Courses

Multiple Energy System: Modeling and Analysis

Multi-Energy Systems (MES) envisage the integration of multiple energy vectors and systems (e.g., electricity, heat, cooling, gas, transport, water, etc.), which is the physical basis of energy internet. MES unlock the flexibility of shifting across multiple energy vectors and result in reduced costs and lower emissions compared to separate energy systems. This course provides a comprehensive introduction to the modelling and analysis of MES. Energy production, conversion, storage, transmission and consumption are analyzed from MES perspective. Specific case study examples and recently developed modelling tools are presented and illustrated.

The topics include:

1. Introduction
   

2. General Methods for Network Analysis
   

3. Network Matrices of Power Systems
   

4. Sparse Techniques
   

5. Modified Solution of Network Equations, Network Reduction and Equivalence
   

6. Mathematical Models of Power Flow Calculations and Basic Solution Methods
   

7. Introduction to energy generation, conversion and storage equipment
   

8. Modeling and Analysis of Heat Network
   

9. Modeling and Analysis of Gas Network
   

10. Standardized Matrix Modeling of Energy Hub
    

11. Modeling of Multiple Energy Networks and Energy Flows Calculations
    

Power Systems Analysis

Power system analysis contributes to analyzing and controlling the complex modern power systems. This course focuses on the power system, introducing the fundamental concepts, mathematical models, analysis methods, computerized methods, control and protection methods. The course is the guide and bridge of power and energy system research. Specific case study examples and recently developed modelling tools are presented and illustrated.

The topics include:

1. Introduction to Power Systems
   

2. Power System Steady State Models
   

3. Power Flow Analysis and Calculation
   

4. Power System Steady State Operation and Control
   

5. Power System Transient Models
   

6. Power System Transient Analysis
   

7. Power System Stability Analysis and Control
   

Energy Internet Planning, Operation and Control

Energy Internet is a new form of energy industry development which integrates Internet with energy production, transmission, storage, consumption and energy market. Energy Internet has the main characteristics of equipment intelligence, energy integration, information symmetry, decentralization, system flattening, open trading and so on. Compared with traditional energy systems, energy Internet has achieved “three major integration”, namely, integration of multiple energy systems, integration of cyber and physical systems, and integration of multiple links from energy production to consumption. “Three major integration” will have a revolutionary impact on the physical form, scheduling, control and trading of energy internet. Energy Internet is an emerging field, which is a very important and promising development direction in the field of energy. Starting from the characteristics and development trend of energy system, this course pays attention to the study of the inherent physical and economic laws of energy Internet, and teaches the scheduling, control and trading modes, and models and key technologies of energy internet.

The topics include:

1. Introduction: Background and Policy System
   

2. Market Mechanism and Business Models of Energy Internet
   

3. Optimal Scheduling of Energy Internet: Models and Methods
   

4. Distributed Control of Energy Internet
   

5. Optimal Scheduling of Energy Internet: Optimization and Application
   

6. Operation of Integrated Electricity and Natural Gas Networks
   

7. Operation of Energy Internet with High Proportion Distributed Energy Storage
   

8. Integrated Demand Response
   

9. Operation of Energy Internet Considering Energy Big Data
   

10. Tradable Energy for Distributed Agents
    

11. Optimal Planning of District Multi-Energy Systems
    

12. Optimal Planning of Multi-Regional Multi-Energy Systems
    

Big Data Technology and its Applications

Big data technology and machine learning methods have produced a series of successful applications in the energy systems in recent years. Data driven techniques become an essential knowledge for students in Energy Internet field. This course provides a comprehensive introduction to classic machine learning methods and the applications in energy systems. This course firstly introduces a series of basic theories and methods of big data and machine learning, including regression method, decision tree, random forest, SVM, neural network, clustering method, dimensionality reduction method, etc. Then the applications of big data technology from the practical problems of the current energy industry are presented and illustrated. Enhancing students’ data thinking to solve energy system problems is a critical target of this course.

The topics include:

1. Introduction: Background
   

2. Python Programming Introduction
   

3. Optimization Theory Introduction
   

4. Machine Learning (1): Regression
   

5. Machine Learning (2): Artificial Neural Networks
   

6. Machine Learning (3): Logistic Regression and SVM
   

7. Machine Learning (4): Decision Tree
   

8. Machine Learning (5): Ensemble Learning and Random Forest
   

9. Machine Learning (6): Unsupervised Learning
   

10. Applications (1): Big Data in the Demand Side and Forecasting
    

11. Applications (2): Big Data in the Demand Side - Electricity Consumption Analysis
    

12. Applications (3): Data-driven Power Flow Calculation
    

13. Applications (4): Power System Topology Identification
    

14. Applications (5): Overview on Computer Vision, Natural Language Processing, and Power System Analytics