Details
Merging Optimization and Control in Power Systems
Physical and Cyber Restrictions in Distributed Frequency Control and BeyondIEEE Press Series on Control Systems Theory and Applications 1. Aufl.
115,99 € |
|
Verlag: | Wiley |
Format: | |
Veröffentl.: | 05.08.2022 |
ISBN/EAN: | 9781119827931 |
Sprache: | englisch |
Anzahl Seiten: | 432 |
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Beschreibungen
<b>Merging Optimization and Control in Power Systems</b> <p><b>A novel exploration of distributed control in power systems with insightful discussions of physical and cyber restrictions</b> <p>In <i>Merging Optimization and Control in Power Systems</i> an accomplished team of engineers deliver a comprehensive introduction to distributed optimal control in power systems. The book re-imagines control design within the framework of cyber-physical systems with restrictions in both the physical and cyber spaces, addressing operational constraints, non-smooth objective functions, rapid power fluctuations caused by renewable generations, partial control coverage, communication delays, and non-identical sampling rates. <p>This book bridges the gap between optimization and control in two ways. First, optimization-based feedback control is explored. The authors describe feedback controllers which automatically drive system states asymptotically to specific, desired optimal working points. Second, the book discusses feedback-based optimization. Leveraging the philosophy of feedback control, the authors envision the online solving of complicated optimization and control problems of power systems to adapt to time-varying environments. <p>Readers will also find: <ul><li>A thorough argument against the traditional and centralized hierarchy of power system control in favor of the merged approach described in the book</li> <li>Comprehensive explorations of the fundamental changes gripping the power system today, including the increasing penetration of renewable and distributed generation, the proliferation of electric vehicles, and increases in load demand</li> <li>Data, tables, illustrations, and case studies covering realistic power systems and experiments</li> <li>In-depth examinations of physical and cyber restrictions, as well as the robustness and adaptability of the proposed model</li></ul> <p>Perfect for postgraduate students and researchers with the prerequisite knowledge of power system analysis, operation, and dynamics, convex optimization theory, and control theory, <i>Merging Optimization and Control in Power Systems</i> is an advanced and timely treatment of distributed optimal controller design.
<p>Foreword xv</p> <p>Preface xvii</p> <p>Acknowledgments xix</p> <p><b>1 Introduction 1</b></p> <p>1.1 Traditional Hierarchical Control Structure 2</p> <p>1.1.1 Hierarchical Frequency Control 2</p> <p>1.1.1.1 Primary Frequency Control 4</p> <p>1.1.1.2 Secondary Frequency Control 5</p> <p>1.1.1.3 Tertiary Frequency Control 5</p> <p>1.1.2 Hierarchical Voltage Control 5</p> <p>1.1.2.1 Primary Voltage Control 6</p> <p>1.1.2.2 Secondary Voltage Control 7</p> <p>1.1.2.3 Tertiary Voltage Control 7</p> <p>1.2 Transitions and Challenges 7</p> <p>1.3 Removing Central Coordinators: Distributed Coordination 8</p> <p>1.3.1 Distributed Control 11</p> <p>1.3.2 Distributed Optimization 12</p> <p>1.4 Merging Optimization and Control 13</p> <p>1.4.1 Optimization-Guided Control 14</p> <p>1.4.2 Feedback-Based Optimization 