Different capacity incentives like feed-in-tariff have been considered to encourage companies to invest in wind power units. One of the main challenges of the electricity market policymakers is the determination of this fixed payment based on limited funding in a way that the investment cost of wind units is compensated and the associated investment risk is reduced. The main contribution of this paper is the introduction of a method to manage the amount of payment or incentives during a time horizon to reach the targeted wind capacity and reduce its investment risk. In this regard, the ratio-based incentive is introduced.To study the effects of such a policy, the long-term behavior of the electricity market is simulated by a dynamic model, which is a useful tool for policymakers to analyze the effects of their policies. Then, conditional value at risk and value at risk concepts are used to measure the risk of wind capacity investment. The results illustrate that the ratio-based incentive is more effective than the feed-in-tariff in the context of decreasing the risk of investment, reducing total CO2 production, electricity price reduction, and speed of providing higher amounts of wind capacity.

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Various incentives are introduced for the expansion of electric vehicle fleets and electricity generation from renewable energy resources. Although many researchers studied the effect of these policies on the related sector, there is no study investigating the indirect effect of renewable energy incentives on the deployment of electric vehicles or the indirect effect of electric vehicle adoption policies on the long-term integration of renewable energy resources. The main contribution of this paper is to analyze the impact of the specific incentives on both deployment of electric vehicles in the transportation system and investment in capacity generation in the electricity market. For this purpose, a new framework was designed to analyze the effect of policies on the electric vehicle deployment and development of DC charging stations based on the system dynamics approach. Then, this framework was combined with the existing dynamic models of the electricity market to study the interaction and behavior of both coupled systems from the policymakers' perspective. The effect of policies implementation was interpreted in a mathematical framework and the Net Present Value method was used for assessing the investment in charging infrastructures. Simulation results of a case study in the United States and sensitivity analysis illustrate that increasing the wind capacity incentives accelerated the electrification of the transportation system and increasing the incentives for electrification of transportation system influences wind capacity positively. Moreover, the sensitivity of the electric vehicle adoption to gas price is more than the sensitivity of the wind capac ity penetration to gas price. INDEX TERMSDC charging stations, Electricity market, Electric vehicles deployment policies, Plug-in electric vehicles, Renewable capacity incentive, System dynamics, Wind capacity investment. NOMENCLATURE Abbreviations ESS Energy storage system RES Renewable energy source PEV Plug-in electric vehicle EVSE Electric vehicle supply equipment HC Hard coal units CCGT Combined cycle gas turbines GT Gas turbines NPV Net Present Value Variables DUCH Number of PEVs that are supplied in DC charging station d Time step (day) TNPEV Total number of plug-in electric vehicles