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第三版目录。
( |" {/ |- J1 ~+ j/ H, P* W. S1 Introduction 1; \( W9 b5 D6 G) |3 b
1.1 Purpose of the Course / 1
" E6 o* f* S3 w' L+ O1.2 Course Scope / 2
3 [! t- B% j. j0 b1.3 Economic Importance / 27 @. ]0 _4 a# c$ D# r2 _
1.4 Deregulation: Vertical to Horizontal / 3
* R0 ?6 h. x' X0 X& r- M1.5 Problems: New and Old / 3
+ |3 _5 h9 o9 H$ P1.6 Characteristics of Steam Units / 6
/ R" E6 o7 G. k0 T9 j6 [ a4 q1.6.1 Variations in Steam Unit Characteristics / 10
( |* ]2 O, |: i. p, C' o1.6.2 Combined Cycle Units / 134 b2 j \$ q; T5 `+ Q8 b
1.6.3 Cogeneration Plants / 14% O' S S _9 f' c: W
1.6.4 Light-Water Moderated Nuclear Reactor Units / 17/ ~5 u5 y* ]; ^! R5 i a
1.6.5 Hydroelectric Units / 18
5 G! t2 ` t x1.6.6 Energy Storage / 211 {% ~. m0 w5 z* ?
1.7 Renewable Energy / 22
1 E( g: P) K6 J% b1.7.1 Wind Power / 23
3 K) ^# B; b- ~! W* V" B5 \1.7.2 Cut-In Speed / 23
/ X9 t7 W; W+ U _1.7.3 Rated Output Power and Rated Output Wind Speed / 24
% Y7 G* n% d- K3 `% A1.7.4 Cut-Out Speed / 24
" E, W+ V* e! H- p1.7.5 Wind Turbine Efficiency or Power Coefficient / 24
. L6 Y$ P, E+ u& A! B3 {* i1.7.6 Solar Power / 25. q7 o1 a& _ D3 b8 e- k1 G" P
APPENDIX 1A Typical Generation Data / 26; ^3 V, v( ^7 Z! r# D
APPENDIX 1B Fossil Fuel Prices / 28+ n7 p; K# O q
APPENDIX 1C Unit Statistics / 29& b8 t& S b X6 X3 J3 g
CONTENTS7 w t+ }7 ~9 P Y- k
viii contents
9 {# s- C" o# y7 k# Z7 `. TReferences for Generation Systems / 31( G% i7 p' t% z$ G; m1 ^
Further Reading / 311 L0 a$ c- A* O
2 Industrial Organization, Managerial Economics, and Finance 35& y* r# h& `/ Y% X3 a
2.1 Introduction / 355 h7 _& r O4 ?" R7 \ ?
2.2 Business Environments / 36
% [5 w. s& V! s' q9 L2.2.1 Regulated Environment / 37
9 m5 Q9 }: [6 J( u2.2.2 Competitive Market Environment / 38
- \8 f2 S4 _* n2 r! v/ S2.3 Theory of the Firm / 402 a0 m& a/ M" i: d6 W
2.4 Competitive Market Solutions / 42
' o1 r$ m7 P7 U2.5 Supplier Solutions / 45
' V$ N K/ h" l+ E, O+ D1 a2.5.1 Supplier Costs / 468 c# t8 u! A& A/ N( h
2.5.2 Individual Supplier Curves / 46. V% J# @8 ?2 M; w
2.5.3 Competitive Environments / 47
+ _6 M5 x1 P% ]8 o2.5.4 Imperfect Competition / 51
0 t" h5 c# ~5 H& D2.5.5 Other Factors / 52
7 X! z6 U# F- N$ j4 ] Q, d. K2.6 Cost of Electric Energy Production / 53& R, h$ W! k* E& L' {+ T$ E' E
2.7 Evolving Markets / 54
) |. k: A/ ?5 o7 U) {2.7.1 Energy Flow Diagram / 57: x% H8 J$ \! C2 S2 y6 G
2.8 Multiple Company Environments / 585 G- s& _( ?* z& d' N
2.8.1 Leontief Model: Input–Output Economics / 58
& F4 ?, T9 g1 T2.8.2 Scarce Fuel Resources / 60 \% g9 n3 ~$ e* ^; E/ {# Z
