1403/03/31
جمال قاسمی

جمال قاسمی

مرتبه علمی: دانشیار
ارکید:
تحصیلات: دکترای تخصصی
اسکاپوس:
دانشکده: دانشکده مهندسی و فناوری
نشانی:
تلفن: 01135302902

مشخصات پژوهش

عنوان
Optimized Neural Network Based Thermal and Electrical Scheduling of Virtual Power Plant in the Presence of Energy Storage
نوع پژوهش
JournalPaper
کلیدواژه‌ها
artificial neural networks, optimization problem, virtual power plants, distributed energy resource
سال
2017
مجله Journal of Renewable and Sustainable Energy
شناسه DOI
پژوهشگران Hajar mohammadinia samakoosh ، Jamal Ghasemi ، Javad KazemiTabar

چکیده

Increasing energy consumption in the world and the presence of distributed generation poses new challenges in the management and operation of electrical grids. Virtual Power Plant (VPP) and optimization methods assist in the integration of distributed energy resources in the power systems. One way of optimization is the use of Artificial Neural Networks (ANNs). This paper presents a new ANN base method for short-term scheduling of internal resources of a VPP in order to maximize its profit in a period of 24-hour. This model is proposed to find an optimized method for connecting to the resources and VPP. The proposed model can be a way to fill the lack of a standard interface in technical problems with VPPs. Because of the different market and regulation conditions, applying an expert system, like ANN that is independent from these rules, can be attractive. It should be mentioned that the proposed method has been examined on filed data that were obtained from power plant in Tuscany, ranging 6 to 20 kW. As it is shown in the results, in peak load condition, Radial Basis Function (RBF) has the average error of 4% and performs better than Multilayer Perceptron (MLP). However, in intermittent load condition, MLP has the average error of 6% and does better than RBF. Nevertheless, both MLP and RBF have similar performance in off-peak load condition.