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A Novel Scheme for P2P Energy Trading Considering Energy Congestion in Microgrid

IEEE ACCESS 2021年 9卷 147649-147664页

作者： Ehjaz, Muhammad Iqbal, Muhammad Zaidi, Syed Sajjad Haider Khan, Bilal Muhammad COMSATS Univ Dept Elect & Comp Engn Wah Campus Islamabad 45550 Pakistan Natl Univ Sci & Technol Dept Elect Engn PNEC Islamabad 44000 Pakistan

The advancements in Renewable Energy Sources (RES), increasing trends of distributed generation and proliferation of prosumer community requires an affective utilization of energy in Microgrid (MG) paradigm. Peer-to-Peer (P2P) energy trading mechanism provides a platform where prosumers can participate in energy sharing which is beneficial for all users of MG. Meanwhile, due to the high penetration of RES and distributed generations in distribution system used in P2P energy trading concept, the control and operation of existing grid is lacking the required functionalities. In P2P energy trading, power congestion occurs on distribution system which can limit effective utilization of RES. As RES and power demands are increasing, congestion problem also tends to increase. Smart Metering (SM) infrastructure is used to handle Energy Congestion Problem (ECP) by more effective management of prosumer community in P2P energy trading mechanism. SM monitors power transactions, maintains status of the network and obtains power profiles of prosumers. In this paper, Normalized P2P (NP2P) energy trading scheme is proposed with objective to decrease load on main grid and cost minimization for all users. NP2P is centralized contracting scheme treats all users in Microgrid equal for energy flow and cost implementation. Three different cases are considered to validate efficiency of proposed P2P energy trading scheme. In this research work, ECP due to NP2P model is analyzed, modeled as Knapsack Problem (KP) and solved using greedy and Simulated Annealing (SA) algorithms. In proposed research, Central Energy Management Unit (CEMU) is responsible for implementation of functions for NP2P model and energy congestion control. To improve the reliability of grid, some prosumers with surplus energy should be disconnected. The results of various considered cases for proposed NP2P scheme and algorithms for ECP are compared to verify effectiveness of the solution.

关键词： Microgrids Costs Optimization Renewable energy sources Power system stability Peer-to-peer computing Power markets Peer-to-Peer energy trading energy congestion microgrid smart meter greedy algorithm simulated annealing algorithm Knapsack problem

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Characterizing Minimum-Time Solar Sail Geostationary Orbit Transfers Using Pseudospectral Optimal Control

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JOURNAL OF SPACECRAFT AND ROCKETS 2021年第4期58卷 997-1009页

作者： Fitzgerald, Riley M. MIT Dept Aeronaut & Astronautics 77 Massachusetts Ave Cambridge MA 02139 USA

This work examines the problem of planet-centered orbit transfers using solar sail propulsion. Though solar sails offer unlimited Delta V, they place coupled time-dependent restrictions on thrust magnitude and direction, and the multiple objectives of an orbit transfer can make locally optimal approaches inefficient;it is therefore advantageous to use optimal control to design such trajectories. This paper presents orbit-averaged equinoctial-element equations of motion for a solar sail under a discretized and interpolated control law. These equations are used to study transfer from a geostationary transfer orbit to geostationary orbit;a locally optimal control law is used to generate initial trajectories, and Gaussian pseudospectral optimal control is applied to yield minimum-time transfers. In the default case, with sail area-mass ratio of 30m2/kg, optimization reduces the transfer time from 1222 to 293 days. Finally, sail area per mass, sail reflectivity, and initial sun-Earth orientation are varied to explore their effects, and to provide general design guidelines for planet-centered solar sail transfers. Distinct performance regimes are observed for transfers taking longer or shorter than one year, and for reflectivities greater or less than 0.4. Additionally, initial solar position was found to drastically affect the behavior of transfers in the fast regime.

