Solar aided power generation from coal fired power stations: THERMSOLV software

Although the thermodynamic advantages of using solar energy to replace the bleed off steam in the regeneration system of Rankine cycle coal fired power stations has been proven theoretically, the practical techno/economic feasibility of the concept has yet to be confirmed relative to real power station applications. To investigate this concept further, a computer modelling software “THERMSOLV” was developed by Deakin University researchers, together with the support of the Victorian power industry and Australian Research Council (ARC). This newly developed software simulates the steam cycle to assess the techno/economic merit of the solar aided concept for various power station structures, locations and local electricity market conditions. Two case studies, one in Victoria Australia and one in Yunnan Province, China, have been carried out to show the application of the software. This paper reports the structure and functions of the software, and the results of the two case studies.

Uncertainty quantification for wind farm power generation

Accurate forecasting of wind farm power generation is essential for successful operation and management of wind farms and to minimize risks associated with their integration into energy systems. However, due to the inherent wind intermittency, wind power forecasts are highly prone to error and often far from being perfect. The purpose of this paper is to develop statistical methods for quantifying uncertainties associated with wind power generation forecasts. Prediction intervals (PIs) with a prescribed confidence level are constructed using the delta and bootstrap methods for neural network forecasts. The moving block bootstrap method is applied to preserve the correlation structure in wind power observations. The effectiveness and efficiency of these two methods for uncertainty quantification is examined using two month datasets taken from a wind farm in Australia. It is demonstrated that while all constructed PIs are theoretically valid, bootstrap PIs are more informative than delta PIs, and are therefore more useful for decision-making.

Power generation from randomly oriented electrospun nanofiber membranes

Randomly orientated electrospun poly(vinylidene fluoride) nanofiber membranes were directly used as active layers to make mechanical-to-electrical energy conversion devices. Without any extra poling treatment, the device can generate high electrical outputs upon receiving a mechanical impact. The device also showed long-term working stability and ability to drive electronic devices. Such a nanofiber membrane device may serve as a simple but efficient energy source for self-powered electronics.

Electrical power generation from randomly-oriented electrospun nanofibrous nonwoven

In this study, we have demonstrated that randomly-oriented electrospun PVDF nanofiber nonwovens can be used directly as an active layer to generate electrical power with a voltage output as high as 4 volt and current 4 micoramp scales on a small nonwoven piece. This discovery may provide a simple, efficient, cost-effective and flexible solution to self-powering of microelectronics for various purposes.

Combined nonparametric prediction intervals for wind power generation

Prediction intervals (PIs) are a promising tool for quantification of uncertainties associated with point forecasts of wind power. However, construction of PIs using parametric methods is questionable, as forecast errors do not follow a standard distribution. This paper proposes a nonparametric method for construction of reliable PIs for neural network (NN) forecasts. A lower upper bound estimation (LUBE) method is adapted for construction of PIs for wind power generation. A new framework is proposed for synthesizing PIs generated using an ensemble of NN models in the LUBE method. This is done to guard against NN performance instability in generating reliable and informative PIs. A validation set is applied for short listing NNs based on the quality of PIs. Then, PIs constructed using filtered NNs are aggregated to obtain combined PIs. Performance of the proposed method is examined using data sets taken from two wind farms in Australia. Simulation results indicate that the quality of combined PIs is significantly superior to the quality of PIs constructed using NN models ranked and filtered by the validation set.

Prediction intervals for short-term wind farm power generation forecasts

Quantification of uncertainties associated with wind power generation forecasts is essential for optimal management of wind farms and their successful integration into power systems. This paper investigates two neural network-based methods for direct and rapid construction of prediction intervals (PIs) for short-term forecasting of power generation in wind farms. The lower upper bound estimation and bootstrap methods are used to quantify uncertainties associated with forecasts. The effectiveness and efficiency of these two general methods for uncertainty quantification is examined using twenty four month data from a wind farm in Australia. PIs with a confidence level of 90% are constructed for four forecasting horizons: five, ten, fifteen, and thirty minutes. Quantitative measures are applied for objective evaluation and unbiased comparison of PI quality. Demonstrated results indicate that reliable PIs can be constructed in a short time without resorting to complicate computational methods or models. Also quantitative comparison reveals that bootstrap PIs are more suitable for short prediction horizon, and lower upper bound estimation PIs are more appropriate for longer forecasting horizons.

