Optimal Battery Energy Storage Placement in Highly PV - Penetrated Distribution Networks

Alzahrani, Ahmed; Alharthi, Hussain; Khalid, Muhammad

doi:10.1109/isgt45199.2020.9087725

Cited by 8 publications

(5 citation statements)

References 32 publications

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“…Distribution losses reduction is the main objective function for optimal location and sizing of BESS as presented in [4][5][6][7][8]. Utilizing BESS as a backup source to supply de-energized zones is a solution to improve network continuity as illustrated in [9].…”

Section: Figure 1: Bess Diagrammentioning

confidence: 99%

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Multifunction Battery Energy Storage System for Distribution Networks

Abdalla¹,

Abdel-Salam²,

Mostafa³

2022

Energy Engineering

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Battery Energy Storage System (BESS) is one of the potential solutions to increase energy system flexibility, as BESS is well suited to solve many challenges in transmission and distribution networks. Examples of distribution network's challenges, which affect network performance, are: (i) Load disconnection or technical constraints violation, which may happen during reconfiguration after fault, (ii) Unpredictable power generation change due to Photovoltaic (PV) penetration, (iii) Undesirable PV reverse power, and (iv) Low Load Factor (LF) which may affect electricity price. In this paper, the BESS is used to support distribution networks in reconfiguration after a fault, increasing Photovoltaic (PV) penetration, cutting peak load, and loading valley filling. The paper presents a methodology for BESS optimal locations and sizing considering technical constraints during reconfiguration after a fault and PV power generation changes. For determining the maximum power generation change due to PV, actual power registration of connected PV plants in South Cairo Electricity Distribution Company (SCEDC) was considered for a year. In addition, the paper provides a procedure for distribution network operator to employ the proposed BESS to perform multi functions such as: the ability to absorb PV power surplus, cut peak load and fill load valley for improving network's performances. The methodology is applied to a modified IEEE 37-node and a real network part consisting of 158 nodes in SCEDC zone. The simulation studies are performed using the DIgSILENT PowerFactory software and DPL programming language. The Mixed Integer Linear Programming optimization technique (MILP) in MATLAB is employed to choose the best locations and sizing of BESS.

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Section: Figure 1: Bess Diagrammentioning

confidence: 99%

“…The modified elements are shown in red while open cables are presented in dotted red lines. To comply with the MSPV capacity requirements, the IEEE 37-node test feeder loads are modified as listed in Table B1 in Appendix B, and load profile is assumed to be similar to the load profile in [4] with the IEEE 33-bus system.…”

Section: System Descriptionmentioning

confidence: 99%

Multifunction Battery Energy Storage System for Distribution Networks

Abdalla¹,

Abdel-Salam²,

Mostafa³

2022

Energy Engineering

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“…Such is the case of the Institute of Electrical and Electronics Engineers (IEEE), which provides users with model networks for implementation or study to obtain results applicable to future electrical network designs. In the literature, there are many studies that have used IEEE test feeders for their research: Scheneider et al (2018) analyzed the different IEEE test feeders, Alvarez-Alvarado et al (2022) used IEEE 39-node test feeder and DIgSILENT PowerFactory® to optimize the location and size of new solar and wind power plants by meeting frequency requirements in a distribution network, Alzahrani et al (2020) verified the optimization of the location of battery energy storage systems in distribution networks with photovoltaics minimizing losses in the IEEE 37-node test feeder, Kaur et al (2010) optimized generation economically to meet the operating requirements of the 30-node IEEE test feeder, Yadav et al (2019) used DIgSILENT PowerFactory® in the IEEE 39-node test feeder in the transmission network of India for the classification of events in networks with integration of renewables, Montoya-Bueno et al (2016) used the IEEE 34-node test feeder to test a new approach to manage uncertainty in distribution networks with integration of renewables minimizing distribution costs.…”

Section: Introductionmentioning

confidence: 99%

Análisis De Flujo De Carga De La Red Ieee 34 Nudos Con Integración De Energías Renovables

2023

Proceedings From the International Congress on Project Management and Engineering

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Honrubia Escribano, Andrés (1) (1) UCLM, (2) E2F INGENIERÍA Nowadays, the commitments made by governments worldwide on the reduction of greenhouse gas emissions, as well as the war situation that arose between Ukraine and Russia, which seriously affects Europe from the energy point of view, has motivated a great development of renewable energies. In order not to limit their expansion, grids must be prepared for the installation of these renewable energy sources and their impact on the electricity system in general. To analyze this impact, the "IEEE 34 Node Test Feeder" network is modelled and simulated in DIgSILENT PowerFactory®, because it is a network comparable to urban distribution networks where a massive implementation of generation system from renewable sources for self-consumption is taking place. Once the base grid has been modelled and simulated, renewable generation, wind and photovoltaic facilities are introduced along the network, with the aim of evaluating their technical-economic performance.

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“…Some researches focus on integrating ESS with power PV station, such as, the optimal siting of battery energy storage system (BESS) in distribution network is solved by PSO to minimize the energy losses of the system [22,23]. With similar objective function, [24][25][26] developed the GA optimization method to reduce daily loss and peak demand by PV stations while deploying ESS, but the cost of installing ESS is not considered. With the same concept, [27] developed a GA optimization approach for minimizing voltage fluctuations induced by PV penetration by distributing BESS across permitted nodes of a distribution system while considering for capital, land-of-use, and installation costs with a qualitative cost model.…”

Section: Introductionmentioning

confidence: 99%

Coordinated Optimal Placement of Energy Storage System and Capacitor Bank Considering Optimal Energy Storage Scheduling for Distribution System Using Mixed-Integer Particle Swarm Optimization

2022

IJIES

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This paper proposes a mixed-integer particle swarm optimization (MIPSO) for coordinated optimal placement of energy storage system (ESS) and capacitor bank (CB). In the propose method, optimal ESS scheduling (OESSS) is solved by particle swarm optimization (PSO), as a subproblem, the optimal coordinated placement (COP) for ESS and CB, simultaneously. The distribution system annual loss minimization (DSALM) is used as the objectives of COP problem. The proposed method was tested with the IEEE 33-bus radial distribution test system. The results demonstrated that the proposed method is successful and robust in minimizing system losses, which loss saving of 35.12% when compare to the based case, which is the best solution among other existing methods.

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Optimal Battery Energy Storage Placement in Highly PV - Penetrated Distribution Networks

Cited by 8 publications

References 32 publications

Multifunction Battery Energy Storage System for Distribution Networks

Multifunction Battery Energy Storage System for Distribution Networks

Análisis De Flujo De Carga De La Red Ieee 34 Nudos Con Integración De Energías Renovables

Coordinated Optimal Placement of Energy Storage System and Capacitor Bank Considering Optimal Energy Storage Scheduling for Distribution System Using Mixed-Integer Particle Swarm Optimization

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