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第三版目录。
5 r: I8 V1 a4 B1 Introduction 1
% v- C1 a( x6 P) O& v; R1.1 Purpose of the Course / 1
5 U7 Y- u/ |9 W+ M: U! y3 L% a1.2 Course Scope / 20 v+ N, c+ u* ]( G+ o
1.3 Economic Importance / 2
6 {& H) d: j9 n1.4 Deregulation: Vertical to Horizontal / 3
4 ~) g9 s3 H) n( }$ g0 x+ ^1.5 Problems: New and Old / 37 T1 [9 {( ~4 Y/ i; H5 ^6 c! `& g
1.6 Characteristics of Steam Units / 6
1 T+ t! b9 T/ H7 `' i; S/ R1.6.1 Variations in Steam Unit Characteristics / 100 p @0 M6 ]. J3 X
1.6.2 Combined Cycle Units / 139 S) M: s' ]/ ?' C. w( Y- C
1.6.3 Cogeneration Plants / 14
0 Z( m5 ]- t1 M' j. ~: `1.6.4 Light-Water Moderated Nuclear Reactor Units / 17
/ K) @/ _6 u9 t6 H8 m. k' g G1.6.5 Hydroelectric Units / 18- v6 [7 |- z" @/ {
1.6.6 Energy Storage / 21' u- P1 @2 N2 H
1.7 Renewable Energy / 22% k) P5 s5 g+ F* T+ k+ q' o
1.7.1 Wind Power / 23
c2 {1 N2 F& ]# U3 M2 |: D1.7.2 Cut-In Speed / 23* i+ q. I' W7 k# K+ D ]0 f! |
1.7.3 Rated Output Power and Rated Output Wind Speed / 24
# s4 x( u! e! ?1.7.4 Cut-Out Speed / 24
2 ~0 x9 L5 E0 M5 @1.7.5 Wind Turbine Efficiency or Power Coefficient / 24$ o. A, Y" `" L* c) V) H
1.7.6 Solar Power / 25
* K5 ` d3 Q& Y" {APPENDIX 1A Typical Generation Data / 26
* A9 ]% W9 H, gAPPENDIX 1B Fossil Fuel Prices / 28; \# d- j, V$ C
APPENDIX 1C Unit Statistics / 29
( @& J( {4 c* y$ ^CONTENTS
# w' b) d4 E6 l( aviii contents
$ w4 S2 Z( I m$ @References for Generation Systems / 31
5 `5 C1 F w/ }1 x; t, m9 Z, HFurther Reading / 31
7 B7 f( T$ r" J' X* H4 H2 Industrial Organization, Managerial Economics, and Finance 35% j% v: z+ v/ {1 X+ E* u$ r
2.1 Introduction / 35 ^, {0 ^6 } h, p% @
2.2 Business Environments / 36) |: h- C: v$ H* o; @4 O
2.2.1 Regulated Environment / 37
B- c0 i" h+ S% m e, i: {8 H$ C2.2.2 Competitive Market Environment / 38+ b, ~/ k# q" w& ]' c+ ]6 G
2.3 Theory of the Firm / 40/ e3 B Y. M Q# V4 `! ?
2.4 Competitive Market Solutions / 42
: m5 A& u0 I8 l a% p2.5 Supplier Solutions / 453 v }8 t( A1 E4 @: m' B, g1 ]
2.5.1 Supplier Costs / 460 C( }# f, i/ ]1 |0 {' W
2.5.2 Individual Supplier Curves / 46
# q2 j$ p j1 P! u ^+ s! l* M2.5.3 Competitive Environments / 470 q0 F: T9 v- X3 k$ ^0 _6 p
2.5.4 Imperfect Competition / 510 e. ~, V/ `% C5 o r! l
2.5.5 Other Factors / 52; y4 @+ o+ |0 ]
2.6 Cost of Electric Energy Production / 53' O/ b- C/ J" p+ N1 r$ Z
2.7 Evolving Markets / 54
" E: _% O( N: F' s8 d. U2.7.1 Energy Flow Diagram / 572 `" z/ q4 Z4 W( g R- l
2.8 Multiple Company Environments / 58
( P1 T/ c8 w* e- h) W& m2 Z% X2.8.1 Leontief Model: Input–Output Economics / 588 m, b+ Q4 a: {. x
2.8.2 Scarce Fuel Resources / 60
8 d2 f8 K; q9 w1 |( Z2.9 Uncertainty and Reliability / 61/ `4 s; W' h3 H- J9 }! o
PROBLEMS / 61
* J- e. t, `- x' BReference / 62
/ V# Q9 ~& t% Z" ~7 M; W3 Economic Dispatch of Thermal Units and Methods of Solution 630 B4 F" l$ Z# |( d- ]# T
3.1 The Economic Dispatch Problem / 63
+ l; }$ @( ]' X1 J3.2 Economic Dispatch with Piecewise Linear Cost Functions / 68. I8 b, u# Z& ?