Modelling and power quality analysis of a grid-connected solar PV system

Increased concern about global warming coupled with the escalating demand of energy has driven the conventional power system to be more reliable one by integrating Renewable Energies (RE) in to grid. Over the recent years, integration of solar PV forming a gridconnected PV is considered as one of the most promisingtechnologies to the developed countries like Australia to meet the growing demand of energy. This rapid increase in grid connected photovoltaic (PV) systems has made the supply utilities concerned about the drastic effects that have to be considered on the distribution network in particular voltage fluctuations, harmonic distortions and the Power factor for sustainable power generation. However, irrespective of thefact that the utility grid can accommodate the variability of load or irregular solar irradiance, it is essential to study the impact of grid connected PV systems during higher penetration levels as the intermittent nature of solar PV adversely effects the grid characteristics in meeting the load demand. Hence, keeping this in track, this paper examines the grid-connected PV system considering a residential network of Geelong region (38◦.09' S and 144◦.21’ E) and explores the level of impacts considering summer load profile with a change in the level of integrations. Initially, a PV power system network model is developed in Matlab-Simulink environment and the simulations are carried out to explore the impacts of solar PV penetration at low voltage distribution network considering power quality (PQ) issues such as voltage fluctuations, harmonics distortion at different load conditions.

Time-series-based maximization of distributed wind power generation integration

Energy policies and technological progress in the development of wind turbines have made wind power the fastest growing renewable power source worldwide. The inherent variability of this resource requires special attention when analyzing the impacts of high penetration on the distribution network. A time-series steady-state analysis is proposed that assesses technical issues such as energy export, losses, and short-circuit levels. A multiobjective programming approach based on the nondominated sorting genetic algorithm (NSGA) is applied in order to find configurations that maximize the integration of distributed wind power generation (DWPG) while satisfying voltage and thermal limits. The approach has been applied to a medium voltage distribution network considering hourly demand and wind profiles for part of the U.K. The Pareto optimal solutions obtained highlight the drawbacks of using a single demand and generation scenario, and indicate the importance of appropriate substation voltage settings for maximizing the connection of MPG.

Discrete-time sliding mode control of input-delay systems applied on a power generation system

This paper presents two discrete sliding mode control (SMC) design. The first one is a discrete-time SMC design that doesn't take into account the time-delay. The second one is a discrete-time SMC design, which takes in consideration the time-delay. The proposed techniques aim at the accomplishment simplicity and robustness for an uncertainty class. Simulations results are shown and the effectiveness of the used techniques is analyzed. © 2006 IEEE.

Probabilistic analysis of the distributed power generation in weakly meshed distribution systems

This paper presents an approach for probabilistic analysis of unbalanced three-phase weakly meshed distribution systems considering uncertainty in load demand. In order to achieve high computational efficiency this approach uses both an efficient method for probabilistic analysis and a radial power flow. The probabilistic approach used is the well-known Two-Point Estimate Method. Meanwhile, the compensation-based radial power flow is used in order to extract benefits from the topological characteristics of the distribution systems. The generation model proposed allows modeling either PQ or PV bus on the connection point between the network and the distributed generator. In addition allows control of the generator operating conditions, such as the field current and the power delivery at terminals. Results on test with IEEE 37 bus system is given to illustrate the operation and effectiveness of the proposed approach. A Monte Carlo Simulations method is used to validate the results. © 2011 IEEE.

The need of subsidy for the implementation of photovoltaic solar energy as supporting of decentralized electrical power generation in Brazil

The growing demand for electrical power and the limited capital invested to provide this power is forcing countries like Brazil to search for new alternatives for electrical power generation. The purpose of this paper is to present a technical and economic study on a 15 kW solar plant installed in an isolated community, highlighting the importance of the need for financial subsidy from the government. It evaluates the importance of parameters such as the annual interest rate, specific investment, the marginal cost of expanding the electrical power supply and the government subsidy on amortization time of capital invested. © 2012 Elsevier Ltd All rights reserved.