关键词： Pseudospectral Optimal Control Low Thrust Propulsion Geostationary Transfer Orbit Earth Reflectivity Orbital Period Thrust Vectoring Attitude Control System Right Ascension greedy algorithm

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Online Configuration Selection for Redundant Arrays of Inertial Sensors: Application to Robotic Systems Covered with a Multimodal Artificial Skin

Online Configuration Selection for Redundant Arrays of Inert...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Leboutet, Quentin Bergner, Florian Cheng, Gordon Tech Univ Munich Inst Cognit Syst Fac Elect Engn & Comp Sci D-80333 Munich Germany

ISBN: (纸本)9781728162126

Multiple approaches to the estimation of high-order motion derivatives for innovative control applications now rely on the data collected by redundant arrays of inertial sensors mounted on robots, with promising results. However, most of these works suffer scalability issues induced by the considerable amount of data generated by such large-scale distributed sensor systems. In this article, we propose a new adaptive sensor-selection algorithm, for distributed inertial measurements. Our approach consists in using the data of a subset of sensors, selected among a larger collection of inertial sensing elements covering a rigid robot link. The sensor selection process is formulated as an optimization problem, and solved using a projected gradient heuristics. The proposed method can run online on a robot and be used to recalculate the selected sensor arrangement on the fly when physical interaction or potential sensor failure is detected. The tests performed on a simulated UR5 industrial manipulator covered with a multimodal artificial skin, demonstrate the consistency and performance of the proposed sensor-selection algorithm.

关键词： Acceleration Feedback Artificial Robot Skin Automatic Sensor Selection greedy algorithm

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An EIM-degradation free reduced basis method via over collocation and residual hyper reduction-based error estimation

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JOURNAL OF COMPUTATIONAL PHYSICS 2021年 444卷 110545-110545页

作者： Chen, Yanlai Gottlieb, Sigal Ji, Lijie Maday, Yvon Univ Massachusetts Dartmouth Dept Math 285 Old Westport Rd N Dartmouth MA 02747 USA Shanghai Jiao Tong Univ Sch Math Sci Shanghai 200240 Peoples R China Sorbonne Univ Paris France Univ Paris Diderot Paris France CNRS Paris France Lab Jacques Louis Lions F-75005 Paris France SPC Paris France Inst Univ France Paris France

The need for multiple interactive, real-time simulations using different parameter values has driven the design of fast numerical algorithms with certifiable accuracies. The reduced basis method (RBM) presents itself as such an option. RBM features a mathematically rigorous error estimator which drives the construction of a low-dimensional subspace. A surrogate solution is then sought in this low-dimensional space approximating the parameter-induced high fidelity solution manifold. However when the system is nonlinear or its parameter dependence nonaffine, this efficiency gain degrades tremendously, an inherent drawback of the application of the empirical interpolation method (EIM). In this paper, we augment and extend the EIM approach as a direct solver, as opposed to an assistant, for solving nonlinear partial differential equations on the reduced level. The resulting method, called Reduced Over-Collocation method (ROC), is stable and capable of avoiding the efficiency degradation. Two critical ingredients of the scheme are collocation at about twice as many locations as the number of basis elements for the reduced approximation space, and an efficient error indicator for the strategic building of the reduced solution space. The latter, the main contribution of this paper, results from an adaptive hyper reduction of the residuals for the reduced solution. Together, these two ingredients render the proposed R2-ROC scheme both offline- and online-efficient. A distinctive feature is that the efficiency degradation appearing in traditional RBM approaches that utilize EIM for nonlinear and nonaffine problems is circumvented, both in the offline and online stages. Numerical tests on different families of time-dependent and steady-state nonlinear problems demonstrate the high efficiency and accuracy of our R2-ROC and its superior stability performance. (C) 2021 Elsevier Inc. All rights reserved.

关键词： Reduced basis method Empirical interpolation method Generalized empirical interpolation method Collocation Over Collocation Hyper reductions greedy algorithm

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A greedy Feature Selection algorithm for Brain-Computer Interface Classification Committees 8

A Greedy Feature Selection Algorithm for Brain-Computer Inte...

