Analysis of Construction and Demolition Waste and its Applications Based on Recent Studies

Construction and Demolition Waste C & D waste is becoming a havoc each coming day. According to government agencies like Building Material Promotion Council (BMPTC) and Centre for Fly Ash Research and Management (C-FARM) estimated 165 million tonnes from construction. Out of municipal solid waste approximately 15% to 20% of solid waste comes from construction and demolition projects. Centre of Science and Environment (CSE) says in their latest release analysis of the C&D waste management sector, titled Another Brick off the Wall, India manages to recover and recycle only about 1% of its construction and demolition (C&D) waste), as the official recycling capacity is a mere 6,500 tons per day (TPD)- just about 1%. In this paper, we will analyze the C & D waste management to maintain the sustainable approach. View Article DOI: 10.47856/ijaast.2022.v09i07.00

Spatial-Temporal Recurrent Graph Neural Networks for Fault Diagnostics in Power Distribution Systems

Fault diagnostics are extremely important to decide proper actions toward fault isolation and system restoration. The growing integration of inverter-based distributed energy resources imposes strong influences on fault detection using traditional overcurrent relays. This paper utilizes emerging graph learning techniques to build a new temporal recurrent graph neural network models for fault diagnostics. The temporal recurrent graph neural network structures can extract the spatial-temporal features from data of voltage measurement units installed at the critical buses. From these features, fault event detection, fault type/phase classification, and fault location are performed. Compared with previous works, the proposed temporal recurrent graph neural networks provide a better generalization for fault diagnostics. Moreover, the proposed scheme retrieves the voltage signals instead of current signals so that there is no need to install relays at all lines of the distribution system. Therefore, the proposed scheme is generalizable and not limited by the number of relays installed. The effectiveness of the proposed method is comprehensively evaluated on the Potsdam microgrid and IEEE 123-node system in comparison with other neural network structures

Hierarchical Control of Grid-Connected Hydrogen Electrolyzer Providing Grid Services

This paper presents the operation modes and control architecture of the grid-connected hydrogen electrolyzer systems for the provision of frequency and voltage supports. The analysis is focused on the primary and secondary loops in the hierarchical control scheme. At the power converter inner control loop, the voltage- and current-control modes are analyzed. At the primary level, the droop and opposite droop control strategies to provide voltage and frequency support are described. Coordination between primary control and secondary, tertiary reserves is discussed. The case studies and real-time simulation results are provided using Typhoon HIL to back the theoretical investigation

1-D Convolutional Graph Convolutional Networks for Fault Detection in Distributed Energy Systems

This paper presents a 1-D convolutional graph neural network for fault detection in microgrids. The combination of 1-D convolutional neural networks (1D-CNN) and graph convolutional networks (GCN) helps extract both spatial-temporal correlations from the voltage measurements in microgrids. The fault detection scheme includes fault event detection, fault type and phase classification, and fault location. There are five neural network model training to handle these tasks. Transfer learning and fine-tuning are applied to reduce training efforts. The combined recurrent graph convolutional neural networks (1D-CGCN) is compared with the traditional ANN structure on the Potsdam 13-bus microgrid dataset. The achievable accuracy of 99.27%, 98.1%, 98.75%, and 95.6% for fault detection, fault type classification, fault phase identification, and fault location respectively.Comment: arXiv admin note: text overlap with arXiv:2210.1517

AN ENHANCED SCHEDULING APPROACH WITH CLOUDLET MIGRATIONS FOR RESOURCE INTENSIVE APPLICATIONS

Cloud computing is one of the most advanced technologies to present computerized generation. Scheduling plays a major role in it. The connectivity of Virtual Machines (VM) to schedule the assigned tasks (cloudlet) is a most attractive field to research. This paper introduces a confined Cloudlet Migration based scheduling algorithm using Enhanced-First Come First Serve (CMeFCFS). The objective of this work is to minimize the makespan, cost and to optimize the resource utilization. The proposed work has been simulated in the CloudSim toolkit package. The results have been compared with pre-existing scheduling algorithms with same experimental configuration. Important parameters like execution time, completion time, cost, makespan and utilization of resources are compared to measure the performance of the proposed algorithm. Extensive simulation results prove that introduced work has better results than existing approaches. 99.8% resource utilization has been achieved by CMeFCFS. Plotted graphs and calculated values show that the proposed algorithm is very effective for cloudlet scheduling

