Semnan University

About: Semnan University is a education organization based out in Semnan, Semnān, Iran. It is known for research contribution in the topics: Heat transfer & Nanofluid. The organization has 5531 authors who have published 9778 publications receiving 119986 citations.

Generation and Transmission Expansion Planning: MILP–Based Probabilistic Model

Abstract: This paper describes a new probabilistic model for generation and transmission expansion planning (G&TEP) problem considering reliability criteria. Probabilistic reliability criteria accounts for random generator or line outages with known historical forced outage rates (FOR). The resultant model considers the installation and operation costs as well as the cost of expected energy not supplied (EENS) to optimally determine the number and location of new generating units and circuits in the network, power generation capacity for those units and the voltage phase angle at each node. Also, efficient linear formulations are introduced in this paper to deal with the nonlinear nature of the problem including objective functions and constraints. Modified 6-bus test system, IEEE 24-bus RTS and IEEE 118-bus test system are utilized to illustrate the effectiveness of the proposed framework.

Rheological behavior characteristics of TiO2-MWCNT/10w40 hybrid nano-oil affected by temperature, concentration and shear rate: An experimental study and a neural network simulating

Abstract: In this article, rheological behavior of TiO2-MWCNT (45–55%)/10w40 hybrid nano-oil was studied experimentally. The nano- oils were tested at temperature ranges of 5–55 °C and in shear rates up to 11,997 s −1 . With respect to viscosity, shear stress and shear rate variations it was cleared that either of the base oil and nano-oil were non-Newtonian fluids. New equations which were based on thickness of the fluid were presented for different temperature values, R-squared values were between 0.9221 and 0.9998 (the precise of correlation changes depend on temperature). Also to predict the nano-oil behavior, neural network method was utilized. an artificial neural network (MLP type) were used to predict the viscosity in terms of temperature, solid volume fraction and shear stress. to compare the prediction precise of neural network and correlation the results of these two were compared with together. ANN showed more accurate results in comparison with correlation results. R 2 and (MSE) were 0.9979 and 0.000016 respectively for the ANN.

Robust Transmission and Energy Storage Expansion Planning in Wind Farm-Integrated Power Systems Considering Transmission Switching

Abstract: This paper presents a new nondeterministic model for joint transmission and energy storage expansion planning along with optimal transmission switching in wind farm-integrated power systems. The proposed approach adopts the underlying idea of robust optimization to characterize the uncertainty sources pertaining to load demands and wind power productions through uncertainty sets. Accordingly, a tractable adaptive min–max–min cost model is introduced to find a robust optimal expansion plan for new lines and storages withstanding the worst-case realization of the uncertain variables. As the adaptive min–max–min cost model cannot be solved directly by the commercial off-the-shelf software packages, a decomposition algorithm using primal cutting planes is introduced to obtain the optimal solution. The proposed approach has been implemented on the IEEE 24-bus and the IEEE 73-bus test systems. Also, the robustness of optimal expansion plans under different circumstances is evaluated through a post-optimization procedure simulating different realizations of the uncertainty sources. Case studies justify the efficiency of the proposed RO-based model.