On the parameters of performance of combined refrigeration and power plants

Combined Refrigeration and Power (CRP) plants generate power and refrigerate a thermal load simultaneously from the same fuel. The overall efficiency is a parameter based on the first law generally used to quantify the fuel saving, in the sense that a plant that has greater overall efficiency saves more fuel than others to generate the same useful energy. However, the literature shows that the overall efficiency and other parameters of performance are defined in several different ways. This heterogeneity is not desirable when considering a coherent and universally accepted parameter of performance based on the first law. In this work, some parameters found in the literature are critically analyzed in order to indicate the most proper one. The indicated parameter is then formally analyzed in order to verify its mathematical consistency. The primary energy rate is considered the most well-suited parameter based on the first law to characterize the performance of a CRP plant. © 2013 Elsevier Ltd and IIR.

1st and 2nd Generation Ethanol from Biomass Crops

In chapter 1 and 2 calcium hydroxide as impregnation agent before steam explosion of sugarcane bagasse and switchgrass, respectively, was compared with auto-hydrolysis, assessing the effects on enzymatic hydrolysis and simultaneous saccharification and fermentation (SSF) at high solid concentration of pretreated solid fraction. In addition, anaerobic digestion of pretreated liquid fraction was carried out, in order to appraise the effectiveness of calcium hydroxide before steam explosion in a more comprehensive way. In As water is an expensive input in both cultivation of biomass crops and subsequent pretreatment, Chapter 3 addressed the effects of variable soil moisture on biomass growth and composition of biomass sorghum. Moreover, the effect of water stress was related to the characteristics of stem juice for 1st generation ethanol and structural carbohydrates for 2nd generation ethanol. In the frame of chapter 1, calcium hydroxide was proven to be a suitable catalyst for sugarcane bagasse before steam explosion, in order to enhance fibre deconstruction. In chapter 2, effect of calcium hydroxide on switchgrass showed a great potential when ethanol was focused, whereas acid addition produced higher methane yield. Regarding chapter 3, during crop cycle the amount of cellulose, hemicellulose and AIL changed causing a decrease of 2G ethanol amount. Biomass physical and chemical properties involved a lower glucose yield and concentration at the end of enzymatic hydrolysis and, consequently, a lower 2G ethanol concentration at the end of simultaneous saccharification and fermentation, proving that there is strong relationship between structure, chemical composition, and fermentable sugar yield. The significantly higher concentration of ethanol at the early crop stage could be an important incentive to consider biomass sorghum as second crop in the season, to be introduced into some agricultural systems, potentially benefiting farmers and, above all, avoiding the exacerbation of the debate about fuel vs food crops.

Micro combined heat and power laboratory development

Micro Combined Heat and Power (Micro-CHP) system produces both electricity and heat required for residential or small business applications. Use of Micro-CHP in a residential application not only creates energy and economic savings but also reduces the carbon foot print of the house or small business. Additionally, micro-CHP can subsidize its cost of operation by selling excess electricity produced back to the grid. Even though Micro-CHP remains attractive on paper, high initial cost and optimization issues in residential scale heat and electrical requirement has kept this technology from becoming a success. To understand and overcome all disadvantages posed my Micro-CHP system, a laboratory is developed to test different scenarios of Micro-CHP applications so that we can learn and improve the current technology. This report focuses on the development of this Micro-CHP laboratory including installation of Ecopower micro-CHP unit, developing fuel line and exhaust line for Ecopower unit, design of electrical and thermal loop, installing all the instrumentation required for data collection on the Ecopower unit and developing controls for heat load simulation using thermal loop. Also a simulation of Micro-CHP running on Syngas is done in Matlab. This work was supported through the donation of ‘Ecopower’ a Micro-CHP unit by Marathon Engine and through the support of Michigan Tech REF-IF grand.