Abstract
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. Electricity load dispatch is concerned with the production of reliable electricity at the lowest costs, both monetary and environmental, within the limitations of the considered network. In this study, we propose a novel DDDAMS-based economic load dispatching framework for the efficient and reliable real-time dispatching of electricity under uncertainty. The proposed framework includes 1) a database fed from electrical and environmental sensors of a power grid, 2) an algorithm for online state estimation of the considered electrical network using particle filtering, 3) an algorithm for effective culling and fidelity selection in simulation considering the trade-off between computational requirements, and the environmental and economic costs attained by the dispatch, and 4) data driven simulation for mimicking the system response and generating a dispatch configuration which minimizes the total operational and environmental costs of the system, without posing security risks to the energy network. Components of the proposed framework are first validated separately through synthetic experimentation, and then the entirety of the proposed approach is successfully demonstrated for different scenarios in a modified version of the IEEE-30 bus test system where sources of distributed generation have been added. The experiments reveal that the proposed work premises significant improvement in the functional performance of the electricity networks while reducing the cost of dynamic computations.