16</p> <p>1.5 Overview of the Book 17</p> <p>Bibliography 19</p> <p><b>2 Preliminaries 23</b></p> <p>2.1 Norm 23</p> <p>2.1.1 Vector Norm 23</p> <p>2.1.2 Matrix Norm 24</p> <p>2.2 Graph Theory 26</p> <p>2.2.1 Basic Concepts 26</p> <p>2.2.2 Laplacian Matrix 26</p> <p>2.3 Convex Optimization 28</p> <p>2.3.1 Convex Set 28</p> <p>2.3.1.1 Basic Concepts 28</p> <p>2.3.1.2 Cone 30</p> <p>2.3.2 Convex Function 31</p> <p>2.3.2.1 Basic Concepts 31</p> <p>2.3.2.2 Jensen’s Inequality 35</p> <p>2.3.3 Convex Programming 35</p> <p>2.3.4 Duality 36</p> <p>2.3.5 Saddle Point 39</p> <p>2.3.6 KKT Conditions 39</p> <p>2.4 Projection Operator 41</p> <p>2.4.1 Basic Concepts 41</p> <p>2.4.2 Projection Operator 42</p> <p>2.5 Stability Theory 44</p> <p>2.5.1 Lyapunov Stability 44</p> <p>2.5.2 Invariance Principle 46</p> <p>2.5.3 Input–Output Stability 47</p> <p>2.6 Passivity and Dissipativity Theory 49</p> <p>2.6.1 Passivity 49</p> <p>2.6.2 Dissipativity 51</p> <p>2.7 Power Flow Model 52</p> <p>2.7.1 Nonlinear Power Flow 53</p> <p>2.7.1.1 Bus Injection Model (BIM) 53</p> <p>2.7.1.2 Branch Flow Model (BFM) 54</p> <p>2.7.2 Linear Power Flow 55</p> <p>2.7.2.1 DC Power Flow 55</p> <p>2.7.2.2 Linearized Branch Flow 56</p> <p>2.8 Power System Dynamics 56</p> <p>2.8.1 Synchronous Generator Model 57</p> <p>2.8.2 Inverter Model 58</p> <p>Bibliography 60</p> <p><b>3 Bridging Control and Optimization in Distributed Optimal Frequency Control 63</b></p> <p>3.1 Background 64</p> <p>3.1.1 Motivation 64</p> <p>3.1.2 Summary 66</p> <p>3.1.3 Organization 67</p> <p>3.2 Power System Model 67</p> <p>3.2.1 Generator Buses 68</p> <p>3.2.2 Load Buses 69</p> <p>3.2.3 Branch Flows 70</p> <p>3.2.4 Dynamic Network Model 72</p> <p>3.3 Design and Stability of Primary Frequency Control 74</p> <p>3.3.1 Optimal Load Control 74</p> <p>3.3.2 Main Results 75</p> <p>3.3.3 Implications 79</p> <p>3.4 Convergence Analysis 79</p> <p>3.5 Case Studies 88</p> <p>3.5.1 Test System 88</p> <p>3.5.2 Simulation Results 89</p> <p>3.6 Conclusion and Notes 92</p> <p>Bibliography 93</p> <p><b>4 Physical Restrictions: Input Saturation in Secondary Frequency Control 97</b></p> <p>4.1 Background 98</p> <p>4.2 Power System Model 100</p> <p>4.3 Control Design for Per-Node Power Balance 101</p> <p>4.3.1 Control Goals 102</p> <p>4.3.2 Decentralized Optimal Controller 103</p> <p>4.3.3 Design Rationale 105</p> <p>4.3.3.1 Primal–Dual Algorithms 105</p> <p>4.3.3.2 Design of Controller (4.6) 105</p> <p>4.4 Optimality and Uniqueness of Equilibrium 108</p> <p>4.5 Stability Analysis 112</p> <p>4.6 Case Studies 120</p> <p>4.6.1 Test System 120</p> <p>4.6.2 Simulation Results 122</p> <p>4.6.2.1 Stability and Optimality 122</p> <p>4.6.2.2 Dynamic Performance 123</p> <p>4.6.2.3 Comparison with AGC 124</p> <p>4.6.2.4 Digital Implementation 124</p> <p>4.7 Conclusion and Notes 128</p> <p>Bibliography 131</p> <p><b>5 Physical Restrictions: Line Flow Limits in Secondary Frequency Control 135</b></p> <p>5.1 Background 136</p> <p>5.2 Power System Model 137</p> <p>5.3 Control Design for Network Power Balance 138</p> <p>5.3.1 Control Goals 139</p> <p>5.3.2 Distributed Optimal Controller 141</p> <p>5.3.3 Design Rationale 142</p> <p>5.3.3.1 Primal–Dual Gradient Algorithms 142</p> <p>5.3.3.2 Controller Design 143</p> <p>5.4 Optimality of Equilibrium 144</p> <p>5.5 Asymptotic Stability 148</p> <p>5.6 Case Studies 155</p> <p>5.6.1 Test System 155</p> <p>5.6.2 Simulation Results 156</p> <p>5.6.2.1 Stability and