2.9 Uncertainty and Reliability / 61. e8 {9 X8 N) N. Z
PROBLEMS / 61
0 t) Y& d- M6 `" G; |Reference / 629 |. K9 ]5 Z7 v) }- N, V* I$ Y
3 Economic Dispatch of Thermal Units and Methods of Solution 634 [6 B. \& h* F& K& M4 E# s
3.1 The Economic Dispatch Problem / 63
2 ?) z% |# m- O$ z |& m* G' I4 c0 n& b3.2 Economic Dispatch with Piecewise Linear Cost Functions / 68
" W0 [* {( C7 e* P }3.3 LP Method / 692 X! f5 [2 e3 |% Z4 C" H
3.3.1 Piecewise Linear Cost Functions / 69
9 E+ x8 T$ l/ I; z, q* n8 ^3.3.2 Economic Dispatch with LP / 718 ^5 @8 e. \" v
3.4 The Lambda Iteration Method / 73
]. U4 q' V6 \) ?! X& I3.5 Economic Dispatch Via Binary Search / 76) U1 b. `$ Z9 v
3.6 Economic Dispatch Using Dynamic Programming / 78
7 g! _8 ]( @2 ~4 y) z: m+ J3.7 Composite Generation Production Cost Function / 817 k7 Z# F4 `' d: E+ o: z1 O
3.8 Base Point and Participation Factors / 852 `' b6 j' M! ~/ p5 Z
3.9 Thermal System Dispatching with Network Losses @$ y+ ?+ j! W6 [/ l$ R
Considered / 88+ c# o5 H h+ P% M8 j
contents ix
2 C0 Z+ J. N( X3.10 The Concept of Locational Marginal Price (LMP) / 92
2 T& E/ s! O' D- X" i3.11 Auction Mechanisms / 953 m1 e$ `8 q5 t
3.11.1 PJM Incremental Price Auction as a
t. B9 a; D8 `* hGraphical Solution / 95" s+ G) v/ X6 m' z- G% a9 y
3.11.2 Auction Theory Introduction / 98, m+ h( J; c/ y
3.11.3 Auction Mechanisms / 100
! q: `* ^7 l9 D$ W0 F3.11.4 English (First-Price Open-Cry = Ascending) / 101
4 ~8 ]) M. ^$ N0 r2 l8 C3.11.5 Dutch (Descending) / 103' e. z: a: h V; D. R6 r& z
3.11.6 First-Price Sealed Bid / 1046 F& a( C0 e& N, P5 \! |, q# N3 ?
3.11.7 Vickrey (Second-Price Sealed Bid) / 105
% g/ x; d. L, l3.11.8 All Pay (e.g., Lobbying Activity) / 105/ J3 v( d. r+ S( j7 \
APPENDIX 3A Optimization Within Constraints / 1067 x( N$ N! f6 H& B4 j/ b
APPENDIX 3B Linear Programming (LP) / 117) e+ w( Z3 f; R D K" S, `
APPENDIX 3C Non-Linear Programming / 128
7 U$ d! Y! H( S4 V5 n: V$ [ zAPPENDIX 3D Dynamic Programming (DP) / 128
B* t; K4 e3 {. VAPPENDIX 3E Convex Optimization / 135
0 T% n8 k' O3 I- zPROBLEMS / 138
d T4 ~: K0 R% nReferences / 146
' e, X2 j- k% `% F4 Unit Commitment 147
7 K* ?4 ?6 Y J+ T, `% w4.1 Introduction / 147 Y# h4 o' o+ M* R
4.1.1 Economic Dispatch versus Unit Commitment / 147
) ^! z) M/ h. E. Z- d( Y* M0 g5 L0 R4.1.2 Constraints in Unit Commitment / 152
5 s4 P- {2 l! l! m& `* i2 q4.1.3 Spinning Reserve / 152) @8 h: p# {* Y
4.1.4 Thermal Unit Constraints / 153
# B/ {( L" Y& X2 G6 \' h# ]4.1.5 Other Constraints / 155
' `3 Z! f* B$ [, {/ R" o3 a8 \$ g4.2 Unit Commitment Solution Methods / 155
7 ^+ e3 `7 \- t |4.2.1 Priority-List Methods / 1567 ^# c7 R1 U2 O2 |4 F6 ?
