Inclusion of Energy Storage System with Renewable Energy Resources in Distribution Networks

Thokar, Rayees Ahmad; Pandey, Vipin Chandra; Gupta, Nikhil; Niazi, K. R.; Swarnkar, Anil; Singh, Pradeep Pratap; Meena, Nand K.

doi:10.1002/9781119760375.ch10

Cited by 2 publications

(2 citation statements)

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“…BESS power is also considered to be 1 kW for 1 kWh capacity. In this analysis, the

SO{C^{min}}

and

SO{C^{max}}

are considered equal to 0.1 and 1, respectively, as also proposed and applied in [33] and [40]. The proposed EMS can efficiently schedule the battery's operation within any

SO{C^{min}}

and

SO{C^{max}}

selected, meaning that no special formulation is required for the application of also widely utilized thresholds of 0.2 and 0.8, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…The roundtrip efficiency

({\eta _{rnp}})

is calculated at the dc‐side of the BESS as the fraction of energy stored in the battery module that can be later retrieved and is typically provided by battery manufacturers. Assuming that the charging efficiency at the dc‐side is equal to the discharging efficiency at the dc‐side, their values can be derived as the square root of

{\eta _{rnp}}

[33]. The total efficiency of the BESS in charging and discharging mode,

{\eta _{ch}}

and

{\eta _{dch}}

, is calculated using (7), based on the notation of Figure 2, where

{\eta _{inv}}

is the inverter efficiency rate.…”

Section: Problem Formulationmentioning

confidence: 99%

See 1 more Smart Citation

Optimal scheduling of prosumer's battery storage and flexible loads for distribution network support

Kelepouris

Nousdilis

Bouhouras

et al. 2023

IET Generation Trans & Dist

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Here, an efficient Energy Management System (EMS) for residential installations is proposed. The EMS handles on site Photovoltaic (PV) power production along with Battery Energy Storage System (BESS) and performs load shifting under a Demand Side Management (DSM) scheme that considers user comfort. The goal of the proposed methodology is the minimization of power exchange between the prosumer and the grid. In this way, the power flow across the Distribution Network (DN) is decreased, thus reducing power losses, and improving the voltage profile along the DN. Moreover, the EMS increases the self-sufficiency of the residence. The minimization problem is solved via a metaheuristic algorithm, namely Unified Particle Swarm Optimization (UPSO) utilizing real consumption patterns for residential appliances. The latter adds complexity to the problem but offers accurate modelling of the power demand and improves the performance of the EMS scheme. The proposed EMS is locally implemented as a plug-and-play scheme and benefits both the prosumer and the DN under low computational burden, proving to be efficient. Simulation results for a case study verify the efficiency of the proposed EMS and the comparison with other similar methods proves its superior performance.

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“…BESS power is also considered to be 1 kW for 1 kWh capacity. In this analysis, the

SO{C^{min}}

and

SO{C^{max}}

are considered equal to 0.1 and 1, respectively, as also proposed and applied in [33] and [40]. The proposed EMS can efficiently schedule the battery's operation within any