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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.

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.

Based on the energy conversion equation and dynamic power model of the semi-transparent crystalline silicon photovoltaic (PV) window (ST-PVW), through an iterative coupling solution to the operating temperature of the cell, a thermal-electric coupling calculation method for the ST-PVW is provided, and, combined with experiments, the method model was verified. Based on this model, the influence of PV cell coverage rate (PVR) on the thermal performance of the ST-PVW was studied. According to the simulation results, in summer, the heat gain of the ST-PVW decreases with the increase of PVR, and in winter, the amount of heat loss increases with the increase of PVR. For the four cities of Guangzhou, Nanjing, Beijing and Harbin, when the PVR is 1, 0.60 to 0.64, 0.28 to 0.32 and 0.26 to 0.30, respectively, the annual power consumption of the air conditioner can reach the minimum, and when the PVR is 0.16 to 0.17, 0.24 to 0.25, 0.22 to 0.23 and 0.19 to 0.20, respectively, the amount of electricity generated can just offset the power consumption of the air conditioner during the day.

Fluidization of non-spherical particles is a common process in energy industries and chemical engineering. Understanding the fluidization of non-spherical particles is important to guide relevant processes. There already have been numerous studies which investigate the behaviors of different non-spherical particles during fluidization, but the investigations of the fluidization of polyhedral particles do not receive much attention. In this study, the investigation of the fluidization of polyhedral particles described by the polyhedron approach is conducted with a numerical CFD-DEM method. Experiments of the fluidization of three kinds of polyhedral particles are conducted under the same condition with corresponding simulations to validate the accuracy of our CFD-DEM model. The results indicate that our CFD-DEM model with the polyhedron approach can predict the behaviors of polyhedral particles with reasonable accuracy. Fluidization behaviors of different polyhedral particles are also investigated in this study. Compared to spherical particles, the motion of polyhedral particles is stronger, and mixing degree is higher under the same fluidization gas velocity.