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第三版目录。
, _3 T$ J* [. q1 B7 ~+ p0 c0 n1 Introduction 1
@0 r7 ?7 h# z0 A1.1 Purpose of the Course / 11 u8 N7 L2 F, V6 c d5 O
1.2 Course Scope / 2
0 E4 i& ]7 H( [# R1.3 Economic Importance / 2
6 a9 M) f/ b5 I6 c: U1.4 Deregulation: Vertical to Horizontal / 34 i+ @( c% i# c1 W: ?% Q
1.5 Problems: New and Old / 3# U5 G+ R) V2 }9 ~4 ~; }
1.6 Characteristics of Steam Units / 6
1 q" d* q) U$ }, f1.6.1 Variations in Steam Unit Characteristics / 10. m5 ?. B: f4 ^) a# t, w0 T
1.6.2 Combined Cycle Units / 13
# g2 t# q. Q1 E% W" m1.6.3 Cogeneration Plants / 14
0 H& h/ P/ n0 q( r1.6.4 Light-Water Moderated Nuclear Reactor Units / 17" b/ G( ^5 W$ i) d0 K5 O) p% y& s! R
1.6.5 Hydroelectric Units / 18
$ q) }5 q/ @+ w, U' a1.6.6 Energy Storage / 21
' x! o- D1 S2 Z. E, ]1.7 Renewable Energy / 22" H( k) ` N3 y( B% G
1.7.1 Wind Power / 23
& L: U4 e' t% i1.7.2 Cut-In Speed / 23
. U# m3 [2 n( {. y7 t1.7.3 Rated Output Power and Rated Output Wind Speed / 24
- `3 ?- ^/ l: s1.7.4 Cut-Out Speed / 24, G/ u( H r6 E+ m2 x
1.7.5 Wind Turbine Efficiency or Power Coefficient / 24/ [) u( F5 ^: C& ]& o1 m4 P; ^& P
1.7.6 Solar Power / 25
* X/ a; M4 w2 O; S: yAPPENDIX 1A Typical Generation Data / 26
& s6 _, R3 s. o& UAPPENDIX 1B Fossil Fuel Prices / 28
0 }, U. W3 }! t5 y2 tAPPENDIX 1C Unit Statistics / 29
. c: x, t* t" u! {1 YCONTENTS. b2 f) P/ {% l
viii contents- z U* e, \' x4 Z0 T' ~& M4 F
References for Generation Systems / 31
2 {' P @8 _) n5 c2 H% o1 ^% R2 _) D# E, fFurther Reading / 31
- Q5 J$ Y* A. a( J2 h7 \4 S2 Industrial Organization, Managerial Economics, and Finance 35' @% f1 w/ I' ?/ i0 k$ W8 r9 S
2.1 Introduction / 35+ d5 ~0 A7 [6 J; q9 o
2.2 Business Environments / 367 d+ S3 r6 u- Y/ T2 K# H$ L
2.2.1 Regulated Environment / 37
/ E% Y C7 y! ~6 }2.2.2 Competitive Market Environment / 38
: B- V3 [ ?9 Y. M" N2.3 Theory of the Firm / 40; C9 G' o7 E( ~& p: e3 s
2.4 Competitive Market Solutions / 42
4 b8 v- H# y8 {6 k6 U2.5 Supplier Solutions / 454 ~- Q7 C' x( r7 s5 |, T
2.5.1 Supplier Costs / 46* h5 K. c1 c' p/ z
2.5.2 Individual Supplier Curves / 46
% @; q7 s8 A& Z2 z1 X2.5.3 Competitive Environments / 478 X6 D. l c" y+ R( C
2.5.4 Imperfect Competition / 514 c1 S/ ?3 F- J9 L4 u
2.5.5 Other Factors / 526 V3 g+ J- D1 t& l. d: _* e, h
2.6 Cost of Electric Energy Production / 53( v* }6 z# h9 B7 {4 c& Z$ ~! \
2.7 Evolving Markets / 54
' t) K4 V. ]+ [1 J5 {2.7.1 Energy Flow Diagram / 57 Q/ }% p; Z0 |9 Z4 K% V/ L, y+ u$ q
2.8 Multiple Company Environments / 58
9 j. Q" ^8 b% E# `+ A2.8.1 Leontief Model: Input–Output Economics / 58) ^" H+ f8 J+ o t8 x5 j: ~/ g
2.8.2 Scarce Fuel Resources / 601 v$ v7 I: N& I* l' Q
