Optimal Operation of Battery and Hydrogen Energy Storage Systems in Electrical Distribution Networks for Peak Shaving

Viola, Luigi; Silva, Luiz C. P. da; Rider, Marcos J.

doi:10.1109/isgt-la.2019.8895509

Cited by 3 publications

(2 citation statements)

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“…In [18], the bin packing algorithm is utilised to control the BESS scheduling in the distribution networks for peak shaving and load levelling. In [19], the peak shaving and energy cost minimisation have been addressed using a combination of battery and hydrogen storage units for distribution networks. The BESS active/reactive power dispatch was optimised in [20] for a multiobjective function that aims to minimise the DG curtailment and BESS operation costs.…”

Section: Introductionmentioning

confidence: 99%

“…To summarise, none of the previous studies considered the coordination, operation, and control of multiple BESS in the distribution network, especially for grid power levelling. Study [19] considered two BESS in their case study. However, the coordination between BESS has not been explained nor investigated clearly in the literature, especially for the real‐time approaches.…”

Section: Introductionmentioning

confidence: 99%

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Distributed battery energy storage systems operation framework for grid power levelling in the distribution networks

et al. 2021

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The integration of battery energy storage systems (BESS) in the electrical grid is accelerating to mitigate the challenges associated with the rapid deployment of low carbon technologies (LCTs). This work investigates the ability of BESS to provide the power networks with important ancillary services such as peak shaving and grid power levelling through two case studies conducted in collaboration with Northern Ireland's distribution network operator. A powerful approach consisting of two strategies is developed to operate the BESS powerfully to enhance the operation of the distribution network. The first strategy is day‐ahead scheduling that aims to dispatch the distributed BESS to smooth the grid power and mitigate voltage and lines stresses. A powerful demand forecasting algorithm is utilised to efficiently apply the day‐ahead scheduling. The second strategy is a real‐time operation to flatten the grid power which can be used separately or to adjust the results obtained from the day‐ahead strategy against the forecasting errors. The proposed approach was validated using real measurements and applied to an 11 kV distribution network located in Northern Ireland, UK. The BESS expected revenues from the participation in different services available in the island of Ireland are quantified and the degradation is considered.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%