Department of Electrical Engineering
University of Colorado Denver
Currently in Energy Management Systems (EMS), the State Estimation (SE) and Optimal Power Flow (OPF) are solved separately by applying the separation principle. This paper proposes solving the SE an OPF concurrently, thus solving a non-liner, convex problem. Solving a nonlinear problem is more accurate, thus leading to a better control action. In addition, the preview capabilities of the modified State Estimation helps in obtaining a good control action.
The maximum power and power dispatch of the distributed generator is not known, thus, the generation dispatch power is computed from the OPF using historical data. This dispatch is related to the maximum power generation available thus a data-driven deterministic model is solved. Using an OPF eliminates the need to use an autoregressive method to determine the apparent power.
SE is used to assign a value to an unknown system state variable based on measurements from that system according to some criteria. SE in Power Systems can be improved by considering measurement forecasts. These measurement forecasts are obtained using the Extended Kalman Filter (EKF) approach for nonlinear state estimation. OPF is used in EMS to obtain the generation dispatch that represents the minimum ($/hr) total generation cost of all generators taking into account the total losses of the power system.
In the proposed model, an input to nonlinear dynamic state model which incorporates power flow equations is first developed. Then, using an EKF approach for nonlinear state estimation with the Holt method of exponential smoothing, the time series of the system is forecasted. Finally, a nonlinear model predictive control (NMPC) based voltage/VAR support strategy is developed for the multi-period OPF. The objective of the control problem is to minimize the aggregate reactive power injected by DG subject to the following constraints 1) voltage regulation, 2) phase imbalance correction 3) maximum and minimum reactive power injection by individual generators. The forecast aided state estimation and NMPC problem presented in this paper is validated using a six--bus system.
Gadi Ogbogu has a Bachelor of Science degree in Electrical Engineering from The University of Texas at Austin and a Master of Science degree in Petroleum Engineering from the University of Oklahoma, Norman. Gadi currently teaches Industrial Electronics and Programmable Logic Controllers at Metropolitan State University. In addition, Gadi teaches Power Systems Laboratory at The University of Colorado, Denver. Gadi is a PhD candidate and is currently conducting research in Power Systems under Prof. Fernando Mancilla-David.
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