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Sigler, Devon

Publications and source records attributed to Sigler, Devon.

46 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Scalable Transmission Expansion Under Uncertainty Using Three-Stage Stochastic Optimization

The intermittent nature of renewable energy poses new challenges for power grids due to its variable and uncertain power output. These features of renewable generation are becoming more relevant to transmission planning as grids reach higher penetration levels of renewable energy. In this paper we present an approach for transmission planning based on scalable computational approaches which enable the explicit consideration of operational uncertainties in the planning process. Using three-stage stochastic programming and the progressive hedging algorithm, we compute transmission expansion decisions on a modified RTS-GMLC test system. We augment the grid with large amounts of wind generation and consider many operational scenarios subject to wind uncertainty.

multi-stage stochastic optimization↗
DOE OSTI2020

Scalable Stochastic Transmission Expansion: A Use Case for ExaSGD

The intermittent nature of renewable energy poses new challenges for power grids due to its variable and un- certain power output. These features of renewable generation are becoming more relevant to transmission planning as grids reach higher penetration levels of renewable energy. In this paper we present an approach for transmission planning based on scalable computational approaches which enable the explicit consideration of operational uncertainties in the planning process. Using three-stage stochastic programming and the progressive hedging algorithm, we compute transmission expansion decisions on a modified RTS-GMLC test system. We augment the grid with large amounts of wind generation and consider many operational scenarios subject to wind uncertainty. This is an example of a possible use of the ExaSGD security constrained AC optimal power flow solver.

Exascale Computing Project↗
DOE OSTI2020

Pareto solutions in multicriteria optimization under uncertainty

We present and analyze several definitions of Pareto optimality for multicriteria optimization or decision problems with uncertainty primarily in their objective function values. In comparison to related notions of Pareto robustness, we first provide a full characterization of an alternative efficient set hierarchy that is based on six different ordering relations both with respect to the multiple objectives and a possibly finite, countably infinite or uncountable number of scenarios. We then establish several scalarization results for the generation of the corresponding efficient points using generalized weighted-sum and epsilon-constraint techniques. Finally, we leverage these scalarization results to also derive more general conditions for the existence of efficient points in each of the corresponding optimality classes, under suitable assumptions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Distributed Reinforcement Learning with ADMM-RL

NREL researcher Peter Graf discusses distributed reinforcement learning with ADMM-RL as part of the Innovative Optimization and Control Methods for Highly Distributed Autonomous Systems workshop. NREL hosted the workshop to exchange ideas on state-of-the-art control and optimization strategies as well as get feedback on work being done at NREL.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Distributed Reinforcement Learning with ADMM-RL

This paper presents a new algorithm for distributed Reinforcement Learning (RL). RL is an artificial intelligence (AI) control strategy such that controls for highly nonlinear systems over multi-step time horizons may be learned by experience, rather than directly computed on the fly by optimization. Here we introduce ADMM-RL, a combination of the Alternating Direction Method of Multipliers (ADMM) and reinforcement learning that allows for integrating learned controllers as subsystems in generally convergent distributed control applications. ADMM has become the workhorse algorithm for distributed control, combining the advantages of dual decomposition (namely, enabling decoupled, parallel, distributed solution) with the advantages of the method of multipliers (namely, convexification/stability). Our ADMM-RL algorithm replaces one or more of the subproblems in ADMM with several steps of RL. When the nested iterations converge, we are left with a pretrained subsolver that can potentially increase the efficiency of the deployed distributed controller by orders of magnitude. We illustrate ADMM-RL in both distributed wind farm yaw control and distributed grid-aware demand aggregation for water heaters.

ADMM↗
DOE OSTI2019

ProgressiveHedging.jl

Implementation of the Progressive Hedging algorithm written in Julia utilizing JuMP. This is used to solve multistage optimization problems (e.g. Stochastic programs or capacity expansion models). The user uses the StructJuMP extension of JuMP to create the model and build up the scenario tree.

Sigler, Devon↗
DOE OSTI2019

Multi-operator Production Cost Modeling

As contiguous power systems are often operated by several regional system operators, optimization models have traditionally been designed to generate optimal system-wide scheduling decisions. This report introduces an approach to reflect the existence of multiple system operators making scheduling decisions within an interconnected power system. The approach is explicitly designed to reflect each operator's limited knowledge about other regions when making scheduling decisions. Detailed analysis using a modified IEEE Reliability Test System reveals that the approach mimics real system operations by providing imperfect information to each operator about external operations and external constraints. The approach scales to a 78,000-bus model of the Eastern Interconnection, where a regression analysis provides quantitative insight into which constraints cause inefficiencies when improperly managed in a multi-operator simulation. Because the approach generates distinct regional scheduling problems, unit commitment and economic dispatch simulations can be applied to individual regions and solved in parallel, enabling production cost simulations at a previously intractable scale.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Importance Sampling with Analog Scenarios for Stochastic Economic Dispatch

Increasing penetrations of renewable energy sources such as wind into power grids motivates the investigation of new approaches to 5-minute economic dispatch computation. In this poster, we investigate the application of importance sampling in the two-stage stochastic economic dispatch problem while using historical data to characterize uncertainty. In our numerical experiments, we observe that importance sampling performs better than the standard Monte Carlo sampling approach in preventing loss-of-load. While first-stage costs are comparable for both sampling methods, the second-stage costs are significantly reduced when the importance sampling method is employed.

97 MATHEMATICS AND COMPUTING↗