Search

The synergistic operation of integrated electricity and natural gas systems (IEGS) in the presence of wind power necessitates a distributed optimization framework that ensures information privacy. However, incorporating wind power penetration leads to distributed optimization problems including uncertainty and unstable distributed algorithm's convergence. With the increasing proportion of wind power in IEGS, fluctuating wind power penetration highly affects the convergence of distributed optimization solutions, resulting in uncontrollable optimization time of the distributed algorithm. Accordingly, this paper investigates the impact of wind fluctuation and uncertainty on distributed IEGS optimization and proposes a novel fast distributed co-optimization framework. Specifically, an adaptive alternating direction method of multipliers (ADMM) is developed to accommodate wind fluctuation. Based on designed rules, the penalty parameter is updated in every step to maximize the gradient of the optimization objective. The experiments are conducted using real wind power generation data sourced from a wind farm in Australia and a classical IEGS framework composed of the IEEE 24-bus electricity system and a 12-node natural gas system. Compared to original ADMM and residual balancing ADMM, the proposed framework achieves an average reduction of 0–91.4% in the number of iterative steps for multiple solution iterations across various scenarios, with a corresponding decrease in the standard deviation by 13.8%–93.0%. • Fast and stable convergence of IEGS distributed optimization problem. • ADMM with an adaptive penalty parameter guarantees the convergence. • Update rate further improves the sensibility of the penalty parameter. • Mean value and standard deviation of running iterations significantly decrease. [ABSTRACT FROM AUTHOR]

Hydrogen is considered a key energy vector and carrier for the decarbonization of global energy systems. However, the economics of green hydrogen systems hinder their widespread application. This paper presents a techno-economic analysis of a green hydrogen production system using high-temperature water electrolysis integrated with a concentrated solar power system (CSP-SOEC) for Western Australia. Real-time solar resource data with 30-min resolution for a typical meteorological year were used to assess the performance of the entire system. The intermittent nature of solar resources is accounted for by integrating the system with a thermal energy storage medium and performing the analysis in off-design mode. The validity of the electrolysis stack model is crucial for overall system performance, which was confirmed through experimental testing conducted on a 15-cell stack. The system was designed to generate a 1 MWe output, and the results showed that a field area of 29,000 m2 and thermal energy storage capacity of 382,500 kWh can fulfil the design criteria. The system generates 0.86 tonne/day of hydrogen at a cost of 8.87 US$/kg-H 2 with a solar-to-hydrogen efficiency of 13.80 %. The cost breakdown revealed that the storage medium has the most significant contribution. Moreover, the sensitivity of the system to the production capacity was analyzed, which showed that larger-scale hydrogen production systems have the potential to further reduce the cost. An 8 MWe system has the capacity to produce 7.18 tonne/day of hydrogen at a cost of 6.1 US$/kg-H 2. The molten salt is currently utilized only 39.3 % for the hydrogen production process. To optimize resource utilization, a cogeneration system is devised and assessed for simultaneous steam and hydrogen production. The results reveal that the cogeneration system can achieve an LCOH reduction of 9 % by reaching 8.07 US$/kg-H 2. These findings are invaluable for academic and industry stakeholders in making informed decisions and fostering the green hydrogen sector in Australia. • A techno economic assessment of green H 2 production for Australia is done. • The electrolysis model is corroborated using experimental results. • The cost of green H 2 for such system in Australia is 8.87 US$/kg-H 2. • Higher production capacity favors the system economics. • The cogeneration system reduces the cost by 9 %. [ABSTRACT FROM AUTHOR]

In response to the high uncertainty associated with residential net load due to the coupling of distributed photovoltaic generation and user demand, this paper proposed a novel cluster-based stacking ensemble learning model for net load interval prediction. Firstly, the k-means algorithm is employed to discover the similarity in user electricity consumption patterns. Then, a RIME optimization algorithm with local enhancement (LRIME) is developed to optimize the parameters and weights of the base learners in stacking ensemble learning. Subsequently, base learners with strong predictive capabilities and significant diversity are chosen as the first-layer predictive models, extreme learning machine (ELM) is utilized as the second-layer predictive model, ultimately resulting in the proposed stacking ensemble learning prediction model. And utilizing the bootstrap method to fit the volatility of point predictions, different prediction intervals are obtained at varying confidence levels, aiming to quantify the integrated uncertainty in photovoltaic generation and load. Testing on the open Ausgrid electricity load data in Australia provided robust validation of the proposed method's effectiveness. In comparison with other outstanding prediction models, the proposed ensemble model can effectively capture the uncertainty in integrating photovoltaic generation and user load. • A RIME optimization algorithm with local enhancement (LRIME) is developed. • A stacking ensemble model is proposed for residential net load interval prediction. • The proposed model demonstrates higher forecasting accuracy and reliability. [ABSTRACT FROM AUTHOR]