Optimality 156</p> <p>5.6.2.2 Dynamic Performance 158</p> <p>5.6.2.3 Comparison with AGC 158</p> <p>5.6.2.4 Congestion Analysis 158</p> <p>5.6.2.5 Time Delay Analysis 161</p> <p>5.7 Conclusion and Notes 165</p> <p>Bibliography 165</p> <p><b>6 Physical Restrictions: Nonsmoothness of Objective Functions in Load-Frequency Control 167</b></p> <p>6.1 Background 167</p> <p>6.2 Notations and Preliminaries 169</p> <p>6.3 Power System Model 170</p> <p>6.4 Control Design 171</p> <p>6.4.1 Optimal Load Frequency Control Problem 172</p> <p>6.4.2 Distributed Controller Design 173</p> <p>6.5 Optimality and Convergence 176</p> <p>6.5.1 Optimality 176</p> <p>6.5.2 Convergence 178</p> <p>6.6 Case Studies 183</p> <p>6.6.1 Test System 183</p> <p>6.6.2 Simulation Results 184</p> <p>6.7 Conclusion and Notes 187</p> <p>Bibliography 188</p> <p><b>7 Cyber Restrictions: Imperfect Communication in Power Control of Microgrids 191</b></p> <p>7.1 Background 192</p> <p>7.2 Preliminaries and Model 193</p> <p>7.2.1 Notations and Preliminaries 193</p> <p>7.2.2 Economic Dispatch Model 194</p> <p>7.3 Distributed Control Algorithms 195</p> <p>7.3.1 Synchronous Algorithm 195</p> <p>7.3.2 Asynchronous Algorithm 196</p> <p>7.4 Optimality and Convergence Analysis 198</p> <p>7.4.1 Virtual Global Clock 199</p> <p>7.4.2 Algorithm Reformulation 200</p> <p>7.4.3 Optimality of Equilibrium 203</p> <p>7.4.4 Convergence Analysis 204</p> <p>7.5 Real-Time Implementation 206</p> <p>7.5.1 Motivation and Main Idea 206</p> <p>7.5.2 Real-Time ASDPD 208</p> <p>7.5.2.1 AC MGs 208</p> <p>7.5.2.2 DC Microgrids 208</p> <p>7.5.3 Control Configuration 210</p> <p>7.5.4 Optimality of the Implementation 211</p> <p>7.6 Numerical Results 213</p> <p>7.6.1 Test System 213</p> <p>7.6.2 Non-identical Sampling Rates 214</p> <p>7.6.3 Random Time Delays 217</p> <p>7.6.4 Comparison with the Synchronous Algorithm 217</p> <p>7.7 Experimental Results 219</p> <p>7.8 Conclusion and Notes 222</p> <p>Bibliography 224</p> <p><b>8 Cyber Restrictions: Imperfect Communication in Voltage Control of Active Distribution Networks 229</b></p> <p>8.1 Background 230</p> <p>8.2 Preliminaries and System Model 232</p> <p>8.2.1 Note and Preliminaries 232</p> <p>8.2.2 System Modeling 233</p> <p>8.3 Problem Formulation 234</p> <p>8.4 Asynchronous Voltage Control 235</p> <p>8.5 Optimality and Convergence 237</p> <p>8.5.1 Algorithm Reformulation 238</p> <p>8.5.2 Optimality of Equilibrium 242</p> <p>8.5.3 Convergence Analysis 243</p> <p>8.6 Implementation 245</p> <p>8.6.1 Communication Graph 245</p> <p>8.6.2 Online Implementation 246</p> <p>8.7 Case Studies 246</p> <p>8.7.1 8-Bus Feeder System 247</p> <p>8.7.2 IEEE 123-Bus Feeder System 250</p> <p>8.8 Conclusion and Notes 253</p> <p>Bibliography 254</p> <p><b>9 Robustness and Adaptability: Unknown Disturbances in Load-Side Frequency Control 257</b></p> <p>9.1 Background 258</p> <p>9.2 Problem Formulation 259</p> <p>9.2.1 Power Network 259</p> <p>9.2.2 Power Imbalance 260</p> <p>9.2.3 Equivalent Transformation of Power Imbalance 261</p> <p>9.3 Controller Design 263</p> <p>9.3.1 Controller for Known P <sup>_in</sup> <sub>j</sub> 263</p> <p>9.3.2 Controller for Time-Varying Power Imbalance 264</p> <p>9.3.3 Closed-Loop Dynamics 265</p> <p>9.4 Equilibrium and Stability Analysis 266</p> <p>9.4.1 Equilibrium 266</p> <p>9.4.2 Asymptotic Stability 269</p> <p>9.5 Robustness Analysis 274</p> <p>9.5.1 Robustness Against Uncertain Parameters 274</p> <p>9.5.2 Robustness