3.3 LP Method / 69" H9 f* R: @2 ]' Z& e( |
3.3.1 Piecewise Linear Cost Functions / 69
7 Y1 H. u! b8 e' ?1 N U1 S6 ]9 q' a* F; d3.3.2 Economic Dispatch with LP / 71* ~0 T. w0 B# N* ~' q z
3.4 The Lambda Iteration Method / 73
3 M) B' f3 y% A* F" o3.5 Economic Dispatch Via Binary Search / 76
, Z. ]8 n/ g- {+ Y$ Z: `3.6 Economic Dispatch Using Dynamic Programming / 78+ Y/ h+ `: V* f9 c0 T0 O# W
3.7 Composite Generation Production Cost Function / 81" R" M K/ Z" x% L1 V
3.8 Base Point and Participation Factors / 85! w& y( C. l D
3.9 Thermal System Dispatching with Network Losses) z: _7 W6 m* z8 K1 s
Considered / 88
# W- c9 Z) ^! g f0 kcontents ix
0 G% q& I" E& g7 X2 U# H, u3.10 The Concept of Locational Marginal Price (LMP) / 924 ]9 j9 V; l' I3 b3 K w
3.11 Auction Mechanisms / 95
' K! |9 e% w+ \% z& z I3.11.1 PJM Incremental Price Auction as a- o; ]* A" z5 }
Graphical Solution / 956 [- e. L W0 y- |5 ]
3.11.2 Auction Theory Introduction / 985 k9 f& e8 P$ W J3 _3 l
3.11.3 Auction Mechanisms / 100
0 Y% R# [+ H4 m8 }3.11.4 English (First-Price Open-Cry = Ascending) / 101
" ?8 X- V* {( B' y* x3.11.5 Dutch (Descending) / 103
+ t& `/ ?" D8 y1 K3.11.6 First-Price Sealed Bid / 104$ D" I! R4 H! }% R' U4 s
3.11.7 Vickrey (Second-Price Sealed Bid) / 105& r- Q% h' Z8 x# g
3.11.8 All Pay (e.g., Lobbying Activity) / 105
6 {' |( t7 d6 g6 N L1 F NAPPENDIX 3A Optimization Within Constraints / 106
E) ]8 v/ K4 MAPPENDIX 3B Linear Programming (LP) / 117
X& G' u4 O! `) x3 FAPPENDIX 3C Non-Linear Programming / 128
) Q d# Y" w$ E+ X4 D2 z' X9 I& QAPPENDIX 3D Dynamic Programming (DP) / 128
* Z5 d0 r: @1 pAPPENDIX 3E Convex Optimization / 135 q7 _4 M8 m) i# D; c9 d' m
PROBLEMS / 138: s# n2 }; S& s
References / 146
& u1 C8 E3 P8 a1 R1 _9 _4 Unit Commitment 1474 T0 f, B( K) Y; R- C+ r! h
4.1 Introduction / 147
; E$ u& g) _: ~4.1.1 Economic Dispatch versus Unit Commitment / 1476 k3 E7 ]4 Q) L6 C: o7 O
4.1.2 Constraints in Unit Commitment / 152
; `# K4 a( W# P4.1.3 Spinning Reserve / 152# H: L7 N5 @% D( ] r1 x
4.1.4 Thermal Unit Constraints / 1532 s9 |3 s3 W# M
4.1.5 Other Constraints / 1552 r Q0 c8 w: u5 m5 V
4.2 Unit Commitment Solution Methods / 1554 t: U0 W* M9 |% O4 f
4.2.1 Priority-List Methods / 156
" @9 B$ c( e2 p* c+ u5 B4.2.2 Lagrange Relaxation Solution / 157" y& R+ _( B9 t$ f+ ^1 t) v
4.2.3 Mixed Integer Linear Programming / 166
8 u1 p) ]! w* _$ l# r. g- _3 g8 U) P4.3 Security-Constrained Unit Commitment (SCUC) / 167: b$ J0 b8 }% y
4.4 Daily Auctions Using a Unit Commitment / 1672 J4 o! Y# X1 T& t- X
APPENDIX 4A Dual Optimization on a Nonconvex
1 r2 m; O$ m( Z5 V. I8 S' QProblem / 167/ [) r- q" t" b, _2 x0 m( x0 X
