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Artificial Intelligence Enabled Computational Methods for Smart Grid Forecast and Dispatch

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With the increasing penetration of renewable energy and distributed energy resources, smart grid is facing great challenges, which could be divided into two categories. On the one hand, the endogenous uncertainties of renewable energy and electricity load lead to great difficulties in smart grid forecast. On the other hand, massive electric devices as well as their complex constraint relationships bring about significant difficulties in smart grid dispatch. 

Owe to the rapid development of artificial intelligence in recent years, several artificial intelligence enabled computational methods have been successfully applied in the smart grid and achieved good performances. Therefore, this book is concerned with the research on the key issues of artificial intelligence enabled computational methods for smart grid forecast and dispatch, which consist of three main parts.

 

(1) Introduction for smart grid forecast and dispatch, in inclusion of reviewing previous contribution of various research methods as well as their drawbacks to analyze characteristics of smart grid forecast and dispatch.

(2) Artificial intelligence enabled computational methods for smart grid forecast problems, which are devoted to present the recent approaches of deep learning and machine learning as well as their successful applications in smart grid forecast.

(3) Artificial intelligence enabled computational methods for smart grid dispatch problems, consisting of edge-cutting intelligent decision-making approaches, which help determine the optimal solution of smart grid dispatch.             

The book is useful for university researchers, engineers, and graduate students in electrical engineering and computer science who wish to learn the core principles, methods, algorithms, and applications of artificial intelligence enabled computational methods.

Author(s): Yuanzheng Li, Yong Zhao, Lei Wu, Zhigang Zeng
Series: Engineering Applications of Computational Methods, 14
Publisher: Springer
Year: 2023

