Optimal Scheduling of Battery Energy Storage System in Distribution Network Considering Uncertainties using hybrid Monte Carlo- Genetic Approach

Document Type : Research paper

Authors

1 Department of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran.

2 Azarbaijan Shahid Madani University

Abstract

This paper proposes a novel hybrid Monte Carlo simulation-genetic approach (MCS-GA) for optimal operation of a distribution network considering renewable energy generation systems (REGSs) and battery energy storage systems (BESSs). The aim of this paper is to design an optimal charging /discharging scheduling of BESSs so that the total daily profit of distribution company (Disco) can be maximized. In this study, the power generation of REGSs such as photovoltaic resources (PVs) and the network electricity prices are studied through their uncertainty natures. The probability distribution function (PDF), is used to account for uncertainties in this paper. Also, the Monte Carlo simulation (MCS) is applied to generate different scenarios of network electricity prices and solar irradiation of PVs. Optimal scheduling of BESSs can be performed by genetic algorithm (GA). In this paper, firstly, the charging and discharging state of BESSs (positive or negative sign of battery power) is determined according to the variable amount of the electricity prices and power produced from PVs, which have been obtained from the Monte Carlo simulation. Then by using the GA, optimal amount of BESSs is determined. Therefore, a hybrid MCS-GA is used to solve this problem. Numerical examples are presented to illustrate the optimal charging/discharging power of the battery for maximizing the total daily profit.

