Data-Driven Distributionally Robust Optimal Power Flow for Distribution Systems

Abstract

Increasing penetration of distributed energy resources complicate operations of electric power distribution systems by amplifying volatility of nodal power injections. On the other hand, these resources can provide additional control means to the distribution system operator (DSO). This paper takes the DSO perspective and leverages a data-driven distributionally robust decision-making framework to overcome the uncertainty of these injections and its impact on the distribution system operations. We develop an AC OPF formulation for radial distribution systems based on the LinDistFlow AC power flow approximation and exploit distributionally robust optimization to immunize the optimized decisions against uncertainty in the probabilistic models of forecast errors obtained from the available observations. The model is reformulated to be computationally tractable and tested on multiple IEEE distribution test systems. We also release the code supplement that implements the proposed model in Julia and can be used to reproduce our numerical results.

6 pages, 6 figures, 27 references. v4: Final Version as accepted for Publication in IEEE L-CSS

References in corpus (9)

• JuMP: A Modeling Language for Mathematical Optimization
• A Robust Approach to Chance Constrained Optimal Power Flow with Renewable Generation
• Local Control of Reactive Power by Distributed Photovoltaic Generators
• Uncertainty Sets For Wind Power Generation
• Chance Constraints for Improving the Security of AC Optimal Power Flow
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Cited by in corpus (8)

• Distribution Electricity Pricing under Uncertainty
• Network-Aware Flexibility Requests for Distribution-Level Flexibility Markets
• Online Learning for Network Constrained Demand Response Pricing in Distribution Systems
• The Value of Including Unimodality Information in Distributionally Robust Optimal Power Flow
• Towards Distributed Energy Services: Decentralizing Optimal Power Flow with Machine Learning
• A Data-driven Probabilistic-based Flexibility Region Estimation Method for Aggregated Distributed Energy Resources
• Data-Driven Moment-Based Distributionally Robust Chance-Constrained Optimization
• Co-Control of VaR and CVaR forData-Driven Stochastic Demand Response Auction