@article { author = {Kian, Ramez}, title = {A production planning problem with lost sales and nonlinear convex production cost function under carbon emission restrictions}, journal = {Journal of Energy Management and Technology}, volume = {4}, number = {2}, pages = {1-13}, year = {2020}, publisher = {Iran Energy Association (IEA)}, issn = {2588-3372}, eissn = {2588-3372}, doi = {10.22109/jemt.2019.192552.1184}, abstract = {In this paper, a finite-horizon production planning problem with possible lost sales under several carbon emission restrictions is investigated. The studied model is deterministic with known demands which may not necessarily be met as lost sales are also allowed to provide reasonable flexibility with carbon emission restrictions. The problem is modeled as mixed integer nonlinear programming which has been reformulated in conic-quadratic form for convex cases. The problem is numerically investigated with respect to the costs incurred, the amount of carbon emissions and the magnitude of resulting demand losses. These issues are considered under different carbon restriction policies imposed over several block of periods over the planning horizon. Different carbon cap policies are defined and examined to with wide range of parameter sets to observe how different policies affect the amount of emission, cost and lost sales as the main KPI's set in this study. Numerical examples and their corresponding observations and managerial insights are provided accordingly.}, keywords = {Carbon emission restrictions,Production Planning,nonlinear convex cost, lost sales}, url = {https://www.jemat.org/article_97245.html}, eprint = {https://www.jemat.org/article_97245_d5b8162334ccdd621e154f85b3aa8d1a.pdf} } @article { author = {Haghighi, Alireza and Babapoor, Aziz and Azizi, Mohammadmehdi and Javanshir, Zahra and Ghasemzade, Hooman}, title = {Optimization of the thermal performance of PCM nanocomposites}, journal = {Journal of Energy Management and Technology}, volume = {4}, number = {2}, pages = {14-19}, year = {2020}, publisher = {Iran Energy Association (IEA)}, issn = {2588-3372}, eissn = {2588-3372}, doi = {10.22109/jemt.2019.152458.1134}, abstract = {Thermal energy storage is among the highly efficient approaches to overcome the energy crisis. Using phase change material (PCM) is one of the most effective techniques in thermal energy storage application. Several types of PCM with distinct characteristics and different ranges of melting and solidification temperature have found their way in various industries. However, commercialized PCMs generally suffer from low thermal conductivity which limits their application. In this study, the effect of adding different weight percentages of various nanoparticles, such as CuO, TiO2, Al2O3 and graphene to paraffin, as a standard PCM, on improvement of the thermal properties of PCM was investigated. Thermophysical properties and morphology of the nanocomposites, such as phase change temperature and latent heat of melting were characterized by Differential Scanning Calorimetry (DSC), Scanning Electron Microscope (SEM), and Fourier Transform Infrared Spectroscopy (FT-IR). SEM images display the proper distribution of nanoparticles in phase change material. FTIR results verified the formation of nanocomposites. Comparison between the investigated PCM nanocomposites showed that the nanocomposites containing 2 wt.% TiO2 with the enthalpy of 179.88 J/g, and 1 wt.% graphene nanocomposite with the enthalpy of 120.38 J/g had the highest and lowest energy storage capacity compared to paraffin, respectively. The results indicated that Nano-enhanced phase change materials (NEPCMs) could be particularly useful in applications in which temperature control is crucial. The new types of nanocomposites used in this study showed remarkable thermal performance, and they are capable of being used in thermal management applications}, keywords = {graphene,Nanoparticle,Nanocomposites,phase change materials,Optimization Thermal}, url = {https://www.jemat.org/article_99355.html}, eprint = {https://www.jemat.org/article_99355_fe13169fb58e89f3b053236fe40b3bad.pdf} } @article { author = {Parhizkari, Ladan and Najafi, Arsalan and Golshan, Mahdi}, title = {Medium term electricity price forecasting using extreme learning machine}, journal = {Journal of Energy Management and Technology}, volume = {4}, number = {2}, pages = {20-27}, year = {2020}, publisher = {Iran Energy Association (IEA)}, issn = {2588-3372}, eissn = {2588-3372}, doi = {10.22109/jemt.2020.204642.1203}, abstract = {Accurate electricity market price forecasting gives a capability to make better decisions in electricity