3,273 research outputs found

    A Game-Theoretic Approach for Elastic Distributed Data Stream Processing

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    Distributed data stream processing applications are structured as graphs of interconnected modules able to ingest high-speed data and to transform them in order to generate results of interest. Elasticity is one of the most appealing features of stream processing applications. It makes it possible to scale up/down the allocated computing resources on demand in response to fluctuations of the workload. On clouds, this represents a necessary feature to keep the operating cost at affordable levels while accommodating user-defined QoS requirements. In this article, we study this problem from a game-theoretic perspective. The control logic driving elasticity is distributed among local control agents capable of choosing the right amount of resources to use by each module. In a first step, we model the problem as a noncooperative game in which agents pursue their self-interest. We identify the Nash equilibria and we design a distributed procedure to reach the best equilibrium in the Pareto sense. As a second step, we extend the noncooperative formulation with a decentralized incentive-based mechanism in order to promote cooperation by moving the agreement point closer to the system optimum. Simulations confirm the results of our theoretical analysis and the quality of our strategies

    A Survey on Formation Control of Small Satellites

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    A Control-Theoretic Methodology for Adaptive Structured Parallel Computations

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    Adaptivity for distributed parallel applications is an essential feature whose impor- tance has been assessed in many research fields (e.g. scientific computations, large- scale real-time simulation systems and emergency management applications). Especially for high-performance computing, this feature is of special interest in order to properly and promptly respond to time-varying QoS requirements, to react to uncontrollable environ- mental effects influencing the underlying execution platform and to efficiently deal with highly irregular parallel problems. In this scenario the Structured Parallel Programming paradigm is a cornerstone for expressing adaptive parallel programs: the high-degree of composability of parallelization schemes, their QoS predictability formally expressed by performance models, are basic tools in order to introduce dynamic reconfiguration processes of adaptive applications. These reconfigurations are not only limited to imple- mentation aspects (e.g. parallelism degree modifications), but also parallel versions with different structures can be expressed for the same computation, featuring different levels of performance, memory utilization, energy consumption, and exploitation of the memory hierarchies. Over the last decade several programming models and research frameworks have been developed aimed at the definition of tools and strategies for expressing adaptive parallel applications. Notwithstanding this notable research effort, properties like the optimal- ity of the application execution and the stability of control decisions are not sufficiently studied in the existing work. For this reason this thesis exploits a pioneer research in the context of providing formal theoretical tools founded on Control Theory and Game Theory techniques. Based on these approaches, we introduce a formal model for control- ling distributed parallel applications represented by computational graphs of structured parallelism schemes (also called skeleton-based parallelism). Starting out from the performance predictability of structured parallelism schemes, in this thesis we provide a formalization of the concept of adaptive parallel module per- forming structured parallel computations. The module behavior is described in terms of a Hybrid System abstraction and reconfigurations are driven by a Predictive Control ap- proach. Experimental results show the effectiveness of this work, in terms of execution cost reduction as well as the stability degree of a system reconfiguration: i.e. how long a reconfiguration choice is useful for targeting the required QoS levels. This thesis also faces with the issue of controlling large-scale distributed applications composed of several interacting adaptive components. After a panoramic view of the existing control-theoretic approaches (e.g. based on decentralized, distributed or hierar- chical structures of controllers), we introduce a methodology for the distributed predictive control. For controlling computational graphs, the overall control problem consists in a set of coupled control sub-problems for each application module. The decomposition is- sue has a twofold nature: first of all we need to model the coupling relationships between control sub-problems, furthermore we need to introduce proper notions of negotiation and convergence in the control decisions collectively taken by the parallel modules of the application graph. This thesis provides a formalization through basic concepts of Non-cooperative Games and Cooperative Optimization. In the notable context of the dis- tributed control of performance and resource utilization, we exploit a formal description of the control problem providing results for equilibrium point existence and the compari- son of the control optimality with different adaptation strategies and interaction protocols. Discussions and a first validation of the proposed techniques are exploited through exper- iments performed in a simulation environment

    Improving stability in Adaptive Distributed Parallel applications: a cooperative predictive approach

