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This is an excerpt from the Fourth Quarter 2013 edition of the Wind Program R&D Newsletter.

The amount of wind power in current electricity supply portfolios around the world is rapidly increasing. To help ensure the power system's reliability and adequacy, grid operators are actively pursuing the development of new rules that fully consider the characteristics of wind power with its variability and forecasting errors. In Dynamic Scheduling of Operating Reserves in Co-Optimized Electricity Markets With Wind Power, an article published in the January 2014 issue of IEEE Transactions on Power Systems, researchers at the U.S. Department of Energy's Argonne National Laboratory are proposing a new concept for operating reserves to help address the challenges of incorporating larger quantities of renewable energy resources into the nation's power grid.

Uncertainty in power systems has traditionally been addressed by scheduling additional generation capacity as an operating reserve that is set by deterministic rules, such as being a fixed percentage of load and/or the largest contingency in the system. There is growing interest, though, in using alternative approaches to ensure reliable and efficient system operations with large shares of renewable energy. Many researchers have recently looked into the use of stochastic programming for this purpose. One downside of the use of stochastic unit commitment models, however, is that they present heavy computational challenges.

At the same time, a longstanding challenge in electricity market design is that investors in new generation capacity need accurate price signals to determine whether and when to expand their capacity and what technologies to build. It is therefore important that the prices of energy and reserves in power markets reflect the supply and demand balance and provide efficient incentives for both operation and investment. For example, if a market is in a "scarcity situation," the prices of both energy and reserves should increase to reflect this reality, thereby signaling a need for new capacity. As renewable energy use continues to expand, forecasting errors will play a more important role in determining the needs for operating reserves and this should also influence prices.

To address these two challenges of integrating wind power more efficiently and at the same time improving scarcity pricing and investment incentives, the Argonne team proposes a probabilistic methodology to estimate a dynamic demand curve for operating reserves. This demand curve represents the amount a system operator is willing to pay for these services. The curve is quantified by the cost of unserved energy and the expected loss of load while accounting for uncertainty from generator contingencies, load forecasting errors, and the predicted uncertainty in the wind power forecast. In contrast to other proposed stochastic scheduling methods, the demand curve for reserves can easily be implemented within current market structures that co-optimize energy and reserves. In fact, several independent system operators in the United States already have simple demand curve structures in place, but they are static and do not account for the impact of forecasting errors.

The Argonne team tested the proposed operating reserve strategies in a case study of an electricity market for energy and reserves with centralized unit commitment and economic dispatch. The objective of the scheduling model is to maximize the total "social welfare," i.e., the difference between the consumer benefits and the costs of operating the power plants for the next day. The performance of the proposed demand curve model is compared to those from three other models where the operating reserve requirements do not respond to prices. Case study findings include the following.

  • Prices for energy and reserves with the dynamic demand curve are less volatile than the ones with price-inelastic reserve requirements because of the price-responsive demand for reserves.
  • The dynamic demand curve introduces some additional flexibility in system dispatch and therefore provides significant operational benefits compared to the other models—benefits that are most significant in low-load periods.
  • Use of the demand curve concept provides energy and reserve prices that better reflect the marginal reliability of the system as a function of the level of operating reserves.
  • The demand curve model offers a more equitable approach to compensating generation capacity for providing reserves through a market mechanism.

"Overall, our study shows that the proposed demand curve for reserves, which accounts for wind power forecast uncertainty, can contribute to more efficient market operations with renewable energy through improved price incentives for short-term operation and long-term system expansion," says Zhi Zhou, lead author of the study.