Finite control set model predictive control (FCS-MPC) is widely researched for converter control, as it incorporates the control objective and system constraints straightforwardly into the control. To address the drawbacks of conventional FCS-MPC, data-driven predictive control schemes, such as reinforcement learning (RL), have been proposed to tackle issues related to parameter sensitivity and unmodeled dynamics. Another challenge with FCS-MPC is the computational burden associated with complex converter systems and longer prediction horizon optimization problems. For FCS-MPC with a longer prediction horizon, the computation cost increases exponentially, rendering it impractical for real-time implementation. Additionally, RL controllers have not been explored for achieving long prediction horizon predictive control in power converters. To bridge this gap, this paper proposes introducing prediction horizons into RL for optimal power converter control, leveraging the nonlinear mapping capability and self-learning characteristics of RL. Thus, a multi-step RL controller is developed to enhance steady-state performance without increasing the computational burden. Both simulation and experimental results confirm the effectiveness of the proposed method.

Grid-forming control is a promising solution for enhancing the performance of AC-AC modular multilevel converters (MMCs) in railway traction systems, facilitating both independent operation and multi-converter synergy. However, implementing grid-forming control for AC-AC MMCs without a DC link, which provides stable voltage and frequency support as well as damping and inertia to the system, requires further investigation. To address this need, this work proposes a grid-forming control scheme for AC-AC MMCs, which includes grid-forming control for the single-phase side, energy maintaining control for the three-phase side, and voltage balancing control. By leveraging the energy stored in the module capacitors, a synchronization structure with virtual admittance is developed for the single-phase grid-forming control, effectively emulating the dynamic behaviors of rotary frequency converters (RFCs). The energy maintaining control for the three-phase side and the voltage balancing control are subsequently derived to support the overall control scheme. Experimental results validate the effectiveness of the proposed grid-forming control scheme using a scaled-down AC-AC MMC.

The modular multilevel converter (MMC) holds promise as a power supply solution for AC-AC conversion in railway traction systems. This paper introduces a indirect finite control set model predictive control (FCS-MPC) approach aimed at enhancing the dynamic performance of the converter. A discrete-time domain state space model is developed to forecast the behavior of both the three-phase side currents and circu-lating currents one step ahead. A cost function is defined to determine optimal insertion indices in each phase, facilitating rapid current tracking. Additionally, controls for phase and arm module capacitor voltages are implemented to further improve performance. Simulation results validate the effectiveness of the proposed strategy, highlighting its superior performance compared to traditional control methods.

Dual-active-bridge (DAB) converter is a promising technology for the high-frequency and high-power application of electric vehicle (EV) chargers. To reduce losses of power exchange between EV battery and power source or load, several control methods, such as tripe phase-shift (TPS) control with minimum-current-stress optimization, are used in DAB converter. However, in most existing works, the dead-time effects are not considered, which will result in a non-zero-voltage-switching (non-ZVS) operation and thus inaccurate optimization results. This paper analyzes the influence of junction capacitance and dead time on the ZVS characteristics, and proposes a TPS control-based optimization scheme aiming at the minimum inductor current stress under the premise of ZVS operation. The proposed approach can further improve the operating efficiency of DAB converters. Simulation results validate the effectiveness of proposed optimization scheme, where the losses can be reduced significantly under light load.

This study investigates the benefits of introducing Li-ion batteries as energy storage unit in the commercial sector by considering a representative building with a photovoltaic system. Only the costs and revenues related to the installation and operation of the battery are considered in this study. The operational strategy of the battery consists in balancing the following processes through day-ahead forecasts for both electricity consumption and photovoltaic production: shaving a targeted peak, performing price arbitrage, and increasing photovoltaic selfconsumption. By reviewing the electricity price cost for commercial buildings from several companies around the world, a general electricity price structure is defined. Afterwards, a Monte Carlo Analysis is applied for three locations with different solar irradiation levels to study the impact of climate, electricity price components, and other seven sensitive parameters on the economic viability of Li-ion batteries. The Monte Carlo Analysis shows that the most sensitive parameters for the net present value are the battery capacity, the battery price, and the component of the electricity price that relates to the peak power consumption. For Stockholm, one of the investigated locations, the corresponding Pearson correlation coefficients are -0.67, -0.66, and 0.19 for the case were no photovoltaic system is installed. For the considered battery operational strategies, the current investment and annual operation costs for the Li-ion battery always lead to negative net present values independently of the location. Battery prices lower than 250 US$/kWh start to manifest positive net present values when combining peak shaving, price arbitrage, and photovoltaic self-consumption. However, the integration of a photovoltaic system leads to a reduced economic viability of the battery by reducing the revenues generated by the battery while performing peak shaving.

Floating wind turbine has become the most promising technology for deep-sea wind power generation. Therefore, some means to reduce the structural load for stabilizing the wind turbine has been developing. In this paper, a semi-active structural control is realized by replacing the damper in passive TMD with the magnetorheological (MR) damper. The damping force of the MR damper can be changed by altering the voltage applied to it. A simple and convenient control method is designed, which includes adaptive control force design and retrogression controller. The simulation results show that the semi-active control method has a good damping effect, which mitigates much of the structural load with respect to the passive structural control.

AAsymmetric synthesis of N-aryloxazolidinones via dynamic kinetic resolution was developed. A ruthenium-based catalyst was used in the racemization of beta-anilino alcohols, while Candida antarctica lipase B (CAL-B) was applied for two selective alkoxycarbonylations operating in cascade. Various N-aryloxazolidinone derivatives were obtained in high yields and good enantiopurities.

The seawater source heat pump (SWHP) utilizes low-grade energy from seawater to satisfy heating/cooling requirements of coastal buildings. Compared with the open-loop SWHP system, the closed-loop system is more stable and reliable when used in cold regions because the seawater heat exchanger can mitigate icing and corrosion problems. This study established mathematical modeling for the seawater heat exchanger, which was validated by the experimental results, to estimate the heat transfer performance. The thermal performance of the seawater heat exchanger under different operating conditions was predicted, and the effects of several key parameters on thermal resistance were analyzed. The results revealed that the pipe wall thermal resistance of the original heat exchanger was the most important factor limiting the heat transfer. Hence, a cost analysis of the heat exchangers with different pipe materials was conducted, and the steel pipe with anticorrosive coating was proven to be optimal in reducing pipe wall thermal resistance and economy. The findings are useful in designing and optimizing the seawater heat exchanger and promoting the application of ocean thermal energy.

Rural electrification programs usually do not consider the impact that the increment of demand has on thereliability of off-grid photovoltaic (PV)/battery systems. Based on meteorological data and electricity consumptionprofiles from the highlands of Bolivian Altiplano, this paper presents a modelling and simulationframework for analysing the performance and reliability of such systems. Reliability, as loss of power supplyprobability (LPSP), and cost were calculated using simulated PV power output and battery state of chargeprofiles. The effect of increasing the suppressed demand (SD) by 20% and 50% was studied to determine howreliable and resilient the system designs are. Simulations were performed for three rural application scenarios: ahousehold, a school, and a health centre. Results for the household and school scenarios indicate that, toovercome the SD effect, it is more cost-effective to increase the PV power rather than to increase the batterycapacity. However, with an increased PV-size, the battery ageing rate would be higher since the cycles areperformed at high state of charge (SOC). For the health centre application, on the other hand, an increase inbattery capacity prevents the risk of electricity blackouts while increasing the energy reliability of the system.These results provide important insights for the application design of off-grid PV-battery systems in ruralelectrification projects, enabling a more efficient and reliable source of electricity.