Stochastic Reactive Power Management in Microgrids with Renewables
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Abstract (arXiv)
Distribution microgrids are being challenged by reverse power flows and voltage fluctuations due to renewable generation, demand response, and electric vehicles. Advances in photovoltaic (PV) inverters offer new opportunities for reactive power management provided PV owners have the right investment incentives. In this context, reactive power compensation is considered here as an ancillary service. Accounting for the increasing time-variability of distributed generation and demand, a stochastic reactive power compensation scheme is developed. Given uncertain active power injections, an online reactive control scheme is devised. This scheme is distribution-free and relies solely on power injection data. Reactive injections are updated using the Lagrange multipliers of a second-order cone program. Numerical tests on an industrial 47-bus microgrid and the residential IEEE 123-bus feeder corroborate the reactive power management efficiency of the novel stochastic scheme over its deterministic alternative, as well as its capability to track variations in solar generation and household demand.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Sep 2014.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 1.
4 papers.
- 2023Distributed Online Convex Optimization with Adversarial Constraints: Reduced Cumulative Constraint Violation Bounds under Slater's Conditioncited by 13
- 2021Controlling Smart Inverters using Proxies: A Chance-Constrained DNN-based Approachcited by 5
- 2022Reducing Learning Difficulties: One-Step Two-Critic Deep Reinforcement Learning for Inverter-based Volt-Var Controlcited by 0
- 2022Reducing Action Space: Reference-Model-Assisted Deep Reinforcement Learning for Inverter-based Volt-Var Controlcited by 0
- 2023Distributed Online Convex Optimization with Adversarial Constraints: Reduced Cumulative Constraint Violation Bounds under Slater's Condition31 May 2023
- 2022Reducing Action Space: Reference-Model-Assisted Deep Reinforcement Learning for Inverter-based Volt-Var Control10 Oct 2022
- 2022Reducing Learning Difficulties: One-Step Two-Critic Deep Reinforcement Learning for Inverter-based Volt-Var Control30 Mar 2022
- 2021Controlling Smart Inverters using Proxies: A Chance-Constrained DNN-based Approach2 May 2021