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An efficient use of solar energy production requires reliable forecast information on surface solar irradiance. This article aims at providing a Model Output Statistics (MOS) method of improving solar irradiance forecasts from Weather Research and Forecasting (WRF) model. The WRF model was used to produce one year of day ahead solar irradiance forecasts covering Reunion Island with an horizontal resolution of 3 km. These forecasts are refined with a Kalman filter using hourly high quality ground measurements. Determination of the relevant data inputs for the Kalman filter method is realized with a bias error analysis. The solar zenith angle and the clear sky index, among others, are used for this analysis. Accuracy of the method is evaluated with a comprehensive testing procedure using different error metrics. The Kalman filtering appears to be a viable method in order to improve the solar irradiance forecasting. Refereed/Peer-reviewed

This study proposes a novel 10-point numerical scale to evaluate long-term thermal comfort satisfaction. The available scale that verbally evaluates the short-term thermal comfort is thermal sensation vote (TSV) and this has substituted for the long-term thermal comfort assessment. However, the TSV has been questioned by many studies due to the ambiguous, widely different interpretation of each score and unequal perceived gaps between the scores. The paper argues the biological similarities between pain and temperature perceptions and proposes a numerical thermal comfort scale like that of long-term pain perception. Participants in a survey rated their long-term summer thermal comfort in the natural ventilation mode through the numerical and TSV scales. Ratings were compared after converting to degree hours based on monitoring temperature data for the same summer. The results show that the numerical scale provides an opportunity for respondents to express their long-term thermal comfort satisfaction level. Scoring by the numerical scale was convenient and clear to the respondents. The verbal scores of TSV, had different interpretations for individuals as each point was attributed to more than two values of degree hours. The score ‘Neutral’ was significantly ambiguous for assessing thermal comfort. Refereed/Peer-reviewed

During the last decade, numerous solar forecasting tools have been developed to predict the energy generation of photovoltaic (PV) farms. The quality of solar forecasts is assessed by comparing predictions with measured solar data. However, this methodology does not consider the added value of the forecasts for their applications. As a consequence, what value could be given to the improvement of forecasts considering this evaluation framework? To answer this question, this work compares the value of different operational solar forecasts for a specific application. The aim is to look for relationships between the economic value and the error metrics defined to evaluate the forecast quality. A new generation of large-scale PV plants integrates ESS. The aim is to add flexibility to the injection of the production into the grid and thus to maximize the profit by taking advantage of the possibilities offered by the electricity market, such as energy arbitrage. To optimize the operation of these specific ESS, forecasting of the solar production is of paramount importance. The study case considered in this work is a large-scale PV farm of several megawatts associated with Li-ion batteries in the Australian energy market context. For this specific case study, the results show that the metrics used to evaluate the forecast quality based on the mean absolute error (MAE) have an almost linear relationship with the economic gain brought by applying the forecast. More precisely, an improvement of 1% point in MAE results approximately in an increase of 2% points in economical gain. Refereed/Peer-reviewed

This paper evaluates the application of a family of VAR-mGARCH (Vector AutoRegressive with multivariate AutoRegressive Conditional Heteroskedasticity) volatility models to the problem of modeling (i.e. generating correct in-sample scenarios) and forecasting in a probabilistic way three univariate but mutually dependent wind speed hourly series. The evaluation starts from the consideration that optimal modeling and optimal forecasting are not necessarily attained by the same models, and that they are assessed in different ways. The proposed VAR-mGARCH family, originated in Finance, consists of the VAR-BEKK (VAR - Baba, Engle, Kraft and Kroner) model and the VAR-DCC (VAR - Dynamic Conditional Correlation) model, the latter seen as the simplified and more scalable version of the former. These models were never used in wind speed studies before. Both models will be used with Gaussian innovations, and the VAR-DCC model will be also used with Student's t innovations. These models, which are able to dynamically couple volatilities, are compared to two benchmark model sets, the first set consisting of three independent univariate AR-GARCH (i.e. non-vector) models, the second set consisting of an individual VAR model without the GARCH sector. These benchmark models cannot dynamically couple volatilities. Both benchmark model sets will be used with Gaussian innovations. The time series used for the evaluation are taken from three North-American wind metering sites located at few tens of kilometers from each other. In order to highlight the usefulness of choosing autoregressive coupled volatility schemes for modeling and forecasting spatially and temporally varying wind speed data, a controlled variance electricity generation portfolio example of Markowitz type, typical of Energy Finance, is also discussed at some length. In all, it is concluded that the VAR-DCC model with Student's t innovations is the most balanced choice for this data set when the three goals of modeling, forecasting and scalability in the number of considered sites are taken into account at once. Refereed/Peer-reviewed

A Photovoltaic/Thermal (PV/T) system combines PV and thermal collector, which is considered promising technology especially for building integrated PV/T system. The PV/T cooling systems using water, water-PCM and nanofluid/nano-PCM moves through the cooling pipes were investigated, in this study. However, this paper focuses on testing different PV/T systems (conventional PV, water-based PVT, water-nanofluid PVT, and nanofluid/nano-PCM) under the same conditions and environment using one artificial neural network (ANN) based Multi-Layer Perceptron (MLP) system. Also, investigate the differences in the efficiency of these systems on both thermal and electrical when using only one simulation system (MLP). The proposed ANN approach proved that using of nanofluid/nano-PCM was enhanced the electrical efficiency from 8.07% to 13.32% and its thermal efficiency reached 72%. Also, the voltage was improved significantly. Many measurement methods were used for validating the results of the proposed ANN model like the Mean Absolute Error (MAE), Mean Square Error (MSE), Correlation (R), and coefficient of determination (R 2 ). The proposed ANN model achieved a final MSE of 0.0229 in the training phase and 0.0282 in the cross-validation phase. The sensitivity analysis showed that the influence of solar irradiation and Amb-temp almost has a constant effect on electrical efficiency. However, the Ambient temperature had a significant impact on thermal efficiency. The results of the network were consistent with the experimental results of the current study and published works. Refereed/Peer-reviewed

