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Deciding on the support schemes for upcoming wind farms in competitive electricity markets

Zamani-Dehkordi, Payam, Rakai, Logan, Zareipour, Hamidreza
Energy 2016 v.116 pp. 8-19
algorithms, case studies, electricity, electricity costs, energy, income, issues and policy, markets, models, prices, regression analysis, wages and remuneration, wind, wind farms, wind power, Alberta, Ontario
A variety of policies have driven escalated global growth of wind-power generation in recent years. However, wind production should be supported through market-based schemes that avoid overcompensation. The present paper aims to determine a justified support scheme for upcoming wind farms in competitive electricity markets. Clustering tools and non-parametric regression algorithms are employed to model the price-formation process in the market and estimate the revenue of forthcoming wind facilities. Accordingly, the premium paid to an upcoming wind farm is calculated by incorporating its estimated revenue and levelized cost of energy. Then, the impact of the proposed wind project on wholesale and retail electricity prices is modelled based on the achieved non-parametric regression models. The methodology is applied to two wind farms in the Alberta and Ontario electricity markets in Canada as the case studies. Our results indicate that electricity consumers in Ontario should pay a higher premium for each unit of energy generated by the wind farm due to its lower revenue from the market. However, it is observed in both markets that the impact of wind farms on wholesale prices exceed the remuneration paid to them and thus, electricity consumers experience a decrease in their ultimate electricity costs.