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.

A regional grid cluster proposal is required to tackle power grid complexities and evaluate the impact of decentralized renewable energy generation. However, implementing regional grid clusters poses challenges in power flow forecasting owing to the inherent variability of renewable power generation and diverse power load behavior. Accurate forecasting is vital for monitoring the imported power during peak regional load periods and surplus power generation exported from the studied region. This study addressed the challenge of multistep bidirectional power flow forecasting by proposing an LSTM autoencoder model. During the training stage, the proposed model and baseline models were developed using autotune hyperparameters to fine-tune the models and maximize their performance. The model utilized the last 6 h leading up to the current time (24 steps of 15 min intervals) to predict the power flow 1 h ahead (4 steps of 15 min intervals) from the current time. In the model evaluation stage, the proposed model achieved the lowest RMSE and MAE scores with values of 32.243 MW and 24.154 MW, respectively. In addition, it achieved a good R² score of 0.93. The evaluation metrics demonstrated that the LSTM autoencoder outperformed the other models for multistep forecasting task in a regional grid cluster proposal.

The wind power generation is highly dependent on current weather conditions. In the course of the energy transition, the generation levels from volatile wind energy are constantly increasing. Accordingly, the prediction of regional wind power generation is a particularly important and challenging task due to the highly distributed installations. This paper presents a study on the role of regional wind power infeed estimation and proposes a multi-aggregated wind power characteristics model based on three scaled Gumbel distribution functions. Multi-levels of wind turbines and their allocation are investigated for the regional aggregated wind power. Relative peak power performance and full load hours are compared for the proposed model and the real measurement obtained from a local distribution system operator. Furthermore, artificial intelligence technologies using neural networks, such as Long Short-Term Memory (LSTM), stacked LSTM and CNN–LSTM, are investigated by using different historical measurement as input data. The results show that the suggested stacked LSTM performs stably and reliably in regional power prediction.

With the massive expansion of decentralised renewable energy in electricity grid networks, the power supply system has been changed from centralised to decentralised one and from directional to bi-directional one. However, due to the regional energy structure difference in the power imbalance between electricity generation and consumption is becoming more and more serious. A grid-scale energy storage system (ESS) can be one solution to balance the local difference. In this paper, two charging/discharging strategies for the grid-scale ESS were proposed to decide when and with how much power to charge/discharge the ESS. In order to realise the two strategies, this paper focuses on the application of fuzzy logic control system. The proposed strategies aim to reduce the peak power generation, consumption and the grid fluctuation. In particular, this paper analysis the ratio between energy-capacity and rated power of ESS. The performance of the proposed strategies is evaluated from two aspects, the normalised power of ESS itself and the influence on the power grid. Simulation studies were carried out on the rule-based control systems with different energy-to-power (e2p) ratios, and the results show that the proposed charging strategy with combination of extreme situation of power imbalance and the rest capacity of ESS provides a smooth load curve for the regional power grid system while the external power exchange is reduced effectively.