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Abstract The effect of vortex generator parameters (Cold orifice angle, Cold orifice diameter and Nozzle area) on vortex tube performance is investigated experimentally. Vortex tube is connected to a natural gas pipeline with constant pressure of 4 bars. To improve vortex tube efficiency, six generators with different cold orifice angle, five generators with different cold orifice diameter and three generators with different nozzle area are studied for each experiment part. Results show variation of nozzle area has no effect on optimum cold mass fraction while cold mass angle and cold mass diameter move this point. Increment in cold orifice diameter increases optimum cold mass fraction and decreases cold temperature. As the angle of cold orifice increases, more mass flow passes through cold outlet and optimum cold mass fraction also increases. The expansion of the gas in the diffuser type cold orifice is investigated as the dominating reason for the different vortex tube performance. These mentioned designing parameters of vortex generator affect the flow pattern and efficiency of vortex tube as a consequence. For cold orifice angle of 4.1°, cold orifice ratio of 0.64 and nozzle area ratio of 0.14, highest efficiency is achieved.

Abstract Recently in electricity markets, a massive focus has been made on setting up opportunities for participating demand side. Such opportunities, also known as demand response (DR) options, are triggered by either a grid reliability problem or high electricity prices. Two important challenges that market operators are facing are appropriate designing and reasonable pricing of DR options. In this paper, time-of-use program (TOU) as a prevalent time-varying program is modeled linearly based on own and cross elasticity definition. In order to decide on TOU rates, a stochastic model is proposed in which the optimum TOU rates are determined based on grid reliability index set by the operator. Expected Load Not Supplied (ELNS) is used to evaluate reliability of the power system in each hour. The proposed stochastic model is formulated as a two-stage stochastic mixed-integer linear programming (SMILP) problem and solved using CPLEX solver. The validity of the method is tested over the IEEE 24-bus test system. In this regard, the impact of the proposed pricing method on system load profile; operational costs and required capacity of up- and down-spinning reserve as well as improvement of load factor is demonstrated. Also the sensitivity of the results to elasticity coefficients is investigated.

Abstract In this study, two-phase flow in a Polymer Electrolyte Membrane (PEM) fuel cell with the converging-diverging flow field was investigated using numerical simulation. A transient, three-dimensional, two-phase flow, and multi-component model, as well as an agglomerate model for oxygen reduction in the cathode catalyst layer, was employed to simulate the performance of the cathode half-cell. The numerical implementation was conducted by developing a new solver in OpenFOAM by the author. An augmentation about 28.2% was observed in the oxygen mass fraction at GDL/Channel interface for a PEM fuel cell with a converging-diverging angle of 0.3° in comparison with the reference cell (straight channels). Moreover, the average of liquid water saturation was decreased by 3.61% in the middle cross-section of gas channels and 9.4% near to the outlet region for reviewed converging-diverging cases. Finally, to investigate the improvement of the cell performance, polarization curve and net output power were presented. It was found that the using converging-diverging flow field was more effective at high current densities, while it had a minor effect at low current densities. The net output power of the PEM fuel cell with converging-diverging channels was enhanced by more than 10% compared with the base cell.

Abstract Short-term power output forecasting is a fundamental and mandatory task in the power plants. Since the restructuring and privatization of power plants in Iran are in progress, this is particularly important in the operational planning and cost control in the power plants. Numerous factors affect power output forecasting especially climate factors. In this paper, besides several climate factors, two factors including wind speed and wind direction that have been rarely considered simultaneously for power output forecasting in previous studies, have been used. These two factors have many fluctuations and usually create a significant noise in forecasting models. To illustrate this claim, the mechanical simulations are used to demonstrate the necessity of these factors for short-term power output forecasting. For this purpose, a neural network-based approach is proposed using six variables. This approach uses the mixture models of Kohonen's self-organizing map (SOM) as clustering method and radial basis function (RBF) as classification method for accurate power output forecasting in a power plant. Furthermore, the real case study in Iranian power plant is used to show the ability of the proposed approach. Furthermore, the statistical tests are provided to indicate the advantages and capabilities of the proposed approach.

