• Energy Research
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A new sizing approach is applied in this paper to stand-alone PV systems design, which is based on systems configurations without shedding load. The investigation is based on a detailed study of the minimum storage requirement and an analysis of the sizing curves. The analysis reveals the importance of using daily series of measured solar radiation data instead of monthly average values. For high-reliability systems, it is important that these data series are as long as possible, while the configuration of large generator and small battery seems to deal better with the unpredictability of solar radiation.

Abstract In some shelf sea regions of the world, the tidal range is sufficient to convert the potential energy of the tides into electricity via tidal range power plants. As an island continent, Australia is one such region – a previous study estimated that Australia hosts up to 30% of the world’s resource. Here, we make use of a gridded tidal dataset (TPXO9) to characterize the tidal range resource of Australia. We examine the theoretical resource, and we also investigate the technical resource through 0D modelling with tidal range power plant operation. We find that the tidal range resource of Australia is 2004 TWh/yr, or about 22% of the global resource. This exceeds Australia’s total energy consumption for 2018/2019 (1721 TWh/yr), suggesting tidal range energy has the potential to make a substantial contribution to Australia’s electricity generation (265 TWh/yr in 2018/2019). Due to local resonance, the resource is concentrated in the sparsely populated Kimberley region of Western Australia. However, the tidal range resource in this region presents a renewable energy export opportunity, connecting to markets in southeast Asia. Combining the electricity from two complementary sites, with some degree of optimization tidal range schemes in this region can produce electricity for 45% of the year.

Small-scale wind turbine operations within the urban environment are exposed to high levels of gusts and turbulence compared to flows over less rough surfaces. There is therefore a need for such systems to not only cope with, but to thrive under such fluctuating flow conditions. This paper addresses the potential importance of gust tracking technologies within the urban environment via the analysis of the additional energy present in the gusty wind resource using high resolution measurements at two urban roof-top locations. Results demonstrate significant additional energy present in the gusty wind resource at high temporal resolution. This energy is usually under-represented by the use of mean wind speeds in quantifying the power in the wind over longer averaging times. The results support the promise of capturing a portion of this extra energy through gust tracking solutions. The sensitivity of this “additional” wind energy to averaging time interval is also explored, providing useful information for the design of gust tracking or dynamic control algorithms for small-scale turbines. Relationships between turbulence intensity and excess energy available are drawn. Thus, an analytical model is proposed which may prove useful in predicting the excess energy available across wide areas from, for example, boundary layer turbulence models.

Abstract Climate change could substantially impact the performance of the buildings in providing thermal comfort to occupants. Recently launched UK climate projections (UKCP09), clearly indicate that all areas of the UK will get warmer in future with the possibility of more frequent and severe extreme events, such as heat waves. This study, as part of the Low Carbon Futures (LCF) Project, explores the consequent risk of overheating and the vulnerability of a building to extreme events. A simple statistical model proposed by the LCF project elsewhere has been employed to emulate the outputs of the dynamic building simulator (ESP-r) which cannot feasibly be used itself with thousands of available probabilistic climate database. Impact of climate change on the daily external and internal temperature profiles has been illustrated by means of 3D plots over the entire overheating period (May–October) and over 3000 equally probable future climates. Frequency of extreme heat events in changing climate and its impact on overheating issues for a virtual case study domestic house has been analyzed. Results are presented relative to a baseline climate (1961–1990) for three future timelines (2030s, 2050s, and 2080s) and three emission scenarios (Low, Medium, and High).

Abstract In this paper, a strategy is proposed in order to introduce in a realistic way wind generation into a transmission power system non sequential Monte Carlo adequacy study with economic dispatch. Thanks to the implemented solution, wind generation is consequently confronted to operational constraints related to high powered thermal units, nuclear parks or thermal machines with technical minimum value. Moreover, during each simulated system state, a DC load flow is also calculated in order to evaluate reinforcements optimizing the large scale integration of wind power production. The simulation tool modified during the present work is called Scanner© and is the property of Tractebel Engineering (Gaz de France – Suez) company. It has been here applied to an academic test system: the Roy Billinton Test System (RBTS).

Abstract An indirect forced convection and desiccant integrated solar dryer is designed and fabricated to investigate its performance under the hot and humid climatic conditions of Chennai, India. The system consists of a flat plate solar air collector, drying chamber and a desiccant unit. The desiccant unit is designed to hold 75 kg of CaCl2-based solid desiccant consisting of 60% bentonite, 10% calcium chloride, 20% vermiculite and 10% cement. Drying experiments have been performed for green peas at different air flow rate. The equilibrium moisture content Me is reached in 14 h at an air flow rate of 0.03 kg/m2 s. The system pickup efficiency, specific moisture extraction rate, dimensionless mass loss, mass shrinkage ratio and drying rate are discussed in this paper.

AbstractSimulating fully resolved Horizontal Axis Tidal Turbine (HATT) geometry for a time period great enough to resolve a fully developed wake, and accurately predict power and thrust characteristics, is computationally very expensive. The BEM-CFD method is an enhanced actuator disk and is able to reduce the computational cost by simulating a time averaged downstream velocity field. Current implementations fall short of accurately determining tip losses, which are a function of the hydrofoil geometry. This work proposes a method of addressing this shortfall by modifying the angle of attack to conform to the constraints outlined in Prandtl's lifting line theory, i.e. the zero lift angle of attack at the hydrofoil tip. The revised model is compared to existing BEM-CFD methods and validated against experimental data. The revised BEM-CFD method presented in this work shows a significant improvement over previous BEM-CFD methods when predicting power and thrust. The coefficient of power is reduced from 0.57 (approx. 30% above experiment) to 0.44 (approx. 3% above experiment). An increase in turbulence intensity in the rotor region, in particular at the wake boundary, improves the recovery of the wake without the addition of empirical turbulence source terms. Good correlation with experimental results for power, thrust and wake prediction, is observed. The model may also be applied to wind turbines.

Abstract Reliability analysis of IESs (Integrated Energy System) is complicated because of the complexity of system topology and dynamics and different kinds of uncertainties. Reliability is often calculated based on statistic methods, which always focus on historical performances and neglect the importance of their dynamics and structure. To overcome this problem, in this paper, a systematic framework for dynamically analysing the real-time reliability of IESs is proposed by integrating different machine learning methods and statistics. Firstly, the bootstrap-based Extreme Learning Machine is developed to forecast the conditional probability distributions of the productions of renewable energies and the energy consumptions. Then, the dynamic behaviour of IESs is simulated based on a stacked auto-encoder model, instead of using traditional mechanism-based simulation models, for improving computational efficiency. Besides, the variables representing the transient properties of natural gas pipeline networks, such as delivery pressures and flow rates, are taken as the indicators for quantifying the energy supply security in natural gas pipeline networks. The time-dependent relationships among these indicators and their statistic correlations are modelled for improving the effectiveness of the analysis results. Finally, the reliability assessment is performed by estimating the probability distribution of each functional state of the target IES. A case study of a realistic bi-directional IES is carried out to demonstrate the effectiveness of the proposed method. The results show that the method is able to effectively evaluate the reliability of IESs, which can provide useful information for system operation and management.