Abstract    In this paper, impacts of various uncertainties such as random outages of generating units and transmission lines, forecasting errors of load demand and wind power, in the presence of Demand response (DR) programs on power generation scheduling are studied. The problem is modelled in the form of a two-stage stochastic unit commitment (UC) which by solving it, the optimal solutions of UC as well as DR are obtained. Generating units’ constraint, DR and transmission network limits are included. Here, DR program is considered as ancillary services (AS) operating reserve which is provided by demand response providers (DRPs. In order to implement the existent uncertainties, Monte Carlo (MC) simulation method is applied. In this respect, scenarios representing the stochastic parameters are generated based on Monte Carlo simulation method which uses the normal distribution of the uncertain parameters. Backward technique is used to reduce the number of scenarios. Then, scenario tree is obtained by combining the reduced scenarios of wind power and demand. The stochastic optimization problem is then modelled as a mixed-integer linear program (MILP). The proposed model is applied to two test systems. Simulation results show that the DR improves the system reliability and also reduces the total operating cost of system under uncertainties.

Keywords    Ancillary services (AS), demand response (DR), stochastic optimization, uncertainty, wind power

چکیده    در این مقاله اثرات عدم قطعیت­های مختلف نظیر خروج تصادفی واحدهای تولید و خطوط انتقال، خطای پیش بینی بار و تولید توان بادی در حضور برنامه­های پاسخگویی تقاضا (DR) بر زمان­بندی تولید مطالعه می­شود. مسئله مورد نظر به صورت یک مسئله تصادفی دومرحله­ای درمدار قرار گرفتن واحدهای تولید (UC) مدل می­شود که با حل آن جوابهای بهینه UC و DR تعیین می­شوند. محدودیتهای واحدهای تولید، برنامه DR و حدود شبکه انتقال در نظر گرفته می­شوند. در این مقاله، برنامه DR بصورت خدمات جانبی ذخیره درنظر گرفته می­شود که توسط فراهم­کنندگان آن ارائه می­گردد. به منظور پیاده­سازی عدم قطعیتها، از شبیه سازی مونت کارلو (MC) استفاده می­شود. به این منظور، سناریوهایی که نماینده عدم قطعیتها هستند بر اساس شبیه سازی مونت کارلو تولید می­شوند که در آن از توزیع نرمال پارامترهای غیرقطعی استفاده می­شود. ، برای کاهش سناریو از تکنیک پسرو استفاده می­شود. سپس با ترکیب سناریوهای توان بادی و تقاضا درخت سناریو تشکیل می­گردد. مسئله بهینه سازی تصادفی بصورت یک مسئله برنامه­ریزی خطی صحیح آمیخته (MILP) مدل می­شود. مدل پیشنهادی روی دو سیستم آزمون بکار گرفته می­شود. نتایج شبیه سازی نشان می­دهد که DR ضمن بهبود قایلست اطمینان سیستم، هزینه کل بهره­برداری را تحت شرایط عدم قطعیت کاهش می­دهد.

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