Machine learning based weighted scheduling scheme for active power control of hybrid microgrid

  title={Machine learning based weighted scheduling scheme for active power control of hybrid microgrid},
  author={Sidra Kanwal and Bilal Ahmed Khan and Sahibzada Muhammad Ali},
  journal={International Journal of Electrical Power \& Energy Systems},
2 Citations



Robust Energy Management for Microgrids With High-Penetration Renewables

To address the intrinsically stochastic availability of renewable energy sources (RES), a novel power scheduling approach is introduced that involves the actual renewable energy as well as the energy traded with the main grid, so that the supply-demand balance is maintained.

Realistic and Transparent Optimum Scheduling Strategy for Hybrid Power System

This paper proposes the optimal scheduling strategy taking into account the impact of uncertainties in wind, solar PV, and load forecasts, and provides the best-fit DA schedule by minimizing both DA and real-time adjustment costs including the revenue from renewable energy certificates.

Short-Term Forecasting of Power Production in a Large-Scale Photovoltaic Plant Based on LSTM

  • GaoLiHongLong
  • Engineering, Environmental Science
    Applied Sciences
  • 2019
Photovoltaic (PV) power is attracting more and more concerns. Power output prediction, as a necessary technical requirement of PV plants, closely relates to the rationality of power grid dispatch. If

Machine Learning Based Photovoltaics (PV) Power Prediction Using Different Environmental Parameters of Qatar

Photovoltaics (PV) output power is highly sensitive to many environmental parameters and the power produced by the PV systems is significantly affected by the harsh environments. The annual PV power

Robust Optimal Power Management System for a Hybrid AC/DC Micro-Grid

Hybrid ac/dc micro-grid is a new concept decoupling dc sources with dc loads and ac sources with ac loads, while power is exchanged between both sides using a bidirectional converter/inverter. This

Robust Energy Management of Microgrid With Uncertain Renewable Generation and Load

A scenario-based robust energy management method accounting for the worst-case amount of renewable generation and load, which is robust against most of the possible realizations of the modeled uncertain set by Monte Carlo verification is developed.

Service Restoration for a Renewable-Powered Microgrid in Unscheduled Island Mode

The proposed two scenario-splitting methods can be solved in a two-step solving procedure, in which a Lagrangian technique and dynamic programming are utilized to provide an analytical sub-optimal yet efficient solution to the original problem.