| Description |
1 online resource (xix, 274 pages) : illustrations (chiefly color) |
| Series |
Power Systems Series |
|
Power systems.
|
| Contents |
Intro -- Foreword -- Preface -- Acknowledgments -- Contents -- 1 Overview of Flexible Load Control -- 1.1 Problem Statement -- 1.2 Economic Dispatch -- 1.3 Energy Management -- 1.4 Demand Response -- 1.5 Problem Properties -- References -- 2 Data Center Flexible Load Control for Renewable Energy Integration -- 2.1 Introduction -- 2.2 Framework Description -- 2.2.1 Structure of Data Center Microgrid -- 2.2.2 Multi-criteria Optimal Operation Framework -- 2.3 Formulations of Multi-criteria Optimization -- 2.3.1 Arrival-Departure Curve Modelling -- 2.3.2 Formulations of Multiple Criteria |
|
2.3.3 System Constraints -- 2.4 Solving Algorithm and Decision Making Method -- 2.4.1 Augmented in-Constraint Optimization -- 2.4.2 Balanced Decision Making Method -- 2.5 Case Study -- 2.5.1 Case Setup -- 2.6 Energy Supply -- 2.6.1 Simulation Results -- 2.7 Conclusion -- References -- 3 Data Center Load Control Based Microgrid Operation via Robust Multi-objective Optimization -- 3.1 Introduction -- 3.2 DCMG Structure and Operational Formulations -- 3.2.1 The Structure of DCMG -- 3.2.2 DCMG Power Demand Models -- 3.2.3 DCMG Power Supply Models |
|
3.3 Robust Multi-objective DCMG Operation Management -- 3.3.1 Affinely Adjustable Policy to Handle Uncertainty -- 3.3.2 Uncertainty Flexible Uncertainty Set to Reduce Conservativeness -- 3.3.3 Uncertainty Set Reformulation to Build a Tractable Policy -- 3.3.4 Robust Multi-objective Optimization Model Formulation -- 3.4 Solution Approach for Robust Multi-objective Operation Optimization Model -- 3.4.1 Equivalent Reformation of Nonlinear Constraints -- 3.4.2 Augmented Epsilon-Constraint Optimization -- 3.4.3 Solution Procedure -- 3.5 Simulation Studies -- 3.5.1 Case Description |
|
3.5.2 Case 1: Conventional Robust Multi-objective Optimization Considering Maximum Uncertainty Set -- 3.5.3 Case 2: Robust Multi-objective Optimization Considering Flexible Uncertainty Set -- 3.5.4 Case 3: Analysis on Effectiveness of the Affinely Adjustable Policy -- 3.5.5 Case 3: Analysis on Parameters Sensitivities of the Solution Approach -- 3.6 Conclusions -- References -- 4 Collaborative Response of Data Center Coupled with Hydrogen Storage System for Renewable Energy Absortion: A Global Interval Optimization Approach -- 4.1 Introduction -- 4.1.1 Backgrounds -- 4.1.2 Related Works |
|
4.1.3 Contribution -- 4.2 Multiple Data Center Collaborative Response Considering Hydrogen Storage System -- 4.2.1 Power Dispatching and Demand Response -- 4.2.2 Modeling of Data Center Load and Hydrogen Storage -- 4.2.3 Modeling of Regional Power System -- 4.2.4 Interval Optimization Model of Cooperative Responding DCCHSS -- 4.3 Interval Optimization Considering Probability Correction of Pessimistic and Optimistic Scenario -- 4.4 Global Interval Optimization Approach in High-Order and Non-convex Model -- 4.5 Case Study -- 4.5.1 Case Setup -- 4.5.2 Analysis of Decision Performance -- 4.5.3 Analysis of WP Connection in the Efficient and Robust Solution |
| Summary |
This book addresses the pressing challenges faced by renewable power system operation (RPSO) due to the increasing penetration of renewable energy and flexible load. These challenges can be divided into two categories. Firstly, the inherent uncertainties associated with renewable energy sources pose significant difficulties in RPSO. Secondly, the presence of various types of flexible load, along with their complex constraint relationships, adds to the operational complexities. Recognizing the growing emphasis on the economic and low-carbon aspects of RPSO, this book focuses on the key issues of flexible load control. It mainly consists of following categories: (1) The control of data centers, a booming flexible load, to enhance RPSO through renewable energy integration and advanced robust multi-objective optimization. (2) The introduction of flexible industrial load control, employing effective demand-supply cooperative responding strategies for RPSO. (3) The exploration of electric vehicle flexible charging load control and centralized electric vehicle charging system control in the context of RPSO. The book also covers the emerging field of flexible integrated load control for renewable energy-based comprehensive energy system operation. Aimed at researchers, engineers, and graduate students in electrical engineering and computer science, this book provides a valuable resource for understanding and implementing flexible load control in the context of RPSO |
| Notes |
Description based upon print version of record |
| Subject |
Electric power systems.
|
|
Renewable energy sources.
|
| Form |
Electronic book
|
| Author |
Li, Yang
|
|
Zeng, Zhigang
|
| ISBN |
9789819703128 |
|
9819703123 |
|