4.2.2 Lagrange Relaxation Solution / 1577 m5 r6 R/ O q2 u+ w8 f1 o; g
4.2.3 Mixed Integer Linear Programming / 166- I% B7 O" z- X2 M/ e$ Z
4.3 Security-Constrained Unit Commitment (SCUC) / 167. ?9 U3 ?0 ^3 O6 f
4.4 Daily Auctions Using a Unit Commitment / 167; H' z5 W6 ^ v) y
APPENDIX 4A Dual Optimization on a Nonconvex
# D) d6 p$ ^$ F+ S5 uProblem / 167
# C3 I3 ^0 G6 xAPPENDIX 4B Dynamic-Programming Solution to
4 H- L7 X( X" m. U1 V1 n! @9 s; q1 N& PUnit Commitment / 173
" p! p4 p2 c) B \: x$ |$ Y' q4B.1 Introduction / 173
2 ], B. M7 ~" G" C3 |$ @4B.2 Forward DP Approach / 1744 C6 ?; x3 j. d' D3 U# ~
PROBLEMS / 1820 C$ u/ F1 a2 r; e2 k2 }
x contents
( [ U* H5 F# N; }- w- _' o5 Generation with Limited Energy Supply 187. h2 e' N' @2 Z c F
5.1 Introduction / 187
1 f) G9 ^; Q0 X% w# M; D# R5.2 Fuel Scheduling / 188
9 w& r1 k( y6 z: x4 I5.3 Take-or-Pay Fuel Supply Contract / 188
$ K4 N: f/ |; Y; U5.4 Complex Take-or-Pay Fuel Supply Models / 194
, t% M2 s0 f3 N' L& Y3 R) S5.4.1 Hard Limits and Slack Variables / 194( R) T& \1 j: R+ B1 x! D; ]
5.5 Fuel Scheduling by Linear Programming / 195 K6 ~" f, O: Z0 }
5.6 Introduction to Hydrothermal Coordination / 202
: \( m# r- ^ V5.6.1 Long-Range Hydro-Scheduling / 203- L% F6 x- a T* G- k
5.6.2 Short-Range Hydro-Scheduling / 2043 n$ A" \. f& w) m' o! v1 g1 b) F" _
5.7 Hydroelectric Plant Models / 2044 o8 |+ Y/ L# T7 D; C+ ^
5.8 Scheduling Problems / 207; A r( T( }; n6 I+ Q) l7 e
5.8.1 Types of Scheduling Problems / 207
( l' C) r2 P1 x' T8 C* z" O P5.8.2 Scheduling Energy / 207" B9 j0 F" L7 k! @1 m, Z f7 @
5.9 The Hydrothermal Scheduling Problem / 211
- @# B" ]2 S* N1 t5.9.1 Hydro-Scheduling with Storage Limitations / 211: }8 Q7 G- x. H G1 D$ f: r2 [" S" I
5.9.2 Hydro-Units in Series (Hydraulically Coupled) / 216
+ u5 T4 d$ u$ R4 B/ Y, g( k, a7 u5.9.3 Pumped-Storage Hydroplants / 218( X: R5 P/ k& i$ q U4 m" M8 L
5.10 Hydro-Scheduling using Linear Programming / 222
! Y. M5 o! M0 K u# SAPPENDIX 5A Dynamic-Programming Solution to hydrothermal
. _( k' _$ `8 R8 o2 }- T# r; jScheduling / 225
( h4 G; a+ D- W) ?2 `' c7 N5.A.1 Dynamic Programming Example / 227% W+ p# ~0 M" \/ O( G. \; e/ B6 c
5.A.1.1 Procedure / 228
/ _6 d, f) i# h5.A.1.2 Extension to Other Cases / 2319 L$ A( q) t( U3 O2 T
5.A.1.3 Dynamic-Programming Solution to Multiple Hydroplant
3 H* ~0 d# h$ N# u/ Y. g" PProblem / 2328 r1 `+ ]( ?# A) Y4 N* w7 q
PROBLEMS / 234
6 }) L, {) w$ [' ?9 a6 Transmission System Effects 243
2 r! E7 G* Q6 G& B- E+ b# ~6.1 Introduction / 2434 p# F; \6 e: h& G- q& u+ v* }
6.2 Conversion of Equipment Data to Bus and Branch Data / 247
/ X1 W0 K3 [) Z: u- f6.3 Substation Bus Processing / 2484 h! |! K3 r: ?4 ^5 ] Z* G# m+ Q