Current and newly built buildings will inevitably experience the effects of climate change, therefore, the design and performance of these buildings should consider weather data that includes some of the effects of climate change, instead of only using historical weather data. However, climate change weather data suitable for buildings performance simulation are typically unavailable. This research presents a method to integrate climate change features into historical weather data to make suitable climate change weather data available for buildings performance simulation. The method separates hourly dry bulb temperature into three time series components to simplify the integration of three climate change features. The method adjusts the maximum and minimum monthly averages, the number of days with maximum temperature above a specified threshold, and the number of consecutive occurrences of days with maximum temperature above a specified threshold (heatwave). The research also presents the adjustment of monthly averages of global solar irradiation. Under the climate–changed weather conditions, the annual heating thermal energy decreases by 21%–22%, the annual cooling thermal energy increases by 29%–31%, and the combined heating and cooling thermal energy decreases by 4%–5% compared to the heating and cooling thermal energy under the current weather conditions. The results indicate that climate–changed weather data is necessary as historical weather data is insufficient for accurately assessing the energy performance of a building. Refereed/Peer-reviewed

We develop a new probabilistic forecasting method for global horizontal irradiation (GHI) by extending our previous bootstrap method to a case of an exponentially decaying heteroscedastic model for tracking dynamics in solar radiance. Our previous method catered for the global systematic variation in variance of solar radiation, whereas our new method also caters for the local variation in variance. We test the performance of our new probabilistic forecasting method against our old probabilistic forecasting method at three locations: Adelaide, Darwin, and Mildura. These locations are chosen to represent three distinct climates. The prediction interval coverage probability, prediction interval normalized averaged width and Winkler score results from our new probabilistic forecasting method are encouraging. Our new method performs better than our previous method at Adelaide and Mildura, regions with a higher proportion of clear-sky days, whereas our previous method performs better than our new method at Darwin, a region with a lower proportion of clear-sky days. These results suggest that the ideal probabilistic forecasting method might be climate specific.

At present, solar energy resources are becoming more and more popular as a substitute for conventional energy sources, in order to reduce greenhouse gas emissions. In order to further improve the utilization rate of solar energy resources, storage, as an important supplement, is more and more widely used. One of the most important research directions is to determine the necessary size of storage capacity by mathematic modeling. This paper will describe a new and efficient decision-making process to determine a series of storage capacities for a grid-connected PV system. The benefit of using a decision-making model is that it can easily determine the most appropriate storage capacity for the system. The paper will show here that the decision-making model will not only find the most effectiveness storage, but also find the relationship between storage capacities and the utilization rates of renewable energy. The case study shows that using 141.25 kWh storage increases the utilization rate of renewable energy in demand by 3.605%. And it would recover the storage cost by 4.66 years in Australia market. Refereed/Peer-reviewed

Despite the variety of irradiance separation models available in the literature, there is not yet a model that performs equally well for every location worldwide. Nevertheless, separation models still represent an interesting approach when only global irradiance is measured. This work presents further developments of the recently proposed Boland-Ridley-Lauret minute model and proposes 1-min diffuse fraction models for each climate zone in the globe. To accomplish this, 1-min worldwide irradiance data from 51 the Baseline Surface Rresearch Network stations and one from Australian Bureau of Meteorology were used. The Köppen-Geiger classification was used to determine the climate zone of each station: both the simple climate classification (A, B, C, D, E) and the complete classification (Aw, Bsh, etc.). Furthermore, a robust nonlinear regression method was used to build the separation models without removing measurement outliers in advance. The climate-specific models proposed herein present better performance than other models from the literature, such as the BRL, Perez and Skartveit models (developed using hourly irradiance data) and the Engerer model (developed using minute data). Generally, the models proposed for climate zones in the complete Köppen classification presented better similitude with the measured data than the models for the simple classification, especially for stations within climate zones B and E. Refereed/Peer-reviewed

The field of energy forecasting has attracted many researchers from different fields (e.g., meteorology, data sciences, mechanical or electrical engineering) over the last decade. Solar forecasting is a fast-growing subdomain of energy forecasting. Despite several previous attempts, the methods and measures used for verification of deterministic (also known as single-valued or point) solar forecasts are still far from being standardized, making forecast analysis and comparison difficult. To analyze and compare solar forecasts, the well-established Murphy–Winkler framework for distribution-oriented forecast verification is recommended as a standard practice. This framework examines aspects of forecast quality, such as reliability, resolution, association, or discrimination, and analyzes the joint distribution of forecasts and observations, which contains all time-independent information relevant to verification. To verify forecasts, one can use any graphical display or mathematical/statistical measure to provide insights and summarize the aspects of forecast quality. The majority of graphical methods and accuracy measures known to solar forecasters are specific methods under this general framework. Additionally, measuring the overall skillfulness of forecasters is also of general interest. The use of the root mean square error (RMSE) skill score based on the optimal convex combination of climatology and persistence methods is highly recommended. By standardizing the accuracy measure and reference forecasting method, the RMSE skill score allows—with appropriate caveats—comparison of forecasts made using different models, across different locations and time periods. Refereed/Peer-reviewed

This article proposes a new hybrid least squares-support vector machine and artificial bee colony algorithm (ABC-LS-SVM) for multi-hour ahead forecasting of global solar radiation (GHI) data. The framework performs on training the least squares-support vector machine (LS-SVM) model by means of the ABC algorithm using the measured data. ABC is developed for free parameters optimization for the LS-SVM model in a search space so as to boost the forecasting performance. The developed ABC-LS-SVM approach is verified on an hourly scale on a database of five years of measurements. The measured data were collected from 2013 to 2017 at the Applied Research Unit for Renewable Energy (URAER) in Ghardaia, south of Algeria. Several combinations of input data have been tested to model the desired output. Forecasting results of 12 h ahead GHI with the ABC-LS-SVM model led to the root-mean-square error (RMSE) equal to 116.22 Wh/m2, Correlation coefficient r = 94.3%. With the classical LS-SVM, the RMSE error equals to 117.73 Wh/m2 and correlation coefficient r = 92.42%; for cuckoo search algorithm combined with LS-SVM, the RMSE = 116.89 Wh/m2 and r = 93.78%. The results achieved reveal that the proposed hybridization scheme provides a more accurate performance compared to cuckoo search-LS-SVM and the stand-alone LS-SVM.

