Probabilistic unit commitment in multi-area grids with high renewable energy penetration by using dynamic programming based on Neural Network

Nowadays, economic and environmental requirements are increased. Therefore, this paper presents a proposed solution of the unit commitment problem for a multi-area grid, which contains conventional and renewable energy sources and storage units. To ensure optimum and economic operation with the stochastic nature sources, it is essential to develop an efficient forecasting model for renewable power generation. Forecasting model was built by using a hybrid Markov to forecast solar power, while, auto regressive integrated moving average model is used to predict wind power. Unit commitment problem incorporate with forecasting model to develop probabilistic unit commitment. The proposed formulation is subject to multi-constraints. To overcome the variation and error of renewable power forecasting, the reserve coefficient is modified to develop two new reserves; up reserve and down reserve. The optimization algorithm used to solve probabilistic unit commitment is Dynamic programming based on Neural Network. The system under study in this paper is a standard IEEE 30, with wind speed and solar radiation data are based on the available data of the city of Florida in USA.