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    With this thesis we take a step further on improving reconfiguration decisions in adaptive distributed parallel computations. The concept of switching cost is introduced with the aim of reducing the amount of reconfigurations and of improving the reconfigurations stability in dynamic execution scenar- ios. Computation modules control is based on the Model-Based Predictive Control (MPC) approach. We study the effectiveness of this approach in parallel distributed computations, where each module cooperates to find global optimal reconfiguration trajectory. Experimental results are obtained by means of experiments performed in a simulation environment

    Antifragility = Elasticity + Resilience + Machine Learning: Models and Algorithms for Open System Fidelity

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    We introduce a model of the fidelity of open systems - fidelity being interpreted here as the compliance between corresponding figures of interest in two separate but communicating domains. A special case of fidelity is given by real-timeliness and synchrony, in which the figure of interest is the physical and the system's notion of time. Our model covers two orthogonal aspects of fidelity, the first one focusing on a system's steady state and the second one capturing that system's dynamic and behavioural characteristics. We discuss how the two aspects correspond respectively to elasticity and resilience and we highlight each aspect's qualities and limitations. Finally we sketch the elements of a new model coupling both of the first model's aspects and complementing them with machine learning. Finally, a conjecture is put forward that the new model may represent a first step towards compositional criteria for antifragile systems.Comment: Preliminary version submitted to the 1st International Workshop "From Dependable to Resilient, from Resilient to Antifragile Ambients and Systems" (ANTIFRAGILE 2014), https://sites.google.com/site/resilience2antifragile

    Review on Multi-Objective Control Strategies for Distributed Generation on Inverter-Based Microgrids

