulating production and demand in the microgrid the Demand Response (DR) program is conducted to improve the contribution of the renewable energy resources. The planning is constructed as an optimization problem. It is formulated as a Mixed Integer Linear Programming (MILP). By solving it, the size and production magnitude of energy sources, as well as storage conditions, are determined. Finally, the proposed method is simulated by GAMS for all seasons of two scenarios. The results show desirable energy management and cost reduction in the studied grid.


[1]  A. Diab et al., “Application of different optimization algorithms for optimal sizing of PV/wind/diesel/battery storage stand-alone hybrid microgrid”, IEEE Access, vol. 7, pp. 119223-45, 2019.
[2]  M. Khan, A. Yadav, L. Mathew, “Techno economic feasibility analysis of different combinations of PV-wind diesel-battery hybrid system for telecommunication applications in different cities of Punjab, India”, Renew. Sustain. Energy Rev., vol. 76, pp. 577-607, 2017.
[3]  A. Agüera-Pérez et al., “Weather forecasts for microgrid energy management: review, discussion and recommendations”, Appl. Energy, vol. 228, pp. 265-78, 2018.
[4]  R. Jane et al., “Characterizing meteorological forecast impact on microgrid optimization performance and design”, Energies, vol. 13, pp. 577, 2020.
[5]  A. Khosravi et al., “Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference system”, Sustain. Energy Tech. Assess., vol. 25, pp.146-160, 2018.
[6]  G. Osório, J. Matias, J. Catalão, “Short-term wind power forecasting using adaptive neuro-fuzzy inference system combined with evolutionary particle swarm optimization, wavelet transform and mutual information”, Renew. Energy, vol. 75, pp.301-7, 2015.
[7]  O. Karakuş, E. Kuruoğlu, M. Altınkaya, “One-day ahead wind speed/ power prediction based on polynomial autoregressive model”, IET Renew. Power Gener., vol. 11, pp. 1430-9, 2017.
[8]   M. Alamaniotis, G. Karagiannis, “Integration of Gaussian processes and particle swarm optimization for very-short term wind speed forecasting in smart power”, Int. J. Monit. Surveill. Tech. Res., vol. 5, pp. 1-14, 2017.
[9]  M. Alamaniotis, G. Karagiannis, “Application of fuzzy multiplexing of learning Gaussian processes for the interval forecasting of wind speed”, IET Renew. Power Gener., vol. 14, pp.100-9, 2019.
[10]  F. Nitsch, O. Turkovska, J. Schmidt, “Observation-based estimates of land availability for wind power: a case study for czechia”, Energy Sustain. Soc., vol. 9, pp. 1-13, 2019.
[11]  A. Santos et al., “Framework for microgrid design using social, economic, and technical analysis”, Energies, vol. 11, pp. 2832, 2018.
[12]  H. Hosseinnia, B. Tousi, “Optimal operation of dg-based micro grid (MG) by considering demand response program (DRP)”, Electric Power Syst. Res., vol. 167, pp. 252-60, 2019.
[13]  B. Dey et al., “Solving energy management of renewable integrated microgrid systems using crow search algorithm”, Soft Comput., vol. 22, pp. 55-66, 2019.
[14]  F. El-Faouri et al., “Modeling of a microgrids power generation cost function in real-time operation for a highly fluctuating load”, Simulation Modell. Practice Theory, vol. 94, pp. 118-133, 2019.
[15]  A. Yasmeen et al., “Optimal energy management in microgrids using meta-heuristic technique”, Int. Conf. Emerging Internetworking, Data Web Tech., 2018.
[16]  A. Ehsana, Q. Yang, “State-of-the-Art techniques for modelling of uncertainties in active distribution network planning”, Rev. Appl. Energy, vol. 239, pp. 1509-23, 2019.
[17]  E. Shahryari et al., “A copula-based method to consider uncertainties for multi-objective energy management of microgrid in presence of demand response”, Energy. vol. 175, pp. 879-90, 2019.
[18]  A. Fathy, K. Kaaniche, T. Alanazi, “Recent approach based social spider optimizer for optimal sizing of hybrid PV/Wind/Battery/Diesel integrated microgrid in aljouf region”, IEEE Access. vol. 8, pp. 57630-45, 2020.
