Saving utility costs optimization in generator operation planning based on scalable alternatives of probabilistic demand-side management

The electric power system network has become more self-sufficient and less dependent on fossil fuel-based units due to the increasing integration of renewable energy resources. It is crucial to have an efficient method or technology for managing the system's economics, security, reliability, environmental damage, and the uncertainties that come with fluctuating loads. In this context, this paper utilizes a framework based on probabilistic simulation of a demand-side management approach and computational intelligence to calculate the optimal value of saving utility cost (SUC). Unlike traditional methods that dispatch peak-clipped resource blocks sequentially, a modified artificial bee colony (MABC) algorithm is employed. The SUC is then reported through a sequential valley-filling procedure. Consequently, the SUC is derived from the overall profitability of the generation system and includes savings in energy costs, capacity costs, and expected cycle costs. Further investigation to obtain the optimal value of SUC was conducted by comparing the SUC determined directly and indirectly, explicitly referring to the peak clipping energy of thermal units (PCETU). The comparisons utilized the MABC algorithm and a standard artificial bee colony, and the results were verified using the modified IEEE RTS-79 with varying peak load demands. The findings illustrate that the proposed method demonstrated robustness in determining the global optimal values of SUC increments, achieving increases of 7.26 % for 2850 MW and 5 % for 3000 MW, compared to indirect estimation based on PCETU. Moreover, SUC increments of 18.13 % and 25.47 % were also achieved over the conventional method.

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