6.4 Equipment Modeling / 2487 |+ L: \1 N9 V) ?+ l4 T# m. `
6.5 Dispatcher Power Flow for Operational Planning / 251/ v: X- Y0 j( \. ]
6.6 Conservation of Energy (Tellegen’s Theorem) / 252+ c1 g! p0 V; j
6.7 Existing Power Flow Techniques / 253" m f3 |0 H8 A! ?. o
6.8 The Newton–Raphson Method Using the Augmented/ X E# ]5 a- J- v: Q2 M
Jacobian Matrix / 254
) w6 r, A6 m3 ^0 h2 i# H1 k6.8.1 Power Flow Statement / 254
; C3 J P% S% U; E6.9 Mathematical Overview / 257
1 t& a4 l z1 g- Y$ |contents xi. F# O+ Y u& B
6.10 AC System Control Modeling / 259
8 F7 m+ [! c: b. F' B* {6.11 Local Voltage Control / 259+ z8 {9 u! _0 `3 z2 A% n
6.12 Modeling of Transmission Lines and Transformers / 2597 x4 ]& D+ |* U9 x# b8 c+ `1 L
6.12.1 Transmission Line Flow Equations / 259
' u/ A3 z2 F: g; H0 R& c6.12.2 Transformer Flow Equations / 2603 ]0 S* e+ P% c, g* e$ b
6.13 HVDC links / 261: c9 }# n# X0 A- n
6.13.1 Modeling of HVDC Converters
1 W4 g0 ^* o0 o# B8 Kand FACT Devices / 264
) t$ S' J. S/ m0 h) b! l6.13.2 Definition of Angular Relationships in
' M# X' ?( U* jHVDC Converters / 2648 l- X* L0 B' ]
6.13.3 Power Equations for a Six-Pole HVDC
5 c3 [8 U0 E2 g" p N! [* tConverter / 264/ ^$ I# T1 I5 b% |& [( F, n
6.14 Brief Review of Jacobian Matrix Processing / 267$ ?& ?: L5 G" J6 c( c( \
6.15 Example 6A: AC Power Flow Case / 269( H2 i$ |3 j% z* F9 z
6.16 The Decoupled Power Flow / 271* j6 ~* a( q |& ~
6.17 The Gauss–Seidel Method / 2759 _& r _$ U1 p5 b& Y$ a% c1 f: H
6.18 The “DC” or Linear Power Flow / 2772 m( W/ n4 r8 @" ]( w6 X
6.18.1 DC Power Flow Calculation / 277
4 r( Y) d3 `$ Y6.18.2 Example 6B: DC Power Flow Example on the" `5 X) E! ?* @: `( p9 O. ]: J
Six-Bus Sample System / 278
" W( D. U" G$ h9 N6 @5 k+ @6.19 Unified Eliminated Variable Hvdc Method / 278
# }& w/ o* w) Q5 c) `! E6.19.1 Changes to Jacobian Matrix Reduced / 279
/ h- n, V: W5 w6 n2 Q }7 Z4 f6.19.2 Control Modes / 280; F$ E* _: H. {: u7 F, B
6.19.3 Analytical Elimination / 2806 |# K$ F( j1 _9 \+ K0 I
6.19.4 Control Mode Switching / 283
# M, w( I8 X9 G" I6.19.5 Bipolar and 12-Pulse Converters / 283
- ^% W$ J2 ]" X% a" z6 ^+ G* U6.20 Transmission Losses / 2842 d' ^# c4 e) F: ]
6.20.1 A Two-Generator System Example / 284, q3 @+ d R2 Z0 B) i1 q! [
6.20.2 Coordination Equations, Incremental Losses,
- {+ v/ P$ B5 |+ N; h+ eand Penalty Factors / 286" A e- R; A. a7 }
6.21 Discussion of Reference Bus Penalty Factors / 2880 @2 Q$ ]8 W. J j/ }2 Q3 I
6.22 Bus Penalty Factors Direct from the AC Power Flow / 289
! Z* M, V3 B) z8 Z) L' E8 H: \PROBLEMS / 291& Z3 ?& @% P$ d- I; G
7 Power System Security 296, y* r$ z( x( L/ Q
7.1 Introduction / 296" }$ _- _ F- S. Q7 F! X
7.2 Factors Affecting Power System Security / 301" i3 H( j8 K& s0 B
