Integrated Operational Planning of Battery Storage Systems for Improved Efficiency in Residential Community Energy Management Using Multistage Stochastic Dual Dynamic Programming: A Finnish Case Study
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| Pubblicato in: | Energies vol. 18, no. 13 (2025), p. 3560-3577 |
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| Autore principale: | |
| Altri autori: | , |
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MDPI AG
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| Accesso online: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Pattanun, Chanpiwat |u Department of Graduate Studies, Command and General Staff College, Royal Thai Army, 820/1 Rama V Rd., Nakhon-Chai-Si Road, Dusit, Bangkok 10300, Thailand | |
| 245 | 1 | |a Integrated Operational Planning of Battery Storage Systems for Improved Efficiency in Residential Community Energy Management Using Multistage Stochastic Dual Dynamic Programming: A Finnish Case Study | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This study introduces a novel approach for optimizing residential energy systems by combining linear policy graphs with stochastic dual dynamic programming (SDDP) algorithms. Our method optimizes residential solar power generation and battery storage systems, reducing costs through strategic charging and discharging patterns. Using stylized test data, we evaluate battery storage optimization strategies by comparing various SDDP model configurations against a linear programming (LP) benchmark model. The SDDP optimization framework demonstrates robust performance in battery operation management, efficiently handling diverse pricing scenarios while maintaining computational efficiency. Our analysis reveals that the SDDP model achieves positive financial returns with small-scale battery installations, even in scenarios with limited photovoltaic generation capacity. The results confirm both the economic viability and environmental benefits of residential solar–battery systems through two key strategies: aligning battery charging with renewable energy availability and shifting energy consumption away from peak periods. The SDDP framework proves effective in managing battery operations across dynamic pricing scenarios, achieving performance comparable to LP methods while handling uncertainties in PV generation, consumption, and pricing. | |
| 651 | 4 | |a Finland | |
| 653 | |a Forecasting techniques | ||
| 653 | |a Datasets | ||
| 653 | |a Dynamic programming | ||
| 653 | |a Random variables | ||
| 653 | |a Electricity | ||
| 653 | |a Planning | ||
| 653 | |a Optimization | ||
| 653 | |a Energy management | ||
| 653 | |a Convex analysis | ||
| 653 | |a Linear programming | ||
| 653 | |a Stochastic models | ||
| 653 | |a Algorithms | ||
| 653 | |a Households | ||
| 653 | |a Efficiency | ||
| 700 | 1 | |a Oliveira Fabricio |u Department Mathematics and Systems Analysis, School of Science, Aalto University, FI-00076 Espoo, Finland; fabricio.oliveira@aalto.fi (F.O.); sgabriel@umd.edu (S.A.G.) | |
| 700 | 1 | |a Gabriel, Steven A |u Department Mathematics and Systems Analysis, School of Science, Aalto University, FI-00076 Espoo, Finland; fabricio.oliveira@aalto.fi (F.O.); sgabriel@umd.edu (S.A.G.) | |
| 773 | 0 | |t Energies |g vol. 18, no. 13 (2025), p. 3560-3577 | |
| 786 | 0 | |d ProQuest |t Publicly Available Content Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3229145590/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3229145590/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3229145590/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |