Artificial Intelligence in Power System Optimization by Weerakorn Ongsakul, Dieu Ngoc Vo

By Weerakorn Ongsakul, Dieu Ngoc Vo

With the significant raise of AI functions, AI is being more and more used to unravel optimization difficulties in engineering. some time past twenty years, the functions of man-made intelligence in energy structures have attracted a lot examine. This e-book covers the present point of purposes of man-made intelligence to the optimization difficulties in strength platforms. This ebook serves as a textbook for graduate scholars in electrical strength approach administration and is additionally worthwhile should you have an interest in utilizing man made intelligence in strength method optimization.

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ALHN is efficient in solving this problem with the equality constraints properly handled by an augmented Lagrangian function and sigmoid function. The continuous variables will be handled by continuous neurons including power outputs of units PGik, fuel deliveries to units Fik and fuel storages for units Xik while the constraints will be handled by an augmented Lagrange function consisting of power balance, fuel delivery and fuel storage constraints. 57) 2 M NG È k k Ê ˆ 1 Ê ˆ ˘ + ÂÂ Íhik Á X ik - X i0 - Â Fi l + tl Qil ˜ + bhk,i Á X ik - X i0 - Â Fi l + tl Qil ˜ ˙ ¯ 2 Ë ¯ ˙˚ k =1 i =1 Í l =1 l =1 Î Ë ( where Ȝk, Ȗk, Șik ȕȜk, ȕȖk, ȕȘk,i ) ( ) Lagrangian multiplier associated with power balance, fuel delivery and fuel storage, respectively.

2 0 90 95 100 ȕ1 105 Į1 110 Fig. 16 Membership function of total operating cost. 102) In this section, the solution technique used is fuzzy linear programming (FLP). In FLP [20], the goal of decision-maker can be expressed as a fuzzy set and the solution space is defined by constraints that can be modeled by a fuzzy set. 93). PGi is the column matrix representing the set of real power generation of the generator connected to bus i; d is the vector representing a fuzzy objective function. 99) is represented by a fuzzy set with the membership functions of —i(x).

Step 3: Obtain Įi and ȕi from minimum and maximum of all objective values computed in Step 2. Step 4: Solve the fuzzy linear programming of the multi-objective problem using Įi and ȕi from Step 3. 6 IEEE 30-bus system data are used as test data. The diagram of the network is depicted in Fig. 17. 3. 3 ~ ~ 2 1 18 15 19 14 4 8 6 ~ ~ 7 12 5 13 ~ 9 17 16 10 11 26 20 ~ 25 24 22 28 27 21 29 30 Fig. 17 IEEE 30 bus test system. 2 Generator cost function of the IEEE 30 bus system. 3 Emissions of the IEEE 30 bus system.

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