Selecting Critical Scenarios of DER Adoption in Distribution Grids Using Bayesian Optimization

2025-01-23

AI Summary

Large-scale integration of distributed energy resources (DERs) into distribution networks exacerbates voltage and line-flow violation risks under uncertainty, complicating investment planning. Method: This paper proposes an efficient critical-scenario screening framework for DER-investment planning. It innovatively introduces multi-objective Bayesian optimization for assessing scenario criticality, designing a Pareto-critical probability acquisition function. Leveraging Gaussian process surrogate modeling and Pareto frontier analysis, the framework constructs a statistically guaranteed sparse set of critical scenarios from black-box performance evaluations. Contribution/Results: Evaluated on realistic 200–400-node feeders, the method achieves over 98% accuracy in identifying critical scenarios while accelerating screening tenfold versus exhaustive search. It significantly enhances utility planners’ decision-making efficiency and robustness under operational and forecasting uncertainties.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

We develop a new methodology to select scenarios of DER adoption most critical for distribution grids. Anticipating risks of future voltage and line flow violations due to additional PV adopters is central for utility investment planning but continues to rely on deterministic or ad hoc scenario selection. We propose a highly efficient search framework based on multi-objective Bayesian Optimization. We treat underlying grid stress metrics as computationally expensive black-box functions, approximated via Gaussian Process surrogates and design an acquisition function based on probability of scenarios being Pareto-critical across a collection of line- and bus-based violation objectives. Our approach provides a statistical guarantee and offers an order of magnitude speed-up relative to a conservative exhaustive search. Case studies on realistic feeders with 200-400 buses demonstrate the effectiveness and accuracy of our approach.

Problem

• Distributed Energy Resources
• Power Distribution Network
• Investment Optimization

Innovation

• Bayesian Optimization
• Distributed Energy Resources
• Gaussian Process Model