Operational controls are designed to support the integration of wind and solar power within microgrids. An aggregated model of renewable wind and solar power generation forecast is proposed to support the quantification of the operational reserve for day-ahead and real-time scheduling. Then, a droop control for power electronic converters connected to battery storage is developed and tested. Compared with the existing droop controls, it is distinguished in that the droop curves are set as a function of the storage state-of-charge (SOC) and can become asymmetric. The adaptation of the slopes ensures that the power output supports the terminal voltage while at the same keeping the SOC within a target range of desired operational reserve. This is shown to maintain the equilibrium of the microgrid's real-time supply and demand. The controls are implemented for the special case of a dc microgrid that is vertically integrated within a high-rise host building of an urban area. Previously untapped wind and solar power are harvested on the roof and sides of a tower, thereby supporting delivery to electric vehicles on the ground. The microgrid vertically integrates with the host building without creating a large footprint.

In Vietnam’s major urban areas, private motorcycles are the main means of transportation that are suitable for socio-economic conditions, current transport infrastructure, and people’s habits. However, in recent years, the negative effects of a large number and high density of motorcycles in urban areas such as traffic congestion and noise and air pollution have resulted in a gradual change in the public’s opinion of private vehicle adoption, especially motorcycles. The public has also urged the authorities to issue policies of limiting or curving the growth in the number of private vehicles powered by fossil fuels in big cities. However, in order to achieve the goal, other alternative means of transport should be proposed to encourage people to move to a more sustainable and eco-friendly form of mobility. The alternatives also should be consistent with the average income level as well as social characteristics. In recent years, along with the development of a highly connected public transport network, efficient and less-polluting vehicles including electric two-wheelers have been emerging, thriving, and drawing more attraction from Vietnamese people and policy makers. The spread in the number of electric two-wheelers in Vietnam’s major cities may be a sign of the transition to a more sustainable and less-polluting means of transport as an alternative to gasoline-powered motorcycles. In this paper, the authors aim to analyze the current road traffic status in Vietnam’s major cities, as well as shed a light on the transition to the greener and more efficient alternative vehicles to motorcycles in Vietnam’s urban traffic. Several recommendations for encouraging this vehicle type development are also important outcomes of this paper.

This paper presents the effectiveness of the ensemble empirical mode decomposition-long short-term memory (EEMD-LSTM) model for short term load prediction. The prediction performance of the proposed model is compared to that of three other models (LR, ANN, LSTM). The contribution of this research lay in developing a novel approach that combined the EEMD-LSTM model to enhance the capability of industrial load forecasting. This was a field where there had been limited proposals for improvement, as these hybrid models had primarily been developed for other industries such as solar power, wind power, CO2 emissions, and had not been widely applied in industrial load forecasting before. First, the raw data was preprocessed using the IQR method, serving as the input for all four models. Second, the processed data was then used to train the four models. The performance of each model was evaluated using regression-based metrics such as mean absolute error (MAE) and mean squared error (MSE) to assess their respective output. The effectiveness of the EEMD-LSTM model was evaluated using Seojin industrial load data in Vietnam, and the results showed that it outperformed other models in terms of RMSE, n-RMSE, and MAPE errors for both 1-step and 24-step forecasting. This highlighted the model's capability to capture intricate and nonlinear patterns in electricity load data. The study underscored the significance of selecting a suitable model for electricity load forecasting and concluded that the EEMD-LSTM model was a dependable and precise approach for predicting future electricity assets. The model's robust performance and accurate forecasts showcased its potential in assisting decision-making processes in the energy sector.

Currently, electrification of mobility could be considered as a sustainable solution for reducing oil dependency and encouraging environmental protection. In Vietnam, socioeconomic condition, current traffic infrastructure and users’ habit are main causes of the prevalence of motorcycles, especially in large urbans. A huge number and high density of personal gasoline-powered vehicles has pressured traffic infrastructure and this is claimed to be the major contributor to air pollution. With reasonable purchase price and low operation cost, electric two-wheelers (e-bikes, electric mopeds, electric motorcycles) could be seen as a promising solution for replacing gasoline-powered motorcycles. In order to promote this transition, supporting infrastructure such as charging facilities should be studied, investigated and deployed. Nevertheless, electrification of mobility is only beneficial to environment if the electricity used to charge EVs comes from renewable sources and not from fossil fuel generation. With high potential of solar energy, local consumption of PV generation for charging stations in Vietnam may provides a promising solution. This paper aims to research the trend of transport electrification and the feasibility of PV-integrated charging stations for electric two-wheelers in Vietnam as well as propose PV-based charging station solution at Electric Power University.

In developing countries as well as in Vietnam, infrastructure for two-wheeled electric vehicles is necessary but has not drawn much attention. Sustainable transportation in urban traffic requires sustainable solutions to first-mile/last-mile services which are essential for eco-friendly and efficient connection to the public transport system. Hence, electric two-wheeler charging stations which coordinate charging a large number of electric bicycles/electric motorcycles at public parking or transit hubs should be considered. In this study, the authors propose a novel approach to effectively allocate power to electric two-wheelers in a charging station which is interconnected with the distribution grid, a photovoltaic system and conventional loads of a building. With a three-stage of allocation, the proposed algorithm proves its effectiveness in reducing load variance as well as peak shaving and valley filling while relieving the complexity of solving multivariable optimal problems. The idea also contributes to increasing the penetration rate of both electric vehicles and distributed photovoltaic sources while alleviating adverse effects of these emergent factors.

In this study, an integrated forecasting model was developed by combining the ensemble empirical mode decomposition (EEMD) model and gated recurrent unit (GRU) neural network to accurately predict the rooftop solar power output at a specific power unit located in Tay Ninh province, Vietnam. The EEMD method was employed to decompose the solar power signals into multiple frequencies, allowing for a more comprehensive analysis. Subsequently, the GRU network, known for its ability to capture long-term dependencies, was utilized to forecast future values for each decomposition series. By merging the forecasted values obtained from the decomposition series, the final prediction for the solar power output was generated. To evaluate the efficacy of our proposed approach, a comparative analysis was undertaken against other forecasting models, including a single artificial neural network, long short-term memory network, and GRU, all of which solely considered the solar power series as input features. The experimental results provided compelling evidence of the superior performance of the EEMD-GRU model, especially when incorporating weather variables into the forecasting process, achieving the best results in all three forecasting scenarios (1-step, 2-step, and 3-step). For both forecasting targets, Inverter 155 and 156, the n-RMSE indices were 1.35%, 3.5%, and 4.8%, respectively, significantly lower than the compared single models. This integration of weather variables enhances the model's accuracy and reliability in predicting rooftop solar power output, establishing it as a valuable tool for efficient energy management in the region.