With the average solar radiation reaching up to 5 kWh/m2, Vietnam is considered as a country showing an excellent potential for solar power production. Since the year 2000, there have been a lot of studies about the potential of this source in Vietnam. So far, many applications of solar power have been implemented on small, medium, and large scales. In fact, the total capacity of current grid-connected solar power plants has exceeded the planned capacity by 2020 nearly 6 times. However, the studies of solar potential in Vietnam are still incomplete. The policies and mechanisms for developing solar power projects have received attention from the authorities but have not been really satisfactory. The infrastructure is still poor and the power system does not keep up with the development of modern grids. This paper reviewed the potential and actual implementation stage of photovoltaic projects in Vietnam. Moreover, the barriers and challenges of institution, technique, economy, and finance have been considered explicitly for the future development of solar energy in Vietnam.

At present, the electric vehicle (EV) market is developing strongly and widely across many countries around the world. Increasing clean energy infrastructure for EVs is a possible solution to reduce greenhouse gas emissions and help improve air quality in urban areas. Electric vehicles charged by electricity from photovoltaic (PV) systems can produce less emissions than conventional EVs charged by the utility grid. Thus, the combination of solar power and EV charging stations is one of the possible methods to achieve sustainable development in the current EV market. EVs in cities in Vietnam have developed very quickly in recent times, but the charging station infrastructure is still very limited, and most existing charging stations use electricity from the utility grid. In this paper, the optimal configuration of PV-powered EV charging stations is analyzed technically and economically under different solar irradiation conditions in Vietnam. The study results show that the optimal configuration and investment efficiency of PV-powered EV charging stations in each urban area are greatly affected by the solar irradiation value and feed-in tariff (FIT) price of rooftop solar power. In Vietnam, a region with high solar irradiation, such as Ho Chi Minh, is more likely to invest in PV-powered EV charging stations than other areas with lower solar irradiation, such as Hanoi.

One of the major issues about the operation of power systems is the prediction of load demand. Moreover, load forecasting is of prime concern to system operators. Recently, the integration of power system elements, such as renewable energy sources, energy storages and electricity vehicle, brings more challenges, particularly when there are large fluctuations in forecasting cycle. This study concentrates on short-term load demand forecasting and proposes a hybrid method that combines Singular Spectrum Analysis (SSA) with deep-learning Neural Network (NN) techniques. In the beginning, the SSA technique is applied as an initial filter to remove noises. Next, a hybrid neural network, including Backpropagation Neural Network (BPNN) and Long Short-Term Memory (LSTM), is developed and trained. Then, the trained network is used as the core forecasting algorithm. Each SSA has different forms to combine with neural networks. The performance of the proposed forecasting algorithm is demonstrated using the power demand data recorded in Taiwan. Furthermore, this study compares the forecasting results by five models, including SSA, SSA-BPNN, ANN, SSA-LSTM, Wavelet Neural Network (WNN) and LSTM. The forecasting results reveal that the proposed forecasting model using Singular Spectrum Analysis provides the best performance on load forecasts.

No abstract

No abstract