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Additional file 1: Table S1. Primers used in this study. Fig. S1. Partial amino acid sequence alignment of three MnSODs of P. ostreatus CCMSSC00389. A g126 (MnSOD1) vs g8468 (MnSOD2). B g126 (MnSOD1) vs g12127 (MnSOD3) . C g8468 (MnSOD2) vs g12127 (MnSOD3)

Additional file 1: Table S1. Primers used in this study. Figure S1. Cellulase activity of mycelia cultured on the medium with 0, 0.3%, 0.6%, 0.9%, and 1.2% concentrations of cellulose for 3, 4, 5, 6, 7, and 8 days. Each value represents the mean ± SD (n = 3), and different lowercase letters denote statistical significance (P < 0.05). Figure S2. Cloning of FfCEL6B. Figure S3. Cloning of FfMYB15.

Environmental concerns and depletion of traditional energy lead to the booming development of renewable distributed generation (RDG) in the past decade. However, due to intermittent nature of renewable energy sources, to what extent RDG could provide capacity to power systems becomes a critical issue to the utility company when implementing long-term system strategic (generation expansion) planning. On the other hand, in a smart-grid frame, the popularization of different varieties of demand-side resources enables the system to operate at more flexible modes ever before. The potential variability in load demand not only introduces additional dynamics and uncertainties to the system, but could also affect the reliability benefits of RDG. Therefore, in practice, to effectively estimate the reliability value of RDG in power systems, the potential interaction between generation- and demand-side must be properly captured. In this paper, a study to assess the capacity credit (CC) of RDG in a context of distributed generation system is performed with consideration of the impacts of demand response (DR). A compound reliability model for DR is presented, which considers the uncertainties involved in both instant response and follow-on load recovery processes. On this basis, an assessment framework for the CC of RDG based on sequential Monte Carlo simulation is developed by which the inter-temporal characteristics of DR resources can be fully captured. The numerical study is implemented based on the IEEE-38 bus test case. The calculation results demonstrate that the CC of RDG would depend on a variety of factors, including penetration level, responsiveness of load demand and the correlations between RDG and DR availability. Also, it is shown that accounting for the underlying effect of DR is of absolute importance, otherwise the CC of RDG might be estimated erroneously in real practices.

This paper proposes a multi-year expansion planning method for enabling distribution systems to support growing penetrations of plug-in electric vehicles. As distinct from the existing studies, the temporal characteristics of charging loads and their reliability impacts are especially focused in our work. To achieve this, a novel dual-stage optimization framework is developed. The proposed method considers the capacity reinforcement of distribution systems in conjunction with their operation decisions and coordinates them under the same frame so as to minimize the total system costs for accommodating electric vehicles. The uncertainties associated with renewable energy generation, charging behaviors, and conventional load demand are represented by multiple probabilistic scenarios. To fully reveal the impacts of electric vehicle integration, both uncontrolled and coordinated charging schemes are considered in our analysis. Furthermore, as charging loads bring about extra demand to the grid, the reliability criteria is also taken into account in the proposed model. Using a heuristic algorithm combined with reliability analysis, the optimal solution for the concerned problem can be determined, which involves the best timing, locations, and capacities for installation of distributed generation units and network components. The effectiveness of the proposed framework is examined based on a 38-bus test system and the obtained results verify the performance of the approach.

This paper presents the concept of the generalized power source in an active distribution network. After the energy storage system (ESS), distributed generators (DG), and demand-side controllable load are connected to the active distribution network, part of the system load can be undertaken by these ESS, DG, and controllable load instead of relying on the capacity of the substation, which greatly improves the reliability of the system. The above-mentioned distributed energy resources in the active distribution network are collectively referred to as the generalized power source (GPS). Substation credible capacity refers to the capacity of the GPS to supply the distribution network that is equivalent at the high voltage distribution network level when considering the faults of the main transformer, 110 kV lines, and circuit breakers. Considering the uncertainties and control strategies of GPS, this paper takes a specific distribution area as the background to study its substituted substation capacity. Based on a sequential Monte Carlo evaluation framework and the principle of constant reliability, an evaluation method of the GPS credible capacity is proposed. In order to verify the effectiveness of the method proposed in this paper, the credible capacity of various typical connection modes of a high voltage distribution network is quantitatively analyzed in the case study, which can provide a reference for the capacity planning of an active distribution network substation and grid structure selection.

Due to the inherent fluctuation nature of wind power, voltage control and frequency regulation in China's power grid become more intricate as the wind power penetration level increasing. Provided with traits of comprehensive response and bidirectional output, wide concern has been arisen by the hybrid energy storage system (HESS), while how to design a smooth strategy is vital to achieve great effect. In order to conform the fluctuation of the wind power to China's technical standards, based on a HESS composed of electrical double layer capacitors (EDLCs) and battery, this paper proposes a strategy utilizing wavelet threshold filtering and mean-value interval as first and second smooth step, to flat the high and low frequency components of wind power fluctuations respectively. Taking a wind farm as an illustration, the validity of this strategy is verified by the calculation and analysis.