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Hybrid offshore renewable energy platforms have been proposed to optimise power production and reduce the levelised cost of energy by integrating or co-locating several renewable technologies. One example is a hybrid wave-wind energy system that combines offshore wind turbines with wave energy converters (WECs) on a single floating foundation. The design of such systems involves multiple parameters and performance measures, making it a complex, multi-modal, and expensive optimisation problem. This paper proposes a novel, robust and effective multi-objective swarm optimisation method (DMOGWA) to provide a design solution that best compromises between maximising WEC power output and minimising the effect on wind turbine nacelle acceleration. The proposed method uses a chaotic adaptive search strategy with a dynamic archive of non-dominated solutions based on diversity to speed up the convergence rate and enhance the Pareto front quality. Furthermore, a modified exploitation technique (Discretisation Strategy) is proposed to handle the large damping and spring coefficient of the Power Take-off (PTO) search space. To evaluate the efficiency of the proposed method, we compare the DMOGWA with four well-known multi-objective swarm intelligence methods (MOPSO, MALO, MODA, and MOGWA) and four popular evolutionary multi-objective algorithms (NSGA-II, MOEA/D, SPEA-II, and PESA-II) based on four potential deployment sites on the South Coast of Australia. The optimisation results demonstrate the dominance of the DMOGWA compared with the other eight methods in terms of convergence speed and quality of solutions proposed. Furthermore, adjusting the hybrid wave-wind model's parameters (WEC design and PTO parameters) using the proposed method (DMOGWA) leads to a considerably improved power output (average proximate boost of 138.5%) and a notable decline in wind turbine nacelle acceleration (41%) throughout the entire operational spectrum compared with the other methods. This improvement could lead to millions of dollars in additional income per year over the lifespan of hybrid offshore renewable energy platforms. • A new multiobjective optimisation method proposed and enhanced hybrid wave-wind platform. • A new combination of chaotic adaptive exploration and dynamic archive is introduced. • Effective and practical discretisation method improved performance of hybrid platform. • The performance of the proposed optimiser is validated on four real sea studies. [ABSTRACT FROM AUTHOR]

• A new energy-as-a-service business model for aggregators of prosumers. • A new techno-economic simulation framework to compute business model benefits. • The new business model increases the average profitability of the aggregator by 438%. • The new business model provides free electricity to prosumers. Traditional retail business models price electricity using volumetric tariffs, which charge customers for the unit of energy consumed. These tariffs were designed for passive consumers with low flexibility. In this paper, we argue that these volumetric tariffs are unsuitable for prosumers with high flexibility since they are unable to adequately value the flexibility of their distributed energy resources in multiple electricity markets. This reduces the interest of prosumers participating in aggregators' business models. To address this issue, we propose a new business model for aggregators of prosumers, based on the concept of energy-as-a-service. In this business model, prosumers pay a monthly fee for aggregators to represent and optimize them in multiple wholesale electricity markets, including in energy and ancillary service markets. The monthly fee is computed by a new techno-economic simulation framework proposed in this paper, which can also be used to estimate the profitability of the new business model from the perspectives of both the aggregator and prosumers. Our experiments on a portfolio of real prosumers from Australia show that the new business model maximizes the economic benefits of both the aggregator and prosumers by increasing the average profit of the aggregator by 438% and reducing the average electricity cost of prosumers from $583/year to $0 when compared to two of the most common retail business models available in the Australian market. In other words, the economic benefit for prosumers is free electricity. In addition to this benefit, the new business model also provides simplicity and predictability to prosumers, as they are offered a guaranteed outcome before providing the services. [ABSTRACT FROM AUTHOR]

Although noteworthy efforts towards energy storage participation in electricity markets have been witnessed worldwide during the last decades, market mechanisms for optimal storage integration are yet to be comprehensively studied. This paper presents a novel approach wherein a multi-period look-ahead market is proposed to progressively solve the optimisation problem over a receding horizon, incorporating intertemporal constraints to enable forward planning. The focus is specifically on modelling the bidding behaviour of energy storage, and to address this, we develop two distinct models for the participation of energy storage in the market. The integrated model aims to schedule the future dispatch of the storage to minimise total operational cost, taking into account the fixed costs of the storage, while the strategic model pursues the dual ambition to maximise storage profit by formulating a bilevel optimisation problem. We validate the viability of our proposed market mechanisms through case studies on the Australian National Electricity Market. We conduct case studies which provide evidence that both the strategic and integrated bidding models effectively address the requirement for forward planning, resulting in reduced market costs and volatility. Notably, the strategic model outperforms in terms of generating higher storage revenue, while the integrated model exhibits a slight advantage in terms of lower market costs compared to the strategic model. • Development of a multi-period look-ahead market for storage integration in electricity markets. • Model of strategic bidding using a bilevel optimisation model, vs an integrated bidding model. • Both bidding models can fulfil need for forward planning, reducing market costs and volatility. • The strategic model increases storage revenue, while the integrated model reduces market costs. • Testing is carried out using a high-fidelity model of Australia's NEM and historical data. [ABSTRACT FROM AUTHOR]