Fifth-generation district heating and cooling (5GDHC) systems have the potential to provide simultaneous heating and cooling, allowing for energy exchange between users with different needs. However, their viability in mild climates with a higher share of cooling demand remains unclear. In this paper, we propose a framework for assessing the engineering, economic and environmental performance of a 5GDHC system compared to a state-of-the-art business-as-usual solution and demonstrate it through a practical case study for a university campus in Melbourne, Australia. When accessible heat sources and sinks are available, the 5GDHC system provides a cost-effective solution, with annual cost savings between 9 and 29 % and GHG emissions reduction between 25 and 58 % compared to an already advanced business-as-usual system. Additionally, by using peak off-peak tariffs and an hourly emission factor for the electricity consumed, we demonstrate the 5GDHC operational flexibility in pursuing different objectives, such as minimising cost or emissions, respectively. The results suggest that 5GDHC systems are an economically and environmentally viable solution in milder climates, and a successful implementation of 5GDHC in Australia can create new market opportunities and pave the way for its adoption in other countries with similar climatic conditions and no established history of district heating systems. • 5GDHC systems are analysed in a new context with a milder climate. • A multi-dimensional assessment framework is proposed. • The framework is demonstrated through a practical case study in Melbourne, Australia. • Flexibility is assessed through varying electricity tariffs and emission factors. • The strong performance of the 5GDHC system offers insights into similar contexts. [ABSTRACT FROM AUTHOR]

As more and more photovoltaic (PV) systems are integrated into the grid, the intelligent operation of the grid system is facing significant challenges. Therefore, accurately forecasting PV power output at various time scales is particularly urgent. To meet this demand, this paper proposes an LSTM-Informer model based on an improved Stacking ensemble algorithm (ISt-LSTM-Informer). The proposed model improves the k-fold cross validation in the traditional Stacking algorithm to a time-series cross validation for integrating time-series forecasting models. Simultaneously, it utilizes long short-term memory (LSTM) and Informer as the base models. By integrating the advantages of the two base models, the ISt-LSTM-Informer achieves accurate short and medium-term PV power forecasting. To validate the effectiveness of the model, a historical dataset from a PV system located in Uluru, Australia, is used for various types of experiments. Among them, comparative experiments validate the superiority of the model. Compared with five other methods, the ISt-LSTM-Informer obtains 21 optimal results for the four evaluation metrics of RMSE, MAE, MAPE, and R 2 across eight forecasting time scales. In addition, different combinations of base models are conducted to verify the advantages of the Stacking ensemble algorithm and the two base models, respectively. • A novel multi-timescale photovoltaic power forecasting model is proposed. • Time-series cross validation is introduced into the Stacking algorithm. • LSTM and Informer are utilized as the base models of the Stacking algorithm. • Various methods are compared to verify the proposed model's effectiveness. • The model's predictive accuracy is illustrated through various visualization methods. [ABSTRACT FROM AUTHOR]

The accurate prediction of power load is helpful to make reasonable power generation plans and scientific dispatching schemes and achieve the goal of energy saving and emission reduction. There are many factors influencing the power load, and there may be a nonlinear relationship between the power load and these influencing factors. A new fractional multivariate grey Bernoulli model, referred to as MFGBM (q, r, N), is developed in this article for the short-term prediction of power load. The fractional differential equation and fractional accumulation generation are integrated into MFGBM (q, r, N). Second, this paper improves the grey wolf algorithm to better optimize many parameters in the model. The algorithm is improved by using a chaotic Tent map to optimize the initial population composition, adding inertia weights to change the position vector of the grey wolf, and introducing a nonlinear function and Lévy flight to enhance local exploitation and global exploration ability. Finally, this paper selects the daytime and nighttime power loads in Australia and takes the electricity price, humidity, and temperature as the influencing factors to validate the prediction capability of MFGBM (q, r, N). Our findings indicate that MFGBM (q, r, N) is highly applicable to short-term power system prediction. • The multivariate grey Bernoulli model is proposed to predict short-term power load. • The improved grey wolf algorithm is proposed to avoid falling into local optimum. • 3. The non-singular Caputo fractional derivative and Laplace transform are introduced. • The fractional-order operator is introduced to reduce the influence of randomness. [ABSTRACT FROM AUTHOR]

