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39). Step 10: If n < Nmax or Err(n)max > İ, n = n + 1 and return to Step 6. Step 11: Calculate total cost using the objective function. where Nmax is the maximum number of iterations and İ is the threshold of the maximum error of ALHN. 00741PG23 $/h F3 ( PG 3 ) 45 d PG 3 d 180 MW These units supply a load demand of 210 MW. 005 Nmax = 2500; İ = 10–4. 7089. 4628. 5269. 57/h The convergence characteristic of ALHN for the problem based on the maximum error is shown in Fig. 13. min = 6. 8517e-005, max = 9.

32). 39). Step 10: If n < Nmax or Err(n)max > İ, n = n + 1 and return to Step 6. Step 11: Calculate total cost using the objective function. where Nmax is the maximum number of iterations and İ is the threshold of the maximum error of ALHN. 00741PG23 $/h F3 ( PG 3 ) 45 d PG 3 d 180 MW These units supply a load demand of 210 MW. 005 Nmax = 2500; İ = 10–4. 7089. 4628. 5269. 57/h The convergence characteristic of ALHN for the problem based on the maximum error is shown in Fig. 13. min = 6. 8517e-005, max = 9.

120) Ô+ 2 bg Í FC (ai + bV i Î i =1 ˚ ˛ Ó 1 - sk 2 k =1 M +Â Ê (Vgk )2 ˆ Á - 2b ˜ Ë g ¯ 2 È k Ê k NG ˘ 1 Ê k NG k ˆ k ˆ +  ÍVl ,h Á Vl , p -  DliVi , p ˜ + bh Á Vl , p -  DliVi , p ˜ ˙ ¯ 2 Ë ¯ ˙˚ k =1 l =1 Í i =1 i =1 Î Ë k k ˆ M Ê NG Vi , p NL Vl , p -1 -1 +  Á Â Ú g (V )dV + Â Ú g (V )dV ˜ ˜¯ k =1 Á l =1 0 Ë i =1 0 M NL where Vik,p output of continuous neuron p(i,k) representing power output of unit i in subinterval k, PGik, Vlk,p output of continuous neuron p(l,k) representing power flow on line l in subinterval k, Plk,p, k VȜ output of multiplier neuron Ȝ(k) representing Lagrangian multiplier associated with power balance constraint Ȝk, VȖk output of multiplier neuron Ȗ(k) representing Lagrangian multiplier for emission constraint Ȗk, k Vl ,Ș output of multiplier neuron Ș(l,k) representing Lagrangian multiplier for transmission constraint Șlk.

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