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Polystyrene (PS) spheres were prepared through an emulsifier-free emulsion polymerization method, in which the reaction time, ionic strength, concentrations of copolymer were studied in detail. The resulting PS microspheres and Ag nanoparticles were used to construct a surface enhanced Raman scattering (SERS) substrate by a layer-by-layer assembly method. A relatively uniform distribution of PS/Ag in the films was obtained, and the multilayer substrate presented excellent SERS reproducibility and a tunable enhancement effect. The SERS substrate was used for detecting harmful pesticides (malachite green and dimetridazole) in food samples, with a limit of detection as low as 3.5 ppb. The obtained plasmonic composite has a promising future in the field of SERS sensing. [ABSTRACT FROM AUTHOR]

To achieve the goal of carbon neutrality, the demand for energy saving by the residential sector has witnessed a soaring increase. As a promising paradigm to monitor and manage residential loads, the existing studies on non-intrusive load monitoring (NILM) either lack the scalability of real-world cases or pay unaffordable attention to identification accuracy. This paper proposes a high accuracy, ultra-sparse sample, and real-time computation based NILM method for residential appliances. The method includes three steps: event detection, feature extraction and load identification. A wavelet decomposition based standard deviation multiple (WDSDM) is first proposed to empower event detection of appliances with complex starting processes. The results indicate a false detection rate of only one out of sixteen samples and a time consumption of only 0.77 s. In addition, an essential feature for NILM is introduced, namely the overshoot multiple (which facilitates an average identification improvement from 82.1% to 100% for similar appliances). Moreover, the combination of modified weighted K-nearest neighbors (KNN) and overshoot multiples achieves 100% appliance identification accuracy under a sampling frequency of 6.25 kHz with only one training sample. The proposed method sheds light on highly efficient, user friendly, scalable, and real-world implementable energy management systems in the expectable future. [ABSTRACT FROM AUTHOR]

Optimal operation of energy storage systems plays an important role in enhancing their lifetime and efficiency. This paper combines the concepts of the cyber–physical system (CPS) and multi-objective optimization into the control structure of the hybrid energy storage system (HESS). Owing to the time-varying characteristics of HESS, combining real-time data with physical models via CPS can significantly promote the performance of HESS. The multi-objective optimization model designed in this paper can improve the utilization of supercapacitors, reduce energy consumption, and prevent the state of charge (SOC) of HESS from exceeding the limitation. The new control scheme takes the characteristics of the components of HESS into account and is beneficial in reducing battery short-term power cycling and high discharge currents. The rain-flow counting algorithm is applied for battery life prediction to quantify the benefits of the HESS under the control scheme proposed. A much better power-sharing relationship between the supercapacitor and the lithium–ion battery (LiB) can be observed from the SIMULINK results and the case study with our new control scheme. Moreover, compared to the traditional low-pass filter control method, the battery lifetime is quantifiably increased from 3.51 years to 10.20 years while the energy efficiency is improved by 1.56%. [ABSTRACT FROM AUTHOR]

The increased installation capacity of grid-connected household photovoltaic (PV) systems has been witnessed worldwide, and the power grid is facing the challenges of overvoltage during peak power generation and limited frequency regulation performance. With the dual purpose of enhancing the power grid safety and improving the PV utilization rate, the maximum feed-in active power can be regulated by modifying the maximum power point tracking (MPPT) algorithm and battery energy storage (BES) accessibility as control instructions. However, the existing methods not only waste installed PV capacity, but it becomes no longer accessible when the state of charge (SOC) of the BES approaches its upper limit. In response to the above problem, this paper proposes a power limit control strategy to coordinate the MPPT algorithm and the BES accessibility. The proposed strategy directly controls the inverter output current according to the power limit instructions from the electric operation control centers, leading to a bus voltage difference. The difference serves as a control signal for BES and PV. Under a power-limiting scenario, priority is given to power regulation through energy storage to absorb the limited active power. When the SOC of the BES reaches the upper limit of charging, modification of the PV MPPT algorithm facilitates the inverter output power to meet the power limit requirements. To further verify the effectiveness of the proposed power limit control strategy, both simulation and experimental studies are conducted, which consistently indicated a synchronized inverter current with grid voltage and a rapid power response of the power-limiting instruction within 0.2 s. The power limit control strategy not only improves the PV energy utilization but also supports the safe and reliable operation of the power gird in the context of soaring renewable energy penetration. [ABSTRACT FROM AUTHOR]