مشخصات پژوهش

صفحه نخست /Deep Neural Network Based ...
عنوان Deep Neural Network Based Data-Driven Framework For Combined Economic Emission Dispatch Including Photovoltaic Integration
نوع پژوهش مقاله ارائه شده
کلیدواژه‌ها CEED, deep neural network, economic dispatch, hybrid algorithm, optimization
چکیده The Combined Economic and Emission Dispatch (CEED) plays a crucial role in balancing cost-effective electricity generation from both traditional and renewable energy sources with environmental considerations. Several existing solutions attempt to solve this problem. 'Black-box' models, while adept at processing large datasets, often fall short in delivering optimal solutions. Conversely, 'white-box' models, despite their theoretical precision, grapple with uncertainties and typically exhibit slower performance. To address these limitations, this paper introduces a 'gray-box' model framework. Our study incorporates a scenario featuring four Thermal Units (TUs) and six Solar Units (SUs). Results indicate that the introduced hybrid algorithms capture over 90% of the optimal behavior, signifying a notable stride in addressing energy optimization challenges. Their swift execution times particularly stand out, making them highly suitable for real-time operational scenarios.
پژوهشگران سعید گلستان (نفر سوم)، سید یوسف موسی زاده موسوی (نفر دوم)، یونس قزاق جاهد (نفر اول)