Optimal Operation of Integrated Energy Systems under Uncertainties : Distributionally Robust and Stochastic Methods.
Optimal Operation of Integrated Energy Systems Under Uncertainties: Distributionally Robust and Stochastic Models discusses new solutions to the rapidly emerging concerns surrounding energy usage and environmental deterioration. Integrated energy systems (IESs) are acknowledged to be a promising app...
Сохранить в:
| Главный автор: | |
|---|---|
| Другие авторы: | , |
| Формат: | Электронный ресурс eКнига |
| Язык: | английский |
| Опубликовано: |
San Diego :
Elsevier,
2024.
|
| Предметы: | |
| Online-ссылка: | ScienceDirect |
| Метки: |
Добавить метку
Нет меток, Требуется 1-ая метка записи!
|
MARC
| LEADER | 00000cam a22000007i 4500 | ||
|---|---|---|---|
| 001 | on1396699068 | ||
| 003 | OCoLC | ||
| 005 | 20260915095227.0 | ||
| 006 | m o d | ||
| 007 | cr cnu---unuuu | ||
| 008 | 230909s2024 cau o ||| 0 eng d | ||
| 040 | |a EBLCP |b eng |e rda |c EBLCP |d YDX |d N$T |d YDX |d OCLCO |d OPELS |d OCLCL |d OCLCF |d OCLCO | ||
| 019 | |a 1396789398 | ||
| 020 | |a 9780443141232 |q electronic book | ||
| 020 | |a 0443141231 |q electronic book | ||
| 020 | |z 0443141223 | ||
| 020 | |z 9780443141225 | ||
| 035 | |a (OCoLC)1396699068 |z (OCoLC)1396789398 | ||
| 050 | 4 | |a QA402.5 |b .Y36 2024 | |
| 082 | 0 | 4 | |a 519.6 |2 23/eng/20231016 |1 https://id.oclc.org/worldcat/ddc/E3Qg3jh8b9byKVyRcCDVXQGcgX |
| 100 | 1 | |a Yang, Bo. | |
| 245 | 1 | 0 | |a Optimal Operation of Integrated Energy Systems under Uncertainties : |b Distributionally Robust and Stochastic Methods. |
| 264 | 1 | |a San Diego : |b Elsevier, |c 2024. | |
| 300 | |a 1 online resource (252 p.) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 505 | 0 | |a Front Cover -- Optimal Operation of Integrated Energy Systems Under Uncertainties -- Copyright -- Contents -- Preface -- 1 Introduction -- 1.1 Integrated energy systems -- 1.1.1 Component model -- 1.1.2 Uncertainty factors of IES -- 1.1.2.1 Uncertainty on the source side -- 1.1.2.2 Uncertainty on the load side -- 1.1.2.3 Uncertainty on the multi-energy trading market -- 1.1.3 Operation scenario of IES -- 1.1.3.1 Terminal IES -- 1.1.3.2 Regional IES -- 1.1.3.3 Cross-regional IES -- 1.2 Challenges on the energy management -- 1.3 Related work -- 1.3.1 Day-ahead IES optimal operation | |
| 505 | 8 | |a 1.3.1.1 Day-ahead energy-optimization scheduling operation -- 1.3.1.2 Day-ahead energy-market trading -- 1.3.2 Intra-day IES optimal operation -- 1.3.2.1 Model-based methods -- 1.3.2.2 Model-free methods -- 1.3.3 Current issues -- 1.4 Overview of the book -- References -- 2 Day-ahead energy management of IES with a distributionally robust approach -- 2.1 Introduction -- 2.2 System model and problem formulation -- 2.2.1 Energy-hub model -- 2.2.2 Day-ahead energy-management formulation -- 2.3 Scheduling with distributionally robust optimization -- 2.3.1 DRO reformulation for chance constraint | |
| 505 | 8 | |a 2.3.2 Approximation with unimodality information -- 2.4 Numerical experiments -- 2.4.1 Simulation setup -- 2.4.2 Day-ahead energy-management results -- 2.4.2.1 Economic profits of storages -- 2.4.3 Impacts of confidence levels -- 2.4.4 Impacts of interval bounds -- 2.4.5 Evaluation on economic comparisons -- 2.5 Conclusion and notes -- References -- 3 Distributionally robust heat-and-electricity pricing for IES with decision-dependent uncertainties -- 3.1 Introduction -- 3.2 System model and problem formulation -- 3.2.1 Mathematical formulation of retailer level | |
