Energy storage and demand response resources, in combination with intermittent renewable generation, are expected to provide domestic customers with the capability of reducing their electricity consumption. This paper highlights the role that an intelligent battery control, in combination with solar generation, could play to increase renewable uptake while reducing customers' electricity bills without intruding on people's daily life. The optimal performance of a home energy management system (HEMS) is investigated through a range of demand-response (DR) interventions, leading to different levels of customer weariness and consumption patterns. Thus, DR is applied with efficient and specific control of domestic appliances through load shifting and curtailment. Regarding the uncertainty associated with PV generation, a chanceconstrained (CC) optimal scheduling is considered subject to the operation constraints from each power component in the HEMS. By applying distributionally robust optimization (DRO), the ambiguity set is accurately built for this distributionally robust chance-constrained (DRCC) problem without the need of any probability distribution associated with uncertainty. Based on the greatly altered consumption profiles in this paper, the proposed DRCC-HEMS is proven to be optimally effective and computationally efficient while considering uncertainty. Nomenclature A. Sets T Set of time slots. A Set of appliances. B. Parameters F Fuse capacity. , , , Minimum charging and discharging power of ESS. , , , Maximum charging and discharging power of ESS. , Minimum and maximum state of charge of ESS. , , , Charging and discharging efficiency of ESS. , Degradation cost coefficient of ESS. , , , , Capital cost, life cycle and total capacity of ESS. 0 Statistical mean of . 0 Statistical covariance of .

In response to the deficiencies that the Carrier Phase Shift (CPS-PWM) and Carrier In-Phase Disposition (IPD-PWM) modulation techniques are applied to Cascaded H-bridge (CHB) multilevel inverters, combined with the advantages of the two techniques, a Hybrid Multi-carrier PWM technique based on carrier reconstruction is proposed. The aforementioned technique uses the carrier segment in the half carrier period of the IPD-PWM technique as the basic unit and optimizes the modulation performance by periodically adjusting its arrangement in the vertical direction. When CHB multilevel inverter adopts Hybrid Multi-carrier PWM technique, as with CPS-PWM technique, the output power balance between the cascaded H-bridge cells is achieved naturally; at the same time, the harmonic spectrum of the output voltage is identical with that of the IPD-PWM technique, that is to say, the harmonic characteristics of the output line voltage of the inverters are effectively improved. In this paper, the CHB seven-level inverter is taken as an example, and the correctness of the relevant conclusions is verified by simulation and experimental results. INDEX TERMS Hybrid multi-carrier PWM, cascaded H-bridge inverter, power balance, harmonics analysis.

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Abstract:The uncertainty and regularity of wind power generation are caused by wind resources' intermittent and randomness. Such volatility brings severe challenges to the wind power grid. The requirements for ultrashort-term and short-term wind power forecasting with high prediction accuracy of the model used, have great significance for reducing the phenomenon of abandoned wind power , optimizing the conventional power generation plan, adjusting the maintenance schedule and developing real-time monitoring systems. Therefore, accurate forecasting of wind power generation is important in electric load forecasting. The echo state network (ESN) is a new recurrent neural network composed of input, hidden layer and output layers. It can approximate well the nonlinear system and achieves great results in nonlinear chaotic time series forecasting. Besides, the ESN is simpler and less computationally demanding than the traditional neural network training, which provides more accurate training results. Aiming at addressing the disadvantages of standard ESN, this paper has made some improvements. Combined with the complementary advantages of particle swarm optimization and tabu search, the generalization of ESN is improved. To verify the validity and applicability of this method, case studies of multitime scale forecasting of wind power output are carried out to reconstruct the chaotic time series of the actual wind power generation data in a certain region to predict wind power generation. Meanwhile, the influence of seasonal factors on wind power is taken into consideration. Compared with the classical ESN and the conventional Back Propagation (BP) neural network, the results verify the superiority of the proposed method.

An arc fault on the DC side of the photovoltaic system is a potential safety hazard and is difficult to detect due to the complexity of photovoltaic systems. The detection method of series arc fault in photovoltaic systems is investigated here. The DC arc fault test platform for a photovoltaic system is established to collect the current signal under normal and fault conditions. In this study, the time domain characteristics, frequency domain characteristics, and time-frequency domain characteristics are compared by analysing the current data from the photovoltaic system in before and after fault states: corresponding feature vectors are used to construct the arc fault feature space of the system, and according to the position of the current signal in the feature space the fault is detected, so as to realise effective arc fault feature information. Then the method of establishing the arc fault feature space is introduced and key parameters of the feature space are determined. Finally, the anti-interference ability of arc fault feature space detection is verified. The results showed that the detection method is both feasible and accurate.

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Hepatitis E virus (HEV) is an emerging zoonotic pathogen with multiple species and genotypes, which may be classified into human, animal, and zoonotic HEV. Codon usage bias of HEV remained unclear. This study aims to characterize the codon usage of HEV and elucidate the main drivers influencing the codon usage bias. A total of seven HEV genotypes, HEV-1 (human HEV), HEV-3 and HEV-4 (zoonotic HEV), HEV-8, HEV-B, HEV-C1, and HEV-C2 (emerging animal HEV), were included in the study. Complete coding sequences, ORF1, ORF2, and ORF3, were accordingly obtained in the GenBank. Except for HEV-8, the other six genotypes tended to use codons ending in G/C. Based on the analysis of relatively synonymous codon usage (RSCU) and principal component analysis (PCA), codon usage bias was determined for HEV genotypes. Codon usage bias differed widely across human, zoonotic, and animal HEV genotypes; furthermore, it varied within certain genotypes such as HEV-4, HEV-8, and HEV-C1. In addition, dinucleotide abundance revealed that HEV was affected by translation selection to form a unique dinucleotide usage pattern. Moreover, parity rule 2 analysis (PR2), effective codon number (ENC)-plot, and neutrality analysis were jointly performed. Natural selection played a leading role in forming HEV codon usage bias, which was predominant in HEV-1, HEV-3, HEV-B and HEV-C1, while affected HEV-4, HEV-8, and HEV-C2 in combination with mutation pressure. Our findings may provide insights into HEV evolution and codon usage bias.