Smart Grid Operation and Control
Unit Commitment and Economic Dispatch of Renewable Generation and Storage in Electric Grid
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Coordination and control of power generation sources are essential tasks of modern smart grids. With the increased penetration of power generation using renewable sources such as wind and solar, the complexity of the unit commitment and economic dispatch problems increases due to the stochastic nature of renewable power. A forecasting model is built to simulate the stochastic nature of renewable sources. A hybrid Markov method is proposed to forecast solar radiation, and an improved autoregressive integrated moving average (ARIMA) model is proposed to predict wind speed. For high penetration o...
Coordination and control of power generation sources are essential tasks of modern smart grids. With the increased penetration of power generation using renewable sources such as wind and solar, the complexity of the unit commitment and economic dispatch problems increases due to the stochastic nature of renewable power. A forecasting model is built to simulate the stochastic nature of renewable sources. A hybrid Markov method is proposed to forecast solar radiation, and an improved autoregressive integrated moving average (ARIMA) model is proposed to predict wind speed. For high penetration of renewable energy, storage media must be used to compensate for the fluctuation of natural sources. Nonlinear model predictive control (NMPC) is introduced to solve the power system ED problems with the presence of renewable energy resources. The work presents a novel mathematical formula for NMPC, integrated with a swarm optimization technique to describe the nonlinear behavior of the problem. This new formulation is called swarm model predictive control. The control model will be able to address the effect of the system disturbances and fluctuations, using a controlled ARIMA.
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