[HTML][HTML] Optimization with constraint learning: A framework and survey
Many real-life optimization problems frequently contain one or more constraints or objectives
for which there are no explicit formulae. If however data on feasible and/or infeasible states …
for which there are no explicit formulae. If however data on feasible and/or infeasible states …
Artificial intelligent techniques for identifying the cause of disturbances in the power grid
M Khaleel, SA Abulifa, AA Abulifa - Brilliance: Research of …, 2023 - jurnal.itscience.org
The intricacy of the power system configuration, coupled with the contemporary trends in
power generation and demand, renders the attainment of adequate supply quality a …
power generation and demand, renders the attainment of adequate supply quality a …
Model-free voltage control of active distribution system with PVs using surrogate model-based deep reinforcement learning
Accurate knowledge of the distribution system topology and parameters is required to
achieve good voltage control performance, but this is difficult to obtain in practice. This paper …
achieve good voltage control performance, but this is difficult to obtain in practice. This paper …
Challenges in smartizing operational management of functionally-smart inverters for distributed energy resources: A review on machine learning aspects
Y Fujimoto, A Kaneko, Y Iino, H Ishii, Y Hayashi - Energies, 2023 - mdpi.com
The widespread introduction of functionally-smart inverters will be an indispensable factor
for the large-scale penetration of distributed energy resources (DERs) via the power system …
for the large-scale penetration of distributed energy resources (DERs) via the power system …
Enforcing policy feasibility constraints through differentiable projection for energy optimization
While reinforcement learning (RL) is gaining popularity in energy systems control, its real-
world applications are limited due to the fact that the actions from learned policies may not …
world applications are limited due to the fact that the actions from learned policies may not …
[HTML][HTML] Deep reinforcement learning-based two-timescale Volt-VAR control with degradation-aware smart inverters in power distribution systems
Higher penetration of intermittent solar photovoltaic (PV) systems in the distribution grid
results in frequent voltage fluctuations. The conventional voltage regulating devices …
results in frequent voltage fluctuations. The conventional voltage regulating devices …
Fast probabilistic hosting capacity analysis for active distribution systems
Interconnection studies for distributed energy resources (DERs) can currently take months
since they entail simulating a large number of power flow scenarios. If DERs are to be …
since they entail simulating a large number of power flow scenarios. If DERs are to be …
Optimal design of Volt/VAR control rules for inverter-interfaced distributed energy resources
I Murzakhanov, S Gupta… - … on Smart Grid, 2023 - ieeexplore.ieee.org
The IEEE 1547 Standard for the interconnection of distributed energy resources (DERs) to
distribution grids provisions that smart inverters could be implementing Volt/VAR control …
distribution grids provisions that smart inverters could be implementing Volt/VAR control …
[HTML][HTML] Decentralized control in active distribution grids via supervised and reinforcement learning
While moving towards a low-carbon, sustainable electricity system, distribution networks are
expected to host a large share of distributed generators, such as photovoltaic units and wind …
expected to host a large share of distributed generators, such as photovoltaic units and wind …
Controlling smart inverters using proxies: A chance-constrained DNN-based approach
Coordinating inverters at scale under uncertainty is the desideratum for integrating
renewables in distribution grids. Unless load demands and solar generation are telemetered …
renewables in distribution grids. Unless load demands and solar generation are telemetered …