Majlesi Journal of Energy Management
Volume:3 Issue: 3, Sep 2014

Significant promise for diversifying energy resources and achieving more distributed energy generation, interests in using Microgirds (MG) to coordination of local generation and consumption is increased more and more. This paper to facilitate power exchanges between isolated generation supplies in MGs by using demand response (DR) and energy storage (ES), a modified conventional energy management system (MCEMS) based on local energy market (LEM) (MCEMS) is represented to facilitate power exchanges between distributed generation (DG) in MGs. The main objective of the proposed algorithm is to minimize the total generation cost by using various model of load consumption, to maximize utilization of available energy in DGs, to optimize the usage of energy storage (ES) and demand response (DR). The obtained results demonstrate the high efficiency of the proposed EMS combined with ES and DR compared to the proposed one in the literature for reduction of load demand during peak hours and reduction of the penalty cost due to undelivered power.

Economic load dispatch (ELD) have been applied to obtain optimal fuel cost of generating units. Genetic Algorithm (GA) is a global search technique based on principles inspired from the genetic and evolution mechanism observed in natural biological systems. This paper presents a novel stochastic Genetic Algorithm approach to solve the Economic Load Dispatch problem considering various generator constraints and also conserves an acceptable system performance in terms of limits on generator real and reactive power outputs bus voltages, shunt capacitors/reactors, transformers tap-setting and power flow of transmission lines. The ELD problem in a power system is to determine the optimal combination of power outputs for all generating units which will minimize the total fuel cost while satisfying all practical constraints. Simulation results on the IEEE 30-bus electrical network with the proposed GA is applied to types of ELD problems with non-smooth cost functions. The experimental results show that the proposed GA approach is comparatively capable of obtaining higher quality solution.

In this paper, an economic dataset is proposed for organization of petroleum exporting countries (OPEC) oil studies. The proposed dataset can be used for estimation tasks of various OPEC oil functions such as price function prediction, demand function estimation etc. The economic parameters of the proposed dataset include inflation rate, interest rate, OPEC oil production level, silver price, gold price, market index and U.S. dollar index. Forecasting OPEC oil functions is vital for decision making in energy management. Hence the proposed dataset can be applied in various energy management-based application domains. In Experimental Results Section, we apply artificial neural networks (ANNs) for oil price prediction. The numerical results show the proposed economic parameters can be effectively used for data-mining in OPEC oil parameters.

This paper proposes new guaranteed convergence based particle swarm optimization (GCPSO) algorithm for solving optimal power flows with security constraints(line flows and bus voltages). This algorithm deals with the situation of non convergence due to very small velocities that occur in a variant inertia weight based PSO [1].By using a new updating formula based on number of successes and failures, this new guaranteed convergence based PSO has been developed. Basically, this algorithm follows the same principle of PSO, but the new updation will take place when the velocities become very smaller. In this paper, Variable constriction factor has been considered for GCPSO. The OPF problem has been considered with Quadratic cost function to realize Optimal Power Flow using GCPSO applied to IEEE 30 and IEEE 14 bus systems. Simulation results show that proposed approaches gives better solution than earlier reported approaches with guaranteed convergence with little sacrifice of speed.

The reason that cogeneration is being used more compared to separate heat and power is because it is more efficient. In this paper the goal is finding the optimized CHP system utility size and thermal storage considering reliability limits of boiler and grid connected bus. Loss of Load Expectation (LOLE) and Expected energy not supplied (EENS) are considered as two reliability indices to insure the security of operation. non-sequentional Monte carlo simulation method is introduced to the reliability assessment of CHP,and a normal distribution electrical load model is built to simulate the hourly electrical load.CHP model combined with a two-state reliability model is applied to monte carlo simulation method, and results show that the CHP reliability model works well with non-sequential monte carlo simulation. Non-Sequential Monte Carlo method is used to generate scenarios. Also in order to reduce computation time and due to the large number of scenarios, a scenario reduction technique is used. GAMS software is used for optimization object.

Due to the increasing energy demand in all over the world and decreasing fossil resources, renewable energy is more biased. The share of wind energy is undeniable in renewable energies market since in recent decades it has the highest development rate among the other sustainable energies. Wind turbines are systems that harness the kinetic energy of the wind for useful power. Design and optimization of wind systems requires aerodynamics analysis. Computational fluid dynamics is a guaranteed method for aerodynamics analysis of wind turbines. In this study two dimensional horizontal axis wind turbine blade profiles flow are simulated and aerodynamic loads are estimated using CFD methods. Spalart-Allmaras turbulence model is used. Thereafter, wind turbine`s Coefficient of Performance is calculated using aerodynamic loads. In this case, two types of commonly used profiles in the manufacture of wind turbine blades are used and the results obtained are compared with each other.