SYNAPCITY-DT: AI-Driven Urban Energy Intelligence for Smart Cities

A Decision Oriented Digital Twin Framework for Uncertainty Aware Renewable Urban Energy Management

Authors

DOI:

https://doi.org/10.62161/sauc.v12.6349

Keywords:

Urban Digital Twin, Smart Cities, AI-Driven Energy Systems, Renewable Energy Intelligence, Risk-Aware Control, PV–BESS Systems, Urban Resilience

Abstract

This study presents SYNAPCITY-DT, an AI-driven urban energy digital twin framework for adaptive renewable energy operation under uncertainty. The framework integrates probabilistic photovoltaic forecasting, risk-aware battery energy storage optimization, and operational intelligence within a unified digital twin environment. Using real operational data from a utility-scale PV–BESS system in Finland, the framework evaluates uncertainty propagation across forecasting, planning, and operational decision-making. Results show that adaptive risk-aware control improves operational resilience, flexibility utilization, and decision robustness under varying forecast horizons. The study demonstrates the potential of AI-enabled digital twins as scalable urban energy intelligence systems supporting resilient and sustainable smart cities.

Downloads

Download data is not yet available.

Global Statistics ℹ️

Cumulative totals since publication
0
Views
0
Downloads
0
Total
Downloads by format:
PDF (Español (España)) 0 PDF 0

References

Abdalla, A. A., El Moursi, M. S., El-Fouly, T. H. M., & Al Hosani, K. H. (2026). A state-of-the-art review of adaptive, predictive, synergistic, and degradation-aware power variability smoothing techniques in photovoltaic power plants. Renewable and Sustainable Energy Reviews, 230, Article 116687. https://doi.org/10.1016/j.rser.2025.116687

Agakishiev, I., Härdle, W. K., Kopa, M., Kozmik, K., & Petukhina, A. (2025). Multivariate probabilistic forecasting of electricity prices with trading applications. Energy Economics, 141, Article 108008. https://doi.org/10.1016/j.eneco.2024.108008

Al-Dahidi, S., Madhiarasan, M., Al-Ghussain, L., Abubaker, A. M., Ahmad, A. D., Alrbai, M., Aghaei, M., Alahmer, H., Alahmer, A., Baraldi, P., & Zio, E. (2024). Forecasting solar photovoltaic power production: A comprehensive review and innovative data-driven modeling framework. Energies, 17(16), 4145. https://doi.org/10.3390/en17164145

Ali, A., Yousif, M., Numan, M., & Kazmi, S. A. A. (2025). Coordinated generation and transmission expansion planning with energy storage systems to facilitate high penetration of renewable energy. IEEE Access, 13, 176801–176812. https://doi.org/10.1109/ACCESS.2025.3617374

Allahvirdizadeh, Y., Galvani, S., & Shayanfar, H. (2021). Data clustering based probabilistic optimal scheduling of an energy hub considering risk-averse. International Journal of Electrical Power & Energy Systems, 128, Article 106774. https://doi.org/10.1016/j.ijepes.2021.106774

Amorim, W. C. S., Cupertino, A. F., Pereira, H. A., & Mendes, V. F. (2024). On sizing of battery energy storage systems for PV plants power smoothing. Electric Power Systems Research, 229, Article 110114. https://doi.org/10.1016/j.epsr.2024.110114

Assaad, C., Leon, J. P. M., Quick, J., Göçmen, T., Ghazouani, S., & Das, K. (2025). Enabling efficient sizing of hybrid power plants: A surrogate-based approach to energy management system modeling. Wind Energy Science, 10, 559–578. https://doi.org/10.5194/wes-10-559-2025

Baccino, F., & Santarelli, M. (2023). Advanced battery energy storage systems for hybrid power and energy management. In 7th International Hybrid Power Plants & Systems Workshop (HYB 2023) (pp. 90–95). https://doi.org/10.1049/icp.2023.1437