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    [EN] Microgrids have emerged as a solution to address new challenges in power systems with the integration of distributed energy resources (DER). Inverter-based microgrids (IBMG) need to implement proper control systems to avoid stability and reliability issues. Thus, several researchers have introduced multi-objective control strategies for distributed generation on IBMG. This paper presents a review of the different approaches that have been proposed by several authors of multi-objective control. This work describes the main features of the inverter as a key component of microgrids. Details related to accomplishing efficient generation from a control systems' view have been observed. This study addresses the potential of multi-objective control to overcome conflicting objectives with balanced results. Finally, this paper shows future trends in control objectives and discussion of the different multi-objective approaches.Gonzales-Zurita, Ó.; Clairand, J.; Peñalvo-López, E.; Escrivá-Escrivá, G. (2020). Review on Multi-Objective Control Strategies for Distributed Generation on Inverter-Based Microgrids. Energies. 13(13):1-29. https://doi.org/10.3390/en13133483S1291313Ross, M., Abbey, C., Bouffard, F., & Joos, G. (2015). Multiobjective Optimization Dispatch for Microgrids With a High Penetration of Renewable Generation. IEEE Transactions on Sustainable Energy, 6(4), 1306-1314. doi:10.1109/tste.2015.2428676Murty, V. V. S. N., & Kumar, A. (2020). Multi-objective energy management in microgrids with hybrid energy sources and battery energy storage systems. Protection and Control of Modern Power Systems, 5(1). doi:10.1186/s41601-019-0147-zKatircioğlu, S., Abasiz, T., Sezer, S., & Katırcıoglu, S. (2019). Volatility of the alternative energy input prices and spillover effects: a VAR [MA]-MGARCH in BEKK approach for the Turkish economy. Environmental Science and Pollution Research, 26(11), 10738-10745. doi:10.1007/s11356-019-04531-5Olivares, D. E., Mehrizi-Sani, A., Etemadi, A. H., Canizares, C. A., Iravani, R., Kazerani, M., … Hatziargyriou, N. D. (2014). Trends in Microgrid Control. IEEE Transactions on Smart Grid, 5(4), 1905-1919. doi:10.1109/tsg.2013.2295514Akinyele, D., Belikov, J., & Levron, Y. (2018). Challenges of Microgrids in Remote Communities: A STEEP Model Application. Energies, 11(2), 432. doi:10.3390/en11020432Benamar, A., Travaillé, P., Clairand, J.-M., & Escrivá-Escrivá, G. (2020). Non-Linear Control of a DC Microgrid for Electric Vehicle Charging Stations. International Journal on Advanced Science, Engineering and Information Technology, 10(2), 593. doi:10.18517/ijaseit.10.2.10815Lakshmi, M., & Hemamalini, S. (2018). Nonisolated High Gain DC–DC Converter for DC Microgrids. IEEE Transactions on Industrial Electronics, 65(2), 1205-1212. doi:10.1109/tie.2017.2733463Yin, C., Wu, H., Locment, F., & Sechilariu, M. (2017). Energy management of DC microgrid based on photovoltaic combined with diesel generator and supercapacitor. Energy Conversion and Management, 132, 14-27. doi:10.1016/j.enconman.2016.11.018Chen, D., Xu, Y., & Huang, A. Q. (2017). Integration of DC Microgrids as Virtual Synchronous Machines Into the AC Grid. IEEE Transactions on Industrial Electronics, 64(9), 7455-7466. doi:10.1109/tie.2017.2674621Abhinav, S., Schizas, I. D., Ferrese, F., & Davoudi, A. (2017). Optimization-Based AC Microgrid Synchronization. IEEE Transactions on Industrial Informatics, 13(5), 2339-2349. doi:10.1109/tii.2017.2702623Liu, Z., Su, M., Sun, Y., Li, L., Han, H., Zhang, X., & Zheng, M. (2019). Optimal criterion and global/sub-optimal control schemes of decentralized economical dispatch for AC microgrid. International Journal of Electrical Power & Energy Systems, 104, 38-42. doi:10.1016/j.ijepes.2018.06.045Khatibzadeh, A., Besmi, M., Mahabadi, A., & Reza Haghifam, M. (2017). Multi-Agent-Based Controller for Voltage Enhancement in AC/DC Hybrid Microgrid Using Energy Storages. Energies, 