Abstract
A chance-constrained programming-based optimization model for the dynamic economic emission dispatch problem (DEED), consisting of both thermal units and wind turbines, is developed. In the proposed model, the probability of scheduled wind power (WP) is included in the set of problem-decision variables and it is determined based on the system spinning reserve and the system load at each hour of the horizon time. This new strategy avoids, on the one hand, the risk of insufficient WP at high system load demand and low spinning reserve and, on the other hand, the failure of the opportunity to properly exploit the WP at low power demand and high spinning reserve. The objective functions of the problem, which are the total production cost and emissions, are minimized using a new hybrid chaotic maps-based artificial bee colony (HCABC) under several operational constraints, such as generation capacity, system loss, ramp rate limits, and spinning reserve constraints. The effectiveness and feasibility of the suggested framework are validated on the 10-unit and 40-unit systems. Moreover, to test the robustness of the suggested HCABC algorithm, a comparative study is performed with various existing techniques.