• Presenting a set of mathematical models for green flowshop scheduling problems. • Considering common carbon reduction policies and their corresponding scenarios. • Providing analysis of models and insights to design a framework for green flowshops. • Industries can use our cost-effective strategies to meet limits mandated by government. • Government can explore our integrated framework to gauge impacts of carbon policies. In this paper we consider, from an environmental policy-maker perspective, how carbon reduction policies impact the economic competitiveness of the manufacturing sector. Specifically, we focus on flowshop scheduling – which typically aims to minimize makespan for purely economic objectives – and consider how three common carbon reduction policies – namely, taxes on emissions, baselines on emissions, and emissions trading schemes – can create competitive green flowshops that balance minimization of makespan and carbon emissions. The goal is to enable policy-makers to understand how to set policies and control parameters to achieve environmental objectives while ensuring global economic competitiveness of industry. We initially present a set of mixed-integer linear programming (MILP) models for flowshop scheduling problems operating in a regulated environment in which each carbon reduction policy is adopted. We then introduce a bi-objective scheduling framework for the corresponding problem to obtain alternative solutions under each policy. These models and their computational results however, are not the main focus of the study, but are presented as a means to demonstrate how green policies co-exist with economic objectives, with policy-makers in control of the balance. To this end, based on financial data from Australia's carbon emissions profile, we provide a policy-oriented analysis of the models, and some managerial insights into the effect of scheduling strategies on carbon emissions under different reduction policies. These insights offer support to both environmental policy-makers and corporate production and sustainability managers to determine whether it is technically feasible and profitable to replace traditional scheduling strategies with environmentally friendly scheduling strategies. [ABSTRACT FROM AUTHOR]

• A novel CElif system is proposed in this paper. • Decomposition and denoising strategy is designed in the CElif system. • The MSsa mechanism is superior both in search ability and operation efficiency. • The construction of the five ANNs covers the inherent modes of sequence. • Differences in people's behavior on weekdays and weekends are considered. Under the trend of the global green and low-carbon transformation, accurate prediction of electric load is an urgent problem for all countries to maintain the normal operation of production and living activities. Currently, a large number of models based on single-point forecasting of electric load have emerged. However, these models are limited by the high randomness of electric load data, which restricts the further improvement of prediction accuracy. To fill this gap, a CElif (Combined Electric Load Interval Forecasting) system is proposed in this paper, which contains decomposition and denoising module, individual forecasting module, optimization module and evaluation module, aiming to tolerate uncertainty to provide policymakers with more information. Decomposition and denoising strategy is adopted to extract and reconstruct the inherent modes of the original sequence. In order to verify the superiority of the CElif system, the electric load data of 30-minute interval in Australia are used to test. The numerical results conclude the CElif system not only has excellent coverage performance in electric load interval prediction, controls the uncertainty to a great extent, but also provides a basis for power system dispatching management. [ABSTRACT FROM AUTHOR]

• Direct and indirect effects of sectors on net energy consumption are analysed. • Level effect is identified as the major driver for increasing energy consumption. • Manufacturing has the largest energy consumption reduction potential. • How to decrease net energy consumption has been offered to policy-makers. 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. [ABSTRACT FROM AUTHOR]

Active load shifting of the electricity demand unlocks a variety of benefits. Examples of such advantages include the increased stability of energy systems, reduced electricity costs and financial savings in the transmission as well as generation infrastructure. Although the technology necessary for demand response has been extensively studied for individual appliances or at the micro-grid level, evaluations of its nationwide impact are scarce. Yet governments and policy-makers require quantitative assessments in order to understand the underlying value and derive appropriate policies. For this purpose, this paper utilizes real-world data from the German-Austrian electricity market in order to calculate ex post the impact of demand response on electricity spot prices and load. As a result, we find that a 25% adoption rate of the available potential for load shifting could have decreased nationwide electricity expenses by approximately €500 million, or 6%, in 2014. At the same time, we observe that the price volatility rises under this scheme and thus impairs non-flexible electricity customers. This observation entails significant implications in terms of designing effective policies. [ABSTRACT FROM AUTHOR]