10(2), 169. doi:10.3390/en10020169Asghar, F., Talha, M., & Kim, S. (2017). Robust Frequency and Voltage Stability Control Strategy for Standalone AC/DC Hybrid Microgrid. Energies, 10(6), 760. doi:10.3390/en10060760Lotfi, H., & Khodaei, A. (2017). Hybrid AC/DC microgrid planning. Energy, 118, 37-46. doi:10.1016/j.energy.2016.12.015Kerdphol, T., Rahman, F., & Mitani, Y. (2018). Virtual Inertia Control Application to Enhance Frequency Stability of Interconnected Power Systems with High Renewable Energy Penetration. Energies, 11(4), 981. doi:10.3390/en11040981Rodrigues, Y. R., Zambroni de Souza, A. C., & Ribeiro, P. F. (2018). An inclusive methodology for Plug-in electrical vehicle operation with G2V and V2G in smart microgrid environments. International Journal of Electrical Power & Energy Systems, 102, 312-323. doi:10.1016/j.ijepes.2018.04.037Ghosh, S., & Chattopadhyay, S. (2020). Three-Loop-Based Universal Control Architecture for Decentralized Operation of Multiple Inverters in an Autonomous Grid-Interactive Microgrid. IEEE Transactions on Industry Applications, 56(2), 1966-1979. doi:10.1109/tia.2020.2964746Mohapatra, S. R., & Agarwal, V. (2020). Model Predictive Control for Flexible Reduction of Active Power Oscillation in Grid-Tied Multilevel Inverters Under Unbalanced and Distorted Microgrid Conditions. IEEE Transactions on Industry Applications, 56(2), 1107-1115. doi:10.1109/tia.2019.2957480Ziouani, I., Boukhetala, D., Darcherif, A.-M., Amghar, B., & El Abbassi, I. (2018). Hierarchical control for flexible microgrid based on three-phase voltage source inverters operated in parallel. International Journal of Electrical Power & Energy Systems, 95, 188-201. doi:10.1016/j.ijepes.2017.08.027Golshannavaz, S., & Mortezapour, V. (2018). A generalized droop control approach for islanded DC microgrids hosting parallel-connected DERs. Sustainable Cities and Society, 36, 237-245. doi:10.1016/j.scs.2017.09.038Safa, A., Madjid Berkouk, E. L., Messlem, Y., & Gouichiche, A. (2018). A robust control algorithm for a multifunctional grid tied inverter to enhance the power quality of a microgrid under unbalanced conditions. International Journal of Electrical Power & Energy Systems, 100, 253-264. doi:10.1016/j.ijepes.2018.02.042Andishgar, M. H., Gholipour, E., & Hooshmand, R. (2017). An overview of control approaches of inverter-based microgrids in islanding mode of operation. Renewable and Sustainable Energy Reviews, 80, 1043-1060. doi:10.1016/j.rser.2017.05.267Li, Z., Zang, C., Zeng, P., Yu, H., Li, S., & Bian, J. (2017). Control of a Grid-Forming Inverter Based on Sliding-Mode and Mixed H2/H{H_2}/{H_\infty } Control. IEEE Transactions on Industrial Electronics, 64(5), 3862-3872. doi:10.1109/tie.2016.2636798Hossain, M. A., Pota, H. R., Squartini, S., & Abdou, A. F. (2019). Modified PSO algorithm for real-time energy management in grid-connected microgrids. Renewable Energy, 136, 746-757. doi:10.1016/j.renene.2019.01.005Shokoohi, S., Golshannavaz, S., Khezri, R., & Bevrani, H. (2018). Intelligent secondary control in smart microgrids: an on-line approach for islanded operations. Optimization and Engineering, 19(4), 917-936. doi:10.1007/s11081-018-9382-9Safari, A., Babaei, F., & Farrokhifar, M. (2019). A load frequency control using a PSO-based ANN for micro-grids in the presence of electric vehicles. International Journal of Ambient Energy, 42(6), 688-700. doi:10.1080/01430750.2018.1563811Miveh, M. R., Rahmat, M. F., Ghadimi, A. A., & Mustafa, M. W. (2016). Control techniques for three-phase four-leg voltage source inverters in autonomous microgrids: A review. Renewable and Sustainable Energy Reviews, 54, 1592-1610. doi:10.1016/j.rser.2015.10.079Rokrok, E., Shafie-khah, M., & Catalão, J. P. S. (2018). Review of primary voltage and frequency control methods for inverter-based islanded microgrids with distributed generation. Renewable and Sustainable Energy Reviews, 82, 3225-3235. doi:10.1016/j.rser.2017.10.022Bouzid, A. M., Guerrero, J. M., Cheriti, A., Bouhamida, M., Sicard, P., & Benghanem, M. (2015). A