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GAMS codes

A.1 Introduction In Chapters 5–11 different decision-making models pertaining to producers, consumers, retailers, and market/system operators are proposed and formulated. Model performances are assessed by solving and discussing examples and realistic case studies. The objective of this Appendix is to help the reader to implement the models presented in this book. For this reason, the GAMS codes used to formulate the examples in the aforementioned chapters are included in this Appendix. We refer the interested reader to [19, 141] for further details on GAMS formulation. Specifically, the list of examples whose GAMS codes are included in this Appendix is 1. 2. 3. 4. 5. 6. 7. 8.

Producer Pool Example (Section 5.7). Wind Producer Example (Section 6.6). Producer Futures Market Example. No Unit Unavailability (Section 7.7). Producer Futures Market Example. Unit Unavailability (Section 7.8). Retailer Example (Section 8.7). Consumer Example (Section 9.5). Market-Clearing Example (Section 10.6). Market-Clearing Example with Wind Generation (Section 11.5).

A.2 GAMS code for the Producer Pool Example (Section 5.7) $title POOL PRODUCER

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A GAMS codes

*************************************************************************** * DATA *************************************************************************** SETS G T W

Units Periods Scenarios

/g1 * g2/ /t1 * t3/ /w1 * w12/;

alias(W,WW); SCALARS beta alpha

Weighting parameter Confidence level

/0/ /0.95/;

PARAMETERS CP(G) Production cost / g1 12 g2 16 / PGmax(G) Capacity / g1 100 g2 50 / PGmin(G) Minimum power ouput / g1 10 g2 10 /

Rup(G) / g1 g2

Ramp-up ramp 25 15 /

Rdw(G) / g1 g2

Ramp-down ramp 25 15 /

PG0(G) / g1 g2

Initial power status 50 25 /

PAmax(G) Maximum range power in the AGC service / g1 15 g2 10 / AD(W) / w1 w2 w3 w4 w5 w6 w7 w8 w9 w10 w11 w12

Day-ahead non-anticipativity vector 1 1 1 0 1 1 1 0 1 1 1 0 /

AR(W) / w1 w2 w3 w4 w5 w6 w7

Regulation non-anticipativity vector 1 1 1 0 1 1 1

A.2 GAMS code for the Producer Pool Example w8 w9 w10 w11 w12 AA(W) / w1 w2 w3 w4 w5 w6 w7 w8 w9 w10 w11 w12

0 1 1 1 0 / Adjustment nonanticipativity vector 1 0 1 0 1 0 1 0 1 0 1 0 /

prob(W);

TABLE lambdaD(W,T) Day-ahead price scenarios t1 t2 t3 w1 11.0 16.5 12.1 w2 11.0 16.5 12.1 w3 11.0 16.5 12.1 w4 11.0 16.5 12.1 w5 13.2 18.7 16.5 w6 13.2 18.7 16.5 w7 13.2 18.7 16.5 w8 13.2 18.7 16.5 w9 15.4 19.8 17.6 w10 15.4 19.8 17.6 w11 15.4 19.8 17.6 w12 15.4 19.8 17.6; TABLE lambdaR(W,T) Regulation price scenarios t1 t2 t3 w1 5.5 8.5 6.5 w2 5.5 8.5 6.5 w3 5.0 7.0 5.5 w4 5.0 7.0 5.5 w5 6.5 9.5 8.0 w6 6.5 9.5 8.0 w7 6.0 8.5 7.5 w8 6.0 8.5 7.5 w9 7.0 10.5 10.5 w10 7.0 10.5 10.5 w11 6.5 9 9.5 w12 6.5 9 9.5; TABLE lambdaA0(W,T) Adjustment price scenarios (inverse demand curve intercept) t1 t2 t3 w1 17 28 15 w2 10 17 13 w3 12 17 15 w4 9 14 11 w5 17 22 19 w6 14 19 15 w7 16 25 21 w8 13 17 14 w9 17 28 24 w10 14 23 20 w11 16 23 21 w12 13 19 18;

449

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A GAMS codes

TABLE gammaA(W,T) Adjustment price scenarios t1 t2 t3 w1 -0.01 -0.03 -0.02 w2 -0.01 -0.03 -0