With the ever-increasing level of bidding freedom bestowed to participants in deregulated electricity markets, bidding strategies have become more diversified and complicated, inevitably giving rise to the growth of market uncertainties. Some researchers have developed tools to predict the bidding behaviors of generation companies (GENCOs) considering uncertainties. However, there still remains a gap in enhancing the performance of probabilistic Bidding Behavior Forecasting (BBF) and understanding the sources of bidding uncertainties in electricity markets. This paper proposes a holistic Bayesian Deep Learning (BDL) framework based on Accurate Variational Inference (AVI) to capture both aleatoric uncertainty and epistemic uncertainty, two important uncertainties in bidding behaviors. The introduced framework also procures higher BBF accuracy and reasonable computation cost compared with existing techniques. Derivatives of GENCOs' bidding uncertainty are ascertained by implementing a new metric to quantify the impact of influence factors. A numerical experiment is conducted using data from National Electricity Market (NEM) in Australia to demonstrate the performance of the proposed framework. • A BDL framework based on AVI is proposed for analyzing bidding uncertainty. • A modified BNN is constructed to perform BBF with higher efficiency. • The epistemic uncertainty and the aleatoric uncertainty in BBF are captured. • The derivatives of bidding uncertainty are ascertained. [ABSTRACT FROM AUTHOR]

Load forecasting is a challenging task in power markets that require attention in generating accurate and stable load to deal with planning and management strategies. In past few years, several intelligence-based models have been introduced for precise load forecast. Among them, artificial neural network (ANN) seems more effective and capable to handle the non-linear behavior of load and generates an accurate forecast. However, it suffers from overfitting problem thus reducing the accuracy of load forecasts. To overcome this problem, a hybrid methodology namely ANN-IEAMCGM-R, for short-term load forecast is proposed in this paper. ANN is integrated with an enhanced evolutionary algorithm (IEAMCGM-R) to find optimal network weights. This evolutionary algorithm is composed of improved environmental adaptation method with real parameters (IEAM-R) and our proposed Controlled Gaussian Mutation (CGM) method to bring greater diversity within the population resulting in a higher convergence of solutions. The electric load data from the New England Power Pool (NEPOOL, ISO New England) and Australian Energy Market Operator (New South Wales (NSW), Australia) have been used to illustrate the efficacy of the proposed hybrid methodology. Results show that the proposed hybrid methodology generates higher accuracy than other state-of-the-art algorithms. • Proposal of Controlled Gaussian Mutation for enhancing the diversity of IEAM-R. • Proposal of enhanced optimization algorithm, namely IEAMCGM-R. • A hybrid methodology is presented for load forecasting. • Experimental results show that ANN-IEAMCGM-R generates least forecasting error. • Statistical test results ensures the statistical signi_cance of forecasted load. [ABSTRACT FROM AUTHOR]

Abstract Short-term electrical load forecasting is an important part in the management of electrical power because electrical load is an extreme, complex non-linear system. To obtain parameter values that provide better performances with high precision, this paper proposes a new hybrid electrical load forecasting model, which combines ensemble empirical mode decomposition, extreme learning machine, and grasshopper optimization algorithm for short-term load forecasting. The most important difference that distinguishes this electrical load forecasting model from other models is that grasshopper optimization can search suitable parameters (weight values and threshold values) of extreme learning machine, while traditional parameters are selected randomly. It is applied in Australia electrical load prediction to show its superiority and applicability. The simulation studies are carried out using a data set collected from five main states (New South Wales, Queensland, Tasmania, South Australia and Victoria) in Australia from February 1 to February 27, 2018. Compared with all considered basic models, the proposed hybrid model has the best performance in predicting electrical load. Graphical abstract Image 1 Highlights • Grasshopper algorithm is incorporated in extreme learning machine. • A hybrid decomposition-and-ensemble model is constructed for forecasting. • The proposed model can predict electricity demands with high accuracy. [ABSTRACT FROM AUTHOR]