Bernecker, M., Sgarciu, S., Kan, X., Anvari, M., Riepin, I., & Müsgens, F. (2026). Adaptive robust optimization for European electricity system planning considering regional Dunkelflaute events. Applied Energy, 412, Article 127671. https://doi.org/10.1016/j.apenergy.2026.127671

Cao, Y., Wu, Q., Zhang, H., & Li, C. (2022). Optimal sizing of hybrid energy storage system considering power smoothing and transient frequency regulation. International Journal of Electrical Power & Energy Systems, 142, Article 108227. https://doi.org/10.1016/j.ijepes.2022.108227

Chen, Y., Li, X., & Zhao, S. (2024). A novel photovoltaic power prediction method based on a long short-term memory network optimized by an improved sparrow search algorithm. Electronics, 13(5), 993. https://doi.org/10.3390/electronics13050993

Conejo, A. J., Carrión, M., & Morales, J. M. (2010). Decision making under uncertainty in electricity markets. Springer. https://doi.org/10.1007/978-1-4419-7421-1

Čović, N., Pavić, I., & Pandžić, H. (2024). Multi-energy balancing services provision from a hybrid power plant: PV, battery, and hydrogen technologies. Applied Energy, 374, Article 123966. https://doi.org/10.1016/j.apenergy.2024.123966

Das, K., Hansen, A. D., Leon, J. P. M., Zhu, R., Gupta, M., Pérez-Rúa, J.-A., Long, Q., Pombo, D. V., Barlas, A., Gocmen, T., Sogachev, A., Koivisto, M., Cutululis, N. A., & Sørensen, P. E. (2025). Research challenges and opportunities of utility-scale hybrid power plants. WIREs Energy and Environment, 14, e70001. https://doi.org/10.1002/wene.70001

Deb, K. (2011). Multi-objective optimisation using evolutionary algorithms: An introduction. In L. Wang, A. Ng, & K. Deb (Eds.), Multi-objective evolutionary optimisation for product design and manufacturing. Springer. https://doi.org/10.1007/978-0-85729-652-8_1

Domínguez, R., & Vitali, S. (2021). Multi-chronological hierarchical clustering to solve capacity expansion problems with renewable sources. Energy, 227, Article 120491. https://doi.org/10.1016/j.energy.2021.120491

Ejuh Che, E., Roland Abeng, K., Iweh, C. D., Tsekouras, G. J., & Fopah-Lele, A. (2025). The impact of integrating variable renewable energy sources into grid-connected power systems: Challenges, mitigation strategies, and prospects. Energies, 18(3), 689. https://doi.org/10.3390/en18030689

Elnosh, A., Calais, M., & Parlevliet, D. (2026). A systematic literature review of digital twin research for photovoltaic systems: Trends, challenges, and opportunities. Renewable and Sustainable Energy Reviews, 226, Article 116326. https://doi.org/10.1016/j.rser.2025.116326

ENTSO-E. (2026, April 15). Electricity balancing and European balancing market framework. https://www.entsoe.eu/network_codes/eb/

Esmaeili Aliabadi, D., & Pinto, T. (2025). Modeling electricity markets and energy systems: Challenges and opportunities. Energies, 18(2), 245. https://doi.org/10.3390/en18020245

Finnish Meteorological Institute. (2025). Open data services. https://en.ilmatieteenlaitos.fi/open-data

Fingrid. (2025). Frequency containment reserves (FCR) data. https://www.fingrid.fi

Gao, Q., Chen, Y., Yang, D., Zhang, H., Yang, G., Shen, Y., Xia, X., & Liu, B. (2025). Firm power generation with photovoltaic overbuilding and pumped hydro storage. Energy, 324, Article 135800. https://doi.org/10.1016/j.energy.2025.135800

Gao, X., Chan, K. W., Xia, S., Zhou, B., Lu, X., & Xu, D. (2019). Risk-constrained offering strategy for a hybrid power plant consisting of wind power producer and electric vehicle aggregator. Energy, 177, 183–191.

