Investment Cost Forecasting for Low Carbon Power System Planning Considering Technical Progress and Scale Effect

• Published in: IEEE access Vol. 10; p. 1
• Main Authors: Sun, Wei, Xiao, Jinyu, Hou, Jinming, Jin, Chen, Nie, Yuanhong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Carbon; Carbon content; Carbon dioxide; Clean energy; Cost analysis; Costs; curve fitting; Forecasting; Investment; investment cost; LCOE; learning curve; log-log regression; long term planning; Low carbon economy; low carbon power system; Modeling; onshore wind power; Predictive models; Scale effect; TRL; Wind power generation
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3218060

Low carbon power system with high penetration of clean energy is an effective way to realize the carbon emission target. Long term power system planning should consider both technical constraints and reasonable investment cost forecasting. For reasonable investment cost forecasting in long term, the effect on investment cost by technical progress and scale effect should be taken into consideration both. Investment cost can affect the planning results of installed capacity, while installed capacity affects the investment cost forecasting result mutually. There is no paper that takes technical effect into investment cost forecasting or analyzes the mutual effect between planned installed capacity and investment cost forecasting. Technical progress can be quantified by TRL (Technical Readiness Level) while scale effect can be quantified by installed capacity. In this paper, the Z curve which describes the relationship between investment cost and TRL is derived. While the learning curve which describes the relationship between investment cost and installed capacity is derived. Then based on the two curves, the 3D relation function with three variables which are TRL (independent variable), installed capacity (independent variable) and investment cost (dependent variable) is derived for the first time which can be used to forecast the investment costs. The 3D curve is combined with the existing GTSEP model (with technical constraints) by feedback for the first time. At last, based on the improved GTSEP model, the planning of low carbon power system in 2060 was given and the investment cost of onshore wind power in 2060 was forecasted. The novelty of this paper is proposing the 3D curve which qualified technical progress and involved it in investment cost for the first time. And combined the 3D curve with the GESTP model to feedback the forecasting investment cost to power system planning which reveals the mutual effect between installed capacity planning and investment cost forecasting for the first time. The study case indicates that decreasing of investment cost is mainly caused by technical progress at the early stage of technical life, and the decreasing of investment cost is caused mainly by increasing installed capacity at the mature stage when technology has been mature, and the effect caused by technical progress becomes less and less weak. The accelerating of technical progress will decrease the investment cost and increase the installed capacity of this technology and affect other technologies with interaction in the whole power system.