On the Viability of Stochastic Economic Dispatch for Real-Time Energy Market Clearing

TL;DR

This paper demonstrates that stochastic economic dispatch, specifically the SLAD formulation with an accelerated Benders' decomposition, is now computationally feasible and offers significant benefits for real-time energy market clearing amidst renewable energy uncertainty.

Contribution

It introduces the SLAD formulation for real-time market clearing and develops an accelerated Benders' decomposition to solve it efficiently, showing practical scalability.

Findings

SLAD solves in under 5 minutes on industry-sized grids.

SLAD achieves over 50% cost savings compared to flexiramp products.

SLAD is more robust to forecasting methodology.

Abstract

Over the past decade, the rapid adoption of intermittent renewable energy sources (RES), especially wind and solar generation, has posed challenges in managing real-time uncertainty and variability. In the U.S., Independent System Operators (ISOs) solve a security-constrained economic dispatch (SCED) every five minutes to clear real-time electricity markets, co-optimizing energy dispatch and reserve to minimize costs while meeting physical and reliability constraints. All SCED formulations in the U.S. are deterministic and mostly consider a single time period, limiting their effectiveness in managing real-time operational uncertainty from RES intermittency. This limitation is highlighted by the recent introduction of multiple short-term ramping products in U.S. markets, aiming to bridge the gap between deterministic and stochastic SCED formulations. While stochastic formulations address uncertainty in a unified and endogenous manner, their adoption has been hindered by high computational costs and, to a lesser extent, the availability of probabilistic forecasts. This paper revisits these concerns and demonstrates that stochastic economic dispatch is now a viable technology for real-time market clearing. It introduces the stochastic look-ahead dispatch (SLAD) formulation for real-time market clearing and presents an accelerated Benders' decomposition to solve it efficiently. Extensive experiments on a real, industry-sized transmission grid demonstrate the computational scalability of the proposed approach, with SLAD instances being solved in under 5 minutes. Furthermore, results show that SLAD provides more than 50% additional savings compared to flexiramp products and is more robust to the forecasting methodology. Therefore, SLAD is a promising approach for uncertainty management in real-time electricity markets.