Corrective Control to Handle Forecast Uncertainty: A Chance Constrained Optimal Power Flow

Roald, Line; Misra, Sidhant; Krause, Thilo; Andersson, Göran

doi:10.1109/tpwrs.2016.2602805

Cited by 79 publications

(55 citation statements)

References 25 publications

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“…While these constraints must all be satisfied, only a limited number will be active at the optimal solution [1]. Since the transmission constraints can be numerically challenging and represent a computational bottleneck, this observation can be exploited to devise more efficient solution algorithms, using methods such as, e.g., constraint generation [2], [3].…”

Section: Introductionmentioning

confidence: 99%

Implied Constraint Satisfaction in Power System optimization: The Impacts of Load Variations

Roald

Molzahn

2019

2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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In many power system optimization problems, we observe that only a small fraction of the line flow constraints ever become active at the optimal solution, despite variations in the load profile and generation costs. This observation has farreaching implications not only for power system optimization, but also for the practical long-term planning, operation, and control of the system. We formalize this empirical observation for problems involving the DC power flow equations. We use a two-step constraint screening approach to identify constraints whose satisfaction is implied by other constraints in the problem, and can therefore be removed from the problem. For the first screening step, we derive analytical bounds that quickly identify redundancies in the flow limits on parallel lines. The second screening step uses optimization-based constraint screening, where we solve a (relaxed) optimization problem for each constraint to identify redundancies. Different from existing methods, we specifically focus our approach on large ranges of load variation such that the results are valid for long periods of time, thus justifying the computational overhead required for the screening method. Numerical results for a wide variety of standard test cases show that even with load variations up to ±100% of nominal loading, we are able to eliminate a significant fraction of the flow constraints. This large reduction in constraints may enable a range of possible applications. As one illustrative example, we demonstrate the computational improvements for the unit commitment problem obtained as a result of the reduced number of constraints.

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Section: Introductionmentioning

confidence: 99%

Implied Constraint Satisfaction in Power System optimization: The Impacts of Load Variations

Roald

Molzahn

2019

2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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“…With growing uncertainty from renewable generation and fluctuating demand (1) needs to be solved at a much faster time scale in order to adjust generation in response to uncertainty realization. Traditionally, these real-time adjustments are modeled in the OPF using an affine policy [1], [2], [3]. However, the affine policy can be restrictive and is sub-optimal with respect to feasibility and optimality [5].…”

Section: A the Case For Learning Optimal Power Flow Solutionsmentioning

confidence: 99%

“…Traditionally, the necessary real-time adjustments to the generation is modelled using an affine control policy [1], [2], [3], which mimics the behavior of the widely utilized automatic generation control (AGC). While affine policies are simple to handle computationally, they are restrictive, and can be sub-optimal in terms of cost and constraint enforcement [4].…”

Section: Introductionmentioning

confidence: 99%

Learning for DC-OPF: Classifying active sets using neural nets

Deka

Misra

2019

2019 IEEE Milan PowerTech

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The optimal power flow is an optimization problem used in power systems operational planning to maximize economic efficiency while satisfying demand and maintaining safety margins. Due to uncertainty and variability in renewable energy generation and demand, the optimal solution needs to be updated in response to observed uncertainty realizations or near real-time forecast updates. To address the challenge of computing such frequent real-time updates to the optimal solution, recent literature has proposed the use of machine learning to learn the mapping between the uncertainty realization and the optimal solution. Further, learning the active set of constraints at optimality, as opposed to directly learning the optimal solution, has been shown to significantly simplify the machine learning task, and the learnt model can be used to predict optimal solutions in real-time. In this paper, we propose the use of classification algorithms to learn the mapping between the uncertainty realization and the active set of constraints at optimality, thus further enhancing the computational efficiency of the real-time prediction. We employ neural net classifiers for this task and demonstrate the excellent performance of this approach on a number of systems in the IEEE PES PGLib-OPF benchmark library.Index Terms-optimal power flow, active set, ReLU, multiclass, neural network.

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“…For example, Refs. [25]- [27] consider stochastic OPF formulations which also incorporate HVDC lines and HVDC grids. However, they all assume a DC-OPF formulation.…”

Section: Contributionsmentioning

confidence: 99%

“…As a result, the computational complexity is increased. To maintain scalability, we propose to use a constraint generation method to solve the AC-OPF in each step of Algorithm 1 based on [25]: First, we solve the AC-OPF excluding all uncertainty margins (i.e. they are set to zero), except the uncertainty margins for the generators (2c) -(2d) and the HVDC active power (16a) -(16b).…”

Section: Iterative Chance-constrained Ac-opf Optimizing Generatormentioning

confidence: 99%

Chance-constrained AC optimal power flow integrating HVDC lines and controllability

Venzke

Halilbašić

Barré

et al. 2020

International Journal of Electrical Power & Energy Systems

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The integration of large-scale renewable generation has major implications on the operation of power systems, two of which we address in this work. First, system operators have to deal with higher degrees of uncertainty due to forecast errors and variability in renewable energy production. Second, with abundant potential of renewable generation in remote locations, there is an increasing interest in the use of High Voltage Direct Current lines (HVDC) to increase transmission capacity. These HVDC transmission lines and the flexibility and controllability they offer must be incorporated effectively and safely into the system. In this work, we introduce an optimization tool that addresses both challenges by incorporating the full AC power flow equations, chance constraints to address the uncertainty of renewable infeed, modelling of point-to-point HVDC lines, and optimized corrective control policies to model the generator and HVDC response to uncertainty. The main contributions are twofold. First, we introduce a HVDC line model and the corresponding HVDC participation factors in a chance-constrained AC-OPF framework. Second, we modify an existing algorithm for solving the chance-constrained AC-OPF to allow for optimization of the generation and HVDC participation factors. Using realistic wind forecast data, for 10 and IEEE 39 bus systems with HVDC lines and wind farms, we show that our proposed OPF formulation achieves good in-and out-of-sample performance whereas not considering uncertainty leads to high constraint violation probabilities. In addition, we find that optimizing the participation factors reduces the cost of uncertainty significantly.

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Corrective Control to Handle Forecast Uncertainty: A Chance Constrained Optimal Power Flow

Cited by 79 publications

References 25 publications

Implied Constraint Satisfaction in Power System optimization: The Impacts of Load Variations

Implied Constraint Satisfaction in Power System optimization: The Impacts of Load Variations

Learning for DC-OPF: Classifying active sets using neural nets

Chance-constrained AC optimal power flow integrating HVDC lines and controllability

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