A modern power grid needs to become smarter in order to provide an affordable, reliable, and sustainable supply of electricity. For these reasons, considerable activity has been carried out in the United States and Europe to formulate and promote a vision for the development of future smart power grids. However, the majority of these activities emphasized only the distribution grid and demand side leaving the big picture of the transmission grid in the context of smart grids unclear. This paper presents a unique vision for the future of smart transmission grids in which their major features are identified. In this vision, each smart transmission grid is regarded as an integrated system that functionally consists of three interactive, smart components, i.e., smart control centers, smart transmission networks, and smart substations. The features and functions of each of the three functional components, as well as the enabling technologies to achieve these features and functions, are discussed in detail in the paper.

Open access transmission has created a deregulated power market and brought new challenges to system planning. This paper proposes a new method to compute a probabilistic load flow in extensive power systems for the purpose of using it as a quick screening tool to determine the major investment on improving transmission system inadequacy. This innovative method combines the concept of Cumulants and Gram-Charlier expansion theory to obtain probabilistic distribution functions of transmission line flows. It has significantly reduced the computational time while maintaining a high degree of accuracy. This enables probabilistic analysis of power flow problems to be treated objectively and allows quantitative assessment of system reliability.

This paper proposes a vision of next-generation monitoring, analysis, and control functions for tomorrow's smart power system control centers. The paper first reviews the present control center technology and then presents the vision of the next-generation monitoring, analysis, and control functions. The paper also identifies the technology and infrastructure gaps that must be filled, and develops a roadmap to realize the proposed vision. This smart control center vision is expected to be a critical part of the future smart transmission grid.

A new intelligent system (IS) is developed for real-time dynamic security assessment (DSA) of power systems. Taking an ensemble learning scheme, the IS structures a series of extreme learning machines (ELMs) and generalizes the randomness of single ELMs during the training. Benefiting from the unique properties of ELM and the strategically designed decision-making rules, the IS learns and works very fast and can estimate the credibility of its DSA results, allowing an accurate and reliable pre-fault DSA mechanism: credible results can be directly adopted while incredible results are decided by alternative tools such as time-domain simulation. This makes the IS promising for practical application since the potential unreliable results can be eliminated for use. Case studies considering classification and prediction are, respectively, conducted on an IEEE 50-machine system and a dynamic equivalent system of a real-world large power grid. The efficiency, robustness, accuracy, and reliability of the IS are demonstrated. In particular, it is observed that the IS could provide 100% classification accuracy and very low prediction error on its decided instances.

System restoration involves status assessment, optimization of generation capability, and load pickup. The optimization problem needs to take complex constraints into consideration, and therefore, it is not practical to formulate the problem as one single optimization problem. The other critical consideration for the development of decision support tools is its generality, i.e., the tools should be portable from a system to another with minimal customization. This paper reports a practical methodology for construction of system restoration strategies. The strategy adopted by each power system differs, depending on system characteristics and policies. A new method based on the concept of “generic restoration milestones (GRMs)” is proposed. A specific restoration strategy can be synthesized by a combination of GRMs based on the actual system conditions. The decision support tool is expected to reduce the restoration time, thereby improving system reliability. The proposed decision support tool has been validated with cases based on a simplified Western Electricity Coordinating Council (WECC) 200-Bus system and Hawaiian Electric Company system.

Parameter identification is the key technology in measurement-based load modeling. A hybrid learning algorithm is proposed to identify parameters for the aggregate load model (ZIP augmented with induction motor). The hybrid learning algorithm combines the genetic algorithm (GA) and the nonlinear Levenberg-Marquardt (L-M) algorithm. It takes advantages of the global search ability of GA and the local search ability of L-M algorithm, which is a more powerful search technique. The proposed algorithm is tested for load parameter identifications using both simulation data and field measurement data. Numerical results illustrate that the hybrid learning algorithm can improve the accuracy and reduce the computation time for load model parameter identifications.