| 总结: | 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 the SUCvalue. 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. The SUC is 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 the SUC is performed between the SUC determined 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 the SUC either obtained directly or by referring to the ERTU. Furthermore, SUC increments of 7.26% and 5% are achieved for 2850 and 3000 MW, respectively. © 2020 by the authors.
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