APPENDIX 4B Dynamic-Programming Solution to
0 X9 U4 q6 T) U( D; u6 VUnit Commitment / 1738 |6 v; B7 U4 {( F4 d1 n2 L
4B.1 Introduction / 173
! u6 ~" J$ R7 g! O1 G4B.2 Forward DP Approach / 174
7 P; v3 a1 s" ^" Q& S( A2 \PROBLEMS / 182
8 S% ^8 l) o" Y) A+ [: k) H, zx contents
3 S9 `8 B9 K, q5 Generation with Limited Energy Supply 1873 n" @. Y7 W0 k6 W" |6 S: b( Q
5.1 Introduction / 187% I. k& w7 O1 c2 [' @7 b
5.2 Fuel Scheduling / 188
1 X) d2 Z4 _, c; ^+ D' |5.3 Take-or-Pay Fuel Supply Contract / 188, K# c) A+ x# G: v: [
5.4 Complex Take-or-Pay Fuel Supply Models / 194; u) s I) f. U" p7 Z4 m9 L! ]
5.4.1 Hard Limits and Slack Variables / 194
: A. j7 D4 a- W. k0 P5.5 Fuel Scheduling by Linear Programming / 195" ?7 c4 v9 H- r5 C3 V7 D7 g
5.6 Introduction to Hydrothermal Coordination / 202
5 ?2 T* S3 b& Q3 L' R3 t5.6.1 Long-Range Hydro-Scheduling / 203
6 T/ Y9 W1 X' L# I5 O l+ J; V+ L5.6.2 Short-Range Hydro-Scheduling / 204# [% V& T4 |" j/ k# C- }; j, G+ X
5.7 Hydroelectric Plant Models / 204
+ @, W, }/ j- B4 N% K5.8 Scheduling Problems / 207+ _ I# X" ~" u" p L9 l) b5 ~
5.8.1 Types of Scheduling Problems / 207
; r5 L% e! H5 q5 Y5.8.2 Scheduling Energy / 207
, X4 L! O/ A \' X3 o8 ?$ E5.9 The Hydrothermal Scheduling Problem / 2116 [5 \" ~ a+ i% x2 b( }3 i
5.9.1 Hydro-Scheduling with Storage Limitations / 211
/ b+ F8 B2 n: ?( h, D8 ]5.9.2 Hydro-Units in Series (Hydraulically Coupled) / 216
' [0 Z9 S' G' [3 i5.9.3 Pumped-Storage Hydroplants / 218" L, B2 F, n2 T" A# z
5.10 Hydro-Scheduling using Linear Programming / 2224 k3 i- m( F; x6 [0 m
APPENDIX 5A Dynamic-Programming Solution to hydrothermal
6 }% m. D, p7 |; ~4 q5 w6 m6 OScheduling / 225
& r3 G" R; O. | a) V5.A.1 Dynamic Programming Example / 227" I1 {$ ~- H& T4 s* X' j8 w$ |8 ^
5.A.1.1 Procedure / 228
% A( Y, L7 {1 }" Q5 B5.A.1.2 Extension to Other Cases / 231
: o* b, G; U" ? D( I. i) F' s5.A.1.3 Dynamic-Programming Solution to Multiple Hydroplant. k. G0 E; a- Y! |( w! z+ y C' w
Problem / 232
3 L2 u: E: ?& q. i! VPROBLEMS / 2343 o' X$ j C8 \$ N) c8 T+ L6 e
6 Transmission System Effects 243/ f5 n9 v3 r8 v$ Q: E$ I5 {
6.1 Introduction / 243
R. }; w! S( T! K2 g" @6.2 Conversion of Equipment Data to Bus and Branch Data / 247
0 ?% A9 U# y1 I7 W; q6.3 Substation Bus Processing / 248
# _1 H$ `, m' v R) d. b6.4 Equipment Modeling / 248
3 w9 c T2 E6 u* W6.5 Dispatcher Power Flow for Operational Planning / 251. s! s6 @8 M6 ?8 Q* @& w$ B* G2 b
6.6 Conservation of Energy (Tellegen’s Theorem) / 252
5 ]4 H1 t/ }9 U, T* ^5 n6.7 Existing Power Flow Techniques / 253$ O1 |( t& S8 _: R' b