Solar radiation components assessment is a highly required activity for solar energy applications. Despite their importance, diffuse and direct solar radiation components are not available in many locations in the world. This is specifically the case in the studied region due to high fiscal demands and technical limitations. In the present work, our main objective is to estimate the two components using global solar radiation components as the only measured input parameter. Machine learning (ML) techniques seem to be a good solution of such an estimation problem but the main issue of ML techniques is the need of long historical data of both desired outputs, direct and diffuse, to build the optimum model in the training phase. However, in the present study, we adopt the use of the Boland–Ridley–Lauret (BRL) model to deal with the problem of estimation of the direct and diffuse components from the global radiation. The adjusted BRL model was applied on two time scales, daily and hourly components. It was found that the estimated direct and diffuse solar radiation values by the adjusted BRL model are in favorable agreement with the measured data. Refereed/Peer-reviewed

Concentrations of building mass and insufficient greenery in cities are identified to contribute significantly to extended heat stress in the built environment, commonly known as the urban heat island (UHI)effect. This paper presents a scenario-based modelling built on climate change projections and potential alterations of urban greenery in 2030 and 2090 in Adelaide, South Australia. The model is based on regional climate change scenarios, potential urban surface cover alternatives and resulted urban microclimate variations in Adelaide. Projected energy demand variations and corresponding carbon emission are calculated in each scenario. Results indicate that an ideal urban landscape transformation scenario with 30% tree canopy can effectively decrease the surface temperature by 1°C in winter and 3°C in summer by 2090. It is estimated that having greener and more heat resilient public spaces could save a total carbon emission of 140,000 tone CO2e in Adelaide annually compared to a business-as-usual scenario. Urban greenery may be used as a mean of increased urban life resilience to climate change by reducing surface temperature, increasing urban resilience and decreasing the energy demand during summer. Refereed/Peer-reviewed

As the frequency and intensity of heatwaves are growing, strategies to improve our resilience are becoming more vital. Policies to increase heat stress resilience are mostly isolated across different disciplines and government departments. A holistic approach would be necessary that mitigates the numerous negative impacts of heatwaves on public health, urban infrastructure and services through adaptation to heatwaves in the realm of public health, building and construction industry, and urban planning and infrastructure. This paper reviews the research on heat stress adaptation measures, before presenting recommendations for a range of integrated policy measures to increase the heat stress resilience of urban populations in Australian cities. The recommended policy measures include information dissemination, incentives and disincentives, promotion, demonstration and regulations. The paper concludes by identifying directions for further research and reinforcing the multiple benefits that can result from the implementation of heat stress resilience policies and strategies. Refereed/Peer-reviewed

Particle size is a fundamental variable in the dust aerosol cycle. A dust layer effective particle size is strongly related to its’ radiative forcing and the extent of its impact on an area of interest. However, in-situ measurements of dust particle size are costly, spatially sparse and time-consuming. This paper presents a simple empirically derived model to estimate effective dust diameter using infrared band brightness temperature difference of the 8.7 and 12.0 μm wavelength bands retrieved from the SEVIRI (Spinning Enhanced Visible and InfaRed Imager) radiometer onboard Meteosat satellites. Three case studies were used to test the model. The results showed consistency between the model and in-situ aircraft fly through sampling of dust particle size. The use of the model is demonstrated by presenting an analysis of a severe dust storm over Western Asia. This model is expected to contribute to addressing the discrepancies between the current particle size retrieval techniques and aircraft measurements. There are many potential applications for this model including providing an independent reference data for atmospheric dust models, forecasting the impact of dust storms on horizontal visibility as wellas the solar energy system performance over regions prone to dust storms. Refereed/Peer-reviewed

Outdoor thermal discomfort discourages outdoor living at the cost of increased demand for indoor air-conditioning. The resulted waste heat from air-conditioning makes a feedback loop with increased outdoor heat stress. Local and seasonal climate expectations, comfort perceptions, demographic specifications, activity choices and socio-cultural norms can affect the adaptation of public life to outdoor heat stress. This paper explores limits of outdoor thermal adaptation in Sydney, Melbourne and Adelaide. Outdoor activity observation and microclimate measurement were conducted in 10 selected public spaces between February 2013 and March 2014. Results indicate that before the outdoor thermal environment of UTCI = 22–34°C, there is no significant decline in outdoor living and people adapted their outdoor activities, clothing and activity rate to achieve thermal comfort. Beyond this neutral thermal threshold, outdoor thermal adaptation shifted towards dismissal of optional and social activities and modification of necessary activities. Space configurations, local climate expectations and flexible activity choices may extend outdoor thermal adaptation by the critical zero-activity thresholds of 48°C. Thereafter, outdoor activity prevention can become the dominant thermal adaptation strategy. Localized limits of outdoor thermal adaptation are to be addressed to facilitate more liveable and healthy cities in the context of climate change. Refereed/Peer-reviewed

In Australia, heatwaves are the deadliest natural hazard and a major driver of peak electricity demand. The disproportionately high peak demand increases electricity prices, causes occasional blackouts and exacerbates energy poverty, all of which limit one's ability to use air conditioning. Meanwhile, increased energy efficiency of dwellings may decrease their heat stress resistance. This paper challenges whether the current Australian Nationwide Energy Rating Scheme encourages heat stress resistance. Cooling consumption, peak cooling demand and the risk of indoor overheating were assessed for a typical single-storey home in Adelaide and Sydney. Design options between 6 and 8 stars, plus two traditional, energy-inefficient homes were simulated with the AccuRate energy simulation program. This paper demonstrates that the NatHERS does not directly encourage heat stress resistance in new homes and can even deliver buildings with worse heat resistance and increased reliance on air conditioning than traditional, energy-inefficient buildings. This overreliance on air conditioning can present a public health hazard, consequently, the integration of heat stress resistance in the Nationwide House Energy Rating Scheme is recommended. Recommendations for the analysis and assessment of heat stress resistance are provided. Refereed/Peer-reviewed

Electrical energy storage can be used to store excess power generated by domestic rooftop PV systems, rather than exporting it to the grid and then buying back energy at a higher price. We have used one-minute PV generation and electrical load data from thirty-eight low-energy homes to simulate the operation of energy storage, and to calculate the impact on the amount and cost of imported electricity. The payback period for energy storage systems depends on factors including the cost of energy storage, the cost of electricity, the price paid for exported energy, the power generated by the PV system, and how and when energy is used by the household. We calculate the payback period for various configurations. Decreasing feed-in tariffs and the decreasing cost of energy storage will lead to an uptake of energy storage system over the next few years. While storage can be used to reduce household electricity cost, it does not lead directly to reductions in CO 2 emissions. However, household energy storage will enable greater use of rooftop PV, and ultimately can be used to match household demand to variable supply from local and centralised renewable energy sources. Refereed/Peer-reviewed