Abstract Australia's east coast gas market has faced transformational shifts in demand with the commencement of three Liquefied Natural Gas facilities in Queensland. Faced with risks of high domestic prices and potential gas shortages, political intervention has been considered as a possible solution. This paper investigates the impact of network interconnectivity on domestic gas prices by employing a long-term planning model underpinned by mathematical optimisation. At optimal system cost, improved network interconnectivity can provide material and sustained price reductions for the gas market with potential flow-on reductions to the electricity market. Increased connectivity is shown to deliver reductions of over $2/GJ in average gas prices across the eastern seaboard, with a subsequent reduction in electricity prices across all mainland National Electricity Market (NEM) regions. The results also highlight the need to unlock new supply as new transmission projects, though having the potential to reduce gas prices through market connectivity, rely on adequate supply to meet long term demand, and sustain market balance. Highlights • Improved network interconnectivity can provide material and sustained price reductions. • Potential reductions of over $2/GJ in average gas prices on the eastern seaboard. • Wallumbilla-Newcastle Gas Pipeline seen to have most value from system and investor perspective. • Flow-on electricity price reductions up to 19% depending on generator bidding behaviour. [ABSTRACT FROM AUTHOR]

Predicting electricity demand data is considered an essential task in decisions taking, and establishing new infrastructure in the power generation network. To deliver a high-quality electricity demand prediction, this paper proposes a hybrid combination technique, based on a deep learning model of Convolutional Neural Networks and Echo State Networks, named as CESN. Daily electricity demand data from four sites (Roderick, Rocklea, Hemmant and Carpendale), located in Southeast Queensland, Australia, have been used to develop the proposed hybrid prediction model. The study also analyzes five other machine learning-based models (support vector regression, multilayer perceptron, extreme gradient boosting, deep neural network, and Light Gradient Boosting) to compare and evaluate the outcomes of the proposed deep learning approach. The results obtained in the experimental study showed that the proposed hybrid deep learning model is able to obtain the highest performance compared to other existing models developed for daily electricity demand data forecasting. Based on the statistical approaches utilized in this study, the proposed hybrid approach presents the highest prediction accuracy among the compared models. The obtained results showed that the proposed hybrid deep learning algorithm is an excellent and accurate electricity demand forecasting method, which outperformed the state of the art algorithms that are currently used in this problem. [Display omitted] • A Hybrid deep learning model is proposed for daily electricity demand prediction. • Convolutional Neural Networks and Echo State Networks are integrated into a single forecasting algorithm. • The proposed deep learning-based approach is excellent in electricity demand prediction. • The proposed algorithm has practical use in inter-daily electricity demand prediction. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) power forecasting (PVPF) plays an important role in the scheduling and operation of modern power systems. Considering the highly varying and complex features of PVPF, this paper constitutes a novel hybrid deep learning forecasting method. A similar day selection method based on the levy-flight beetle antennae search algorithm is proposed to select historical days similar to the forecast day from the real-time massive data. The Pearson correlation coefficient method is utilized to select the main meteorological factors while wavelet packet decomposition is used to decompose and reconstruct the original PV power into a series of sub-signals. A deep learning model taking the sub-signals of similar days as network inputs is established with a group of gated recurrent units (GRU), where the hyperparameters of each GRU network are effectively optimized. The forecasting result of each sub-signal is integrated to obtain the final forecasting PV power value. The simulation regarding the 5-min ahead PVPF is carried out based on a real-world dataset from Alice Spring, Australia. The simulation comparisons indicate that the proposed hybrid deep learning method outperforms other competitive PVPF methods in terms of both forecasting accuracy and computational efficiency. • Application of SDS-based GRU model to PV power forecasting. • A similar day selection method is developed based on the Levy-flight BAS algorithm. • Application of WPD to decompose the PV signal to extract the frequency feature. • Levy-flight BAS is used to optimize the hyperparameters of GRU. [ABSTRACT FROM AUTHOR]

