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Theoretical Background
Published in Valery Rudnev, Don Loveless, Raymond L. Cook, Handbook of Induction Heating, 2017
Valery Rudnev, Don Loveless, Raymond L. Cook
An understanding of the physics of the electromagnetic proximity and skin effects is important not only in IH but also in power supply design and bus network design. The proper design of a bus network will significantly decrease its impedance, minimizing voltage drop and reducing transmission power losses, and thus improving overall energy efficiency.
Communication Infrastructure for Smart Microgrids
Published in Sasi K. Kottayil, Smart Microgrids, 2020
Bus topology: A single central cable acts as the shared medium in the bus network and interconnects all members of the network. Any member node of the network can transmit a data packet to any other node by sending the data to the bus. Though all connected nodes can receive the data, only the intended receiver will process it.
Two-stage control for transfer synchronisation and regularity of subsequent bus line service
Published in Transportmetrica A: Transport Science, 2022
Hu Zhang, Shidong Liang, Shengxue He, Pengcheng Yuan, Jing Zhao
Step 1. Input related parameters of bus network. Based on the random distribution, generate a stochastic sample for the travel time of each bus between stops (denoted as ). Following He et al. (2019), is the average travel time plus a random variable , as shown in Equations (39) and (40). Given the distance between stops and average bus speed from historical data, the can be obtained. The random variable is assumed to follow the normal distribution . Variance can represent the level of randomness. On this basis, through travel time sampling, the actual bus movement can be simulated.
A New Method to Reduce Harmonic Magnitude Based on Simultaneous Determination of Maximum Voltage and Current Harmonic Contribution in Interconnected Networks
Published in Electric Power Components and Systems, 2019
Javad Momenpour Akerdi, Mehdi Torabian Esfahani, Behrooz Vahidi
A similar study of the IEEE 6-BUS network on the IEEE 14-BUS network, shown in Figure 6, has been done. The goal is to determine the contribution of each harmonic source to the 7th harmonic reduction of voltage on bus 4. The results of the vector projection method and proposed method are given in Tables 8 and 9, respectively. According to the results of the vector projection method, although the harmonic contribution of S2 is negative (), the reduction rate that it generates is positive (10.21). In addition, for S14, a positive harmonic contribution is also achieved () while the downside is negative (–6.23). Therefore, determining the voltage harmonic contribution by the vector projection method in this network also did not result in logical responses. In Table 9, the results of determining the voltage harmonic contribution are presented by the proposed method. According to this table and the vector projection method, S4, S3, and S9 is the three sources that have most contribution. The lowest contribution is for the S12. S4, S3, and S9 can be 11.81, 7.95, and 5.12 times the S12, respectively, in contributing to the reduction of the 7th harmonic voltage amplitude on bus 4.
Structured storage policies for energy distribution networks
Published in IISE Transactions, 2018
Arnab Bhattacharya, Jeffrey P. Kharoufeh, Bo Zeng
We have examined optimal energy storage and flow strategies in a two-bus distribution network with storage devices and line losses. The network operator's objective is to minimize the total expected discounted costs incurred over a finite planning horizon by optimally selecting the amount of energy to charge to, or discharge from, the storage devices; the amount of energy to buy from, or sell to, the grid; and the amount of energy to transmit between the buses. By way of a finite-horizon, discounted cost MDP model, we established the monotonicity of the optimal policy with respect to the storage levels. Moreover, we proved the multimodularity of the value function in the storage levels and that the optimal storage decisions at each stage exhibit bounded sensitivities. Significantly, we also established bounds that compare the cost of the two-bus network to the costs of two comparable networks with pooled and decentralized storage configurations, respectively. The results of the two-bus network were extended to more general multi-bus network topologies. The usefulness of the main results was illustrated by way of a numerical example using real pricing and wind generation data. Our results highlighted the benefits of using the network model over those of single-storage models that do not account for interactions between buses in a distribution network.