بهینه سازی زمانبندی غلط غالب و وابسته به تعامل اتفاقی برای میکروپردرهای متصل شده با توجه به نسلهای توزیع مبتنی بر فتوولتائیک تحت عدم قطعیت

This study develops a dominant-subordinate-interactive stochastic programming (DSISP) model for the wind-photovoltaic-based distributed energy resource systems in Lize Financial Business District of Beijing, China. During the synergistic optimization process with 24-h prediction outputs of solar and wind power as constraints, the dominant level puts more emphasis on renewable energy utilization, while the minimization both of pollutant emissions and system cost are modeled as a multi-objective programming (MOP) problem placed at the subordinate level. Results indicate that: (a) renewable energy technologies would exert an increasing paramount role in power systems, with an average share of 15.54% through a whole year; (b) a higher violation risk would lead to a decreased strictness of the constraints for facility capacity availabilities, and then to a higher system cost, more green electricity generation, and lower non-renewable electricity generation. As a consequence, thermal supplies especially in winner days would depend strongly on gas-fired boiler, which would result in more pollutant emissions as a violation risk increases. Moreover, the performance of the DSISP model is enhanced by comparing with three single-objective programs and the MOP approach. Results demonstrate that the DSISP model can provide much comprehensive and systematic strategies as considering the bi-level structure within the systems.