2.9 Uncertainty and Reliability / 61
- C& G9 e! e5 C& o% RPROBLEMS / 61
: U0 l y# ?5 R9 S, wReference / 62- ?- @. M6 M, J6 D' B
3 Economic Dispatch of Thermal Units and Methods of Solution 63
( i! g. y& b3 T, K& `3.1 The Economic Dispatch Problem / 63' p* @+ C" T6 z7 J$ o1 C
3.2 Economic Dispatch with Piecewise Linear Cost Functions / 683 v p$ u6 [9 j4 W# _
3.3 LP Method / 69
, |: _. a/ y* }0 X3.3.1 Piecewise Linear Cost Functions / 695 u- v2 ]& [1 Q
3.3.2 Economic Dispatch with LP / 710 e! @! n/ m, w
3.4 The Lambda Iteration Method / 73/ G5 s: s; ~3 Q
3.5 Economic Dispatch Via Binary Search / 76
7 s- ?' N y' ^1 ~: _& f1 v. M, }3.6 Economic Dispatch Using Dynamic Programming / 78
4 ~; N5 b# i% v w$ l3.7 Composite Generation Production Cost Function / 81
* W- x6 R& s4 t1 Z3.8 Base Point and Participation Factors / 858 @6 L* \0 W: F- x
3.9 Thermal System Dispatching with Network Losses' `* H( E* [6 R. k
Considered / 88
" ^/ G/ ^. D! r1 [, `contents ix; K( M9 Y8 L0 ~5 b1 R& @" c
3.10 The Concept of Locational Marginal Price (LMP) / 929 g* I6 B( c1 m# g% h3 B% f
3.11 Auction Mechanisms / 95
* E: N# {/ e+ g5 s$ Q3.11.1 PJM Incremental Price Auction as a; I0 B* v. P# n" g& h
Graphical Solution / 95 x. l9 d6 s2 W% X
3.11.2 Auction Theory Introduction / 98
: \% b/ ^ I$ C. @6 ^1 f3.11.3 Auction Mechanisms / 100# ?6 A) i' b2 I- M3 ]# z" O$ a
3.11.4 English (First-Price Open-Cry = Ascending) / 1014 [4 J' ]* _1 s8 C7 P
3.11.5 Dutch (Descending) / 103
0 L& b; R' k; Z4 J' I3.11.6 First-Price Sealed Bid / 104
& F/ s: |8 y. V, o% e( z: y$ b3.11.7 Vickrey (Second-Price Sealed Bid) / 105
0 _! f" q) j5 P5 e3 i3.11.8 All Pay (e.g., Lobbying Activity) / 105( c. s5 u$ H/ v/ g
APPENDIX 3A Optimization Within Constraints / 106; @" B& f G5 M. m' F: N0 y; a
APPENDIX 3B Linear Programming (LP) / 117* m9 e0 ^& [0 E$ ]
APPENDIX 3C Non-Linear Programming / 128
0 L5 W) E; w" F" N( `APPENDIX 3D Dynamic Programming (DP) / 128
4 }7 O' Z# @5 U6 `/ HAPPENDIX 3E Convex Optimization / 135- v6 b. b+ w7 I2 }
PROBLEMS / 138
8 I* d5 P/ G% A4 XReferences / 146/ R( ?2 ]3 X9 r3 l
4 Unit Commitment 147% K. a% W' V) H. u+ o9 z
4.1 Introduction / 147
: S2 c2 B/ \5 ~! s4.1.1 Economic Dispatch versus Unit Commitment / 147
9 s6 e' K. n0 i- _4.1.2 Constraints in Unit Commitment / 152/ M0 m' T. w' q
4.1.3 Spinning Reserve / 152; _ e. F) T* _9 P) G6 A
4.1.4 Thermal Unit Constraints / 153
" k& I5 f [. V5 o) H3 ^4.1.5 Other Constraints / 1551 L" u+ E& p8 P! }$ h. }+ z2 F( f
4.2 Unit Commitment Solution Methods / 155, D1 L( ^4 b. [6 r& ?) p
4.2.1 Priority-List Methods / 156
( _% U, K9 k: g' ^/ j4.2.2 Lagrange Relaxation Solution / 157: [7 B' |: f) M7 {( `- ]* }. V
