Valuing Uncertainties in Wind Generation: An Agent-Based Optimization Approach
Daniel Shen, Marija Ilic
TL;DR
The paper tackles the problem of integrating wind and other VRE uncertainties by shifting uncertainty management from quantity-based constraints to price-based signals. It introduces uncertainty-aware offers derived from a CVaR optimization framework within a DYMONDS-inspired distributed setting and evaluates them on a modified RTS-GMLC system against a centralized percentile-dispatch baseline. Results indicate that risk-aware offers can substantially reduce revenue volatility for wind (by roughly $50$–$75\%$) with only a small increase (about $1\%$) in total generator payments, though some scenarios may yield negative wind profits due to distribution estimation challenges. The work highlights market-design implications for price-domain uncertainty management, privacy-preserving distributed optimization, and the need for robust, data-driven estimation of price and shortfall distributions to ensure reliable, economically sound operation.
Abstract
The increasing integration of variable renewable energy sources such as wind and solar will require new methods of managing generation uncertainty. Existing practices of uncertainty management for these resources largely focuses around modifying the energy offers of such resources in the quantity domain and from a centralized system operator consideration of these uncertainties. This paper proposes an approach to instead consider these uncertainties in the price domain, where more uncertain power is offered at a higher price instead of restricting the quantity offered. We demonstrate system-level impacts on a modified version of the RTS-GMLC system where wind generators create market offers valuing their uncertainties over scenario set of day-ahead production forecasts. The results are compared with a dispatch method in which wind energy is offered at zero marginal price and restricted based on the forecast percentile.