Distributed Stochastic Market Clearing with High-Penetration Wind Power

TL;DR

This paper presents a risk-aware stochastic market clearing model for high-penetration wind power integration, minimizing social costs and system re-dispatch risks using CVaR and a distributed ADMM solver.

Contribution

It introduces a convex stochastic market clearing framework incorporating CVaR for wind power risk mitigation, with a scalable distributed solution method.

Findings

Effective risk mitigation of wind forecast errors demonstrated.

Scalable distributed solver achieves convergence on large systems.

Numerical results validate the proposed framework's advantages.

Abstract

Integrating renewable energy into the modern power grid requires risk-cognizant dispatch of resources to account for the stochastic availability of renewables. Toward this goal, day-ahead stochastic market clearing with high-penetration wind energy is pursued in this paper based on the DC optimal power flow (OPF). The objective is to minimize the social cost which consists of conventional generation costs, end-user disutility, as well as a risk measure of the system re-dispatching cost. Capitalizing on the conditional value-at-risk (CVaR), the novel model is able to mitigate the potentially high risk of the recourse actions to compensate wind forecast errors. The resulting convex optimization task is tackled via a distribution-free sample average based approximation to bypass the prohibitively complex high-dimensional integration. Furthermore, to cope with possibly large-scale dispatchable loads, a fast distributed solver is developed with guaranteed convergence using the alternating direction method of multipliers (ADMM). Numerical results tested on a modified benchmark system are reported to corroborate the merits of the novel framework and proposed approaches.