Value-added trade has increased rapidly in recent decades in the context of globalisation. This paper aims to measure the energy embodied in the value-added trade and the ecological relationships in the networks of the international construction industries. A hybrid method which combines ecological network analysis and the input–output method has been adopted. The direct energy intensities in most countries ranged from 0 to 3.0 trillion joules per million US$ and the dominant relationships were control and exploitation in the selected 15 years. For the construction sector, the negative relationships were dominant at 63.9% in Australia and 79.19% in China. The ratio of positive relationships increased from 24.3% to 31.5%, while the ratio of negative relationships declined from 75.7% to 68.5% over the 15 years. The mutualism index ranged from 0.72 to 0.98 over the 15 years, maintaining a value less than 1. In contrast, the synergism index had an increase of 12.2 over the 15 years. The results indicate that both direct energy intensity and the value-added ratio are important factors in relation to the energy embodied in value-added trade. Sectoral influence on the national industrial system has been interpreted in this paper to stimulate further research. • The energy embodied in the value-added trade of the international construction industries. • The combination of ecological and economic connections in an analysis of value-added trade. • The influence of specific construction sectors on the network depending on flow capacity. • The exploitation and control relationships that dominate the value-added trade system. • The balance between the value-added ratio and energy intensity in sustainable development. [ABSTRACT FROM AUTHOR]

Based on extensive research work on the application of energy trading models, energy sharing strategies are applied to smart homes. This paper addresses an energy sharing platform as an energy storage solution for renewable distributed generation (RDG) integration in microgrids. An energy-sharing service platform for renewable distributed generation smart home applications is proposed. This platform aims to suit various kinds of energy users, energy generation and energy storage devices. It is based on a decentralized approach, which means a central operator is not required. In the proposed energy management model, the up-reserve (UR) is defined as the maximum state of charge (SOC) value in every participant. The conditional Value-at-Risk (CVaR) method is used to undertake risk analysis and UR calculation. In a shared strategy, the Conditional Value at Risk (CVaR) method is used for risk analysis and energy operations. Moreover, this energy sharing platform refers to a trading mechanism called the call-auction method, which is similar to the opening auction trading in the stock market. The objective of the trading principle is to achieve the maximum trading volume; thus the platform facilitates an increase in the utilization of RDG. This paper details the rigorous proof of the energy sharing strategy used by the algorithm in the energy trading model. A rigorous proof of the algorithm is also given. Furthermore, the proposed energy sharing strategy has been compared with existing offerings of the frequency control ancillary services (FCAS). The historical data in Australia's electricity market is used to verify the proposed approach. Moreover, the proposed energy sharing platform compares with the FCAS provision. Simulation results show that the surplus energy can be shared among participants who hold different quantities of demand and generation/storage. Therefore, the proposed approach is a cost-effective energy storage solution, especially when energy storage capital cost is high. • An energy sharing platform is presented for renewable distributed generation. • An independent energy management strategy can be customized for each participant. • The call auction model of stock exchange is cited in the energy sharing platform. • The maximum transaction volume is applied in the call auction model. • The award of energy sharing can be self-ruled by participants. [ABSTRACT FROM AUTHOR]

This paper is the first of a two part study that considers long term, least cost, GHG (greenhouse gas) abatement pathways for an electricity system. Part 1 formulates a planning model to optimise these pathways and presents results for a single reference scenario. Part 2 applies this model to different scenarios and considers the policy implications. The planning model formulated has several constraints which are important when considering GHG abatement and widespread uptake of intermittent renewable generation. These constraints do not appear to have been integrated into a single planning model previously, and include constraints on annual GHG emissions, unit commitment, storage, plant dynamics and intermittent renewable generation. The model prioritises overall abatement, and therefore does not include a price on carbon or support for any particular technology. The model is applied to Australia's NEM (National Electricity Market) as an example. All model inputs – for technologies, demand, and meteorological data – are from the most current and authoritative public sources. As such, the results are transparently derived and both policy and technology neutral. For the reference scenario presented here, key technologies are wind from 2015, gas generation from 2030, and solar generation from 2040. [ABSTRACT FROM AUTHOR]

This paper is the second of a two part study that considers least cost, greenhouse gas abatement pathways for an electricity system. Part 1 of this study formulated a model for determining these abatement pathways, and applied this model to Australia's NEM (National Electricity Market) for a single reference scenario. Part 2 of this study applies this model to different scenarios and considers the policy implications. These include cases where nuclear power generation and CCS (carbon capture and storage) are implemented in Australia, which is presently not the case, as well as a more detailed examination of how an extended, RPS (renewable portfolio standard) might perform. The effect of future fuel costs and different discount rates are also examined. Several results from this study are thought to be significant. Most importantly, this study suggests that Australia already has utility scale technologies, renewable and non-renewable resources, an electricity market design and an abatement policy that permit continued progress towards deep greenhouse gas abatement in its electricity sector. In particular, a RPS (renewable portfolio standard) appears to be close to optimal as a greenhouse gas abatement policy for Australia's electricity sector for at least the next 10–15 years. [ABSTRACT FROM AUTHOR]

