Data-Driven Energy Management and Tariff Optimization in Power Systems
Description
Presents a comprehensive guide to transforming power systems through data
Data-Driven Energy Management and Tariff Optimization in Power Systems offers an authoritative examination of how data science is reshaping the energy landscape. As the electricity sector grapples with increasing complexity, this timely volume responds to a growing demand for adaptive strategies that enable accurate forecasting, intelligent tariff design, and optimized resource allocation, underpinned by advanced analytics and machine learning.
Drawing on global expertise and real-world case studies, the book bridges the theoretical and practical dimensions of energy systems management, providing deep insight into how data collected from smart meters, SCADA systems, and IoT devices can be mined for predictive modeling, demand response, and peak load management. The book's accessible structure and didactic approach make it suitable for a wide readership, while its breadth of topics ensures relevance across the spectrum of energy challenges.
Integrating rigorous analysis with application-oriented strategies, this book:
- Presents advanced techniques in machine learning, predictive modeling, and pattern recognition tailored to energy management and tariff design
- Provides accessible explanations of complex algorithms through a didactic and visual teaching style, including informative tables and illustrations
- Highlights tools for grid stability, demand forecasting, and peak load management using high-resolution energy data
- Addresses the integration of renewable energy sources into existing infrastructures through data-driven optimization
Designed for a broad audience, Data-Driven Energy Management and Tariff Optimization in Power Systems is ideal for upper-level undergraduate and graduate courses in energy management, power systems analytics, and smart grids as part of electrical engineering or energy policy programs. It is also an essential reference for power system engineers, energy analysts, researchers, and policymakers involved in grid planning and optimization.
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Persons
Hamidreza Arasteh is an Assistant Professor in the Power Systems Operation and Planning Research Department at the Niroo Research Institute, Tehran, Iran, and a Research Assistant at the Center for Research on Microgrids (CROM), Huanjiang Laboratory, Zhuji, Shaoxing, Zhejiang, China. He specializes in energy management, smart grids, microgrids, and electricity markets, with numerous research contributions in energy management and the integration of data analytics into power system operations.
Pierluigi Siano is a Professor and Scientific Director of the Smart Grids and Smart Cities Laboratory at the University of Salerno, Italy. A Senior Member of IEEE, his research focuses on demand response, distributed energy resources, and power system planning. He serves on editorial boards for several prestigious journals in the field.
Niki Moslemi is Head of the Power Systems Operation and Planning Research Department at the Niroo Research Institute in Tehran, Iran. She brings decades of experience in power quality, load forecasting, system resiliency, and data-driven energy strategies. Her leadership and research span multiple high-impact projects within the energy sector.
Josep M. Guerrero is with Zhejiang University, Hangzhou, Zhejiang, China, a Director of the Center for Research on Microgrids (CROM), Huanjiang Laboratory, Zhuji, Shaoxing, China, and a Distinguished Senior Researcher at the Department of Electrical Engineering, University of Valladolid, Spain. His research interests include various aspects of microgrids, including power electronics and distributed energy resources.
Content
About the Editors xiii
List of Contributors xv
Preface xix
1 Fundamentals of Power System Data and Analytics 1
Pouya Ramezanzadeh, Mohsen Parsa Moghaddam, and Reza Zamani
1.1 Introduction 1
1.2 Background 2
1.2.1 Concept, Opportunities, and Challenges of Present and Future Power Systems 2
1.2.2 Transformation in the Power Industry 3
1.2.3 Drivers and Barriers 6
1.3 Data-rich Power Systems 6
