Conference proceedings article
Innovation of price adjustment mechanisms to support investment in solar power in Germany
Publication Details
Authors: | Do, T.; Hoffmann, C. |
Editor: | E3S Web of Conference |
Edition name or number: | E3S Web of Conference |
Publication year: | 2018 |
Journal: | E3S Web of Conferences |
Pages range : | UNSP 02007 |
Book title: | 2018 The 3rd International Conference on Power and Renewable Energy |
Volume number: | 64 |
Number of pages: | 6 |
ISSN: | 2267-1242 |
DOI-Link der Erstveröffentlichung: |
Abstract
It has been widely agreed that to incentivize renewables integration into the power system, not only pricing mechanisms, but price adjustment mechanisms have played a vital role, and it has been true for the German Energiewende. This study is to carry out a detailed analysis of investment results influenced by innovative price adjustment mechanisms from an auto degression rate to a feedback system. Employing linear regression models for the historical data of investment in small-scale rooftop PV projects in Germany, we have found out a better correlation between PV system price and feed-in tariff (92.09%) under quarter feedback and monthly adjustment mechanism compared to an annual feedback system. However, the underinvestment in recent years reveals that a feedback mechanism without specific mathematical shapes was not effective enough in term of meeting the targeted volume. Therefore, further researches are to design mathematical images of feedback mechanism in order to find out the trajectory of electricity price in the future which at the same time satisfies the target of investment and economic effectiveness.
It has been widely agreed that to incentivize renewables integration into the power system, not only pricing mechanisms, but price adjustment mechanisms have played a vital role, and it has been true for the German Energiewende. This study is to carry out a detailed analysis of investment results influenced by innovative price adjustment mechanisms from an auto degression rate to a feedback system. Employing linear regression models for the historical data of investment in small-scale rooftop PV projects in Germany, we have found out a better correlation between PV system price and feed-in tariff (92.09%) under quarter feedback and monthly adjustment mechanism compared to an annual feedback system. However, the underinvestment in recent years reveals that a feedback mechanism without specific mathematical shapes was not effective enough in term of meeting the targeted volume. Therefore, further researches are to design mathematical images of feedback mechanism in order to find out the trajectory of electricity price in the future which at the same time satisfies the target of investment and economic effectiveness.