This paper evaluates the economic feasibility of SPCC (solar-assisted post-combustion carbon capture) at a coal power plant near Sydney, Townsville and Melbourne for implementation in 2020. A review of Australia's carbon and renewable energy policy is performed first, after which the merits of application of each incentive for SPCC are compared. Incentives such as subsidies, preferential discount rate, carbon tax and RECs (renewable energy certificates) are evaluated. No single incentive on its own, and within reasonable limits, is able to provide a favourable economic performance for power plant based renewable powered CCS (carbon capture and storage). However, once the compounded effect of carbon tax and electricity price increase is taken into account, a carbon price as low as A$ 25/tonne-CO 2 and a REC price as low as A$ 35/MWh e is able to produce a positive net benefit in Townsville, while a slightly higher carbon price and REC price is required for Sydney. Melbourne performs poorest amongst the three locations, where substantially higher incentives would be needed for economic feasibility. The level of incentives required for SPCC to become feasible are found to fall in the range of current Australian policy incentives accessible to renewable energy technologies for stand-alone power generation. [ABSTRACT FROM AUTHOR]

The Asia-Pacific has experienced prodigious growth in energy use and is by far the world's largest greenhouse-gas emitting region. Australia has played a leading role in meeting the region's energy and resource needs, becoming the world's largest exporter of coal, liquefied natural gas, iron ore, and alumina. Our analysis shows that these exports are tied to sizeable consequential emissions at the point of use or processing, accounting for about 8.6% of the total greenhouse gas emissions of the Asia-Pacific. The paper investigates three pathways by which Australia could instead export zero-carbon energy and products: direct exports of renewable electricity via sub-sea cables, exports of zero-carbon fuels such as green hydrogen, and the export of "green" metals processed from Australian ores using renewable energy. Carrying out robust, high-level calculations we find that Australia has the land and renewable energy resources to become a key exporter of these commodities. Realization of this potential relies on ongoing cost reductions, growing demand-side interest linked to meeting ambitious emission reduction targets in the region, and the development of cross-border frameworks for clean energy trade. If it were to achieve this goal, Australia could make a sizeable contribution to regional decarbonization via renewable-energy based exports. • Consequential emissions from existing key Australian exports are estimated. • The potential for replacement zero-carbon exports from Australia is analyzed. • Land, water, and energy requirements for new zero-carbon exports are calculated. • There is the potential to reduce Asia-Pacific greenhouse gas emissions by about 8.6%. • Around 2% of Australia's land mass would be required for solar and wind farms. [ABSTRACT FROM AUTHOR]

Ground source heat pump systems (GSHP) for residential building heating, cooling, and hot water are highly energy efficient but capital intensive when sized for peak demands. The use of supplemental sources of energy with GSHP systems enables improved life-cycle economics through the reduction in the size and cost of the GSHP components. This paper investigates the life-cycle economics of hybrid solar-assisted ground source heat pump systems (SAGSHP) using simulations validated from field data. The economics and optimal sizing of SAGSHP systems for heating dominant climates in four locations in Australia and ten locations elsewhere are evaluated in order to explore the suitability and relative merits of SAGSHP systems in a range of heating dominant climates. In locations having high or moderate levels of solar irradiation, high electricity prices, and high or moderate gas prices, SAGSHP systems are shown to have the lowest life cycle cost amongst alternatives, with predicted savings of up to 30%. • A comprehensive investigation of the design and performance of hybrid GHSPs. • A comparison of hybrid GSHPs and conventional systems on cost and CO 2 emissions. • Effects of local climatic and economic conditions are evaluated for 14 global cities. • Hybrid GSHPs have shown to be the most economical system for 10 out of 14 locations. • Local energy price is a key factor that influences the feasibility of hybrid GSHPs. [ABSTRACT FROM AUTHOR]

