Voltage Control by Smart Sustainable Buildings: Data-Driven vs OPF-based Techniques

Article Engineering, Electrical & Electronic

DeepOPF-V: Solving AC-OPF Problems Efficiently

Wanjun Huang et al.

Summary: This paper proposes a voltage-constrained approach based on deep neural networks to solve AC optimal power flow problems. The method predicts the voltage of all buses to calculate the remaining variables, avoiding the need to solve nonlinear equations. It achieves high computational efficiency and preserves the feasibility of solutions.

IEEE TRANSACTIONS ON POWER SYSTEMS (2022)

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Article Engineering, Electrical & Electronic

A Comprehensive Multi-Period Optimal Power Flow Framework for Smart LV Networks

Iason I. Avramidis et al.

Summary: This paper introduces an extensive multi-period optimal power flow framework for smart LV distribution systems, focusing on residential flexibility to combat operational issues. The study includes detailed performance assessments on ZIP flexible loads, controllability of conventional residential devices, and exploitation of inverter technologies. The analysis highlights the de-congestion potential of common residential devices and explores setups with partial adjustments on customer profiles.

IEEE TRANSACTIONS ON POWER SYSTEMS (2021)

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Article Engineering, Electrical & Electronic

Data-Driven Optimal Power Flow: A Physics-Informed Machine Learning Approach

Xingyu Lei et al.

Summary: This study presents a data-driven approach for OPF that decomposes OPF model features into three stages to reduce learning complexity and correct learning bias. An enhanced feature attraction is achieved through a sample pre-classification strategy based on active constraint identification.

IEEE TRANSACTIONS ON POWER SYSTEMS (2021)

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Article Engineering, Electrical & Electronic