A new methodology for optimal location and sizing of battery energy storage system in distribution networks for loss reduction

Abstract In this study, a new methodology has been proposed for optimal allocation and optimal sizing of a lithium-ion battery energy storage system (BESS). The main purpose is to minimize the total loss reduction in the distribution system. The optimization process is applied using a newly developed type of Cayote Optimization Algorithm (COA). The proposed technique includes two different approaches. In the first approach, the optimization for allocation and the sizing are performed one by one and in the second approach, the optimization has been done simultaneously. To analyze the proposed system, four different scenarios have been analyzed which include different conditions without/with PVs and also using single/two BESS. The results showed that using two BESS can reduce the total error of the distribution system. the results also showed that using PVs can also decrease the total losses. Finally, the proposed approach based on ICOA is compared with Firefly Algorithm (FA), Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO) to show the proposed method's prominence efficiency.

[1]  Mohammad Javad Sanjari,et al.  Optimal placement and sizing of battery energy storage system for losses reduction using whale optimization algorithm , 2019 .

[2]  Noradin Ghadimi,et al.  A new feature selection and hybrid forecast engine for day-ahead price forecasting of electricity markets , 2017, J. Intell. Fuzzy Syst..

[3]  Hadi Zayandehroodi,et al.  A New Formulation to Reduce the Number of Variables and Constraints to Expedite SCUC in Bulky Power Systems , 2019 .

[4]  Navid Razmjooy,et al.  Experimental modeling of PEM fuel cells using a new improved seagull optimization algorithm , 2019, Energy Reports.

[5]  Guido Carpinelli,et al.  A Hybrid Method for Optimal Siting and Sizing of Battery Energy Storage Systems in Unbalanced Low Voltage Microgrids , 2018 .

[6]  Zhiwei Wang,et al.  Optimization of Battery Energy Storage to Improve Power System Oscillation Damping , 2018, IEEE Transactions on Sustainable Energy.

[7]  Josep M. Guerrero,et al.  Optimal sizing of a lithium battery energy storage system for grid-connected photovoltaic systems , 2017, 2017 IEEE Second International Conference on DC Microgrids (ICDCM).

[8]  L. F. Grisales-Norena,et al.  Integration of energy storage systems in AC distribution networks: Optimal location, selecting, and operation approach based on genetic algorithms , 2019, Journal of Energy Storage.

[9]  Leandro dos Santos Coelho,et al.  Coyote Optimization Algorithm: A New Metaheuristic for Global Optimization Problems , 2018, 2018 IEEE Congress on Evolutionary Computation (CEC).

[10]  Noradin Ghadimi,et al.  Robust optimization based optimal chiller loading under cooling demand uncertainty , 2019, Applied Thermal Engineering.

[11]  Juan C. Vasquez,et al.  Optimal utilization of microgrids supplemented with battery energy storage systems in grid support applications , 2015, 2015 IEEE First International Conference on DC Microgrids (ICDCM).

[12]  Amjad Anvari-Moghaddam,et al.  Optimal planning and operation management of a ship electrical power system with energy storage system , 2016, IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society.

[13]  Navid Razmjooy,et al.  Multi-objective optimization of a PEMFC based CCHP system by meta-heuristics , 2019, Energy Reports.

[14]  Vijay Kumar,et al.  Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems , 2019, Knowl. Based Syst..

[15]  Noradin Ghadimi,et al.  Optimal preventive maintenance policy for electric power distribution systems based on the fuzzy AHP methods , 2016, Complex..

[16]  Karzan Wakil,et al.  RETRACTED: Risk-assessment of photovoltaic-wind-battery-grid based large industrial consumer using information gap decision theory , 2018, Solar Energy.

[17]  Guo Li,et al.  A niching chaos optimization algorithm for multimodal optimization , 2018, Soft Comput..

[18]  Mohammad Ghiasi,et al.  Extracting Appropriate Nodal Marginal Prices for All Types of Committed Reserve , 2019 .

[19]  G. Cheng,et al.  On the efficiency of chaos optimization algorithms for global optimization , 2007 .

[20]  Alireza Nouri,et al.  Planning in Microgrids With Conservation of Voltage Reduction , 2018, IEEE Systems Journal.

[21]  Di Wu,et al.  Assigning value to energy storage systems at multiple points in an electrical grid , 2018 .

[22]  Satvir Singh,et al.  Butterfly optimization algorithm: a novel approach for global optimization , 2018, Soft Computing.

[23]  Noradin Ghadimi,et al.  Concordant controllers based on FACTS and FPSS for solving wide-area in multi-machine power system , 2016, Journal of Intelligent & Fuzzy Systems.

[24]  Rui LI,et al.  Optimal planning of energy storage system in active distribution system based on fuzzy multi-objective bi-level optimization , 2018 .

[25]  Wei Wang,et al.  Electricity load forecasting by an improved forecast engine for building level consumers , 2017 .

[26]  Noradin Ghadimi,et al.  The price prediction for the energy market based on a new method , 2018 .

[27]  Jagdish Chand Bansal Particle Swarm Optimization , 2018, Studies in Computational Intelligence.

[28]  Noradin Ghadimi,et al.  Reliability assessment for components of large scale photovoltaic systems , 2014 .

[29]  Xin-She Yang,et al.  Firefly Algorithms for Multimodal Optimization , 2009, SAGA.

[30]  Taher Niknam,et al.  Multi-operation management of a typical micro-grids using Particle Swarm Optimization: A comparative study , 2012 .

[31]  Leandro dos Santos Coelho,et al.  A Novel Metaheuristic Algorithm Inspired by Rhino Herd Behavior , 2018 .

[32]  Mehdi Hosseinzadeh,et al.  A framework to expedite joint energy-reserve payment cost minimization using a custom-designed method based on Mixed Integer Genetic Algorithm , 2018, Eng. Appl. Artif. Intell..

[33]  Haiguo Tang,et al.  A new wind power prediction method based on ridgelet transforms, hybrid feature selection and closed-loop forecasting , 2018, Adv. Eng. Informatics.

[34]  Noradin Ghadimi,et al.  A new prediction model of battery and wind-solar output in hybrid power system , 2019, J. Ambient Intell. Humaniz. Comput..

[35]  Vijay Kumar,et al.  Emperor penguin optimizer: A bio-inspired algorithm for engineering problems , 2018, Knowl. Based Syst..

[36]  Navid Razmjooy,et al.  Optimal configuration and energy management for combined solar chimney, solid oxide electrolysis, and fuel cell: a case study in Iran , 2019, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects.

[37]  Navid Razmjooy,et al.  A Hybrid Neural Network – World Cup Optimization Algorithm for Melanoma Detection , 2018, Open medicine.