• Optimization model for the integrated assessment of the green ammonia value chain. • Case study for ammonia trade between Australia and Germany in 2030. • Sensitivity analysis on ammonia prices for technical and economic parameters. • Cost comparison of conventional and green ammonia production. This paper contributes to understanding the transformation of global energy trade to green energy carriers, focusing on green ammonia as the foreseeable first green hydrogen carrier. We provide a comprehensive overview of today's ammonia trade and assess scaling options for the trade of green ammonia. To that aim, we develop an optimization model for the integrated assessment of the green ammonia value chain that covers all steps from green ammonia production in an exporting country, up to delivery to a harbor in an importing country. The model endogenously chooses among different technology options and determines cost minimal operation. In a case study, we apply the model to the large-scale import of ammonia from Australia to Germany in a scenario for 2030. The results show that green ammonia can reach cost parity with gray ammonia even for moderate gas prices (but not necessarily with blue ammonia) if CO 2 prices are high enough. We also provide a sensitivity analysis with respect to the interest rate and other key technical and economic parameters and show that cracking ammonia to provide pure hydrogen comes at a 45 % cost markup per MWh at the destination. [ABSTRACT FROM AUTHOR]

• A novel integrated short-term power load forecasting system is proposed. • The fuzzy granulation technique simplifies the large-scale dataset and extracts the effective characteristics of the raw data. • A novel multi-objective variant of the tuna swarm optimizer is utilized to integrate multiple forecasting sequences. • The optimal weight coefficients of the ensemble model are theoretically demonstrated to be the Pareto optimal solutions. • Our proposed forecasting system can gauge the uncertainty of future electricity load. As an irreplaceable power source, electricity is responsible for sustaining the national economy and social development, and the precondition for the power system's stable operation is to perform an accurate short-term load forecast (STLF). However, with the increasing forms of social power consumption and the emergence of large-scale sustainable resources on the grid, which make STLF increasingly challenging as the power load exhibits greater stochasticity and instability. Therefore, a novel STLF system is developed in this paper, which incorporates data fuzzy granulation, a high-performance optimizer for integrating forecasting sequences, point and interval forecasts. Moreover, the performance tests of the optimization algorithm verify that our proposed optimizer can obtain more approximate solution sets to the real Pareto front and outshines the traditional optimization algorithm concerning convergence and diversity. Load data from three regions of Australia demonstrate that our developed system can remarkably contribute to the accuracy and stability of the STLF , and also quantify the volatility and uncertainty of the power load, which allows power workers to better capture the fluctuation interval of future loads and effectively enhance the flexibility of grid operation. [ABSTRACT FROM AUTHOR]

Control of the superheated steam temperature is a significant technical challenge to coal-fired power plants due to the strong nonlinearity, large inertia and parameter uncertainties with external disturbances. This paper proposes a robust proportion–integration–differentiation (PID) control of the superheated steam temperature with proven stability and the external disturbance rejection. The design is based on the proposed data-driven feedback compensator (DFC) which is a neural network (NN) trained to isolate the nonlinearity and inertia from the PID controller, allowing the system for tuning the PID controller can be simplified as an approximate linear system with the modeling error based-feedback and feed-forward compensations. Then, the PID controller is tuned by the Nyquist stability criterion to place every closed-loop pole to a specific region in the left-half s-plane to guarantee the stability with considering the modeling error. Besides, a NN based feed-forward compensator is trained as the inverse model of the feedback compensator to further smooth the temperature fluctuation caused by the disturbance. The simulations and engineering implementation of the superheated steam temperature controls for a coal-fired power plant in Australia show the effectiveness. • A data-driven compensation based robust control with considering uncertainties. • A neural feedback compensator to compensate the nonlinearity and inertia. • Approximation errors handled by the robust PID control with proven stability. • The effectiveness verified by the implementation in a physical coal-fired plant. [ABSTRACT FROM AUTHOR]