Gardemeister, L., Liikkanen, J., Meriläinen, A., Kosonen, A., Ruusunen, J., Lindfors, A. V., Atlaskin, E., & Ahola, J. (2025). Spatial optimization of solar PV and wind power capacity in Finland and correlation analysis. International Journal of Electrical Power & Energy Systems, 173, Article 111386. https://doi.org/10.1016/j.ijepes.2025.111386

Glasserman, P. (2003). Monte Carlo methods in financial engineering. Springer. https://doi.org/10.1007/978-0-387-21617-1

Holttinen, H., Lindroos, T. J., Lehtilä, A., Koljonen, T., Kiviluoma, J., & Korpås, M. (2025). Estimating the CO2 impacts of wind energy in the transition towards carbon-neutral energy systems. Energies, 18(6), 1548. https://doi.org/10.3390/en18061548

Hofbauer, L., McDowall, W., & Pye, S. (2022). Challenges and opportunities for energy system modelling to foster multi-level governance of energy transitions. Renewable and Sustainable Energy Reviews, 161, Article 112330. https://doi.org/10.1016/j.rser.2022.112330

Iheanetu, K. J. (2022). Solar photovoltaic power forecasting: A review. Sustainability, 14(24), 17005. https://doi.org/10.3390/su142417005

International Energy Agency. (2025). Renewables 2025: Analysis and forecast to 2030. https://www.iea.org/reports/renewables-2025

Jadidoleslam, M. (2025). Risk-constrained participation of virtual power plants in day-ahead energy and reserve markets based on multi-objective operation of active distribution network. Scientific Reports, 15, 9145. https://doi.org/10.1038/s41598-025-93688-w

Karrari, S., Ludwig, N., De Carne, G., & Noe, M. (2022). Sizing of hybrid energy storage systems using recurring daily patterns. IEEE Transactions on Smart Grid, 13(4), 3290–3300. https://doi.org/10.1109/TSG.2022.3156860

Lappalainen, K., & Valkealahti, S. (2022). Sizing of energy storage systems for ramp rate control of photovoltaic strings. Renewable Energy, 196, 1366–1375. https://doi.org/10.1016/j.renene.2022.07.069

Lindberg, O., Zhu, R., & Widén, J. (2024). Quantifying the value of probabilistic forecasts when trading renewable hybrid power parks in day-ahead markets: A Nordic case study. Renewable Energy, 237, Article 121617. https://doi.org/10.1016/j.renene.2024.121617

Liu, Y., Zhong, Y., & Tang, C. (2023). Optimal sizing of photovoltaic/energy storage hybrid power systems: Considering output characteristics and uncertainty factors. Energies, 16(14), 5549. https://doi.org/10.3390/en16145549

Luo, C., Al-Messabi, N., Kuang, Z., Ma, C., El-Amin, I., Deng, H., & Li, Y. (2025). Photovoltaic system modeling and forecasting techniques: A survey. Engineering Applications of Artificial Intelligence, 162, Article 112516. https://doi.org/10.1016/j.engappai.2025.112516

Maciejowska, K., Serafin, T., & Uniejewski, B. (2024). Probabilistic forecasting with a hybrid Factor-QRA approach: Application to electricity trading. Electric Power Systems Research, 234, Article 110541. https://doi.org/10.1016/j.epsr.2024.110541

Meng, A., Wang, P., Zhai, G., Zeng, C., Chen, S., Yang, X., & Yin, H. (2022). Electricity price forecasting with high penetration of renewable energy using attention-based LSTM network trained by crisscross optimization. Energy, 254, Article 124212. https://doi.org/10.1016/j.energy.2022.124212

Mottola, F., Proto, D., & Russo, A. (2024). Probabilistic planning of a battery energy storage system in a hybrid microgrid based on the Taguchi arrays. International Journal of Electrical Power & Energy Systems, 157, Article 109886. https://doi.org/10.1016/j.ijepes.2024.109886