7.3 Contingency Analysis: Detection of Network Problems / 301) {9 ]7 l2 B* x& W5 H- [) w
7.3.1 Generation Outages / 3018 K' T# {& m7 Y" w5 r# p& |: o
7.3.2 Transmission Outages / 3028 @9 `' _9 c" d- r* f/ q, h
xii contents
9 u; E3 `/ {' `: r2 Q/ j6 q5 f: p7.4 An Overview of Security Analysis / 306. J4 ~9 A( g9 [
7.4.1 Linear Sensitivity Factors / 307
/ F3 ^ T: X0 S/ k# W" P) s2 n2 S7.5 Monitoring Power Transactions Using “Flowgates” / 313
( x. X x, z* H5 L6 y7.6 Voltage Collapse / 315
$ d, c9 x: K# m6 l0 p. B% J! z7.6.1 AC Power Flow Methods / 317
1 T0 c* X5 i) R9 N5 ^1 s7.6.2 Contingency Selection / 320
- m' I/ a3 q8 u3 L! z" m9 W7.6.3 Concentric Relaxation / 323
- a" ], B3 e, i$ \( ]7.6.4 Bounding / 325
% S) l2 m. u7 ]. U7.6.5 Adaptive Localization / 3257 V$ s7 k* \% F Q3 l/ G) V
APPENDIX 7A AC Power Flow Sample Cases / 3276 G5 \2 U+ @5 u1 [( h2 M
APPENDIX 7B Calculation of Network Sensitivity Factors / 336
y9 U, s) F& X* E; b! m7B.1 Calculation of PTDF Factors / 3366 p6 W! r5 L: R' B# F
7B.2 Calculation of LODF Factors / 339
$ f5 L8 F& j/ f ^5 `3 P% D7B.2.1 Special Cases / 341' c+ E, Y- P' `+ ^3 {/ d
7B.3 Compensated PTDF Factors / 343/ E+ r1 ^( g0 H2 T! I
Problems / 3438 }8 T9 y g7 O0 g
References / 349
- J# y% z; c0 t! k# P$ H0 G7 w8 Optimal Power Flow 350
( j% j9 f# e) O0 j3 d8.1 Introduction / 350* b$ n( ]4 s' q7 J8 P2 }0 w* R
8.2 The Economic Dispatch Formulation / 351
+ I9 M! @$ D. N3 C8.3 The Optimal Power Flow Calculation Combining8 a1 F2 l* r3 N$ ]0 m( X
Economic Dispatch and the Power Flow / 352, R* m6 U8 u4 `
8.4 Optimal Power Flow Using the DC Power Flow / 354! _( n8 c+ R$ g( ]# m, T6 v' h
8.5 Example 8A: Solution of the DC Power Flow OPF / 356
) S$ S- ^+ v5 S+ H; v& S7 Z8.6 Example 8B: DCOPF with Transmission Line
, F9 C! V) h1 f9 Y, e( `Limit Imposed / 361( _# ~2 C& p0 W2 `
8.7 Formal Solution of the DCOPF / 3653 |& o# F5 x: t+ B8 z
8.8 Adding Line Flow Constraints to the Linear
9 W* z8 p6 p6 s. rProgramming Solution / 3655 ]5 C; j' {7 J
8.8.1 Solving the DCOPF Using Quadratic Programming / 3679 o) \" M0 C z7 c
8.9 Solution of the ACOPF / 368
7 T* {" P; L, ?/ ~/ e8.10 Algorithms for Solution of the ACOPF / 369
- ~2 p- x, {. o8.11 Relationship Between LMP, Incremental Losses,5 y# W+ a1 T, Q2 A
and Line Flow Constraints / 3760 E _ [1 ^9 L7 |2 Z, [" U3 M5 |0 D+ }
8.11.1 Locational Marginal Price at a Bus with No Lines
1 J: L0 }* C2 e' @Being Held at Limit / 377
' t- M' M) x F# j& G( P8.11.2 Locational Marginal Price with a Line Held at its Limit / 378
6 I; I" T; t8 o0 G9 Ycontents xiii7 ^& I; q- C1 f) i0 C
8.12 Security-Constrained OPF / 382
8 B4 b$ [1 u( J/ v! z8.12.1 Security Constrained OPF Using the DC Power Flow/ e& R9 b4 w5 d/ e ?1 [: B' B