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8th Annual International Conference of the Biologically-Inspired-Cognitive Architectures-Society on Biologically Inspired Cognitive Architectures (BICA)

作者： Trofimov, Alexander G. Shishkin, Sergei L. Kozyrskiy, Bogdan L. Velichkovsky, Boris M. Natl Res Nucl Univ MEPhI Moscow Russia Natl Res Ctr Kurchatov Inst Moscow Russia

We propose an approach to electroencephalogram feature selection and classification problems in brain-computer interfaces based on a committee of weak classifiers. The design of a classification committee is formulated as an optimization problem and the greedy algorithm for its solving is considered. The proposed approach is applicable when the objects to be classified are characterized by a large number of features while a few train samples are available. Classification performance of the committee was evaluated on real data and improvement over traditional classification methods was observed. (C) 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/3.0/) Peer-review under responsibility of the scientific committee of the 8th Annual International Conference on Biologically Inspired Cognitive Architectures

关键词： electroencephalogram brain-computer interface feature selection classification committee greedy algorithm

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A Novel Sparrow Search algorithm for the Traveling Salesman Problem

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IEEE ACCESS 2021年 9卷 153456-153471页

作者： Wu, Changyou Fu, Xisong Pei, Junke Dong, Zhigui Shandong Inst Business & Technol Sch Management Sci & Engn Yantai 264005 Peoples R China Liaoning Inst Sci & Technol Benxi 117004 Peoples R China

The sparrow search algorithm (SSA) tends to fall into local optima and to have insufficient stagnation when applied to the traveling salesman problem (TSP). To address this issue, we propose a novel greedy genetic sparrow search algorithm based on a sine and cosine search strategy (GGSC-SSA). First, the greedy algorithm is introduced to initialize the population and to increase the diversity of the population. Second, genetic operators are used to update the population, balancing global search and local development capabilities. Finally, the adaptive weight is introduced in the producer update to increase the adaptability of the algorithm and to optimize the quality of the solution, and a sin-cosine search strategy is introduced to update the scroungers. In addition, the GGSC-SSA is compared with the genetic algorithm (GA), simulated annealing (SA), particle swarm optimization (PSO), grey wolf optimization (GWO), ant colony optimization (ACO) and the artificial fish (AF) algorithm on TSP datasets for performance testing. We also compare it with some recently improved algorithms. The results of the simulations are encouraging;the GGSC-SSA significantly enhances the solution precision, optimization speed and robustness.

关键词： Heuristic algorithms Optimization Traveling salesman problems Search problems Particle swarm optimization Genetic algorithms Statistics Sparrow search algorithm traveling salesman problem greedy algorithm genetic operators sin-cosine search strategy combinatorial optimization

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Optimal Multi-objective Units Start-up Strategy Based on greedy algorithm

Optimal Multi-objective Units Start-up Strategy Based on Gre...

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IEEE International Conference of Region 10 (TENCON)

作者： Zhang, Bowen Shi, Libao Ni, Yixin Yao, Liangzhong Bazargan, Masoud Tsinghua Univ Grad Sch Shenzhen Natl Key Lab Power Syst Shenzhen Shenzhen 518055 Peoples R China China Elect Power Res Inst Beijing 100192 Peoples R China Alstom Grid Res & Technol Ctr Stafford ST17 4LX England

ISBN: (纸本)9781479928255;9781479928279

In this paper, a multi-objective units start-up optimization strategy is proposed. In the proposed model, the economic factors during generator start-up are introduced into the conventional units start-up objective function. Furthermore, the system frequency characteristics as well as generator ramp constraints are considered during analysis. The greedy algorithm is employed to solve the proposed model. Finally, the case studies are carried out based on the IEEE 30-bus test system as benchmark to demonstrate the effectiveness and validity of the proposed model and method.

