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The decreasing cost of energy storage technologies and the increasing utilization of renewable energy sources have made distributed transmission-scale energy storage economically viable. Merchant energy storage investors can collect significant profits through spatiotemporal energy arbitrage from congested transmission lines. This paper formulates a three-stage, four-level min–min–max–min problem from the viewpoint of power system operators and planners such that they will build new transmission lines and identify potential locations for profitable merchant energy storage investments. The first level determines transmission expansion decisions from the system operator's perspective. The second level plans potential investments in merchant energy storage to ensure profitability at a desired rate of return. The third level realizes the worst-case situations of long-term uncertainties related to the expansion of wind farms and peak load growth in each regional zone, while the fourth level simulates market clearing for representative days, modeling short-term uncertainties. A novel iterative nested column and constraint generation (C&CG) technique is adopted to cope with numerical issues caused by binary variables of transmission lines and storage blocks and the enormous size of the problem. The case study of a modified IEEE 24-bus test system was used to assess the effectiveness of the proposed algorithm. • Proposing a new robust co-planning of transmission expansion and profit-driven merchant energy storage. • Considering short-term variability and long-term uncertainty of wind farm expansion and future load growth. • Adopting a novel iterative nested column and constraint generation (C&CG) technique to solve the four-level problem. • Validating the effectiveness of the proposed method and sensitivity analysis in 24-bus IEEE test system. [ABSTRACT FROM AUTHOR]

Abstract: The main goal of this paper is the determination of the effect of the metal oxide varistor (MOV) on various ferroresonance modes including fundamental resonance, subharmonic and chaos mode which are generated in electrical power systems. Chaos theory is used for analyzing this effect. Also, the bifurcation, phase plan diagram and time domain simulation are used for this purpose. The proposed power system contains a no-load or lightly loaded power transformer. The magnetization curve of the transformer core is modeled by a single-value two-term polynomial. The core loss is modeled based on the flux of the transformer. The MOV modeled as a nonlinear voltage dependent resistance. The suppression effect of MOV on chaotic ferroresonance in power transformer is studied in this paper. The simulation results confirm that connecting the MOV to the transformer has a considerable suppression effect on ferroresonance phenomena. [Copyright &y& Elsevier]

Abstract: Based on the concept of the external power system dynamic equivalent obtained for the study system, in this paper a reduced-order artificial neural network is proposed, to construct a model for the external part. The mastermind behind the proposed method is to identify the external part as a dynamic–algebraic ANN, and this separation between dynamic equations in the state space and algebraic equations is useful to solve the prediction problem. Moreover, using similarity transformations, the state space model can be simplified, such that all the nonlinearities are embedded in the algebraic part. Since usually the study system equations are available in the continuous time domain, the external part is converted to the continuous time domain by a novel method. To obtain this model, the system should be excited first by a sort of random disturbances, and then data measured on the boundary nodes is used to identify the model. Identification process is accomplished by training the proposed network which can be used to predict behavior of the external system with a high degree of accuracy. Such an equivalent has wide applications for dynamic stability studies. [Copyright &y& Elsevier]