4.2.3 Mixed Integer Linear Programming / 166' w: o9 m, S6 V& l
4.3 Security-Constrained Unit Commitment (SCUC) / 167
& @$ G' n# f% T6 x5 S. U4.4 Daily Auctions Using a Unit Commitment / 167) ?) z6 }9 `( X3 p1 y- u1 F* i& v
APPENDIX 4A Dual Optimization on a Nonconvex9 _4 W9 j2 L+ [4 l0 F( t% A
Problem / 167% d7 `" {! b6 _( Y" Q
APPENDIX 4B Dynamic-Programming Solution to- R/ M0 V8 t8 |+ \3 H6 W7 r2 h
Unit Commitment / 1736 Q7 t0 h, e4 f; G5 x9 Q
4B.1 Introduction / 1731 y! b% l/ V" X8 t" [3 V9 ~* |/ k
4B.2 Forward DP Approach / 1749 g# }) ~ w; m! ]
PROBLEMS / 182! y+ F1 l |/ G" L: h* X
x contents* o0 p/ G$ @6 f9 i- ^$ f
5 Generation with Limited Energy Supply 1872 R; U+ I. }1 T- Z8 H
5.1 Introduction / 187 \, V7 k" ]5 G, b y2 S
5.2 Fuel Scheduling / 1886 A! |/ [! p9 T( Y
5.3 Take-or-Pay Fuel Supply Contract / 188
) p2 q5 P) Y8 b1 \# [4 ]5.4 Complex Take-or-Pay Fuel Supply Models / 1940 @, Q; m6 K. ] N! W& W1 d
5.4.1 Hard Limits and Slack Variables / 194
: R% f8 F' {- G* y5.5 Fuel Scheduling by Linear Programming / 195
& n- k. e7 ^( H- j* L" P7 o# i5.6 Introduction to Hydrothermal Coordination / 2028 a; u. T) }$ a
5.6.1 Long-Range Hydro-Scheduling / 203
6 O. ]8 m$ j. m4 D/ u" w9 k5.6.2 Short-Range Hydro-Scheduling / 204
5 H3 a& f. F) q4 e! P+ h/ y5.7 Hydroelectric Plant Models / 204 ?4 M9 X" g/ x2 C# H
5.8 Scheduling Problems / 207) \5 N A2 j5 V2 u1 k
5.8.1 Types of Scheduling Problems / 207
8 R4 Y+ _+ s" j- h) v5.8.2 Scheduling Energy / 207
* X- A; A' G( J- o% Z4 P3 ^5.9 The Hydrothermal Scheduling Problem / 211
' n! U" I# B" F% s5.9.1 Hydro-Scheduling with Storage Limitations / 2118 ^. ~9 z8 P% t" M5 I; ]+ q$ M
5.9.2 Hydro-Units in Series (Hydraulically Coupled) / 216
- c8 P, S( |! K4 k3 L N- H5.9.3 Pumped-Storage Hydroplants / 218
_9 L: b. R9 l" N3 V5.10 Hydro-Scheduling using Linear Programming / 222+ S0 v) |) c( B+ [
APPENDIX 5A Dynamic-Programming Solution to hydrothermal
4 T: d: _7 Q+ EScheduling / 225) W; H) g( H0 H, z4 n9 e% ~
5.A.1 Dynamic Programming Example / 227
1 ~% v5 d5 r$ a4 @4 T, s& H5.A.1.1 Procedure / 228
+ ~2 \ |& N; ?3 ?% T5.A.1.2 Extension to Other Cases / 231
* d* A( b8 c' ~" Z7 v5.A.1.3 Dynamic-Programming Solution to Multiple Hydroplant% ]* P. [. D6 t" X/ h6 M: E4 O$ f
Problem / 232
3 l" O, S( t, j) APROBLEMS / 234+ ]# {* U; V2 [
6 Transmission System Effects 243- R+ ]4 R( E7 B8 N' j+ o/ F. i
6.1 Introduction / 243
/ Z! L7 @: C, b( s* V2 p6.2 Conversion of Equipment Data to Bus and Branch Data / 247
1 a3 X# x7 d' i. Y; h6.3 Substation Bus Processing / 248
$ u# S- [$ d9 M ?6 G( N: b6.4 Equipment Modeling / 248; {7 I j8 h: ?3 q1 X* }
6.5 Dispatcher Power Flow for Operational Planning / 251; J3 A; l, b, I, w8 H
