introduction of an efficient incentive for investment in wind turbines based on system dynamics modelling approach

Due to the stochastic nature of wind energy, allocating an appropriate investment incentive for wind generation technology (wgt) is a complicated issue. we propose an improvement on the traditional incentive, known as capacity payment mechanism (cpm), to reward the wind generators based on their performance exogenously affected by the wind energy potential of the location where the turbines are installed, and therefore, lead the investments towards locations with more generation potential. in cpm, a part of investment cost of each generator is recovered through fixed payments. however, in our proposal, wind generators are rewarded according to dynamic forecasts of the wind energy potential of the wind farm where they are located. we use an auto-regressive moving average (arma) model to forecast the wind speed fluctuations in long-term while capturing the auto-correlation of wind velocity variation in consecutive time intervals. using the system dynamics (sd) modelling approach a competitive electricity market is designed to examine the efficiency of the proposed incentive. performing a simulation analysis, we conclude that while a fixed cpm for wind generation can decrease the loss of load durations and average prices in long-term, the proposed improvement can provide quite similar results more efficiently.

introduction of an efficient incentive for investment in wind turbines based on system dynamics modelling approach

due to the stochastic nature of wind energy, allocating an appropriate investment incentive for wind generation technology (wgt) is a complicated issue. we propose an improvement on the traditional incentive, known as capacity payment mechanism (cpm), to reward the wind generators based on their performance exogenously affected by the wind energy potential of the location where the turbines are installed, and therefore, lead the investments towards locations with more generation potential. in cpm, a part of investment cost of each generator is recovered through fixed payments. however, in our proposal, wind generators are rewarded according to dynamic forecasts of the wind energy potential of the wind farm where they are located. we use an auto-regressive moving average (arma) model to forecast the wind speed fluctuations in long-term while capturing the auto-correlation of wind velocity variation in consecutive time intervals. using the system dynamics (sd) modelling approach a competitive electricity market is designed to examine the efficiency of the proposed incentive. performing a simulation analysis, we conclude that while a fixed cpm for wind generation can decrease the loss of load durations and average prices in long-term, the proposed improvement can provide quite similar results more efficiently.