Decision-Aware Pre-Feasibility Screening for Branch-Level Battery Storage Investment Under Spatiotemporal Grid Stress: Evidence from the Tokyo Transmission Network

Yanxue Li; Tianxiao Huang; Weijun Gao

doi:10.59717/ipj.energy-use.2026.100062

Energy Use > 2026 Vol. 2 > No. 3 > 100062

Next Article Previous Article

ARTICLE Open Access

Cite PDF

Decision-Aware Pre-Feasibility Screening for Branch-Level Battery Storage Investment Under Spatiotemporal Grid Stress: Evidence from the Tokyo Transmission Network

1.
Innovation Institute for Sustainable Maritime Architecture Research and Technology, Qingdao University of Technology, Qingdao 266033 China
2.
Faculty of Environmental Engineering, The University of Kitakyushu, Fukuoka 808-0135, Japan

Fund Project: This study was supported by Shandong Provincial Youth Innovation Team Project for Higher Education Institutions (No. 2024KJH025).

More Information

Corresponding author: liyanxue@qut.edu.cn
Received Date: September 07, 2029

Accepted Date: September 22, 2026

Published Online: September 23, 2026

Energy Use 2(3), https://doi.org/10.59717/ipj.energy-use.2026.100062 | Cite this article

GRAPHICAL ABSTRACT

GRAPHICAL ABSTRACT

DownLoad: Full size image

PUBLIC SUMMARY

PUBLIC SUMMARY
1. Public grid, electricity-price and temperature records support early screening of battery storage investments in Tokyo.
  
  Across 283 transmission branches, the model selects six two-hour battery projects totalling 1.33 GW within a JPY 200 billion budget.
  
  Assumed savings from postponing grid upgrades account for 86% of modelled annual value before costs and determine project profitability.
  
  Fifty-seven branches show strong flow responses to hot and cold evenings; none coincides with the six selected battery locations.
  
  Economic and weather assessments identify distinct planning priorities, guiding targeted engineering studies and verification of potential savings.

ABSTRACT

ABSTRACT

Public transmission-flow records reveal recurring operational stress, but their value for storage investment depends on a transparent link between observed flows, economic decisions, and subsequent engineering validation. This study develops a decision-aware spatiotemporal framework for branch-level battery energy storage system (BESS) pre-feasibility screening in the Tokyo transmission network. Fiscal-year 2024 TEPCO branch flows are aligned with Tokyo-area day-ahead prices and temperature observations across 283 unique branches and 8,760 hours. A composite stress screen identifies 12 candidates; branch-level linear programs co-optimize BESS power, energy, dispatch, and empirical peak reduction; and a binary portfolio model selects projects under a JPY 200 billion budget. A separate 20:00 weather model estimates branch-specific heating and cooling responses. The selected portfolio contains six two-hour projects totalling 1,328.5 MW/2,657 MWh and JPY 199.3 billion of capital, with a conditional 15-year net present value of JPY 208.0 billion. Valuation depends chiefly on the peak-deferral credit, which supplies 86.1% of gross annual operating value, and the fixed-design break-even value is JPY 14,913/kW-year. Without the credit, annualised net benefit falls to −JPY 19.8 billion/year; at the base value of JPY 30,000/kW-year it reaches JPY 20.0 billion/year. Fifty-seven of 283 branches exhibit robust two-sided temperature responses, the all-branch pressure–temperature rank correlation is weak, and none of the selected projects passes the weather screen. The framework separates economic peak-shaving candidates from weather-responsive corridors and routes each toward an appropriate validation pathway.
- battery storage /
- spatiotemporal grid stress /
- peak shaving /
- electricity arbitrage /
- resilience screening /
- resource adequacy

FullText(HTML)