6.6 Conservation of Energy (Tellegen’s Theorem) / 252
. ?" o- `/ u, b+ \! F( w3 v6.7 Existing Power Flow Techniques / 2538 ?- m. [' C4 E" d9 q% M4 ^
6.8 The Newton–Raphson Method Using the Augmented
1 X4 f# P2 U! ?: w: p5 u4 ^Jacobian Matrix / 254. h% l8 n; K. @( d% A4 G
6.8.1 Power Flow Statement / 254
, \: p, C( B/ B! D5 v6.9 Mathematical Overview / 257 j* t% w8 u5 O3 M" X
contents xi
/ f2 {' k! c w3 i+ Z. @% Z6.10 AC System Control Modeling / 259
~$ A/ o' Y( c' {+ F8 \% z6.11 Local Voltage Control / 259; `! H( A/ C$ U( O' F
6.12 Modeling of Transmission Lines and Transformers / 259
* _* X' w. M: l) h% J) i6.12.1 Transmission Line Flow Equations / 259
5 m. `5 @) J2 T' P0 c6.12.2 Transformer Flow Equations / 260
* `+ Y4 c' [, a/ _) h2 B( M6.13 HVDC links / 261
* v( J/ Q; \& e% {( z6.13.1 Modeling of HVDC Converters- S, C# f" Z3 D4 @
and FACT Devices / 2646 ~0 Y( k: Z& n8 A' t5 Q4 j
6.13.2 Definition of Angular Relationships in* ]8 O" Q) K* O
HVDC Converters / 264
' F/ V; g# Q |; j4 l; e. E0 h1 F: V6.13.3 Power Equations for a Six-Pole HVDC6 p$ P# z Q0 H4 _* T( i
Converter / 264* m8 c) }- m. @, N. x% @# a" d9 x* N
6.14 Brief Review of Jacobian Matrix Processing / 267
. E- m2 t# M; u1 v% k0 r( _6.15 Example 6A: AC Power Flow Case / 269' `1 S) b9 i7 i$ |. ^# O
6.16 The Decoupled Power Flow / 271
' g, L; k4 m2 i3 Z( ]6.17 The Gauss–Seidel Method / 275
( b9 t# C: l" _9 S9 h6.18 The “DC” or Linear Power Flow / 277" ^& {( R; C1 C6 |
6.18.1 DC Power Flow Calculation / 277
8 j# r$ R6 r0 A7 j7 Q7 X8 W+ B# m6.18.2 Example 6B: DC Power Flow Example on the
6 H$ E& l! h5 D# u; }4 m& g8 vSix-Bus Sample System / 278
5 c/ o5 ~) S: S3 H( i7 ^8 z% G: f6.19 Unified Eliminated Variable Hvdc Method / 278
8 U& H2 M* E2 i+ @ |7 ]- I' x* z6.19.1 Changes to Jacobian Matrix Reduced / 279
" c3 _! ~! Y! h+ B7 Q1 Q6.19.2 Control Modes / 2805 g4 d9 ^0 I+ n
6.19.3 Analytical Elimination / 2801 W! V# ?9 }4 o( D
6.19.4 Control Mode Switching / 283" F0 G. [+ G. u' f' P3 q* M2 l
6.19.5 Bipolar and 12-Pulse Converters / 283
! k7 N* t9 `8 h! s7 G& Y8 C/ E6.20 Transmission Losses / 284
1 r, [, L% w- @: e) P6.20.1 A Two-Generator System Example / 284& b' {% x3 D* b( M3 R9 a$ Z0 i
6.20.2 Coordination Equations, Incremental Losses,; A( |( r. N' @+ O: j: ]
and Penalty Factors / 2867 L. U6 r2 c( X$ I, I
6.21 Discussion of Reference Bus Penalty Factors / 288
7 s: g/ v% Z5 W2 q6.22 Bus Penalty Factors Direct from the AC Power Flow / 289
: f, _) A( r" b7 l- i* }PROBLEMS / 291
/ i; `' Y7 M, |5 ^" i7 Power System Security 296
4 Z; F0 y9 A2 Z C4 U' f! P& R7.1 Introduction / 2967 }( ^$ T. r$ ~% E9 }
7.2 Factors Affecting Power System Security / 301
