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A multi-objective optimal phasor measurement unit placement model using integer linear programming is presented in this article. The proposed model simultaneously optimizes two objectives, i.e., minimization of phasor measurement unit numbers and maximization of measurement redundancy. To calculate the redundancy criteria, the single-line outage and the phasor measurement unit loss are considered simultaneously. A linear formulation is presented for both objective functions. Also herein, to address conflicting attributes and identify Pareto optimal solutions of the multi-objective optimal phasor measurement unit placement problem, a new multi-objective mathematical programming method is proposed. Finally, a new index, i.e., minimum distance to utopia point, is implemented to select the most preferred solution among the available Pareto front based options on the goal to achieve judicious decision makers. Two test systems, i.e., a modified 9-bus and an IEEE 118-bus test systems, are used to illustr...

The aim of this paper is to select the optimal size, location, and parameter setting of static var compensator (SVC) to improve power system stability in long transmission lines. Two objectives are considered in this paper to improve both the transient stability of the system and the SVC investment cost. In this study, the actual model of the long transmission line is considered to accurately determine the SVC location. By using the actual model of the line for long transmission lines, the SVC investment cost is reduced and the results have more accuracy than that found when the simplified model was used. In addition to placement of SVC for improving the system transient stability, an extra controller is used and adjusted to enhance the performance of the SVC. The problem is formulated as a form of multi-objective function and then multi-objective particle swarm optimization (MOPSO) is used to solve the problem and find the solution. In this research, results are presented as a set of responses to...

This article extends a hybrid evolutionary algorithm to cope with the feeder reconfiguration problem in distribution networks. The proposed method combines the Self-Adaptive Modified Particle Swarm Optimization (SAMPSO) with Modified Shuffled Frog Leaping Algorithm (MSFLA) to proceed toward the global solution. As with other population-based algorithms, PSO has parameters which should be tuned to have a suitable performance. Thus, a self-adaptive framework is proposed to adjust the parameters dynamically. In SAMPSO, the PSO learning factors are considered to be the new control variables and are changed in the evolutionary process. To enhance the quality of the solutions, the SAMPSO is combined with MSFLA and a new hybrid algorithm is proposed to minimize the electrical energy losses of the distribution system by feeder reconfiguration. The effectiveness of the proposed method is demonstrated through two test systems.

This paper proposes a model of security-constrained joint market clearing of energy and reserves (AGC, spinning, non-spinning and operating reserves) auctions. The model takes into consideration lost opportunity cost (LOC) and multi-block offers in the market clearing framework. Furthermore, this paper presents typical market clearing and dispatch mechanisms based on the security-constrained optimal power flow (OPF) technique that is currently being utilised in various independent system operators (ISOs) throughout the world. The proposed model has been applied to the IEEE 24-bus Reliability Test System and detailed simulation studies have been carried out to examine the performance of the proposed scheme.