Showing 4 results for Esmaili
M. Esmaili, H. A Shayanfar, N. Amjady,
Volume 6, Issue 1 (March 2010)
Abstract
Congestion management in electricity markets is traditionally done using deterministic values of power system parameters considering a fixed network configuration. In this paper, a stochastic programming framework is proposed for congestion management considering the power system uncertainties. The uncertainty sources that are modeled in the proposed stochastic framework consist of contingencies of generating units and branches as well as load forecast errors. The Forced Outage Rate of equipment and the normal distribution function to model load forecast errors are employed in the stochastic programming. Using the roulette wheel mechanism and Monte-Carlo analysis, possible scenarios of power system operating states are generated and a probability is assigned to each scenario. Scenario reduction is adopted as a tradeoff between computation time and solution accuracy. After scenario reduction, stochastic congestion management solution is extracted by aggregation of solutions obtained from feasible scenarios. Congestion management using the proposed stochastic framework provides a more realistic solution compared with the deterministic solution by a reasonable uncertainty cost. Results of testing the proposed stochastic congestion management on the 24-bus reliability test system indicate the efficiency of the proposed framework.
M. Moravvej-Farshi, F. Esmailifard, K. Saghafi,
Volume 7, Issue 1 (March 2011)
Abstract
We present an optimized design for GaAs/AlGaAs quantum cascade lasers operating at 4.1THz. This was based on a three-well active module with diagonal radiative transition. This was performed by modifying the existing model structure, to reduce the parasitic anticrossings (leakage currents) as well as the optical gain linewidth. While the gain FWHM was reduced by more than 50% the gain peak was increased by about 23.3%.
M. Esmaili, H. A. Shayanfar, K. Gharani,
Volume 10, Issue 4 (December 2014)
Abstract
Phasor Measurement Units (PMUs) are in growing attention in recent power systems because of their paramount abilities in state estimation. PMUs are placed in existing power systems where there are already installed conventional measurements, which can be helpful if they are considered in PMU optimal placement. In this paper, a method is proposed for optimal placement of PMUs incorporating conventional measurements of zero injection buses and branch flow measurements using a permutation matrix. Furthermore, the effect of single branch outage and single PMU failure is included in the proposed method. When a branch with a flow measurement goes out, the network loses one observability path (the branch) and one conventional measurement (the flow measurement). The permutation matrix proposed here is able to model the outage of a branch equipped with a flow measurement or connected to a zero injection bus. Also, measurement redundancy, and consequently measurement reliability, is enhanced without increasing the number of PMUs this implies a more efficient usage of PMUs than previous methods. The PMU placement problem is formulated as a mixed-integer linear programming that results in the global optimal solution. Results obtained from testing the proposed method on four well-known test systems in diverse situations confirm its efficiency.
M. Sedighizadeh, M. Esmaili, M. M. Mahmoodi,
Volume 13, Issue 3 (September 2017)
Abstract
Distribution systems can be operated in multiple configurations since they are possible combinations of radial and loop feeders. Each configuration leads to its own power losses and reliability level of supplying electric energy to customers. In order to obtain the optimal configuration of power networks, their reconfiguration is formulated as a complex optimization problem with different objective functions and network operating constraints. In this paper, a multi-objective framework is proposed for optimal network reconfiguration with objective functions of minimization of power losses, System Average Interruption Frequency Index (SAIFI), System Average Interruption Duration Index (SAIDI), Average Energy Not Supplied (AENS), and Average Service Unavailability Index (ASUI). The optimization problem is solved by the Imperialist Competitive Algorithm (ICA) as one of the most modern heuristic tools. Since objective functions have different scales, a fuzzy membership is utilized here to transform objective functions into a same scale and then to determine the satisfaction level of the afforded solution using the fuzzy fitness. The efficiency of the proposed method is confirmed by testing it on 32-bus and 69-bus distribution test systems. Simulation results demonstrate that the proposed method not only presents intensified exploration ability but also has a better converge rate compared with previous methods.