A production simulation tool for systems with an integrated concentrated solar plant with thermal energy storage

The global awareness of the impacts of climate change is a key driver of the growing interest in effectively harnessing renewable energy resources. The concentrated solar plant (CSP) technology has emerged as a promising approach to harness solar energy, with several implement-tations under way around the world. CSP is, typically, coupled with the deployment of thermal energy storage (TES) so as to provide the CSP operator the flexibility to produce electricity beyond the sunrise-to-sunset period. Indeed, the effective utilization of TES requires a scheduler to optimize the value of the CSP-produced energy. The assessment of CSP sets up an acute need for a practical simulation tool to study the effects of the integrated CSP with TES on the systems. Such a tool must explicitly represent the uncertainty, variability and intermittency of the solar resource and its interactions with the loads and other resources. We report on the development of a probabilistic simulation tool aimed at addressing these needs. The identification of distinct direct normal irradiation (DNI) patterns is an important step in the extension of the conventional probabilistic simulation approach to the systems with the integrated CSP resource. We use clustering techniques to identify the various pattern groups - referred to as regimes - and construct the CSP power output model based on the identified regimes. We make detailed use of conditional probability concepts in the incorporation of this model into the probabilistic production simulation approach. We carry out an extensive set of simulations for testing the extended approach. To illustrate the capabilities of this approach to quantify the variable effects of the systems with the integrated CSP resources over longer-term periods, we present the representative simulation results on a modified version of the IEEE Reliability Test System (RTS), with an integrated CSP resource. The load data is based from the scaled 2011 ERCOT load data. The study results provide insights into the CSP impacts on the variable effects of the systems and demonstrate the effectiveness of the extended simulation approach.