The paper presents a power-quality analysis in the utility low-voltage network focusing on harmonic currents’ pollution. Usually, to forecast the modern electrical and electronic devices’ contribution to increasing the current total harmonic distortion factor $\left(TH{D}_I\right)$ and exceeding the regulation limit, analyses based on tests and models of individual devices are conducted. In this article, a composite approach was applied. The performance of harmonic currents produced by sets of devices commonly used in commercial and residential facilities’ nonlinear loads was investigated. The measurements were conducted with the class A PQ analyzer (FLUKE 435) and dedicated to the specialized PC software. The experimental tests show that the harmonic currents produced by multiple types of nonlinear loads tend to reduce the current total harmonic distortion factor $\left(TH{D}_I\right)$. The changes of harmonic content caused by summation and/or cancellation effects in total current drawn from the grid by nonlinear loads should be a key factor in harmonic currents’ pollution study. Proper forecasting of the level of harmonic currents injected into the utility grid helps to maintain the quality of electricity at an appropriate level and reduce active power losses, which have a direct impact on the price of electricity generation.

The stochastic nature of renewable energy sources, especially solar PV output, has created uncertainties for the power sector. It threatens the stability of the power system and results in an inability to match power consumption and production. This paper presents a Convolutional Neural Network (CNN) approach consisting of different architectures, such as the regular CNN, multi-headed CNN, and CNN-LSTM (CNN-Long Short-Term Memory), which utilizes a sliding window data-level approach and other data pre-processing techniques to make accurate forecasts. The output of the solar panels is linked to input parameters such as irradiation, module temperature, ambient temperature, and windspeed. The benchmarking and accuracy metrics are calculated for 1 h, 1 day, and 1 week for the CNN based methods which are then compared with the results from the autoregressive moving average and multiple linear regression models in order to demonstrate its efficacy in making short-term and medium-term forecasts.

The massive installation of renewable energy sources together with energy storage in the power grid can lead to fluctuating energy consumption when there is a bi-directional power flow due to the surplus of electricity generation. To ensure the security and reliability of the power grid, high-quality bi-directional power flow prediction is required. However, predicting bi-directional power flow remains a challenge due to the ever-changing characteristics of power flow and the influence of weather on renewable power generation. To overcome these challenges, we present two of the most popular hybrid deep learning (HDL) models based on a combination of a convolutional neural network (CNN) and long-term memory (LSTM) to predict the power flow in the investigated network cluster. In our approach, the models CNN-LSTM and LSTM-CNN were trained with two different datasets in terms of size and included parameters. The aim was to see whether the size of the dataset and the additional weather data can affect the performance of the proposed model to predict power flow. The result shows that both proposed models can achieve a small error under certain conditions. While the size and parameters of the dataset can affect the training time and accuracy of the HDL model.

This paper presents an energy management system for the microgrid present at Wroclaw University of Science and Technology. It has three components: a forecasting system, an optimizer and an optimized electrical vehicle charging station as a separate load for the system. The forecasting system is based on a deep learning model utilizing a Long Short-Term Memory (LSTM) – Autoencoder based architecture. The study provides a statistical analysis of its performance over several runs and addresses reliability and running time issues thereby building a case for its adoption. A MIDACO – MATPOWER combined optimization algorithm has been used as the optimization algorithm for energy management which intends to harness the speed of MATPOWER and the search capabilities of Mixed Integer Distributed Ant Colony Optimization (MIDACO) in finding an appropriate global minimum solution. The objective of the system is to minimize the import of power from the main grid resulting in improved self-sufficiency. Finally, an optimized electrical vehicle charging station model to maximize the renewable energy utilization within the facility is incorporated into the same.

This article presents the selection of an appropriate deep learning Long Short-Term Memory (LSTM) based probabilistic hour-ahead forecasting model for a grid connected industrial solar PV power plant located in Poland. It has a 317 kW peak power capacity and is connected with a metallurgical plant producing steel for car parts. The purpose of the study is to present a model that could be used by the plant to participate in the Polish intra-day electricity market. Four different LSTM models were investigated which include the vanilla model, the stacked LSTM model, the Bi-directional LSTM model and the LSTM-Autoencoder model. Out of the investigated models it was observed that the LSTM-Autoencoder model was the best performing one in terms of reliability. The average Root Mean Squared Error (RMSE) and the average Mean Absolute Error (MAE) for the Autoencoder model over 100 runs were 15.59 kW and 8.36 kW which represent 4.9% and 2.6% of the peak power respectively. Moreover, it was observed that it has the shortest width for the 95% confidence interval of only 0.5% for both the RMSE and the MAE. In terms of accuracy the best performing model was the LSTM bi-directional model with the average RMSE and MAE values of 12.87 kW and 6.91 kW which represent 4% and 2.1% of the peak power. The 95% confidence intervals width for both the RMSE and MAE over the 100 runs were 0.8% and 0.5% respectively.