Adaptive Robust Optimization With Dynamic Uncertainty Sets for Multi-Period Economic Dispatch Under Significant Wind

By Á. Lorca, Andy X. Sun. IEEE Transactions on Power Systems, 2014

406 citations recorded by the source corpus.

The exceptional benefits of wind power as an environmentally responsible renewable energy resource have led to an increasing penetration of wind energy in today's power systems. This trend has started to reshape the paradigms of power system operations, as dealing with uncertainty caused by the highly intermittent and uncertain wind power becomes a significant issue. Motivated by this, we present a new framework using adaptive robust optimization for the economic dispatch of power systems with high level of wind penetration. In particular, we propose an adaptive robust optimization model for multi-period economic dispatch, and introduce the concept of dynamic uncertainty sets and methods to construct such sets to model temporal and spatial correlations of uncertainty. We also develop a simulation platform which combines the proposed robust economic dispatch model with statistical prediction tools in a rolling horizon framework. We have conducted extensive computational experiments on this platform using real wind data. The results are promising and demonstrate the benefits of our approach in terms of cost and reliability over existing robust optimization models as well as recent look-ahead dispatch models.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/71adc6621bff4b3d2d65bba1c7f47645964c4865

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Citations in the corpus

Data-driven Affinely Adjustable Distributionally Robust Unit Commitment (2018, background): “Recently, dynamic uncertainty set [29], [37] and data-driven uncertainty set [38] have been proposed to reduce the conservatism of RO.”
Strategic Bidding for a Virtual Power Plant in the Day-Ahead and Real-Time Markets: A Price-Taker Robust Optimization Approach (2016, methodology): “In those cases in which the VPP includes multiple wind farms, the uncertainty modeling technique proposed in [24] can be used to forecast the confidence bounds of the WP productions dynamically, while considering their spatio-temporal correlation.”
Distributionally Robust Co-Optimization of Energy and Reserve Dispatch (2016, methodology): “BLP (A3) can be solved by the alternating direction method in [27] and [44], or can be reformulated as a MILP such as the method in [23], [31], and”