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• 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]

Microgrids are extensively integrated into electrical systems due to their many technical, economic, and environmental advantages. However, they encounter a challenge as they experience high-frequency fluctuations caused by the stochastic nature of renewable energy generation, electric loads, and the presence of Electric Vehicles (EVs). Therefore, various techniques, algorithms, and controllers have been introduced to ensure effective Load Frequency Control (LFC) and maintain a stable power system in microgrids. These methods aim to ensure that the system's frequency remains stable and within an acceptable range, especially when faced with changing load demands and other factors. This paper presents a novel enhanced control approach, Particle Swarm Optimization-Trained Artificial Neural Network (PSO-TANN), to optimize the load frequency model of a microgrid with vehicle-to-grid integration. The results are then compared under various scenarios, including renewable energy integration, EV charging and discharging dynamics, and varying load demands. The comparative analysis involves assessing the performance of the conventional Proportional–Integral–Derivative (PID) controller, the PSO-PID controller, and the newly proposed controlling technique. The suggested controller attains 99.904% efficiency with a negligible mean squared error of 1.1112 × 10−7, decreasing the integrated time absolute error to 1.0 × 10−4. It shows rapid response, precise targeting, and quick peak output ability, with marginal overshoot and undershoot, and a transient time of 28.5626 s, efficiently controlling microgrid frequency. Stability analysis validates the effectiveness of the proposed PSO-TANN controller in ensuring stability within the microgrid's LFC system during uncertainties and disturbances. This establishes resilience, diminishes settling time, and maintains reliable performance while controlling frequency. • An investigation into an islanded microgrid's Load Frequency Control (LFC) model framework has been conducted, comprising diesel generator, solar PV system, wind turbine, and EVs operating in vehicle-to-grid mode to supply power to electric loads, consequently impacting the frequency dynamics of the microgrid system. • A novel enhanced control approach, Particle Swarm Optimization-Trained Artificial Neural Network (PSO-TANN) has been implemented to optimize the load frequency model of a microgrid. • An extensive comparative analysis with other controllers, including PID and PSO-PID, is conducted across diverse scenarios involving renewable energy integration and EV charging and discharging dynamics under various load disturbances to reduce settling time, control system frequency, and ensure reliable performance. • The stability analysis validates the efficiency of the proposed PSO-TANN controller in ensuring reliability during uncertainties and disturbances, maintaining overall robustness and stability of the LFC system within the microgrid. [ABSTRACT FROM AUTHOR]

• Qualitative outcomes of energy poverty across Australian climate zones are compared. • Outcomes influenced by energy costs and local energy, housing and welfare policies. • Measures of energy poverty and policy responses need to reflect local contexts. Academic interests into energy poverty have been growing since the 1990s. These have ranged from quantitative measures and qualitative reflections, as well as ones that aimed to provide more nuanced definitions. These studies have, however, typically focused on single jurisdictions or across national borders, with very few examining the issue across different climatic and policy contexts within the same nation. This paper aims to address this gap by focussing on differences in climate conditions, local policies and quality of the housing stock across Australia and reflects on how these factors each played a role, to varying degrees, in the prevalence of energy poverty amongst lower-income households across eight metropolitan and regional settings conducted as part of a recent study in Australia. Through a review of focus group, interview and workshop data, it especially highlights the more qualitative impacts of energy poverty, and how each of these factors come to influence the extent to which lower-income households experience energy poverty and the support they were able to access. Additional to the need for more contextualised measures of energy poverty, this paper calls for localised responses to alleviate the negative outcomes reflected by our participants in regard to both energy and housing policies. [ABSTRACT FROM AUTHOR]

Global climatic change is driving research and development in low emissions technologies. One such technology is the use of charcoal from biomass in steelmaking. This paper adapts social life cycle assessment methodologies to analyse the social dimensions of energy supply alternatives in steelmaking using regionalised production scenarios in Australia. Three energy supply alternatives are investigated: charcoal produced from Radiata pine plantation forestry; charcoal produced from Mallee eucalypt revegetation on agricultural land; and metallurgical coal. Impact indicators analysed include land-use, employment, workplace health & safety and a qualitative analysis of identified stakeholder issues. The research finds that biomass alternatives are significant generators of direct employment at the regional level; have concomitantly higher rates of workplace injuries and represent a significant change in land-use. Charcoal produced from Mallee biomass planted as a conservation measure on farmland, however, has the benefit of representing a shared land-use that provides an additional farm revenue stream and assists dryland salinity management. The paper finds that full substitution of coal by pine or Mallee charcoal does not provide a unique solution for optimising the social performance of the energy supply alternatives across all indicators. [ABSTRACT FROM AUTHOR]

