Evaluating Resilience of Electricity Distribution Networks via A Modification of Generalized Benders Decomposition Method

This paper presents a computational approach to evaluate the resilience of electricity Distribution Networks (DNs) to cyber-physical failures. In our model, we consider an attacker who targets multiple DN components to maximize the loss of the DN operator. We consider two types of operator response: (i) Coordinated emergency response; (ii) Uncoordinated autonomous disconnects, which may lead to cascading failures. To evaluate resilience under response (i), we solve a Bilevel Mixed-Integer Second-Order Cone Program which is computationally challenging due to mixed-integer variables in the inner problem and non-convex constraints. Our solution approach is based on the Generalized Benders Decomposition method, which achieves a reasonable tradeoff between computational time and solution accuracy. Our approach involves modifying the Benders cut based on structural insights on power flow over radial DNs. We evaluate DN resilience under response (ii) by sequentially computing autonomous component disconnects due to operating bound violations resulting from the initial attack and the potential cascading failures. Our approach helps estimate the gain in resilience under response (i), relative to (ii)

Stochastic Resource Allocation for Electricity Distribution Network Resilience

In recent years, it has become crucial to improve the resilience of electricity distribution networks (DNs) against storm-induced failures. Microgrids enabled by Distributed Energy Resources (DERs) can significantly help speed up re-energization of loads, particularly in the complete absence of bulk power supply. We describe an integrated approach which considers a pre-storm DER allocation problem under the uncertainty of failure scenarios as well as a post-storm dispatch problem in microgrids during the multi-period repair of the failed components. This problem is computationally challenging because the number of scenarios (resp. binary variables) increases exponentially (resp. quadratically) in the network size. Our overall solution approach for solving the resulting two-stage mixed-integer linear program (MILP) involves implementing the sample average approximation (SAA) method and Benders Decomposition. Additionally, we implement a greedy approach to reduce the computational time requirements of the post-storm repair scheduling and dispatch problem. The optimality of the resulting solution is evaluated on a modified IEEE 36-node network.Comment: 6 pages, 5 figures, accepted to 2020 American Control Conferenc

Solar Panel Recycling in The United States

The last decade has seen enormous growth in the solar industry across the globe. According to the International Renewable Energy Agency (IREnA) in 2015 solar energy produced about 222 gigawatts worldwide with expected growth to top 4500 gigawatts by 2050. By comparison the United States is expected to see an additional 10.6 gigawatts in 2018. The Midwest has seen similar growth in the solar industry within the past decade. The problem is the technology used to convert the sun’s rays into electrical energy does not last forever. On average the industry rates a solar panel\u27s life span around 25-30 years. Up to now limited quantities from early installations have been retired. The forecast for such systems will continue to grow. The once small quantities of glass and photovoltaic components will begin to grow from hundreds to thousands to millions of tons of material waste. This research provide a roadmap to the potential growth and opportunity to recycling solar technologies. It also illustrates some of the possible economic barriers including policy, transportation, value, and cost effective processes

Solar Powered Optimal Battery Charging Scheme For Moving Robotic Vehicle

This paper focuses on the design and development of an optimization charging system for Li–Po batteries with the help of solar tracking panels. Therefore, the implementation of a complete energy management system applied to a robotic vehicle. The proposed system is to design Moving robotic Vehicle. The design concept, based on a microcontroller. On this basis, our proposal makes a dual signi?cant contribution. First upon, it presents the construction of a solar tracking mechanism aimed at increasing the robots power regardless of its mobility. Secondly, it proposes an alternative design of power system performance based on a pack of two batteries. The aim is completing the process of charging a battery independently while the other battery provides all the energy consumed by the robotic vehicle. DOI: 10.17762/ijritcc2321-8169.160414

Characterization of the mechanical properties and microstructural evolution of martensitic steel in repeated tempering cycles

The purpose of this study was to understand the behavior of martensitic H13 steel in accordance with the microstructural evolution, mechanical properties and wear in repeated tempering cycles. The microstructures were characterized by axio image observer microscope, scanning electron microscope (SEM), x-ray diffraction (XRD). Uniaxial tensile test, charpy v-notch impact test, rockwell hardness test and wear test were conducted to analyze the changes in mechanical properties, impact properties, hardness and wear in repeated tempering cycles. The specimen prepared were subjected to the hardening at 1030 °C for 20 minutes, oil quenched and subjected to repeated tempering cycles at 570 °C for 2hrs holding time each. The mechanical properties recorded indicates that the maximum ultimate tensile strength obtained was at double tempering due to secondary hardening effect i.e., alloy carbides precipitation offering strength to the matrix and corresponding wear was found to be minimum. The annealed specimen revealed bainitic microstructure and with hardening and repeated tempering cycles, fine needle like structure and carbides was observed in microstructure and retained austenite was converted into martensite and martensite was converted into tempered martensite. Carbide size and martensite lath distribution controls the strength and fracture rate

An Efficient Query Optimizer with Materialized Intermediate Views in Distributed and Cloud Environment

In cloud computing environment hardware resources required for the execution of query using distributed relational database system are scaled up or scaled down according to the query workload performance. Complex queries require large scale of resources in order to complete their execution efficiently. The large scale of resource requirements can be reduced by minimizing query execution time that maximizes resource utilization and decreases payment overhead of customers. Complex queries or batch queries contain some common subexpressions. If these common subexpressions evaluated once and their results are cached, they can be used for execution of further queries. In this research, we have come up with an algorithm for query optimization, which aims at storing intermediate results of the queries and use these by-products for execution of future queries. Extensive experiments have been carried out with the help of simulation model to test the algorithm efficiency

QOTUM: The Query Optimizer for Distributed Database in Cloud Environment

In distributed and cloud database system, large amount of resources are used by complex queries that increase in payment overheads of cloud users. These resource needs can be minimised by evaluating common join operations and caching their results so that they could be applied to the execution of additional queries. In this research, the Query Optimization Technique Using Materialization (QOTUM) system is designed and developed that uses the mechanism of materialized views during query execution at distributed database in cloud environment. Extensive experiments are conducted with the help of standard benchmarks datasets and query workloads in cloud environment. The performance of QOTUM system is studied and evaluated against conventional SQL system based on various performance parameters