Power Management of Nanogrid Cluster with P2P Electricity Trading Based on Future Trends of Load Demand and PV Power Production

This paper presents the power management of the nanogrid clusters assisted by a novel peer-to-peer(P2P) electricity trading. In our work, unbalance of power consumption among clusters is mitigated by the proposed P2P trading method. For power management of individual clusters, multi-objective optimization simultaneously minimizing total power consumption, portion of grid power consumption, and total delay incurred by scheduling is attempted. A renewable power source photovoltaic(PV) system is adopted for each cluster as a secondary source. The temporal surplus of self-supply PV power of a cluster can be sold through P2P trading to another cluster (s) experiencing temporal power shortage. The cluster in temporal shortage of electric power buys the PV power to reduce peak load and total delay. In P2P trading, a cooperative game model is used for buyers and sellers to maximize their welfare. To increase P2P trading efficiency, future trends of load demand and PV power production are considered for power management of each cluster to resolve instantaneous unbalance between load demand and PV power production. To this end, a gated recurrent unit network is used to forecast future load demand and future PV power production. Simulations verify the effectiveness of the proposed P2P trading for nanogrid clusters.Comment: This article is submitted for publication in Sustainable Cities and Societ

Reinforcement Learning Based Cooperative P2P Energy Trading between DC Nanogrid Clusters with Wind and PV Energy Resources

In order to replace fossil fuels with the use of renewable energy resources, unbalanced resource production of intermittent wind and photovoltaic (PV) power is a critical issue for peer-to-peer (P2P) power trading. To resolve this problem, a reinforcement learning (RL) technique is introduced in this paper. For RL, graph convolutional network (GCN) and bi-directional long short-term memory (Bi-LSTM) network are jointly applied to P2P power trading between nanogrid clusters based on cooperative game theory. The flexible and reliable DC nanogrid is suitable to integrate renewable energy for distribution system. Each local nanogrid cluster takes the position of prosumer, focusing on power production and consumption simultaneously. For the power management of nanogrid clusters, multi-objective optimization is applied to each local nanogrid cluster with the Internet of Things (IoT) technology. Charging/discharging of electric vehicle (EV) is performed considering the intermittent characteristics of wind and PV power production. RL algorithms, such as deep Q-learning network (DQN), deep recurrent Q-learning network (DRQN), Bi-DRQN, proximal policy optimization (PPO), GCN-DQN, GCN-DRQN, GCN-Bi-DRQN, and GCN-PPO, are used for simulations. Consequently, the cooperative P2P power trading system maximizes the profit utilizing the time of use (ToU) tariff-based electricity cost and system marginal price (SMP), and minimizes the amount of grid power consumption. Power management of nanogrid clusters with P2P power trading is simulated on the distribution test feeder in real-time and proposed GCN-PPO technique reduces the electricity cost of nanogrid clusters by 36.7%.Comment: 22 pages, 8 figures, to be submitted to Applied Energy of Elsevie

Phenotypic and Genomic Properties of Brachybacterium vulturis sp. nov. and Brachybacterium avium sp. nov.

Two strains, VM2412T and VR2415T, were isolated from the feces of an Andean condor (Vultur gryphus) living in Seoul Grand Park, Gyeonggi-do, South Korea. Cells of both strains were observed to be Gram-stain positive, non-motile, aerobic, catalase positive and oxidase negative. Growth was found to occur at 10-30°C, showing optimum growth at 30°C. The strains could tolerate up to 15% (w/v) NaCl concentration and grow at pH 6-9. The strains shared 99.3% 16S rRNA gene sequence similarity to each other but were identified as two distinct species based on 89.0-89.2% ANIb, 90.3% ANIm, 89.7% OrthoANI and 38.0% dDDH values calculated using whole genome sequences. Among species with validly published names, Brachybacterium ginsengisoli DCY80T shared high 16S rRNA gene sequence similarities with strains VM2412T (98.7%) and VR2415T (98.4%) and close genetic relatedness with strains VM2412T (83.3–83.5% ANIb, 87.0% ANIm, 84.3% OrthoANI and 27.8% dDDH) and VR2415T (82.8–83.2% ANIb, 86.7% ANIm, 83.9% OrthoANI and 27.2% dDDH). The major fatty acid of the two strains was identified as anteiso-C15:0 and the polar lipids consisted of phosphatidylglycerol, diphosphatidylglycerol, presumptively phosphatidylethanolamine and three unidentified glycolipids. Strain VR2415T also produced an unidentified phospholipid. The cell walls of the two strains contained meso-diaminopimelic acid as diagnostic diamino acid and the whole cell sugars were ribose, glucose, and galactose. The strains contained MK-7 as their predominant menaquinone. The genomes of strains VM2412T, VR2415T, and B. ginsengisoli DCY80T were sequenced in this study. The genomic G+C contents of strains VM2412T and VR2415T were determined to be 70.8 and 70.4 mol%, respectively. A genome-based phylogenetic tree constructed using an up-to-date bacterial core gene set (UBCG) showed that the strains formed a clade with members of the genus Brachybacterium, supporting their taxonomic classification into the genus Brachybacterium. Based on phenotypic and genotypic analyses in this study, strains VM2412T and VR2415T are considered to represent two novel species of the genus Brachybacterium and the names Brachybacterium vulturis sp. nov. and Brachybacterium avium sp. nov. are proposed for strains VM2412T (=KCTC 39996T = JCM 32142T) and VR2415T (=KCTC 39997T = JCM 32143T), respectively

