Better Weather Forecasting Reduces Wind Integration Costs
October 13, 2014
By Amelia Schlusser
Wind energy is an increasingly cost-effective source of electricity. According to Lazard’s Levelized Cost of Energy Analysis version 8.0, the levelized cost of unsubsidized wind energy currently ranges from $37 to $81 per megawatt hour (MWh). When the calculation includes federal tax incentives, levelized costs fall to between $14 and $67 per MWh. In contrast, the levelized cost of natural gas combined cycle technologies ranges from $61 to $87 per MWh, while the cost of coal ranges from $66 to $151 per MWh. On a levelized cost basis, therefore, wind energy is currently cost competitive with conventional fossil fuel resources.
However, the intermittent nature of wind energy presents challenges for integrating this resource onto the grid. Grid operators face significant uncertainty regarding the availability of wind energy, which varies significantly from hour-to-hour and day-to-day. This variability forces grid operators to make rapid adjustments to accommodate load increases and decreases that fluctuate with weather conditions. Operators must keep other generating resources on reserve to provide back-up power for low-wind periods, which can add significant cost on a per-MWh basis.
Idaho Power Company recently developed a new forecasting tool that allows grid operators to more cost-effectively integrate variable wind energy onto the grid. This Renewables Integration Tool, or RIT, consists of a number of models and databases that forecast hourly and daily wind conditions and project the amount of wind energy the utility can procure on an hourly basis. The RIT incorporates data on weather conditions, turbine performance, and supply and demand conditions within Idaho Power’s service territory, and allows the utility to better predict wind energy availability from 72 to 180 hours into the future.
During the first quarter of 2014, Idaho Power determined that the RIT was 26% to 32% more accurate than the utility’s previous forecasting methods. This increased forecasting accuracy has enabled the utility to reduce grid integration and operating costs by $287,000 over a three-month period. While these cost savings are significant, Idaho Power notes that the accuracy of the RIT’s forecasts vary during periods of unpredictable weather, and plans to continue refining the software to further improve the utility’s ability to integrate wind energy onto the system.
While the electrical sector still has a long way to go to fully integrate intermittent renewable energy generation onto the grid, better forecasting methods such as Idaho Power’s RIT enable utilities to integrate additional renewable resources at a lower cost to consumers. These reduced integration costs in turn increase the value of renewable energy. Other utilities should follow in Idaho Power’s footsteps to develop customized forecasting tools to facilitate integrating variable renewable energy resources onto the grid.