Determining placement and capacity of reactive-power compensators in distribution networks is an optimization problem with various objectives and technical constraints. The large number of variables and constraints in this problem can be an obstacle for the success of classical and meta-heuristic optimization methods. Therefore, having a guess of the suitable buses for installing compensators as an initial solution, improves performance of the mentioned methods. Using the network impedance matrix, an initial guess is obtained in this paper in the form of suitable or higher-priority buses. The applied matrix is a revised version in which loads connected to the buses are taken into account. The reasons for considering the loads are that, firstly, the list of bus-priorities becomes more consistent with network load pattern, and secondly, difficulties in forming the impedance matrix in radial distribution networks are obviated. Comparison of the list of suitable buses for installing the compensator obtained by the proposed method with the list of buses extracted from the sensitivity analysis indicates the effectiveness of the former method. پرونده مقاله

The emergency demand response program (EDRP) is a type of program that can be utilized as a tool for controlling the price of electricity when there is a lack of reliability in the distribution system. In this study, a formulation is proposed for determining the optimum amount of demands in the EDRP according to the viewpoints of the regional market manager (RMM) aimed at reducing the EDRP costs and smoothening the load curve based on the logarithmic model and the matrix of demand elasticity. The probability that the aggregators should present their available reserves to the RMM in response to the received incentives has also been included in the proposed formulations. The market manager then prioritizes the available reserves using the reserve-margin factor (RMF). Three algorithms including co-evolutionary particle swarm optimization (C-PSO), co-evolutionary teaching learning-based optimization (C-TLBO) and co-evolutionary improved teaching learning-based optimization (C-ITLBO) are used for reducing the EDRP costs. The results show that the proposed formulations are effective in improving the economic performance of the regional market and the load curve. Furthermore, the results indicate the superiority of the C-ITLBO algorithm in terms of the total cost, incentive rate and peak shaving in comparison with C-PSO and C-TLBO algorithms. پرونده مقاله