The Integration Costs of Large-Scale Wind Power

The Integration Costs of Large-scale Wind Power

Roger Lueken, Carnegie Mellon Unversity, 240-476-8547,

Jared Moore, Carnegie Mellon University, 317-985-7822,

Jay Apt, Carnegie Mellon University, 412-268-3003,

Overview

Renewable sources such as wind induce system-level costs into the management of the electricity grid that are generally not included in traditional levelized cost of electricity (LCOE) estimates. These costs arise from the need to manage the inherent variability and unpredictability of such sources. Renewable generators also provide societal benefits in the form of pollution reduction that are generally unpriced and left out of traditional LCOE estimates. In this work, we develop a comprehensive LCOE for wind energy that includes these unpriced costs and benefits.

Wind and solar power are driven by meteorological and astronomical processes, which make their generation variable. Variability refers to uncontrollable changes in power output. These variations occur at all timescales from second-to-second changes to annual variations that may affect long term planning. We term costsarising from fluctuations from 48 hours to real time "operational costs". Although forecasting tools are in use, no forecast is perfect, and the unforecast fluctuations of renewable energy can cause the grid to be operated suboptimally and require additional reserves.

Non-operational costs include transmission costs, curtailment costs, and long-term capacity costs. An unpriced benefit of renewable energy is the reduction in pollutants due to displacement of fossil-fueled. In this analysis, we define and detail each of these cost and benefit categories and describe our methods for quantifying each for the PJM Interconnection. Each of these findings are informed by our review of existing literature and internal modelling and analysis efforts.

The observed distributions of these costs are inherently wide, since local conditions and historical cost trends make selecting a single representative cost number misleading. Thus, we use the standard technique for treating uncertainty: creating distributions for each parameter that contributes to the total cost, then statistically sampling from these distributions to create the final cost distribution.

Methodology

We divide wind integration costs and benefits into five separate categories:

-Operational costs

-Transmission costs

-Curtailment costs

-Long-term capacity costs

-Pollution reduction benefits

For each category, we provide a concise definition, discuss methods in existing literature for calculating the associated cost or benefit, and provide an estimate for the PJM Interconnection. Specifically, we quantify costs and benefits for two scenarios: a ‘current’ scenario, which represents PJM as it is today with small amounts of wind and high reserve margins, and a ‘future’ scenario, with 20% of energy from wind and low reserve margins.

Because the costs and benefits associated with each category are highly uncertain, we develop probabilistic distributions of the costs/benefits for each category. We then use Monte Carlo simulation to calculate a probabilistic estimate of the ‘true’ LCOE of wind power in both the current and future scenarios.

Results

We find that the unpriced integration costs of wind energy are highly uncertain, but average $13/MWh in the current scenario and $30/MWh in the future scenario. These are cost increases of 20% - 40% over the traditional LCOE estimates that only include capital costs and operations and maintenance costs. However, the pollution reduction benefits of wind power greatly exceed the unpriced costs of wind in both scenarios. In both the current and future scenario, it is highly probable that wind has a positive net benefit to society, meaning pollution reduction benefits exceed all costs, including unpriced integration costs and traditional LCOE costs.

Both the unpriced costs and benefits of wind are highly uncertain. We find that in the current scenario, the cost of transmission is the primary driver of uncertainty. In the future scenario with 20% wind and low reserve margins, the cost of building backup generation capacity for variable wind is the largest driver of uncertainty.

Conclusions

We conclude that although the unpriced costs and benefits of wind are highly uncertain, it is highly probable that wind provides net benefits to society both currently and in the future. Due to the complex nature of the electrical system and inherent difficulties in estimating these integration costs, we believe that probabilistic methods are the appropriate tool for these types of analyses. Simple point estimates of costs and benefits do not capture the entire story and are likely to provide misleading results.

References

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