Abstract Coal plays a key role in China's economy as a dominant primary energy resource. In this paper, we provide probability density forecasts for weekly steam coal prices in China based on daily factors such as renewable energy source, Daqing oil, Japanese natural gas, Australia steam coal prices, coal mining industry index, A-share power sector index, A-share index, coal industry index, and temperature. The empirical results show that the influence of temperature lasts longer than other factors, while the Australia steam coal prices, renewable energy source and A-share index are the three best predictors for steam coal prices. It is also shown that the high-frequency factors are useful to forecast steam coal prices and that considering the nonlinearity of coal prices can improve the forecast accuracy by about 22%. We further provide the probability density forecasts for steam coal prices based on the influence of all the selected factors, the results suggest that our proposed method can provide accurate and satisfying probability density forecasts. Given these results, the policy-makers can make effective strategies which can not only adjust the energy structure but also ensure economic growth. • High-frequency factors are useful to forecast coal prices. • C-MIDAS-X model considers the high-frequency factors and the nonlinearity of coal prices. • The probability density forecasts are satisfying. [ABSTRACT FROM AUTHOR]

This study aims to analyse whether the Australian commitment to the Paris Agreement in 2016 and national energy policies will have a visible effect in decreasing the historical dependence of the NEM (National Electricity Market) on fossil fuels by 2030 and consequently decreasing the carbon emissions generated. In this paper, we model the 2030 electricity mix in the NEM in Australia. EnergyPlan , which is the modelling tool adopted for this study, is described and a reference model is conceived and calibrated based on the 2018 electricity situation on the NEM. The simulations are implemented considering three different supply scenarios and three potential electricity demands, to evaluate how different variables can influence the NEM electricity mix by 2030. This study concludes that by 2030 wind and solar power (including rooftop-PV systems) will be the major renewable energy contributors in the NEM, as opposed to 2018 where hydropower represented the main renewable energy source. Nonetheless, the electricity system in 2030 will continue to rely on fossil fuels, which will account for more than 50% of the final energy mix in every scenario, depicting that new efforts will still be needed beyond 2030. • We foresee the electricity mix in Australia by 2030. • We study three generation and three demand scenarios. • Fossil fuels will still account for more than 50% of the final electricity mix. • We expect a visible CO2 emissions reduction, due to the bet in RES in recent years. [ABSTRACT FROM AUTHOR]

Solar power forecasting is important for the reliable and economic operation of power systems with high penetration of solar energy. The solar power forecasts for the day-ahead time horizon are more erroneous than the hour-ahead time horizon. Numerical weather prediction (NWP) variables such as irradiance, cloud cover, precipitation etc. are used as input to day-ahead forecasting models. The uncertainty in NWP varies with weather conditions. Different forecasting algorithms based on a single method are available in the literature. Combination of individual forecasting algorithms increases the accuracy of the forecasts. However, the combined-forecast has yet not been analysed much in the area of day-ahead solar power forecasting. This paper thus explores different combined-forecast methods such as mean, median, linear regression and non-linear regressions using supervised machine learning algorithms. The number of models required for day-ahead solar power forecasts is studied. One for all hour (same) or separate models for each hour of the day are possible. The effects of retraining frequency on the performance of the forecasting models, which is important for the computational burden of the system, are also studied. Forecasting algorithms are applied to three solar plants in Australia. • Different combined-forecasts for day-ahead solar power forecasting were explored. • The number of forecasting models required for solar power forecasting was analysed. • The effect of different retraining frequencies for day-ahead solar power forecasting models was examined. • Several machine-learning based forecasting models were implemented. [ABSTRACT FROM AUTHOR]

Liquefied Natural Gas (LNG) as a fuel is seen as a solution to curb harmful emissions in shipping and its quick uptake is now significant. As heat leaks into the LNG fuel tanks, composition of the vapor and liquid phase change. This process, known as ageing, alters the fuel properties and can impact negatively the main engine performance. In this paper, a dynamical model to assess the methane number at engine inlet from the bunkered composition is presented. The fuel system is made of a 40 ft container tank and two heat exchangers. The model considers the two-phase real mixture in the tank and solves explicitly the heat and mass conservation laws. Then a strategy is developed to avoid "off-spec" methane number at engine inlet. The approach is tested on two bunkered compositions, one from Libya and one from Australia. The simulations have been conducted for a concept LNG/wind electric ship using multiple removable 40 ft container LNG tanks. Results show that with the proposed mixing strategy, the methane number remains above its limit value at engine inlet, insuring optimal operating conditions of engines. • A model to assess quality of natural gas at engine inlet over a voyage is proposed. • A control strategy maintains the methane number above 80 (optimal operating point). • The control strategy relies on Forced Boil-off Gas and composition mixing. • The case study is a concept ship using removable containerized LNG tanks. [ABSTRACT FROM AUTHOR]

