Stochastic Economic-Emission Dispatch with Confidence Interval Constraint

Aiming to reduce the fuel cost and pollution emission of power grid with high penetration of intermittent renewable energy, a novel stochastic dispatch scheme considering the economy and emission is proposed in this paper. Different from conventional stochastic dispatch that merely focuses on the minimization of the objectives on fuel cost and emission, the proposed method takes Confidence Interval (CI) constraint into consideration when selecting the optimal Probability Distribution Function (PDF) of fuel cost. The PDF with the narrowest CI width is demonstrated to have superior performance in terms of reliability. Moreover, with the advantage of fast convergence, bacterial swarm algorithm (BSA) is applied to in this paper to reduce the computational complexity. Numerical studies are conducted on a modified IEEE 30-bus system that includes 3 wind farms and 2 solar farms. Comprehensive comparisons are carried out on the results achieved by the proposed method, other Evolutionary Algorithms (EAs) and the deterministic dispatch, which verifies the superiority of CI-BSA. Besides, a trade-off relationship between the fuel cost and pollution emission is analyzed based on the Pareto front in the experimental studies.