دانلود مقاله ISI انگلیسی شماره 86886
ترجمه فارسی عنوان مقاله

برنامه ریزی مطلوب تولید و سیستم های نسل در تولید برنامه تقاضای برق تقاضای کاهش تقاضا

عنوان انگلیسی
Optimal scheduling of manufacturing and onsite generation systems in over-generation mitigation oriented electricity demand response program
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
86886 2018 28 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Computers & Industrial Engineering, Volume 115, January 2018, Pages 381-388

ترجمه کلمات کلیدی
برنامه ریزی مطلوب، سیستم تولید، پاسخ تقاضای کاهش تقاضای بیش از حد تولید، تجزیه و تحلیل میزان حساسیت، بهینه سازی ذرات ذرات،
کلمات کلیدی انگلیسی
Optimal scheduling; Manufacturing system; Over-generation mitigation oriented demand response; Sensitivity analysis; Particle swarm optimization;
پیش نمایش مقاله
پیش نمایش مقاله  برنامه ریزی مطلوب تولید و سیستم های نسل در تولید برنامه تقاضای برق تقاضای کاهش تقاضا

چکیده انگلیسی

Manufacturing system is considered a valuable source that can provide electricity load adjustment in electricity demand response program to balance the supply and demand of the electricity throughout the grid. In this paper, we propose a mathematical model to identify the optimal participation strategy for manufacturing end use customers with onsite energy generation system in the demand response program designed for mitigating electricity over-generation due to high penetration of renewable sources in electricity grid. The background of over-generation mitigation oriented demand response program is described first. Then, the manufacturer’s decision making procedure for identifying the optimal participation strategy is modeled as a mixed nonlinear integer programming. In particular, the manufacturers’ participation strategies including the decision of participating or not, and corresponding production schedule of manufacturing system as well as utilization schedule of onsite generation system, are modeled as decision variables in the objective function to minimize the overall cost considering the benefits due to the participation, energy billing cost, onsite generation cost, and production loss penalty cost. Particle swarm optimization is used to find a near optimal solution for the formulated problem. A numerical case study with sensitivity analysis is then conducted to demonstrate the effectiveness and robustness of the proposed model.