Search

• Evaluate the effect of uncertain operating and market conditions on dispatch planning. • Apply Model Predictive Control (MPC) for dispatch scheduling of CSP plants. • Monte-Carlo simulation is to evaluate the MPC performance for a case-study in Australia. • The plant profitability from electricity sales under spot prices is assessed. The aim at dispatch scheduling for concentrating solar power plants is to utilize thermal storage to maximize profit from electricity generation. The dispatching plan, however, must contend with significant uncertainty in both weather forecasts (particularly solar irradiation) and, in some locations, real-time electricity prices governed by potentially volatile markets. This paper proposes a Model-Predictive Control (MPC) which employs a novel similarity-based forecast for weather variables and exploits the predictions of electricity prices provided by the market operator. A Monte-Carlo simulation is developed to evaluate true performance of the proposed MPC against two benchmarks, with perfect knowledge (PK) forecasts and day-ahead optimized using prototypical weather (DAPW). A case study is performed on a 115 MW Solar Tower plant with 8-hour storage, hypothetically located in South Australia, and operates under either a fixed-price or a real-time spot price scenario. A Monte-Carlo simulation is conducted for 150 tests for January (summer) and August (winter). The results show in fixed-price scenario MPC achieves 82.4 % of optimal profit (i.e. obtained via PK) in January and 72.4 % in August whereas that in DAPW falls to 71.5 % and 56.9 % of optimal, respectively. In the spot market scenario, MPC reaches 71.3 % and 63.6 % of optimal profit in January and August, whereas that for DAPW reaches 61.4 % and 55.1 %, respectively. In conclusion, PK forecast assumption in dispatch planning over-estimates the achievable profit by ∼ 28–40 % particularly in spot market scenario. Moreover, MPC can mitigate the influence of uncertainties on the plant economic performance by 8.5 %–15.5 % compared to DAPW benchmarks. [ABSTRACT FROM AUTHOR]

Concentrating Solar Power (CSP) plants can make a significant contribution to renewable energy supply in many regions of the world. Several bottom-up engineering performance models for CSP plants have been developed. However, they require the capacity of the plant to be known. In whole-of-grid optimization models, the capacity of the plants is an optimization variable and therefore not known in advance. In this case, researchers have taken a simplified modelling approach that overestimated CSP performance, especially for low-DNI conditions. In this paper, we offer a novel density-based engineering modelling approach in which CSP performance is determined independently of the capacity of the power plant, and which can be used in whole-of-grid optimization models. We investigate a case study of the site of the new Aurora CSP project in Port Augusta, South Australia, to be one of the world's largest solar thermal power plants by 2020. Using a Geographical Information System (GIS)-grid representation of Australia, we compare CSP performance resulting from a simplified model with our new approach. • A novel density-based engineering modelling approach for Concentrating Solar Power (CSP) plants. • CSP performance is determined independently of the capacity of the power plant. • The approach can be used in whole-of-grid optimisation models. • We investigate the site of the new Aurora CSP project in Port Augusta, South Australia to be constructed by 2020. • The new approach is compared to the simplified description of CSP performance, using a GIS-grid representation of Australia. [ABSTRACT FROM AUTHOR]