Читать книгу Mathematical Programming for Power Systems Operation - Alejandro Garcés Ruiz - Страница 26

A receding horizon control can implement most of the optimization algorithms presented in this book. Figure 1.7 depicts the main architecture for this simple but powerful strategy, for real-time implementation of operation models. These optimization models may be the optimal power flow, economic dispatch, energy storage management, or a mixed model that includes multiple models. An unbiased forecast predicts variables such as wind speed, solar radiation, and power demand. Moreover, a state estimator gives accurate measurements of the system variables.

Figure 1.7 A possible architecture for implementing an optimization model for power systems operation.

(Equation 1.10) represents the optimization model, where x is the vector of decision variables for each time t, and α are the parameters predicted by the forecast module. Of course, this forecast may change since renewable resources and loads may be highly variable in modern power systems. Therefore, the optimization model must be continuously executed and the solution updated.

(1.10)

In many cases, the forecast has an error that introduces uncertainty in the model. Either stochastic or robust optimization is a suitable option to face this uncertainty. Chapter 6 presents the latter option.