* d" H+ P I; Z4 v$ }7.3 Contingency Analysis: Detection of Network Problems / 301
+ B! e8 |) c) Z7.3.1 Generation Outages / 301
+ y+ g; I5 l1 r% A( p5 l7.3.2 Transmission Outages / 302
: ]- z1 Z' p( ?8 \; Gxii contents! ~1 o: p; I- ~. j& p; J
7.4 An Overview of Security Analysis / 306" m) \: u3 c+ _' N4 C
7.4.1 Linear Sensitivity Factors / 307
7 ^! m7 R* x9 y# I) T; e( E' y0 D$ c7.5 Monitoring Power Transactions Using “Flowgates” / 3138 F( U) I' j* @
7.6 Voltage Collapse / 315 {* [! `! ?" D- q- u3 _
7.6.1 AC Power Flow Methods / 3170 v' \- ~9 ?- k
7.6.2 Contingency Selection / 320
3 |) j% P/ ~( N7 s( ?7.6.3 Concentric Relaxation / 323
) _/ A) I) A( T* I f. O$ C. `8 x7.6.4 Bounding / 325
) }; N1 `5 R* {2 ?' d7.6.5 Adaptive Localization / 325
- c$ z& c( x" D+ Y5 O, a+ [APPENDIX 7A AC Power Flow Sample Cases / 3273 \1 Q% ?5 y5 K! x+ i
APPENDIX 7B Calculation of Network Sensitivity Factors / 336
: O4 x3 x6 R, ^4 x% f# E1 Y' x7B.1 Calculation of PTDF Factors / 3368 Q* @6 g$ |" M4 W7 J4 j
7B.2 Calculation of LODF Factors / 3397 B1 o. s+ \7 p9 q* k
7B.2.1 Special Cases / 341- m3 O) w) m1 S2 w- @1 X- x% I) j
7B.3 Compensated PTDF Factors / 343' t! P' z; h3 x5 U+ N b% J
Problems / 343$ Y6 @* a6 v" c4 b
References / 349
' g: r: f6 v% }: m$ \8 Optimal Power Flow 350
0 `' h2 A& I7 c' H( G7 y8.1 Introduction / 350' ?3 I3 z$ U8 ~
8.2 The Economic Dispatch Formulation / 351
( y9 x. M9 Y7 x! S* k8.3 The Optimal Power Flow Calculation Combining9 c( \5 T7 H( E6 O
Economic Dispatch and the Power Flow / 3526 `( f& M) X2 G3 t" F; @
8.4 Optimal Power Flow Using the DC Power Flow / 3541 l' P' H+ w \6 M: ?& J2 ?
8.5 Example 8A: Solution of the DC Power Flow OPF / 356( A) U8 t$ G' Z
8.6 Example 8B: DCOPF with Transmission Line2 \3 `$ Z( F' B
Limit Imposed / 361
* a" v2 ]; @4 B( P8.7 Formal Solution of the DCOPF / 365' }1 p7 k2 u, V
8.8 Adding Line Flow Constraints to the Linear- P$ P3 K6 g0 M1 N; O0 t: C( A
Programming Solution / 3653 R5 K t! k t) i% p% S
8.8.1 Solving the DCOPF Using Quadratic Programming / 367
! p) f$ \* M& A/ L8.9 Solution of the ACOPF / 368( o7 T* W. x" R S# d( t5 D5 C8 P
8.10 Algorithms for Solution of the ACOPF / 369
( j9 H h' m5 q3 I) r3 H! `3 r& k$ g8.11 Relationship Between LMP, Incremental Losses,) A5 V1 K- Y' @. J& ]
and Line Flow Constraints / 376% P# R8 @; p( e5 Z$ C: S a
8.11.1 Locational Marginal Price at a Bus with No Lines0 n& y, }% z- {: x$ [
Being Held at Limit / 377 A" }* [4 I t
8.11.2 Locational Marginal Price with a Line Held at its Limit / 378# o: y, T. V9 x2 S* h% i
contents xiii
d' y, }8 b1 S& {( @: U8.12 Security-Constrained OPF / 382' Z1 U) M4 W# k/ e$ U$ f8 U
8.12.1 Security Constrained OPF Using the DC Power Flow
