Industrial Demand Response: Methods, best practices, case studies, and applications

Cover
Contents
About the editors
Foreword
Introduction
1 A comprehensive review on industrial demand response strategies and applications
1.1 Introduction
1.2 Demand side management and ancillary services in smart grid
1.2.1 Smart grid
1.2.2 Demand response automation schemes
1.2.3 Ancillary services in the industrial sector
1.3 Industrial DR case study implementations
1.3.1 Manufacturing processes
1.3.2 Refrigerator warehouses
1.3.3 IT industry/data centers
1.4 Barriers and limitations
1.4.1 Financial
1.4.2 Behavioral/social
1.4.3 Regulatory
1.4.4 Technological
1.5 Conclusions
References
2 Demand response cybersecurity for power systems with high renewable power share
2.1 Introduction
2.2 An overview of DR and EV-based DR
2.3 An overview of demand side cybersecurity
2.4 Modeling power system with DR
2.5 Discussions on the results of cyberattacks on EV aggregator
2.6 Conclusion
References
3 Recurrent neural networks for electrical load forecasting to use in demand response
3.1 Introduction
3.2 DR programs
3.2.1 Load forecasting in DR
3.3 Review on load forecasting
3.4 RNNs in electric load forecasting
3.4.1 Scaling data, normalizing
3.5 PCA for electrical load forecasting
3.6 Load data pre-processing with time organization and training, validation and testing: case study of Urban Area of New South Wales
3.7 Results and discussion
3.8 Conclusion
References
4 Optimal demand response strategy of an industrial customer
4.1 Demand side management categories
4.2 What is DR?
4.3 Why DR?
4.4 DR classification
4.4.1 Competitive DR
4.4.2 Non-competitive DR
4.4.3 Incentivebased DR
4.5 Benefits of DR
4.6 Challenges in DR implementation
4.7 DR provisions
4.8 Applications of DR
4.9 Motivation about DR
4.10 DR of an industrial buyer
4.11 Problem formulation
4.11.1 Market clearing sub-problem
4.11.2 Proposed purchase cost-saving optimization sub-problem
4.12 Proposed solution algorithm
4.13 Case study
4.14 Conclusion
References
5 Price-based demand response for thermostatically controlled loads
5.1 Demand response
5.2 Smart grid control
5.3 Modeling of thermostatically controlled loads (TCL)
5.4 DR from aggregated TCLs—load model
5.4.1 Transfer function of aggregated response of TCL units
5.5 Automatic generation control (AGC)
5.5.1 Primary frequency control
5.5.2 Secondary frequency control
5.6 Dynamic demand control (DDC)
5.7 Simulink model
Appendix A: Modeling of aggregated TCL loads using coupled Fokker–Planck equations
Appendix B: Single areaAGC system parameters
References
6 Electric vehicle massive resources mining and demand response application
6.1 Introduction
6.2 Development status and trend of EVs and charging infrastructure
6.2.1 Development status of EVs
6.2.2 Construction situation of charging infrastructure
6.2.3 Governments’ supporting policies
6.3 EV massive resources digging and DR capability/potential evaluation
6.3.1 EV massive resources digging
6.3.2 EVs in DR capability/potential evaluation
6.4 The mode of EVs participating in DR
6.4.1 Research on multi-station mode participating in power grid DR
6.4.2 Research on single-station mode participating in power grid DR
6.5 Practical experience on EVs participating in DR
6.5.1 DR pilot projects – in structural mode
6.5.2 DR pilot projects – in event mode
6.5.3 Practical experience
6.6 Summary and prospect
6.6.1 Summary
6.6.2 Prospect
References
7 Demand response measurement and verification approaches: analyses and guidelines
7.1 Introduction
7.1.1 Concepts
7.1.2 Literature review
7.1.3 Classification of CBL estimation methods
7.1.4 Features of CBL estimation methods
7.2 An overview of different CBL estimation approaches
7.2.1 Averaging method
7.2.2 Regression method
7.2.3 Other CBL calculation methods
7.3 Comparison of different baseline estimation methods
7.4 Accuracy evaluation indexes
7.4.1 RMSE and RRMSE
7.4.2 MAPE and MAE [57, 58]
7.5 Guidelines and suggestions to select a proper baseline estimation method
7.6 Practical results
7.7 Concluding remarks and outlook
Acknowledgments
References
8 Transactive energy industry demand response management market
8.1 Demand response
8.2 Transactive control
8.3 DR modeling and simulation results
8.3.1 Model A
8.3.2 Model-B
8.3.3 Simulation results and discussion
8.4 TE management
8.5 Methodology
8.5.1 Bidding/offering strategy of energy storage devices (ESD)
8.5.2 Bidding strategy of HVAC
8.5.3 Offering strategy of PVs
8.6 Problem formulation
8.7 Simulation results and discussions
8.8 Future works
8.9 Conclusion
References
9 Industrial demand response opportunities with residential appliances in smart grids
Nomenclature
9.1 Introduction
9.2 Demand peaks
9.3 Demand response
9.4 Thermostatically controlled loads (TCLS)
9.5 Case study 1: hybrid control approach for frequency regulation
9.5.1 Refrigerator modelling
9.5.2 DR controller description
9.5.3 HillClimbing method
9.5.4 System description
9.5.5 Simulation results
9.5.6 Discussion
9.6 Case study 2: appliance level data analysis of summer demand reduction potential from residential aircons
9.6.1 Summer peak demand analysis
9.6.2 DR opportunities with aircons
9.7 Conclusion
References
10 Modelling and optimal scheduling of flexibility in energy-intensive industry
10.1 Introduction