and Quadratic Programming / 3849 _. }! v, |" U; ]+ T# u& O8 U
8.12.2 DC Power Flow / 3852 l: g' B# k$ L
8.12.3 Line Flow Limits / 385) i5 Q: M* `, Q
8.12.4 Contingency Limits / 386
7 ]5 E0 f, N1 o' y" zAPPENDIX 8A Interior Point Method / 3911 g. y' R2 K2 i6 k4 m7 f, e s
APPENDIX 8B Data for the 12-Bus System / 393
* D7 S) w* B" xAPPENDIX 8C Line Flow Sensitivity Factors / 3953 p1 y* i* X" E3 v, ~! R
APPENDIX 8D Linear Sensitivity Analysis of the
9 [8 ^8 \5 F. O U) |% jAC Power Flow / 397% z, H" z/ U5 q/ r3 Z
PROBLEMS / 399
. ?3 [$ K/ g% t! ?& n4 p9 Introduction to State Estimation in Power Systems 403# o9 U+ z" A* d
9.1 Introduction / 403
8 Z' H2 G/ a; \' ?: W9.2 Power System State Estimation / 404
3 f2 Q% q L( A' r0 e. S9.3 Maximum Likelihood Weighted Least-Squares
0 O$ S4 Y+ n/ h" ?3 ]! Y3 z+ k! hEstimation / 408 S$ n# }$ w& Z! E, h
9.3.1 Introduction / 408) n, e" _0 u& q. @
9.3.2 Maximum Likelihood Concepts / 410
! Y1 {/ @4 s/ u+ F9.3.3 Matrix Formulation / 414
2 J" ?4 @2 m; i- {9 x2 k( B9.3.4 An Example of Weighted Least-Squares
0 o7 z& ^2 v5 uState Estimation / 4178 @ f. B. j$ p2 c5 P
9.4 State Estimation of an Ac Network / 421- @3 w( ]( T, @8 d; Z# [& g% c! E
9.4.1 Development of Method / 421
, R* o! T4 ~3 E) ~9.4.2 Typical Results of State Estimation on an5 P6 u# F. D4 y( y4 N! i
AC Network / 4241 D# C1 ~3 F5 j/ P
9.5 State Estimation by Orthogonal Decomposition / 428
' g {( M: H% R0 g3 R9.5.1 The Orthogonal Decomposition Algorithm / 431
7 m. m2 L! w! n8 A+ B; F0 c9.6 An Introduction to Advanced Topics in State Estimation / 435" D6 R6 p( f5 T( n) T+ K8 V
9.6.1 Sources of Error in State Estimation / 435
' P( g1 s, n9 g, B! V9.6.2 Detection and Identification of Bad Measurements / 436
) d7 b$ P/ Y) h# }9 g( Q1 _* j- V9.6.3 Estimation of Quantities Not Being Measured / 443 T7 [8 F( s$ k& d# L1 a8 N
9.6.4 Network Observability and Pseudo-measurements / 4444 u! X; m! B* t$ @8 h
9.7 The Use of Phasor Measurement Units (PMUS) / 447% I( j) ^* \5 a6 O9 a
9.8 Application of Power Systems State Estimation / 451
2 V+ N( q, z, x1 d9.9 Importance of Data Verification and Validation / 4544 q8 }0 S* E7 v7 z" P, X
9.10 Power System Control Centers / 454
- Y& }. N* y* c( G* g. V+ Yxiv contents
P7 V# B- B* B1 @- lAPPENDIX 9A Derivation of Least-Squares Equations / 456$ r/ `; A3 _7 J6 M4 w
9A.1 The Overdetermined Case (Nm > Ns) / 457/ {* J6 P$ O; l5 t v9 M( L8 |
9A.2 The Fully Determined Case (Nm = Ns) / 462
o- P: @' w8 `9A.3 The Underdetermined Case (Nm < Ns) / 462. q) Y5 X9 W9 R
PROBLEMS / 464
# p/ l2 ^- ]. T- n- j0 A# C' w. X10 Control of Generation 468
6 r) B+ N2 |2 g0 d, e2 R10.1 Introduction / 468
