An exact and scalable problem decomposition for security-constrained optimal power flow

Velloso, Alexandre; Hentenryck, Pascal Van; Johnson, Emma S.

doi:10.1016/j.epsr.2020.106677

Cited by 17 publications

(22 citation statements)

References 18 publications

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“…The results also show that the predictions can be used to seed a load flow optimization that returns a feasible solution within 0.03% of the AC-OPF objective, while reducing the running times by a factor close to 3. Future work will focus on generalizing the approach to security-constrained OPF, by studying how to merge the algorithm proposed in [3] to the AC setting and the proposed 2-stage approach.…”

Section: Discussionmentioning

confidence: 99%

“…. However, assume that flows (p f 4,3 , q f 4,3 ), along with the voltage magnitudes v 3 , v 4 are fixed and respect constraints ( 4), (5r), (5i) associated with line (3,4). In that case, the setpoints for the generator at bus 2 can be computed without the knowledge of (p d 4 , q d 4 , p d 5 , q d 5 , p d 6 , q d 6 ): the vector (p f 4,3 , q f 4,3 , v 3 ) encodes all the information from area 2 needed to compute the generator setpoint.…”

Section: Capturing Flows On Coupling Linesmentioning

confidence: 99%

“…It determines the least-cost generation dispatch that meets the demand of the power grid subject to engineering and physical constraints. It is non-convex and NP-hard [1], and the basic block of many applications, including security-constrained OPF [2], [3], security-constrained unit commitment [4], optimal transmission switching [5], capacitor placement [6], and expansion planning [7], among others.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Spatial Network Decomposition for Fast and Scalable AC-OPF Learning

Chatzos¹,

Mak²,

Hentenryck³

2021

Preprint

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This paper proposes a novel machine-learning approach for predicting AC-OPF solutions that features a fast and scalable training. It is motivated by the two critical considerations: (1) the fact that topology optimization and the stochasticity induced by renewable energy sources may lead to fundamentally different AC-OPF instances; and (2) the significant training time needed by existing machine-learning approaches for predicting AC-OPF. The proposed approach is a 2-stage methodology that exploits a spatial decomposition of the power network that is viewed as a set of regions. The first stage learns to predict the flows and voltages on the buses and lines coupling the regions, and the second stage trains, in parallel, the machine-learning models for each region. Experimental results on the French transmission system (up to 6,700 buses and 9,000 lines) demonstrate the potential of the approach. Within a short training time, the approach predicts AC-OPF solutions with very high fidelity and minor constraint violations, producing significant improvements over the state-of-the-art. The results also show that the predictions can seed a load flow optimization to return a feasible solution within 0.03% of the AC-OPF objective, while reducing running times significantly.

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Section: Discussionmentioning

confidence: 99%

Section: Capturing Flows On Coupling Linesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spatial Network Decomposition for Fast and Scalable AC-OPF Learning

Chatzos¹,

Mak²,

Hentenryck³

2021

Preprint

Self Cite

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“…Recent studies in SCACOPF have focused on improving the representation and tractability of realistic coupling constraints between the base case and contingencies (Dvorkin et al 2018, Velloso et al 2020, which permits not only optimizing the base case solution to make it secure, but also optimize the primary response of generators to contingencies. At the same time, novel machine learning techniques have begun to be proposed and theoretically validated for SCACOPF (Venzke and Chatzivasileiadis 2021), opening the door for future progress in the area, as is required by the evolving needs of power grids.…”

Section: State-of-the-art and Contributionsmentioning

confidence: 99%

Solving realistic security-constrained optimal power flow problems

Petra,

Aravena

2021

Preprint

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We present a decomposition approach for obtaining good feasible solutions for the security-constrained alternating-current optimal power flow (SCACOPF) problem at an industrial scale and under real-world time and computational limits. The approach aims at complementing the existing body of literature on bounding the problem via convex relaxations. It was designed for the participation in ARPA-E's Grid Optimization (GO) Competition Challenge 1. The challenge focused on a near-real-time version of the SCACOPF problem where a base case operating point is optimized taking into account possible single-element contingencies, after which the system adapts its operating point following the response of automatic frequency drop controllers and voltage regulators. Our solution approach for this problem relies on state-of-the-art nonlinear programming algorithms and employs nonconvex relaxations for complementarity constraints, a specialized two-stage decomposition technique with sparse approximations of recourse terms, and contingency ranking and pre-screening. The paper also outlines the salient features of our implementation, such as fast model functions and derivatives evaluation, warm-starting strategies, and asynchronous parallelism. We discuss the results of the independent benchmark of our approach done by ARPA-E's GO team in Challenge 1, which found that our methodology consistently produces high quality solutions across a wide range of network sizes and difficulty. Finally, we conclude by outlining potential extensions and improvements of our methodology.

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“…In the US, TSOs like MISO and PJM execute a SCED every five minutes, which means that the optimization problem must be solved in an even tighter time frame, i.e., well under a minute [2]. Security constraints, which enforce robustness against the loss of any individual component, render SCED models particularly challenging for large systems [3]- [5] unless only a subset of contingencies is considered. With more distributed resources and increased operational uncertainty, such computational bottlenecks will only become more critical [2].…”

Section: Introductionmentioning

confidence: 99%

Learning Optimization Proxies for Large-Scale Security-Constrained Economic Dispatch

Chen¹,

Park²,

Tanneau³

et al. 2021

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The Security-Constrained Economic Dispatch (SCED) is a fundamental optimization model for Transmission System Operators (TSO) to clear real-time energy markets while ensuring reliable operations of power grids. In a context of growing operational uncertainty, due to increased penetration of renewable generators and distributed energy resources, operators must continuously monitor risk in real-time, i.e., they must quickly assess the system's behavior under various changes in load and renewable production. Unfortunately, systematically solving an optimization problem for each such scenario is not practical given the tight constraints of real-time operations. To overcome this limitation, this paper proposes to learn an optimization proxy for SCED, i.e., a Machine Learning (ML) model that can predict an optimal solution for SCED in milliseconds. Motivated by a principled analysis of the market-clearing optimizations of MISO, the paper proposes a novel ML pipeline that addresses the main challenges of learning SCED solutions, i.e., the variability in load, renewable output and production costs, as well as the combinatorial structure of commitment decisions. A novel Classification-Then-Regression architecture is also proposed, to further capture the behavior of SCED solutions. Numerical experiments are reported on the French transmission system, and demonstrate the approach's ability to produce, within a time frame that is compatible with real-time operations, accurate optimization proxies that produce relative errors below 0.6%.

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An exact and scalable problem decomposition for security-constrained optimal power flow

Cited by 17 publications

References 18 publications

Spatial Network Decomposition for Fast and Scalable AC-OPF Learning

Spatial Network Decomposition for Fast and Scalable AC-OPF Learning

Solving realistic security-constrained optimal power flow problems

Learning Optimization Proxies for Large-Scale Security-Constrained Economic Dispatch

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