科研之友
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摘要
In consideration of the deteriorating global warming and air pollution, a multi-objective electricity-gas flow (MOEGF) is proposed in this paper. Different from the existing emission control modes, a novel stochastic dispersion (SD) control for air pollutants is formulated in the MOEGF. The SD control considering district-varying environmental tolerance introduces the Gauss puff dispersion model to precisely describe the emission dispersion, and further reduces the influence on air pollutant concentration. In addition, a scenario-based strategy is developed to enhance the robustness of SD control in the atmospheric condition uncertainty. To solve the MOEGF involving complex constraints and multiple conflictive objectives, a two-stage Pareto optimization framework is proposed. In the first stage, the nonconvex natural gas flow is linearized, and then an improved homogenized adjacent points method (I-HAPM) is developed to calculate a high-quality Pareto solution set of the simplified MOEGF, to provide diversified trade-off between objectives. Owing to the cooperation of the linearized gas flow and the Pareto optimizer I-HAPM, the computational quality and efficiency of the Pareto solution set is improved. In the second stage, a tailored compromise solution for operation is determined from the Pareto solution set according to real dispatch requirements, which is convexified with the penalty convex-concave procedure to guarantee the operation security. Case studies demonstrate that the MOEGF effectively reduces carbon emissions and influence on air pollution of the combined system, with a low-level cost sacrifice. Besides, the effectiveness of the two-stage Pareto optimization is validated.
