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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.