Language: English
Pages: 270
City: Singapore

Foreword
Preface
Acknowledgments
Contents
1 Introduction for Smart Grid Forecast and Dispatch
1.1 Smart Grid Forecast
1.2 Smart Grid Dispatch
1.2.1 Problem Statement
1.2.2 Problem Properties
References
2 Review for Smart Grid Forecast
2.1 Introduction
2.2 The Load and Netload Forecasting
2.2.1 The Representative Patterns of Load Forecasting
2.2.2 The Statistical Model of Load/Net Load Forecasting
2.2.3 The Machine Learning Model of Load and Netload Forecasting
2.3 The Electrical Price Forecasting
2.3.1 The Mathematical Method for Electrical Price Forecasting
2.3.2 The Learning Method for Electrical Price Forecasting
2.4 The Electrical Vehicle Charging Station Charging Power Forecasting
2.4.1 Model-Based Electrical Vehicle Charging Station Charging Power Forecasting Method
2.4.2 Data-Driven Electrical Vehicle Charging Station Charging Power Forecasting Method
References
3 Review for Smart Grid Dispatch
3.1 Introduction
3.2 Real-World Applications
3.2.1 Distribution Network
3.2.2 Microgrid Network
3.2.3 Electric Vehicles
3.2.4 Integrated Energy System
3.3 The Methods for Smart Grid Dispatch
3.3.1 Mathematical Programming
3.3.2 Evolutionary Algorithms
3.3.3 AI-Enabled Methods
References
4 Deep Learning-Based Densely Connected Network for Load Forecast
4.1 Introduction
4.2 Residual Architecture
4.3 Unshared Convolution
4.4 Densely Connected Network
4.4.1 Overall Framework
4.4.2 Densely Connected Block
4.4.3 Clipped L2-norm
4.4.4 Smooth Loss
4.4.5 Smooth Quantile Regression
4.5 Case Study
4.5.1 Data Description
4.5.2 Case 1: Methods Validation
4.5.3 Case 2: Deterministic Forecasting
4.5.4 Case 3: Probabilistic Forecasting
4.6 Conclusion
References
5 Reinforcement Learning Assisted Deep Learning for Probabilistic Charging Power Forecasting of EVCS
5.1 Introduction
5.2 Framework
5.2.1 Problem Formulation
5.2.2 The Probabilistic Forecast Framework of EVCS Charging Power
5.3 Data Transformer Method
5.4 Reinforcement Learning Assisted Deep Learning Algorithm
5.4.1 Long Short-Term Memory
5.4.2 The Modeling of LSTM Cell State Variation
5.4.3 Proximal Policy Optimization
5.5 Adaptive Exploration Proximal Policy Optimization
5.6 Case Study
5.6.1 Data Description and Experiential Initialization
5.6.2 The Performance of Probabilistic Forecasting Obtained by LSTM-AePPO
5.6.3 Metrics Comparison Among Different Algorithms
5.6.4 The Effectiveness of AePPO
5.7 Conclusion
References
6 Dense Skip Attention-Based Deep Learning for Day-Ahead Electricity Price Forecasting with a Drop-Connected Structure
6.1 Introduction
6.2 Structure of the Proposed Framework
6.2.1 Data Preprocessing
6.2.2 Feature Extraction
6.2.3 Autoweighting of Features
6.2.4 Target Regression
6.3 Drop-Connected UCNN-GRU
6.3.1 Advanced Residual UCNN Block
6.3.2 Drop-Connected Structure
6.4 Dense Skip Attention Mechanism
6.4.1 Dense Skip Connection
6.4.2 Feature-Wise Attention Block
6.5 Case Study
6.5.1 Data Description
6.5.2 Implementation Details
6.5.3 Case 1: Model Effectiveness Evaluation
6.5.4 Case 2: Comparison with Statistical Techniques
6.5.5 Case 3: Comparison with Conventional DL Techniques
6.6 Conclusion
6.7 Quantile Regression
6.8 Formulation of the Evaluation Index
6.9 PReLU: A Solution to the Neuron Inactivation
References
7 Uncertainty Characterization of Power Grid Net Load of Dirichlet Process Mixture Model Based on Relevant Data
7.1 Introduction
7.2 A Bayesian Framework Based on the Dirichlet Mixture Model of Data Association
7.2.1 Net Load Time-Series Correlation
7.2.2 Bayesian Framework Based on the Dirichlet Mixture Model of Data Association
7.2.3 Dirichlet Process and Folded Stick Construction Representation
7.2.4 Nonparametric Dirichlet Mixture Model
7.3 The Dirichlet Mixture Model Based on VBI for Data Association
7.3.1 Nonparametric Dirichlet Mixture Model
7.3.2 Variational Posterior Distribution Considering Data Association
7.4 Example Analysis
7.4.1 Description of the Algorithm
7.4.2 DDPMM Convergence Analysis
7.4.3 Analysis of DDPMM Fitting Effect
7.4.4 DDPMM Interval Indicator Analysis
7.5 Conclusion
References
8 Extreme Learning Machine for Economic Dispatch with High Penetration of Wind Power
8.1 Introduction
8.1.1 Background and Motivation
8.1.2 Literature Review
8.1.3 Contribution of This Paper
8.2 Multi-objective Economic Dispatch Model
8.2.1 Formulations of Economic Dispatch
8.2.2 Multi-objective Economic Dispatch Model
8.3 Extreme Learning Machine Assisted Group Search Optimizer with Multiple Producers
8.3.1 Group Search Optimizer with Multiple Producers
8.3.2 ELM Assisted GSOMP
8.4 Simulation Studies
8.4.1 Simulation Settings
8.4.2 Simulation Results
8.5 Conclusion
References
9 Multi-objective Optimization Approach for Coordinated Scheduling of Electric Vehicles-Wind Integrated Power Systems
9.1 Introduction
9.2 Operation Models of EV and Wind Power
9.2.1 Operational Model of EV Charging Station
9.2.2 Model of Uncertain Wind Power
9.2.3 Wind Power Curtailment Based on Probability Model
9.3 Coordinated Scheduling Model Integarated EV and Wind Power
9.3.1 Objective Functions
9.3.2 Decision Variables
9.3.3 Constraints
9.4 Solution of Coordinated Stochastic Scheduling Model
9.4.1 The Parameter Adaptive DE Algorithm
9.4.2 Decision-Making Method
9.4.3 Solution Procedure
9.5 Case Study
9.5.1 Case Description
9.5.2 Result Analysis
9.5.3 Algorithm Performance Analysis
9.6 Conclusion
References
10 Many-Objective Distribution Network Reconfiguration Using Deep Reinforcement Learning-Assisted Optimization Algorithm
10.1 Introduction
10.2 Many-Objective Distribution Network Reconfiguration Model
10.2.1 Problem Formulations
10.2.2 Objectives
10.3 Deep Reinforcement Learning-Assisted Multi-objective Bacterial Foraging Optimization Algorithm
10.3.1 Multi-objective Bacterial Foraging Optimization Algorithm
10.3.2 Deep Reinforcement Learning
10.3.3 Multi-objective Material Foraging Optimization Algorithm Based on Deep Reinforcement Learning
10.4 Simulation Studies
10.4.1 Simulation Settings
10.4.2 Simulation Result and Analysis
10.4.3 Comparison with Other Algorithms
10.5 Conclusion
References
11 Federated Multi-agent Deep Reinforcement Learning for Multi-microgrid Energy Management
11.1 Introduction
11.2 Theoretical Basis of Reinforcement Learning
11.3 Decentralized Multi-microgrid Energy Management Model
11.3.1 Isolated Microgrid Energy Management Model
11.3.2 Isolated MG Energy Management Model via MDP
11.3.3 Decentralized Multi-microgrid Energy Management Model
11.4 Federated Multi-agent Deep Reinforcement Learning Algorithm
11.4.1 Proximal Policy Optimization
11.4.2 Federated Learning
11.4.3 Federated Multi-agent Deep Reinforcement Learning Algorithm
11.5 Case Study
11.5.1 Experiment Setup
11.5.2 Analysis of the F-MADRL Algorithm
11.5.3 Performance Comparison
11.6 Conclusion
References
12 Prospects of Future Research Issues
12.1 Smart Grid Forecast Issues
12.1.1 Challenges
12.1.2 Future Research Directions
12.2 Smart Grid Dispatch Issues
12.2.1 Challenges
12.2.2 Future Research Directions