| Summary: | In the generation of operating system planning, saving utility cost (SUC) is customarily implemented to attain the forecasted optimal economic benefits in a generating system associated with renewable energy integration. In this paper, an improved approach for the probabilistic peak-shaving technique (PPS) based on computational intelligence is proposed to increase theSUCvalue. Contrary to the dispatch processing of the PPS technique, which mainly relies on the dispatching of each limited energy unit in sequential order, a modified artificial bee colony with a new searching mechanism (MABC-NSM) is proposed. TheSUCis originated from the summation of the Saving Energy Cost and Saving Expected Cycling Cost of the generating system. In addition, further investigation for obtaining the optimal value of theSUCis performed between theSUCdetermined directly and indirectly estimated by referring to the energy reduction of thermal units (ERTU). Comparisons were made using MABC-NSM and a standard artificial bee colony and verified on the modified IEEE RTS-79 with different peak load demands. A compendium of the results has shown that the proposed method is constituted with robustness to determine the global optimal values of theSUCeither obtained directly or by referring to theERTU. Furthermore,SUCincrements of 7.26% and 5% are achieved for 2850 and 3000 MW, respectively.
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