+ f0 z: N, S2 q L, ^and Quadratic Programming / 384
/ \ Z5 b+ f* f& W# H+ T1 p8.12.2 DC Power Flow / 385
# v* ]7 v0 e5 b7 P# a) s, o2 b8.12.3 Line Flow Limits / 385
' p9 E6 X$ O; Z4 L+ w' u( D8.12.4 Contingency Limits / 3863 Y. v4 e l& X# h1 s
APPENDIX 8A Interior Point Method / 3911 W6 W; s5 m7 G! O# _" K- u& O* z; A
APPENDIX 8B Data for the 12-Bus System / 393
- a( q- o0 Y4 R5 B& S8 D& WAPPENDIX 8C Line Flow Sensitivity Factors / 395) R1 L A8 k5 O9 ^
APPENDIX 8D Linear Sensitivity Analysis of the; R: s" d% d7 k, b8 o
AC Power Flow / 3978 l, u0 T1 a9 y7 L( ^
PROBLEMS / 399
( n {) r0 W0 C9 Introduction to State Estimation in Power Systems 4034 m4 v& A/ r- C& [6 `
9.1 Introduction / 403/ t5 r3 x5 B% y6 W
9.2 Power System State Estimation / 404
0 k; g3 f; C- a7 p0 q9.3 Maximum Likelihood Weighted Least-Squares
# q! ]' x( ?; V( ~/ u yEstimation / 408( s0 w2 P5 _- |( W; p
9.3.1 Introduction / 4082 ]8 _& M( O! ]8 f; ?
9.3.2 Maximum Likelihood Concepts / 4105 ?! C* k0 @ U% x
9.3.3 Matrix Formulation / 4142 s5 |4 W, p+ S
9.3.4 An Example of Weighted Least-Squares
- p8 Q& `0 a# q, T# }( |9 d; ]State Estimation / 417
; @+ p* ^4 r0 f. J$ C3 A0 f1 i9.4 State Estimation of an Ac Network / 421
( l- {' _/ v7 X+ o; ?* `2 N9.4.1 Development of Method / 4214 p. t M& B' Y' B
9.4.2 Typical Results of State Estimation on an4 ~7 E, G7 V$ K1 T4 e
AC Network / 4244 D- R5 F7 S0 e1 H7 S
9.5 State Estimation by Orthogonal Decomposition / 428# _9 P( N( Z" j, L$ n: c3 M
9.5.1 The Orthogonal Decomposition Algorithm / 431: i3 H0 z1 b2 a* _8 i4 {
9.6 An Introduction to Advanced Topics in State Estimation / 4353 |, c! l, Z6 ~3 n
9.6.1 Sources of Error in State Estimation / 435
; Y" x7 p+ h+ N$ v; t6 e8 n9.6.2 Detection and Identification of Bad Measurements / 436
0 f: j7 N7 C5 X; t0 _1 s9.6.3 Estimation of Quantities Not Being Measured / 443
! R$ e- T( ]! e. v2 t& o# u f: M/ {9.6.4 Network Observability and Pseudo-measurements / 444
3 E I( m" Z4 B* ~; A9.7 The Use of Phasor Measurement Units (PMUS) / 4472 r2 _5 e2 _) ?9 ^
9.8 Application of Power Systems State Estimation / 451! f2 T2 A6 y! ]# c
9.9 Importance of Data Verification and Validation / 454& ?3 q; A+ L2 O1 t0 }& f% a
9.10 Power System Control Centers / 454
8 ?. d7 O4 L4 C9 k# l4 A9 R2 z' V Ixiv contents
0 @8 i" H; ]8 e& z8 mAPPENDIX 9A Derivation of Least-Squares Equations / 4562 Q% {0 U/ p4 k, O! Z
9A.1 The Overdetermined Case (Nm > Ns) / 457
* x V8 N* V7 X8 i/ u9A.2 The Fully Determined Case (Nm = Ns) / 462) d4 S2 ? V+ _# A/ i8 |5 l t
9A.3 The Underdetermined Case (Nm < Ns) / 462
7 w; F: `2 Z; Y1 WPROBLEMS / 4642 [7 r% T# D2 P8 D \
10 Control of Generation 468- E0 G2 ~7 \9 K3 a