market environment when, this market is complicated due to the severe fluctuations. The main purpose of a prediction model is forecasting the future prices. For doing this, the predicted variable (as output) and historical data (as input) should be close to each other. Machine learning is known as one of the most successful way of forecasting time series. Extreme learning machine (ELM) is a feed- forward neural network with one hidden layer. Hence, in this paper an extreme learning machine has been used for predicting electricity prices in a medium term time horizon. The real data of New York City electricity market has been utilized to simulate and predict the electricity price in four seasons of the year. Finally, the findings will be compared with multi-layer perceptron (MLP) results, which proves the efficiency of the model.}, keywords = {electricity price,Forecasting,time series,Extreme learning machine}, url = {https://www.jemat.org/article_101898.html}, eprint = {https://www.jemat.org/article_101898_7af77ce6fca4133459d5020993fa30d3.pdf} } @article { author = {mirzadeh, Mostafa and Simab, Mohsen and Ghaedi, Amir}, title = {Reliability evaluation of power systems containing tidal power plant}, journal = {Journal of Energy Management and Technology}, volume = {4}, number = {2}, pages = {28-38}, year = {2020}, publisher = {Iran Energy Association (IEA)}, issn = {2588-3372}, eissn = {2588-3372}, doi = {10.22109/jemt.2020.176501.1167}, abstract = {Nowadays renewable energy resources have been experiencing rapid progresses in electric power networks all over the world. As a result, it is important that these clean electrical power resources be utilized for power generation in the modern power systems in the planning and operation studies. However, the uncertainty related to these energy resources and their intermittent nature result in some challenges when integrating to the power networks. The reliability of power systems is affected when the penetration level of these intermittent renewable power generations is significant. In this regard, this paper introduces an analytical technique to study the adequacy of a power system containing significant tidal power generation. For this purpose, a multi-state reliability model of a tidal plant considering both failure rate of composed components and variation in the generated power arisen from variation in the tidal current speed is developed. At first, an equivalent reliability model considering failure effects of composed components is obtained. Then Fuzzy-C-means clustering method is applied for reducing numerous values of the output power of the plant. For determining the optimum number of states in the model, XB index is calculated. The proposed multi-state model is then used for adequacy studies of RBTS and IEEE-RTS and the related indices of these systems in presence of tidal units is evaluated.}, keywords = {Tidal power plant, Adequacy indices,Fuzzy c-means clustering,Tidal current speed,state reduction}, url = {https://www.jemat.org/article_102582.html}, eprint = {https://www.jemat.org/article_102582_351b4dc7b6c67644c2a58da2345b6a78.pdf} } @article { author = {Mohseni, Adel and Doroudi, Aref and Karrari, Mehdi}, title = {Robust Kalman Filter-based Method for Excitation System Parameters Identification using Real Measured Data}, journal = {Journal of Energy Management and Technology}, volume = {4}, number = {2}, pages = {39-48}, year = {2020}, publisher = {Iran Energy Association (IEA)}, issn = {2588-3372}, eissn = {2588-3372}, doi = {10.22109/jemt.2019.200701.1198}, abstract = {The excitation system is one of the most important components of a power plant. The network operator awareness of excitation system parameters is vital for accurate and efficient power system dynamic studies and optimal retuning. In this paper, the use of KFs for estimating generator excitation system parameters is proposed. The CKF is reformulated and developed for this purpose. A step disturbance in the reference voltage of the UNITROL 6800 excitation system -manufactured by ABB- is used to confirm the proposed method. The simulations are established based on a real case in Iran's grid, and the results are compared with the metaheuristics such as GA and PSO which have been widely employed in literature since now. The case studies indicate that the proposed method is more accurate and robust than the optimization algorithms, not only from mean value point of view but also from statistical point of view. Moreover, it is much more helpful to identify the parameters whose actual values are needed for optimal retuning.