Recent trends in increased public environmental awareness coupled with the introduction of governmental incentives, has resulted in a remarkably rapid growth in rooftop photovoltaic (PV) installations at a residential level in Australia. Despite the widely perceived economic and environmental benefits of rooftop PV, the economic and environmental performance of rooftop PV under Australian conditions are yet to be thoroughly examined. Whilst prior studies on the subject of rooftop PV are extensively overseas based, the conditions under which they are performed differ immensely from the Australian conditions. Consequently, this paper focuses on evaluating the life cycle economic and environmental merit of rooftop PV in Australia. By making use of component-wise costs and embedded emissions associated with each component of the rooftop PV generation system, the life cycle cost and emission models have been developed. For this to occur, the Australian costs and, energy and emission intensities have been used. All the life cycle factors relating to the manufacturing, purchasing, installation and operation of rooftop PV are considered. A sensitivity analysis is performed with respect to electricity price, feed-in tariff, battery price, battery lifetime and emission intensities. The results of the cost and emissions analyses are collectively used to estimate the cost of emissions mitigation through the use of rooftop PV for current and potential future scenarios in Australia. The cost payback and the energy payback times are found to range between 11 to more than 25 years and 1.75 to 14 years, respectively. The study also reveals that local load profile can have a significant impact on payback times. For example, the cost payback without battery storage is found to be the least for Tasmania which hosts the least favourable solar insolation conditions and lowest feed-in tariff amongst the Australian states however possesses a high daily load and electricity price. [ABSTRACT FROM AUTHOR]

This paper presents a new hybrid approach for Global Solar Radiation (GSR) prediction problems, based on deep learning approaches. Predictive models are useful ploys in solar energy industries to optimize the performance of photovoltaic power systems. Specifically, in this work we develop a new 4-phase hybrid CXGBRFR framework, which includes a deep learning Convolutional Neural Network, an Extreme Gradient Boosting with Random Forest Regression, and a Harris Hawks Optimization for initial feature selection. The proposed system has been tested in a problem of daily GSR prediction at six solar farms in Australia. Data from three global climate models (GCM) (CSIRO-BOM ACCESS1-0, MOHC Hadley-GEM2-CC and MRI MRI-CGCM3) have been considered as predictive (input) variables for the proposed approach. The variables from these GCMs contain enough information to obtain an accurate prediction of the GSR at each solar farm. The performance of the proposed approach is compared against different deep and shallows learning approaches: Deep Belief Network, Deep Neural Network, Artificial Neural Network, Extreme Learning Machine and Multivariate Auto-Regressive Spline models. We show that the proposed approach exhibits an excellent performance in GSR prediction, against all alternative approaches in all solar farms considered. [Display omitted] • We develop hybrid deep learning model to predict solar radiation • We optimize the algorithm with Harris Hawks, gradient boosting and random forest • We adopt global climate model and observed predictors as learning model inputs • We build an expert system with relevant knowledge in context of solar application • Our methods benefit solar photovoltaic power monitoring in solar engineering problems. [ABSTRACT FROM AUTHOR]

This paper examines the cost-effectiveness of subsidies (feed-in tariffs and renewable energy credits) paid for by electricity consumers to support the uptake of roof top photovoltaic (PV) systems by households in Australia. We estimate annual payback periods, and then regress these against the actual uptake of household PV and associated emission reductions, creating a relationship not apparent in other research. Sensitivity analysis reveals that the declining cost of PV panels had most impact on PV uptake followed by feed-in tariffs, renewable energy credits and the increasing cost of household electricity tariffs. Our modelling shows that feed-in tariffs were higher than necessary to achieve the resultant levels of PV uptake and that the low cost of PV panels and comparatively high electricity tariffs are likely to result in a continuing strong uptake of household PV in Australia. Our modelling shows that subsidies peaked in 2011 and 2012, with payback periods of three to four years, having since increased to five to six years. Emission reduction costs are expected to reduce from over AU$200 per t CO 2 e in 2013 to between AU$65 and AU$100 per t CO 2 e in 2020. Household PV reduced Australia’s emissions by 3.7 million t CO 2 e in 2013 (1.7% of Australia’s total emissions) and is expected to reach eight million tonnes (3.7% of Australia’s total emissions) by 2020. [ABSTRACT FROM AUTHOR]