This study aims to test the appropriateness of multivariate skew-t copula and checkerboard copula of maximum entropy in generating monthly rainfall total data. The generation of synthetic data is important, as it provides hypothetical data in areas for which data availability remains limited. Three selected meteorological stations in Kelantan, Malaysia, Stesen Pertanian Melor, Rumah Pam Salor, and Ladang Lepan Kabu, are considered in this study. Monthly rainfall total data for the driest and wettest months in the year are tested in this study. For these three stations, the identified month with the least total of rainfall received (driest) is May, while the month with the highest total of rainfall received (wettest) is November. The data is fitted to gamma distribution with the corresponding parameters estimated. The observed data will be transformed to be in unit uniform using the gamma marginal. The resulting data is compared to simulated uniform data generated using multivariate skew-t copula and checkerboard copula of maximum entropy models based on the correlation values of the observed and simulated data. Next, the Kolmogorov-Smirnov test is used to assess the fit between the observed and generated data. The results show that the values of simulated correlation coefficients do not differ much for gamma distribution, multivariate skew-t, and maximum entropy approaches. This implies that the multivariate skew-t and maximum entropy may be used to generate monthly rainfall total for cases in which actual data is unavailable. Refereed/Peer-reviewed

Heatwaves have been subject to significant attention in Australia and globally due to their negative impacts on the ecosystem, infrastructure, human health and social life. Measures to increase resilience to heatwaves, however, are mostly isolated in different disciplines. This paper proposes a framework integrating urban and infrastructure planning, building design, public health and social research to comprehensively assess heat stress resilience. The proposed framework can assist decision makers in the evaluation of different policy changes addressing heat stress resilience and contribute to more comprehensive and effective heatwave management. An online survey was undertaken with a representative sample (N = 393) from Adelaide, South Australia, to explore heat stress resistance of the built environment, adaptation and heat-related, self-reported health problems. The study established the magnitudes of the secondary negative impacts of heatwaves on public health and daily routines. The findings identify a low level of resistance to heat stress by the built environment. It was concluded that community education along with focused building and planning regulations has the potential to significantly increase heat stress resilience. Refereed/Peer-reviewed

This paper provides a comprehensive analysis of Australian net energy consumption between 2004–05 and 2014–15. Results from environmentally-extended input-output (EEIO) analysis show that the Transport sector has the largest direct effect on net energy consumption in industrial sectors, which decreased by about 35% for net energy consumption per million $AUD in the period. The Export sector has the largest direct net energy consumption while Households consumption results in the largest net energy consumption embodied in different categories of Final demand. The structural decomposition analysis (SDA) decomposes the change of net energy consumption into five drivers, in which net energy intensity mainly reduces Australian net energy consumption by about 8000 Petajoules, while the level effect of Final demand increases it by about 10,000 Petajoules. Analysis of forward and backward linkages highlights the Manufacturing sector as the key industrial sector with the largest energy consumption reduction potential via minor changes in its input and Final demand. This indicates that more attention should be given to the reduction of energy demand from the consumption patterns of Households consumption, the improvement of energy intensity, and the application of cleaner technologies in the Transport and Manufacturing sectors. The Australian Environmental-Economic Accounts is combined with Australian input-output tables to construct the EEIO tables for net energy consumption. The combination of economic and environmental data sets provides a depth of understanding their potential to inform environmental policy decisions. The novelty of the research is the combination of economic and energy data sets, the application of EEIO model, the implementation of the additive SDA method, and the use of forward and backward linkages for the Australian energy system. Refereed/Peer-reviewed

In this paper a model is used to estimate the diffuse radiation using the Boland-Ridley-Lauret (BRL) model, which is developed for Chile, Costa Rica, and Australia. Additionally, artificial Direct Normal Irradiance time series are determined from the diffuse fraction results estimated by the model. Both estimates achieve excellent agreement based on the bias and normalized scatter. The relationship between the predictors of the model and the climatic conditions of each case of study, according to the Köppen-Geiger climatic classification was analyzed, which to the best of the authors’ knowledge, it’s the first study of this kind. An analysis on correlation and statistical significance was carried out between the model predictors and four determining geographic and climatic variables: altitude, latitude, precipitation and temperature. The statistical analysis shows that two of the six predictors correlate with temperature and precipitation and one predictor is correlated with latitude. Therefore, it can be suggested that the BRL model seems largely insensitive to the different regional climatic conditions, nevertheless, evaluation of the effect of the apparent correlation of the respective predictors and microclimatic and geographical variables points to further research in this area considering a wider selection of locations Refereed/Peer-reviewed

Waste generation is linked to consumption both in households (Final demand) and in the supply chain. Gaining understanding into the driving forces that change of waste generation in the supply chain can contribute to solving issues of waste management. The environmentally-extend input-output model is an effective tool with which to investigate the relationship between economic activities and waste generation. In this paper structural decomposition analysis (SDA) is employed to analyse the determinants of changes of waste generation in Australian economy from 2007–2008 to 2013–2014. Empirical results indicate that the major determinant for the increase of waste generation was change in Final demand's overall level of economic activity. Changes in the production mix of Final demand (mix effect) was responsible for a decrease of waste generation in Australian economy during the period. The Manufacturing sector was found to have the highest waste generation intensity. Meaning that each million $AUD output of the Manufacturing sector resulted in the most amount of waste generation. In addition, technological change has contributed the largest waste generation effect for the Construction sector in 2011–2012. These findings suggest that Final demand, technological changes and sectoral changes are identified as the drivers of Australian waste generation historically. To reduce waste generation, policy must be targeted at altering behaviour of consumption and waste generation, and increasing innovation of new ecological technologies for Australian industry. Refereed/Peer-reviewed

The current deep concerns on energy independence and global society's security at the face of climate change have empowered the new "green energy" paradigm and led to a rapid development of new methodology for modeling sustainable energy resources. However, clean renewables such as wind and solar energies are inherently intermittent, and their integration into a electric power grid require accurate and reliable estimation of uncertainties. And, if probabilistic forecasting of wind power is generally well developed, probabilistic forecasting of solar power is still in its infancy. In this paper we propose a new data-driven method for constructing a full predictive density of solar radiance based on a nonparametric bootstrap. We illustrate utility of the new bootstrapped statistical ensembles for probabilistic one-hour ahead forecasting in Mildura, Australia. We show that the new approach delivers sharp and calibrated ensembles of one-hour forecasts, and is computationally inexpensive and easily tractable. Refereed/Peer-reviewed