REFERENCES

[1]	Hesse H., Schimpe M., Kucevic D., et al. (2017). Lithium-Ion Battery Storage for the Grid-A Review of Stationary Battery Storage System Design Tailored for Applications in Modern Power Grids. Energies 10. 2107,DOI:10.3390/en10122107 View in Article Google Scholar
[2]	Liu Z., Su T., Quan Z., et al. (2023). Review on the Optimal Configuration of Distributed Energy Storage. Energies 16:5426. DOI:10.3390/en16145426 View in Article CrossRef Google Scholar
[3]	Levin T., Bistline J., Sioshansi R., et al. (2023). Energy storage solutions to decarbonize electricity through enhanced capacity expansion modelling. Nat. Energy 8:1199−1208. DOI:10.1038/s41560-023-01340-6 View in Article CrossRef Google Scholar
[4]	Mallapragada D., Sepulveda N. and Jenkins J. (2020). Long-run system value of battery energy storage in future grids with increasing wind and solar generation. Appl. Energy 275:115390. DOI:10.1016/j.apenergy.2020.115390 View in Article CrossRef Google Scholar
[5]	Sepulveda N., Jenkins J., Edington A., et al. (2021). The design space for long-duration energy storage in decarbonized power systems. Nat. Energy :506-516. DOI:10.1038/s41560-021-00796-8 View in Article Google Scholar
[6]	Ziegler M., Mueller J., Pereira G., et al. (2019). Storage Requirements and Costs of Shaping Renewable Energy Toward Grid Decarbonization. Joule 3:2134−2153. DOI:10.1016/j.joule.2019.06.012 View in Article CrossRef Google Scholar
[7]	Antweiler W. (2021). Microeconomic models of electricity storage: Price Forecasting, arbitrage limits, curtailment insurance, and transmission line utilization. Energy Eco. 101:105390. DOI:10.1016/j.eneco.2021.105390 View in Article CrossRef Google Scholar
[8]	Schill W. and Zerrahn A. (2018). Long-run power storage requirements for high shares of renewables: Results and sensitivities. Renew. Sustain. Energy Rev. 83:156−171. DOI:10.1016/j.rser.2017.05.205 View in Article CrossRef Google Scholar
[9]	Massucco S., Pongiglione P., Silvestro F., et al. (2021). Siting and Sizing of Energy Storage Systems: Towards a Unified Approach for Transmission and Distribution System Operators for Reserve Provision and Grid Support. Elect. Power Sys. Res. 190:106660. DOI:10.1016/j.epsr.2020.106660 View in Article CrossRef Google Scholar
[10]	Khajeh H., Parthasarathy C., Doroudchi E., et al. (2023). Optimized siting and sizing of distribution-network-connected battery energy storage system providing flexibility services for system operators. Energy 285:129490. DOI:10.1016/j.energy.2023.129490 View in Article CrossRef Google Scholar
[11]	Zhao Y., Zhang Y., Li Y., et al. (2024). Optimal configuration of energy storage for alleviating transmission congestion in renewable energy enrichment region. J. Energy Stor. 82:110398. DOI:10.1016/j.est.2023.110398 View in Article CrossRef Google Scholar
[12]	Inaolaji A., Wu X., Roychowdhury R., et al. (2024). Optimal allocation of battery energy storage systems for peak shaving and reliability enhancement in distribution systems. J Energy Stor. 95:112305. DOI:10.1016/j.est.2024.112305 View in Article CrossRef Google Scholar
[13]	Lamp S. and Samano M. (2022). Large-scale battery storage, short-term market outcomes, and arbitrage. Energy Eco. 107:105786. DOI:10.1016/j.eneco.2021.105786 View in Article CrossRef Google Scholar
[14]	Hjalmarsson J., Thomas K. and Boström C. (2023). Service stacking using energy storage systems for grid applications – A review. J. Energy Stor. 60:106639. DOI:10.1016/j.est.2023.106639 View in Article CrossRef Google Scholar
[15]	Marnell K., Obi M. and Bass R. (2019). Transmission-Scale Battery Energy Storage Systems: A Systematic Literature Review. Energies 12:4603. DOI:10.3390/en12234603 View in Article CrossRef Google Scholar
[16]	Ma M., Huang H., Song X., et al. (2022). Optimal sizing and operations of shared energy storage systems in distribution networks: A bi-level programming approach. Appl. Energy 307:118170. DOI:10.1016/j.apenergy.2021.118170 View in Article CrossRef Google Scholar
[17]	Chung A., Abdulai J., Bayoh P., et al. (2026). Improving access to essential medicines via decision-aware machine learning. Nature :1178-1183. DOI:10.1038/s41586-026-10433-7 View in Article Google Scholar
[18]	Zheng Y., Lin Y., Zhao L., et al. (2023). Spatial planning of urban communities via deep reinforcement learning. Nat. Comput. Sci. :748-762. DOI:10.1038/s43588-023-00503-5. View in Article Google Scholar
[19]	Wessel J., Chowdhury A., Wild T., et al. (2025). Regional coordination can alleviate the cost burden of a low-carbon electricity system. Nat. Commun. 16:9033. DOI:10.1038/s41467-025-64093-8 View in Article CrossRef Google Scholar
[20]	Donti P. L., Amos B. and Kolter J. Z. (2017). Task-based End-to-end Model Learning in Stochastic Optimization. Adv. Neural Inform. Proces. Sys. 30:5484−5494. DOI:10.48550/arXiv.1703.04529 View in Article CrossRef Google Scholar
[21]	Wilder B., Dilkina B. and Tambe M. (2019). Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization. Proceed. AAAI Conf. A.I. 33:1658−1665. DOI:10.1609/aaai.v33i01.33011658 View in Article CrossRef Google Scholar
[22]	Elmachtoub A. and Grigas P. (2022). Smart "Predict, then Optimize". Manag. Sci. 68:9−26. DOI:10.1287/mnsc.2020.3922 View in Article CrossRef Google Scholar
[23]	Mandi J., Kotary J., Berden S., et al. (2024). Decision-Focused Learning: Foundations, State of the Art,Benchmark and Future Opportunities. J. A.I. Res. 81:1623−1701. DOI:10.1613/jair.1.15320 View in Article CrossRef Google Scholar
[24]	Kotary J., Fioretto F., Van Hentenryck P., et al. (2021). End-to-End Constrained Optimization Learning: A Survey. Proceed. 13 Int. Joint Conf. A.I. :4475–4482. DOI: 10.24963/ijcai.2021/610 View in Article Google Scholar
[25]	Hiruta Y., Gao L. and Ashina S. (2022). A novel method for acquiring rigorous temperature response functions for electricity demand at a regional scale. Sci. Total Environ. 819:152893. DOI:10.1016/j.scitotenv.2021.152893 View in Article CrossRef Google Scholar
[26]	Takane Y., Nakajima K., Yamaguchi K., et al. (2023). Decarbonisation technologies can halve the nonlinear increase in electricity demand in densely populated areas due to climate change. Sustain. Cit. Soc. 99:104966. DOI:10.1016/j.scs.2023.104966 View in Article CrossRef Google Scholar
[27]	Ludwig N., Arora S. and Taylor J. W. (2023). Probabilistic load forecasting using post-processed weather ensemble predictions. J. Operat. Res. Soc. 74:1008−1020. DOI:10.1080/01605682.2022.2115411 View in Article CrossRef Google Scholar
[28]	Miller J. I. and Nam K. (2022). Modeling peak electricity demand: A semiparametric approach using weather-driven cross-temperature response functions. Energy Econ. 114:106291. DOI:10.1016/j.eneco.2022.106291 View in Article CrossRef Google Scholar
[29]	Hou H., Liu C., Wang Q., et al. (2022). Review of load forecasting based on artificial intelligence methodologies, models, and challenges. Elec. Power Sys. Res. 210:108067. DOI:10.1016/j.epsr.2022.108067 View in Article CrossRef Google Scholar
[30]	Brown T., Hörsch J. and Schlachtberger D. (2018). PyPSA: Python for Power System Analysis. J. Open Res. Soft. 6:4. DOI:10.5334/jors.188 View in Article CrossRef Google Scholar
[31]	Hörsch J., Hofmann F., Schlachtberger D., et al. (2018). PyPSA-Eur: An open optimisation model of the European transmission system. Energy Strat. Rev. 22:207−215. DOI:10.1016/j.esr.2018.08.012 View in Article CrossRef Google Scholar
[32]	Birchfield A., Xu T., Gegner K., et al. (2017). Grid Structural Characteristics as Validation Criteria for Synthetic Networks. IEEE TPS 32:3258−3265. DOI:10.1109/tpwrs.2016.2616385 View in Article CrossRef Google Scholar
[33]	Grochowicz A., Bloomfield H. and Victoria M. (2026). Resilience metrics to guide back-up investments in the power system during extreme weather. Nat. Sustain. DOI:10.1038/s41893-026-01897-6 View in Article Google Scholar
[34]	Qiu L., Khorramfar R., Wang S., et al. (2026). Climate change reshapes resource adequacy risks and optimal renewable energy siting in wind and solar energy systems. Nat. Energy. DOI:10.1038/s41560-026-02109-3 View in Article Google Scholar