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Synergistic Effects of Proprioceptive Neuromuscular Facilitation and Manual Lymphatic Drainage in Patients with Mastectomy-Related Lymphedema

Purpose: Manual lymphatic drainage (MLD) and proprioceptive neuromuscular facilitation (PNF) are potential therapeutic strategies to reduce mastectomy-induced edema. The purpose of this study was to investigate whether the combination of these therapies would induce synergistic effects to treat lymphedema-related complications and to analyze a possible physiological mechanism involved in the observed effects.Methods: A total of 55 patients diagnosed with mastectomy-induced lymphedema were recruited and randomized into three experimental groups: PNF group (n = 17), MLD group (n = 20), and PNF + MLD group (n = 18). They were subjected to designated rehabilitation program three times a week for 16 weeks. ROM (flexion of the shoulder joint), edema size, arterial blood flow velocity, and degree of pain and depression were measured every 4 weeks over experimental period.Results: Lymphedema volume, VAS pain scale, and Beck depression scale were decreased in PNF and MLD groups for 16 weeks in a time-dependent manner. In combination, a greater reduction of these variables was observed over 16 weeks compared to each PNF and MLD. While axillary arterial blood circulation rate in the affected extremity was increased in both PNF and PNF + MLD groups over 16 weeks, this value was not increased in MLD group throughout the experimental period. A greater reduction of scales of VAS pain and Beck Depression Inventory (BDI) was observed in PNF + MLD group after the 16 week-treatment, as compared to each PNF and MLD group. Pearson's coefficients test demonstrated that there are significant correlation of depression against pain (r = 0.616, p < 0.01), ROM (r = −0.478, p < 0.01), and lymphedema size (r = 0.492, p < 0.01).Conclusion: The combination of MLD and PNF induces potent synergistic effects on edema volume, shoulder range of motion (ROM), pain, and depression in patients with lymphedema. In addition, an increased rate of axillary arterial blood flow in PNF-treated patients provide a potential physiological mechanism by which local arterial pulsation in the affected extremity plays a positive role in the treatment of lymphedema. Therefore, it is suggested to incorporate an element of PNF into traditional MLD method to facilitate treatment process for patients with lymphedema

Development of Charging/Discharging Scheduling Algorithm for Economical and Energy-Efficient Operation of Multi-EV Charging Station

As the number of electric vehicles (EVs) significantly increases, the excessive charging demand of parked EVs in the charging station may incur an instability problem to the electricity network during peak hours. For the charging station to take a microgrid (MG) structure, an economical and energy-efficient power management scheme is required for the power provision of EVs while considering the local load demand of the MG. For these purposes, this study presents the power management scheme of interdependent MG and EV fleets aided by a novel EV charging/discharging scheduling algorithm. In this algorithm, the maximum amount of discharging power from parked EVs is determined based on the difference between local load demand and photovoltaic (PV) power production to alleviate imbalances occurred between them. For the power management of the MG with charging/discharging scheduling of parked EVs in the PV-based charging station, multi-objective optimization is performed to minimize the operating cost and grid dependency. In addition, the proposed scheme maximizes the utilization of EV charging/discharging while satisfying the charging requirements of parked EVs. Moreover, a more economical and energy-efficient PV-based charging station is established using the future trends of local load demand and PV power production predicted by a gated recurrent unit (GRU) network. With the proposed EV charging/discharging scheduling algorithm, the operating cost of PV-based charging station is decreased by 167.71% and 28.85% compared with the EV charging scheduling algorithm and the conventional EV charging/discharging scheduling algorithm, respectively. It is obvious that the economical and energy-efficient operation of PV-based charging station can be accomplished by applying the power management scheme with the proposed EV charging/discharging scheduling strategy

Development of Charging/Discharging Scheduling Algorithm for Economical and Energy-Efficient Operation of Multi-EV Charging Station

As the number of electric vehicles (EVs) significantly increases, the excessive charging demand of parked EVs in the charging station may incur an instability problem to the electricity network during peak hours. For the charging station to take a microgrid (MG) structure, an economical and energy-efficient power management scheme is required for the power provision of EVs while considering the local load demand of the MG. For these purposes, this study presents the power management scheme of interdependent MG and EV fleets aided by a novel EV charging/discharging scheduling algorithm. In this algorithm, the maximum amount of discharging power from parked EVs is determined based on the difference between local load demand and photovoltaic (PV) power production to alleviate imbalances occurred between them. For the power management of the MG with charging/discharging scheduling of parked EVs in the PV-based charging station, multi-objective optimization is performed to minimize the operating cost and grid dependency. In addition, the proposed scheme maximizes the utilization of EV charging/discharging while satisfying the charging requirements of parked EVs. Moreover, a more economical and energy-efficient PV-based charging station is established using the future trends of local load demand and PV power production predicted by a gated recurrent unit (GRU) network. With the proposed EV charging/discharging scheduling algorithm, the operating cost of PV-based charging station is decreased by 167.71% and 28.85% compared with the EV charging scheduling algorithm and the conventional EV charging/discharging scheduling algorithm, respectively. It is obvious that the economical and energy-efficient operation of PV-based charging station can be accomplished by applying the power management scheme with the proposed EV charging/discharging scheduling strategy

Special Issue on Advanced Wireless Sensor Networks for Emerging Applications

Wireless sensor networks (WSNs) have been widely used due to their extensive range of applications [...

Special Issue on Advanced Wireless Sensor Networks for Emerging Applications

Wireless sensor networks (WSNs) have been widely used due to their extensive range of applications [...