Optimal allocation of electric vehicle charging stations and renewable distributed generation with battery energy storage in radial distribution system considering time sequence characteristics of generation and load demand

Balu, Korra; Mukherjee, V.

doi:10.1016/j.est.2022.106533

Cited by 48 publications

(20 citation statements)

References 48 publications

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“…CSs have a negative effect, whereas DG provides positive effect Unbalanced radial distribution system evaluation Emphasized the use of DG for system improvement Balu and Mukherjee [14] Best location for EVCS/ EVBSSs, impact on RDS Real power loss, voltage profile, energy loss cost, overall operating cost, EVCS/EVBSS demand Consideration of industrial load, constant power, commercial load, residential load models Advocated integration of DGs for selfsustainability, modeling EVCS/EVBSS as constant current load Yuvaraj et al [15] Optimal allocation of DSTATCOM and EVCS Reduced real power loss reduction, voltage stability, cost reductions BESA optimization technique Benefits in terms of voltage stability and power loss reduction have been shown…”

Section: Contribution and Noveltymentioning

confidence: 99%

“…Bhadoriya et al [13] have evaluated the negative effects of charging stations (CSs) on unbalanced radial distribution systems and the positive effects of DG to balance single and multiple DG to minimize the active and reactive power loss and improve voltage stability. Balu and Mukherjee [14] have introduced a new technique for attaining the best location of EV CSs (EVCSs)/EV battery storage systems (EVBSSs) in the radial-distribution-system (RDS). Yuvaraj et al [15] have presented a new nature-inspired bald eagle search algorithm (BESA)-based optimization approach for determining DOI: 10.1002/ente.202300874 Proper planning for electric vehicle (EV) charging stations, as well as the necessary network development, is critical for the continued growth of EVs and conventional loads.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Technique for Optimal Placement of Fast‐Charging Stations of Electric Vehicles for the Reliability of Distribution Network

Ranjith Kumar,

Vallimurugan

2023

Energy Tech

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Proper planning for electric vehicle (EV) charging stations, as well as the necessary network development, is critical for the continued growth of EVs and conventional loads. This article proposes a hybrid method for the allocation of fast‐charging stations (FCSs) and photovoltaic (PV) with battery energy storage (BES) and scheduling. The proposed hybrid technique is the wrapper of golden jackal optimization (GJO) and random forest algorithms (RFA), commonly named the GJO–RFA technique. Using the GJO approach, the allocation and EV assignment problems are resolved. RFA is used to address EV, traffic flow, and PV‐related uncertainties. The objective of the proposed approach is to minimize the loss of energy, investments, index of voltage deviation, and operation and maintenance costs of FCS, PV, and BES. The associated important factors are also assessed, including the number of charging ports, FCS capacity, and EV flow caught using a FCS. As a test example, a 33‐node radial distribution network with the appropriate traffic network is used. For the final problem, the proposed approach to minimal energy loss is 2622.7270 kWh. By then, the performance of the proposed approach is executed in MATLAB, and it is contrasted with other existing techniques. The result shows that the proposed method‐related energy loss is less than existing approaches.

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Section: Contribution and Noveltymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Technique for Optimal Placement of Fast‐Charging Stations of Electric Vehicles for the Reliability of Distribution Network

Ranjith Kumar,

Vallimurugan

2023

Energy Tech

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“…Furthermore, there is a substantial body of literature on the simultaneous installation of EVCS with DGs and DSTAT-COMs/capacitors. Table-1 [2], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36] provides a complete review of current research on EVCS planning using Distribution Generator (DG), Capacitor, Network Reconfiguration (NR), Battery Energy Storage System (BESS), and DSTATCOM for various types of distribution systems, optimization methodologies, and objective functions.…”

Section: Related Workmentioning

confidence: 99%

Integration of Electric Vehicle Charging Stations and DSTATCOM in Practical Indian Distribution Systems Using Bald Eagle Search Algorithm

