Chance-Constrained Optimal Power Flow: Risk-Aware Network Control under Uncertainty

By D. Bienstock, M. Chertkov, Sean Harnett. SIAM Review, 2012

537 citations recorded by the source corpus.

When uncontrollable resources fluctuate, optimal power flow (OPF), routinely used by the electric power industry to redispatch hourly controllable generation (coal, gas, and hydro plants) over control areas of transmission networks, can result in grid instability and, potentially, cascading outages. This risk arises because OPF dispatch is computed without awareness of major uncertainty, in particular fluctuations in renewable output. As a result, grid operation under OPF with renewable variability can lead to frequent conditions where power line flow ratings are significantly exceeded. Such a condition, which is borne by our simulations of real grids, is considered undesirable in power engineering practice. Possibly, it can lead to a risky outcome that compromises grid stability---line tripping. Smart grid goals include a commitment to large penetration of highly fluctuating renewables, thus calling to reconsider current practices, in particular the use of standard OPF. Our chance-constrained (CC) OPF co...

Subfields: Electrical and Electronic Engineering, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/439b4ad017a96546d95b58c57f6b509556b78fa1

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Citations in the corpus

Distributionally Robust Chance Constrained Optimal Power Flow with Renewables: A Conic Reformulation (2018, methodology): “Recently, including [4], [5], [8], [9], many studies tried to approximate the two-side chance constrained set (4) by two single-sided chance constraints.”
Chance-Constrained Economic Dispatch With Non-Gaussian Correlated Wind Power Uncertainty (2017, methodology): “A comparison is included in Section V. 3) If the GMM has only one Gaussian component, which means a Gaussian assumption, then the proposed method can degenerate into a SOCP as in [10].”
Uncertainty Sets for Wind Power Generation (2015, methodology): “Thismethod is suitable foruncertaintysets in theRUC [2] andRCC-OPF [3], [4],which can be interpreted in terms variability and uncertainty ofWPG.”
Chance-Constrained Two-Stage Unit Commitment Under Uncertain Load and Wind Power Output Using Bilinear Benders Decomposition (2016, background): “ecial cases, chance constraint can be converted into a closed form expression and the whole model can be reformulated into a regular mixed integer programming (MIP) model (e.g., [20, 24, 26, 29]). In [30, 31], such property is used to study chance constrained optimal power flow problems subject to u”
Data-driven Affinely Adjustable Distributionally Robust Unit Commitment (2018, methodology): “As for the continuous dispatch problem, we adopt the affinely adjustable approach [21], [26]–[29], i.”
Solving Multiperiod OPF Problems Using an AC-QP Algorithm Initialized With an SOCP Relaxation (2017, methodology): “Some recent techniques for addressing uncertainty include adding a heuristically chosen set of scenarios to the OPF problem [50]– [52], assuming a closed-form uncertainty distribution to analytically reformulate the OPF problem [53], [54], and scenario approaches that offer performance guarantees [5”
Security Constrained Optimal Power Flow with Distributionally Robust Chance Constraints (2015, background): “The same type of Gaus sian reformulation is performed for an OPF without security constraints in [6].”
Distributionally Robust Chance-Constrained Optimal Power Flow With Uncertain Renewables and Uncertain Reserves Provided by Loads (2017, methodology): “Assuming Gaussian uncertainty, the CC-OPF can be reformulated as a convex program [13], [14].”
On the Interaction Between Aggregators, Electricity Markets and Residential Demand Response Providers (2020, methodology): “Leveraging the reformulation of the chance constraint and these linearizations, one obtains two MIQCP problems that can be solved with commercial solvers (Cplex, Gurobi) [34].”