Long-term solar PV planning: An economic-driven robust optimization approach

Abstract

Renewable energy represents an excellent opportunity for improving the living standard of communities. This is even more important in high-density urban systems, where space becomes a key resource and a growing population drives the demand to levels that traditional electricity sources can hardly manage. In this context, the presence of multiple stakeholders with competing objectives needs to be taken into account for long-term planning. Moreover, as the decisions of adopting renewable energy options may be driven by economic considerations and risk-aversion of consumers when facing uncertain future scenarios, the behavior of stakeholders should be considered. To address these challenges, we propose an efficient robust-optimization-based approach for long-term solar PV planning, where the objective is the maximization of the total economic surplus between producers and consumers of solar PV systems. The uncertainty affects the price that risk-averse consumers (i.e., residential, commercial, and industrial) are willing to pay for solar-PV systems. Land-space constraints are included to describe the relationship between installed capacity and available space, which is a scarce resource. Singapore is employed as a case study because of its high solar-energy production potential, limited space profile, ambitious solar PV capacity installation targets, and past data availability that made it possible to calibrate the model. Results show that the projections of our model are compatible with existing assumptions. The analysis of the solution allows quantifying the incentives required for achieving national capacity installation targets in 2025 and 2030, which may otherwise be underestimated by 20–30%.