Recent Advances in Electrical & Electronic Engineering - Volume 18, Issue 10, 2025

The multi-microgrid interconnection system is an effective method to solve the problem of distributed energy consumption, enhance the stability and reliability of the power grid, connect multiple microgrids with the main power grid, and achieve energy sharing and exchange, thereby improving energy utilization and reducing transaction costs.

Develop advanced multi-microgrid energy optimization scheduling strategy, which can better integrate and utilize renewable energy, reduce carbon emissions, obtain more economic energy scheduling scheme, and reduce operation and maintenance costs.

It achieves a three-layer scheduling strategy by coordinating microgrids with the main grid, microgrids with other microgrids, and distributed power sources within microgrids. It first satisfies the maximum consumption of renewable energy in multi-microgrid systems, and then satisfies the economic scheduling between multi-microgrids and the main grid, thus achieving the goal of low-carbon economic operation. When scheduling, priority should be given to coordinating the energy flow among microgrids. When the distributed power sources within a microgrid cannot satisfy the electricity demand, or when the electricity demand is too low, resulting in the energy storage system (ESS) being fully charged and leading to an excess of renewable energy, the interconnection scheduling between microgrids and the main grid should be initiated to achieve optimal energy sharing scheduling.

To validate the effectiveness of the proposed model, a case study involving three interconnected microgrids was conducted. A comparison is made between the proposed optimized dispatch model and the scenario where microgrids are not interconnected. The results indicate a total cost reduction of 128.1 CNY and an environmental governance cost reduction of 13 CNY.

The results demonstrate that the proposed economic dispatch model for interconnected microgrids reduces both operational and environmental governance costs, enhances the utilization of renewable energy, decreases pollutant emissions, and aligns with the operational goals of a low-carbon economy. The feasibility and effectiveness of this method for energy scheduling in multi-microgrid interconnected systems are verified.

Wind power generation is becoming increasingly important as a renewable energy source, but its intermittent nature poses significant challenges to power system operation. Accurate forecasting of wind power output is crucial to ensure a stable and efficient power grid.

The scientific aim of the work is to propose a novel wind power forecasting method by combining the squeeze-and-excitation network (SENet), convolutional neural network (CNN), and long short-term memory (LSTM) network into a hybrid model. The subject of the research was to obtain a more accurate and reliable wind power forecasting approach and to explore the effectiveness of the proposed SENet-CNN-LSTM model in improving the forecasting performance of wind power generation.

Firstly, the isolated forest algorithm is used to detect abnormal values in wind power historical data, and the linear interpolation method is used to fill in the missing data. Secondly, CNN is used to extract the spatio-temporal characteristics of wind power data, SENet is used to assign different weights to the extracted feature information, and LSTM’s unique gating mechanism is used to memorize and forget the information. Finally, taking the measured historical data of a wind power farm as the sample, six algorithms including CNN, LSTM, CNN-LSTM, SENet-CNN, SENet-LSTM, and SENet-CNN-LSTM are used to predict the example.

The prediction results show that the SENet-CNN-LSTM model achieves an RMSE of 1.781MW, MAE of 1.894MW, and MAPE of 9.032%. Compared to the other five prediction models, SENet-CNN-LSTM exhibits significantly improved performance with lower RMSE, MAE, and MAPE values.

The prediction accuracy of wind power based on the SENet-CNN-LSTM model is significantly improved, which provides an important analysis basis for the safe operation of wind farms and the economic dispatching of power grids.

Reducing power consumption in digital circuits can be achieved by minimizing the number of transitions, and Gray code provides a binary numeral system optimized for this purpose. Traditional CMOS-based counters face limitations in power efficiency and performance at nanoscale levels. This research presents a novel design of a Gray code counter utilizing Carbon Nanotube Field-Effect Transistors (CNTFETs) as a high-performance alternative to CMOS technology.

The CNTFET-based Gray code counter was evaluated across a range of temperatures (25°C to 100°C), input voltages (0.7V to 1.3V), and clock frequencies (200 MHz to 800 MHz). Supervised machine learning was employed to predict and analyze key performance metrics, including propagation delay, power consumption, and Power-Delay Product (PDP), for both CMOS and CNTFET Gray code counters under varying conditions.

The results demonstrate that the CNTFET-based Gray code counter exhibits significantly lower power dissipation, faster operation, and a minimum PDP compared to its CMOS counterpart across the tested temperature, voltage, and frequency variations. The machine learning predictions aligned closely with simulation results, confirming the accuracy of this approach in optimizing the design.

The study validates the CNTFET Gray code counter as a highly efficient, low-power solution suited for high-performance applications. Its superior performance characteristics suggest that CNTFET technology, coupled with AI-driven optimization, holds promise for advanced low-power VLSI circuit designs.