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Multi-Scale Transactive Control in Interconnected Bulk Power Systems Under High Renewable Energy Supply and High Demand Response Scenarios

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Multi-Scale Transactive Control in Interconnected Bulk Power Systems Under High Renewable Energy Supply and High Demand Response Scenarios Multi-scale Transactive Control In Interconnected Bulk Power Systems Under High Renewable Energy Supply and High Demand Response Scenarios by David P. Chassin BSc., Building Science, Rensselaer Polytechnic Institute, 1987 MASc., Mechanical Engineering, University of Victoria, 2015 A Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY in the Department of Mechanical Engineering arXiv:1711.09704v2 [cs.SY] 6 Dec 2017 © David P. Chassin, 2017 University of Victoria All rights reserved. This dissertation may not be reproduced in whole or in part, by photocopying or other means, without the permission of the author. ii Multi-scale Transactive Control In Interconnected Bulk Power Systems Under High Renewable Energy Supply and High Demand Response Scenarios by David P. Chassin BSc., Building Science, Rensselaer Polytechnic Institute, 1987 MASc., Mechanical Engineering, University of Victoria, 2015 Supervisory Committee Dr. Ned Djilali, Supervisor (Department of Mechanical Engineering) Dr. Yang Shi, Departmental Member (Department of Mechanical Engineering) Dr. Panajotis Agothoklis, Outside Member (Department of Electric Engineering) iii ABSTRACT This dissertation presents the design, analysis, and validation of a hierarchical transactive control system that engages demand response resources to enhance the integration of renewable electricity generation resources. This control system joins energy, capacity and regulation markets together in a unified homeostatic and eco- nomically efficient electricity operation that increases total surplus while improving reliability and decreasing carbon emissions from fossil-based generation resources. The work encompasses: (1) the derivation of a short-term demand response model suitable for transactive control systems and its validation with field demonstration data; (2) an aggregate load model that enables effective control of large populations of thermal loads using a new type of thermostat (discrete time with zero deadband); (3) a methodology for optimally controlling response to frequency deviations while tracking schedule area exports in areas that have high penetration of both intermittent renewable resources and fast-acting demand response; and (4) the development of a system-wide (continental interconnection) scale strategy for optimal power trajectory and resource dispatch based on a shift from primarily energy cost-based approach to a primarily ramping cost-based one. The results show that multi-layer transactive control systems can be constructed, will enhance renewable resource utilization, and will operate in a coordinated manner with bulk power systems that include both regions with and without organized power markets. Estimates of Western Electric Coordinating Council (WECC) system cost savings under target renewable energy generation levels resulting from the proposed system exceed US$150B annually by the year 2024, when compared to the existing control system. iv Contents Supervisory Committee ii Abstract iii Table of Contents iv List of Tables viii List of Figures x Nomenclature xiv Acknowledgements xxiv Funding Sources xxix Dedication xxx 1 Introduction 1 1.1 Motivation . 4 1.2 Main Contributions . 5 1.3 Outline of the Thesis . 6 2 Problem Statement 8 2.1 Barriers To Integrated Demand Response . 9 2.2 Achieving Optimality in Transactive Systems . 14 3 Demand Response 17 3.1 Background . 18 3.2 Random Utility Model . 21 3.2.1 First-principles Model . 22 v 3.2.2 Model Assumptions . 24 3.2.3 Equilibrium Demand Response . 26 3.3 Model Validation . 27 3.4 Summary of Results . 33 4 Aggregation 34 4.1 Background . 34 4.2 Aggregate Load Curtailment Model . 36 4.2.1 Aggregate Load Model . 37 4.2.2 Load Control Model . 41 4.2.3 Open-Loop Response . 42 4.2.4 Model Identification . 43 4.3 Aggregate Demand Response Controller Design . 45 4.3.1 Proportional Control . 47 4.3.2 Proportional-Derivative Control . 48 4.3.3 Unity Damped Control . 50 4.3.4 Deadbeat Control . 52 4.3.5 Pole Placement Control . 52 4.3.6 Integral Error Feedback . 53 4.4 Agent-based Simulation Results . 54 4.5 Summary of Results . 57 5 Regulation 58 5.1 Methodology . 58 5.1.1 Frequency control mechanism . 59 5.1.2 Transactive control platform . 60 5.1.3 Demand response integration in the 5-minute market . 61 5.1.4 -optimal control policy . 64 H2 5.2 Implementation . 67 5.2.1 State-space realization . 70 5.2.2 Model Validation . 71 5.3 Control Performance . 72 5.4 Summary of Results . 75 6 Dispatch 76 6.1 Background . 77 vi 6.2 Methodology . 78 6.3 Optimal Dispatch Controller . 82 6.4 Performance Evaluation . 84 6.5 Case Study: WECC 2024 . 87 6.6 Summary of Results . 