}, keywords = {Parameter identification,Excitation system,Kalman Filter,Metaheuristics}, url = {https://www.jemat.org/article_100331.html}, eprint = {https://www.jemat.org/article_100331_15f7088d1948a2588d3540da327cb847.pdf} } @article { author = {Seyed Matin, Pouria and Ghaebi, Hadi}, title = {Energy and exergy analysis of a multi-stage cooling cycle of scramjet to produce electricity and hydrogen}, journal = {Journal of Energy Management and Technology}, volume = {4}, number = {2}, pages = {49-59}, year = {2020}, publisher = {Iran Energy Association (IEA)}, issn = {2588-3372}, eissn = {2588-3372}, doi = {10.22109/jemt.2020.200875.1196}, abstract = {A multi-stage open cooling cycle of scramjet for electricity and hydrogen co-production is proposed in which the fuel of scramjet is used as coolant of cooling cycle.Thermodynamic and exergetic examinations of the advanced system have been conducted to appraise the performance of the cycle, electricity and hydrogen production. In this integral system, the waste heat of scramjet drives the power sub-cycle whilst the PEM electrolyzer input electricity is supplied by a portion of net electricity output of the cycle. It is figured out that the multi-expansion process reveals more advantages in comparison to the single-expansion process in terms of more cooling capacity, electricity and H_2 production.For the fuel mass flow rate of 0.4 kg/s, the cooling capacity of the new proposed cycle is computed 9.16 MW, the net electricity output is calculated about 3.38 MW and the hydrogen production rate is attained 42.16 kg/h. On the other hand, the exergetic analysis results have proved the fact that PEM electrolyzer has the highest exergy destruction ratio by 48% among all components of the cycle. In this case, the energy and exergy efficiency of the overall set-up are acquired by 12.95% and 22.16%, correspondingly.The outcomes of parametric evaluation demonstrated that the electricity and hydrogen production are directly proportional to the back pressure of pump accordingly, more electricity and hydrogen are generated by higher back pressure. But, increasing the mass flow rate of fuel does not have any tangible impact on energy and exergy efficiency of whole set-up thus both remain approximately constant.}, keywords = {Thermodynamic analysis,Scramjet,hydrogen,Multi-expansion,M-OCC,PEM electrolyzer}, url = {https://www.jemat.org/article_100903.html}, eprint = {https://www.jemat.org/article_100903_de9c1670a674c0320f51b6634c4e4ebf.pdf} } @article { author = {Emarati, Mohammad Reza and Keynia, Farshid and Rashidi Nejad, Masoud}, title = {Determination of the optimal strategy for the presence of a retailer in the next day's electricity market based on the risk index}, journal = {Journal of Energy Management and Technology}, volume = {4}, number = {2}, pages = {60-68}, year = {2020}, publisher = {Iran Energy Association (IEA)}, issn = {2588-3372}, eissn = {2588-3372}, doi = {10.22109/jemt.2020.204029.1201}, abstract = {Recently in Iran, due to the growing trend of electric energy consumption and the limitations of state resources in creating new capacities in the sectors of production, transmission, and distribution, the issue of privatization and restructuring in the electricity industry has been the same as in other countries of the world. At the moment, the wholesale market has begun its business, and soon it will include other sectors of the electricity industry, and distribution companies will be the primary buyers of electricity in this market. In this paper, due to the importance of the issue, the determination of the optimal strategy for the presence of a retailer in the competitive markets has been addressed. A linear model, which considers the behavior of a retailer in wholesale and retail markets, has been presented. Artificial neural networks are used to generate scenarios of price and consumer demand. In addition, the retailer determines its maximum profit based on taking into account different pricing methods offered to the end consumers. In this study, by presenting an indicator for calculating the risk in the decisions of the retail company, the role of this factor is discussed in the company's profits. According to the results, in the real-time pricing mode, the retailer increases its dependency on the pool, compared with the time of use and fixed pricing methods. In addition, the expected profit of the retailer has an inverse relationship with the amount of risk-aversion by the retailer.}, keywords = {Competitive Electricity Market,Neural Networks,Retailer,Risk Indices}, url = {https://www.jemat.org/article_103118.html}, eprint = {https://www.jemat.org/article_103118_38ac8d149a70b4680bf55e6720ab40bc.pdf} }