survey on control of electric power distributed generation systems for microgrid applications. Renewable and Sustainable Energy Reviews, 44, 751-766. doi:10.1016/j.rser.2015.01.016Vásquez, V., Ortega, L. M., Romero, D., Ortega, R., Carranza, O., & Rodríguez, J. J. (2017). Comparison of methods for controllers design of single phase inverter operating in island mode in a microgrid: Review. Renewable and Sustainable Energy Reviews, 76, 256-267. doi:10.1016/j.rser.2017.03.060Shen, X., Wang, H., Li, J., Su, Q., & Gao, L. (2019). Distributed Secondary Voltage Control of Islanded Microgrids Based on RBF-Neural-Network Sliding-Mode Technique. IEEE Access, 7, 65616-65623. doi:10.1109/access.2019.2915509Arbab-Zavar, B., Palacios-Garcia, E., Vasquez, J., & Guerrero, J. (2019). Smart Inverters for Microgrid Applications: A Review. Energies, 12(5), 840. doi:10.3390/en12050840Bullich-Massagué, E., Díaz-González, F., Aragüés-Peñalba, M., Girbau-Llistuella, F., Olivella-Rosell, P., & Sumper, A. (2018). Microgrid clustering architectures. Applied Energy, 212, 340-361. doi:10.1016/j.apenergy.2017.12.048Kerdphol, T., Rahman, F., Mitani, Y., Hongesombut, K., & Küfeoğlu, S. (2017). Virtual Inertia Control-Based Model Predictive Control for Microgrid Frequency Stabilization Considering High Renewable Energy Integration. Sustainability, 9(5), 773. doi:10.3390/su9050773Hajiakbari Fini, M., & Hamedani Golshan, M. E. (2018). Determining optimal virtual inertia and frequency control parameters to preserve the frequency stability in islanded microgrids with high penetration of renewables. Electric Power Systems Research, 154, 13-22. doi:10.1016/j.epsr.2017.08.007Jung, J., & Villaran, M. (2017). Optimal planning and design of hybrid renewable energy systems for microgrids. Renewable and Sustainable Energy Reviews, 75, 180-191. doi:10.1016/j.rser.2016.10.061Baharizadeh, M., Karshenas, H. R., & Guerrero, J. M. (2018). An improved power control strategy for hybrid AC-DC microgrids. International Journal of Electrical Power & Energy Systems, 95, 364-373. doi:10.1016/j.ijepes.2017.08.036Serban, I., & Ion, C. P. (2017). Microgrid control based on a grid-forming inverter operating as virtual synchronous generator with enhanced dynamic response capability. International Journal of Electrical Power & Energy Systems, 89, 94-105. doi:10.1016/j.ijepes.2017.01.009Tavakoli, M., Shokridehaki, F., Marzband, M., Godina, R., & Pouresmaeil, E. (2018). A two stage hierarchical control approach for the optimal energy management in commercial building microgrids based on local wind power and PEVs. Sustainable Cities and Society, 41, 332-340. doi:10.1016/j.scs.2018.05.035Cagnano, A., De Tuglie, E., & Cicognani, L. (2017). Prince — Electrical Energy Systems Lab. Electric Power Systems Research, 148, 10-17. doi:10.1016/j.epsr.2017.03.011Zhang, H., Meng, W., Qi, J., Wang, X., & Zheng, W. X. (2019). Distributed Load Sharing Under False Data Injection Attack in an Inverter-Based Microgrid. IEEE Transactions on Industrial Electronics, 66(2), 1543-1551. doi:10.1109/tie.2018.2793241Yang, L., Hu, Z., Xie, S., Kong, S., & Lin, W. (2019). Adjustable virtual inertia control of supercapacitors in PV-based AC microgrid cluster. Electric Power Systems Research, 173, 71-85. doi:10.1016/j.epsr.2019.04.011Rahman, F. S., Kerdphol, T., Watanabe, M., & Mitani, Y. (2019). Optimization of virtual inertia considering system frequency protection scheme. Electric Power Systems Research, 170, 294-302. doi:10.1016/j.epsr.2019.01.025Farrokhabadi, M., Canizares, C. A., Simpson-Porco, J. W., Nasr, E., Fan, L., Mendoza-Araya, P. A., … Reilly, J. (2020). Microgrid Stability Definitions, Analysis, and Examples. IEEE Transactions on Power Systems, 35(1), 13-29. doi:10.1109/tpwrs.2019.2925703Yoldaş, Y., Önen, A., Muyeen, S. M., Vasilakos, A. V., & Alan, İ. (2017). Enhancing smart grid with microgrids: Challenges and opportunities. Renewable and Sustainable