Techno-Economic Planning of Spatially-Resolved Battery Storage Systems in Renewable-Dominant Grids Under Weather Variability

Fuente: arXiv
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Main Authors: Ahmadi, Seyed Ehsan, Kabir, Elnaz, Fattahi, Mohammad, Marzband, Mousa, Li, Dongjun
Format: Preprint
Published: 2025
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author Ahmadi, Seyed Ehsan
Kabir, Elnaz
Fattahi, Mohammad
Marzband, Mousa
Li, Dongjun
author_facet Ahmadi, Seyed Ehsan
Kabir, Elnaz
Fattahi, Mohammad
Marzband, Mousa
Li, Dongjun
contents The ongoing energy transition is significantly increasing the share of renewable energy sources (RES) in power systems; however, their intermittency and variability pose substantial challenges, including load shedding and system congestion. This study examines the role of the battery storage system (BSS) in mitigating these challenges by balancing power supply and demand. We optimize the location, size, and type of batteries using a two-stage stochastic program, with the second stage involving hourly operational decisions over an entire year. Unlike previous research, we incorporate the comprehensive technical and economic characteristics of battery technologies. The New York State (NYS) power system, currently undergoing a significant shift towards increased RES generation, serves as our case study. Using available load and weather data from 1980-2019, we account for the uncertainty of both load and RES generation through a sample average approximation approach. Our findings indicate that BSS can reduce renewable curtailment by 34% and load shedding by 21%, contributing to a more resilient power system in achieving NYS 2030 energy targets. Furthermore, the cost of employing BSS for the reduction of load shedding and RES curtailment does not increase linearly with additional capacity, revealing a complex relationship between costs and renewable penetration. This study provides valuable insights for the strategic BSS deployment to achieve a cost-effective and reliable power system in the energy transition as well as the feasibility of the NYS 2030 energy targets.
format Preprint
id arxiv_https___arxiv_org_abs_2508_12526
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Techno-Economic Planning of Spatially-Resolved Battery Storage Systems in Renewable-Dominant Grids Under Weather Variability
Ahmadi, Seyed Ehsan
Kabir, Elnaz
Fattahi, Mohammad
Marzband, Mousa
Li, Dongjun
Systems and Control
The ongoing energy transition is significantly increasing the share of renewable energy sources (RES) in power systems; however, their intermittency and variability pose substantial challenges, including load shedding and system congestion. This study examines the role of the battery storage system (BSS) in mitigating these challenges by balancing power supply and demand. We optimize the location, size, and type of batteries using a two-stage stochastic program, with the second stage involving hourly operational decisions over an entire year. Unlike previous research, we incorporate the comprehensive technical and economic characteristics of battery technologies. The New York State (NYS) power system, currently undergoing a significant shift towards increased RES generation, serves as our case study. Using available load and weather data from 1980-2019, we account for the uncertainty of both load and RES generation through a sample average approximation approach. Our findings indicate that BSS can reduce renewable curtailment by 34% and load shedding by 21%, contributing to a more resilient power system in achieving NYS 2030 energy targets. Furthermore, the cost of employing BSS for the reduction of load shedding and RES curtailment does not increase linearly with additional capacity, revealing a complex relationship between costs and renewable penetration. This study provides valuable insights for the strategic BSS deployment to achieve a cost-effective and reliable power system in the energy transition as well as the feasibility of the NYS 2030 energy targets.
title Techno-Economic Planning of Spatially-Resolved Battery Storage Systems in Renewable-Dominant Grids Under Weather Variability
topic Systems and Control
url https://arxiv.org/abs/2508.12526