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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]

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 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]