Search

In Vietnam, energy generation accounts for more than half of the national greenhouse gas (GHG) emission. This sector has tremendous potential for emission reduction through the exploitation of renewable energy resources. This study examines the environmental impact of grid-connected solar and wind power in Vietnam, with a focus on GHG emissions. A life cycle assessment was conducted for these purposes. A case study of an integrated 50 kWp solar photovoltaics (PV) and 6 kW wind power model in the Central Highland of Vietnam was selected to illustrate the environmental impact of solar and wind power in Vietnam. The environmental inflows and outflows were quantified from raw material extraction for manufacturing components of the model, such as the panels, turbines, inverters and subsidiary components, to the end of life of the model. OpenLCA software was used for the calculation, with background data from publications and free LCA databases. The results obtained indicate that the life cycle GHG emissions are 20 gCO2e/kWh of solar PV, 3.7 gCO2e/kWh of wind power, and the total emission of the model during its 25-year lifetime is 38.28 tCO2e. If solar and wind power replace grid power, the lifetime emission reduction of the integrated solar and wind power model would be 1.8 thousand tCO2e. DOWNLOAD HISTORY | This article has been downloaded 57 times in Digital Commons before migrating into this platform.

This study presents how to improve the short-term forecast of photovoltaic plant's output power by applying the Long Short-Term Memory, LSTM, neural networks for industrial-scale solar power plants in Vietnam under possible curtailment operation. Since the actual output power does not correspond to the available power, new techniques (Global Horizontal Irradiance - GHI interval division, P/GHI factor addition (P - Power)) have been designed and applied for processing errors and missing data. The prediction model (LSTM network, structure of hidden layers, number of nodes) has been developed by the authors in a previous work. In this new version of the model, the training technique is improved by using validation and experiments to determine the appropriate relevant parameters. The forecast results show that the proposed new method is more efficient than the old method, as the MAPE (Mean Absolute Percentage Error) forecast error is reduced by 6.059% and the RMSE (Root Mean Square Error) is reduced by 6.710%.

The Vietnamese government has initiated systematic policies for promoting renewable energy since the early 2000s, and in 2017, the country became the first major developing economy in East Asia to give up developing nuclear power. However, to meet the growing demand for lower-emission electricity, Vietnam needs market-based policies in order to build a favorable environment for expanding renewable energy. With stronger commitments from the government, more effective policies such as renewable portfolio standard frameworks and auctions are expected to deliver more renewable electricity to customers. With increasing penetration of solar and wind power causing congestion on the grid, there are considerable opportunities for building local and smart grids that could facilitate more reliable and secure electricity services. Abundant hydroelectric capacity offers large potential storage capacity that can also facilitate the expansion of variable solar and wind renewable energy.

This paper proposes a new model for short-term forecasting power generation capacity of large-scale solar power plant (SPP) in Vietnam considering the fluctuations of weather factors when applying the Long Short-Term Memory networks (LSTM) algorithm. At first, a configuration of the model based on the LSTM algorithm is selected in accordance with the weather and operating conditions of SPP in Vietnam. Not only different structures of LSTM model but also other conventional forecasting methods for time series data are compared in terms of error accuracy of forecast on test data set to evaluate the effectiveness and select the most suitable LSTM configuration. The most suitable configuration has been selected and applied on Thanh Thanh Cong No 1 (TTC) SPP with 2 input cases: real historical weather data and forecasted weather data. The results show that second case gives a much larger Mean Absolute Percentage Error (MAPE) than that of first case (10.857% versus 3.491%). Based on above experiment, new additional features are proposed to improve the selected LSTM model precision and cope with the problem of error due to weather forecast data. The result of the application of the new prediction model for TTC solar plant indicates that the MAPE is reduced from 10.857% to 9.881%.

The energy supply and demand balance is one of the important issues to address when proposing options for energy system development. Currently, several tools are utilized for calculating the supply and demand balance for Vietnam's energy systems. These tools are instrumental in developing the national energy system. However, most of them treat the national energy system as a unified whole, with no specific focus on regional energy supply and demand balances. This paper presents a methodology to calculate the supply and demand balance for Vietnam's energy systems by region, with the practical application of Corrective Module1, a piece of software which is a result of the joint research and development effort by the Institute of Energy Science, Vietnam Academy of Science and Technology (VAST) and the Melentiev Energy Systems Institute, Siberian Branch of the Russian Academy of Sciences (RAS). The findings of this study demonstrate that the energy system supply and demand balance calculated by region is the scientific basis for researchers, managers, and policymakers to get a clear vision of possible energy system development at the national and regional scale and to provide directions and policies that are consistent with the conditions of each region and the country. This approach is appropriate for Vietnam's geographical conditions, infrastructure, energy resources, load distribution, and policy framework.

