Vietnam became the world’s third largest market for solar photovoltaic energy in 2020. Especially after the Vietnamese government issued feed-in tariffs for grid-connected solar photovoltaic systems, the installed capacity of solar photovoltaic applications exploded in 2019. From studies carried out in the relevant literature, it can be said that support policies are highly important for the initial development of the renewable energy industry in most countries. This is especially true in emerging countries such as Vietnam. This paper reviews the feed-in tariffs issued and deployed in different regions of Vietnam for grid-connected solar photovoltaic applications. Moreover, the paper takes a closer look at the costs of electricity production from these systems in relation to the feed-in tariffs issued in Vietnam. The results show that the gap between the levelized cost of electricity and the feed-in tariff for solar photovoltaic electricity is relatively high, particularly in regions with a lower irradiation potential.

The levelized cost of energy (LCOE) of offshore wind energy conversion systems using permanent magnet and fully superconducting generators is compared. Scaling laws are used to calculate the masses and the costs of all the components, to the exception of the generators that are designed and optimized by using the finite element method. The comparisons are done for various turbine’s nominal power, ranging from 2 to 23 MW. The results show that for each generator type, there exists an optimal nominal power that minimizes the LCOE. In addition, the use of a superconducting generator could significantly lower (about 7% at 20 MW) the LCOE compared with using a permanent magnet generator.

Purpose: In many countries, innovation in building design for improving energy performance, reducing CO2 emissions and minimizing life cycle cost has received much attention for sustainable development. This paper investigates the importance of optimization tools for enhancing the design performance in the early stages of Vietnam's cooling-dominated buildings in hot and humid climates using an integrated building design approach.Design/methodology/approach: The methodology of this study exploits the non-dominated sorting genetic algorithm (NSGA-II) optimization algorithm coupled with building simulation to research a trade-off between the optimization of investment cost and energy consumption. Our approach focuses on the whole optimization problem of thermal envelope, glazing and energy systems from preliminary design phases. The methodology is then tested for a case study of a non-residential building located in Hanoi.Findings: The results show a considerable improvement in design performance by our method compared to current building design. The optimal solutions present the trade-off between energy consumption and capital cost in the form of a Pareto front. This helps architects, engineers and investors make important decisions in the early design stages with a large view of impacts of all factors on energy performance and cost.Originality/value: This is one of the original research to study integrated building design applying the simulation-based genetic optimization algorithm for cooling-dominated buildings in Vietnam. The case study in this article is for a non-residential building in the north of Vietnam but the methodology can also be applied to residential buildings and other regions.

Data play an essential role in the optimal control of smart buildings’ operation, especially in building energy-management for the target of nearly zero buildings. The building monitoring system is in charge of collecting and managing building data. However, device imperfections and failures of the monitoring system are likely to produce low-quality data, such as data loss and inconsistent data, which then seriously affect the control quality of the buildings. This paper proposes a new approach based on Gaussian process regression for data-quality monitoring and sensor network data compensation in smart buildings. The proposed method is proven to effectively detect and compensate for low-quality data thanks to the application of data analysis to the energy management monitoring system of a building model in Viet Nam. The research results provide a good opportunity to improve the efficiency of building energy-management systems and support the development of low-cost smart buildings.

Buildings with solar rooftops have become vital objects in the energy transition in Vietnam. In this context, the demand for research on energy management solutions to use energy efficiently and increase PV energy absorption capacity is rising. In this paper, we present a practical route to developing a low-cost monitoring platform to meet the building energy management in the country. First, our project built a monitoring architecture with high-density wireless sensors in an office building in Vietnam. Next, we discussed the influence of significant obstacles such as technical issues, users, and cost on the resilience and reliability of the monitoring system. Then, we proposed essential solutions for data quality improvement by testing sensors, detecting wireless sensor network errors, and compensating for data losses by embedding machine learning. We found the platform’s potential in developing a rich database of building characteristics and occupants. Finally, we proposed plans exploiting the data to reduce wasted energy in equipment operation, change user behaviors, and increase auto-consumption PV power. The effectiveness of the monitoring platform was an approximate 62% energy reduction in the first year. The results are a cornerstone for implementing advanced research as modeling and real-time optimal control toward nearly zero-energy buildings.