Against Unknown Disturbances 275</p> <p>9.6 Case Studies 277</p> <p>9.6.1 System Configuration 277</p> <p>9.6.2 Self-Generated Data 279</p> <p>9.6.3 Performance Under Unknown Disturbances 282</p> <p>9.6.4 Simulation with Real Data 282</p> <p>9.6.5 Comparison with Existing Control Methods 284</p> <p>9.7 Conclusion and Notes 286</p> <p>Bibliography 287</p> <p><b>10 Robustness and Adaptability: Partial Control Coverage in Transient Frequency Control 289</b></p> <p>10.1 Background 289</p> <p>10.2 Structure-Preserving Model of Nonlinear Power System Dynamics 291</p> <p>10.2.1 Power Network 291</p> <p>10.2.2 Synchronous Generators 292</p> <p>10.2.3 Dynamics of Voltage Phase Angles 293</p> <p>10.2.4 Communication Network 294</p> <p>10.3 Formulation of Optimal Frequency Control 294</p> <p>10.3.1 Optimal Power-Sharing Among Controllable Generators 294</p> <p>10.3.2 Equivalent Model With Virtual Load 295</p> <p>10.4 Control Design 296</p> <p>10.4.1 Controller for Controllable Generators 296</p> <p>10.4.2 Active Power Dynamics of Uncontrollable Generators 297</p> <p>10.4.3 Excitation Voltage Dynamics of Generators 298</p> <p>10.5 Optimality and Stability 298</p> <p>10.5.1 Optimality 298</p> <p>10.5.2 Stability 300</p> <p>10.6 Implementation With Frequency Measurement 306</p> <p>10.6.1 Estimating Μ I Using Frequency Feedback 306</p> <p>10.6.2 Stability Analysis 307</p> <p>10.7 Case Studies 310</p> <p>10.7.1 Test System and Data 310</p> <p>10.7.2 Performance Under Small Disturbances 312</p> <p>10.7.2.1 Equilibrium and its Optimality 312</p> <p>10.7.2.2 Performance of Frequency Dynamics 313</p> <p>10.7.3 Performance Under Large Disturbances 316</p> <p>10.7.3.1 Generator Tripping 317</p> <p>10.7.3.2 Short-Circuit Fault 318</p> <p>10.8 Conclusion and Notes 321</p> <p>Bibliography 322</p> <p><b>11 Robustness and Adaptability: Heterogeneity in Power Controls of DC Microgrids 325</b></p> <p>11.1 Background 325</p> <p>11.2 Network Model 328</p> <p>11.3 Optimal Power Flow of DC Networks 329</p> <p>11.3.1 OPF Model 329</p> <p>11.3.2 Uniqueness of Optimal Solution 331</p> <p>11.4 Control Design 334</p> <p>11.4.1 Distributed Optimization Algorithm 334</p> <p>11.4.2 Optimality of Equilibrium 335</p> <p>11.4.3 Convergence Analysis 338</p> <p>11.5 Implementation 344</p> <p>11.6 Case Studies 346</p> <p>11.6.1 Test System and Data 346</p> <p>11.6.2 Accuracy Analysis 348</p> <p>11.6.3 Dynamic Performance Verification 348</p> <p>11.6.4 Performance in Plug-n-play Operations 352</p> <p>11.7 Conclusion and Notes 353</p> <p>Bibliography 354</p> <p><b>Appendix A Typical Distributed Optimization Algorithms 357</b></p> <p>A.1 Consensus-Based Algorithms 357</p> <p>A.1.1 Consensus Algorithms 358</p> <p>A.1.2 Cutting-Plane Consensus Algorithm 359</p> <p>A.2 First-Order Gradient-Based Algorithms 362</p> <p>A.2.1 Dual Decomposition 363</p> <p>A.2.2 Alternating Direction Method of Multipliers 366</p> <p>A.2.3 Primal–Dual Gradient Algorithm 368</p> <p>A.2.4 Proximal Gradient Method 371</p> <p>A.3 Second-Order Newton-Based Algorithms 374</p> <p>A.3.1 Barrier Method 374</p> <p>A.3.2 Primal–Dual Interior-Point Method 375</p> <p>A.4 Zeroth-Order Online Algorithms 377</p> <p>Bibliography 379</p> <p><b>Appendix B Optimal Power Flow of Direct Current Networks 385</b></p> <p>B. 1 Mathematical Model 385</p> <p>B.. 1 Formulation 385</p> <p>B.1. 2 Equivalent Transformation 387</p> <p>B. 2 Exactness of SOC Relaxation 388</p> <p>B.2. 