et al. 2023

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Because of the increasing growth of Electric Vehicle (EV) in India, more electricity is required to power such vehicles. It is also gaining popularity because of its low maintenance, improved performance, and zero carbon impact. As the usage of electric vehicles grows, the distribution system's performance is impacted. As an outcome, the reliability of the distribution system (DS) is dependent on the position of the electric vehicle charging station (EVCS). The fundamental difficulty is the deterioration of the DS due to an incorrect EVCS location. The DS is linked to the charging station and works with the distribution static compensator (DSTATCOM) to minimize the impact of the EVCS. A new nature-inspired Bald Eagle Search Algorithm (BESA) based optimization technique was utilized to find the optimal allocation of DSTATCOM and EVCS in the DS. The proposed strategy for mitigating the real power loss has been tested on practical Indian 28-bus and 108-bus distribution networks. Power loss reduction optimizes the system's net savings, voltage stability, and bus voltage. The test case findings show that the BESA-based optimization is more accurate regarding power loss mitigation, bus voltage enhancement, and annual net saving improvement than the BA-based optimization in the DS. INDEX TERMS Electrical vehicle (EV), bald eagle search algorithm (BESA), bat algorithm (BA), distribution STATicCOMpensator (DSTATCOM), voltage stability index (VSI), distribution system (DS), electric vehicle charging station (EVCS). I. INTRODUCTIONLong-term development and poverty eradication depend heavily on energy. In India, 97.2% of the population has access to electricity. Natural gas and coal are the most frequent fuels utilized in the power sector. India has a total capacity for electricity generation of 388.134GW, with offgrid renewable energy (RE) accounting for 21.26% of that capacity. The lack of coal and natural gas might threaten the production of power. To address the nation's expandingThe associate editor coordinating the review of this manuscript and approving it for publication was Junho Hong .

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“…1 The main task of distribution network planning is to determine the optimal power supply or grid expansion construction scheme according to the load forecasting results and the original grid structure on the basis of meeting the load growth demand and various constraints in a certain planning period, so as to build a safe, reliable, cost-effective and flexible distribution network. 2 Different researchers analyzed the multiscale dynamic time domain of integrated energy in the distribution grid area. Yongjun et al 3 propose a constant capacity planning method for wind optical storage access distribution network, constructs constraints, such as power balance and voltage deviation, and establishes a multiobjective optimization model for wind optical storage access distribution network considering economy and stability based on the consideration of the timing characteristics of distributed generation, the timing characteristics of different seasonal loads, and the real-time price.…”

Section: Introductionmentioning

confidence: 99%

“…The main task of distribution network planning is to determine the optimal power supply or grid expansion construction scheme according to the load forecasting results and the original grid structure on the basis of meeting the load growth demand and various constraints in a certain planning period, so as to build a safe, reliable, cost‐effective and flexible distribution network 2 . Different researchers analyzed the multiscale dynamic time domain of integrated energy in the distribution grid area.…”

Section: Introductionmentioning

confidence: 99%

Multiscale dynamic time‐domain simulation of regional integrated energy considering seasonal load difference

Li,

Qian,

Liu

et al. 2023

Energy Science & Engineering

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To improve the multiscale dynamic dispatching and distribution capacity of regional integrated energy, a multiscale dynamic time‐domain dispatching model of regional integrated energy with seasonal load difference is proposed. The master–slave game coordination planning model of regional comprehensive energy multiscale dispatching is constructed, and the difference fusion model of regional comprehensive energy multiscale dynamic dispatching is established by using the active and reactive power coordination control method according to the influence factors of voltage deviation and line overload absorption. Maximize the weight factors of the positive contribution of distributed photovoltaic (PV) power to the active distribution network. On the basis of the quantitative regulation analysis method of demand response power based on the equivalent slope of the load curve, establish an energy absorption capacity analysis model of seasonal load differentiation. On the basis of the dynamic interaction between different systems, a multiscale dynamic time‐domain analysis and dynamic simulation model of regional integrated energy is constructed by using the method of PV absorptive capacity assessment on both sides of demand uncertainty, to achieve multiscale dynamic time‐domain feature extraction of integrated energy and integrated scheduling of seasonal load differences. The simulation results show that the multiscale dynamic time‐domain allocation of regional comprehensive energy using this method improves the energy dispatching and balanced allocation capability, and the time‐domain dynamic allocation capability is good. Considering the terminal load demand and the electricity price demand formulated by the distribution network investment operators, the seasonal load response and comprehensive dispatching capability are improved.

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Optimal allocation of electric vehicle charging stations and renewable distributed generation with battery energy storage in radial distribution system considering time sequence characteristics of generation and load demand

Cited by 48 publications

References 48 publications

A Hybrid Technique for Optimal Placement of Fast‐Charging Stations of Electric Vehicles for the Reliability of Distribution Network

A Hybrid Technique for Optimal Placement of Fast‐Charging Stations of Electric Vehicles for the Reliability of Distribution Network

Integration of Electric Vehicle Charging Stations and DSTATCOM in Practical Indian Distribution Systems Using Bald Eagle Search Algorithm

Multiscale dynamic time‐domain simulation of regional integrated energy considering seasonal load difference

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