A Probabilistic Production Simulation Approach for Systems with Integrated Concentrated Solar Plants with Thermal Energy Storage by Ti Xu Thesis

The global awareness of the impacts of climate change is a key driver of the quick pace of development of renewable energy technologies. The concentrated solar plant (CSP) technology has emerged as a promising approach to harness solar energy, with several implementations under way around the world. Unlike PV and wind resources, a CSP allows the deployment of the thermal energy storage (TES), which provides the CSP operator the flexibility to produce electricity beyond the sunrise-to-sunset periods. For a system with integrated CSPs at distinct locations on its footprint, the effective utilization of the TES devices requires a scheduler to optimize the value of the total CSP-produced energy for the system. However, the assessment of impacts of CSP resources poses major challenges due to the inherent uncertainty, variability and intermittency of direct normal irradiation (DNI), which markedly influence the times and the quantities of total CSP energy production. The geographic correlations among the multi-site DNI and its intrinsic seasonality further complicate the effective quantification of the multi-site CSP variable effects in power systems into which they are integrated. Thus, the assessment of CSPs sets up an acute need for a practical simulation approach to emulate operations of the systems with integrated CSP resources and to evaluate their variable impacts. Such an approach must explicitly represent the uncertainty, variability and intermittency of the CSP resources, the geographic correlation among them, as well as the flexibility imparted by TES devices. The approach also needs to take into account the seasonality of the CSP resources and their interactions with the load seasonal changes. To address these needs, we construct the multi-site CSP power output model and formulate the associated scheduling problem (SP) under some specific TES operational iii objective in a system with integrated multi-site CSP resources. The power outputs of the multi-site CSPs depend not only on the specific details of the CSP configurations and the operational schedule, but also on the nature of the solar energy input. The identification of distinct multi-site DNI data in a given season is a key step to obtain the analytic representation of the multi-site CSP power outputs. We use statistical clustering techniques to classify the distinct data into various groups – referred to as regimes – and utilize the power output model to probabilistically characterize the multi-site CSP power outputs based on the identified DNI regimes. We make detailed use of the conditional probability concepts …