Reliability quantification and visualization for electric microgrids

2012 Fall.Includes bibliographical references.The electric grid in the United States is undergoing modernization from the state of an aging infrastructure of the past to a more robust and reliable power system of the future. The primary efforts in this direction have come from the federal government through the American Recovery and Reinvestment Act of 2009 (Recovery Act). This has provided the U.S. Department of Energy (DOE) with $4.5 billion to develop and implement programs through DOE's Office of Electricity Delivery and Energy Reliability (OE) over the a period of 5 years (2008-2012). This was initially a part of Title XIII of the Energy Independence and Security Act of 2007 (EISA) which was later modified by Recovery Act. As a part of DOE's Smart Grid Programs, Smart Grid Investment Grants (SGIG), and Smart Grid Demonstration Projects (SGDP) were developed as two of the largest programs with federal grants of$3.4 billion and $600 million respectively. The Renewable and Distributed Systems Integration (RDSI) demonstration projects were launched in 2008 with the aim of reducing peak electricity demand by 15 percent at distribution feeders. Nine such projects were competitively selected located around the nation. The City of Fort Collins in co-operative partnership with other federal and commercial entities was identified to research, develop and demonstrate a 3.5MW integrated mix of heterogeneous distributed energy resources (DER) to reduce peak load on two feeders by 20-30 percent. This project was called FortZED RDSI and provided an opportunity to demonstrate integrated operation of group of assets including demand response (DR), as a single controllable entity which is often called a microgrid. As per IEEE Standard 1547.4-2011 (IEEE Guide for Design, Operation, and Integration of Distributed Resource Island Systems with Electric Power Systems), a microgrid can be defined as an electric power system which has following characteristics: (1) DR and load are present, (2) has the ability to disconnect from and parallel with the area Electric Power Systems (EPS), (3) includes the local EPS and may include portions of the area EPS, and (4) is intentionally planned. A more reliable electric power grid requires microgrids to operate in tandem with the EPS. The reliability can be quantified through various metrics for performance measure. This is done through North American Electric Reliability Corporation (NERC) metrics in North America. The microgrid differs significantly from the traditional EPS, especially at asset level due to heterogeneity in assets. Thus, the performance cannot be quantified by the same metrics as used for EPS. Some of the NERC metrics are calculated and interpreted in this work to quantify performance for a single asset and group of assets in a microgrid. Two more metrics are introduced for system level performance quantification. The next step is a better representation of the large amount of data generated by the microgrid. Visualization is one such form of representation which is explored in detail and a graphical user interface (GUI) is developed as a deliverable tool to the operator for informative decision making and planning. Electronic appendices-I and II contain data and MATLAB© program codes for analysis and visualization for this work

Operation of electric microgrids under uncertainty

2017 Spring.Includes bibliographical references.Optimization and decision-making are non-trivial in case of multiple, incommensurable, and conflicting objectives. Decision-making becomes more complicated with uncertainty in inputs. Power system operation with electric microgrids subsumes all of the abovementioned aspects. Centralized decision-making in day-ahead dispatch of microgrids with multiple objectives in a grid-connected mode is addressed from the perspective of a power distribution system operator. Uncertainties in the electrical output of variable distributed energy resources and load demand due to forecasting errors are treated statistically by using empirical distributions. Scenarios for simulation are generated using statistics of actual data for solar and load demand forecast. Kantorovich distance measure is used for scenario reduction to maintain computational tractability of the problem. Discrete compromise programming is used for multi-criteria decision-analysis to obtain non-dominated dispatch solutions without generating a computationally expensive Pareto front. Two step look-ahead dynamic program routine is used for dispatch optimization of dispatchable, non-dispatchable solar, and energy storage asset. New performance metrics are developed for reserve management in microgrids using North American Electric Reliability Corporation (NERC) metrics and some previously developed metrics by this researcher. The economic dispatch problem is formulated as a constrained optimization problem with the new metric for reserve as a constraint. Optimization programs are implemented using MATLAB® and power system simulations are performed on standard IEEE 13-node test distribution feeder using the real-time simulation platform—RTDS®. Some potential future developments and applications of performance metrics are presented as future work

A Performance Metric for Co-optimization of Day-Ahead Dispatch and Reserves in Electric Microgrids

International audienceWe present a new performance metric for co-optimization of dispatch and reserves in microgrids. A metric based on NERC criteria is used for each asset to quantity its reliability-based value. A system level metric is obtained through the individual performance metrics, dispatched capacity, and net dispatchable capacity available as reserve. Simulations are performed on a notional microgrid with a dispatchable and a non-dispatchable distributed energy resource to demonstrate the calculation of the metric

Efficient Reinforcement Learning for Real-Time Hardware-Based Energy System Experiments

In the context of urgent climate challenges and the pressing need for rapid technology development, Reinforcement Learning (RL) stands as a compelling data-driven method for controlling real-world physical systems. However, RL implementation often entails time-consuming and computationally intensive data collection and training processes, rendering them inefficient for real-time applications that lack non-real-time models. To address these limitations, real-time emulation techniques have emerged as valuable tools for the lab-scale rapid prototyping of intricate energy systems. While emulated systems offer a bridge between simulation and reality, they too face constraints, hindering comprehensive characterization, testing, and development. In this research, we construct a surrogate model using limited data from simulated systems, enabling an efficient and effective training process for a Double Deep Q-Network (DDQN) agent for future deployment. Our approach is illustrated through a hydropower application, demonstrating the practical impact of our approach on climate-related technology development