• MERRA-2 outperforms CAMS in capturing seasonal dust, consistent with Himawari-8 observations. • Dust emissions and depositions in Australia peak at 1.4 gm−2 during Spring-Summer. • Seasonal soiling show reductions in energy of ∼3% near the Lake Eyre Basin. • Rainfall bursts remove soiling of 3 gm−2 at solar farms, improving energy yield by 9%. Dust plays a vital role in the Earth's climate system and can also modulate solar energy yield from photovoltaics due to soiling and it also impacts concentrated solar power outputs by up to 30%. Research examining decreased energy production on solar panels due to dust accumulation is limited. This is particularly important for dry arid regions of Australia, which are prone to dust storms. Thus, this paper examines the dust characteristics over Australia by comparing two reanalysis products, the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) and the Copernicus Atmosphere Monitoring Service (CAMS) with the satellite data (Himawari-8) to ascertain the capabilities of the reanalysis products in capturing dust cycles over Australia. The results are then used to explore the seasonal variability and trends in the radiative impacts, emissions, and depositions of dust. The energy reductions from seasonal averaged dust accumulations without natural removal or cleaning and later the impact of soiling with removal from precipitation bursts on selected operational solar farms are presented. Results show that MERRA-2 outperforms CAMS in capturing Dust Aerosol Optical Depth (AOD) near the Lake Eyre Basin in all seasons, consistent with satellite observations. The radiative trends showed contrasting differences during the Spring and Summer, whereas seasonal dust emissions and depositions peaked at 1.4 gm−2. Similarly, seasonal dust accumulations without natural removal on panels showed significant reductions in the energy of up to 3% around the Lake Eyre Basin, but most operational solar farms in Australia depicted energy reductions of 8–9% due to accumulated dust of up to 2.5–3 gm−2, which was naturally removed from regular seasonal rainfall bursts. [ABSTRACT FROM AUTHOR]

• A novel CTSGAN deep learning framework is first proposed and applied to model day-ahead electricity price scenarios. • The CTSGAN point forecasting model can eventually be transformed into a probabilistic forecasting model by enhancing the diversity of random input. • The CTSGAN probabilistic forecasting model can directly yield high-quality forecasting intervals with different coverage probabilities as a multi-objective forecasting model. • PSO-based optimal conditions selection method can increase the forecasting accuracy. Electricity prices in spot markets are volatile and can be affected by various factors, such as generation and demand, system contingencies, local weather patterns, bidding strategies of market participants, and uncertain renewable energy outputs. Because of these factors, electricity price forecasting is challenging. This paper proposes a scenario modeling approach to improve forecasting accuracy, conditioning time series generative adversarial networks on external factors. After data pre-processing and condition selection, a conditional TSGAN or CTSGAN is designed to forecast electricity prices. Wasserstein Distance, weights limitation, and RMSProp optimizer are used to ensure that the CTGAN training process is stable. By changing the dimensionality of random noise input, the point forecasting model can be transformed into a probabilistic forecasting model. For electricity price point forecasting, the proposed CTSGAN model has better accuracy and has better generalization ability than the TSGAN and other deep learning methods. For probabilistic forecasting, the proposed CTSGAN model can significantly improve the continuously ranked probability score and Winkler score. The effectiveness and superiority of the proposed CTSGAN forecasting model are verified by case studies. [ABSTRACT FROM AUTHOR]

• Bottom-up estimation tool to develop electric public transport (EPT) charging loads. • Uses GTFS public data and can be scaled to hundreds of cities/states globally. • EPT could add 17% to critical peak demand on some distribution zone substations. Battery electrified public transit (BEPT), including buses and passenger ferries, is a promising solution to transport-related climate emissions and urban air pollution, but introduces potentially challenging large, coincident demand in the low-voltage distribution network. This paper presents a tool for estimating the energy and charging demand of electrified public transit using public data that is available in over 150 cities/states globally and demonstrates it in two case studies. The tool applies heuristic vehicle scheduling to existing public General Transit Feed Specification data to model the charging profiles and electricity consumption of BEPT for various charging regimes across different geographical scales. For the case study of New South Wales, Australia, the impacts of BEPT are most significant at the low-voltage network, where adding a battery-electric bus depot was found to increase the annual critical peak demand at the local zone substation in Summer by up to 17% while exacerbating demand during the daily evening peak period by 20–30%. Across the entire state of NSW, a full transition to BEPT increases annual electricity consumption by 1.28–1.34% and peak daily demand by 1–3%. [ABSTRACT FROM AUTHOR]

