ECONOMIC Potential of Renewable Energy in Vietnam's Power Sector

A bottom-up Integrated Resource Planning model is used to examine the economic potential of renewable energy in Vietnam’s power sector. In a baseline scenario without renewables, coal provides 44% of electricity generated from 2010 to 2030. The use of renewables could reduce that figure to 39%, as well as decrease the sector’s cumulative emission of CO2 by 8%, SO2 by 3%, and NOx by 4%. In addition,renewables could avoid installing 4.4GW in fossil fuel generating capacity, conserve domestic coal,decrease coal and gases imports, improving energy independence and security. Wind could become cost-competitive assuming high but plausible on fossil fuel prices, if the cost of the technology falls to 900 US$/kW

Optimal placement of battery energy storage system considering penetration of distributed generations

This paper proposes the optimal problem of location and power of the battery-energy-storage-system (BESS) on the distribution system (DS) considering different penetration levels of distributed generations (DGs). The objective is to minimize electricity cost of the DS in a typical day considering the power limit of DG fed to the DS. Growth optimizer (GO) is first applied to search the BESS’s location and power for each interval of the day. The considered problem and GO method are evaluated on the 18-node DS with two penetrations levels of photovoltaic system and wind turbine. The results demonstrate that the optimal BESS placement significantly reduces electricity cost. Furthermore, the optimal BESS location and power also help to reduce the cut capacity of DGs as their power greater than the load demand. The compared results between GO and particle swarm optimization (PSO) method have shown that GO reaches the better performance than PSO in term the optimal solution and the statistical results. Thus, GO is an effective approach for the BESS placement problem

Engineering nanoparticles using chemical and biological approaches for tumor targeted delivery

Doctor of PhilosophyDepartment of ChemistrySantosh AryalNanotechnology offers exciting options for the site-selective delivery of chemotherapeutics and diagnostic agents using nanoparticles. Varieties of organic and inorganic nanomaterials have been explored extensively as a delivery system either in the form of drug carriers or imaging agents. Successful stories include the clinical translation of anticancer nanomedicines such as PEGylated liposomal doxorubicin (DOXIL®), albumin-bound paclitaxel (Abraxane®), and polymeric micelle loaded paclitaxel (Genexol®), which are currently used in the clinic as one of the first lines for cancer chemotherapies. These conventional nanomedicines rely on passive-drug targeting taking advantage of leaky tumor vasculature, called the Enhanced Permeability and Retention (EPR) effect. However, delivering biologically active components selectively to the diseased cell, for example, cancer, is highly challenging due to the biological barriers in the body including blood pool cells/proteins, heterogeneous microenvironment, and intracellular degradation. Therefore, the goal of this dissertation is to develop nanoplatforms that can deliver the agents of interest in targeted fashion to cancer while bypassing or collaborating with the biological barriers. The design consideration of these nanoplatforms centralizes on using simple chemical reactions and cell biology to engineer nanoparticles. The presented nanoparticles were extensively studied and evaluated for their biological functions using in vitro and in vivo models. These nanoconstructs described herein address current limitations of conventional nanomedicine such as (1) the lack of understanding of the interaction of nanoparticle and biological system, and (2) the lack of an effective targeting strategy to deliver drugs to the cancer cell in the tumors. The significant findings of each system will be highlighted and discussed throughout this dissertation. Results obtained highlight key findings such as NP intracellular fate, maximized tumor accumulation, and unique pharmacokinetics could open the avenues for systemic investigations for personalized medicine and lay the foundation for nanomedicine design to accelerate clinical translation

Rate Effects on Peak and Residual Strengths of Overconsolidated Clay in Ring Shear Tests

Overconsolidated (OC) clay soil is widely distributed in landslide slopes. This soil is often fissured, jointed, contains slickensides, and is prone to sliding. Thus, the shear strength behavior of OC clayey soil is complicated and has received much attention in the literature and in practice in terms of evaluating and predicting landslide stability. However, the behavior of the shear strength of OC clayey soil at different shear rates, as seen in ring shear tests, is still only understood to a limited extent and should be examined further, especially in terms of the residual strength characteristics. In this study, a number of ring shear tests were conducted on kaolin clay at overconsolidation ratios (OCRs) ranging from 1 to 6 under different shear displacement rates in the wide range of 0.02 mm/min to 20.0 mm/min to investigate the shear behavior and rate dependency of the shear strength of OC clay. Variations in the cohesion and friction angles of OC clay under different shear rates were also examined. The results indicated that the rate effects on the peak strength of OC and normally consolidated (NC) clays are opposite at fast shear displacement rates. At the residual state, as with NC clay, the positive rate effect on the residual strength is also exhibited in OC clay, but at a lower magnitude. Regarding the shear strength parameters, the variations in the cohesion and friction angles of OC clay at different shear rates were found to be different at peak and residual states

