Smart Battery Management Scheme for V2G Based EV Smart Charger – A Better approach of Allocation of EV Based Distributed Generation

Misra, Srikant; Panigrahi, Pratap Kumar; Ghosh, Sandip

doi:10.1109/isssc50941.2020.9358817

Cited by 2 publications

(1 citation statement)

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“…Injection of DRGs implies a dominance of fluctuatory overall grid power that must be evened out from time to time, thus in the process intricating overall grid power flow management. The fluctuations in the grid power (voltage) can lead to issues such as voltage instability, power factor quality degradation, high level of harmonics, and overall power grid system unreliability [1], [2]. The voltage stability can be partly addressed by the incorporation of energy storage systems (ESSs) to supply stabilizing power when the need arises.…”

Section: Introductionmentioning

confidence: 99%

Optimal Allocation and Control of EV Energy Storage in Microgrids

Nleya¹

2021

TURCOMAT

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Next Generation smart grid (SG) systems blend legacy power system networks and the latest state-of-the-art ICT technologies to ensure the efficiency, robustness as well as reliability of the former (power systems). The duplex flow of both information and energy enhances energy supply and de-mand response, as well as SG-related innovative business-oriented applications and services. Renewable generators (RGs) and Electric Vehicles (EVs) are becoming prominent in any SG setup as they promote environmental friendliness. The presence of both necessitates the optimal allocation of dis-tributed renewable generation (DRG) and energy storage sys-tems (ESS) at both SG and microgrid (MG) levels. In that way, grid stability will be always ensured. The paper describes and discusses an optimized ESS deployment approach for serving EVs (as they are one of the largest consumers of stored energy) and DRGs. Careful consideration of the state of the ESS is also considered by developing and applying a dynamic capacity adjustment algorithm to deal with the none-smooth cost func-tions. The proposed cost function takes into consideration the operation as well as investment cost minimization concurrently. The matrix real-coded genetic algorithm (MRCGA) is used to minimize the cost function of the system while constraining it to meet the customer demand, as well as the security of the system overall. The computational simulation results are presented to verify the effectiveness of the proposed method. The electricity network model is simplified using a virtual subnode concept to alleviate the computational load burden of a node's agent. Simulation results demonstrate the feasibility and stability of this dispatch strategy. Overall, our proposed framework and obtained results set a benchmark for the realization of agent-based coordination algorithms to solve the optimal dispatch problem

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Section: Introductionmentioning

confidence: 99%