Two-Stage Distributionally Robust Optimal Scheduling for Integrated Energy Systems Considering Uncertainties in Renewable Generation and Loads
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| Publicado en: | Mathematics vol. 13, no. 9 (2025), p. 1439 |
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| Otros Autores: | , , , , |
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 024 | 7 | |a 10.3390/math13091439 |2 doi | |
| 035 | |a 3203211403 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231533 |2 nlm | ||
| 100 | 1 | |a Hu Keyong |u School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China; 2024111011029@stu.hznu.edu.cn (Q.Y.); 2024112011060@stu.hznu.edu.cn (L.L.); watersun@hznu.edu.cn (S.S.) | |
| 245 | 1 | |a Two-Stage Distributionally Robust Optimal Scheduling for Integrated Energy Systems Considering Uncertainties in Renewable Generation and Loads | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a To effectively account for the impact of fluctuations in the power generation efficiency of renewable energy sources such as photovoltaics (PVs) and wind turbines (WTs), as well as the uncertainties in load demand within an integrated energy system (IES), this article develops an IES model incorporating power generation units such as PV, WT, microturbines (MTs), Electrolyzer (EL), and Hydrogen Fuel Cell (HFC), along with energy storage components including batteries and heating storage systems. Furthermore, a demand response (DR) mechanism is introduced to dynamically regulate the energy supply–demand balance. In modeling uncertainties, this article utilizes historical data on PV, WT, and loads, combined with the adjustability of decision variables, to generate a large set of initial scenarios through the Monte Carlo (MC) sampling algorithm. These scenarios are subsequently reduced using a combination of the K-means clustering algorithm and the Simultaneous Backward Reduction (SBR) technique to obtain representative scenarios. To further manage uncertainties, a distributionally robust optimization (DRO) approach is introduced. This method uses 1-norm and ∞-norm constraints to define an ambiguity set of probability distributions, thereby restricting the fluctuation range of probability distributions, mitigating the impact of deviations on optimization results, and achieving a balance between robustness and economic efficiency in the optimization process. Finally, the model is solved using the column and constraint generation algorithm, and its robustness and effectiveness are validated through case studies. The MC sampling method adopted in this article, compared to Latin hypercube sampling followed by clustering-based scenario reduction, achieves a maximum reduction of approximately 17.81% in total system cost. Additionally, the results confirm that as the number of generated scenarios increases, the optimized cost decreases, with a maximum reduction of 1.14%. Furthermore, a comprehensive cost analysis of different uncertainties modeling approaches is conducted, demonstrating that the optimization results lie between those obtained from stochastic optimization (SO) and robust optimization (RO), effectively balancing conservatism and economic efficiency. | |
| 653 | |a Energy management | ||
| 653 | |a Electrical loads | ||
| 653 | |a Modelling | ||
| 653 | |a Optimization | ||
| 653 | |a Hypercubes | ||
| 653 | |a Energy storage | ||
| 653 | |a Energy resources | ||
| 653 | |a Uncertainty | ||
| 653 | |a Probability distribution | ||
| 653 | |a Robustness | ||
| 653 | |a Fuel cells | ||
| 653 | |a Photovoltaic cells | ||
| 653 | |a Hydrofluorocarbons | ||
| 653 | |a Electric power generation | ||
| 653 | |a Scheduling | ||
| 653 | |a Cluster analysis | ||
| 653 | |a Cost analysis | ||
| 653 | |a Wind power | ||
| 653 | |a Electricity | ||
| 653 | |a Storage systems | ||
| 653 | |a Energy industry | ||
| 653 | |a Costs | ||
| 653 | |a Sampling methods | ||
| 653 | |a Clustering | ||
| 653 | |a Carbon | ||
| 653 | |a Integrated energy systems | ||
| 653 | |a Renewable energy sources | ||
| 653 | |a Renewable resources | ||
| 653 | |a Hydrogen fuels | ||
| 653 | |a Algorithms | ||
| 653 | |a Probability | ||
| 653 | |a Integrated approach | ||
| 653 | |a Linear programming | ||
| 653 | |a Wind turbines | ||
| 653 | |a Electric power demand | ||
| 653 | |a Methods | ||
| 653 | |a Alternative energy sources | ||
| 653 | |a Constraints | ||
| 653 | |a Vector quantization | ||
| 653 | |a Latin hypercube sampling | ||
| 700 | 1 | |a Yang, Qingqing |u School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China; 2024111011029@stu.hznu.edu.cn (Q.Y.); 2024112011060@stu.hznu.edu.cn (L.L.); watersun@hznu.edu.cn (S.S.) | |
| 700 | 1 | |a Lu, Lei |u School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China; 2024111011029@stu.hznu.edu.cn (Q.Y.); 2024112011060@stu.hznu.edu.cn (L.L.); watersun@hznu.edu.cn (S.S.) | |
| 700 | 1 | |a Zhang, Yu |u School of Engineering, Hangzhou Normal University, Hangzhou 311121, China; 2024112032014@stu.hznu.edu.cn | |
| 700 | 1 | |a Sun Shuifa |u School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China; 2024111011029@stu.hznu.edu.cn (Q.Y.); 2024112011060@stu.hznu.edu.cn (L.L.); watersun@hznu.edu.cn (S.S.) | |
| 700 | 1 | |a Wang, Ben |u School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China; 2024111011029@stu.hznu.edu.cn (Q.Y.); 2024112011060@stu.hznu.edu.cn (L.L.); watersun@hznu.edu.cn (S.S.) | |
| 773 | 0 | |t Mathematics |g vol. 13, no. 9 (2025), p. 1439 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3203211403/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3203211403/fulltextwithgraphics/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3203211403/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |