A Battery Energy Storage interface for wind power systems with the use of grid side inverter

This paper presents a novel concept of Energy Storage System (ESS) interfacing with the grid side inverter in wind energy conversion systems. The inverter system used here is formed by cascading a 2-level inverter and a three level inverter through a coupling transformer. The constituent inverters are named as the “main inverter” and the “auxiliary inverter” respectively. The main inverter is connected with the rectified output of the wind generator while the auxiliary inverter is attached to a Battery Energy Storage System (BESS). The BESS ensures constant power dispatch to the grid irrespective of change in wind condition. Furthermore, this unique combination of BESS and inverter eliminates the need of additional dc-dc converters. Novel modulation and control techniques are proposed to address the problem of non-integer, dynamically-changing dc-link voltage ratio, which is due to random wind changes. Strategies used to handle auxiliary inverter dc-link voltage imbalances and controllers used to charge batteries at different rates are explained in detail. Simulation results are presented to verify the efficacy of the proposed modulation and control techniques in suppressing random wind power fluctuations.

A dual inverter with integrated energy storage for wind power systems

This paper explores the possibility of using grid side inverter as an interface to connect energy storage systems. A dual inverter system, formed by cascading two 2-level inverters through a coupling transformer, is used as the testing model. The inverters are named as “main inverter” and “auxiliary inverter”. The main inverter is powered by the rectified output of the wind generator while the auxiliary inverter is attached to a Battery Energy Storage System (BESS). If there is a surplus of wind power compared to the demand, then that would be stored in BESS while if there is a deficit in wind power then the demand will be satisfied by supplying power from the BESS. This enables constant power dispatch to the grid irrespective of wind changes. Novel modulation and control techniques are proposed to address the problem of non-integer, dynamically-varying dc-link voltage ratio, which is due to random wind changes. Furthermore, a maximum power tracking controller for this unique system is explained in detail. Simulation results verify the efficacy of proposed modulation and control techniques in suppressing random power fluctuations.

Making optimal and justifiable asset maintenance decisions

Maintenance decisions for large-scale asset systems are often beyond an asset manager's capacity to handle. The presence of a number of possibly conflicting decision criteria, the large number of possible maintenance policies, and the reality of budget constraints often produce complex problems, where the underlying trade-offs are not apparent to the asset manager. This paper presents the decision support tool "JOB" (Justification and Optimisation of Budgets), which has been designed to help asset managers of large systems assess, select, interpret and optimise the effects of their maintenance policies in the presence of limited budgets. This decision support capability is realized through an efficient, scalable backtracking- based algorithm for the optimisation of maintenance policies, while enabling the user to view a number of solutions near this optimum and explore tradeoffs with other decision criteria. To assist the asset manager in selecting between various policies, JOB also provides the capability of Multiple Criteria Decision Making. In this paper, the JOB tool is presented and its applicability for the maintenance of a complex power plant system.

Utilizing reactive capability of PV inverters and battery systems to improve voltage profile of a residential distribution feeder

Significant increase in installation of rooftop Photovoltaic (PV) in the Low-Voltage (LV) residential distribution network has resulted in over voltage problems. Moreover, increasing peak demand creates voltage dip problems and make voltage profile even worse. Utilizing the reactive power capability of PV inverter (RCPVI) can improve the voltage profile to some extent. Resistive caharcteristic (higher R/X ratio) limits the effectiveness of reactive power to provide voltage support in distribution network. Battery Energy Storage (BES), whereas, can store the excess PV generation during high solar insolation time and supply the stored energy back to the grid during peak demand. A coordinated algorithm is developed in this paper to use the reactive capability of PV inverter and BES with droop control. Proposed algorithm is capable to cater the severe voltage violation problem using RCPVI and BES. A signal flow is also mentioned in this research work to ensure smooth communication between all the equipments. Finally the developed algorithm is validated in a test distribution network.

Making climate change manageable : how accounting constructs new power-knowledge regimes

This paper examines the modern power of accounting to permeate new spheres and create new objects by examining how climate change becomes a new category for accounting’s attention. It outlines the socio-political problem space where accounting and climate change connect by tracing the emergence of the UK’s Climate Change Act (2008) to a specifically modern calculating attitude described here as ‘managing by the numbers’. It suggests the intersection of accounting and climate change was made possible by accounting’s role in tying disciplinary subjectivities and objectivities together whilst operating simultaneously at the level of individuals, organisations and government. Such that when faced with new unknowns we revert to previous ways of managing we have come to know and experienced throughout our formative years in schools, hospitals, firms and government departments. In this way, accounting’s emergence in the domain of managing climate change implies a transformation that cannot be explained merely as a practical response to a global warming problem, but rather as an example of a new power-knowledge regime that makes possible the management and control of a new organisational phenomenon called climate change.

Smart coordinated distribution system control to enable high level penetration of rooftop PVs

This thesis addresses voltage violation problem, the most critical issue associated with high level penetration of photovoltaic (PV) in electricity distribution network. A coordinated control algorithm using the reactive power from PV inverter and integrated battery energy storage has been developed and investigated in different network scenarios in the thesis. Probable variations associated with solar generation, end-user participation and network parameters are also considered. Furthermore, a unified data model and well-defined communication protocol to ensure the smooth coordination between all the components during the operation of the algorithm is described. Finally this thesis incorporated the uncertainties of solar generation using probabilistic load flow analysis.

Battery energy storage systems to improve power system frequency response

Battery energy storage systems (BESS) are becoming feasible to provide system frequency support due to recent developments in technologies and plummeting cost. Adequate response of these devices becomes critical as the penetration of the renewable energy sources increases in the power system. This paper proposes effective use of BESS to improve system frequency performance. The optimal capacity and the operation scheme of BESS for frequency regulation are obtained using two staged optimization process. Furthermore, the effectiveness of BESS for improving the system frequency response is verified using dynamic simulations.

Optimal allocation of conservation effort among subpopulations of a threatened species: How important is patch quality?

Money is often a limiting factor in conservation, and attempting to conserve endangered species can be costly. Consequently, a framework for optimizing fiscally constrained conservation decisions for a single species is needed. In this paper we find the optimal budget allocation among isolated subpopulations of a threatened species to minimize local extinction probability. We solve the problem using stochastic dynamic programming, derive a useful and simple alternative guideline for allocating funds, and test its performance using forward simulation. The model considers subpopulations that persist in habitat patches of differing quality, which in our model is reflected in different relationships between money invested and extinction risk. We discover that, in most cases, subpopulations that are less efficient to manage should receive more money than those that are more efficient to manage, due to higher investment needed to reduce extinction risk. Our simple investment guideline performs almost as well as the exact optimal strategy. We illustrate our approach with a case study of the management of the Sumatran tiger, Panthera tigris sumatrae, in Kerinci Seblat National Park (KSNP), Indonesia. We find that different budgets should be allocated to the separate tiger subpopulations in KSNP. The subpopulation that is not at risk of extinction does not require any management investment. Based on the combination of risks of extinction and habitat quality, the optimal allocation for these particular tiger subpopulations is an unusual case: subpopulations that occur in higher-quality habitat (more efficient to manage) should receive more funds than the remaining subpopulation that is in lower-quality habitat. Because the yearly budget allocated to the KSNP for tiger conservation is small, to guarantee the persistence of all the subpopulations that are currently under threat we need to prioritize those that are easier to save. When allocating resources among subpopulations of a threatened species, the combined effects of differences in habitat quality, cost of action, and current subpopulation probability of extinction need to be integrated. We provide a useful guideline for allocating resources among isolated subpopulations of any threatened species. © 2010 by the Ecological Society of America.

Open problem: Shifting experts on easy data

A number of online algorithms have been developed that have small additional loss (regret) compared to the best “shifting expert”. In this model, there is a set of experts and the comparator is the best partition of the trial sequence into a small number of segments, where the expert of smallest loss is chosen in each segment. The regret is typically defined for worst-case data / loss sequences. There has been a recent surge of interest in online algorithms that combine good worst-case guarantees with much improved performance on easy data. A practically relevant class of easy data is the case when the loss of each expert is iid and the best and second best experts have a gap between their mean loss. In the full information setting, the FlipFlop algorithm by De Rooij et al. (2014) combines the best of the iid optimal Follow-The-Leader (FL) and the worst-case-safe Hedge algorithms, whereas in the bandit information case SAO by Bubeck and Slivkins (2012) competes with the iid optimal UCB and the worst-case-safe EXP3. We ask the same question for the shifting expert problem. First, we ask what are the simple and efficient algorithms for the shifting experts problem when the loss sequence in each segment is iid with respect to a fixed but unknown distribution. Second, we ask how to efficiently unite the performance of such algorithms on easy data with worst-case robustness. A particular intriguing open problem is the case when the comparator shifts within a small subset of experts from a large set under the assumption that the losses in each segment are iid.

Limitations of a laboratory scale model in predicting optimal pilot scale conditions for dilute acid pretreatment of sugarcane bagasse

Pilot and industrial scale dilute acid pretreatment data can be difficult to obtain due to the significant infrastructure investment required. Consequently, models of dilute acid pretreatment by necessity use laboratory scale data to determine kinetic parameters and make predictions about optimal pretreatment conditions at larger scales. In order for these recommendations to be meaningful, the ability of laboratory scale models to predict pilot and industrial scale yields must be investigated. A mathematical model of the dilute acid pretreatment of sugarcane bagasse has previously been developed by the authors. This model was able to successfully reproduce the experimental yields of xylose and short chain xylooligomers obtained at the laboratory scale. In this paper, the ability of the model to reproduce pilot scale yield and composition data is examined. It was found that in general the model over predicted the pilot scale reactor yields by a significant margin. Models that appear very promising at the laboratory scale may have limitations when predicting yields on a pilot or industrial scale. It is difficult to comment whether there are any consistent trends in optimal operating conditions between reactor scale and laboratory scale hydrolysis due to the limited reactor datasets available. Further investigation is needed to determine whether the model has some efficacy when the kinetic parameters are re-evaluated by parameter fitting to reactor scale data, however, this requires the compilation of larger datasets. Alternatively, laboratory scale mathematical models may have enhanced utility for predicting larger scale reactor performance if bulk mass transport and fluid flow considerations are incorporated into the fibre scale equations. This work reinforces the need for appropriate attention to be paid to pilot scale experimental development when moving from laboratory to pilot and industrial scales for new technologies.

Is comfort important for optimal use of child restraints?

Suboptimal restraint use, particularly the incorrect use of restraints, is a significant and widespread problem among child vehicle occupants, and increases the risk of injury. Previous research has identified comfort as a potential factor influencing suboptimal restraint use. Both the real comfort experienced by the child and the parent’s perception of the child’s comfort are reported to influence the optimal use of restraints. Problems with real comfort may lead the child to misuse the restraint in their attempt to achieve better comfort whilst parent-perceived discomfort has been reported as a driver for premature graduation and inappropriate restraint choice. However, this work has largely been qualitative. There has been no research that objectively studies either the association between real and parental perceived comfort, or any association between comfort and suboptimal restraint use. One barrier to such studies is the absence of validated tools for quantifying real comfort in children. We aimed to develop methods to examine both real and parent-perceived comfort and examine their effects on suboptimal restraint use. We conducted online parent surveys (n=470) to explore what drives parental perceptions of their child’s comfort in restraint systems (study 1) and used data from field observation studies (n=497) to examine parent-perceived comfort and its relationship with observed restraint use (study 2). We developed methods to measure comfort in children in a laboratory setting (n=14) using video analysis to estimate a Discomfort Avoidance Behaviour (DAB) score, pressure mapping and adapted survey tools to differentiate between comfortable and induced discomfort conditions (study 3). Preliminary analysis of our recent online survey of Australian parents (study 1) indicates that 23% of parents report comfort as a consideration when making a decision to change restraints. Logistic regression modelling of data collected during the field observation study (study 2) revealed that parent-perceived discomfort was not significantly associated with premature graduation. Contrary to expectation, children of parents who reported that their child was comfortable were almost twice as likely to have been incorrectly restrained (p<0.01, 95% CI 1.24 - 2.77). In the laboratory study (study 3) we found our adapted survey tools did not provide a reliable measurement of real comfort among children. However our DAB score was able to differentiate between comfortable and induced discomfort conditions and correlated well with pressure mapping. Our results suggest that while some parents report concern about their child’s comfort, parent-reported comfort levels were not associated with restraint choice. If comfort is important for optimal restraint use, it is likely to be the real comfort of the child rather than that reported by the parent. The method we have developed for studying real comfort can be used in naturalistic studies involving child occupants to further understand this relationship. This work will be of interest to vehicle and child restraint manufacturers interested in improving restraint design for young occupants as well as researchers and other stakeholders interested in reducing the incidence of restraint misuse among children.

Modeling of the hemodynamic responses in block design fMRI studies

The hemodynamic response function (HRF) describes the local response of brain vasculature to functional activation. Accurate HRF modeling enables the investigation of cerebral blood flow regulation and improves our ability to interpret fMRI results. Block designs have been used extensively as fMRI paradigms because detection power is maximized; however, block designs are not optimal for HRF parameter estimation. Here we assessed the utility of block design fMRI data for HRF modeling. The trueness (relative deviation), precision (relative uncertainty), and identifiability (goodness-of-fit) of different HRF models were examined and test-retest reproducibility of HRF parameter estimates was assessed using computer simulations and fMRI data from 82 healthy young adult twins acquired on two occasions 3 to 4 months apart. The effects of systematically varying attributes of the block design paradigm were also examined. In our comparison of five HRF models, the model comprising the sum of two gamma functions with six free parameters had greatest parameter accuracy and identifiability. Hemodynamic response function height and time to peak were highly reproducible between studies and width was moderately reproducible but the reproducibility of onset time was low. This study established the feasibility and test-retest reliability of estimating HRF parameters using data from block design fMRI studies.

A robust optimization approach for index tracking problem

Index tracking is an investment approach where the primary objective is to keep portfolio return as close as possible to a target index without purchasing all index components. The main purpose is to minimize the tracking error between the returns of the selected portfolio and a benchmark. In this paper, quadratic as well as linear models are presented for minimizing the tracking error. The uncertainty is considered in the input data using a tractable robust framework that controls the level of conservatism while maintaining linearity. The linearity of the proposed robust optimization models allows a simple implementation of an ordinary optimization software package to find the optimal robust solution. The proposed model of this paper employs Morgan Stanley Capital International Index as the target index and the results are reported for six national indices including Japan, the USA, the UK, Germany, Switzerland and France. The performance of the proposed models is evaluated using several financial criteria e.g. information ratio, market ratio, Sharpe ratio and Treynor ratio. The preliminary results demonstrate that the proposed model lowers the amount of tracking error while raising values of portfolio performance measures.