1 SOC Relaxation of OPF in DC Networks 388</p> <p>B.. 2 Assumptions 388</p> <p>B.2. 3 Exactness of the SOC Relaxation 389</p> <p>B.2. 4 Topological Independence 396</p> <p>B.2. 5 Uniqueness of the Optimal Solution 396</p> <p>B.2. 6 Branch Flow Model 397</p> <p>B. 3 Case Studies 399</p> <p>B.3. 1 16-Bus System 399</p> <p>B.3. 2 Larger-Scale Systems 401</p> <p>B. 4 Discussion on Line Constraints 402</p> <p>B.4. 1 OPF with Line Constraints 402</p> <p>B.4. 2 Exactness Conditions with Line Constraints 403</p> <p>B.4. 3 Constructing Approximate Optimal Solutions 406</p> <p>B.4.3. 1 Direct Construction Method 407</p> <p>B.4.3. 2 Slack Variable Method 408</p> <p>Bibliography 409</p> <p>Index 411</p>
<p><b>Feng Liu, PhD,</b> is Associate Professor in the Department of Electrical Engineering at Tsinghua University in Beijing, China.</p> <p><b>Zhaojian Wang, PhD,</b> is Assistant Professor in the Department of Automation at Shanghai Jiao Tong University in Shanghai, China. <p><b>Changhong Zhao, PhD,</b> is Assistant Professor in the Department of Information Engineering at the Chinese University of Hong Kong, Hong Kong SAR, China. <p><b>Peng Yang</b> is a PhD Candidate in the Department of Electrical Engineering at Tsinghua University in Beijing, China.
<p><b>A novel exploration of distributed control in power systems with insightful discussions of physical and cyber restrictions</b></p> <p>In <i>Merging Optimization and Control in Power Systems</i> an accomplished team of engineers deliver a comprehensive introduction to distributed optimal control in power systems. The book re-imagines control design within the framework of cyber-physical systems with restrictions in both the physical and cyber spaces, addressing operational constraints, non-smooth objective functions, rapid power fluctuations caused by renewable generations, partial control coverage, communication delays, and non-identical sampling rates. <p>This book bridges the gap between optimization and control in two ways. First, optimization-based feedback control is explored. The authors describe feedback controllers which automatically drive system states asymptotically to specific, desired optimal working points. Second, the book discusses feedback-based optimization. Leveraging the philosophy of feedback control, the authors envision the online solving of complicated optimization and control problems of power systems to adapt to time-varying environments. <p>Readers will also find: <ul><li>A thorough argument against the traditional and centralized hierarchy of power system control in favor of the merged approach described in the book</li> <li>Comprehensive explorations of the fundamental changes gripping the power system today, including the increasing penetration of renewable and distributed generation, the proliferation of electric vehicles, and increases in load demand</li> <li>Data, tables, illustrations, and case studies covering realistic power systems and experiments</li> <li>In-depth examinations of physical and cyber restrictions, as well as the robustness and adaptability of the proposed model</li></ul> <p> Perfect for postgraduate students and researchers with the prerequisite knowledge of power system analysis, operation, and dynamics, convex optimization theory, and control theory, <i>Merging Optimization and Control in Power Systems</i> is an advanced and timely treatment of distributed optimal controller design.
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