Waste input-output (WIO) model is a suitable method to explore the nexus between economic activities and waste management. Contemporary research that typically explores this nexus follows two main aspects: either they consider Final demand as a whole, or they identify the nexus between households,with different types of socio-demographic indicators and household waste generation. However, it is complex to apply the WIO model from the perspective of household consumption- a major component of Final demand d because of a lack of economic and environmental data related to household consumption.This paper proposes a new perspective, applying the WIO model to assess the nexus between different patterns of household consumption and indirect waste generation and treatment. This novelty is to combine macro- and micro-economic and environmental data related to Australian industrialsectors, different patterns of household consumption (Mosaic data), and direct waste generation into the WIO model for exploring this nexus in two scenarios. Results indicate that the total amount of indirectwaste generation caused by B05 (couples without children who spend the majority of their time at the office) are 99.24 kg more than that of D16 (couples without children who are retired and stay at home)for scenario I. The correlation coefficients for differences of output of economy and indirect waste generation between B05 and D16 are 0.9796 and 0.9773 in scenarios I and II, respectively. Sensitivity analysis indicates the change of the amount of direct waste generation in a reasonable range cannot dramatically affect the major economic activities and waste generation. This research suggests a different perspective of household consumption to estimate indirect waste generation through a WIO model to provide more reliable information for waste management in the supply chain. Refereed/Peer-reviewed

With the recent rapid increase in the use of roof top photovoltaic solar systems worldwide,and also, more recently, the dramatic escalation in building grid connected solar farms, especially in Australia, the need for more accurate methods of very short-term forecasting has become a focus of research. The International Energy Agency Tasks 46 and 16 have brought together groups of experts to further this research. In Australia, the Australian Renewable Energy Agency is funding consortia to improve the five minute forecasting of solar farm output, as this is the time scale of the electricity market. The first step in forecasting of either solar radiation or output from solar farms requires there presentation of the inherent seasonality. One can characterise the seasonality in climate variables by using either a multiplicative or additive modelling approach. The multiplicative approach with respect to solar radiation can be done by calculating the clearness index, or alternatively estimating the clear sky index. The clearness index is defined as the division of the global solar radiation by the extraterrestrial radiation, a quantity determined only via astronomical formulae. To form the clear sky index one divides the global radiation by a clear sky model. For additive de-seasoning, one subtracts some form of a mean function from the solar radiation. That function could be simply the long term average at the time steps involved, or more formally the addition of terms involving a basis of the function space. An appropriate way to perform this operation is by using a Fourier series set of basis functions. This article will show that for various reasons the additive approach is superior.Also, the differences between the representation for solar energy versus solar farm output will be demonstrated. Finally, there is a short description of the subsequent steps in short-term forecasting. Refereed/Peer-reviewed

This paper discusses the use on wind speed data from NREL of a hidden Markov model specially crafted to reproduce the features of hourly multi-site wind speed time series, and to forecast them. Differently from the usual conditional multivariate Gaussian HMM, in this model the emissions are chosen as conditional multivariate truncated Gaussian distributions, which have non-negative support and are thus more suitable for non-negative data like wind speeds Refereed/Peer-reviewed

Building energy performance simulations are limited to typical meteorological weather conditions available in simulation software. Such simulations are insufficient for analysing energy performance sensitivity to a range of probable weather conditions. This research presents a method for developing robust meteorological weather data that can be used for energy performance sensitivity analysis without the need to access historical weather data. The method decomposes dry bulb temperature (DBT) and global horizontal solar radiation (H) into deterministic and stochastic components. For the typical weather data of the City of Adelaide, the deterministic component for each of DBT and H consists of a single frequency Fourier series. The stochastic components consist of 1-lag and 2-lags autoregressive models for DBT and H respectively. The stochastic components also include randomly selected values from the residuals of the autoregressive models. Based on this method, the coldest and hottest weather conditions were selected to simulate the energy performance of a single space. The results revealed 39% more cooling and 15% less heating in the hottest year, and 14% more heating and 64% less cooling in the coldest year. The results indicate that using simulations based on typical weather conditions only are insufficient for assessing buildings' energy performance. Refereed/Peer-reviewed

For design and simulation of solar energy systems, quality information about all components of solar irradiance is crucial. In cases when only global irradiance measurements are available, separation models are a useful method to estimate DNI and diffuse irradiation. Several of such models have been developed since the 1960s, most of them aiming to deliver estimates in hourly resolution. For higher data resolution, such as in minute data, those models are not able to describe fast transient and cloud enhancement phenomena commonly observed in data with smaller time-steps. This paper proposes an adaptation of the BRL separation model, making it capable of delivering more precise irradiance estimates for higher resolution data. Two models result from this adaptation: one for Brazil and other for Australia. The proposed models yield a more precise DNI and diffuse fraction estimates to their respective countries, compared to other separation models commonly used in the technical literature. For example, using the recommended Combined Performance Index (CPI) as a single statistical indicator, the proposed model yields DNI estimates with CPI from 230 to 350% for Australia, and from 270 to 800% for Brazil, while the Engerer model, recently recommended as a ''quasi-universal" 1-min separation model, yields DNI estimates with CPI from 500 to 700% for Australia, and from 600 to 1800% for Brazil. Refereed/Peer-reviewed

We combine ambient (air) and ultrahigh vacuum (UHV) scanning tunneling microscopy (STM) and spectroscopy (STS) investigations together with density functional theory (DFT) calculations to gain a subnanometer insight into the structure and dynamic of two-dimensional (2D) surface-supported molecular networks. The planar tetraferrocene-porphyrin molecules employed in this study undergo spontaneous self-assembly via the formation of hydrogen bonded networks at the gold substrate-solution interface. To mimic liquid phase ambient deposition conditions, film formation was accomplished in UHV by electro-spraying a solution of the molecule in chloroform onto an Au(111) substrate, thereby providing access to the full spectroscopic capabilities of STM that can be hardly attained under ambient conditions. We show that molecular assembly on Au (111) is identical in films prepared under the two different conditions, and in good agreement with the theoretical predictions. However, we observe the contrast found for a given STM bias condition to be different in ambient and UHV conditions despite the similarity of the structures, and we propose possible origins of the different imaging contrast. This approach could be valuable for the thorough characterization of surface systems that involve large molecules and are prepared mainly in ambient conditions.