[19]  S. Bandyopadhyay et al., “Techno-economical model based optimal sizing of PV-battery systems for microgrids”, IEEE Trans. Sustain. Energy, vol. 11, pp. 1657–68, 2019.
[20]  D. Wu et al., “Stochastic optimal sizing of distributed energy resources for a cost-effective and resilient microgrid”, Energy, vol. 5, pp. 117284-96, 2020.
[21]  U. Salman, F. Al-Ismail, M. Khalid, “Optimal sizing of battery energy storage for grid-connected and isolated wind-penetrated microgrid”, IEEE Access. vol. 8, pp. 91129-38, 2020.
[22]  V. Thang, “Optimal sizing of distributed energy resources and battery energy storage system in planning of islanded micro-grids based on life cycle cost”, Energy Syst., vol. 8, pp.1-20, 2020.
[23]  M. Kiptoo et al., “Integrated approach for optimal techno-economic planning for high renewable energy-based isolated microgrid considering cost of energy storage and demand response strategies”, Energy Convers. Manage., vol. 215, pp. 112917-33, 2020.
[24]  S. Mohseni, A. Brent, D. Burmester, “A demand response centred approach to the long-term equipment capacity planning of grid-independent micro-grids optimized by the moth-flame optimization algorithm”, Energy Convers. Manage., vol. 200, pp. 112105-23, 2019.
[25]  A. Dejamkhooy et al., “Fuel consumption reduction and energy management in stand-alone hybrid microgrid under load uncertainty and demand response by linear programming”, J. Oper. Autom. Power Eng., vol. 8, pp. 273-81, 2020.
[26]  K. Das Choton et al., “Overview of energy storage systems in distribution networks: placement, sizing, operation, and power quality”, Renew. Sustain. Energy Rev., vol. 91, pp. 1205-30, 2018.
[27]  A. Jalali, M. Aldeen, “Risk-based stochastic allocation of ESS to ensure voltage stability margin for distribution systems”, IEEE Trans. Power Syst., vol. 34, pp. 1264-77, 2019.
[28]  M. RasolJannesar et al., “Optimal placement, sizing, and daily charge/discharge of battery energy storage in low voltage distribution network with high photovoltaic penetration”, Appl. Energy, vol. 226, pp. 957-66, 2018.
[29]  H. Mousavi, M. Jadidbonab, B. Mohammadi, “Stochastic assessment of the renewable–based multiple energy system in the presence of thermal energy market and demand response program”, J. Oper. Autom. Power Eng., vol. 8, pp. 22-31, 2020.
[30]  P. Wais, “A review of weibull functions in wind sector”, Renew. Sustain. Energy Rev., vol. 70, pp. 1099-107, 2017.
[31]  T. Ishihara, A. Yamaguchi, “Prediction of the extreme wind speed in the mixed climate region by using monte carlo simulation and measure correlate predict method”, Wind Energy, vol. 18, pp. 171-86, 2015.
[32]  H. Lan et al., “Optimal sizing of hybrid PV/diesel/battery in ship power system”, Appl. Energy, vol. 158, pp. 26-34, 2015.
[33]  S. Tito, T. Lie, T. Anderson, “Optimal sizing of a wind-photovoltaic-battery hybrid renewable energy system considering socio-demographic factors”, Solar Energy, vol. 136, pp. 525-32, 2016.
[34]  A. Daud, M. Ismail, “Design of isolated hybrid systems minimizing costs and pollutant emission”, Renew. Energy, vol. 44, pp. 215-24, 2012.
[35]  L. Wang, C. Singh, “PSO-based multi-criteria optimum design of a grid connected hybrid power system with multiple renewable sources of energy”, Swarm Intell. Sympos., 2007.
[36]  M. Amrollahi, S. Bathaee, “Techno-economic optimization of hybrid photovoltaic/wind generation together with energy storage system in a stand-alone micro-grid subjected to demand response”, Appl. Energy, vol. 202, pp. 66-77, 2017.
[37]  Iran Renewable Energy Organization (SUNA) Available At: <http://www.suna.>.
[38]  NASA Surface meteorology and Solar Energy-Location Available At: <https://>
[39]  H. Borhanazad et al., “Optimization of micro-grid system using MOPSO”, Renew. Energy”, vol. 71, pp. 295-306, 2014.