A Unified Approach for Power System Predictive Operations Using Viterbi Algorithm

• Published in: IEEE transactions on sustainable energy Vol. 5; no. 3; pp. 757 - 766
• Main Authors: Livani, Hanif, Jafarzadeh, Saeed, Evrenosoglu, Cansin Yaman, Fadali, M. Sami
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-07-2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Alternative energy; Dynamic state estimation; Electric utilities; Forecasting; Hidden Markov models; Load forecasting; Load modeling; Markov model (MM); Power supply; Renewable resources; State estimation; state forecasting; Viterbi algorithm (VA); Wind forecasting; wind power
• ISSN: 1949-3029; 1949-3037
• DOI: 10.1109/TSTE.2014.2301915

A paradigm shift in the renewable energy proliferation in the U.S. necessitates a paradigm shift in power system operations to accommodate large-scale intermittent power while keeping the grid reliable and secure. Energy management systems (EMS) will benefit from an auxiliary function, which integrates the wind and load forecasting to state estimation and forecasting. This auxiliary function will create a predictive database for the power system states using the historical states as well as wind and load forecasts. The predictive database can be utilized to provide pseudo-measurements to a static state estimator in the case of loss of observability and bad data processing, or it can be used for short-term congestion and ramping predictions. This paper proposes an auxiliary tool for look-ahead power system state forecasting in electrical power systems with high intermittent renewable energy penetration. The method utilizes Markov models (MMs) and the Viterbi algorithm (VA) with a grid of feasible power system states obtained and updated by using the past states. The proposed algorithm is evaluated on the IEEE 14-bus and 118-bus systems using wind and load data available from the Bonneville Power Administration (BPA). The results show good correlation between the predictions and the actual power system states.