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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...

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Bibliographic Details
Main Author: Yang, Bo
Corporate Author: ScienceDirect (Online service)
Other Authors: Wang, Zhaojian, Guan, Xinping
Format: eBook
Language:English
Published: San Diego : Elsevier, 2024.
Subjects:
Mathematical optimization.
Energy consumption.
Renewable resource integration.
Uncertainty.
Optimisation mathématique.
Énergie > Consommation.
Intégration des énergies renouvelables.
Incertitude.
energy consumption.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • 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
  • 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
  • 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
  • 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
  • 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