Keywords

Main Subjects

References

[1]     N. Rugthaicharoencheep and S. Auchariyamet, “Technical and economic impacts of distributed generation on distribution system”, Int. J. Electr. Comput. Energetic Electron. Commun. Eng., vol. 6, no. 4, pp.385-389,2012.
[2]     I. Dincer, “Renewable energy and sustainable development: a crucial review”, Renewable Sustainable Energy Rev., vol. 4, no. 2, pp. 157-175, 2000.
[3]     F. Shaaban, M. and E.F. El-Saadany “Optimal allocation of renewable DG for reliability improvement and losses reduction” in Proce. of  Power Energy Soc. Gen. Meeting, San Diego, CA, USA, 2012, pp.1-8.
[4]     S. Surender Reddy and P.R. Bijwe, “Real time economic dispatch considering renewable energy resources”, Renewable Energy, vol. 83, pp. 1215-1226, 2015.
[5]     E. Mohammadi, and S. Esmaeili, “A Novel optimal placement of PV system for loss reduction and voltage profile improvement”, Tech. Phys. Prob. Eng., vol.4, no. 4, pp. 11-16, 2012.
[6]     B. Zeng, J. Zhang, Xu. Yang, J. Wang, J. Dong, and Yu. Zhang, “Integrated planning for transition to low-carbon distribution system with renewable energy generation and demand response”, IEEE Trans. power syst., vol. 29, no. 3, pp.1153-1165,2014.
[7]     Y.M. Atwa, E.F. El-Saadany, and M.M.A. Salama, “Optimal renewable resources mix for distribution system energy loss minimization”, IEEE Trans. Power Syst., vol. 25, no. 1, pp. 360-370, 2010.
[8]     S. Talari, M. Yazdaninejad, and M.-R. Haghifam, “Stochastic-based scheduling of the microgrid operation including wind turbines, photovoltaic cells, energy storages and responsive loads”, IET Gen., Transm. Distrib., vol. 9, pp. 1498-1509, 2015.
[9]     A. Badri, K. Hoseinpour Lonbar, “Stochastic multiperiod decision making framework of an electricity retailer considering aggregated optimal charging and discharging of electric vehicles”, J. Oper. Autom. Power Eng., vol. 3, no. 1, pp. 34-46, 2015.
[10]  S.M. Mohseni-Bonab, A. Rabiee, S. Jalilzadeh, B. Mohammadi-Ivatloo, S. Nojavan, “Probabilistic multi objective optimal reactive power dispatch considering load uncertainties using Monte Carlo simulations”, J. Oper. Autom. Power Eng., vol. 3, no. 1, pp. 83-93, 2015.
[11]  N. Nikmehr, and S. Najafi-Ravadanegh, “Optimal operation of distributed generations in micro-grids under uncertainties in load and renewable power generation using heuristic algorithm”, IET Renewable Power Gen., vol. 9, pp. 982-990,2015.
[12]  A. Zakariazadeh, S. Jadid, and P. Siano, "Stochastic operational scheduling of smart distribution system considering wind generation and demand response programs", Int. J. Electr. Power Energy Syst., vol. 63, pp. 218-225,2014.
[13]  M. Haghifam, H. Falaghi, and O. Malik, “Risk-based distributed generation placement”, IET Gen. Transm. Distrib., vol. 2, pp. 252-260, 2008.
[14]  A. Soroudi, “Possibilistic-scenario model for DG impact assessment on distribution networks in an uncertain environment”, IEEE Trans. Power Syst., vol. 27, no. 3, pp.1283-1293, 2012.
[15]  J. Eyer, G. Corey, “Energy storage for the electricity grid: Benefits and market potential assessment guide”, Sandia National Laboratories,  2010, pp. 380.
[16]  B. Berseneff, et al., “The significance of energy storage for renewable energy generation and the role of instrumentation and measurement”, in IEEE Instrum. Meas. Magazine, pp.32-40, 2014.
[17]  T. Zhang, “The economic benefits of battery energy storage system in electric distribution system”, Worcester Polytechnic Institute, pp.324, 2013.
[18]  B. Lu, M. Shahidehpour, “Short-term scheduling of battery in grid-connected PV/battery system”, IEEE Trans. Power Syst., vol. 20, no.2, pp.1053-1061, 2005.
[19]  J.-H. Teng, et al. “Optimal charging/discharging scheduling of battery storage systems for distribution systems interconnected with sizeable PV generation systems”, IEEE Trans. Power Syst., vol. 28, no. 2, pp. 1425-1433, 2013.
[20]  C.A. Hill, M.C. Such, Do. Chen, J. Gonzalez and W. Mack Grady, “Battery energy storage for enabling integration of distributed solar power generation”, IEEE Trans. Smart Grid, vol. 3, no. 2, pp. 850-857, 2012.
[21]  D.Q. Hung, N. Mithulananthan, R. Bansal, “Integration of PV and BES units in commercial distribution systems considering energy loss and voltage stability”, Appl. Energy, vol. 113, pp. 1162-1170,2014.
[22]  J. Tant, F. Geth, D. Six, P. Tant, and J. Driesen, "Multi-objective battery storage to improve PV integration in residential distribution grids", IEEE Trans. Sustainable Energy, vol. 4, no. 1, pp. 182-191, 2013.
[23]  R. Khalilpour, A. Vassallo, “Planning and operation scheduling of PV-battery systems: A novel methodology”, Renewable Sustainable Energy Reviews, vol. 53, pp. 194-208, 2016.
[24]  Y. Zheng, Z.Y. Dong, F.J. Luo, “Optimal allocation of energy storage system for risk mitigation of DISCOs with high renewable penetrations”, IEEE Trans. Power Syst., vol. 29, pp. 212-220, 2014.
[25]  A. Gabash, “Flexible optimal operation of battery storage systems for energy supply networks”, IEEE Trans. Power Syst., vol. 28, no. 3, pp. 2788-2797, 2013.
[26]  R. Afshan, J.  Salehi, “Optimal operation of distribution networks with presence of distributed generations and battery energy storage systems considering uncertainties and risk analysis”, J. Renewable Sustainable Energy, vol. 9, pp. 12, 2017.
[27]  V. H. Johnson, “Battery Performance Models in ADVISOR,” J. Power Sources, vol. 110, pp. 321-329, 2002.
[28]  M. Amelin, “On Monte Carlo simulation and analysis of electricity markets”, Ph.D. dissertation, Dept. Elect. Eng. Royal Inst. Tech, Stockholm, 2004.
[29]  R. Billinton, H. Chen, R. Ghajar, “Time-series models for reliability ealuation of power systems including wind energy”, Microelectron. Reliab., vol. 36, pp.1253-1291, 1996.
[30] M. Dicorato, G. Forte, M. Trovato, and E. Caruso, “Risk-constrained profit maximization in day-ahead electricity market”, IEEE Trans. Power Syst., vol. 24, no. 3, pp. 1107-1114, 2009.
Journal of Operation and Automation in Power Engineering
Volume 6, Issue 1
June 2018
Pages 1-12

Files

History

  • Receive Date: 04 March 2017
  • Revise Date: 08 July 2017
  • Accept Date: 15 September 2017
  • First Publish Date: 01 June 2018

Share

How to cite

Statistics