Multi-objective electricity-gas flow with stochastic dispersion control for air pollutants using two-stage Pareto optimization. (1st December 2020)

• Record Type: Journal Article
• Authors: Chen, Yixuan; Qu, Kaiping; Pan, Zhenning; Yu, Tao
• Is Part Of: Applied energy. Volume 279(2020)
• Publication Date: 2020-12-01
• Subjects: Multi-objective electricity-gas flow -- Stochastic dispersion control -- Two-stage Pareto optimization -- Improved homogenized adjacent points method -- Penalty convex–concave procedure; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2020.115773
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A novel stochastic dispersion control is proposed for air pollution in the MOEGF. A novel two-stage Pareto optimization is established to solve the MOEGF. A Pareto optimizer I-HAPM is developed for Pareto solution set in the first stage. The stochastic dispersion control mitigates air pollution with enhanced robustness. I-HAPM surpasses existing algorithms to obtain a high-quality Pareto solution set. Abstract: 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.