Operational Electricity Dispatch Based on Direct Normal Irradiance (DNI) and Load Forecasting: Case Study: STTP with TES system

Abstract

The rise in the use of concentrated solar power (CSP) systems has drawn attention to the fluctuations that affect the grid due to the variability nature of solar resources especially Direct Normal Irradiance (DNI), the main component of these systems. These variations can cause serious damages to the grid causing sometimes black-outs. In terms of technical operations, the chaotic behavior of DNI makes the dispatch in many cases impossible to provide electricity for the right place at the right time when the need is there. This study proposes an operational method to dispatch electricity based on DNI and load forecasting using statistical and machine learning techniques combined with the dispatch tool provided in SAM (System Advisor Model) software. The case study explored is the Solar Tower with the molten salt combined with Thermal Energy Storage (TES) system used in NOOR 3 Ouarzazate, Morocco. The aim of the proposed method is to reduce the effect of fluctuations on the electrical grid by anticipating them and providing an easier and more accurate operational way of scheduling and making dispatch decisions. The results show a considerable increase in the performance of the simulated grid due to the new proposed dispatch method and the machine learning techniques used.