6.8 The Newton–Raphson Method Using the Augmented. q" c$ {- C! L0 u
Jacobian Matrix / 254
/ O5 t5 Z# z; I; g# \6.8.1 Power Flow Statement / 254
8 V: ~& L7 N- Y7 P) Q: m6.9 Mathematical Overview / 2576 v4 R" C3 a2 S' c! \1 O
contents xi
1 X5 i; L2 a) a/ ^. y6.10 AC System Control Modeling / 2596 `+ D3 Z: L' c+ S
6.11 Local Voltage Control / 2596 y0 h* Y0 I. ~0 c r
6.12 Modeling of Transmission Lines and Transformers / 259& }0 j! v0 j+ w% {0 a% t* `
6.12.1 Transmission Line Flow Equations / 259
% z$ U+ Q" u7 t5 i' u/ s6.12.2 Transformer Flow Equations / 260, c$ j# }2 P' {$ X, C; X. ?" v, u
6.13 HVDC links / 261% i! e" j g- o2 x
6.13.1 Modeling of HVDC Converters- j! y7 q$ J S
and FACT Devices / 264( p, P; j# x7 F1 `8 w
6.13.2 Definition of Angular Relationships in
. `6 z! d; U( z" qHVDC Converters / 264* H/ }1 o# _% V8 g8 `' c' X3 d
6.13.3 Power Equations for a Six-Pole HVDC
5 n/ h- }9 U B# QConverter / 264+ _/ k7 B) C' _* r; G
6.14 Brief Review of Jacobian Matrix Processing / 2671 f& I( V0 @- X6 W' J' X
6.15 Example 6A: AC Power Flow Case / 269( u& v$ p' M9 T
6.16 The Decoupled Power Flow / 2718 j c( I7 C, B! {; o
6.17 The Gauss–Seidel Method / 275
4 {/ U: C9 ?3 w6 f, p. B6.18 The “DC” or Linear Power Flow / 277
& |) @! X/ E# U7 o* Z P1 h6.18.1 DC Power Flow Calculation / 277
$ h/ r) M2 E1 K6.18.2 Example 6B: DC Power Flow Example on the
& o B1 l( q* e7 f; e3 dSix-Bus Sample System / 278
# O( |8 e& E! W" @6 D4 p3 `" R6.19 Unified Eliminated Variable Hvdc Method / 278
. |1 f6 P; C) \6.19.1 Changes to Jacobian Matrix Reduced / 279$ `7 h% U& b# v
6.19.2 Control Modes / 280/ {* P0 B e! x
6.19.3 Analytical Elimination / 280
7 ]4 G T U1 h8 h$ g% }6.19.4 Control Mode Switching / 283
0 |- ]4 [- r0 j+ @4 Q. b" G; B6.19.5 Bipolar and 12-Pulse Converters / 283
m4 z9 X0 B" H% L' G c6.20 Transmission Losses / 2849 O3 ?3 b, B; u) e5 h
6.20.1 A Two-Generator System Example / 284
8 K& l; ?1 r7 |4 {3 W. B6.20.2 Coordination Equations, Incremental Losses,* W1 @ p) B, i0 m" a1 t; Y
and Penalty Factors / 286
7 r& q4 t3 w6 ]/ L1 J! T: E6.21 Discussion of Reference Bus Penalty Factors / 288
+ }0 p5 G# d) @7 U/ b2 L: n6.22 Bus Penalty Factors Direct from the AC Power Flow / 289
. D5 B; {( R+ _, g$ s4 b! {PROBLEMS / 291
N- ]9 P% D- Y2 g. {7 Power System Security 296: n, N p; H! j+ N' O9 w
7.1 Introduction / 296
# h, J* s3 Y, `! x$ f% r7.2 Factors Affecting Power System Security / 301
0 L4 e0 x T, ]/ r7.3 Contingency Analysis: Detection of Network Problems / 301- n- p1 i* q- o1 S: Y1 D4 I
7.3.1 Generation Outages / 3019 k/ Q! k' l: z
7.3.2 Transmission Outages / 3022 }& }4 q1 Y9 U( e/ I
xii contents: F, W) s- v3 \1 X