Mystakidis, A., Koukaras, P., Tsalikidis, N., Ioannidis, D., & Tjortjis, C. (2024). Energy forecasting: A comprehensive review of techniques and technologies. Energies, 17(7), 1662. https://doi.org/10.3390/en17071662

NASA POWER. (2025). NASA POWER data access viewer. https://power.larc.nasa.gov

Nord Pool. (2025). Day-ahead market data. https://www.nordpoolgroup.com

Pavlík, M., Kurimský, F., & Ševc, K. (2025). Renewable energy and price stability: An analysis of volatility and market shifts in the European electricity sector (2015–2025). Applied Sciences, 15(12), 6397. https://doi.org/10.3390/app15126397

Rezaeimozafar, M., Barrett, E., Monaghan, R. F. D., & Duffy, M. (2024). A stochastic method for behind-the-meter PV-battery energy storage systems sizing with degradation minimization by limiting battery cycling. Journal of Energy Storage, 86, Article 111199. https://doi.org/10.1016/j.est.2024.111199

Ruan, P., Su, Q., Zhang, L., Luo, J., Diao, Y., Xie, L., & Zheng, H. (2025). Optimal siting and sizing of hybrid energy storage systems in high-penetration renewable energy systems. Energies, 18(9), 2196. https://doi.org/10.3390/en18092196

Ruiz-Abellón, M. C., Fernández-Jiménez, L. A., Guillamón, A., & Gabaldón, A. (2024). Applications of probabilistic forecasting in demand response. Applied Sciences, 14(21), 9716. https://doi.org/10.3390/app14219716

Shapiro, A., Dentcheva, D., & Ruszczyński, A. (2021). Lectures on stochastic programming: Modeling and theory (3rd ed.). Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611976595

Smyl, S., Pełka, P., & Dudek, G. (2026). Probabilistic multi-regional solar power forecasting with any-quantile recurrent neural networks. arXiv, Article abs/2602.05660.

Soroudi, A. (2017). Power system optimization modeling in GAMS. Springer. https://doi.org/10.1007/978-3-319-62350-4

Sun, M., Feng, C., & Zhang, J. (2020). Probabilistic solar power forecasting based on weather scenario generation. Applied Energy, 266, Article 114823. https://doi.org/10.1016/j.apenergy.2020.114823

Sweeney, C., Bessa, R. J., Browell, J., & Pinson, P. (2020). The future of forecasting for renewable energy. WIREs Energy and Environment, 9, e365. https://doi.org/10.1002/wene.365

Talvi, M., & Lappalainen, K. (2024). Sizing of energy storage systems for different levels of PV and wind power in combined PV-wind power plants. In 41st European Photovoltaic Solar Energy Conference and Exhibition (EU PVSEC) proceedings. https://doi.org/10.4229/EUPVSEC2024/5DV.2.4

Teixeira, R., Cerveira, A., Pires, E. J. S., & Baptista, J. (2024). Advancing renewable energy forecasting: A comprehensive review of renewable energy forecasting methods. Energies, 17(14), 3480. https://doi.org/10.3390/en17143480

Tkac, M., Kajanova, M., & Bracinik, P. (2023). A review of advanced control strategies of microgrids with charging stations. Energies, 16(18), 6692. https://doi.org/10.3390/en16186692

Visser, L. R., AlSkaif, T. A., Khurram, A., Kleissl, J., & van Sark, W. G. H. J. M. (2024). Probabilistic solar power forecasting: An economic and technical evaluation of an optimal market bidding strategy. Applied Energy, 370, Article 123573. https://doi.org/10.1016/j.apenergy.2024.123573

Wang, J., Zhou, Y., Zhang, Y., Lin, F., & Wang, J. (2024). Risk-averse optimal combining forecasts for renewable energy trading under CVaR assessment of forecast errors. IEEE Transactions on Power Systems, 39(1), 2296–2309. https://doi.org/10.1109/TPWRS.2023.3268337

