Optimal Scheduling of Battery Energy Storage System in Distribution Network Considering Uncertainties using hybrid Monte Carlo- Genetic Approach
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...
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| Format: | Article |
| Language: | English |
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University of Mohaghegh Ardabili
2018-06-01
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| Series: | Journal of Operation and Automation in Power Engineering |
| Subjects: | |
| Online Access: | http://joape.uma.ac.ir/article_632_02d445095ef5de28bcf76f5fee404d52.pdf |
| _version_ | 1818603034514554880 |
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| author | R. Afshan J. Salehi |
| author_facet | R. Afshan J. Salehi |
| author_sort | R. Afshan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-16T13:16:45Z |
| format | Article |
| id | doaj.art-d5c8505619ed4b81b673ff9afb78ea8f |
| institution | Directory Open Access Journal |
| issn | 2322-4576 2423-4567 |
| language | English |
| last_indexed | 2024-12-16T13:16:45Z |
| publishDate | 2018-06-01 |
| publisher | University of Mohaghegh Ardabili |
| record_format | Article |
| series | Journal of Operation and Automation in Power Engineering |
| spelling | doaj.art-d5c8505619ed4b81b673ff9afb78ea8f2022-12-21T22:30:27ZengUniversity of Mohaghegh ArdabiliJournal of Operation and Automation in Power Engineering2322-45762423-45672018-06-016111210.22098/joape.2017.3385.1271632Optimal Scheduling of Battery Energy Storage System in Distribution Network Considering Uncertainties using hybrid Monte Carlo- Genetic ApproachR. Afshan0J. Salehi1Department of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran.Azarbaijan Shahid Madani UniversityThis 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.http://joape.uma.ac.ir/article_632_02d445095ef5de28bcf76f5fee404d52.pdfBattery Energy Storage SystemsOptimal OperationUncertainty ModelingMonte Carlo simulationgenetic algorithm |
| spellingShingle | R. Afshan J. Salehi Optimal Scheduling of Battery Energy Storage System in Distribution Network Considering Uncertainties using hybrid Monte Carlo- Genetic Approach Journal of Operation and Automation in Power Engineering Battery Energy Storage Systems Optimal Operation Uncertainty Modeling Monte Carlo simulation genetic algorithm |
| title | Optimal Scheduling of Battery Energy Storage System in Distribution Network Considering Uncertainties using hybrid Monte Carlo- Genetic Approach |
| title_full | Optimal Scheduling of Battery Energy Storage System in Distribution Network Considering Uncertainties using hybrid Monte Carlo- Genetic Approach |
| title_fullStr | Optimal Scheduling of Battery Energy Storage System in Distribution Network Considering Uncertainties using hybrid Monte Carlo- Genetic Approach |
| title_full_unstemmed | Optimal Scheduling of Battery Energy Storage System in Distribution Network Considering Uncertainties using hybrid Monte Carlo- Genetic Approach |
| title_short | Optimal Scheduling of Battery Energy Storage System in Distribution Network Considering Uncertainties using hybrid Monte Carlo- Genetic Approach |
| title_sort | optimal scheduling of battery energy storage system in distribution network considering uncertainties using hybrid monte carlo genetic approach |
| topic | Battery Energy Storage Systems Optimal Operation Uncertainty Modeling Monte Carlo simulation genetic algorithm |
| url | http://joape.uma.ac.ir/article_632_02d445095ef5de28bcf76f5fee404d52.pdf |
| work_keys_str_mv | AT rafshan optimalschedulingofbatteryenergystoragesystemindistributionnetworkconsideringuncertaintiesusinghybridmontecarlogeneticapproach AT jsalehi optimalschedulingofbatteryenergystoragesystemindistributionnetworkconsideringuncertaintiesusinghybridmontecarlogeneticapproach |