Thursday, October 22, 2015

Multi-criteria decision making on strategic selection of wind farms

Amy H.I. Lee, Hsing Hung Chen, & He-Yau Kang. (2009).

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.
  1. Form a committee of experts in the industry.
  2. Construct a control hierarchy for the problem.
  3. Determine the priorities of the strategic criteria.
  4. Determine the importance of benefits, opportunities, costs, and risks to each strategic criterion.
  5. Determine the priorities of the BOCR merits.
  6. Decompose the problem into a BOCR hierarchy with four sub-hierarchies.
  7. 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.
  8. Calculate the relative priorities in each sub-hierarchy.
  9. Calculate the priorities of alternatives for each merit sub-hierarchy.
  10. 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.


  1. Andrew, you stated that one weakness of MCIM is the necessity to weight or prioritize criteria or alternatives, and the use of experts reduced the inability to accurately weight options. Do you think limiting the questionnaires to experts contributes to the weakness of prioritizing criteria? Or do you think the problem is inherent to the method?

    1. The problem is definitely inherent to the method, particularly when done as an intelligence function. Not only are you as an analyst trying to weight an option (which humans are poor at doing), but you are also trying to weight it from an outside perspective. In regards to limiting the questionnaire to experts, I think that is dependent on the issue. More technical issues would likely benefit by only surveying experts, but more general issues would likely be just fine for a general population.

  2. I felt it was interesting that they broke the sub-criteria results down into local priorities and global priorities. I assume the local priorities are based on this specific case and global are for wind farms around the world?

    1. That's the assumption I came to as well. The authors did not explicitly state as much, but it seems to be a reasonable conclusion.

  3. I thought this article was interesting, especially since the article I wrote about also referenced how MCDM is useful in understanding the inner and outer environments of an industry. The weighted criterion seemed to mainly focus on the perspective of the inner logistics of the problem, with the exception of "concept conflict," emphasizing the political disagreement. Does the article discuss any other issues that setting up a wind farm could run into, or even factors that make it easier to achieve this goal? If so, do you think these should have been added to the criteria and weighted, or are they too general and not location specific enough to impact the problem?

    1. Katie, there are a couple other risks, specifically technical complexity issues and land suitability/ownership issues, but the authors only pointed out conceptual conflicts because it was the highest priority risk. Technical complexity scored a 0.1208 ((k) on Table 2 above), and land suitability/ownership (l) scored a 0.3153. I think the authors left these out in the narrative for the sake of brevity, but they were noted in the authors' full tables of results.

  4. I think what this article demonstrates is that the intrinsic tie between intelligence analysis and human being involvement in that process.

    1. I agree. I think it places a lot of importance on the human aspect of intelligence analysis in this method.

    2. I agree. I think it places a lot of importance on the human aspect of intelligence analysis in this method.

  5. I thought it was interesting in that it kinda combined some elements of SWOT into the process. I suppose that is possibly a good system to use as it puts a numeric value on the options.