The potential for mitigation of CO2 emissions in Vietnam's power sector

This manuscript examines CO2 emissions from Vietnam's power sector using an expanded Integrated Resource Planning model. The potential effects of the following alternative policy options are examined: energy efficiency, favorably imported generation fuels, nuclear energy, renewable energy, and an internalized positive carbon value. The baseline in terms of cumulative CO2 emissions over 2010-2030 is 3.6 Gt. Lighting energy efficiency improvements offers 14% of no-regret abatement of CO2 emissions. Developing nuclear and renewable energy could help meet the challenges of the increases in electricity demand, the dependence on imported fuels for electricity generation in the context of carbon constraints applied in a developing country. When CO2 costs increase from 1 $/t to 30$/t, building 10 GW of nuclear generation capacity implies an increase in abatement levels from 24% to 46%. Using renewable energy abates CO2 levels by between 14% and 46%. At 2 $/tCO2, the model predicts an abatement of 0.77 Gt from using wind power at prime locations as well as energy from small hydro, wood residue and wood plantations, suggesting Clean Development Mechanism opportunities. At 10$/tCO2, the model predicts an abatement of 1.4 Gt when efficient gas plants are substituted for coal generation and when the potential for wind energy is economically developed further than in the former model

Gendec: A Machine Learning-based Framework for Gender Detection from Japanese Names

Every human has their own name, a fundamental aspect of their identity and cultural heritage. The name often conveys a wealth of information, including details about an individual's background, ethnicity, and, especially, their gender. By detecting gender through the analysis of names, researchers can unlock valuable insights into linguistic patterns and cultural norms, which can be applied to practical applications. Hence, this work presents a novel dataset for Japanese name gender detection comprising 64,139 full names in romaji, hiragana, and kanji forms, along with their biological genders. Moreover, we propose Gendec, a framework for gender detection from Japanese names that leverages diverse approaches, including traditional machine learning techniques or cutting-edge transfer learning models, to predict the gender associated with Japanese names accurately. Through a thorough investigation, the proposed framework is expected to be effective and serve potential applications in various domains.Comment: This paper is accepted for presentation at ISDA'2

Modified sunflower optimization for network reconfiguration and distributed generation placement

This paper proposed modified sunflower optimization (MSFO) for the combination of network reconfiguration and distributed generation placement problem (NR-DGP) to minimize power loss of the electric distribution system (EDS). Sunflower optimization (SFO) is inspired form the ideal of sunflower plant motion to get the sunlight and its reproduction. To enhance the performance of SFO, it is modified to MSFO wherein, the pollination and mortality techniques have been modified by using Levy distribution and mutation of the best solutions. The results are evaluated on two test systems. The efficiency of MSFO is compared with that of the original SFO and other algorithms in literature. The comparisons show that MSFO outperforms to SFO and other methods in obtained optimal solution. Furthermore, MSFO demonstrates the better statistical results than SFO. So, MSFO can be a powerful approach for the NR-DGP problem

Optimal solutions for fixed head short-term hydrothermal system scheduling problem

In this paper, optimal short-term hydrothermal operation (STHTO) problem is determined by a proposed high-performance particle swarm optimization (HPPSO). Control variables of the problem are regarded as an optimal solution including reservoir volumes of hydropower plants (HdPs) and power generation of thermal power plants (ThPs) with respect to scheduled time periods. This problem focuses on reduction of electric power generation cost (EPGC) of ThPs and exact satisfactory of all constraints of HdPs, ThPs and power system. The proposed method is compared to earlier methods and other implemented methods such as particle swarm optimization (PSO), constriction factor (CF) and inertia weight factor (IWF)-based PSO (FCIW-PSO), two time-varying acceleration coefficient (TTVACs)-based PSO (TVAC-PSO), salp swarm algorithm (SSA), and Harris hawk algorithm (HHA). By comparing EPGC from 100 trial runs, speed of search and simulation time, the suggested HPPSO method sees it is more robust than other ones. Thus, HPPSO is recommended for applying to the considered and other problems in power systems

Determining optimal location and size of capacitors in radial distribution networks using moth swarm algorithm

In this study, the problem of optimal capacitor location and size determination (OCLSD) in radial distribution networks for reducing losses is unraveled by moth swarm algorithm (MSA). MSA is one of the most powerful meta-heuristic algorithm that is taken from the inspiration of the food source finding behavior of moths. Four study cases of installing different numbers of capacitors in the 15-bus radial distribution test system including two, three, four and five capacitors areemployed to run the applied MSA for an investigation of behavior and assessment of performances. Power loss and the improvement of voltage profile obtained by MSA are compared with those fromother methods. As a result, it can be concluded that MSA can give a good truthful and effective solution method for OCLSD problem