关键词： power system restoration units start-up economic benefit frequency constraint greedy algorithm

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A Big Data Placement Strategy in Geographically Distributed Datacenters 5

A Big Data Placement Strategy in Geographically Distributed ...

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5th International Conference on Cloud Computing and Artificial Intelligence - Technologies and Applications (CloudTech)

作者： Bouhouch, Laila Zbakh, Mostapha Tadonki, Claude Mohamed V Univ Natl Sch Comp Sci & Syst Anal Rabat Morocco Mines Paris Tech PSL Ctr Rech Informat CRI Paris France

ISBN: (纸本)9781728161754

With the pervasiveness of the "Big Data" characteristic together with the expansion of geographically distributed datacenters in the Cloud computing context, processing large-scale data applications has become a crucial issue. Indeed, the task of finding the most efficient way of storing massive data across distributed locations is increasingly complex. Furthermore, the execution time of a given task that requires several datasets might be dominated by the cost of data migrations/exchanges, which depends on the initial placement of the input datasets over the set of datacenters in the Cloud and also on the dynamic data management strategy. In this paper, we propose a data placement strategy to improve the workflow execution time through the reduction of the cost associated to data movements between geographically distributed datacenters, considering their characteristics such as storage capacity and read/write speeds. We formalize the overall problem and then propose a data placement algorithm structured into two phases. First, we compute the estimated transfer time to move all involved datasets from their respective locations to the one where the corresponding tasks are executed. Second, we apply a greedy algorithm in order to assign each dataset to the optimal datacenter w.r.t the overall cost of data migrations. The heterogeneity of the datacenters together with their characteristics (storage and bandwidth) are both taken into account. Our experiments are conducted using Cloudsim simulator. The obtained results show that our proposed strategy produces an efficient placement and actually reduces the overheads of the data movement compared to both a random assignment and a selected placement algorithm from the literature.

关键词： Big Data Cloud Computing Data Placement greedy algorithm Cloudsim

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Cost-aware Dynamic Data Center Federation Formation for Short-term Service Requests

Cost-aware Dynamic Data Center Federation Formation for Shor...

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4th World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4)

作者： Nayak, Niharika Tripathi, Rakesh Gupta, Govind P. Natl Inst Technol Dept Informat Technol Raipur Madhya Pradesh India

ISBN: (纸本)9781728168234

In current environment, many web services map to the distributed data centers, to respond to variable and increasing workloads. It is not cost efficient for one data center to increase number of servers in order to fulfill all requests coming to it. For this reason, federated data center is a key solution for sharing unused resources and gain sonic profit. In this paper, we have used demand multiplexing and exploits the spatio-temporal variation of electricity price of distributed data center across locations and also consider the power consumption cost of servers at data center. We develop an Integer Linear Programming Problem (ILPP) for Profit Maximization Problem of federated data centers and proposes greedy algorithm for computing an optimal federation formation of data centers. The performance of the proposed greedy federation formation algorithm (G-FM) compares with two other algorithms which are Optimal Federation Formation algorithm (OEM) and Random Federation Formation algorithm (REM). Experiments are performed on real world traffic demands at data center in which the proposed (G-EM) algorithm gives an optimize solution to the optimization problem.

关键词： greedy algorithm Integer Linear Programming Problem Demand Multiplexing Spatio-temporal Variation in Electricity Price

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greedy algorithms for the single-demand facility location problem

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OPERATIONS RESEARCH LETTERS 2017年第5期45卷 452-455页

作者： Cheung, Sin-Shuen Williamson, David P. Cornell Univ Sch Operat Res & Informat Engn Ithaca NY 14853 USA

In this note, we give greedy approximation algorithms for the single-demand facility location problem inspired by the greedy algorithms for the min-knapsack problem originally given by Gens and Levner (1979) and later analyzed by Csirik et al. (1991). The simplest algorithm is a 2-approximation algorithm running in O(n log n) time;in general, we give a k+1/k-approximation algorithm running in O(n(k) log n) time. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Facility location Approximation algorithm greedy algorithm

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