10.2 Understanding flexibility across electricity consumer sectors
10.3 Basis for an industrial flexibility model
10.3.1 European grid balancing services
10.3.2 Models in contemporary research
10.4 Modelling framework formulation
10.4.1 Definitions
10.4.2 Modelling blocks
10.5 Case study
10.5.1 Model
10.5.2 Results
10.6 Conclusions
Acknowledgements
References
11 Industrial demand response: coordination with asset management
11.1 Introduction
11.2 Proposed strategy
11.2.1 General idea
11.2.2 Problem formulation
11.2.3 Solution methodology
11.3 Case study
11.3.1 System description
11.3.2 Results
11.3.3 Discussion
11.4 Conclusions
11.5 Nomenclature
11.5.1 Indices
11.5.2 Parameters
11.5.3 Variables
11.6 Appendix
References
12 A machine learning-based approach for industrial demand response
12.1 Introduction
12.2 Industrial load
12.2.1 Characteristics of industrial load
12.3 Industrial DR
12.3.1 Industrial load forecasting
12.3.2 Role of technology in IDR
12.3.3 Role of policy in IDR
12.3.4 Incentives and price-based DR
12.3.5 Ancillary services
12.4 Machine learning in IDR
12.4.1 Genetic algorithm (GA)
12.4.2 Support vector machine (SVM)
12.4.3 Artificial neural network (ANN)
12.4.4 Fuzzy logic
12.4.5 Adaptive neuro-fuzzy inference system (ANFIS)
12.4.6 Linear regressions
12.5 Conclusion
References
13 Feasibility assessment of industrial demand response
13.1 Cost assessment of IDR
13.1.1 Measurement of flexibility potential
13.1.2 Design and deployment
13.1.3 Operation and management
13.1.4 Communication and control
13.1.5 Feedback system
13.2 IDR benefits
13.2.1 Regulation services
13.2.2 Reserves
13.2.3 Self-consumption
13.2.4 Changes in energy purchasing and flexibility trade
13.2.5 Transmission and distribution network support
13.2.6 Other benefits
13.3 Feasibility assessment
13.3.1 Indicators
13.4 Case studies
13.4.1 Chlor-alkali production industry
13.4.2 Paper industry in Germany
13.5 Conclusions and final considerations
References
14 Measurement and verification of demand response: the customer load baseline
14.1 Introduction
14.2 Literature review
14.3 Customer baseline load, non-intrusive load monitoring and physical-based load models
14.3.1 The necessary linkage between DR methodologies
14.3.2 Physical-based load models
14.3.3 Unadjusted customer baseline load: a review of the main methodologies
14.3.4 Adjustment coefficients for CBL
14.4 Case study
14.4.1 Detecting pre-heating and gaming through PBLM and NIALM
14.5 Results and discussion
14.5.1 Comparisons of unadjusted CBLs based on historical data
14.5.2 Adjustment coefficients: weather sensitive (WS) and PBLM
14.5.3 DR control events: effects on energy calculations
14.6 Conclusions
Acknowledgements
References
15 Modeling and optimizing the value of flexible industrial processes in the UK electricity market
15.1 Introduction
15.1.1 Decarbonization challenges and value of demand response
15.1.2 Industrial DR: significance and relevant work
15.1.3 Chapter motivation and contributions
15.1.4 Chapter outline
15.2 Modeling framework
15.2.1 Assumptions and generic formulation of industrial consumer’s optimization problem
15.2.2 Uninterruptible processes with fixed power
15.2.3 Interruptible processes with fixed power
15.2.4 Uninterruptible and interruptible processes with discretely adjustable power
15.2.5 Uninterruptible and interruptible processes with continuously adjustable power
15.2.6 Material storage buffers
15.3 Case study
15.3.1 Description and input data
15.3.2 Benefits of flexibility types with fixed power
15.3.3 Benefits of flexibility types with adjustable power
15.3.4 Benefits of material storage buffers
15.3.5 Summary of benefits of different flexibility types
15.4 Conclusions and future work
Acknowledgement
References
16 Case study ofAran Islands: optimal demand response control of heat pumps and appliances
16.1 Origins of demand response programmes
16.1.1 Traditional (industrial) DR applications
16.1.2 Transition towards the residential sector
16.2 RESPOND control loop and methodology
16.2.1 IoT backend platform
16.2.2 Forecasting services
16.2.3 Optimisation services
16.2.4 Control services
16.3 Use case setup
16.3.1 Pilot installations
16.3.2 User interface
16.4 Case studies and assessment
16.4.1 Test case #1
16.4.2 Test case #2
16.4.3 Test case #3
16.4.4 Test case #4
Conclusion
Acknowledgement
References
17 Use case of artificial intelligence, and neural networks in energy consumption markets, and industrial demand response
17.1 AI in energy market
17.2 NN
17.3 Power consumption and importance of its prediction
17.4 Cogeneration and dual fuels
17.5 DR and its importance
17.6 Power consumption prediction using artificial NNs (ANNs)
17.7 Framework of the NN-LSTM-based model
17.7.1 Shell layer
17.7.2 Input layer
17.7.3 Hidden layer
17.7.4 Attention layer
17.7.5 Output layer
17.8 Use case of NNs
17.8.1 Overview and benefit
17.9 RNN or LSTM:Which one is better for prediction?
17.9.1 Overview
17.10 Quantum technology
17.10.1 Quantum computing
17.10.2 Quantum fundamentals
17.11 Quantum technology general applications
17.12 Quantum technology and smart grids
17.13 Forecasting in smart grids using quantum technology
17.14 Final overview and conclusion
Acronyms
References
Index
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