The Household sector (HS) is not only the major cause of waste generation in industrial sectors, but also the same function as an industrial sector to generate waste. Current researches mainly focus on waste generation caused by the demand of the HS based on the environmentally-extend input-output (EEIO) models while the effect of the HS as an industrial sector on waste flow has not been analysed. In addition, there is uncertainty around the economic cost of waste management discussed in EEIO models due to the lack of the calculation of the cost of labor. We adjust waste supply-use table to analyse effects of the HS as an industrial sector on waste flow, resulting in closed waste supply-use table (CWSUT). The novelty of the method lies in a shift in the effect of the HS, from an exogenous factor to an endogenous factor. Results derived from Australian CWSUT in 2009–10 illustrate waste generation effects of intermediate sectors and the mixed waste flows of the HS. The definition of “intermediate sector” is that the sector consumes intermediate inputs from producing sectors and generates intermediate outputs to Final demand. They show that: (1) the Construction sector has the largest waste generation effects, in which the amount of masonry waste has accounted for the most direct and total effects of waste generation; (2) investigations regarding the HS in CWSUT can calculate the amount of direct and total waste generation, the monetary flow, and effects of the Income for the Household sector. Base on the above results, the paper puts forwards the application of the CWSUT on other types of environmental issues and waste policies. Refereed/Peer-reviewed

Understanding the relationships between the Australian economic system and waste generation from intermediate sectors and households is a prerequisite for planning and implementing waste management strategies at a national scale. Data of waste generation accounts link to those of national economic accounts. However, in Australia, some years’ data are absent and so these links cannot be made. To rectify this data gap, this paper interpolates and extrapolates the Australian input-output table (IOT) of 2010–2011. Waste input-output (WIO) analysis is then used to assess the effects of the Australian economy on waste generation and treatment between 2009–2010 and 2010–2011. Analysis indicated that the result of interpolation was more reasonable than that of extrapolation, and the interpolation of the Australian IOT of 2010–2011 can be applicable. This comparative analysis of the time series data in WIO model has identified that: (1) per million $AUD of output of the Construction sector generated the most amount of direct and total waste during the period; (2) the relationships between the development of Australian economy and waste generation illustrate that the Australian economy is currently a traditional linear economy; (3) the effectiveness of waste-related policies are shown by the growth of the sums of direct and total effects of intermediate sectors on the Recovery sector; and (4) the amount of waste generated by households increased sharply over the two years. The physical flows of waste footprint show details of waste generation and treatment in the Australian economic system. The information provided in this paper is beneficial to formulate tailor-made policies for waste management in Australia. Refereed/Peer-reviewed

This study investigates the land surface temperature (LST) distribution from thermal infrared data for analyzing the characteristics of surface coverage using the Vegetation-Impervious-Soil (VIS) approach. A set of ten images, obtained from Landsat-5 Thematic Mapper, between 2001 and 2010, were used to study the urban environmental conditions of 47 neighborhoods of Porto Alegre city, Brazil. Porto Alegre has had the smallest population growth rate of all 27 state capitals in the last two decades in Brazil, with an increase of 11.55% in inhabitants from 1.263 million in 1991 to 1.409 million in 2010. We applied the environmental Kuznets curve (EKC) theory in order to test the influence of the economically-related scenario on the spatial nature of social-environmental arrangement of the city at neighborhood scale. Our results suggest that the economically-related scenario exerts a non-negligible influence on the physically driven characteristics of the urban environmental conditions as predicted by EKC theory. The linear inverse correlation R2 between household income (HI) and LST is 0.36 and has shown to be comparable to all other studied variables. Future research may investigate the relation between other economically-related indicators to specific land surface characteristics. Refereed/Peer-reviewed

The process of finding a correct forecast equation for photovoltaic electricity production from renewable sources is an important matter, since knowing the factors affecting the increase in the proportion of renewable energy production and reducing the cost of the product has economic and scientific benefits. This paper proposes a mathematical model for forecasting energy production in photovoltaic (PV) panels based on a self-organizing feature map (SOFM) model. The proposed model is compared with other models, including the multi-layer perceptron (MLP) and support vector machine (SVM) models. Moreover, a mathematical model based on a polynomial function for fitting the desired output is proposed. Different practical measurement methods are used to validate the findings of the proposed neural and mathematical models such as mean square error (MSE), mean absolute error (MAE), correlation (R), and coefficient of determination (R2). The proposed SOFM model achieved a final MSE of 0.0007 in the training phase and 0.0005 in the cross-validation phase. In contrast, the SVM model resulted in a small MSE value equal to 0.0058, while the MLP model achieved a final MSE of 0.026 with a correlation coefficient of 0.9989, which indicates a strong relationship between input and output variables. The proposed SOFM model closely fits the desired results based on the R2 value, which is equal to 0.9555. Finally, the comparison results of MAE for the three models show that the SOFM model achieved a best result of 0.36156, whereas the SVM and MLP models yielded 4.53761 and 3.63927, respectively. A small MAE value indicates that the output of the SOFM model closely fits the actual results and predicts the desired output. Refereed/Peer-reviewed

Outdoor thermal discomfort pushes citizens into air-conditioned buildings and causes increased demand for water and electricity in the majority of Australian urban heat islands. Citizens’ spatial and activity preferences during heat stress conditions are under investigation in this paper. Citizens’ outdoor activity choices in different thermal environments were surveyed in Adelaide from September 2013 to April 2014. The post-activity questionnaire survey of outdoor activities in Adelaide indicates that necessary, optional and social activities decreased during outdoor heat stress more than any other thermal conditions. Outdoor activities were chosen the most in neutral and warm thermal environments. Outdoor activity choices were affected significantly by the urban microclimate parameter of solar radiation. Tree canopy, shading (from buildings or temporary elements) and water features were the most attractive public space features for outdoor participants during heat stress conditions in Adelaide. Meanwhile, essential shopping and dining facilities and social events affect citizens’ outdoor activity choices during heat stress conditions. Thus, increased green infrastructures and supportive land uses are a prerequisite of urban transformation towards high-performance built environment in the context of climate change. Refereed/Peer-reviewed

Quality data regarding direct and diffuse solar irradiation is crucial for the proper design and simulation of solar systems. This information, however, is not available for the entire Brazilian territory. However, hourly measurements of global irradiation for more than seven hundred stations over the territory are available. Several mathematical models have been developed over the past few decades aiming to deliver estimations of solar irradiation components when only measurement of global irradiation is available. In order to provide reliable estimates of diffuse and direct radiation in Brazil, the recently presented Boland–Ridley–Laurent (BRL) model is adjusted to the particular features of Brazilian climate data, developing adjusted BRL models on minute and hourly bases. The model is adjusted using global, diffuse and direct solar irradiation measurements at nine stations, which are maintained by INPE in the frame of the SONDA project. The methodology for processing and analyzing the quality of the data-sets and the procedures to build the adjusted BRL model is thoroughly described. The error indicators show that the adjusted BRL model performs better or similarly to the original one, for both diffuse and DNI estimates calculated for each analyzed Brazilian station. For instance, the original BRL model diffuse fraction estimates have MeAPE errors ranging from 16% to 51%, while the adjusted BRL model gives errors from 9% to 26%. Regarding the comparison between the minute and hourly adjusted models, it can be concluded that both performed similarly, indicating that the logistic behavior of the original BRL model is well suited to make estimates in sub-hourly data sets. Based on the results, the proposed adjusted model can be used to provide reliable estimates of the distribution of direct and diffuse irradiation, and therefore, can help to properly design and reduce the risks associated to solar energy systems. Refereed/Peer-reviewed

