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

By Weerakorn Ongsakul, Dieu Ngoc Vo

With the massive bring up of AI functions, AI is being more and more used to resolve optimization difficulties in engineering. some time past 20 years, the purposes of synthetic intelligence in energy structures have attracted a lot study. This e-book covers the present point of functions of man-made intelligence to the optimization difficulties in energy structures. This ebook serves as a textbook for graduate scholars in electrical strength approach administration and is usually invaluable in the event you have an interest in utilizing synthetic intelligence in strength approach optimization.

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The load demand for each week is 700, 800, and 650 MW, respectively. The coal supplier is to deliver a total of 7,000 tons of coal at the beginning of each week to Economic Dispatch 39 the five generating units, and the initial fuel storage levels are assumed to be 2000, 5000, 5000, 8000, and 8000 tons at the beginning of the first week. 1. In this example, the number of subintervals is 3 with a 168 hour duration each. The sub-cases will be considered here including minimization of fuel cost only, minimization of emissions only and minimization of two or multi objectives to find a trade-off solution.

3. 5 ECONOMIC DISPATCH WITH RAMP RATE CONSTRAINT In an ED design without ramp rate constraint, it is assumed that generating units have the capability to increase stepwise from zero to the rated capacity and vice versa. However, in practice, because of the generating units’ physical limitations, their capability to increase output is constrained by a ramp function, called rampup. Likewise, when a generating unit has to reduce its generation, the decrease is constrained by a ramp function called ramp-down.

PGi3 + cSO2i . PGi2 + bSO2i . PGi + aSO2i ENOx ( PGi ) = d NOxi . PGi3 + cNOxi . PGi2 + bNOxi . PGi + aNOxi ECO2 ( PGi ) = dCO2i . PGi3 + cCO2i . PGi2 + bCO2i . 0 For the implementation of the linear fuzzy programming method, these functions are linearized into 5 piece-wise linear functions. 7 address the dispatch results of minimum operating cost, SO2, NOx and CO2 emissions, respectively. 8 shows the dispatch results of the multi-objective solution. The results show that the single objective approaches result in inferior results in the other objectives and a lower degree of satisfaction.

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