10.1 Introduction / 4683 v& ~+ ~. k" S7 p9 Y6 ]
10.2 Generator Model / 470
, U+ m# l8 s3 D) O8 W10.3 Load Model / 473: @0 i) s. g6 [
10.4 Prime-Mover Model / 475# M. _) {5 Y3 e0 s5 s& L
10.5 Governor Model / 476
0 Y$ ^8 g' U3 k* g7 O9 D1 k10.6 Tie-Line Model / 481
: ]2 ^" {1 G! p; U5 u6 M6 b0 }10.7 Generation Control / 485
7 d; e9 Z1 R9 K) a( q10.7.1 Supplementary Control Action / 485
$ B" t. d& T* {2 G( _10.7.2 Tie-Line Control / 486
`2 n6 j) u1 Z0 o" s10.7.3 Generation Allocation / 4891 J, {. z& U- M
10.7.4 Automatic Generation Control (AGC)
" y! r( E# K8 WImplementation / 491
- T( l3 Y+ V4 j- H; M10.7.5 AGC Features / 495
6 V4 l( R1 S7 j10.7.6 NERC Generation Control Criteria / 496
% K- r. U1 Y1 C) F; {PROBLEMS / 497+ K. `& B& m, ^% P) s" C
References / 500
* C8 r$ M% k f2 H! A5 `. b, J7 }+ ^11 Interchange, Pooling, Brokers, and Auctions 501
$ n: o) E: ^1 u6 c11.1 Introduction / 501! ? R# ?9 F0 y4 n7 D& S' u. ]
11.2 Interchange Contracts / 504. @, _- k7 j9 z
11.2.1 Energy / 504
7 F/ `2 q; @1 x4 x) R' ]11.2.2 Dynamic Energy / 506
5 X5 A. S/ w( X- l11.2.3 Contingent / 5066 n5 o/ R9 f2 w2 a! Y5 g
11.2.4 Market Based / 507
# W6 S. q6 Y! v2 N- E) o- g" ]11.2.5 Transmission Use / 5087 Z* \( C- h% S/ N
11.2.6 Reliability / 517# O7 s; y' Z" b+ [+ M
11.3 Energy Interchange between Utilities / 517. p* c l& n* M
11.4 Interutility Economy Energy Evaluation / 521, v8 B% T' b, J
11.5 Interchange Evaluation with Unit Commitment / 522
" c+ Q3 l S! U6 T- ^+ o4 O11.6 Multiple Utility Interchange Transactions—Wheeling / 523
8 z8 t* m) X0 ] p. W( T0 l11.7 Power Pools / 526
" M: _* H$ {3 lcontents xv- o- r+ n1 _0 s0 t8 W8 ~" V7 |. R
11.8 The Energy-Broker System / 529: D0 z! d* s# U) m5 H: P! O, W
11.9 Transmission Capability General Issues / 533$ V% |# U* e/ R7 f" w& q5 \
11.10 Available Transfer Capability and Flowgates / 535& ^$ ~1 [; @" C4 Z: Q
11.10.1 Definitions / 536
8 t2 G) g Z- p1 ~11.10.2 Process / 539
& v5 T- c5 K$ k( D, c11.10.3 Calculation ATC Methodology / 5401 V7 J: I' h# T+ r
11.11 Security Constrained Unit Commitment (SCUC) / 550 `; a% n7 g; }+ {
11.11.1 Loads and Generation in a Spot Market Auction / 550
. F8 x! s( S5 c11.11.2 Shape of the Two Functions / 552
# k' v! j5 k7 ]11.11.3 Meaning of the Lagrange Multipliers / 553
! l0 ?6 K' a* l8 |11.11.4 The Day-Ahead Market Dispatch / 554
5 x8 H" I1 n+ z* y11.12 Auction Emulation using Network LP / 555
/ {, r7 S+ K# k' G3 z/ F9 G11.13 Sealed Bid Discrete Auctions / 555
8 I8 B P h4 ~0 B* `' ePROBLEMS / 5606 [2 R/ \+ R& i* k
12 Short-Term Demand Forecasting 566
, n; }# x+ h3 O y/ R12.1 Perspective / 566" R) [+ ]' \: Z8 `4 r1 _" p, d
12.2 Analytic Methods / 569