• A private reliability service is proposed and can be traded in the electricity market. • A novel group double side auction method for market clearing is designed and verified. • Dynamic elasticities considering real-time preference are applied in this model. • The model provides the advised price interval for customers. • Real historical data of customers in Australia are used to verify the proposed model. Due to the rapid development of distributed renewable generation, emerging prosumers are encouraged to participate in the energy market. Meanwhile, blackout events, planned and unplanned outages due to weather, component failures and other causes, can also impact users' experience. To maintain reliability and improve users' experience under outage conditions, it is possible to transform the service reliability from a public good (compulsory and uniform) to a private good (self-selection). In this paper, a new restore mechanism based on private reliability service is proposed, the reliability service is no longer uniform. Customers with a higher reliability requirement are willing to pay to maintain their desired consumption in outage conditions. The customers are classified by dynamic elasticity considering historical data and real-time customer preferences. Then the advised unit price, trading amount and reliability level are provided to customer. Once they submit this information to market operator, the price and transaction amount is matched in each group, and floated average clearing price is applied to obtain the bidding results. The trading mechanism is proved to satisfy incentive compatibility, and the transaction process for a specific area in Australia is analyzed in a case study to verify the proposed model. [ABSTRACT FROM AUTHOR]

• A framework for aggregating large-scale prosumers in electricity markets is proposed. • The proposed bi-level framework is linearized by duality theory and big-M method. • Cases studies based on 120,000 prosumers from Australia are conducted. • Increasing prosumers enlarges the gap between strategic and non-strategic bidding. Increased penetration of distributed energy resources is unleashing the flexibility of large-scale prosumers in deregulated markets. To explore prosumers' potential market revenues, some existing studies have focused on the strategic bidding of prosumers aggregation. A majority of those studies assume the price-taker role of the aggregator while a few studies assume the price-maker role of the aggregator. However, it remains an open question as to how the increasing number of prosumers influences the profit of a strategic aggregator. Therefore, we conduct a numerical analysis in this paper to quantify the profits of aggregating large-scale prosumers. A stochastic bi-level optimization model is proposed to depict the strategic behavior of prosumers aggregation bidding in joint energy and regulation markets. This bi-level model is transformed into a mixed-integer linear programming model by employing the Karush-Kuhn-Tucker conditions based on strong duality theory. Case studies based on 120,000 prosumers from Australia demonstrate that the strategic bidding behavior of an aggregator can lead to a 7.5% decrease in operation costs, and increasing the number of prosumers will lead to a larger gap between non-strategic and strategic behavior. [ABSTRACT FROM AUTHOR]

• A novel evaluation of Virtual Net Metering for Community housing is conducted. • Virtual Net Metering provides cost effective aggregations for energy pooling. • By pooling resources savings up to 18% could be offered for solar only schemes. • Battery prices need to decline by 30% for solar and batteries to achieve parity. In a climate of soaring energy prices in Australia not all consumers are able to afford the upfront investment costs in photovoltaics to manage their energy costs. Virtual Net Metering enables the sharing of generation and storage benefits against the load of decentralised customers including apartments. The aggregation of resources can reduce the cost for monitoring and managing distributed generation. This is possible by the use of innovative technology for monitoring, billing and settlement software. This paper provides a novel evaluation of virtual net metering to address social equity in the energy transition with regard to the Australian community housing sector. Modelling was conducted with Monte Carlo simulations of weather variables, demand and solar generation. Scenarios of solar only and solar combined with batteries are evaluated. Results showed that by pooling resources savings up to 18% of total energy costs could be offered to participants for solar only schemes whereas battery prices would have to decline by 30% for combined solar and battery schemes to achieve the same. [ABSTRACT FROM AUTHOR]

