In this study, Lee, Chen, and Kang stress the importance of wind farms as a source of renewable energy, but note that deciding where to build a wind farm is a complicated decision that requires many factors, both positive and negative, to be considered. The authors emphasize the importance of utilizing a framework that evaluates projects while considering markets, technologies, social and environmental impacts, etc. The framework they suggest is the analytic hierarchy process (AHP) model with the inclusions of benefits, opportunities, costs, and risks (the BOCR merits).
The authors summarize the ten steps in conducting an AHP model with BOCR.
- Form a committee of experts in the industry.
- Construct a control hierarchy for the problem.
- Determine the priorities of the strategic criteria.
- Determine the importance of benefits, opportunities, costs, and risks to each strategic criterion.
- Determine the priorities of the BOCR merits.
- Decompose the problem into a BOCR hierarchy with four sub-hierarchies.
- Formulate a questionnaire based on the BOCR hierarchy to pairwise compare elements, or factors, in each level with respect to the same upper level element.
- Calculate the relative priorities in each sub-hierarchy.
- Calculate the priorities of alternatives for each merit sub-hierarchy.
- Calculate the overall priorities of alternatives by synthesizing each alternative under each merit from Step 9 with corresponding normalized weights from Step 5 using one of five methods: additive, probabilistic additive, subtractive, multiplicative priority powers, or multiplicative (for equations please see the article, p. 123).
Lee, Chen, and Kang test this method with a case study of the Chinese wind farm project. The wind farm developers were looking to install 500 wind turbines and needed to determine where to construct the farm. The criteria and sub-criteria for the wind farm project can be seen in Table 1, below.
|Table 1: The criteria and sub-criteria for the wind farm project|
A questionnaire is designed asking the experts to evaluate the priorities of benefits, opportunities, costs, and risks. Afterwards, the priorities of the alternatives under each merit are calculated. The priority results can be found in Table 2.
|Table 2: Relative priorities of criteria and sub-criteria|
The most important benefit criterion was wind availability (criteria (a)), with a score of 0.6317. The important sub-criteria were mean wind power density (a2) and mean wind speed (a3), with scores of 0.2637 and 0.1971, respectively. This means that the greatest benefit for a specific site is having sufficient wind for operations. The most important opportunities were wind power concession program (e1, 0.1945), and clean development mechanisms program (e2, 0.1680), implying that policy support is a major driver to develop wind power. The cost of wind turbines (g, 0.5595) is a major concern, while the risk of concept conflict (j, 0.5639), implies that not all political parties agree on the need for wind power.
After determining these priorities, the criteria are scored. High scores for benefit and opportunity merits indicate better performance, whereas high scores for cost and risk merits indicate worse performance. The authors evaluated five potential sites, and calculated that one specific site (Site B in the study) was clearly the superior option. All five calculation methods (as previously mentioned in Step 10) agreed that Site B was expected to be the best location for a wind farm, primarily due to the highest wind availability (benefit, the highest overall priority) and is also the second least costly location.
The authors do a good job explaining how AHP with BOCR works and their application of the method to a real-world case helps clarify the steps. Despite this method being quite math-heavy, the principles that are derived from it can be very useful to an intelligence application of multi-criteria decision making (MCDM), which would be multi-criteria intelligence matrix (MCIM). Specifically, one can draw out the importance of a structured approach, prioritization, and the ability to support the estimate. Additionally, one weakness of MCIM is the necessity to weight or prioritize criteria or alternatives. In this study, the authors distribute questionnaires to a panel of experts and synthesize the results, and in doing so reduce our inability to accurately weight options.
Lee, A.H.I., Chen, H.H., & Kang, H.Y. (2009). Multi-criteria decision making on strategic selection of wind farms. Renewable Energy, 34, 120-126. Retrieved from: Science Direct.