| Work Detail |
A new Italian study shows the importance of taking into account the dispatchability of power plants when planning photovoltaic projects. Scientists say that assessing a projects levelized cost of energy (LCOE) could be misleading, especially with variable and sometimes negative electricity prices.
A research group led by the Polytechnic University of Milan (Italy) has created a mathematical model to optimize the design of photovoltaic plants based on optimized dispatch strategies taking into account actual grid demand and prices.
“We started from the evolution of the electricity curve, which is also called the duck curve, and arrived at the extreme case of negative electricity prices,” Gianpaolo Manzolini, corresponding author of the study, explains to pv magazine . “The study was started three years ago, before negative prices were recorded in Europe, but they were already present in Australia, in particular in South Australia. The collaboration with the Queensland University of Technology helped from this point of view.”
The research aims to demonstrate that the levelized cost of energy (LCOE) is not the correct optimization parameter for photovoltaic and concentrated solar power (CSP) installations. “The proposed methodology can be applied to any other source,” Manzolini explained. “It certainly becomes more relevant when it comes to technologies that rely on variable renewable sources. The same goes for storage. In principle, it is not necessary in the methodology, and yet it amplifies the differences.”
The proposed model is based on the Aggregated Energy System Optimization (AESOPT) developed by the Polytechnic University of Milan itself. This tool considers the effects of size on both the costs and the efficiency of energy generation technologies. Its default objective function is the optimization of the net present value (NPV) of a project, which corresponds to the value of all future cash flows over the entire life of an investment discounted to the present.
“AESOPT has been extended to include detailed models of ESTC plants, namely the power block, the solar field with linear collectors and the molten salt storages,” the research team specifies, noting that the modelling uses mixed integer linear programming (MILP) and takes into account the minimum and maximum size of components, the maximum power exchanged with the grid, the evolution of energy storage and energy balances. It also takes into account economic parameters such as revenue, capital expenditure (capex), operating expenditure (opex) and the capital recovery factor. “The LCOE is non-linear, as it involves the relationship between two variables. Therefore, the LCOE cannot be directly used as an objective function of the AESOPT tool.”
The scientists worked on two case studies involving a PV plant and a CSP facility that were supposed to be installed in South Australia and Southern California. The analysis was based on actual electricity prices in 2022 along with meteorological conditions during the same year and temporal resolution, and 16 different cases were investigated considering PV and CSP applied to the two locations.
“The results show that solar plants designed specifically to optimise profits based on real electricity market prices lead to relevant differences compared to the standard LCOE-based approach,” the researchers explained. “PV plant designs optimised according to the latter do not include the installation of a storage system, while this becomes crucial to ensure the profitability of the plant when the real grid situation is considered.”
They highlighted that considering network specificity can increase a projects NPV by up to 10-fold, while LCOE could increase by up to three-fold compared to conventional LCOE-based approaches.
The academics presented their findings in the study “ Limitations of using LCOE as an economic indicator for solar power plants”, recently published in Renewable and Sustainable Energy Reviews . The research involved scientists from the Queensland University of Technology.
“Future work will focus on evaluating plant dispatch and design based on forecast weather conditions and electricity prices to see the impact of uncertainties on plant design,” they concluded. “Furthermore, the model will be extended to other schemes such as residential, including energy communities.” |
