Storage management in a rolling horizon Dynamic Real-Time Optimization (DRTO) methodology for a non-concentrating solar thermal plant for low temperature heat production

The intermittency and uncertain forecasts of solar irradiation complicate the operation of a solar thermal plant with thermal storage for heat production. In this work, a rolling horizon Dynamic Real-Time Optimization (DRTO) methodology is proposed to determine the economic optimal operation of a non-concentrating solar thermal plant for low temperature heat production, using a planning phase to improve storage management. The methodology is tested online on a detailed simulation model representing a large-scale solar thermal plant in case studies, using variable heat demand and real data for the weather forecasts and measurements. It was shown that DRTO performs better than offline Dynamic Optimization thanks to the use of updated weather forecasts and the regular re-initialization of the system state using measurements. An increase up to 57% in supplied energy and a decrease up to 35% in operating costs were achieved with DRTO compared to DO. Guidelines on the best storage management policy at the DRTO level, using the planning phase, are formulated. While storing the maximum energy possible for later use is the best option when overheating is not a risk, following the planned storage state helps to prevent overheating when the solar irradiation is high and the heat demand low.