7.4 An Overview of Security Analysis / 3063 {* N+ o+ l8 ~* |2 b) [# k
7.4.1 Linear Sensitivity Factors / 307
! S2 x! A6 N, k6 W6 v7.5 Monitoring Power Transactions Using “Flowgates” / 313 L4 a) o% I, t* ^
7.6 Voltage Collapse / 315
- c& L U4 j1 z6 o$ j7.6.1 AC Power Flow Methods / 317
3 G W: o/ p8 G7.6.2 Contingency Selection / 320! o3 G' q2 j8 [3 b( T: G
7.6.3 Concentric Relaxation / 323" I' {( R, Q8 s
7.6.4 Bounding / 325& ~/ w7 H: ?- r( ]7 h$ S$ V
7.6.5 Adaptive Localization / 3256 B. P$ ~) m5 V! n0 {7 \
APPENDIX 7A AC Power Flow Sample Cases / 327
. h! P4 k' T1 ?& K' MAPPENDIX 7B Calculation of Network Sensitivity Factors / 336
7 a! D9 S) s* r) |7B.1 Calculation of PTDF Factors / 3364 P* p8 {1 V* T* j" g& m# R/ k
7B.2 Calculation of LODF Factors / 339- }! C/ E7 v; r( K7 u
7B.2.1 Special Cases / 341
3 {8 o; |7 i' \3 v4 M5 v9 {+ T! `7B.3 Compensated PTDF Factors / 343
, F8 l) U, C! _1 z* D; hProblems / 343* @1 N! ]4 F9 {( V$ X2 A) G
References / 349
( o( J9 T% z1 o& w- y. i) Q8 Optimal Power Flow 350
9 X% s5 N7 l, J; m8.1 Introduction / 350) J: x8 M+ O: N# \1 R
8.2 The Economic Dispatch Formulation / 351
4 G6 V' P7 l9 G% \6 j8.3 The Optimal Power Flow Calculation Combining/ S& \0 w6 M8 U) T0 J# v
Economic Dispatch and the Power Flow / 352
1 O8 K$ y8 d4 J8.4 Optimal Power Flow Using the DC Power Flow / 3548 S# |9 ]- B( u0 R) q: g6 g9 O
8.5 Example 8A: Solution of the DC Power Flow OPF / 356. U; _) c+ E; g& @3 r/ p+ y
8.6 Example 8B: DCOPF with Transmission Line/ `) J2 g9 e! A5 `* p
Limit Imposed / 3616 c; [9 }4 A2 _2 o# f; S
8.7 Formal Solution of the DCOPF / 365
: E" Q7 n7 _. F) [; K, s8.8 Adding Line Flow Constraints to the Linear- x% X; Q; V1 I2 m
Programming Solution / 365
8 i! g5 N7 S$ t) U; z8.8.1 Solving the DCOPF Using Quadratic Programming / 367
0 I- l! C, @0 H5 S8.9 Solution of the ACOPF / 368( g" j: A) y% g x9 @8 k% u. g
8.10 Algorithms for Solution of the ACOPF / 369
: V, }2 f8 K. Z$ _3 r9 ]7 A W- C8.11 Relationship Between LMP, Incremental Losses,# r7 K: G9 k) c/ F ]
and Line Flow Constraints / 376+ e6 F9 M+ V; V' y0 H: `- j6 v8 p; t
8.11.1 Locational Marginal Price at a Bus with No Lines9 `3 j( D/ Y4 F2 h, O3 l
Being Held at Limit / 3776 |6 w) J: _' b4 B* F
8.11.2 Locational Marginal Price with a Line Held at its Limit / 378( g( ?/ D% W K/ E* D
contents xiii i9 x7 b9 {5 i
8.12 Security-Constrained OPF / 382
' j7 n, b' f5 X/ N0 m" [) I8.12.1 Security Constrained OPF Using the DC Power Flow4 C4 N+ S$ R4 g& D
and Quadratic Programming / 384
6 B1 i% l/ V" ~& q8.12.2 DC Power Flow / 3856 a: p* N/ I8 F! U
8.12.3 Line Flow Limits / 385
) {1 A7 l% E% y" g% ?8.12.4 Contingency Limits / 386