& s# t; m* O2 [: y5 ?10.2 Generator Model / 470+ @" y9 p) S/ g x% C- I- y
10.3 Load Model / 473$ e8 N1 u/ e1 M
10.4 Prime-Mover Model / 475/ l h$ ?+ {$ _7 R2 ]3 v: f
10.5 Governor Model / 476
3 a i" l7 }8 ^- c# A3 J1 r10.6 Tie-Line Model / 481
/ w+ p* K6 K5 K2 W6 _ D10.7 Generation Control / 485% g% |, d: Z; u
10.7.1 Supplementary Control Action / 485
& c1 S. y# ?% C! P1 {% a: a' Z10.7.2 Tie-Line Control / 486
! n7 z' x2 g; }/ t10.7.3 Generation Allocation / 489. d9 g5 T$ x5 ^6 O
10.7.4 Automatic Generation Control (AGC)
6 M7 D: |1 b f6 YImplementation / 491
6 w# J, b: y1 M y4 p6 o10.7.5 AGC Features / 495
+ i, x) B3 F4 W0 O) f9 l3 x10.7.6 NERC Generation Control Criteria / 496
; J2 k* q; ]- w& `2 }4 DPROBLEMS / 497 n. b/ A% v' ]; p- l' A! r. \4 w
References / 500
: s# x8 @1 P) I' G11 Interchange, Pooling, Brokers, and Auctions 5015 l8 g! `# W4 _& G" w& v
11.1 Introduction / 501) W+ Q& q/ [. a+ P) v! B# P/ T9 |
11.2 Interchange Contracts / 504* j5 W6 ]# f' M7 w
11.2.1 Energy / 504( Y* M3 C. s0 P+ Z4 u9 V
11.2.2 Dynamic Energy / 506
% s# E @: q* |6 X1 q# C; S" `. Q11.2.3 Contingent / 506
$ U$ J; k0 ^3 i- |# B9 h7 n1 f2 x11.2.4 Market Based / 507
3 r! C+ L8 ~) m/ B- W11.2.5 Transmission Use / 508
8 ~, _) G! s; r' N11.2.6 Reliability / 517" {: }6 M7 _2 D8 d) Z4 t
11.3 Energy Interchange between Utilities / 517( I% {& D6 B. N( I% i; w3 _* D' s
11.4 Interutility Economy Energy Evaluation / 521
* u& n* z q/ b+ K7 R" q11.5 Interchange Evaluation with Unit Commitment / 5229 c+ N9 [) m" f; Q6 U P
11.6 Multiple Utility Interchange Transactions—Wheeling / 523
; W1 @+ p! L6 ?( w) R, d' l0 k6 o0 y11.7 Power Pools / 526' s) w) n: ]3 }- Y5 o+ Q# F
contents xv
% s' K$ V( a: @$ D/ p11.8 The Energy-Broker System / 5299 U. l7 [% c: J0 ~
11.9 Transmission Capability General Issues / 5335 ]! y0 M* B3 b/ D x: I
11.10 Available Transfer Capability and Flowgates / 5359 \5 u0 k6 p2 A: l
11.10.1 Definitions / 536
, {7 A: @9 g* O. X11.10.2 Process / 539( \) q9 `9 R/ M1 ^$ K, D
11.10.3 Calculation ATC Methodology / 540
5 Q5 ^6 J& r9 a8 Y, o11.11 Security Constrained Unit Commitment (SCUC) / 550
' o, i1 ]) m# Y11.11.1 Loads and Generation in a Spot Market Auction / 5508 x+ A- k- U* S( a+ R
11.11.2 Shape of the Two Functions / 552
4 Y, D6 o& A& {; J4 g& g- \! ^" m) H11.11.3 Meaning of the Lagrange Multipliers / 553
8 E& c: Q% a" i+ G! [. X, q* p( g11.11.4 The Day-Ahead Market Dispatch / 554" a& y+ C7 G X
11.12 Auction Emulation using Network LP / 5555 N, ?( ?. Q% z8 M
11.13 Sealed Bid Discrete Auctions / 555
5 a1 \* Y3 e k7 W \PROBLEMS / 560
, W9 m" Y* c a12 Short-Term Demand Forecasting 566
- ^, q8 ?' Y& q0 D4 w* N12.1 Perspective / 566
, c4 Q! O4 R5 n# [12.2 Analytic Methods / 569
