A Downsizing Strategy for Combinatorial PMSG Based Wind Turbine and Micro-SMES System Applied in Standalone DC Microgrid

This paper presents a combinatorial standalone permanent magnet synchronous generator (PMSG) based variable speed wind turbine (VSWT) and small-size superconducting magnetic energy storage (SMES) system into the DC microgrid system. The principal purpose of SMES system is to preserve power balance by absorbing power during peak wind generation and to release it during low power generation. This work accomplished by describing the optimized design of the SMES solenoid coil, ensuring the desired energy storage capacity based on the simulated annealing (SA) algorithm. More importantly, the new control technique is developed for bi-directional DC-DC converter to level output power of the wind turbine depending on the demand thereby reducing the capacity of the DC-DC converter system. Detailed simulation studies implemented in PSCAD/EMTDC corroborate the superior robustness and balancing performance of the proposed micro-SMES controller with an optimal coil size under various situations including variable wind speed. This combination will result in “scaling-factors” knowledge through downsizing strategy which will lead to the most efficient system from cost cutting, energy savings, and downsizing viewpoints

Hybrid integration of multilayer perceptrons and parametric models for reliability forecasting in the smart grid

The reliable power system operation is a major goal for electric utilities, which requires the accurate reliability forecasting to minimize the duration of power interruptions. Since weather conditions are usually the leading causes for power interruptions in the smart grid, especially for its distribution networks, this paper comprehensively investigates the combined effect of various weather parameters on the reliability performance of distribution networks. Specially, a multilayer perceptron (MLP) based framework is proposed to forecast the daily numbers of sustained and momentary power interruptions in one distribution management area using time series of common weather data. First, the parametric regression models are implemented to analyze the relationship between the daily numbers of power interruptions and various common weather parameters, such as temperature, precipitation, air pressure, wind speed, and lightning. The selected weather parameters and corresponding parametric models are then integrated as inputs to formulate a MLP neural network model to predict the daily numbers of power interruptions. A modified extreme learning machine (ELM) based hierarchical learning algorithm is introduced for training the formulated model using realtime reliability data from an electric utility in Florida and common weather data from National Climatic Data Center (NCDC). In addition, the sensitivity analysis is implemented to determine the various impacts of different weather parameters on the daily numbers of power interruptions

A Spectrum Sharing based Metering Infrastructure for Smart Grid Utilizing LTE and WiFi

In advanced metering infrastructure (AMI) of smart grid, WiFi is an appropriate choice for its bidirectional communication requirement to transmit data to the billing center. But, WiFi functions in the free spectrum bands and LTE also requires to use the same free bands for its network expansion being licensed spectrum is limited and expansive. LTE and WiFi can operate simultaneously in the 3.5 GHz band (also known as citizen broadband radio service (CBRS)), which has large amount of free and clean spectrum. In this paper, we propose a smart grid metering infrastructure based on fixed duty cycled LTE and WiFi, where smart meters and its’ data collectors (known as Access Point) use WiFi and LTE, respectively, for transferring data. Under a system level simulation environment, we investigated the LTE-WiFi coexistence performance in CBRS band considering a time division duplexing (TDD)-LTE associated with FTP traffic, and IEEE 802.11n (WiFi). The simulation performance demonstrates a good neighborhood coexistence between WiFi and LTE, which makes it a potential communication solution for the AMI

Optimal Reliability-based Placement of Plug-In Electric Vehicles in Smart Distribution Network

Distributed renewable resources are one of the major technological inclinations that play a pivotal role in the future green power system. In this context, high penetration of Plug-In Electric Vehicles (PIEVs) may bring many challenges for the smart grid. PIEVs’ unscheduled utilization will cause technical issues for both generation side and demand side. High penetration of PIEVs may lead to reliability deterioration of smart distribution network. In this paper, firstly a PIEV owner behavior model is introduced. The output of this model is the hourly demand of PIEVs per day. Secondly, a comprehensive reliability-based objective function is proposed to allocate these modeled PIEVs in smart distribution network. Finally, IEEE RBTS bus 2 is used for implementing allocation algorithm utilizing both genetic algorithm and particle swarm optimizatio

Performance evaluation of optimal photovoltaic-electrolyzer system with the purpose of maximum Hydrogen storage

Power electronics-based electrolyzer systems are prevalently in current use. This paper proposes the more recently employed directly coupled photovoltaic (PV) electrolyzer systems. Equipped with accurate electrical models of the advanced alkaline electrolyzer, PV system and Hydrogen storage tank simulated using MATLAB, the system\u27s performance for a full week is analyzed using Miami, Florida\u27s meteorological data. A multi-level Genetic Algorithm (GA)-based optimization facilitates maximum hydrogen production, minimum excess power generation, and minimum energy transfer loss. The crucial effect of temperature on the overall system performance is also accounted for by optimizing this parameter using GA, maintaining operating conditions close to the Maximum Power Point (MPP) of the PV array. The results of the analysis have been documented to show that the optimal system for a 10 kW electrolyzer can produce, on an average, Hydrogen of 0.0176 mol/s, when the system is operating with 6.3% power loss and 2.4% power transfer loss