The purpose of all market assessments is to match potential consumers with a suitable product or service. In this situation, the ‘consumer’ is those with inadequate access to energy, and the ‘product’ is a small wind energy system (and ancillary services). Previously, market assessments have been carried out at a regional or local scale, with mixed findings. The global market assessment aims to highlight which countries, on an international level, have the right conditions for small wind to be successful. This information is intended to inform future national scale assessments, providing some level of prior guarantee of viability within a given country.
Initially, in order to reduce the number of countries under consideration, countries classified as ‘High Income’ Countries by the World Bank were removed from the study, along with countries reporting 100% electrification to the UN. The rationale behind this was that wealthy countries, or completely electrified countries, will not be in need of energy for development or rural electrification. A wind filter was applied, where countries that were judged to have a poor wind resource were removed from the study based on the DTU Wind Atlas at 50m as displayed on http://irena.masdar.ac.ae/. This judgement was performed collectively by the Market Assessment Working Group based on how high the annual average wind speed was, and how much land area any adequate resource covered.
After these filters were applied, a list of ‘indicators’ of factors for successful small wind projects was assembled. It was important that all data collected was global in coverage, so some indicators were removed at this stage, such as those in the ‘community’ category, due to lack of full data. The remaining indicators were categorised, and a value tree (below) constructed in order to simplify the following weighting process.
Clearly these indicators should not be considered as having equal impact on the success of a small wind installation, and so a weighting process was carried out. The technique used is called SMART (Simple Multi-Attribute Ranking Technique) and is a type of swing weighting. For each ‘branch’ of the tree, the indicator considered as having the most impact is assigned a value of 100, then each remaining ‘sibling’ indicator is scored relatively, from a score of 0 (having no impact) to 100 (having equal impact as the most important indicator). This is carried out for each branch, and then each sub-category and each category, until all ‘nodes’ are weighted. The weights are multiplied down so each end-indicator has a normalised weight, and the sum of all indicators is 1.
All data was normalised to a scale of 0-1, often placing the zero point at the least desirable value and 1 at the most desirable value occurring in the relevant dataset. These indicator scores were then weighted according to the value determined through the SMART process, and summed, to give a final value for every country under consideration. The graph below shows the fifteen highest scoring countries and a breakdown by the categories present in the value tree.
The main limitation of the methodology used is that the methodology cannot satisfactorily function with missing data for an indicator/country pair, which means that small island nations cannot be considered on parity with larger continental states. The study is currently being revisited with a GIS-based methodology in order to mitigate this limitation.