Cited by

ABOUT THIS ARTICLE

Cite this article:

Li Y., Huang T. and Gao W. (2026). Decision-Aware Pre-Feasibility Screening for Branch-Level Battery Storage Investment Under Spatiotemporal Grid Stress: Evidence from the Tokyo Transmission Network. Energy Use 2:100062. https://doi.org/10.59717/ipj.energy-use.2026.100062

Li Y., Huang T. and Gao W. (2026). Decision-Aware Pre-Feasibility Screening for Branch-Level Battery Storage Investment Under Spatiotemporal Grid Stress: Evidence from the Tokyo Transmission Network. Energy Use 2:100062. https://doi.org/10.59717/ipj.energy-use.2026.100062

Welcome!

To request copyright permission to republish or share portions of our works, please visit Copyright Clearance Center's (CCC) Marketplace website at marketplace.copyright.com.

PDF

Figures(8)

Supplementary Information

download_supplementary
SI-Extended-TIE-DOI-62

Article Metrics

Article views(168) PDF downloads(50)

Decision-Aware Pre-Feasibility Screening for Branch-Level Battery Storage Investment Under Spatiotemporal Grid Stress: Evidence from the Tokyo Transmission Network

GRAPHICAL ABSTRACT

PUBLIC SUMMARY

ABSTRACT