JOURNAL OF ENERGY MANAGEMENT
Year:2016 | Volume:6 | Issue:3|Papers Count:6

Combined heat and power production technology has energy efficiency and environmental advantages over other forms of energy supply. Solving combined heat and power economic dispatch problem considering all system demand and operational constraints determines optimal utilization of power and heat sources. In micro grids, dual dependency of combined heat and power production makes economic dispatch problem as a complicated non-linear optimization issue. In this paper, a bacterial foraging algorithm oriented by particle swarm optimization method is applied to solve the problem considering valve point effects and prohibited zones for power-only units models. These considerations cause no convexity in both the objective function and constraints. To exhibit the effectiveness of the proposed algorithm, it is implemented on small and large scale systems. Comparison of the results obtained by the proposed approach with some other recently presented in literature shows overall superiority of the method and illustrates that it can be utilized as an efficient method in solving combined heat and power economic dispatch problem.

Modeling of frequency-dependent components of power system is often based on a terminal description by an admittance matrix in the frequency domain. One challenge in the extraction of state-space models from such data is to prevent possible error magnification when the model is to be applied in time-domain simulations. The error magnification is a consequence of inaccurate representation of small eigenvalues of the admittance matrix. In the case of multi terminal problems, the output response is sometimes strongly dependent on the distribution of the applied input on the terminals. Such behavior is often observed in the low-frequency range, due to large differences between short-circuit and open- circuit modal characteristics. Direct application of standard modeling techniques to such cases gives a model with inaccurate representation of the small modes. So this paper represents a novel method to model multi-port networks using vector fitting approach. In the suggested method, the modeling error is avoidable.

Restructuring in electrical power distribution utilities, energy loss management is one of the critical activities to enhance network efficiency. Accordingly, monitoring of technical loss and strategic planning for loss reduction would be a main target of asset manager. For loss monitoring, the usual methods of loss calculation and estimation need numerous data which is not used mostly to utilize real networks. In this research, a practical method for loss monitoring is introduced according to the viewpoint of components’ supply chain to network. For this purpose, indictors are extracted by the focus group for each process of component inserting and their probability distribution functions are calculated. Then, permissible values of indicators are determined by the Delphi method and to calculate new probability functions, the probability distance of each process is calculated. Finally, a real distribution network with historical data is used here to study the proposed method. The results illustrate that the most deviation is in the design process from the power loss viewpoint.

In this paper a novel power management method for an electric vehicle (EV) equipped with two energy storage systems is presented. In this way, an optimized nonlinear controller based on fuzzy system is developed. The main stage to design a fuzzy controller is proper determination of fuzzy rules and membership functions that in this paper, the fuzzy rules and input and output membership functions of fuzzy controller are determined via mixed integer genetic algorithm (GA). The model of EV and optimized fuzzy controller as well as conventional fuzzy controller and optimized on/off controller are simulated in ADVISOR environment for the standard driving cycle EPA urban UDDS. Simulation results confirm a significant reduction in the consumed energy by the proposed optimal fuzzy controller in comparison with the conventional fuzzy and optimized on/off controllers.

Due to thermal sensitivity, costs and importance of dying process for foods, it seems to be necessary looking for new optimal approaches which could consider effects of various parameters in this research, drying process of kohlrabi in a fluid bed, exposed to air and microwave, will be analyzed using response surface method. Microwave power, temperature and flow rate of inlet air will be studied in different values as three maim parameters. Optimal value of these parameters are 900 watt, 60oC and 1800 SCFH for microwave power, temperature and flow rate of inlet air, respectively. In this condition kohlrabi will be dried in 7.62 minutes. The correlation coefficient of simulated model (R2) for drying time is 0.9995 which shows an excellent fit with the experimental data.

Full Text [PDF 1025KB] Increasing energy cost and reduction of fossil fuel resources have been resulted in increasing demand of renewable energy, such as micro biomass particles in small scale Stirling engines to generate combined heat and power. In such Stirling engines, biomass particles are burnt in external combustion chamber and then, the generated heat is transferred to the working fluid of the engine cycle. Therefore, combustion of fine particle plays basic role in operation and effectiveness of these Stirling engines. Simulation of combustion chambers of those engines using computational fluid dynamics, as a first step of engine design, can reveal more insight to optimization process. In this study, combustion chamber of 55 kW combined heat and power Stirling engine has been simulated and the effects of some parameter have been investigated. The numerical results show good agreement with experiments. Increasing secondary air mass flow rate yields hotter flue gas and higher Stirling engine efficiency. The smaller particles yield to more evaporation and combustion rate of particles which cause to increase outlet temperature. Increasing particle injection velocity causes to decrease burning rate of particle, because some particles leave out the combustor before evaporation and burning.