+ n" u/ L' z f; cAPPENDIX 8A Interior Point Method / 391- ~# I# P3 c. R- I& p' L- E
APPENDIX 8B Data for the 12-Bus System / 393
# w9 N( E* ^! Z, [APPENDIX 8C Line Flow Sensitivity Factors / 395: o0 Y% w. ^' z
APPENDIX 8D Linear Sensitivity Analysis of the
1 h# ] `/ k# N# g+ s" tAC Power Flow / 397
4 n9 ]$ ]* }1 u5 G1 oPROBLEMS / 399' a0 M0 O p4 `! B5 n& x
9 Introduction to State Estimation in Power Systems 403
9 p N( x# c; I5 c4 X9.1 Introduction / 403
. N7 R: x- \+ y$ X$ j& E' X9.2 Power System State Estimation / 404
" E! |$ Y) k& y8 d! O. [8 f9.3 Maximum Likelihood Weighted Least-Squares
6 I6 A8 t: g: }/ y, w1 |3 u3 JEstimation / 4082 b1 a; Y% y4 [* M. ~
9.3.1 Introduction / 4085 M$ q9 Q+ U3 ?* Y6 f; a
9.3.2 Maximum Likelihood Concepts / 4105 B1 C! R% Q- h
9.3.3 Matrix Formulation / 414- F0 N% m) ?/ k% O$ {0 I# h0 W) K3 a
9.3.4 An Example of Weighted Least-Squares
1 M' n2 O" w! _State Estimation / 417
, L# T! u6 M* w' r9.4 State Estimation of an Ac Network / 421, E. {+ C5 J" V: q1 e |7 t
9.4.1 Development of Method / 4218 C/ y' \* s0 X: J- k9 k' L
9.4.2 Typical Results of State Estimation on an, l5 T: a$ K- X4 n
AC Network / 424
1 ^2 d' a3 ?& B. h& H$ p. D9.5 State Estimation by Orthogonal Decomposition / 428
( a# s# Y) ?( A" Z0 Q9.5.1 The Orthogonal Decomposition Algorithm / 431% `* H* R6 _1 Q
9.6 An Introduction to Advanced Topics in State Estimation / 435
. p/ l; m7 g2 ]1 z9 c) O+ C+ ~: }9.6.1 Sources of Error in State Estimation / 435
, Y% U/ q) m6 O! N/ y9.6.2 Detection and Identification of Bad Measurements / 436
8 n* |8 V" |' _" ~- q) K$ B9.6.3 Estimation of Quantities Not Being Measured / 443
! {- N' c& ]5 E9.6.4 Network Observability and Pseudo-measurements / 444+ M0 G/ m3 A, s0 [
9.7 The Use of Phasor Measurement Units (PMUS) / 447
" O: }& p$ E& |! P! G9.8 Application of Power Systems State Estimation / 451( Q& l- @. h! s, H! C1 T
9.9 Importance of Data Verification and Validation / 454
/ I- q6 ?- p( z# I* W' U. L. {9.10 Power System Control Centers / 454" U+ Q7 L, h! y" R* v
xiv contents* k% s; \5 R1 B0 G9 }
APPENDIX 9A Derivation of Least-Squares Equations / 4565 [7 E/ Y4 P$ `+ b. P
9A.1 The Overdetermined Case (Nm > Ns) / 457
7 F! { B& `( \* }# S1 n0 v7 l) [9A.2 The Fully Determined Case (Nm = Ns) / 4622 b, b8 K& \" p( T1 d X, q
9A.3 The Underdetermined Case (Nm < Ns) / 4629 t9 W3 k, m, H0 |. ^9 B: @& Z! s/ t
PROBLEMS / 464
* W" S) y4 ~0 q+ D6 O) i0 Z10 Control of Generation 468
% V! X- Y _/ z4 @10.1 Introduction / 468
6 c9 c" d u/ c* L$ J1 C- g10.2 Generator Model / 470
. T4 I% M# d0 v, s10.3 Load Model / 473; ^! V) s& K4 l3 {: h6 \
