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Accurate and reliable PV power probabilistic-forecasting results can help grid operators and market participants better understand and cope with PV energy volatility and uncertainty and improve the efficiency of energy dispatch and operation, which plays an important role in application scenarios such as power market trading, risk management, and grid scheduling. In this paper, an innovative deep learning quantile regression ultra-short-term PV power-forecasting method is proposed. This method employs a two-branch deep learning architecture to forecast the conditional quantile of PV power; one branch is a QR-based stacked conventional convolutional neural network (QR_CNN), and the other is a QR-based temporal convolutional network (QR_TCN). The stacked CNN is used to focus on learning short-term local dependencies in PV power sequences, and the TCN is used to learn long-term temporal constraints between multi-feature data. These two branches extract different features from input data with different prior knowledge. By jointly training the two branches, the model is able to learn the probability distribution of PV power and obtain discrete conditional quantile forecasts of PV power in the ultra-short term. Then, based on these conditional quantile forecasts, a kernel density estimation method is used to estimate the PV power probability density function. The proposed method innovatively employs two ways of a priori knowledge injection: constructing a differential sequence of historical power as an input feature to provide more information about the ultrashort-term dynamics of the PV power and, at the same time, dividing it, together with all the other features, into two sets of inputs that contain different a priori features according to the demand of the forecasting task; and the dual-branching model architecture is designed to deeply match the data of the two sets of input features to the corresponding branching model computational mechanisms. The two a priori knowledge injection methods provide more effective features for the model and improve the forecasting performance and understandability of the model. The performance of the proposed model in point forecasting, interval forecasting, and probabilistic forecasting is comprehensively evaluated through the case of a real PV plant. The experimental results show that the proposed model performs well on the task of ultra-short-term PV power probabilistic forecasting and outperforms other state-of-the-art deep learning models in the field combined with QR. The proposed method in this paper can provide technical support for application scenarios such as energy scheduling, market trading, and risk management on the ultra-short-term time scale of the power system.

This paper endeavors to apply a novel intelligent damping controller (NIDC) for the static synchronous compensator (STATCOM) to reduce the power fluctuations, voltage support and damping in a hybrid power multi-system. In this paper, we discuss the integration of an offshore wind farm (OWF) and a seashore wave power farm (SWPF) via a high-voltage, alternating current (HVAC) electric power transmission line that connects the STATCOM and the 12-bus hybrid power multi-system. The hybrid multi-system consists of a battery energy storage system (BESS) and a micro-turbine generation (MTG). The proposed NIDC consists of a designed proportional–integral–derivative (PID) linear controller, an adaptive critic network and a proposed functional link-based novel recurrent fuzzy neural network (FLNRFNN). Test results show that the proposed controller can achieve better damping characteristics and effectively stabilize the network under unstable conditions.

As a major technical route to utilize biomass energy, biomass combustion power generation (BCPG) has been shown to be of environmental and economic significance. According to the operating experience, the installed capacity has a decisive impact on the operation and economic return of BCPG projects. In China, an installed capacity of either 30 MW or 12 MW is often chosen for constructing a BCPG project. To explore which one is more suitable for China, this paper uses actual operating data to compare the operation performance and techno-economics of two representative BCPG projects with an installed capacity of 30 MW and 12 MW. The results show that the operation situation and electricity production of the 30 MW project are better than those of the 12 MW project. The 30 MW project has a lower biomass consumption than the 12 MW project to produce per unit of electricity. The Internal Rate of Return (IRR) of the 30 MW project is greater than the industry benchmark in China and is almost three times the IRR of the 12 MW project. Therefore, it is recommended to construct BCPG projects with installed capacity of 30 MW in China.

In this paper, recent achievements in the application of the lattice Boltzmann method (LBM) to complex fluid flows are reported. More specifically, we focus on flows through reactive porous media, such as the flow through the substrate of a selective catalytic reactor (SCR) for the reduction of gaseous pollutants in the automotive field; pulsed-flow analysis through heterogeneous catalyst architectures; and transport and electro-chemical phenomena in microbial fuel cells (MFC) for novel waste-to-energy applications. To the authors’ knowledge, this is the first known application of LBM modeling to the study of MFCs, which represents by itself a highly innovative and challenging research area. The results discussed here essentially confirm the capabilities of the LBM approach as a flexible and accurate computational tool for the simulation of complex multi-physics phenomena of scientific and technological interest, across physical scales.