Wang, Z., Wu, S., Huang, Y., Liu, R., & Liu, X. (2026). A comprehensive review of deep learning for solar nowcasting: Enhancing accuracy, reliability, and interpretability. Applied Energy, 407, Article 127378. https://doi.org/10.1016/j.apenergy.2026.127378

Wipplinger, E. (2007). Philippe Jorion: Value at risk – The new benchmark for managing financial risk. Financial Markets and Portfolio Management, 21, 397–398. https://doi.org/10.1007/s11408-007-0057-3

Xiong, B., Chen, Y., Chen, D., Fu, J., & Zhang, D. (2025). Deep probabilistic solar power forecasting with Transformer and Gaussian process approximation. Applied Energy, 382.

Xuan, A., Shen, X., Guo, Q., & Sun, H. (2021). A conditional value-at-risk based planning model for integrated energy system with energy storage and renewables. Applied Energy, 294, Article 116971. https://doi.org/10.1016/j.apenergy.2021.116971

Yang, G., Yang, D., Liu, B., & Zhang, H. (2024). The role of short- and long-duration energy storage in reducing the cost of firm photovoltaic generation. Applied Energy, 374, Article 123914. https://doi.org/10.1016/j.apenergy.2024.123914

Yang, Z., Kong, D., Chen, Z., Zhang, Z., Du, D., & Zhu, Z. (2025). A data-driven battery energy storage regulation approach integrating machine learning forecasting models for enhancing building energy flexibility A case study of a net-zero carbon building in China. Buildings, 15(19), 3611. https://doi.org/10.3390/buildings15193611

Zhang, X., Li, Y., Li,T., Gui, Y., Sun, Q., & Gao, D. W. (2024). Digital twin empowered PV power prediction. Journal of Modern Power Systems and Clean Energy, 12(5), 1472–1483. https://doi.org/10.35833/MPCE.2023.000351

Zhu, R., Das, K., Sørensen, P. E., & Hansen, A. D. (2025). A review on energy management system for grid-connected utility-scale renewable hybrid power plants. WIREs Energy and Environment, 14, e70004. https://doi.org/10.1002/wene.70004

Zhu, R., Murcia Leon, J. P., Friis-Møller, M., Gupta, M., & Das, K. (2026). Optimal sizing of renewable hybrid power plants considering component reliability as a multi-discipline optimization under uncertainty. Applied Energy, 404, Article 127138. https://doi.org/10.1016/j.apenergy.2025.127138

Downloads

Published

2026-09-03

How to Cite

Shikdar, T. A., & Laaksonen, H. (2026). SYNAPCITY-DT: AI-Driven Urban Energy Intelligence for Smart Cities: A Decision Oriented Digital Twin Framework for Uncertainty Aware Renewable Urban Energy Management. Street Art & Urban Creativity, 12(5), 285–321. https://doi.org/10.62161/sauc.v12.6349

Issue

Vol. 12 No. 5 (2026)

Section

Research articles

License

Copyright (c) 2026 Authors retain copyright and transfer to the journal the right of first publication and publishing rights

Creative Commons License

This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.

Those authors who publish in this journal accept the following terms:

  1. Authors retain copyright.

  2. Authors transfer to the journal the right of first publication. The journal also owns the publishing rights.

  3. All published contents are governed by an Attribution-NoDerivatives 4.0 International License.
    Access the informative version and legal text of the license. By virtue of this, third parties are allowed to use what is published as long as they mention the authorship of the work and the first publication in this journal. If you transform the material, you may not distribute the modified work.

  4. Authors may make other independent and additional contractual arrangements for non-exclusive distribution of the version of the article published in this journal (e.g., inclusion in an institutional repository or publication in a book) as long as they clearly indicate that the work was first published in this journal.

  5. Authors are allowed and recommended to publish their work on the Internet (for example on institutional and personal websites), following the publication of, and referencing the journal, as this could lead to constructive exchanges and a more extensive and quick circulation of published works (see The Effect of Open Access).