This paper presents an online method of monitoring the health of distribution service transformers using data from a real-time data acquisition and analysis system located at the transformer. The results can be provided to utility operators in near real-time, enabling them to assess the status of the distribution transformer and take actions to avoid or mitigate costly transformer failures and service outages. After thorough review of potential non-invasive monitoring techniques, top oil temperature, vibration, transformer loading, and power factor were chosen as monitored parameters, and the overall health index is determined using fuzzy logic. A case study is implemented on a specific distribution transformer to show the advantages of the proposed method.

Vietnam energy demand is currently growing at a very high annual rate, with the government being very interested in investments in renewable energies. Since the region with the highest solar and wind potential is far away from the big load centers, an investigation of offshore energy resources is here proposed. In this study, a review of previous energy potential assessments is provided. Moreover, the minimum feed-in-tariff to make the investments profitable is evaluated, showing that the current tariff for offshore wind plants is largely unattractive.

Vietnam’s Green Growth Action Plan for the period 2014–2020 sets out an overall plan for the sustainable development of the country. One of the cornerstones of that plan is the development of a greenhouse gas inventory and emission reduction opportunity assessment for Ho Chi Minh City. This provides a basis for the roadmap towards green growth in Ho Chi Minh City, one of Vietnam’s largest cities. Marginal costs of 38 emission reduction measures were determined for two main economy sectors, namely energy (32 measures) and agriculture, forestry, and other land use (6 measures). From that analysis, a marginal abatement cost curve was compiled for the energy sector. The total emission reduction opportunities available in 2020 are estimated to be 8.97 million tCO2e, which represents a 14.7% reduction compared to business as usual. Even when the city only implements reduction measures with negative marginal costs (i.e., abatement that saves money), significant reductions of 5.25 MtCO2e (8.6% reduction compared to business as usual) would be achieved. There are significant similarities in abatement opportunities between Ho Chi Minh City and Hanoi, Vietnam’s capital. The case study shows significant reduction in greenhouse gas emissions can be achieved in fast-growing cities with an appropriate level of investment and government support.

This paper presents a method that is applied to optimize the placement of Static VAR compensators in a real low-voltage grid in the Vietnamese territory. In this way, the voltage profile of the distribution grid turns to be improved. A heuristic method, the Binary Particle Swarm Optimization, is used to find a solution to this problem within the Matlab environment. A case study that considers a high penetration of rooftop PV systems in a branch of Soc Son distribution grid is implemented to show the efficiency of the optimization method for this specific application.

With the increase of load demand and distributed photovoltaic (PV) systems on the electric grid, maintaining the required voltage tolerance at the point delivery (customer homes/businesses) is becoming more challenging for electric power utilities. In a residential neighborhood, the peak load typically occurs in the early evening hours while maximum PV generation occurs during mid day. As a result, the lowest voltage operating points occur in the evening hours; whereas the highest voltage operating point occur during the day, when the PV systems are injecting more power than what is locally consumed. Static VAR Compensators (SVCs) can be used to mitigate voltage violations and smooth out the overall profile of a distribution feeder by managing the voltage at the secondary side of distribution service transformers. In this paper, a new method to optimize the placement of SVC devices in a residential neighborhood on the island of Maui, Hawaii, USA with highly distributed PV penetration is presented as a case study using real data to show the efficiency of the proposed optimal placement methodology.

In this paper, a droop coefficients regulation methodology is proposed for an islanded microgrid to optimize the power losses. Based on the P-f and Q-V relations to adjust the droop coefficients at every loading condition, the optimized operating set point generates minimum power losses operation while satisfying the constraints of voltage and frequency limitation. A 9-bus case study is here implemented to show the effectiveness of this new approach as well as the improved operation quality of the system.

This paper shows how minimum cost energy management can be carried out for islanded micro-grids considering an expanded state that also includes the system's frequency. Each of the configurations outputted by the energy management system at each hour are indeed technically sound and coherent from the point of view of generation-consumption balancing by exploiting a frequency dependent load flow algorithm. A Glow-worm Swarm Optimization (GSO) algorithm carried out in a 24 hour time frame provides optimized results. A test has been carried out for a residential PV-Storage-Microturbine islanded micro-grid to show the feasibility as well as the efficiency of the proposed approach and results are compared to an existing solution method recently proposed by the authors.