Comparison of intelligent charging algorithms for electric vehicles to reduce peak load and demand variability in a distribution grid

A potential breakthrough of the electrification of the vehicle fleet will incur a steep rise in the load on the electrical power grid. To avoid huge grid investments, coordinated charging of those vehicles is a must. In this paper, we assess algorithms to schedule charging of plug-in (hybrid) electric vehicles as to minimize the additional peak load they might cause. We first introduce two approaches, one based on a classical optimization approach using quadratic programming, and a second one, market based coordination, which is a multi-agent system that uses bidding on a virtual market to reach an equilibrium, price that matches demand and supply. We benchmark these two methods against each other, as well as to a baseline scenario of uncontrolled charging. Our simulation results covering a residential area with 63 households show that controlled charging reduces peak load, load variability, and deviations from the nominal grid voltage

Distributed multi-agent algorithm for residential energy management in smart grids

Distributed renewable power generators, such as solar cells and wind turbines are difficult to predict, making the demand-supply problem more complex than in the traditional energy production scenario. They also introduce bidirectional energy flows in the low-voltage power grid, possibly causing voltage violations and grid instabilities. In this article we describe a distributed algorithm for residential energy management in smart power grids. This algorithm consists of a market-oriented multi-agent system using virtual energy prices, levels of renewable energy in the real-time production mix, and historical price information, to achieve a shifting of loads to periods with a high production of renewable energy. Evaluations in our smart grid simulator for three scenarios show that the designed algorithm is capable of improving the self consumption of renewable energy in a residential area and reducing the average and peak loads for externally supplied power

Experimental Study of a Centralized Control Strategy of a DC Microgrid Working in Grid Connected Mode