1.3.1 Data Sources and Types 8
1.3.2 Data Structure 10
1.4 Data Analytics in Power Systems 11
1.4.1 What Is Data Analytics? 12
1.4.2 Analytics Techniques 12
1.5 Data Analytics-Based Decision-Making in Future Power Systems 13
1.5.1 Decision Framework 15
1.5.1.1 Uncertainty Issues 15
1.5.1.2 Behavioral Analytics 15
1.5.1.3 Policy Mechanisms 15
1.5.2 Computational Aspects 16
1.6 Conclusion 16
1.7 Future Trends and Challenges 16
References 17
2 Advanced Predictive Modeling for Energy Consumption and Demand 21
Seyed Mohsen Hashemi and Abbas Marini
2.1 The Role of Load Forecasting in Power System Planning 21
2.2 Need for Short-Term Demand Forecasting 22
2.3 Components of Power Demand and Factors Affecting Demand Growth 22
2.3.1 Electricity Demand from the Consumer Type Perspective 23
2.3.2 Electricity Demand from the Supply Perspective 23
2.4 Electricity Demand in Networks with High Renewable Energy Sources 24
2.5 Machine Learning and Its Applications in Demand Forecast 25
2.5.1 Application of Clustering in Load Forecasting 27
2.6 The Impact of Macro-decisions on Long-term Load Forecasting 28
2.6.1 Natural Gas as a Primary Energy Carrier for Heating Demand 29
2.7 Conclusion 34
References 35
3 Demand Response and Customer-Centric Energy Management 39
Alireza Mansoori, Mohsen Parsa Moghaddam, and Reza Zamani
3.1 Introduction 39
3.2 Background 39
3.3 Future Power Systems Aspects, Trends, and Challenges 41
3.4 Transforming to Customer-Centric Era 41
3.4.1 Differences Between Customer-Centric DR Solution and OtherWays in the Future
Power System 42
3.4.2 Drivers and Enablers 42
3.5 Customer-Centric Power System Structure 45
3.5.1 Physical Layer 45
3.5.1.1 Physical Resources 45
3.5.1.2 Physical Constraints of the System 46
3.5.2 Cyber-Social Layers 49
3.5.2.1 Centralized Approach (Traditional) 50
3.5.2.2 Decentralized Approach (Future) 50
3.6 Conclusion and Future Trends 54
References 57
4 Applications of Data Mining in Industrial Tariff Design and Energy Management: Concepts and Practical Insights 61
Hamidreza Arasteh, Niki Moslemi, Majid Miri Larimi, Pierluigi Siano, Sobhan Naderian, andJosep M. Guerrero
4.1 Introduction 61
4.1.1 Data Mining: Concepts, Procedures, and Tools 61
4.1.2 Energy Management and the Role of Data Mining 65
4.1.3 Aims and Scope 66
4.2 Investigating Industrial Load Data: Analysis Through Various Indexes 67
4.3 Classification of Industries 86
4.4 Discussion and Conclusions 90
References 92
5 Data-Driven Tariff Design for Equitable Energy Distribution 95
Salah Bahramara, Hamidreza Arasteh, Asrin Seyedzahedi, and Khabat Ghamari
5.1 Introduction 95
5.1.1 Literature Review and Contributions 96
5.1.2 Chapter Organization 97
5.2 Proposed Approach and Formulations 97
5.3 Describing the Case Study 98
5.4 Simulation Results 100
5.5 Conclusions and Future Works 100
References 105
6 Applying Artificial Intelligence to Improve the Penetration of Renewable Energy in Power Systems 107
Abbas Marini and Seyed Mohsen Hashemi
6.1 Introduction 107
6.2 Machine Learning Techniques 109
6.2.1 Artificial Neural Network and Deep Neural Network 110
6.2.2 Convolutional Neural Network 111
6.2.3 Recurrent Neural Network 111
6.2.4 Long Short-Term Memory 112
6.3 General View of ML/DL Methods for RES Integration 112
6.3.1 Data Preprocessing 114
6.3.1.1 Normalization 115
6.3.1.2 Wrong/Missing Values and Outliers 115
6.3.1.3 Data Resolution 115
6.3.1.4 Inactive Time Data 116
6.3.1.5 Data Augmentation 116
6.3.1.6 Correlation 116
6.3.1.7 Data Clustering 116
6.3.2 Deterministic/Probabilistic Forecasting Methods 116
6.3.2.1 Deterministic Methods 116
6.3.2.2 Probabilistic Forecasting Methods 119
6.3.3 Evaluation Measures 119
6.4 ML/DL Application for Integration of RES 121
6.4.1 Renewable Resources Data Prediction/Planning 122
6.4.2 RES Power Generation Prediction/Operation 125
6.4.3 Electric Load and Demand Forecasting 126
6.4.4 Stability Analysis 127
6.4.4.1 Security Assessment 128
6.4.4.2 Stability Assessment 129
6.5 Integrated Machine Learning and Optimization Approach 129
6.6 Conclusion 131
References 132
7 Machine Learning-Based Solutions for Renewable Energy Integration: Applications, Optimization, and Grid Stability 135
