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A large proportion of photovoltaic (PV) power generation is connected to the power grid, and its volatility and stochasticity have significant impacts on the power system. Accurate PV power forecasting is of great significance in optimizing the safe operation of the power grid and power market transactions. In this paper, a novel dual-channel PV power forecasting method based on a temporal convolutional network (TCN) is proposed. The method deeply integrates the PV station feature data with the model computing mechanism through the dual-channel model architecture; utilizes the combination of multihead attention (MHA) and TCN to extract the multidimensional spatio-temporal features between other meteorological variables and the PV power; and utilizes a single TCN to fully extract the temporal constraints of the power sequence elements. The weighted fusion of the dual-channel feature data ultimately yields the ideal forecasting results. The experimental data in this study are from a 26.52 kW PV power plant in central Australia. The experiments were carried out over seven different input window widths, and the two models that currently show superior performance within the field of PV power forecasting: the convolutional neural network (CNN), and the convolutional neural network combined with a long and short-term memory network (CNN_LSTM), are used as the baseline models. The experimental results show that the proposed model and the baseline models both obtained the best forecasting performance over a 1-day input window width, while the proposed model exhibited superior forecasting performance compared to the baseline model. It also shows that designing model architectures that deeply integrate the data input method with the model mechanism has research potential in the field of PV power forecasting. [ABSTRACT FROM AUTHOR]

Recent events within the Australian National Electricity Market have demonstrated that the system of an energy-only market (a market that only compensates power that has been produced) is no longer fit for purpose. The rate of change in installed capacity and generation requires better planning to ensure reliability is maintained at the lowest total system cost during the transition to net zero. Australian National Electricity Market participants will need sufficient incentives and confidence to invest in new capacity. This paper assesses a "no constraints" scenario and recommends a range of policy and market mechanisms that could be utilized to achieve a net zero National Electricity Market in Australia by 2050. This paper adopts the perspective of total system cost, which allows multiple factors relating to decision-making to be incorporated. In the absence of a carbon price, this paper seeks to put forward technology-based policy and market mechanisms to incentivise the changes required. The "Modelling Energy and Grid Services" model used in this study has shown that this "no constraints" future grid will need to contain approximately 100 GW of variable renewable energy, almost 20 GW of firm, low-emissions generation, such as carbon capture, utilisation and storage, bioenergy with carbon capture and storage, hydroelectric power, or nuclear power. It will also require more than 10 GW of storage, including pumped hydro energy storage and other energy storage technologies, and over 30 GW of firm, dispatchable peaking plants, including thermal power generation. [ABSTRACT FROM AUTHOR]

Accurate electricity price forecasting (EPF) can provide a necessary basis for market decision making by power market participants to reduce the operating cost of the power system and ensure the system's stable operation. To address the characteristics of high frequency, strong nonlinearity, and high volatility of electricity prices, this paper proposes a short-term electricity price forecasting model based on a two-layer variational modal decomposition (VMD) technique, using the sparrow search algorithm (SSA) to optimize the long and short-term memory network (LSTM). The original electricity price sequence is decomposed into multiple modal components using VMD. Then, each piece is predicted separately using an SSA-optimized LSTM. For the element with the worst prediction accuracy, IMF-worst is decomposed for a second time using VMD to explore the price characteristics further. Finally, the prediction results of each modal component are reconstructed to obtain the final prediction results. To verify the validity and accuracy of the proposed model, this paper uses data from three electricity markets, Australia, Spain, and France, for validation analysis. The experimental results show that the proposed model has MAPE of 0.39%, 1.58%, and 0.95%, RMSE of 0.25, 0.9, and 0.3, and MAE of 0.19, 0.68, and 0.31 in three different cases, indicating that the proposed model can well handle the nonlinear and non-stationarity characteristics of the electricity price series and has superior forecasting performance. [ABSTRACT FROM AUTHOR]

Conventional fossil-fuel-based power systems are undergoing rapid transformation via the replacement of coal-fired generation with wind and solar farms. The stochastic and intermittent nature of such renewable sources demands alternative dispatchable technology capable of meeting system stability and reliability needs. Battery energy storage can play a crucial role in enabling the high uptake of wind and solar generation. However, battery life is very sensitive to the way battery energy storage systems (BESS) are operated. In this paper, we propose a framework to analyse battery operation in the Australian National Electricity Market (NEM) electricity spot and contingency reserve markets. We investigate battery operation in different states of Australia under various operating strategies. By considering battery degradation costs within the operating strategy, BESS can generate revenue from the energy market without significantly compromising battery life. Participating in contingency markets, batteries can substantially increase their revenue with almost no impact on battery health. Finally, when battery systems are introduced into highly volatile markets (such as South Australia) more aggressive cycling of batteries leads to accelerated battery aging, which may be justified by increased revenue. The findings also suggest that with falling replacement costs, the operation of battery energy systems can be adjusted, increasing immediate revenues and moving the battery end-of-life conditions closer. [ABSTRACT FROM AUTHOR]