• Energy Research
• 7. Clean energy close
• 13. Climate action close
• 8. Economic growth close
• JP close
• Energies close

Thermoelectric (TE) material is a class of materials that can convert heat to electrical energy directly in a solid-state-device without any moving parts and that is environmentally friendly. The study and development of TE materials have grown quickly in the past decade. However, their development goes slowly by the lack of cheap TE materials with high Seebeck coefficient and good electrical conductivity. Carbon nanotubes (CNTs) are particularly attractive as TE materials because of at least three reasons: (1) CNTs possess various band gaps depending on their structure, (2) CNTs represent unique one-dimensional carbon materials which naturally satisfies the conditions of quantum confinement effect to enhance the TE efficiency and (3) CNTs provide us with a platform for developing lightweight and flexible TE devices due to their mechanical properties. The TE power factor is reported to reach 700–1000 μ W / m K 2 for both p-type and n-type CNTs when purified to contain only doped semiconducting CNT species. Therefore, CNTs are promising for a variety of TE applications in which the heat source is unlimited, such as waste heat or solar heat although their figure of merit Z T is still modest (0.05 at 300 K). In this paper, we review in detail from the basic concept of TE field to the fundamental TE properties of CNTs, as well as their applications. Furthermore, the strategies are discussed to improve the TE properties of CNTs. Finally, we give our perspectives on the tremendous potential of CNTs-based TE materials and composites.

Photovoltaic (PV) technology has been the focus of interest due to its nonpolluting operation and good installation flexibility. Irradiation and temperature are the two main factors which impact the performance of the PV system. Accordingly, when partial shading from surroundings occurs, its incident shadow diminishes the irradiation and reduces the generated power. Since the conventional maximum power point tracking methods (MPPT) could not distinguish the global maximum power of the power-voltage (P-V) characteristic curve, a new tracking method needs to be developed. This paper proposes a global maximum power point tracking method using shading detection and the trend of slopes from each section of the curve. Full mathematical equations and algorithms are presented. Simulations based on real weather data were performed both in short-term and long-term studies. Moreover, this paper also presents the experiment using the DC-DC synchronous and interleaved boost converter. Results from the simulation show an accurate tracking result and the system can enhance the total energy generated by 8.55% compared to the conventional scanning method. Moreover, the experiment also confirms the success of the proposed tracking algorithm.

The number of houses with large, continuous spaces has increased recently. With improvements in insulation performance, it has become possible to efficiently air condition such spaces using a single air conditioner. However, the air conditioning efficiency depends on the placement of the air conditioner. The only way to determine the optimal placement of such air conditioners is to conduct an experiment or use computational fluid dynamic analysis. However, because the analysis is performed over a limited period, it is difficult to consider non-stationarity effects without using an energy simulation. Therefore, in this study, energy simulations and computational fluid dynamics analyses were coupled to develop a thermal environment analysis method that considers non-stationarity effects, and various air conditioner arrangements were investigated to demonstrate the applicability of the proposed method. The accuracy verification results generally followed the experimental results. A case study was conducted using the calculated boundary conditions, and the results showed that the placement of two air conditioners in the target experimental house could provide sufficient air conditioning during both winter and summer. Our results suggest that this method can be used to conduct preliminary studies if the necessary data are available during design or if an experimental house is used.

In this paper, an advanced electrothermal simulation strategy is applied to a 3.3 kV silicon carbide MOSFET power module. The approach is based on a full circuital representation of the module, where use is made of the thermal equivalent of the Ohm’s law. The individual transistors are described with subcircuits, while the dynamic power-temperature feedback is accounted for through an equivalent thermal network enriched with controlled sources enabling nonlinear thermal effects. A synchronous step-up DC-DC converter and a single-phase inverter, both incorporating the aforementioned power module, are simulated. Good accuracy was ensured by considering electromagnetic effects due to parasitics, which were experimentally extracted in a preliminary stage. Low CPU times are needed, and no convergence issues are encountered in spite of the high switching frequencies. The impact of some key parameters is effortlessly quantified. The analysis witnesses the efficiency and versatility of the approach, and suggests its adoption for design, analysis, and synthesis of high-frequency power converters in wide-band-gap semiconductor technology.

In the international movement to combat the threat of climate change, the timely implementation of residential energy-saving practises is becoming an urgent issue. Because the number of apartments is increasing, we analysed data from home energy management systems (HEMSs) and data from questionnaire surveys of 309 households in a condominium. We focused on the seasonal variation in air-conditioning (AC) use in living-dining rooms to determine the tendency of energy use for heating/cooling related to the characteristics of flats, the profiles of residents, and energy-saving behaviours. In winter, 80% of residents mainly used gas floor heating rather than AC and 24% did not use AC in winter. In households where someone stays home for long hours, they prefer gas floor heating rather than AC in winter. These households also tend to engage in energy-saving behaviours to adjust the indoor thermal environment. There are several types of energy-saving lifestyles; therefore, effective energy-saving measures should be considered for both energy efficiency and the thermal comfort of residents.

Accurate and timely macro forecasting requires new and powerful predictors. Carbon emissions data with high trading frequency and short releasing lag could play such a role under the framework of mixed data sampling regression techniques. This paper explores the China case in this regard. We find that our multiple autoregressive distributed lag model with mixed data sampling method setup outperforms either the auto-regressive or autoregressive distributed lag benchmark in both in-sample and out-of-sample nowcasting for not only the monthly changes of the purchasing managers’ index in China but also the Chinese quarterly GDP growth. Moreover, it is demonstrated that such capability operates better in nowcasting than h-step ahead forecasting, and remains prominent even after we account for commonly-used macroeconomic predictive factors. The underlying mechanism lies in the critical connection between the demand for carbon emission in excess of the expected quota and the production expansion decision of manufacturers.

At present, primary power generation depends on non-renewable energy resources, which will become extinct. Solar is the best option in renewable energy sources to achieve clean and green power extraction. Solar PV transforms light energy into electrical energy. However, the output power of solar PV changes with solar insolation. It is also affected by environmental factors and the shading effect. One of the key factors that can reduce the PV system output power is partial shading condition (PSC). The reduction in power output not only depends on shaded region but also depends on pattern of shading and physical position of shaded modules in the array. Due to PSCs, mismatch losses are induced between the shaded modules which can cause several peaks in the output power-voltage (P-V) characteristic. This article describes the non-symmetrical reconfiguration technique and compares it with the primary total cross tied connection. The performance of non-symmetrical reconfiguration techniques is evaluated and compared in terms of global maximum power (GMP), voltage and currents at GMP, open and short circuit voltage and currents, mismatch power loss (MPL), fill factor, efficiency, and number of local maximum power peaks (LMPPs) on a 9 × 9 PV array.

An optimal tilt-angle control based on artificial intelligence (AI control) for tracking bifacial photovoltaic (BPV) systems is developed in this study, and its effectiveness and characteristics are examined by simulating a virtual system over five years. Using deep reinforcement learning (deep RL), the algorithm autonomously learns the control strategy in real time from when the system starts to operate. Even with limited deep RL input variables, such as global horizontal irradiance, time, tilt angle, and power, the proposed AI control successfully learns and achieves a 4.0–9.2% higher electrical-energy yield in high-albedo cases (0.5 and 0.8) as compared to traditional sun-tracking control; however, the energy yield of AI control is slightly lower in low-albedo cases (0.2). AI control also demonstrates a superior performance when there are seasonal changes in albedo. Moreover, AI control is robust against long-term system degradation by manipulating the database used for reward setting.