Influence of wind power on hourly electricity prices and GHG (greenhouse gas) emissions: Evidence that congestion matters from Ontario zonal data

• Published in: Energy (Oxford) Vol. 66; pp. 458 - 469
• Main Authors: Amor, Mourad Ben, Billette de Villemeur, Etienne, Pellat, Marie, Pineau, Pierre-Olivier
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.03.2014; Elsevier
• Subjects: Applied sciences; Congestion; Electricity prices; Electricity pricing; Energy; Estimates; Exact sciences and technology; Fluctuation; Greenhouse effect; Greenhouse gases; Marginal GHG emissions; Natural energy; Reduction; Wind energy; Wind power generation; Canada, Ontario; Marginal GHG emissions; Wind energy; Congestion; Electricity prices; Electricity; Price; Electric power production; Wind power; Pollutant emission
• DOI: 10.1016/j.energy.2014.01.059
• ISSN: 0360-5442

With the growing share of wind production, understanding its impacts on electricity price and greenhouse gas (GHG) emissions becomes increasingly relevant, especially to design better wind-supporting policies. Internal grid congestion is usually not taken into account when assessing the price impact of fluctuating wind output. Using 2006–2011 hourly data from Ontario (Canada), we establish that the impact of wind output, both on price level and marginal GHG emissions, greatly differs depending on the congestion level. Indeed, from an average of 3.3% price reduction when wind production doubles, the reduction jumps to 5.5% during uncongested hours, but is only 0.8% when congestion prevails. Similarly, avoided GHG emissions due to wind are estimated to 331.93 kilograms per megawatt-hour (kg/MWh) using all data, while for uncongested and congested hours, estimates are respectively 283.49 and 393.68 kg/MWh. These empirical estimates, being based on 2006–2011 Ontario data, cannot be generalized to other contexts. The main contribution of this paper is to underscore the importance of congestion in assessing the price and GHG impacts of wind. We also contribute by developing an approach to create clusters of data according to the congestion status and location. Finally, we compare different approaches to estimate avoided GHG emissions. •Accounting for congestion is important for assessing the price and GHG impacts of Wind. A new method is introduced to create cluster of data with respect to the congestion status and geographic coverage.•Inter-cluster differences in statistical distribution result in correlation estimates at the aggregate level that are almost unrelated to intra-cluster estimates.•Four different approaches are introduced and compared to estimate avoided GHG emissions, thanks to wind generation.•Given the variability of Wind generation impacts, constant feed-in tariffs are very likely to be introduce improper incentives.