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vehicles [21]. It is predicted that the DoD will own or lease 92,000 hybrid and electric vehicles through 2020 to help lower its fuel consumption, and reduce the risk and associated impact of fuel price volatility [22]. Furthermore, through Phase 2 of the Smart Power Infrastructure Demonstration for Energy Reliability and Security (SPIDERS) program, the DoD along with the Department of Energy and the US Army ran the first V2G test at Fort Carson, Colorado. The test integrated a 1 MW solar microgrid with five electric vehicles coupled “with advanced bi-directional vehicle chargers to integrate the battery capacity of electric vehicles in both microgrid and normal operations” [23].

      With the current DoD experience in mind, we believe that future non-tactical EVs could be used during summer peak times. Such vehicles, if coupled with PV installations already in place on many military installations should also have sufficient SoCs during the 4 to 9 pm window selected for study.

      6.1.Model building

At the early stage of the technology development, this study adopted the Hierarchical Decision Model (HDM), which was created and developed by Dundar Kocaoglu and Tugrul Daim [24] to better understand and track decision. The HDM is a methodology to analyze and evaluate best fitting alternatives in order to accomplish a specific objective. It uses a multicriterion that flows into alternatives selection process. Our study applied the HDM into four levels, which were Objective, Perspectives, Criteria and Alternatives, that contributed to the best option for the main objective. The HDM used the judgment of experts to prioritize the important perspectives, criteria and alternatives through the pairwise comparison technique [24, 25]. Those perspectives and criteria were then weighed by these experts, who then evaluated and estimated the complex and complicated system to gain the best decision strategically. However, the results from the HDM provided inconsistent and disagreement ratios, which indicated how much their responses did not agree with each other.

      As discussed above, the objective was to determine the best opportunity behind the meter transportation technologies to use for future summer peak V2G programs. As Figure 1 shows, the model was created based on the HDM analysis to accomplish the goal. The decision model is illustrated in Figure 1. This model was created through the HDM link website to collect data from the experts. The respondents did pairwise comparison through the link for all three perspectives and a separate comparison in each node among the criteria is seen in Figure 1.

      Figure 1.The HDM in four levels.

      Finally, the experts completed weighing the pairwise comparison of all the perspectives, criteria and potential alternatives. Then, their opinion is submitted to the model and contributed to the result as the best opportunity of technology options.

      6.2.Data analysis and results

The HDM results showed the best option of potential alternatives through the highest score. Moreover, there was a critical statistic result which is the inconsistency that explores how consistent and careful the experts weighted different factors. The standard acceptable rate for inconsistency was less than 0.1. If it had been more than 0.1, the quality of judgment should not be considered [26]. However, it also depends on the variety of perspectives, criteria and different tolerance levels. In this study, the inconsistency for each expert was less than 0.1, as shown in Table 2, therefore the results from all the experts can be considered as consistent judgment. Moreover, it shows that the disagreement rate is less than 0.1, which means that all the experts are in the same agreement with regard to weighting the criteria and perspectives relating to the objective.

      The F-test value was calculated through pairwise comparison in the HDM model from all the participating experts, as shown in Table 3. The value indicated a degree of agreement due to the benchmark value of 2.33 at 0.1 level (90% confidence level) and the final value of 2.61, which is over 2.33. Therefore, it proves that the HDM weights from the selected experts were in agreement with a 90% confidence.

Table 2.HDM results based on the alternatives.

Table 3. HDM statistical results.

      Through the pairwise comparison in HDM methodology, the important perspectives and criteria reveal overall scores from all the experts. Figure 2 illustrates the score of each element in all levels calculated through the mean score of all the experts. In the perspective level, the Likelihood of Owner Participation tends to be the most important, which can also potentially influence decision making. This perspective is important because the power of owner participation can persuade them to participate in the program. This perspective is influenced by two criterions, which includes the highest impactable criterion—incentives and benefits for owners to participate in a bi-directional grid support program. Therefore, to make the best decision with regard to the objective, the critical, important perspective and criteria need to be first considered carefully.

      Figure 2.The model with HDM results in all levels.

      Considering relevant application alternatives, the Individual Owned Vehicles alternative, for example, electric vehicles in the United States, has the highest score based on many contributions. Such a huge market adoption and technology readiness factor could make this alternative become the first option for this opportunity to become the objective. Moreover, the direct power a vehicle owner could introduce to the program through incentives and benefits could potentially be key to making this the best option.

      The School Bus Fleets is the second option because buses have large stored potential energy and significant downtime during the summer’s peak, which highly contributes to all the three criteria in the Availability perspective. This can be the best option to support the peak V2G program; however, there is a barrier—the cost to transform these buses into electric models. Therefore, the lack of market adoption has a huge impact on this alternative.

      The remaining three options— Municipal (Non-Bus and Non-Emergency), Military and Garbage Truck Fleets—have similar scores. They have immediate market availability and market adoption, which give them opportunities to participate in the program. These options could help the program to manage the energy consumption time. However, it also depends on their availability, readiness and market adoption factor with regard to the V2G integration. Nevertheless, the Garbage Truck Fleets has the lowest score because of the transformation cost to electric models in the first period of time, which is related to the market adoption and incentive/benefit of the owner criterion.

      Conclusion

      This paper analyzed the most opportune behind-the-meter transportation technologies and products to use for future summer peak

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