; \# k- K" q: O6 ?. M12.3 Demand Models / 571
6 N. m6 C7 {% V C12.4 Commodity Price Forecasting / 572
/ H1 R, x$ Y5 T( E- ^12.5 Forecasting Errors / 573* B3 {" ? E+ h4 H
12.6 System Identification / 573
7 y' _4 s% r' f12.7 Econometric Models / 574
; a' q; W+ k6 k# G# L @12.7.1 Linear Environmental Model / 574% {; S. N9 V# V! _6 |( d* c# D4 `3 k
12.7.2 Weather-Sensitive Models / 576
+ _) K" Y' l' D% k, U, ~. S12.8 Time Series / 5788 `: _4 _$ E+ }" Q; J* O. h- U
12.8.1 Time Series Models Seasonal Component / 578
8 T& T, O6 q6 X6 y12.8.2 Auto-Regressive (AR) / 580
; P& ]0 h- l* ?/ a, u; x( a' O12.8.3 Moving Average (MA) / 581# P% b: `2 `" _! O! z7 {
12.8.4 Auto-Regressive Moving Average (ARMA):0 }- a O) t U4 k0 r3 Q
Box-Jenkins / 582
9 _% U8 `4 _: j12.8.5 Auto-Regressive Integrated Moving-Average
7 _% y, b2 X/ e(ARIMA): Box-Jenkins / 584# j# \; }1 d3 B0 K2 w! {
12.8.6 Others (ARMAX, ARIMAX, SARMAX, NARMA) / 585
1 ?/ t* R% [' S$ }- ?1 r12.9 Time Series Model Development / 585, g; `6 ^1 w4 {" V
12.9.1 Base Demand Models / 586/ E1 \/ @$ L& p# i( V6 t6 G
12.9.2 Trend Models / 586, ?. N: k( c1 q* u* v
12.9.3 Linear Regression Method / 5860 b& L/ y6 n' G% M: X! i+ l+ A* z* Y' b
xvi contents
# p# z' N2 a3 k& U12.9.4 Seasonal Models / 588
4 L0 u& p; H# o; N* K12.9.5 Stationarity / 588
" z. g9 ?6 m. m12.9.6 WLS Estimation Process / 590
# z. S; l6 ]( _" P2 c5 B12.9.7 Order and Variance Estimation / 591
% x7 I' V# f# Z4 f) u2 y# v( {+ X' G12.9.8 Yule-Walker Equations / 592
& ]; }; W4 @5 S$ m; q6 [12.9.9 Durbin-Levinson Algorithm / 595
& N( |8 v) Q. O, Z; E% i12.9.10 Innovations Estimation for MA and ARMA
' G2 y4 a5 c9 V, CProcesses / 598
% v& S# D& u: l" _' `" y* M12.9.11 ARIMA Overall Process / 600
) ?& A5 S$ y/ |& E* q12.10 Artificial Neural Networks / 6036 p0 y$ V$ _7 z1 n' w
12.10.1 Introduction to Artificial Neural Networks / 604 T- R9 z/ U# c% D# I+ a
12.10.2 Artificial Neurons / 605' h' T" g3 p1 { I9 E
12.10.3 Neural network applications / 606- c9 y, j& C2 s( p* r
12.10.4 Hopfield Neural Networks / 6068 h% S' V$ T( G0 X+ {% J: @
12.10.5 Feed-Forward Networks / 607
8 T- a3 O, ~# m3 {3 S12.10.6 Back-Propagation Algorithm / 610
$ E" T1 P/ X, r12.10.7 Interior Point Linear Programming Algorithms / 613" O. N! G: L9 `" R
12.11 Model Integration / 614% N, H- \8 z+ e9 K5 m
12.12 Demand Prediction / 614
! D! b+ J9 v7 B$ o7 H7 l' W. Z12.12.1 Hourly System Demand Forecasts / 615
8 l' j, O. _7 l* C A# `12.12.2 One-Step Ahead Forecasts / 6150 Q5 S4 b/ {! `1 a m
12.12.3 Hourly Bus Demand Forecasts / 6167 I' O/ o1 ]; l) z v" r: x: [# f5 U
12.13 Conclusion / 616' ^, Y- k o' p. U8 Q
PROBLEMS / 617 |
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