Summary
Situation awareness plays a main role in the security monitoring and control of power system operation. Previous approaches for enhancement of situation awareness mainly focus on state estimation, security analysis, and visual perception. As an effective tool to automatically identify the trend features of a continuously changing process, the trend analysis technique may be used to enhance the situation awareness of the power system. This paper proposes an enhanced framework of situation awareness by means of the qualitative trend analysis. The enhanced framework consists of 3 levels: the perception of information, the security assessment of the current state, as well as the prediction of the future system states and their trends. Based on the perceived information, the N‐1 steady‐state security distance model is introduced to assess the security levels of both the current and future system states. With the combination of the security distance model and the qualitative trend analysis, a trend‐based approach is derived to capture trend features—the moving direction of the operating points and the changing rate of tendency—of future changing states automatically. Application results of IEEE 118‐bus system and a practical power system show that the trend‐based approach could be effectively used to assist system operators in monitoring, predicting, and preventing potential problems.
This paper proposes an efficient power management approach for the 24 h-ahead optimal maneuver of Mega–scale grid–connected microgrids containing a huge penetration of wind power, dispatchable distributed generation (diesel generator), energy storage system and local loads. The proposed energy management optimization objective aims to minimize the microgrid expenditure for fuel, operation and maintenance and main grid power import. It also aims to maximize the microgrid revenue by exporting energy to the upstream utility grid. The optimization model considers the uncertainties of the wind energy and power consumptions in the microgrids, and appropriate forecasting techniques are implemented to handle the uncertainties. The optimization model is formulated for a day-ahead optimization timeline with one-hour time steps, and it is solved using the ant colony optimization (ACO)-based metaheuristic approach. Actual data and parameters obtained from a practical microgrid platform in Atlanta, GA, USA are employed to formulate and validate the proposed energy management approach. Several simulations considering various operational scenarios are achieved to reveal the efficacy of the devised methodology. The obtained findings show the efficacy of the devised approach in various operational cases of the microgrids. To further confirm the efficacy of the devised approach, the achieved findings are compared to a pattern search (PS) optimization-based energy management approach and demonstrate outperformed performances with respect to solution optimality and computing time.
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