Scientific

The dynamics of the atmospheric boundary layer (ABL) play a fundamental role in wind farm power production, governing the velocity field that enters the farm as well as the turbulent mixing that regenerates energy for extraction at downstream rows. Understanding the dynamic interactions between turbines, wind farms, and the ABL can therefore be beneficial in improving the efficiency of wind farm design and control approaches. This talk introduces a suite of models that exploit this knowledge to improve predictions of both static and dynamic conditions in the wind farm. We first introduce the area localized coupled (ALC) model, which couples the steady state solution of a dynamic wake model with a localized top-down model that focuses on the effect of the farm on the ABL. The ALC model improves the accuracy of power output and local velocity predictions over both conventional wake models and top-down models, while extending the applicability of this type of coupled model to arbitrary wind farm layouts. In the second part of the talk, we focus on using attributes of the turbulent ABL to provide improved models for power production and wake behavior under turbine yawing, which has been shown to increase turbine power output potential. Finally, we demonstrate how these ideas can be extended to a control setting.

Judith Sayers is the President of the Nuu-chah-nulth Tribal Council, a lawyer, renewable energy leader, and chancellor of Vancouver Island University. This presentation was given ahead of a panel discussion on Tackling Climate Change and the Just Transition to Renewable Energy.

Gaël Giraud is the Founding director of the Georgetown University Environmental Justice Program. He is also a professor at Georgetown and the former chief economist of the French Development agency CNRS. This presentation was given ahead of a panel discussion on Tackling Climate Change and the Just Transition to Renewable Energy in which Dr. Giraud also participated.

We formulate an equilibrium model of intraday trading in electricity markets. Agents face balancing constraints between their customers consumption plus intraday sales and their production plus intraday purchases. They have continuously updated forecast of their customers consumption at maturity. Forecasts are prone to idiosyncratic noise as well as common noise (weather). Agents production capacities are subject to independent random outages, which are each modeled by a Markov chain. The equilibrium price is defined as the price that minimizes trading cost plus imbalance cost of each agent and satisfies the usual market clearing condition. Existence and uniqueness of the equilibrium are proved, and we show that the equilibrium price and the optimal trading strategies are martingales. The main economic insights are the following. (i) When there is no uncertainty on generation, it is shown that the market price is a convex combination of forecasted marginal cost of each agent, with deterministic weights. Furthermore, the equilibrium market price is consistent with Almgren and Chriss's model, and we identify the fundamental part as well as the permanent market impact. It turns out that heterogeneity across agents is a necessary condition for Samuelson's effect to hold. We show that when heterogeneity lies only on costs, Samuelson's effect holds true. A similar result stands when heterogeneity lies only on market access quality. (ii) When there is production uncertainty only, we provide an approximation of the equilibrium for large number of players. The resulting price exhibits increasing volatility with time. (Joint work with Andrea Cosso and René Aïd)

Electricity markets balance an increasingly intermittent supply with price-inelastic demand, while climate change and electrification of mobility are contributing to transforming diurnal and seasonal demand patterns. Electricity systems face an increasing level of stochasticity, and market participants need to inform their dispatch decisions based on 24-hour price forecasts for participation in Day-Ahead Markets, which in turn depend on supply and demand forecasts. The arrival of grid-scale electricity storage is also creating new scope for prices forecasts, while smaller scale storage systems act as price takers. In the long term, large-scale deployment of grid-scale electricity storage has the potential of significantly reducing price variation through arbitrage, which could shift the “value added” of forecasting from short-term (intra-day) to long-term predictions, and from supporting operational (dispatch) decisions to supporting capacity (investment) decisions.