Abstract: Narratives and discourses are central to how we interpret and understand the world. The capacity to construct and disseminate compelling stories about particular issues is hence critical to an agent''s capacity to advance their interests. This paper examines some of the main narratives through which the conversation about renewable energy in Australia takes place. We label these narratives feasibility (‘Pie in the Sky’); security (‘Keeping the Lights on’); cost (‘Costing the Earth’); and employment (‘Jobs Carnage’). Some of the most effective narratives are those that are constructed around some of the ‘legitimate’ concerns that pertain to an issue. The renewable energy narratives identified in this paper build on ‘reasonableness’ and ‘common sense’ concerns, and their effectiveness is determined as much by contextualising information absent from the energy conversation as present. While our focus is Australia, some of the narrative dynamics revealed have application to energy politics more broadly. [Copyright &y& Elsevier]

Abstract To support alternative forms of energy resources, the prediction of global incident solar radiation (I rad) is critical to establish the efficacy of solar energy resources as a free and clean energy, and to identify and screen solar powered sites. Solar radiation data for construction of energy feasibility studies are not available in many locations due to the absence of meteorological stations, especially in remote or regional sites. To surmount the challenge in solar energy site identification, the universally gridded data integrated into predictive models used to generate reliable I rad forecasts can be considered as a viable medium for future energy utilization. The objective of this paper is to review, develop and evaluate a suite of machine learning (ML) models based on the artificial neural network (ANN) versus several other kinds of data-driven models such as support vector regression (SVR), Gaussian process machine learning (GPML) and genetic programming (GP) models for the prediction of daily I rad generated through the European Centre for Medium Range Weather Forecasting (ECMWF) Reanalysis fields. The performance of the ML models are benchmarked against several statistical tools: auto regressive moving integrated average (ARIMA), Temperature Model (TM), Time series and Fourier Series (TSFS) models. To train these models, 87 different predictor variables from the ERA-Interim reanalysis dataset (01-January-1979 to 31-December-2015) were extracted for 5 solar-rich metropolitan sites (i.e., Brisbane, Gold Coast, Sunshine Coast, Ipswich and Toowoomba, Australia) targeted against surface level I rad available from the measured Scientific Information for Land Owners dataset. For daily forecast models, a total of the 20 most important predictors related to the I rad dataset were screened with nearest component analysis: " fsrnca " feature selection, and partitioned into training (70%), validation (15%) and testing (15%) sets for model design. To benchmark the ANN, TSFS and TM models were developed with Fourier series and regression analysis, respectively and the statistical performance was benchmarked with root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (E NS), Willmott's Index (WI), Mean Bias error (MBE), Legates and McCabe Index (E 1), and relative MAE , RMSE and diagnostic plots. The performance of ANN was significantly better than the other models (SVR, GPML, GP, TM), resulting in lower RMSE (1.715–2.27 MJm−2/day relative to 2.14–5.90 MJm−2/day), relative RMSE (9.07–12.47 vs 10.98–29.15), relative RMAE (7.97–11.74 vs 9.27–33.96) and larger WI, E NS and E 1 (0.938–0.967 vs. 0.462–0.955, 0.935–0.872 vs. 0.355–0.915, 0.672–0.783 vs. 0.252–0.740). Additionally, models assessed with predictors grouped into El Niño, La Niña and the positive, negative and neutral periods of Indian Ocean Dipole, affirmed the merits of ANN model (RRMSE ≤ 11%). Seasonal analysis showed that ANN was an elite tool over SVR, GPML and GP for I rad prediction. The study concludes that an ANN approach integrated with ECMWF fields, incorporating physical interactions of I rad with atmospheric data, is an efficacious alternative to forecast solar energy and assist with energy modelling for solar-rich sites that have diverse climatic conditions to further support clean energy utilization. Graphical abstract Image 1 Highlights • ANN, SVR, GPML and GP models were designed for prediction of daily solar radiation with reanalysis data. • Determinstic model ARIMA, temperature model and time series with Fourier transform model was used for benchmarking. • Nearest component analysis (fsrnca) was used for feature selection to get optimum inputs. • ANN outperform all ML model and deterministic models. • Solar energy potential is assessed for 5 cities of Queensland, Australia. [ABSTRACT FROM AUTHOR]

