During the lifecycle of this project it was concluded that upon analysing the data from both Sgurr and our case studies, the costs relating to BoP were found to be stochastic in nature and dificult to predict. This enabled the group to construct a tool that would optimize the costings by taking this into account.


BoP Tool Conclusions

Although the major cost of a wind farm project is the wind turbines, analysing the cost of the Balance of Plant (BoP) is very important for the viability of future developments. The cost of a certain wind turbine is practically independent to the location of the wind farm. The same group of turbines installed in a windy location will have the same cost as in other locations. The factor which normally makes it costlier to install a wind farm in a windy location is the cost of the BoP. This factor makes really important to be able to compare the BoP cost for different locations and layouts and to check whether the energy production in a windy area offsets the increase of BoP cost to reach this area.

Offering an estimate of the BoP cost for a wind project has been found to be a complex problem where many uncertainties are involved. One of the guidelines to be applied in order to improve the understanding of this part of a wind farm design is developing a standardized process to report the cost of the BoP elements. Using the same cost breakdown related to the same characteristic parameters on large number of projects is the starting point in order to be able to estimate the cost of future projects.

The use of the BoP cost tool will support the standardization of this costing process. The BoP cost tool combined with the access to a large amount of historical data will increase its value. A large data base obtained from projects broken down by the standard process will make the results obtained more reliable. The visualization of the pie chart for the categories cost share and the box plots are a convenient way to support decision making. The pie chart highlights the elements which have a more important weight in the BoP cost. The box plots show the typical cost range of the different elements, important for the risk assessment of cost variation and which can help to identify outlying cost estimates.

The Tool can be used to act as a what if scenario generator. What if the site was moved closer to the Grid, What if the site was moved further away from the road network. What if these destinations had less or more wind. In this way a skilled tender can optimize their decision making process. Also with the input of more and more tender data over time, the internal database with it's ability to highlight high cost outliers will enable a skilled tender negotiator to target these outliers in the negotiation process and drive down tender costs.

BoP Case Study Conclusion

According to the tool results and the analysis carried out it has been concluded that the size of turbine selected can dramatically affect the balance of plant cost, therefore careful examination for different turbine sizes has to be taken while decision making. The elements that can be adjusted to optimise the cost are foundations, transformers, roads and cabling works that are vary from one size to another. In regards to the sensitivity of the tool, it has been concluded that the increase of turbine numbers can affect the accuracy of the tool and therefore more data input is needed to improve the accuracy of the tool. It has been also concluded that wind farm location can seriously affects both the BoP cost and the annual energy production. Therefore, careful consideration for that should be taken during the decision making process.

There are different factors that have to be considered for the construction of wind turbine plants. In addition to the balance of plant cost, the annual energy production is also important to consider during the decision making prosees. Although it was not part of the initial scope of the project, different locations at different wind speed can affect the payback period for each project. Therefore, the tool has been designed in a way that can be used as "what if" scenaro generator which can give different tender costings dependent on these parameters.

Carbon Tool Conclusions

It can be seen from the case study that the elements of BoP have a great environmental performance, taking well under a year (around 2 months) for the carbon savings to exceed the carbon costs.  Furthermore the carbon footprint is low when compared to fossil fuel electrical generation ranging from 400 – 1200 g CO2e/kWh depending on the type of fossil fuel and boundary of LCA.

Essentially the carbon costs can be deemed insignificant; however the true results would likely be higher due to the boundaries set by the tool.