Results-based financing (RBF) programmes in the clean cooking sector have gained increasing donor interest over the last decade. Although the risks and advantages of RBF have been discussed quite extensively for other sectors, especially health services, there is limited research-documented experience of its application to clean cooking. Due to the sheer scale of the important transition from ‘dirty’ to clean cooking for the 4 billion people who lack access, especially in the Global South, efficient and performance-proven solutions are urgently required. This paper, undertaken as part of the work of the UKAid-funded Modern Energy Cooking Services (MECS) programme, aims to close an important research gap by reviewing evidence-based support mechanisms and documenting essential experiences from previous and ongoing RBF programmes in the clean cooking and other sectors. On this basis, the paper derives key strategic implications and learning lessons for the global scaling of RBF programmes and finds that qualitative key performance indicators such as consumer acceptance as well as longer-term monitoring are critical long-term success factors for RBF to ensure the continued uptake and use of clean cooking solutions (CCS), however securing the inclusion of these indicators within programmes remains challenging. Finally, by discussing the opportunities for the evolution of RBF into broader impact funding programmes and the integration of energy access and clean cooking strategies through multi-sector approaches, the paper illustrates potential steps to enhance the impact of RBF in this sector in the future.

Under a two-part tariff, the user-side installation of photovoltaic and energy storage systems can simultaneously lower the electricity charge and demand charge. How to plan the energy storage capacity and location against the backdrop of a fully installed photovoltaic system is a critical element in determining the economic benefits of users. In view of this, we propose an optimal configuration of user-side energy storage for a multi-transformer-integrated industrial park microgrid. First, the objective function of user-side energy storage planning is built with the income and cost of energy storage in the whole life cycle as the core elements. This is conducted by taking into consideration the time-of-use electricity price, demand price, on-grid electricity price, and energy storage operation and maintenance costs. Then, considering the load characteristics and bidirectional energy interaction of different nodes, a user-side decentralized energy storage configuration model is developed for a multi-transformer-integrated industrial park microgrid. Finally, combined with the engineering practice constraints, the configuration model is solved by mixed integer linear programming. The simulation test demonstrates how the proposed model can successfully increase the economic benefits of an industrial park. Electricity and demand costs are reduced by 11.90% and 19.35%, respectively, and the photovoltaic accommodation level is increased by 4.2%, compared to those without the installation of energy storage system.

Numerical simulations are performed within the time domain to investigate the dynamic behaviors of an SPAR-type FOWT under wave group conditions. Towards this goal, the OC3 Hywind SPAR-type FOWT is adopted, and a JONSWAP (Joint North Sea Wave Project)-based wave group is generated by the envelope amplitude approach. The FOWT motion under wave group conditions, as well as the aerodynamic, hydrodynamic, and mooring performances, is simulated by our established in-house code. The rotating blades are modelled by the blade element momentum theory. The wave-body interaction effect is calculated by the three-dimensional potential theory. The mooring dynamics are also taken into consideration. According to the numerical results, the SPAR buoy motions are slightly increased by the wave group, while the heave motion is significantly amplified. Both the aerodynamic performance and the mooring tension are also influenced by the wave group. Furthermore, the low-frequency resonant response could be more easily excited by the wave group.

To optimize energy structure and efficiently utilize renewable energy sources, it is necessary to establish a new electrical power–gas mutual transformation virtual power plant that has low-carbon benefits. To promote the economic and low-carbon operation of a virtual power plant and reduce uncertainty regarding the use of new energy, a multi-timescale (day-ahead to intraday) optimal scheduling model is proposed. First, a basic model of a new interconnected power–gas virtual power plant (power-to-gas demand response virtual power plant, PD-VPP) was established with P2G and comprehensive demand response as the main body. Second, in response to the high volatility of new energy, a day-ahead to intraday multi-timescale collaborative operation optimization model is proposed. In the day-ahead optimization period, the next day’s internal electricity price is formulated, and the price-based demand response load is regulated in advance so as to ensure profit maximization for the virtual power plant. Based on the results of day-ahead modeling, intraday optimization was performed on the output of each distributed unit, considering the cost of the carbon emission reductions to achieve low-carbon economic dispatch with minimal operating costs. Finally, several operation scenarios are established for a simulation case analysis. The validity of the proposed model was verified via comparison.