Reliable and Bankable Solar PV
In the last decade and longer, photovoltaic module manufacturers have experienced a rapidly growing market along with a dramatic decrease in module price
Such cost pressures have resulted in a drive to develop and implement new module designs, which either increase performance and/ or lifetime of the modules or decrease the cost to produce them.
The reliability and lifetime of a PV plant depends mainly on the quality of the components.
Various international activities are actively studying how to increase perfor- mance and reliability of PV modules and systems, e.g. the IEA PVPS Task 13 (68), the COST Action PEARL-PV (69) or the H2020 project Solar Bankability (70) which provided the basis for the establishment of a common practice for professional risk assessments. The most effective strategy for reliable and bankable solar PV is to prevent the occurrence of failures and by reducing the impact of failures once they become evident. In new PV projects, the focus must be on the ap- plication of novel preventive mitigation measures to minimise the probability of failure occurring once the PV plant is in operation. For existing PV projects, advanced data driven mitigation measures need to be developed to go beyond the state-of-the-art concept of corrective maintenance as well as progressive repowering interventions to extend plant lifetime and increase the production capacity without requesting additional space. All data coming from the various phases carry important information that can only be fully exploited by the com- munity as a whole if the data can be stored and transferred along the value chain. The ultimate goal is to be able to “quantify” quality in a “value chain” ap- proach by not being locked in a specific phase so that a PV project in the future can have access to lower WACCs by presenting bankable approaches, products and services.
PV projects in any market segment require a dedicated technical risk frame- work where the requirements may vary depending on the complexity of the project. In general terms, initial risks need to be identified and quantified and all the stakeholders operating in various phases along the value chain need to be involved to vastly reduce and minimise the residual risks. This can be done by preventing the occurrence of failures and by reducing the impact of fail- ures once they become evident. Technical risks which cannot be transferred to other stakeholders will ultimately stay in the hands of the PV project owner. A clear technical risk framework is important as it can “quantify” the quality of a PV project and thus demonstrate the advantages in terms of business model (more reliable generation for a longer lifetime) compared to other projects of lower quality. A project perceived of high quality by lenders (in terms of equity or debt) will have access to lower WACC (Weighted Average Cost of Capital) which is the most important parameter affecting the LCOE (71) (Levelised Cost of Electricity), and ultimately the IRR (or other benchmarks). Quality in PV projects starts from the planning phase where a fundamental role is played by the accuracy and uncertainties related with the yield assessments. A yield assessment with reduced uncertainties (thanks to improved models and access to better site dependent data, e.g. irradiance) can lead to a much more favourable business model. Procurement is the next important step where extended testing beyond what is prescribed by the standards can increase the confidence of the right choice of PV components.
It is during these two steps that the remuneration of PV projects can be vastly improved by ensuring a reduction in failure rates and a more positive business case. After a successful implementation of these preventive mitigation measures, the PV project needs to focus on the transporta- tion and installation phase where quality assurance needs to be included to make sure that all the components are in their best conditions for the operational phase. A reliable generation for a longer lifetime can then be ensured by in- novative O&M practices which include data-driven meas- ures coming from both field experience and monitoring. Finally, all the information collected along the whole value chain need to stream into digital platforms that can act as a decision support system for the best actions to follow in case of deviations.