On the Disruptive Innovation Strategy of Renewable Energy Technology Diffusion: An Agent-Based Model
Abstract
:1. Introduction
2. Materials and Methods
2.1. Framework of the Model
2.2. Model Dynamics
3. Results
3.1. One-Dimensional Competition
3.2. Two-Dimensional Competition
3.2.1. The Impact of Price Variation
3.2.2. Impact of Preference Changing
3.2.3. The Impact of RET Improvement Rate
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
Variable | Meaning |
the probability that a potential adopter independently adopts CETs. | |
the probability that a potential adopter independently adopts RETs. | |
the probability that a potential adopter becomes aware of CETs. | |
the probability that a potential adopter becomes aware of RETs. | |
the probability that a neighboring adopter of CETs successfully influences the potential adopter of CETs. | |
the probability that a neighboring adopter of RETs successfully influences the potential adopter of RETs. | |
the number of neighboring adopters of CETs. | |
the number of neighboring adopters of RETs. | |
the preference of agent i. | |
the weight that agent i assigns to a technology’s conventional dimension. | |
the weight that agent i assigns to a technology’s non-conventional dimension. | |
the initial weight agent i assign to RETs’ non-conventional dimension. | |
a coefficient adjusting the degree of the influence on preference changing | |
the performance of CETs. | |
the performance of CETs in terms of the conventional dimension. | |
the performance of CETs in terms of the non-conventional dimension. | |
the initial performance of CETs when they enter the market at first. | |
the constant that adjusts the speed of technology progress. | |
the maximal cumulative adopters of CETs at year t. | |
M | the total number of potential adopters in a market. |
the performance of RETs. | |
the performance of RETs in terms of the conventional dimension. | |
the performance of RETs in terms of the non-conventional dimension. | |
the initial performance of RETs. | |
the speed of the technological progress of RETs. | |
the maximum cumulative adopters of RETs within t years. | |
the probability that consumer i decides to purchase a CET. | |
the probability that consumer i decides to purchase a RET. | |
the reservation price of consumers willing to pay for CETs. | |
the reservation price of consumers willing to pay for RETs. | |
the actual price of CETs. | |
the actual price of RETs. | |
the utility of CETs that a consumer perceives. | |
the utility of RETs that a consumer perceives. | |
d | the deviation between the utility a consumer perceives and the reservation price the consumer is willing to pay. |
the unit price of . | |
the unit price of . |
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Parameter Name | Value | Remark |
---|---|---|
M | 1000 | The absolute number of agents is not important when potential adopters are treated as 1. |
0.005 | Network density () is set according to the calibration. This is consistent with [47], which found that the networks in the real world are normally sparse. | |
0.01 | Set according to the calibration. The value is located in the range of previous literature [36,48]. | |
0.01 | Controlled for the current experiment. | |
0.26 | Set according to the calibration. The value is located in the range of previous literature [36,48]. | |
0.26 | Controlled for the current experiment. | |
0.01 | Assume that CETs start with a very low performance, which intends to mimic a complete maturation process of CETs. | |
2.6 | Set according to the calibration. This value indicates that this technology becomes fully mature after 38.9% of the consumers have adopted, according to Equation (8). | |
Controlled for the current experiment. Indicating that the emergent technology appears with the same performance as the incumbent technology. | ||
2.6 | Controlled for the current experiment. | |
1 | The unit price of a technology’s conventional dimension. The number “1” is a convenient benchmark. |
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Zeng, Y.; Dong, P.; Shi, Y.; Li, Y. On the Disruptive Innovation Strategy of Renewable Energy Technology Diffusion: An Agent-Based Model. Energies 2018, 11, 3217. https://doi.org/10.3390/en11113217
Zeng Y, Dong P, Shi Y, Li Y. On the Disruptive Innovation Strategy of Renewable Energy Technology Diffusion: An Agent-Based Model. Energies. 2018; 11(11):3217. https://doi.org/10.3390/en11113217
Chicago/Turabian StyleZeng, Yongchao, Peiwu Dong, Yingying Shi, and Yang Li. 2018. "On the Disruptive Innovation Strategy of Renewable Energy Technology Diffusion: An Agent-Based Model" Energies 11, no. 11: 3217. https://doi.org/10.3390/en11113217