Smarter urban futures require resilient built environment in the context of climate change. This chapter demonstrates the application of satellite-based surface cover and temperature data to support planning for urban heat resilience. Landsat 7 ETM+ and Landsat 8 data is used to analyse the correlation of urban surface covers to the urban heat island effect in Adelaide. Methods for data source selection, surface cover classification, surface temperature calculation and analysis are detailed in this chapter. Results indicate that tree canopy and surface water covers had the least surface temperature variations in mesoscale. The average minimum surface temperature of tree canopy cover was 2.79 °C lower than asphalt and 4.74 °C lower than paved areas. Freely available satellite urban surface temperature data can assist urban planning authorities in planning heat resilient urban spaces for smarter urban futures in the context of climate change. Refereed/Peer-reviewed

Compiling, deploying and utilising large-scale databases that integrate environmental and economic data have traditionally been labour- and cost-intensive processes, hindered by the large amount of disparate and misaligned data that must be collected and harmonised. The Australian Industrial Ecology Virtual Laboratory (IELab) is a novel, collaborative approach to compiling large-scale environmentally extended multi-region input-output (MRIO) models.The utility of the IELab product is greatly enhanced by avoiding the need to lock in an MRIO structure at the time the MRIO system is developed. The IELab advances the idea of the "mother-daughter" construction principle, whereby a regionally and sectorally very detailed "mother" table is set up, from which "daughter" tables are derived to suit specific research questions. By introducing a third tier - the "root classification" - IELab users are able to define their own mother-MRIO configuration, at no additional cost in terms of data handling. Customised mother-MRIOs can then be built, which maximise disaggregation in aspects that are useful to a family of research questions.The second innovation in the IELab system is to provide a highly automated collaborative research platform in a cloud-computing environment, greatly expediting workflows and making these computational benefits accessible to all users.Combining these two aspects realises many benefits. The collaborative nature of the IELab development project allows significant savings in resources. Timely deployment is possible by coupling automation procedures with the comprehensive input from multiple teams. User-defined MRIO tables, coupled with high performance computing, mean that MRIO analysis will be useful and accessible for a great many more research applications than would otherwise be possible. By ensuring that a common set of analytical tools such as for hybrid life-cycle assessment is adopted, the IELab will facilitate the harmonisation of fragmented, dispersed and misaligned raw data for the benefit of all interested parties. © 2014 Elsevier B.V.

Separation models predict diffuse horizontal irradiance from other meteorological parameters such as the global horizontal irradiance or zenith angle. From a mathematical point of view, the separation modeling problem is a regression, where the regressors are observed or computed variables and the regressand is the unobserved diffuse fraction. The most successful minute-resolution separation model prior to 2016 was proposed by Engerer, which is constructed using a trend component (cloud enhancement) and a main effect (logistic function). Subsequently, the Starke model published in 2018 further improved the Engerer model. It is herein shown that the logistic-function type of model, and many other separation models, can be transformed into a (condition-based) multiple linear regression problem. Under this transformation framework, two new models are proposed, which strictly dominate the performance of the Engerer model and the Starke model, at all 7 test sites across the continental United States, making them probably the most accurate separation models to date. The new models are also tested at 5 European sites with unseen data (i.e., not involved during model parameter fitting); their performance again dominates all benchmarking models. The new separation models leverage half-hourly satellite-derived diffuse fraction. Since satellite data are available globally, the satellite-augmented separation models have universal applicability. However, despite their good performance, empirical separation models suffer from a series of issues. Hence, models driven by atmospheric physics are the “true gems” that one should pursue.Separation models predict diffuse horizontal irradiance from other meteorological parameters such as the global horizontal irradiance or zenith angle. From a mathematical point of view, the separation modeling problem is a regression, where the regressors are observed or computed variables and the regressand is the unobserved diffuse fraction. The most successful minute-resolution separation model prior to 2016 was proposed by Engerer, which is constructed using a trend component (cloud enhancement) and a main effect (logistic function). Subsequently, the Starke model published in 2018 further improved the Engerer model. It is herein shown that the logistic-function type of model, and many other separation models, can be transformed into a (condition-based) multiple linear regression problem. Under this transformation framework, two new models are proposed, which strictly dominate the performance of the Engerer model and the Starke model, at all 7 test sites across the continental United States, making th...

In publicly available waste reports only the totals of waste produced for municipal, or industry waste typically feature. The types of waste generated and the generating industry sector are usually omitted. We propose the direct inputs waste estimation methodology to create a detailed estimate of municipal solid waste and industrial solid waste for an economy (including sectoral and waste type disaggregation) using a top-down estimation methodology that links the aforementioned limited publicly available waste data with an input-output table's direct inputs (A) matrix. We then provide an application of the direct inputs waste estimation methodology upon the 2008 waste generation of Australia resulting in a 344 industry sector and 14 waste type data set. The resulting estimation gives unique insights into Australian waste generation; including the large C&I tonnages of waste estimated to be produced from the Service sectors such as the Education, Hospitality, and Health sectors as well as the large amount of food waste produced throughout the economy Refereed/Peer-reviewed

Due its intermittency, the large-scale integration of solar energy into electricity grids is an issue and more specifically in an insular context. Thus, forecasting the output of solar energy is a key feature to efficiently manage the supply-demand balance. In this paper, three short term forecasting procedures are applied to island locations in order to see how they perform in situations that are potentially more volatile than continental locations. Two continental locations, one coastal and one inland are chosen for comparison. At the two time scales studied, ten minute and hourly, the island locations prove to be more difficult to forecast, as shown by larger forecast errors. It is found that the three methods, one purely statistical combining Fourier series plus linear ARMA models, one combining clear sky index models plus neural net models, and a third using a clear sky index plus ARMA, give similar forecasting results. It is also suggested that there is great potential of merging modelling approaches on different horizons. Refereed/Peer-reviewed