10.4 Prime-Mover Model / 475 A' ?# L" T, c6 Z
10.5 Governor Model / 476. V0 n) N$ i" l, `- [ w4 v
10.6 Tie-Line Model / 481
) k F$ o; ?; h" a6 g) B10.7 Generation Control / 485
$ @. s# G m- n5 v4 c10.7.1 Supplementary Control Action / 485
" `" C! l5 u$ H* h3 r10.7.2 Tie-Line Control / 486
" L4 D( U7 y% t+ M10.7.3 Generation Allocation / 489. Q; e& w7 L% q W# L
10.7.4 Automatic Generation Control (AGC)
( S" N! o0 _9 g# ]: C" k/ JImplementation / 491, E Y4 \8 z' U5 T
10.7.5 AGC Features / 495
1 g! A2 K9 ?% Y4 h- Y3 {/ |10.7.6 NERC Generation Control Criteria / 496$ [2 l! q/ y. h+ R b
PROBLEMS / 497
; H* C0 ], B8 m* @References / 500
2 ]- s5 |( E8 s0 }3 a11 Interchange, Pooling, Brokers, and Auctions 5019 g9 m7 k% \% n- z5 ?* g0 g
11.1 Introduction / 5010 e- U N. e4 M+ C! j7 {/ y
11.2 Interchange Contracts / 504* j3 m& ?& y7 i8 z8 t8 B5 S% M) t
11.2.1 Energy / 504# K5 w( X j, P
11.2.2 Dynamic Energy / 506
! T) n! a' z( R/ w! q ^( M7 [5 I11.2.3 Contingent / 506
+ w- Z+ n/ z( P- k: V- O11.2.4 Market Based / 507
2 H$ X! h! c( F5 {) F11.2.5 Transmission Use / 5085 p- K) v- ~. H# J- o
11.2.6 Reliability / 517$ e5 [" ~! U+ ]& n* L
11.3 Energy Interchange between Utilities / 517& o+ S, _2 E5 x1 u
11.4 Interutility Economy Energy Evaluation / 521( ?; f: u; d8 u0 b0 R
11.5 Interchange Evaluation with Unit Commitment / 522
- C! s6 E4 x& w4 w' X! w* I% Q11.6 Multiple Utility Interchange Transactions—Wheeling / 523
7 Z" {3 N3 i" r) Y# B" q' S8 c0 Z11.7 Power Pools / 5262 |9 D1 G8 r1 J' d* ]
contents xv
9 d* o" C; ] j$ @# o- }11.8 The Energy-Broker System / 529
+ i1 o$ ~$ j5 i* r; X+ V6 o2 Z11.9 Transmission Capability General Issues / 533: Q+ v& S2 P. k/ O7 P$ w; a2 X) r
11.10 Available Transfer Capability and Flowgates / 535
* N1 a8 z& i0 g2 \ N11.10.1 Definitions / 536
% G8 k* ~( j: Q: ?- N/ u( \11.10.2 Process / 539- v( p- n+ g: W) d& F
11.10.3 Calculation ATC Methodology / 540
7 w0 I b. i& d) Z, m. ?2 B11.11 Security Constrained Unit Commitment (SCUC) / 550$ O( H/ G. t# ~! ]2 ]! H
11.11.1 Loads and Generation in a Spot Market Auction / 550% O9 j3 C; D& D5 U+ Y9 W
11.11.2 Shape of the Two Functions / 5524 P' Q5 {! Z2 R& ~/ `
11.11.3 Meaning of the Lagrange Multipliers / 553! j3 n, g& V3 p7 T" K
11.11.4 The Day-Ahead Market Dispatch / 554) V2 r5 t) s, i; \0 T! l
11.12 Auction Emulation using Network LP / 555
$ G. g8 J [9 Q11.13 Sealed Bid Discrete Auctions / 555+ I2 h+ u2 r) ~5 u+ W5 M
PROBLEMS / 560
( g! m3 K g( ^8 T1 t12 Short-Term Demand Forecasting 566
$ ]8 H& u0 L8 {5 F/ a12.1 Perspective / 566
# ] _# S) r$ j; S2 x12.2 Analytic Methods / 569
( \0 X3 s J: N4 B1 [% h2 K# t12.3 Demand Models / 571& K, ~4 X+ n8 w( c