Ali Paeizi, Mohammad Mehdi Amiri, Sasan Azad, and Mohammad Taghi Ameli
7.1 Introduction 135
7.2 Machine Learning Importance in RESs Sector 137
7.2.1 AI-Based Algorithms in RESs 137
7.2.2 ML Algorithms Application in RESs 140
7.3 Role of ML in Optimizing Renewable Energy Generation 150
7.3.1 Different Programming Models in RES Optimization 150
7.3.2 Optimization Objectives in RESs 150
7.3.3 ML Applications in Optimizing Renewable Energy Generation 151
7.4 Ensuring Grid Stability Through ML-Based Forecasting 155
7.4.1 Grid Stability Forecasting 155
7.4.2 Grid Stability Through ML-Based Forecasting 157
7.5 Challenges and Future Direction in ML-Based Approaches to RESs 159
7.5.1 Challenges in ML-Based Approaches to RESs 160
7.5.2 Future Directions in ML-Based Approaches to RESs 161
7.6 Conclusion 162
References 163
8 Application of Artificial Neural Networks in Solar Photovoltaic Power Forecasting 167
Hamid Jabari, Afshin Ebrahimi, Ardalan Shafiei-Ghazani, and Farkhondeh Jabari
8.1 RES Share inWorld Energy Transition 167
8.2 Applications of PV Panels in Energy Systems 168
8.3 Disadvantages of PV Panels 169
8.4 Importance of PV Power Forecasting 170
8.5 Proposed Algorithm for PV Power Prediction 170
8.6 Numerical Results and Discussions 172
8.7 Concluding Remarks 172
References 175
9 Power System Resilience Evaluation: Data Challenges and Solutions 179
Mohammad Reza Sheibani, Habibollah Raoufi, and Javad Nezafat Namini
9.1 Introduction 179
9.2 A Review of Power System Resilience Metrics 180
9.3 The General Framework for the Resilience Assessment of the Power System 182
9.4 Data Required for Power System Resilience Studies 182
9.4.1 Data of Natural Origin 184
9.4.2 Basic Data of the Power System 184
9.4.3 Data on Failure and Restoration Rates 186
9.5 Data Analysis and Correction 187
9.6 Disaster Forecasting in Power System Resilience Studies 188
9.7 Modeling the Impact of Disaster on Power System Performance 189
9.8 Static Model in Machine Learning 190
9.9 Spatiotemporal Random Process 192
9.9.1 Dynamic Model for Chain Failures 192
9.9.2 Nonstationary Failure-Recovery-Impact Processes 192
9.10 Lessons Learned and Concluding Remarks 193
9.11 Future Work 194
References 194
10 Nonintrusive Load Monitoring in Smart Grids Using Deep Learning Approach 197
Sobhan Naderian and Hamidreza Arasteh
10.1 Introduction 197
10.2 Deep Learning Neural Networks 199
10.2.1 RNN 199
10.2.2 LSTM 199
10.2.3 CNN 200
10.2.4 Convolutional Layer 201
10.2.5 Pooling Layer 201
10.2.6 Fully Connected Layer 201
10.3 The Proposed Method 201
10.3.1 Pre-Processing and Preparing Data 201
10.3.2 Proposed Method Architecture 202
10.3.3 Proposed Method's Parameters 202
10.3.4 Performance Evaluation 203
10.4 Results and Discussion 204
10.5 Challenges and Future Trends 206
10.6 Conclusion 206
References 207
11 Power System Cyber-Physical Security and Resiliency Based on Data-Driven Methods 211
Hamed Delkhosh, Mahdi Ghaedi, and Maryam Azimi
11.1 Introduction 211
11.2 Fundamental Concepts 212
11.2.1 Cyber-Physical Power System (CPPS) 212
11.2.2 Security and Resiliency 214
11.3 Role of Data Analytics 215
11.3.1 Basic Methods 215
11.3.1.1 Supervised Learning (SL) 215
11.3.1.2 Unsupervised Learning (UL) 216
11.3.2 Advanced Techniques 216
11.3.2.1 Dimensionality Reduction (DR) 217
11.3.2.2 Feature Engineering 217
11.3.2.3 Reinforcement Learning 217
11.3.2.4 Integrated Models 218
11.4 Interdependency Modeling 218
11.4.1 Direct Modeling 220
11.4.2 Testbeds 220
11.4.3 Game-Theoretic 221
11.4.4 Machine Learning 222
11.5 Cyber-Physical Threats 223
11.5.1 Physical Attacks 224
11.5.2 Cyberattacks 225
11.5.2.1 Confidentiality 225
11.5.2.2 Availability 226
11.5.2.3 Integrity 226
11.5.3 Coordinated Attacks 227
11.6 Defense Framework 228
11.6.1 Preventive Measures 228
11.6.1.1 Supply Chain Security 229
11.6.1.2 Access Control 229
11.6.1.3 Personnel Training 230
11.6.1.4 Resource Allocation 230
11.6.1.5 Infrastructure Hardening 231
11.6.1.6 Moving Target Defense 231
11.6.2 Mitigation Actions 232
11.6.2.1 Attack Detection 232
11.6.2.2 Data Recovery 233
11.6.2.3 Reconfiguration and Restoration 233
11.6.2.4 Forensic Analysis 234
11.7 Conclusion 234
References 235
12 Application of Artificial Intelligence in Undervoltage Load Shedding in Digitalized Power Systems: An In-Depth Review 239
