Economic and environmental load dispatching framework using particle filtering

Efficient and reliable electricity plays a vital role in today's society affecting a broad range of aspects of everyday life. Electricity networks need robust decision making mechanisms that enable the system to respond swiftly and effectively to any type of disruption or anomaly in order to ensure reliable electricity flow. Economic load dispatch (ELD) is the operation of generation plants producing reliable electricity at the lowest cost, while recognizing limitations of the system. The environmental economic load dispatch (EELD) problem extends the ELD to include environmental considerations which makes it even more challenging. In this study, we propose a novel two-stage economic and environmental load dispatching framework using particle filtering for the efficient and reliable dynamic dispatching of electricity under uncertainty. The proposed framework includes 1) a short term demand forecasting algorithm using wavelet transform adaptive modeling, and 2) a dynamic load dispatching algorithm using particle filtering developed in a simulation environment. The proposed approach has been successfully demonstrated for different scenarios in the original IEEE-30 bus test system, which has been benchmarked against the literature; and a modified version of the IEEE-30 bus test system, where the loads are updated according to the weather and other external conditions.