No abstract

The depletion of fossil fuel reserves, emission of greenhouse gases and the uneven distribution of existing reserves led the countries to look for sustainable alternatives, especially wind power. In India mostly Squirrel cage induction generators (SCIG) are used for extracting energy from the wind. Induction generators inject real power to the grid and absorb reactive power from grid. Normally, fixed capacitors of rating equal to no-load compensation are installed at the wind-turbine. Reactive power absorbed by the SCIG over and above the no-lad compensation is dependent on the operating condition. To compensate for the reactive power (over and above the no-load compensation) dynamic VAR compensator can be installed at the point of common coupling. When the wind-farm is connected to a weak grid there may be a problem with wind penetration into the grid. UPFC (a versatile FACTS controller) will be able to alleviate the problems associated with fixed speed wind-farms that are connected to a weak grid. In this paper finding the location and capacity of the UPFC for minimisation of power generation cost is posed as a non-linear optimization problem. An efficient Primal-Dual Interior Point algorithm in conjunction with second order sensitivity analysis is made use for solving the above problem. The optimal line placement for wind penetration in terms of marginal values of UPFC variables are identified using first order sensitivity analysis. Second order sensitivity analysis has been employed to identify the optimal line placement for highest cost savings. Further actual cost savings and optimal control settings of UPFC are evaluated by actually placing UPFC in each line. The proposed approach is tested on a sample 9-bus system using the program developed in Matlab and the results are encouraging. The results indicate that the estimation of optimal placement of UPFC for a large system is possible reducing the computation time involved.

This paper discusses the application of covariance matrix adapted evolution strategy (CMAES) algorithm on wind energy conversion systems. CMAES is a class of continuous evolutionary algorithm that generates new population members by sampling from a probability distribution that is constructed during the optimization process. Modified IEEE 14 bus system is considered for simulation purpose. The critical evaluation of maximum loadability of the system is determined. Statistical performance of CMAES algorithm reveals that the best value of maximum loadability is obtained when compared to primal dual interior point method. Even though CMAES takes higher computation time, this method gives the best loadability margin. V C 2014 AIP Publishing LLC.

This paper discusses application of Covariance Matrix Adapted Evolution Strategy (CMAES) algorithm for maximizing loadability margin of power system. CMAES is a class of continuous evolutionary algorithm that generates new population members by sampling from a probability distribution that is constructed during the optimization process. IEEE 14 bus , 30 bus and 118 bus systems are considered for simulation purpose. For comparison of the results, primal dual interior point (PDIP), continuation power flow (CPF), Particle swarm optimization algorithms are considered. Statistical performance of CMAES algorithm reveals that even the mean value of maximum loadability is better than maximum loadability obtained in other methods. Even though CMAES takes higher computation time due to the determination of covariance matrix, only this algorithm gives maximum loadability margin.

The optimal reactive power flow (ORPF) helps to effectively utilize the existing reactive power sources for minimizing the network loss. The chemical reaction optimization (CRO), inspired from the interactions of molecules in a chemical reaction to reach a low energy stable state and searches for optimal solution through reactions involving the on-wall ineffective collisions, decomposition, inter-molecular ineffective collision and synthesis. This paper attempts to obtain global best solution of ORPF using CRO. The results of IEEE 30 bus system are presented to demonstrate its performance.