Nazanin Pourmoradi, Sasan Azad, Mohammad Mehdi Amiri, and Miadreza Shafie-khah
12.1 Introduction 239
12.2 Load-Shedding Strategies 240
12.2.1 Conventional LS 240
12.2.2 Adaptive LS 240
12.2.3 AI-Based LS 241
12.3 Principles of UVLS 242
12.3.1 Amount of Load Shed 242
12.3.2 Location for LS 243
12.3.3 Application of VSI for UVLS 243
12.4 AI-Based Methods 244
12.5 Case Study 248
12.5.1 Database Generation 248
12.5.2 Offline Training 248
12.5.3 Online Application 249
12.6 Future Challenges and Transfer Learning 249
12.7 Conclusion 251
References 252
Index 257
1
Fundamentals of Power System Data and Analytics
Seyed Pouya Ramezanzadeh, Mohsen Parsa Moghaddam, and Reza Zamani
Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
1.1 Introduction
Imagine a world where power systems can predict outages before they happen, optimize energy distribution in real time, and seamlessly integrate renewable energy sources. This image is no longer a distant dream but a rapidly approaching reality, thanks to the transformative power of data analytics. The convergence of digital technologies and power systems heralds a new era of data-based operation in the energy sector [1]. As traditional power grids evolve into interconnected, data-rich ecosystems, data analytics becomes increasingly critical.
Data analytics enables grid operators and market participants to glean actionable insights from vast volumes of data, empowering proactive grid management, demand response optimization, and risk mitigation strategies [2]. By leveraging advanced data analytics techniques, power systems can enhance reliability, optimize energy efficiency, and support the integration of renewable energy sources, paving the way for a sustainable energy future.
As adopting renewable energy sources grows, it introduces more significant variability into the electrical grid, complicating the balance between supply and demand [3]. Innovative solutions must be developed to manage this challenge and enhance system flexibility on both the supply and demand sides. One of the most promising strategies involves the integration of smart grids, supported by big data analytics, to strengthen the resilience and management of power systems [4]. Smart grids allow for more adaptive energy distribution by enabling two-way communication and power flow between consumers and utilities.
A vital element of this transformation is the widespread deployment of smart meters, which have been installed extensively in recent years. These devices monitor near-real-time energy usage at customer sites and relay this information back to utility companies [5]. This system supports a dynamic interaction between consumers and the grid, facilitating better energy management.
The shift toward intelligent grids has been partly driven by the deregulation of power systems, which has moved away from traditional, vertically integrated utility models [6, 7]. By decentralizing energy production and distribution, smart grids can more effectively respond to fluctuating energy demands, incorporating renewable energy into the system more efficiently.
Big data plays a crucial role in the operation of smart grids, particularly in processing the vast amounts of information generated by these systems [8]. Big data analytics applications include real-time monitoring and management of energy consumption, automatic billing systems, predictive forecasting for load and energy generation, and identifying faults or anomalies in the grid [9]. This data-driven approach improves the grid's capacity to respond to unpredictability and increases the overall efficiency of the energy system.
The characteristics of the data collected by intelligent grids align with the core attributes of big data: volume, velocity, and variety. Additionally, it introduces other critical "V" characteristics, such as veracity (accuracy of the data), visibility (transparency), and value (the actionable insights drawn from the data). However, these benefits come with significant challenges, particularly in the areas of security and privacy [10], which must be carefully managed to fully realize the potential of smart grid technologies [11].