Abstract This paper addresses optimal day-ahead scheduling of Combined Heat and Power (CHP) units of an Active Distribution Network (ADN) with Electric Storage Systems (ESS) and Thermal Storage Systems (TSS) considering Industrial Customers (ICs) inter-zonal power exchanges. The ADN operator may use CHP units to supply its ICs and based on smart grid conceptual model, it can transact electricity with upward wholesale electricity market and its downward ICs' systems; meanwhile its ICs can transact energy with each other through the ADN main grid. Basically, the optimal scheduling of CHP units problem is a Mixed Integer Non Linear Programing (MINLP) problem with many stochastic and deterministic variables. However, the electricity transactions between the ADN and its ICs in normal and contingency scenarios may highly complicate this problem. In this paper, linearization techniques are adopted to linearize equations and a two-stage Stochastic Mixed-Integer Linear Programming (SMILP) model is utilized to solve the problem. The first stage models behavior of operation parameters and minimizes the operation costs; check the feasibility of the ICs' requested firm and non-firm power exchanges, and the second stage considers the system's stochastic contingency scenarios. The competitiveness of ADN in the deregulated market can be improved by adjustment of the proposed decision variables in the two stage optimization procedure. The proposed method is applied to 18-bus, 33-bus IEEE test systems. The effectiveness of the proposed algorithm has been investigated.

Abstract In this study, a comprehensive and detailed exergy analysis was carried out to investigate an industrial-scale pasteurized yogurt production plant located in the north-west of Iran, West Azarbaijan province. For this goal, the plant was subdivided into four main subsystems, including steam generation, above-zero refrigeration, milk standardization and pasteurization, and yogurt production lines. Accordingly, exergetic efficiency and exergy destruction rate were defined and computed for each component of the four lines individually. Moreover, this analysis was conducted to find the amount of exergy consumed in producing a given amount of the pasteurized yogurt. The main contributors to the exergy destruction of the entire plant were in descending order of importance: boiler & air compressor combination of the steam generator (12484.88 kW), ice-water tank & agitator combination of the above-zero refrigeration system (2900.59 kW), and pressure reducer #2 of the steam generator (731.82 kW). Moreover, the specific exergy consumption of the pasteurized yogurt was found to be 841.34 kJ/kg based on the mass allocation concept. More specifically, the percentile contributions of the steam generation, above-zero refrigeration, milk standardization and pasteurization, and yogurt production lines to the specific exergy consumption were determined as 82.62%, 9.36%, 2.80%, and 5.21%, respectively.

Abstract This paper presents a strategic bidding model for several price-taker plug-in electric vehicle aggregators sharing the same distribution network that participate in both day-ahead energy and ancillary services (up/down-regulation reserve) markets. Since the strategic feasible space of an aggregator depends on the actions of the other aggregators due to the limited capacity of the existing feeders, the proposed problem forms a generalized Nash equilibrium problem. The aggregators’ objective is considered to be the cost of purchased energy from the day-ahead and real-time market minus the revenue from the day-ahead regulation market. A hybrid stochastic/robust optimization model is employed to deal with different uncertainties an aggregator faces in the bidding strategy problem. These uncertainties include day-ahead energy prices, day-ahead up/down-regulation prices, and real-time energy prices. Day-ahead prices are modeled by different scenarios, while real-time prices are represented by the confidence bounds. Results of a case study are shown to demonstrate the applicability and tractability of the proposed model.

Abstract For decades, energy has prevailed as a critical policymaking concern at national and international levels. Today, energy systems, the global markets and their trends are more complex, and it is crucial for any nation or organization which seeks to grow its share in the energy markets to develop insights about potential future trends and changes. Although Iran has one the largest natural gas reserves in the world, it currently contributes little to international market supply and recently has targeted the enhancement of its role in the market. To achieve this, it must carefully consider the complexity of existing global energy markets and how they are likely to evolve in the future. Here, we develop and discuss a novel scenario synthesizing model to address the inherent uncertainty of the energy future. The model starts with a structured environmental analysis step to establish the meaningful driving forces and other influences on the natural gas global markets. The influences identified are then categorized under four classes: critical uncertainties, driving forces, descriptive, and neutral (which are removed from the study). Applying a simulation-based method, a layered scenario development model is constructed to develop plausible scenarios for two feature classes: critical uncertainties and driving forces. The developed scenarios are then combined to generate possible scenario streams. A third layer simulation is applied to generate final plausible scenarios. As a final step, scenarios are clustered to define relatively independent scenario streams, and each is discussed using descriptive features.