| 505 | 8 | |a 3.2.2 Mathematical formulation of users level -- 3.3 Solution approach -- 3.3.1 DRO reformulation for chance constraints -- 3.3.1.1 Reformulation of linearization -- 3.3.2 MPEC reformulation -- 3.4 Computational experiments -- 3.4.1 Simulation setup -- 3.4.2 Results and discussions -- 3.5 Conclusion and notes -- References -- 4 Multi-level coordinated energy management for IES in hybrid markets -- 4.1 Introduction -- 4.2 Energy-management architecture -- 4.3 Day-ahead energy-management mechanism in hybrid markets -- 4.3.1 Characterization of uncertainties -- 4.3.2 Two-stage day-ahead optimization | |
| 505 | 8 | |a 4.4 Intra-day energy-management mechanism -- 4.4.1 Hour-ahead level optimization -- 4.4.2 Intra-hour level optimization -- 4.5 Solution methodology -- 4.5.1 Linearized reconstruction of carbon-trading function -- 4.5.2 Transformation of two-stage DRO -- 4.5.3 Transformation of chance constraints -- 4.6 Computational experiments -- 4.6.1 Simulation setup -- 4.6.2 Optimal results of triple-layered energy management -- 4.6.3 Impacts of uncertain parameters -- 4.6.4 Impacts of carbon trading -- 4.6.5 Impacts of multi-timescale optimization -- 4.6.6 Comparisons of economic performance | |
| 500 | |a 4.6.7 Scalability analysis | ||
| 520 | |a Optimal Operation of Integrated Energy Systems Under Uncertainties: Distributionally Robust and Stochastic Models discusses new solutions to the rapidly emerging concerns surrounding energy usage and environmental deterioration. Integrated energy systems (IESs) are acknowledged to be a promising approach to increasing the efficiency of energy utilization by exploiting complementary (alternative) energy sources and storages. IESs show favorable performance for improving the penetration of renewable energy sources (RESs) and accelerating low-carbon transition. However, as more renewables penetrate the energy system, their highly uncertain characteristics challenge the system, with significant impacts on safety and economic issues. To this end, this book provides systematic methods to address the aggravating uncertainties in IESs from two aspects: distributionally robust optimization and online operation. | ||
| 650 | 0 | |a Mathematical optimization. | |
| 650 | 0 | |a Energy consumption. | |
| 650 | 0 | |a Renewable resource integration. | |
| 650 | 0 | |a Uncertainty. | |
| 650 | 6 | |a Optimisation mathématique. |0 (CaQQLa)201-0007680 | |
| 650 | 6 | |a Énergie |x Consommation. |0 (CaQQLa)201-0077410 | |
| 650 | 6 | |a Intégration des énergies renouvelables. |0 (CaQQLa)000299155 | |
| 650 | 6 | |a Incertitude. |0 (CaQQLa)201-0031721 | |
| 650 | 7 | |a energy consumption. |2 aat |0 (CStmoGRI)aat300055212 | |
| 650 | 7 | |a Energy consumption |2 fast |0 (OCoLC)fst00909986 | |
| 650 | 7 | |a Mathematical optimization |2 fast |0 (OCoLC)fst01012099 | |
| 650 | 7 | |a Renewable resource integration |2 fast |0 (OCoLC)fst01910116 | |
| 650 | 7 | |a Uncertainty |2 fast |0 (OCoLC)fst01160832 | |
| 700 | 1 | |a Wang, Zhaojian. | |
| 700 | 1 | |a Guan, Xinping. | |
| 776 | 0 | 8 | |i Print version: |a Yang, Bo |t Optimal Operation of Integrated Energy Systems under Uncertainties |d San Diego : Elsevier,c2023 |z 9780443141225 |
| 856 | 4 | 0 | |3 ScienceDirect |u https://www.sciencedirect.com/science/book/9780443141225 |