When distributed solar photovoltaic systems (PV) provide the majority of supply in electricity grids, system operators and network service providers require increased control of customer-owned assets to maintain system security, reliability and equity. Several programs of reform for distributed energy resources (DER) in Australia, USA and Europe have emphasised the importance of placing customers at the centre of any energy transition, but this has occurred against a haphazard backdrop of proposed solar export taxes, decreasing feed-in tariffs, time-varying export incentives and updated inverter standards. Absent is a conversation of how the social contract of connecting to the electricity grid is evolving beyond the passive expectation of electricity supply of the 19th and 20th centuries to a two-way engagement that must support fairness for both DER-owners and non-owners in grids that are at times dominated by DER. This paper proposes a first attempt at a 'DER Bill of Rights and Responsibilities' to provide a template for this required social-license, distinguishing between passive grid participation (passive loads, traditional use, PV self-consumption) and active grid participation (grid interaction for remuneration). Guiding principles of fairness are presented with practical definitions for implementation, referencing existing instruments including inverter standards, network connection agreements, reliability standards, and central ancillary service markets. The intent is that clarity on rights to self-consumption and passive participation will support customer trust in the responsibilities for active DER integration and control. We highlight how proposed rights are already being breached regularly in Australia, as an example jurisdiction, before outlining a pathway to enshrine them for a DER-dominated future with broad sector endorsement. These questions are critical for Australia to address now; other jurisdictions will likely be required to do so in the near future. [ABSTRACT FROM AUTHOR]

To be sustainable in energy usage in the future, there are two aspects that need to be considered: the energy supply or generation and the consumption side, including the closely linked construction and building industries which consume a large amount of energy. Essential requirements for energy efficiency are to produce less greenhouse gas emissions and to rely more on renewable energy sources for future sustainability. Policies for mitigation of the environment impact are having effects on both the supply and demand. While the former requires more alternate sources in smart grids and improved technologies for carbon capture and storage, the latter involves the reduction of energy wastes and greenhouse gas (GHG) emissions as prerequisites to green certification within the construction and building sector. Thus, access to sustainable, affordable, and secure energy is one of the major global strategic priorities to maintain and improve public health, sustain economic growth, and mitigate the effects of climate change. Toward this goal, many countries, including Australia, are investing in clean, efficient, reliable energy systems for a prosperous and environmentally sustainable future. Hence, exploring various options to ensure energy security by diversification of energy sources is an important step in meeting the future requirements and delivering clean energy to different industry sectors. This paper discusses options to manage the use of energy sources in the power generation and construction industries. Options for mitigation of environmental impact and for achievement of sustainable energy usage, such as building design with BIM, are discussed. [ABSTRACT FROM AUTHOR]

Abstract: Coal fired electricity is a major factor in Australia’s greenhouse gas emissions (GHG) emissions. The country has adopted a mandatory renewable energy target (MRET) to ensure that 20% of electricity comes from renewable sources by 2020. In order to support the MRET, a market scheme of tradable Renewable Energy Certificates (RECs) has been implemented since 2001. Generators using biomass from eligible sources are able to contribute to GHG emission reduction through the substitution of coal for electricity production and are eligible to create and trade RECs. This paper quantifies the potential biomass resources available for energy generation from forestry and agriculture in the Green Triangle, one of the most promising Australian Regions for biomass production. We analyse the cost of electricity generation using direct firing of biomass, and estimate the required REC prices to make it competitive with coal fired electricity generation. Major findings suggest that more than 2.6 million tonnes of biomass are produced every year within 200 km of the regional hub of Mount Gambier and biomass fired electricity is viable using feedstock with a plant gate cost of 46 Australian Dollars (AUD) per tonne under the current REC price of 34 AUD per MWh. These findings are then discussed in the context of regional energy security and existing targets and incentives for renewable energies. [Copyright &y& Elsevier]

Abstract: In this paper, our goal is to examine the unit root null hypothesis in energy consumption for Australian states and territory. We consider sectoral energy consumption for Australia and its six states and one territory using time series data for the period 1973–2007. This is the first study that does this. Generally, except for some cases in South Australia, we find strong support that shocks to energy consumption have a temporary effect on energy consumption in Australia. [Copyright &y& Elsevier]

• 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]

• 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]