: `$ J2 Q. Q+ X' c! K# R! K! e12.3 Demand Models / 5716 r1 S6 B% E- l5 w# O4 ^7 E( t. A
12.4 Commodity Price Forecasting / 572
) u3 F# p- h3 g+ P" I( X12.5 Forecasting Errors / 573
& G1 l+ w2 N" V. y7 U% G12.6 System Identification / 573( u6 _" U, z6 b5 K$ @" X/ R
12.7 Econometric Models / 574
$ I1 _3 h8 u" h12.7.1 Linear Environmental Model / 574
) v- L0 u1 o d$ | b' O12.7.2 Weather-Sensitive Models / 576
- v9 `6 z, s- O' X: j12.8 Time Series / 578
; U; d5 t. c7 [* W& P12.8.1 Time Series Models Seasonal Component / 578
) J, G5 j+ v" P1 x12.8.2 Auto-Regressive (AR) / 580# s3 [3 @6 \4 l% @' t3 E/ h/ q
12.8.3 Moving Average (MA) / 581
) O( a K9 j, s$ R8 l12.8.4 Auto-Regressive Moving Average (ARMA):0 d- D2 y9 A+ L! C$ s: O2 W
Box-Jenkins / 582; p( _( F- @9 u2 w9 v
12.8.5 Auto-Regressive Integrated Moving-Average
; B% N7 h; W' e {3 L& s(ARIMA): Box-Jenkins / 584
1 `1 p x- I+ E12.8.6 Others (ARMAX, ARIMAX, SARMAX, NARMA) / 585
! i+ o0 p, O) M: [! D3 x12.9 Time Series Model Development / 5859 e/ y/ T+ ?1 i8 W1 P, J. d
12.9.1 Base Demand Models / 586
1 B; L# d5 Y! u- D1 f! V/ j0 ~* X5 p12.9.2 Trend Models / 5867 H j8 c/ r+ a4 e+ L
12.9.3 Linear Regression Method / 586* d' ]7 R/ W' F3 ]
xvi contents
6 {( F- v: x" f& I o12.9.4 Seasonal Models / 588
9 g8 c! c; b$ Q g. X+ j. W12.9.5 Stationarity / 588+ q' a$ A+ ^& k6 m# B
12.9.6 WLS Estimation Process / 590! t8 U1 V# c2 Z) v- Y! w
12.9.7 Order and Variance Estimation / 5913 d* |. ~4 n$ S) f, i2 o5 }
12.9.8 Yule-Walker Equations / 592
6 o* U* g% K! X% v12.9.9 Durbin-Levinson Algorithm / 595
2 X8 A) O& Z; h. Y% J12.9.10 Innovations Estimation for MA and ARMA( C1 t P0 I) w, U# v
Processes / 598
; V! D" j' D. g) ]12.9.11 ARIMA Overall Process / 6009 C$ {6 k \& u. V0 C
12.10 Artificial Neural Networks / 603! M" D+ `; ?0 V! s5 w+ F9 e
12.10.1 Introduction to Artificial Neural Networks / 604
* _$ t5 N3 r- `) T+ ?12.10.2 Artificial Neurons / 6055 @- F8 Y8 y6 p5 i- s# a% \
12.10.3 Neural network applications / 606
" ]. u+ O9 j* m" B6 ^12.10.4 Hopfield Neural Networks / 606
$ h' _! x3 G. E1 s9 _12.10.5 Feed-Forward Networks / 607
$ n/ A a/ Z7 ^6 |% Q12.10.6 Back-Propagation Algorithm / 610, S T! O) Z2 [, W& w, i, G
12.10.7 Interior Point Linear Programming Algorithms / 613
/ {- Q7 S5 S( F8 \12.11 Model Integration / 614( A9 U2 w% G6 K3 _
12.12 Demand Prediction / 614* I* n+ D5 B$ H8 S3 m. s# a9 \
12.12.1 Hourly System Demand Forecasts / 615
/ T# d4 S" X7 F12.12.2 One-Step Ahead Forecasts / 6153 G, k8 g$ z U( A- G; U' g
12.12.3 Hourly Bus Demand Forecasts / 616" a' |5 s: ^8 e1 l% Q1 O! Z
12.13 Conclusion / 616; s4 V1 ]% Y. Z: L, _3 L' p7 P
PROBLEMS / 617 |
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