12.4 Commodity Price Forecasting / 5725 C$ J4 T5 e5 n! b+ h
12.5 Forecasting Errors / 573
& {6 }1 a5 o; n9 b12.6 System Identification / 573: M& d, V; W/ P
12.7 Econometric Models / 5740 j( ]1 D( e9 V- N
12.7.1 Linear Environmental Model / 574* @* U Q3 r8 g2 k& m
12.7.2 Weather-Sensitive Models / 5761 j) n4 Z, b- m: N, h# j5 x
12.8 Time Series / 578
) W6 e4 g9 X- Z2 W- n! U9 x12.8.1 Time Series Models Seasonal Component / 578
& K7 g4 q$ X3 V7 L1 G( G12.8.2 Auto-Regressive (AR) / 580
+ ]( G/ L2 ?; T: a r% X6 \' S6 K12.8.3 Moving Average (MA) / 581
# l3 J/ i- g2 _3 j1 \% B% S12.8.4 Auto-Regressive Moving Average (ARMA):0 Y* ^1 S o6 {5 H
Box-Jenkins / 582
7 ~( J9 {+ M3 b: b) H% B( p12.8.5 Auto-Regressive Integrated Moving-Average
9 R7 d. L" R6 o6 P9 W* J(ARIMA): Box-Jenkins / 584
J3 d2 U5 a g7 F, F12.8.6 Others (ARMAX, ARIMAX, SARMAX, NARMA) / 585
7 c' U1 D/ j3 l12.9 Time Series Model Development / 585
* u/ ^% D- d6 R8 z! E8 @$ ~8 b12.9.1 Base Demand Models / 586
" k5 ?/ E y% t12.9.2 Trend Models / 5869 w8 P7 j7 p3 ~* {7 j/ F
12.9.3 Linear Regression Method / 586' d9 O' `% m, j9 ]
xvi contents8 L6 _! e; Y4 b% r" ^. ^
12.9.4 Seasonal Models / 588
! u$ q0 h- c8 I- S# J12.9.5 Stationarity / 5882 Z- \! z% e- g' O
12.9.6 WLS Estimation Process / 590
4 n) ~; J8 r, t( M12.9.7 Order and Variance Estimation / 5918 m5 v+ d: @) C2 O' z2 z$ v
12.9.8 Yule-Walker Equations / 592& `1 B! |* v! M% X* ]) h. G
12.9.9 Durbin-Levinson Algorithm / 595, a% T3 f& m$ s0 @( H
12.9.10 Innovations Estimation for MA and ARMA7 P. L/ Y( }; O3 [- w2 V; ]. i
Processes / 598; r6 O* ?* k4 G
12.9.11 ARIMA Overall Process / 600
1 O; G1 H: A+ q1 e" E# ^12.10 Artificial Neural Networks / 603& m9 c/ V% ?2 Y
12.10.1 Introduction to Artificial Neural Networks / 604( t$ @/ j# [4 h* K/ W2 X
12.10.2 Artificial Neurons / 605
# U8 v( \9 [2 g2 V" w+ r12.10.3 Neural network applications / 606
( A p1 z' a! P5 z1 d5 \" ^12.10.4 Hopfield Neural Networks / 606% a3 T, l& z- T$ _; ~+ ?" Y
12.10.5 Feed-Forward Networks / 6075 T% d/ d% Z1 l8 c( b4 `
12.10.6 Back-Propagation Algorithm / 610% U# k' S& Y9 w
12.10.7 Interior Point Linear Programming Algorithms / 613
6 b* T0 H# m& K3 y12.11 Model Integration / 614
/ k5 b) D; J- v- x' G/ G12.12 Demand Prediction / 614
5 q# r' n, ]1 y% y3 N7 o) J12.12.1 Hourly System Demand Forecasts / 615) ^* k5 B9 d3 S* _5 }: p, q
12.12.2 One-Step Ahead Forecasts / 615 `# e7 L& ^* o3 d( v1 v& d$ x) h
12.12.3 Hourly Bus Demand Forecasts / 6168 ^) M1 X: M$ a0 V
12.13 Conclusion / 6168 u! \( f- f, a. W
PROBLEMS / 617 |
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