Figure 1: Proposed system for Econo-Environmental Dispatch (SEED).
Under this project, we are developing an Econo-Environmental dispatch system for power grids with substantial renewable penetration. The proposed system predicts the fluctuations in the demand as well as generation through machine learning. The grid system is also modeled for its capabilities and constraints. Then the generation is optimally matched with the demand for minimum cost of energy as well as the lowest level of emissions.
The temporal variations in the output from potential renewable sources like solar and wind are considered in the generation side. Ramping behavior of these renewable systems are modeled based on the long term performance data using Artificial Intelligence techniques. These models can give an insight to the contributions that can be expected from renewable generation in different time scales. To predict the load in a given time, a load forecasting system based on the novel “Environmental Sensitivity Index (ESI) approach has been developed. Once the load and generation requirements are quantified, the optimal dispatch solution could be suggested by the optimization engine.