93 7 Discussion 94 7.1 Demand Response . 95 7.1.1 Model Limitations . 95 7.1.2 Technical and Regulatory Impacts . 99 7.2 Aggregation . 101 7.3 Regulation . 103 7.3.1 Robustness to FADR Uncertainty . 103 7.4 Dispatch . 105 7.5 Ramping Market Price . 107 7.5.1 Unified Market Design . 109 7.6 Recommendations . 111 8 Conclusions 113 8.1 Principal Contributions and Findings . 115 8.2 Future Research . 116 8.2.1 Demand Response . 117 8.2.2 Aggregate Thermostatic Load Control . 117 8.2.3 Regulation . 118 8.2.4 Dispatch . 119 8.3 Summary . 120 A Demand Elasticity 121 A.1 Analysis . 122 A.2 Discussion . 123 A.2.1 Device Control . 123 A.2.2 Business Models . 124 A.2.3 Regulatory Oversight . 124 A.3 Conclusion . 125 B Price Negotiation Convergence 126 vii B.1 Transactive Price-Discovery . 127 B.2 Stable Mechanism Design . 130 B.2.1 Demand Response Uncertainty . 132 B.3 Conclusions . 134 C Model Specifications 137 D Simulation Results 143 Bibliography 144 viii List of Tables Table 3.1 Feeder characteristics . 29 Table 3.2 Olympic data analysis results . 30 Table 3.3 Columbus analysis results for Feeders 120, 140, 160 and 180 . 31 Table 3.4 Columbus analysis results for only non-experiment days . 32 Table 4.1 House thermal parameters. 41 Table 4.2 Controller design configurations. 47 Table 4.3 Maximum attenuating proportional control gains for various con- ditions. 48 Table 4.4 Proportional-derivative controller design parameters. 49 Table 4.5 Controller design parameters for peak load ( 15oC). 55 − Table 5.1 Cost, generation, and net export impacts of ACE control versus -optimal control . 74 H2 Table 6.1 Marginal prices and marginal costs for 105 GWh schedule at 100 GW initial power and 10 GW/h ramp for cases in Figure 6.5 90 Table 6.2 Single hour cost savings under low and high renewable for a ramp from 100 GW to 110 GW at 5 minute discrete dispatch control update rate, with varying energy error redispatch . 90 Table 6.3 WECC 2024 cost savings from optimal dispatch under different transmission constraint and renewable scenarios . 91 Table 6.4 Summary of energy and price impacts of optimal dispatch control for the WECC 2024 base case . 91 Table 7.1 Bid price entropy statistics . 99 Table C.1 Aggregate Load Model Parameters . 137 Table C.2 WECC 2024 demand forecast and internal area losses [1] . 138 Table C.3 WECC 2024 aggregated installed supply capacity [1] . 139 ix Table C.4 WECC 2024 producer cost and surplus difference for 100% in- elastic load (in M$/year) [1] . 140 Table C.5 WECC 2025 production cost per unit in $/MWh [1] . 141 Table C.6 WECC 2024 model inter-area transfer limits [1] . 142 Table D.1 Estimated, simulated, and errors of aggregate thermostat load state transition probability ρ using joint PDF (N=normal, Ln=log 6 normal, Log=logistic) using 10 homes and td = 1 minute. 143 x List of Figures Figure 2.1 Top-to-bottom rethink of electricity infrastructure, including providers of transmission and distribution infrastructure, sys- tem operators and resource aggregators. 10 Figure 2.2 The California “Duck Curve” [Source: CAISO] . 11 Figure 2.3 Inter-temporal data flow diagram. 14 Figure 3.1 Demand curves for steady state thermostatic loads in a trans- active control system. 26 Figure 3.2 Example of demand function model validation with Columbus demonstration data. The bids shown are from 2013-06-22 22:45 EDT. The clearing price PC and quantity QC are indicated by the circle. The expected price P¯ and quantity Q¯ are indicated by the plus sign. The standard deviation of price P˜ and quantity Q˜ are indicated by the ellipse. 29 Figure 4.1 State-space model of aggregate conventional thermostatic loads in heating regime with refractory states non∗ and noff∗ . ∆τ is the difference between the indoor and outdoor temperatures and δ is the hysteresis band limit. 36 Figure 4.2 General state-space model of discrete-time zero-deadband ag- gregate thermostatic loads. 37 Figure 4.3 Discrete-time heating thermostat transition probabililities for on and off states with PDF of 106 homes with a setpoint change of 0.1◦F. ............................. 39 − Figure 4.4 Zero and pole locations of Equation (4.4) for a random popula- tion of 1 million homes with thermal parameters given in Table IV at various outdoor air temperatures. 42 xi Figure 4.5 Open loop impulse (left), decay (center), and step (right) re- sponse of aggregate load model compared to agent-based simu- lation for 100,000 thermostats per unit input u at -10◦C. 43 Figure 4.6 General structure of the controller (top): Block (A) is the ag- gregate load model, (B) is the reduced-order observer, (C) is the load controller, and (D) is the integral error feedback. 46 Figure 4.7 Discrete-time root-locus of aggregate T δ0 thermostatic loads. 47 Figure 4.8 100 MW proportional load control step response with maximum attenuating proportional control gains based on the load param- eters in Table 4.1 (left) and proportional-derivative control step response (right).
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