• The proposed method can solve voltage issues and enable higher PV penetration. • PV imbalance makes traditional methods less effective on overvoltage mitigation. • A joint centralized-decentralized method is developed for unbalanced PV scenarios. • The new approach controls fast PV fluctuations in a coordinated and distributed way. Due to random connection of small-size single-phase rooftop photovoltaic (PV) generators in residential areas, low voltage distribution systems tend to have unbalanced PV integration across phases. In such situations, traditional reactive power compensation methods may cause unintended Voltage-Reactive Power responses across phases due to line coupling, which consequently affects their effectiveness on overvoltage mitigation. Such overvoltage problems significantly hinder the future integration of clean and renewable solar energy resources in power networks. In this paper, the limitations of traditional methods in PV imbalance scenarios are revealed and comprehensively analyzed by a voltage sensitivity method for the first time. On this basis, an innovative joint centralized-decentralized method is developed to overcome the PV imbalance-induced voltage regulation challenge. Specifically, the inverter's reactive power response to PV power variations in different phases is adaptively scheduled in order to avoid adverse effect of inter-phase Voltage-Reactive Power interaction on voltage regulation. The effectiveness of the proposed method is validated by time-series simulations with recorded data and a real-life low voltage distribution system in Australia. With the traditional method (i.e. power factor droop control strategy), 93 minutes' overvoltage can be observed in the case study, which will trigger overvoltage protection and disconnect PV inverters from the grid. While, the proposed method can always control the system voltage within the allowable range, and it consequently avoids PV disconnection. By solving this bottleneck issue, more PV systems can be integrated into distribution networks, which consequently contributes to CO 2 emission reduction in future. [ABSTRACT FROM AUTHOR]

• Deep learning hybrid model is proposed for solar radiation prediction. • Convolutional network is used to extract features for solar radiation. • Long Short-Term Memory is used for prediction of solar radiation. • Deep learning hybrid model outperforms all comparative models. • The model can be adopted as a decision-support tool in solar energy simulation. This paper designs a hybridized deep learning framework that integrates the Convolutional Neural Network for pattern recognition with the Long Short-Term Memory Network for half-hourly global solar radiation (GSR) forecasting. The Convolution network is applied to robustly extract data input features from predictive variables (i.e., statistically significant antecedent inputs) while Long Short-Term Memory absorbs them for prediction. Half-hourly GSR for Alice Springs (Australia: 01 January 2006 to 31 August 2018) are extracted with stationarity checks applied via unit-root and mutual information test to capture antecedent GSR values required to forecast future GSR. The proposed hybrid model is benchmarked with standalone models as well as other Deep Learning, Single Hidden Layer and Tree based models. The results show that the benchmarked models are not able to generate satisfactory GSR predictions and the proposed hybrid model outperforms all other counterparts. The hybrid model registers superior results with over 70% of predictive errors lying below ±10 Wm−2 and outperforms the benchmark model for 1-Day half-hourly GSR prediction with low Relative Root Mean Square Error (≈1.515%), Mean Absolute Percentage Error (≈4.672%) and Absolute Percentage Bias (≈1.233%). This study ascertains that a proposed hybrid model based on a convolution network framework can accurately predict GSR and enable energy availability to be regularly monitored over multi-step horizons when coupled with a low latency Long Short-Term Memory network. Furthermore, it also concludes that the proposed model can have practical implications in forecasting GSR , capitalizing its versatility as a stratagem in monitoring solar powered systems by integrating freely available solar radiation into a real power grid system. [ABSTRACT FROM AUTHOR]

• Development of a new enthalpy-based Climatic Cooling Potential index. • Critical evaluation of using enthalpy to evaluate Climatic Cooling Potential. • Practical significance of the enthalpy-based Climatic Cooling Potential. • Critical discussion of climatic effects on applicability of Ventilative Cooling. • Spatiotemporal analysis of the Climatic Cooling Potential in Australia. Ventilative Cooling is a promising technique to improve the energy efficiency of buildings through reduced need for mechanical cooling to maintain thermal comfort. To assess the feasibility of Ventilative Cooling in a specific location, it is useful for designers to be able to evaluate the climate potential for cooling. This paper describes a new and enhanced version of the Climatic Cooling Potential (CCP) method, which is a simplified method to evaluate the climate potential for passive cooling that does not require detailed knowledge of the building characteristics. The proposed Enthalpy Climatic Cooling Potential (CCP h) integrates humidity into the climate evaluation, using the enthalpy difference between indoors and outdoors, instead of the temperature difference. It accounts for latent heat, providing a better estimate of locations where the use of outside air will be beneficial from an energy perspective. The methods were compared by mapping the Ventilative Cooling potential for the Australian climate, using weather data from 391 weather stations across Australia. Results showed that most of the highly populated cities in the south of Australia have a climate suitable for Ventilative Cooling. This is true even in the summer period, although the area where the climate is beneficial is significantly larger during the shoulder seasons and winter. The analysis showed that using the CCP index, rather than the new CCP h index, can lead to an under-estimation of the cooling potential during dry winters and an over-estimation during humid summers, due to the contribution of the latent heat. [ABSTRACT FROM AUTHOR]