Abstract
With the gradual opening up of China's power sector, electricity investment is growing. Risk analysis should be applied to the investment optimization decisions. This study describes a CVaR-based investment optimization model, which established electricity portfolio decision-making model to optimize the ratio of investment decision-making and achieve the maximum yield of the total investment target between the various modes of generation. An example was given to verify the validity of the model based on the actual data. Based on simulation results of the example, the ratio of investment in a certain confidence level has been well optimized. The model can play purposes for overall investment risk reduction.
Highlights
With Chinese rapid economic development, the society's demand for electricity is increasing
The model, the change in the minimum expected return ρ can obtained mean-Conditional Value-at-Risk (CVaR) efficient frontier, which is equivalent to the objective function for maximize revenue while meeting certain risk constraints
It is proved thatܨఉሺݔ, ߙሻ can replace the optimization problem hazard function ∅ఉሺܺሻ.The study (Glasserman et al, 2002) gives two theorems to prove that optimization problems formula (1) and formula (3) have the same solution with the following optimization problem formula (4) and formula (5): CVaR-based electricity portfolio optimization allocation model: High-yield power project development is always accompanied by high risk
Summary
With Chinese rapid economic development, the society's demand for electricity is increasing. The model, the change in the minimum expected return ρ can obtained mean-CVaR efficient frontier, which is equivalent to the objective function for maximize revenue while meeting certain risk constraints.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Research Journal of Applied Sciences, Engineering and Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