This chapter explores the digital transformation of power systems, the structure of data-driven power systems, and the pivotal role of data-driven decision-making. It delves into state-of-the-art data analytics techniques explicitly tailored for power systems and presents real-world examples and case studies. Finally, it addresses this rapidly evolving field's challenges and future directions.
To understand the full potential of data analytics in power systems, it is essential to first explore the current landscape and the transformative changes underway in the power industry. The following section provides a comprehensive background on these topics.
1.2 Background
Digital trends have gradually changed traditional power systems over the last decade. Therefore, it is necessary to understand these digital trends and their impact on power systems. Figure 1.1 shows current trends in power systems and the expected results they will cause when the digital transformation enables the power industry to respond to digital trends.
The following sections discuss concepts, opportunities, and challenges of present and future power systems; transformation in the power industry; and drivers and barriers to such transformation.
1.2.1 Concept, Opportunities, and Challenges of Present and Future Power Systems
Traditionally composed of generation, transmission, and distribution networks, legacy power systems are designed to deliver electricity from power plants to end consumers reliably and efficiently [12]. Modernizing power systems opens new opportunities for an increased role of distributed sources, improved participation of stakeholders, and the integration of renewable energy sources, ultimately contributing to a more sustainable energy future [13]. Advanced data analytics can optimize power flow, reduce operational costs, and improve demand forecasting, making the grid more adaptable and resilient [14]. Meanwhile, these groundbreaking digital technologies expose power systems to new challenges.
Figure 1.1 Digital trends and their results in the power industry.
However, this transformation comes with challenges such as maintaining grid stability with intermittent renewable sources, ensuring cybersecurity, and managing the vast amounts of data generated by smart devices [15, 16]. Balancing the benefits of modernization with these challenges is critical for the future of power systems.
Figure 1.2 shows critical opportunities and challenges of future power systems. It is worth mentioning that some unknown opportunities and challenges will emerge in the operational phase of digital transformation.
1.2.2 Transformation in the Power Industry
The power industry is undergoing a significant transformation, driven by a convergence of technological advancements, environmental imperatives, regulatory changes, and evolving consumer expectations [17, 18]. This transformation is not merely a shift in operational practices but a profound rethinking of how energy is generated, distributed, and consumed and how different parts of the energy sector work together [19]. The philosophy behind this transformation centers on sustainability, resilience, and decentralization.
Figure 1.2 Opportunities and challenges of future power systems.
Digitalization revolutionizes the power industry by enabling real-time monitoring, predictive maintenance, and optimized energy management through advanced data analytics. Integrating digital communication technologies with the electrical grid has led to smart grids. Smart grids allow for more efficient and reliable electricity delivery, enhanced by the capability to dynamically respond to changes in demand and supply [2]. Digitalization of power systems relies on the process by which data is gathered, prepared, and used for decision-making.
Decentralization involves generating power closer to where it is used, reducing transmission losses, and enhancing grid resilience [20]. Distributed energy resources like solar panels and battery storage play a crucial role in this shift, allowing consumers to become prosumers who both produce and consume electricity. Historically, power systems were designed with a centralized generation model, where large power plants produced electricity, which was then transmitted over long distances to consumers. This model was efficient for many decades but is increasingly seen as inadequate in addressing the challenges of modern energy demands and environmental concerns. The new philosophy emphasizes a more distributed and dynamic approach, integrating renewable energy sources, enhancing grid flexibility, and prioritizing energy efficiency.
One of the primary drivers of transformation is the integration of renewable energy sources such as wind, solar, and hydropower. These sources are inherently variable and decentralized, requiring a shift from the traditional central control to a more flexible and adaptive grid management system [21]. For instance, the rise of solar power has led to millions of households and businesses installing photovoltaic panels, turning them from mere consumers into prosumers who both produce and consume electricity and data [22]. Data is the core of the transformation by which consumers can manage their participation behavior in energy markets, and utilities can establish new profit opportunities [23].
Advanced data analytics and smart grid technologies are crucial in managing the complexities introduced by renewable energy. Smart grids use sensors, advanced metering infrastructure, and data analytics to monitor and manage real-time energy flow. For example, predictive analytics can forecast energy demand and supply patterns, allowing grid operators to balance loads more...