The frequency and intensity of urban heatwaves (UHWs) have been growing worldwide due to climate change and the exacerbating effects of urban heat islands (UHIEs). UHWs have many negative impacts, including excess negative health outcomes (e.g.morbidity), energy (consumption and peak demand) and water consumption. Most studies have evaluated these impacts separately even though there is an interplay between them. The study assessed the daily excess morbidity, energy demand and consumption,and water supply in the Adelaide metropolitan region during heatwaves, between January 2008 and March 2014. The assessment quantifies the thresholds and the increase in each impact relative to temperature increase. The demonstrated negative impacts on public health, and energy and water resources, potentially exacerbated by UHIEs, justify the importance of interdisciplinary research and integrated policy changes on the mitigation of and adaptation to heatwaves. Refereed/Peer-reviewed

With ever increasing demand for electricity and the huge potential of renewable energy, an increasing number of renewable-energy sources are being used to generate electricity. However, due to the intermittency of renewable-energy generation, many researchers try to overcome the variable nature of renewable energy. A hybrid renewable-energy system is one possible way to introduce smoothing of the supply. Many hybrid renewable-energy studies focus on system optimization and management. This paper mainly researches the performance prediction accuracy of a hybrid solar and wind system. Through a mixed autoregressive and dynamical system model, we test the predictability of the hybrid system and compare it with individual solar and wind series forecasting. After error analysis, the predictability of the hybrid system shows a better performance than solar or wind for Adelaide global solar radiation and Starfish Hill wind farm data. The prediction errors were reduced by 13% to more than 30% according to various error analyses. This result indicates an advantage of the hybrid solar and wind system compared to solar and wind systems taken individually. Refereed/Peer-reviewed

International audience; For some locations both global and diffuse solar radiation are measured. However, for many locations, only global radiation is measured, or inferred from satellite data. For modelling solar energy applications, the amount of radiation on a tilted surface is needed. Since only the direct component on a tilted surface can be calculated from direct on some other plane using trigonometry, we need to have diffuse radiation on the horizontal plane available. There are regression relationships for estimating the diffuse on a tilted surface from diffuse on the horizontal. Models for estimating the diffuse on the horizontal from horizontal global that have been developed in Europe or North America have proved to be inadequate for Australia [13]. Boland et al. [2] developed a validated model for Australian conditions. Boland et al. [3] detailed our recent advances in developing the theoretical framework for the use of the logistic function instead of piecewise linear or simple nonlinear functions and was the first step in identifying the means for developing a generic model for estimating diffuse from global and other predictors. We have developed a multiple predictor model, which is much simpler than previous models, and uses hourly clearness index, daily clearness index, solar altitude, apparent solar time and a measure of persistence of global radiation level as predictors. This model performs marginally better than currently used models for locations in the Northern Hemisphere and substantially better for Southern Hemisphere locations. We suggest it can be used as a universal model.

International audience; Integration of solar energy into the electricity network is becoming essential because of its continually increasing growth in usage. An efficient use of the fluctuating energy output of photovoltaic (PV) systems requires reliable forecast information. In fact, this integration can offer a better quality of service if the solar irradiance variation can be predicted with great accuracy. This paper presents an in-depth review of the current methods used to forecast solar irradiance in order to facilitate selection of the appropriate forecast method according to needs. The study starts with a presentation of statistical approaches and techniques based on cloud images. Next nu-merical weather prediction or NWP models are detailed before discussing hybrid models. Finally, we give indications for future solar irradiance forecasting approaches dedicated to the management of small-scale insu-lar grids.

International audience; This work aims at assessing the performances of four models for their relative merits in estimating diffuse solar radiation on tilted planes for a southern location (Reunion Island). This comparative study benefits from a sound and consistent experimental set-up and is more detailed than the previous research works as 14 inclined planes are available for testing.

Electricity generation is vital in developed countries to power the many mechanical and electrical devices that people require. Unfortunately electricity generation is costly. Although electricity can be generated easily but not normally stored. Electricity generation is also difficult to manage because exact demand is unknown from one instant to the next. A number of services are required to manage fluctuations in electricity demand, and to protect the system when frequency falls below acceptable levels. A current approach is called automatic under frequency load shedding. This article proposes new methods for optimising this approach in New Zealand's power system. The core ideas were developed during the 2015 Maths and Industry Study Group in Brisbane, Australia. The problem was motivated by Transpower Limited, a company that manages New Zealand's power system and transports bulk electricity from where it is generated to where it is needed. The approaches developed in this article can be used in electrical power systems anywhere in the world. References Arroyo J. M. and Galiana F. D. Energy and reserve pricing in security and network constrained electricity markets. IEEE Transactions on Power Systems , 20(2):C634–C643, 2005. Burdett R. and Kozan, E. Performance profiling for predictive train schedules. Journal of Rail Transport Planning and Management , 4:C98–C114, 2014. doi:10.1016/j.jrtpm.2014.11.002. Hiscock J., Hiscock N. and Kennedy A. Advanced voltage control for networks with distributed generation. 19th International Conference on Electricity Distribution . Vienna, 21–24 May, 2007. Just S. and Weber C. Pricing of reserves. Energy Economics , 30:C3198–C3221, 2008. El-Khattam W., Bhattacharya K., Hegazy Y. and Salama M. M. A. Optimal investment planning for distributed generation in a competitive electricity market. IEEE Transactions On Power Systems , 19(3). Kusano N. New trends in protection relays and substation automation systems in Japan. Transmission and Distribution Conference and Exhibition 2002: Asia Pacific. IEEE/PES , 1, 624–628. Lopes J. A., Hatziargyriou N., Mutale J., Djapic P. and Jenkins, N. Integrating distributed generation into electric power systems: A review of drivers, challenges and opportunities. Electric Power Systems Research , 77, 1189–1203. Thomson M. Automatic voltage-control relays and embedded generation. Power Engineering Journal , April, 71–76. Wong S. and Fuller D. J. Pricing energy and reserves using stochastic optimization in an alternative electricity market. IEEE Transactions on Power Systems , 22(2), 631–638. Transpower AUFLS Summary Card. WL 13309 https://www.systemoperator.co.nz/sites/.../AUFLS_Summary_Card.pdf Transpower Electricity reserve technical requirements schedule–-ERTWG https://www.systemoperator.co.nz/sites/default/files/bulk-upload/documents/ERTWG%20Trial%20Procurement%20Schedule%20Guide.pdf AIMMS AIMMS 3.13 - AIMMS Modeling Guide - Integer Programming Tricks. http://www.aimms.com