Energy Reviews, 72, 205-214. doi:10.1016/j.rser.2017.01.064Rajesh, K. S., Dash, S. S., Rajagopal, R., & Sridhar, R. (2017). A review on control of ac microgrid. Renewable and Sustainable Energy Reviews, 71, 814-819. doi:10.1016/j.rser.2016.12.106Marzal, S., Salas, R., González-Medina, R., Garcerá, G., & Figueres, E. (2018). Current challenges and future trends in the field of communication architectures for microgrids. Renewable and Sustainable Energy Reviews, 82, 3610-3622. doi:10.1016/j.rser.2017.10.101Singh, A., & Suhag, S. (2018). Trends in Islanded Microgrid Frequency Regulation – A Review. Smart Science, 7(2), 91-115. doi:10.1080/23080477.2018.1540380Hou, X., Sun, Y., Lu, J., Zhang, X., Koh, L. H., Su, M., & Guerrero, J. M. (2018). Distributed Hierarchical Control of AC Microgrid Operating in Grid-Connected, Islanded and Their Transition Modes. IEEE Access, 6, 77388-77401. doi:10.1109/access.2018.2882678SHI, R., ZHANG, X., HU, C., XU, H., GU, J., & CAO, W. (2017). Self-tuning virtual synchronous generator control for improving frequency stability in autonomous photovoltaic-diesel microgrids. Journal of Modern Power Systems and Clean Energy, 6(3), 482-494. doi:10.1007/s40565-017-0347-3Toub, M., Bijaieh, M. M., Weaver, W. W., III, R. D. R., Maaroufi, M., & Aniba, G. (2019). Droop Control in DQ Coordinates for Fixed Frequency Inverter-Based AC Microgrids. Electronics, 8(10), 1168. doi:10.3390/electronics8101168Shuai, Z., Fang, J., Ning, F., & Shen, Z. J. (2018). Hierarchical structure and bus voltage control of DC microgrid. Renewable and Sustainable Energy Reviews, 82, 3670-3682. doi:10.1016/j.rser.2017.10.096Agundis-Tinajero, G., Segundo-Ramírez, J., Visairo-Cruz, N., Savaghebi, M., Guerrero, J. M., & Barocio, E. (2019). Power flow modeling of islanded AC microgrids with hierarchical control. International Journal of Electrical Power & Energy Systems, 105, 28-36. doi:10.1016/j.ijepes.2018.08.002Ali, A., Li, W., Hussain, R., He, X., Williams, B., & Memon, A. (2017). Overview of Current Microgrid Policies, Incentives and Barriers in the European Union, United States and China. Sustainability, 9(7), 1146. doi:10.3390/su9071146Cui, Y., Geng, Z., Zhu, Q., & Han, Y. (2017). Review: Multi-objective optimization methods and application in energy saving. Energy, 125, 681-704. doi:10.1016/j.energy.2017.02.174Yazdi, F., & Hosseinian, S. H. (2019). A novel «Smart Branch» for power quality improvement in microgrids. International Journal of Electrical Power & Energy Systems, 110, 161-170. doi:10.1016/j.ijepes.2019.02.026Bassey, O., Butler-Purry, K. L., & Chen, B. (2020). Dynamic Modeling of Sequential Service Restoration in Islanded Single Master Microgrids. IEEE Transactions on Power Systems, 35(1), 202-214. doi:10.1109/tpwrs.2019.2929268Chang, E.-C. (2018). Study and Application of Intelligent Sliding Mode Control for Voltage Source Inverters. Energies, 11(10), 2544. doi:10.3390/en11102544Das, D., Gurrala, G., & Shenoy, U. J. (2018). Linear Quadratic Regulator-Based Bumpless Transfer in Microgrids. IEEE Transactions on Smart Grid, 9(1), 416-425. doi:10.1109/tsg.2016.2580159Nguyen, H. K., Khodaei, A., & Han, Z. (2018). Incentive Mechanism Design for Integrated Microgrids in Peak Ramp Minimization Problem. IEEE Transactions on Smart Grid, 9(6), 5774-5785. doi:10.1109/tsg.2017.2696903Xiao, Z., Guerrero, J. M., Shuang, J., Sera, D., Schaltz, E., & Vásquez, J. C. (2018). Flat tie-line power scheduling control of grid-connected hybrid microgrids. Applied Energy, 210, 786-799. doi:10.1016/j.apenergy.2017.07.066Baghaee, H. R., Mirsalim, M., Gharehpetian, G. B., & Talebi, H. A. (2018). A Decentralized Robust Mixed H2/HH_{{2}}/ H_{{{\infty }}} Voltage Control Scheme to Improve Small/Large-Signal Stability and FRT Capability of Islanded Multi-DER Microgrid Considering Load Disturbances. IEEE Systems Journal, 12(3), 2610-2621. doi:10.1109/jsyst.2017.2716351Panda, S. K., & Ghosh, A. (2020). 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