Outdoor and indoor temperature prediction of local buildings is important for optimal building operation and energy-demand management. This study collects data from a commercial building, covering outdoor and indoor climate, and variables of occupants and building system operation. Based on the selected data, two different data-driven methodologies using machine learning techniques are proposed to predict local outdoor and indoor temperatures at a high resolution. The proposed data-driven models with learning capabilities are based on k-nearest neighbor and artificial neural networks, showing good prediction performance for the case study building.
Over the past two centuries, the research on working fluids drove the tremendous progress of organic Rankine cycle to convert medium- and low-temperature heat into power efficiently. With the increasingly stringent requirements on working fluids, the search for alternative working fluids is a never-ending task. In the present work, a comprehensive review of working fluids selection of ORC is presented to summary the current research results, find out the issues and guide the future developments. The research of working fluid selection is divided into three stages according to research method firstly. Then, the research progress of each stages is summarized. In addition, the research challenges and recommendations for further research of working fluids selection and even for novel thermodynamic cycle are highlighted as well. The results show that for traditional ORC, the optimal working fluid could be selected almost by key parameters such as critical temperature, acentric factor and Jacob number, etc. More importantly, the development direction of novel thermodynamic cycle is presented.
Performance characteristics of hydrogen generator using sodium borohydride storage for uninterrupted power system was evaluated in the present study. For fuel cell-based uninterrupted power system, compressed and liquefied hydrogen have been mainly used as a hydrogen storage, but safety for long-term storage is still problematic due to its storage characteristics. In this paper, the reaction of sodium borohydride with an acidic solution was employed to generate hydrogen in order to compensate the storage problem. Using this mechanism, a hydrogen generator for 1 kW fuel cell was developed and the performance evaluation was carried out in the various operation conditions.
Modular multilevel converter based high voltage direct current (MMC-HVDC) technology has been the preferred choice for integration of renewable energy due to its advantages in power control and transmission loss etc. This paper focused on single-line-to-ground (SLG) and three phases to ground fault at valve bottom of MMC station. Theoretical analysis of variation of AC voltage and current of MMC-HVDC, DC voltage and current, and neutral to ground current of transformer after fault occurred were conducted. Characteristics of MMC-HVDC system with fault at bottom of the valve were summarized. The theoretical analysis was verified by simulation examples. The work in this paper laid foundation for configuration of converter protection in MMC-HVDC project, thus had important engineering significance in renewable energy integration process.
This study focuses on the design of segmented thermoelectric generator (STEG) system to achieve the system maximum output power. Multi-objective genetic algorithm (MOGA) design was applied to increase the output power. The STEG structure was optimized by altering the length of the cold side thermoelectric materials. The results showed that the optimized STEG module has the maximum output power when the lengths of p-type and n-leg cold side thermoelectric material are 0.78 mm and 0.5 mm. The cold side length which is not in the range of optimized value will reduce the maximum output power. In addition, the heat transfer rate and power generation of the STEG model was optimized. The most suitable length of the cold side thermoelectric material was found by MOGA. The output power of the optimized STEG compared with half in leg length of the segmented STEG was increased 21.94 % at ΔT= 400 K. Therefore, MOGA was an effective tool for designing STEG geometries.
The reliability evaluation of distribution network is an important part of power system. In the extreme situation caused by aging or weather, the multi-fault affect the distribution network reliability. There are only few reliability assessment approach considered the impact of multi-faults. It’s necessary to have a method that can ensure both accuracy and efficiency. The impact increment method based on Monte Carlo sampling (IIMC) can meet the requirements in transmission network. After improving the independent faults identification by distribution network structure, the RBTS Bus6 system is used as an example to test the effectiveness of IIMC. Compared with traditional Monte Carlo sampling. When the failure rate of components is high for the aging and other reasons, the result can show the advantage of IIMC.
Due to its noiselessness, low energy consumption and compact structure, ionic wind has become a hot research field in recent years. In this study, an ionic wind pump with multi-wire corona electrodes is designed. Each wire electrode corresponds to a pair of parallel plate electrodes which are used to collect charged ions. The change of ionic wind velocity caused by different electrodes configurations and its influence on the cooling effect of a heated plate with a constant power were studied by numerical simulation. The result shows that both the air flow velocity and the mass flow rate increase with the increasing plate electrodes length, and there is approximate a 1 °C temperature drop with a 10 mm increase in electrode length when using the ionic wind pump to cool a plate heated by a 1.5 W power. The maximum temperature drop is approximate 110 °C, compared with the natural convection condition.
In the design of renewable energy capacity, due to the existence of weather uncertainty, the conventional design method can easily lead to overdesign or unsatisfactory performance in order to ensure the system is completely reliable. Most existing solar heating system (SHS) design optimization studies are based on deterministic data or information. However, weather uncertainty is one of the key factors affecting the reliability of the system and the rationality of design results. Therefore, this study proposed a multi-objective optimal design method for SHS considering the uncertainty of the weather and reliability of the system. Taking a SHS of an office building in Tianjin as an example, based on the actual meteorological parameters of 20 years, the uncertainty of the performance of buildings and equipment are simulated by TRNSYS and EnergyPlus. Multi-objective optimization under economic, environment and reliability of the system are considered in optimization algorithm. The optimization results show that compared with the conventional fchart method, total annual cost and CO2 emissions are respectively reduced by 8.3% and 2.5%. Compared with the exhaustive method, it can shorten the calculation time by 98.5%, which can greatly improve the efficiency of optimization.
Transport sector around the world is in a transition era by experiencing a disruption of electric vehicle (EV) technology. This transition brings both challenges and opportunities to energy system and energy pattern in transport sector, such as increasing of electricity demand and reduction of greenhouse gas (GHG) emissions. This study aims to analyse the potential future scenarios of the penetration of EV in Thailand’s road transport sector. In addition, the impacts of such technology to energy demand and supply and potential of GHG emission reduction in transport sector will also be assessed.
Policy commitment of the government plays a crucial role for EVs market in Thailand. Therefore, the future scenarios can be explored by two cases: Current Policy Scenario (CPS) represents the current actions of government support on EVs, whereas Proactive Policy Scenario (PPS) represents full package of government supports on both supply and demand sides. High penetration rate of EVs will impacts on Thailand energy system, especially road transport sector. This include total energy demand pattern, load profile of electricity demand and GHG emission. The results present that total number of EVs in PPS scenario will consume electricity more than that of CPS scenario around 1,650 ktoe (19,363 GWh), however, they can reduce 474 ktoe of total energy consumption and 10 MtCO2eq by 2040.
Conventionally, excessive indoor CO2 condition is related to abnormal occupancy condition and can be solved by increasing overall outdoor air rate through air handling unit (AHU). More energy is consumed to condition and deliver the outdoor air by AHU and deliver outdoor air and fans. To be energy-efficient, this study proposes a multi-room outdoor air coordination strategy in response to abnormal occupancy condition by utilizing wireless sensor network (WSN) technology without increasing overall outdoor air rate through AHU. For critical rooms, a simplified occupancy estimation method is applied, and one-step model-based predictive control for dampers is developed. For non-critical rooms, an optimization problem is formed to reallocate the rest part of overall outdoor air rate. The proposed strategy is applied in a real case in Hong Kong. The reduced highest CO2 and exposure time to undesirable CO2 in critical and non-critical rooms prove the ability of proposed strategy in response to abnormal occupancy condition. And compared with extra energy consumption for AHU and fans in conventional strategy, smaller energy for adjusting dampers proves the energy efficiency of proposed strategy.