During a solar project’s development phase determining future impacts to plant performance is paramount. This performance will determine whether or not a project is bankable and ultimately decide if the project will move forward. An important piece of this planning is the Operations and Maintenance budget, which largely consists of plant cleaning costs. These cleaning costs vary greatly depending on environmental conditions (how quickly the plant accumulates soiling material), difficulty to clean, water availability at the site, local labor rates, etc. Determining when and how to clean is challenging enough in an operating plant, and even more difficult during the development phase, when location and historic meteorological data is all that’s available.
For most projects, the industry is familiar with some degree of production/yield estimation during the planning stage. Whether it is from a system component level tool like PVsyst or a design tool which calculates a value with minimal input, at some point a designer will need to designate a soiling loss value. This value can be monthly or annual and is generally a broad haircut on the annual system production. This value must also include any planned cleanings which will be accounted for in the O&M budget for the project (usually a single annual cleaning). For larger projects Independent Engineers offer services to calculate these values based on local conditions. These values are important for system designers, asset managers and underwriters alike as they will impact cashflow for the duration of the plants life.
Once a plant is commissioned the actual soiling loss values can be directly measured on-site to determine when to clean using actionable data. Locally measured soiling loss values aide O&M teams in ensuring that a PV asset continues to meet operational targets for the life of the system. This study looks at these local measurements for 40 locations and compares them to IE estimates within 30 km on an annual basis. Local measurements were made using the Fracsun ARES soiling station hardware. Using this comparison, we can see the accuracy of the IE measurements as compared to empirical field measurements made within the vicinity of the estimations target location. In the chart below, the horizontal axis is an empirical value and the vertical axis represents the IE estimate at a matching geographic location.
The orange line in this chart represents the boundary of overestimation and under-estimation. Above this line, IE estimates tend to be higher than values measured in the field and below it we have estimates coming in lower than measured in the field. As can be seen, there is no region of the chart where estimations come in close to measured values save for possible the 1.5% to 3% annual loss region where the values have the most similarity, though variations still exist in this region.
This chart indicates that much more data is needed during the planning phase of project development and having local data to validate forecasts can have a large impact on how we plan for O&M activities and plant cleaning budgets.