Spatial bayesian approach for socio-economic assessment of pumped hydro storage

Abstract

Pumped hydro energy storage (PHES) is a key enabler for transitioning to 100 % renewable energy sources. However, PHES site selection is multi-faceted and challenging, including from a socio-economic perspective, due to complex economic issues and the heterogeneity of social factors. To overcome this challenge, this study developed a spatial probabilistic decision-making approach to identify and rank the best PHES sites from a shortlisted number of sites determined from a previous techno-environmental assessment. This model was developed in a participatory environment, where both study evidence and experts’ judgements were utilised to determine the key socio-economic factors, develop the Bayesian Network (BN), and populate the conditional probability tables (CPT). The BN parent node data were retrieved from publicly available sources in a high-resolution spatial and statistical format. An ArcGIS 10.3 tool was utilised to process the data with spatial characteristics, whereas GeNIe 2.5 was used to develop the BN model. Forward propagation, sensitivity, and strength analysis were performed to validate the developed BN model. Application of the spatial-BN model to northern Queensland, Australia reduced the fourteen previously determined techno-environmentally suitable PHES sites, to nine sites that are suitable from a socio-economic perspective for future developments. These nine sites could store and generate over 323 TWh of electricity over their life expectancy and at a levelised cost of 0.040–0.274 AU$/kWh. The developed procedure and model streamline the PHES site selection pre-feasibility process, thereby expediting renewable energy transition.