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.
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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. |
| [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 |
| [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 |
| [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 |
| [22] | Elmachtoub A. and Grigas P. (2022). Smart "Predict, then Optimize". Manag. Sci. 68:9−26. DOI:10.1287/mnsc.2020.3922 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| 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 |
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.
Research workflow.
TEPCO 500/275/154 kV research topology and selected storage sites
Peak-window dispatch for the first selected project
Annualized economics of the six selected branch-storage projects
Fixed-design annualized-profit sensitivity to the assumed peak-deferral value
Temperature-sensitive branches
Integrated branch priorities. Pressure (congestion-screening) and temperature-evidence scores are weakly rank-correlated
Correlation of fixed-design annualized profit with branch pressure and temperature evidence under alternative peak-deferral values