This paper is concerned about the optimal placement of phasor measurement units (PMUs) so as to make a system completely observable. Observability assessment is done by the aid of the topological observability rules. Moreover a new rule is added which can decrease the number of required PMUs for complete system observability. A modified binary particle swarm is used as an optimization tool for obtaining the minimal number of PMUs and corresponding configuration. In order to improve the speed of convergence, an initial PMU placement is provided by graph-theoretic procedure. The simulation results of proposed approach are presented for several IEEE test systems. Index Terms-Observability analysis, optimal placement, particle swarm optimization, phasor measurement unit.

Multicarrier energy systems have provided the opportunity for users who are experiencing only must-run loads to participate in demand response programs by using self-generation units. In this paper, a bilevel game between so-called energy retailers and consumers with firm loads (as subsystems) is formulated in a multicarrier energy system. In the first level of this game, energy retailers offer selling prices for different energy carriers to maximize their profit. Consequently, consumers react to the offered prices by managing their purchasing power from the retail market to minimize their energy bill in the second level. The proposed approach leads to a mixed integer nonlinear programming problem which is sophisticated to solve. To simplify this complex problem, a solution method based on discretization technique is introduced to approach to an epsilon Nash point. The better performance of the proposed energy system model is shown in comparison with the base case and its effectiveness is validated experimentally over a practical benchmark system. The simulation results show a noticeable reduction in the energy production cost and users’ energy bill. Moreover, energy retailers gain more profit in the proposed approach.

In the restructured power markets, price of electricity has been the key of all activities in the power market. Accurately and efficiently forecasting electricity price becomes more and more important. Therefore in this paper, an artificial neural network (ANN) model is designed for short term price forecasting of electricity in the environment of restructured power market. The proposed ANN model is a four-layered perceptron neural network, which consists of, input layer, two hidden layers and output layer. Instead of conventional back propagation (BP) method, Levenberg-Marquardt BP (LMBP) method has been used for the ANN training to increase the speed of convergence. Matlab is used for training the proposed ANN model, also it is performed on the Ontario electricity market to illustrate its high capability and performance.

This article proposes a bang–bang auxiliary frequency control scheme for doubly-fed induction generator (DFIG)-based wind turbines. It results in the DFIGs emulate the primary frequency control of conventional units and, thus, increases the power system frequency stability. Indeed, the suggested hysteresis controller causes to deliver the maximum extractable kinetic energy of DFIGs to the grid and consequently further enhance the power system frequency. This is accomplished using different frequency deviation indices, which determine the size of power mismatch and lead to an appropriate frequency response by the DFIGs. The operational constraints of the DFIGs are taken into account in the design procedure of the controller. The proposed controller implies negligible implementation cost and its parameters must be chosen considering a compromise between the amount of DFIGs’ provided power during the frequency support period and the subsequent wear and tear due to the frequency response. Extensive time domain simulations on an IEEE 9-bus test system verify the efficiency of proposed controller.

In this paper, security constrained reserve management (SCRM) problem is formulated. In this trend, security constrained economic dispatch as well as operating reserve in both normal and ${N-1}$ contingency condition are considered. To address the computational complexity associated with the SCRM problem, the distributed-SCRM (D-SCRM) is proposed. In the suggested method, the power system is decomposed to several subsystems called zone. These zones are interconnected via some tie-lines. By defining two virtual generation units at the middle of each tie-line, all neighbor zones become isolated. This causes the main SCRM problem to be converted to some subproblems. To solve these subproblems, some initial values are first dedicated to the virtual generation units. Next, the independent SCRM subproblems are solved simultaneously using the genetic algorithm (GA). The objective function is then calculated for the main problem and the virtual values are updated using the GA again. This process continues until the global optimum is obtained. IEEE 24-bus, 39-bus, and 118-bus test systems are considered to evaluate the proposed algorithm. Results confirm the effectiveness of proposed algorithm.