Challenging frequency control issues, such as the reliability and security of the power system, arise when increasing penetration levels of inverter-interfaced generation are imposed. As a result of the displacement of convention generation in favour of renewable energy sources, the reduction of frequency response capabilities can be seen. A promising method of overcoming the aforementioned challenges is to utilise Battery Energy Storage Systems (BESS), which provides frequency support by injecting instantaneous power to the grid and back up the conventional generation systems. However, large battery systems increase the cost while inadequate battery capacities result in poor performance. This paper, therefore, proposes an approach for finding the optimum BESS size for an islanded microgrid power system. The determination of the optimum BESS size is based on an existing case study, under which the most severe contingencies of generation loss and load loss have been accounted for, as well as different levels of penetration of renewable energy sources. As a result of using meta-heuristic optimization (Grey Wolf Optimization), the constraint optimization problem has been identified as BESS sizing. Through the use of real-time simulation DIgSILENT PowerFactory software, estimated BESS size can be applied to a standalone microgrid to test the frequency of support capabilities. The simulation has made it apparent that through the selection of the optimum BESS size, the system frequency response is not only mitigated, but improved. • Proposing a novel optimization model for determining the optimal size of the battery devices of standalone microgrids. • Propose an intelligent approach to mitigate the frequency response of standalone microgrids based on hybridization sources. • Combining the advantages of the BESS controller and the meta-heuristic Grey Wolf Optimizer (GWO) algorithm. • Validate the system performance based on a real case study under different scenarios. [ABSTRACT FROM AUTHOR]

Incentive schemes and policies play an important role in reducing carbon emissions from electricity generation. This paper investigates designing tax and subsidy incentives towards a reliable and low emission electricity market, using Australia's National Electricity Market as a case study. In this work, a novel framework is proposed to design interactive tax/subsidy incentives on both emission reduction and resource adequacy in competitive electricity markets as a game model. In our model, market participants decide on their capacity expansion/retirement strategies considering the impact of designed incentive schemes on their long-term operation such that the desired levels of emission reduction and fast response generation are achieved in the network. The simulation results for Australia's electricity market during 2017–2052, indicate the necessity of incentive policies, in spite of the cost reduction trajectory for renewable technologies, to reach the emission intensity reduction above 45% in the market by 2052. In 80% emission intensity reduction scenario, the designed incentive schemes highly encourage the investment on synchronous renewables, +17 GW, storage technologies, +15.7 GW, and transmission lines, +1.6 GW, to support high additional penetration of Variable Renewable Energy, wind and solar, +39 GW, which paves the way to transition to a green and reliable electricity market. • Our model designs emission abatement and reliability schemes in electricity markets. • The schemes are designed considering the strategic behaviour of market players. • The designed emission policies must be dynamic due to outage of aging generators. • Reliability schemes are taken into account to enable high penetration of renewables. • Emission abatement schemes might lower the average prices at some years. [ABSTRACT FROM AUTHOR]

To a large extent, electricity price prediction is a daunting task because it depends on factors, such as weather, fuel, load and bidding strategies etc. Those features generate a lot of fluctuations to electricity price. As a type of RNN, LSTM has a good performance on processing time series data as well as some nonlinear and complex problems. To explore more accurate electricity price forecasting approach, in this paper, a new hybrid model based on wavelet transform and Adam optimized LSTM neural network, denoted as WT-Adam-LSTM, is proposed. After the wavelet transform, nonlinear sequence of electricity price can be decomposed and processed data will have a more stable variance, and the combination of Adam, one of efficient stochastic gradient-based optimizers, and LSTM can capture appropriate behaviors precisely for electricity price. This study presented four cases to verify the performance of the hybrid model, and the dataset from New South Wales of Australia and French were adopted to illustrate the excellence of the hybrid model. The results show that the proposed model can significantly improve the prediction accuracy. • Long short-term Memory (LSTM) is applied for electricity price forecasting. • Adam is used as optimizer for LSTM. • Pauta criterion and Min-max normalization are used as preprocessing methods for price data. • Wavelet transform is used to decompose the electricity price series into a set of better-performing constitutive series. • Four cases proved that proposed WT-Adam-LSTM outperforms the existing models reported in the literature. [ABSTRACT FROM AUTHOR]