An Approach for Evaluating Incentive Policy of Wind Resources Considering the Uncertainties in the Deregulated Power Market

Author(s):
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Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:
Background and Objectives
Development of intermittent wind generation has necessitated the inclusion of several creative approaches in modeling the deregulated power market with presence of wind sources. The uncertain nature of wind resources will cause the private companies meet several risk in their medium and long term planning in a restructured power market. In addition to considering the uncertainties such as load, fuel costs, wind power generation and technical actions of rivals for modeling the restructured power market, the regulatory policies i.e incentive policy for wind resources and Carbon tax should be assumed in this approach.
Methods
The first contribution of this article is to propose a developed mathematical model in order to evaluate the medium term deregulated power market by assuming the hybrid wind-thermal power plants. The second contribution is to evaluate the impact of different types of Feed in Tariff supports on Market Clearing Price, Expected Cost for Government, profits and contribution of each firm in electricity generation in the restructured power market. Also the scenario based method has been used to generate the scenarios for wind uncertainties and then their reliability validate based on the statistical methods.
Results
The proposed mathematical model in the first contribution is calculated for each season and load levels based on the proposed wind scenarios. The functionality and feasibility of this model is demonstrated by simulation studies.
Conclusion
The proposed model in this article can be so useful for evaluating the different types of incentive policies for renewable energies. Moreover, this study confirms the previous researches that selected the Feed in Tariff as an efficient approach for developing the wind resources.
Language:
English
Published:
Journal of Electrical and Computer Engineering Innovations, Volume:11 Issue: 1, Winter-Spring 2023
Pages:
203 to 216
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