[EN] The results concerning the integration of a set of power management strategies and serial communications for the efficient coordination of the power converters composing an experimental DC microgrid is presented. The DC microgrid operates in grid connected mode by means of an interlinking converter. The overall control is carried out by means of a centralized microgrid controller implemented on a Texas Instruments TMS320F28335 DSP. The main objectives of the applied control strategies are to ensure the extract/inject power limits established by the grid operator as well as the renewable generation limits if it is required; to devise a realistic charging procedure of the energy storage batteries as a function of the microgrid status; to manage sudden changes of the available power from the photovoltaic energy sources, of the load power demand and of the power references established by the central controller; and to implement a load shedding functionality. The experimental results demonstrate that the proposed power management methodology allows the control of the power dispatch inside the DC microgrid properly.This work has been cofinanced by the Spanish Ministry of Economy and Competitiveness (MINECO) and by the European Regional Development Fund (ERDF) under Grant ENE2015-64087-C2-2.Salas-Puente, RA.; Marzal-Romeu, S.; González-Medina, R.; Figueres Amorós, E.; Garcerá, G. (2017). Experimental Study of a Centralized Control Strategy of a DC Microgrid Working in Grid Connected Mode. Energies. 10(10):1-25. https://doi.org/10.3390/en10101627S1251010Baek, J., Choi, W., & Chae, S. (2017). Distributed Control Strategy for Autonomous Operation of Hybrid AC/DC Microgrid. Energies, 10(3), 373. doi:10.3390/en10030373Patrao, I., Figueres, E., Garcerá, G., & González-Medina, R. (2015). Microgrid architectures for low voltage distributed generation. Renewable and Sustainable Energy Reviews, 43, 415-424. doi:10.1016/j.rser.2014.11.054Ma, T., Yahoui, H., Vu, H., Siauve, N., & Morel, H. (2017). A Control Strategy of DC Building Microgrid Connected to the Neighborhood and AC Power Network. Buildings, 7(4), 42. doi:10.3390/buildings7020042Lin, P., Wang, P., Xiao, J., Wang, J., Jin, C., & Tang, Y. (2018). An Integral Droop for Transient Power Allocation and Output Impedance Shaping of Hybrid Energy Storage System in DC Microgrid. IEEE Transactions on Power Electronics, 33(7), 6262-6277. doi:10.1109/tpel.2017.2741262Kakigano, H., Miura, Y., & Ise, T. (2010). Low-Voltage Bipolar-Type DC Microgrid for Super High Quality Distribution. IEEE Transactions on Power Electronics, 25(12), 3066-3075. doi:10.1109/tpel.2010.2077682Salomonsson, D., Soder, L., & Sannino, A. (2008). An Adaptive Control System for a DC Microgrid for Data Centers. IEEE Transactions on Industry Applications, 44(6), 1910-1917. doi:10.1109/tia.2008.2006398Xu, L., & Chen, D. (2011). Control and Operation of a DC Microgrid With Variable Generation and Energy Storage. IEEE Transactions on Power Delivery, 26(4), 2513-2522. doi:10.1109/tpwrd.2011.2158456Nejabatkhah, F., & Li, Y. W. (2015). Overview of Power Management Strategies of Hybrid AC/DC Microgrid. IEEE Transactions on Power Electronics, 30(12), 7072-7089. doi:10.1109/tpel.2014.2384999Lu, X., Guerrero, J. M., Sun, K., & Vasquez, J. C. (2014). An Improved Droop Control Method for DC Microgrids Based on Low Bandwidth Communication With DC Bus Voltage Restoration and Enhanced Current Sharing Accuracy. IEEE Transactions on Power Electronics, 29(4), 1800-1812. doi:10.1109/tpel.2013.2266419Chen, D., & Xu, L. (2012). Autonomous DC Voltage Control of a DC Microgrid With Multiple Slack Terminals. IEEE Transactions on Power Systems, 27(4), 1897-1905. doi:10.1109/tpwrs.2012.2189441Guerrero, J. M., Vasquez, J. C., Matas, J., de Vicuna, L. G., & Castilla, M. (2011). Hierarchical Control of Droop-Controlled AC and DC Microgrids—A General Approach Toward Standardization. IEEE Transactions on Industrial Electronics, 58(1), 158-172. doi:10.1109/tie.2010.2066534Vasquez, J., Guerrero, J., Miret, J., Castilla, M., & Garcia de Vicuna, L. (2010). Hierarchical Control of Intelligent Microgrids. IEEE Industrial Electronics Magazine, 4(4), 23-29. doi:10.1109/mie.2010.938720Unamuno, E., & Barrena, J. A. (2015). Hybrid ac/dc microgrids—Part II: Review and classification of control strategies. Renewable and Sustainable Energy Reviews, 52, 1123-1134. doi:10.1016/j.rser.2015.07.186Feng, X., Shekhar, A., Yang, F., E. Hebner, R., & Bauer, P. (2017). Comparison of Hierarchical Control and Distributed Control for Microgrid. Electric Power Components and Systems, 45(10), 1043-1056. doi:10.1080/15325008.2017.1318982Kaur, A., Kaushal, J., & Basak, P. (2016). A review on microgrid central controller. Renewable and Sustainable Energy Reviews, 55, 338-345. doi:10.1016/j.rser.2015.10.141Wu, D., Tang, F., Dragicevic, T., Guerrero, J. M., & Vasquez, J. C. (2015). Coordinated Control Based on Bus-Signaling and Virtual Inertia for Islanded DC Microgrids. IEEE Transactions on Smart Grid, 6(6), 2627-2638. doi:10.1109/tsg.2014.2387357Shi, D., Chen, X., Wang, Z., Zhang, X., Yu, Z., Wang, X., & Bian, D. (2018). A Distributed Cooperative Control Framework for Synchronized Reconnection of a Multi-Bus Microgrid. IEEE Transactions on Smart Grid, 9(6), 6646-6655. doi:10.1109/tsg.2017.2717806Dou, C., Zhang, Z., Yue, D., & Zheng, Y. (2017). MAS-Based Hierarchical Distributed Coordinate Control Strategy of Virtual Power Source Voltage in Low-Voltage Microgrid. IEEE Access, 5, 11381-11390. doi:10.1109/access.2017.2717493Bracale, A., Caramia, P., Carpinelli, G., Mancini, E., & Mottola, F. (2015). Optimal control strategy of a DC micro grid. International Journal of Electrical Power & Energy Systems, 67, 25-38. doi:10.1016/j.ijepes.2014.11.003Yue, J., Hu, Z., Li, C., Vasquez, J. C., & Guerrero, J. M. (2017). Economic Power Schedule and Transactive Energy through an Intelligent Centralized Energy Management System for a DC Residential Distribution System. Energies, 10(7), 916. doi:10.3390/en10070916Gao, L., Liu, Y., Ren, H., & Guerrero, J. (2017). A DC Microgrid Coordinated Control Strategy Based on Integrator Current-Sharing. Energies, 10(8), 1116. doi:10.3390/en10081116Operating Instructions Valve Regulated Stationary Lead-Acid Batterieshttp://www.hoppecke-us.com/tl_files/hoppecke/Documents/HO-US/Operating_Instructions_sealed_stationary_lead_acid_batteries_en1111.pdfTAB Batterieshttp://www.tabspain.com/wp-content/uploads/informacion-tecnica/renovables/curvas-y-tablas/din-41773-y-din-41774-para-baterias-pzs.pdfZhao, J., & Dörfler, F. (2015). Distributed control and optimization in DC microgrids. Automatica, 61, 18-26. doi:10.1016/j.automatica.2015.07.015Eghtedarpour, N., & Farjah, E. (2014). Power Control and Management in a Hybrid AC/DC Microgrid. IEEE Transactions on Smart Grid, 5(3), 1494-1505. doi:10.1109/tsg.2013.2294275Installation, Commissioning and Operation Handbook for Gel-Vrla-Batterieshttp://www.sonnenschein.org/PDF%20files/GelHandbookPart2.pdfAlLee, G., & Tschudi, W. (2012). Edison Redux: 380 Vdc Brings Reliability and Efficiency to Sustainable Data Centers. IEEE Power and Energy Magazine, 10(6), 50-59. doi:10.1109/mpe.2012.2212607Aryani, D., & Song, H. (2016). Coordination Control Strategy for AC/DC Hybrid Microgrids in Stand-Alone Mode. Energies, 9(6), 469. doi:10.3390/en9060469Dragicevic, T., Guerrero, J. M., Vasquez, J. C., & Skrlec, D. (2014). Supervisory Control of an Adaptive-Droop Regulated DC Microgrid With Battery Management Capability. IEEE Transactions on Power Electronics, 29(2), 695-706. doi:10.1109/tpel.2013.2257857Tian, Y., Li, D., Tian, J., & Xia, B. (2017). State of charge estimation of lithium-ion batteries using an optimal adaptive gain nonlinear observer. Electrochimica Acta, 225, 225-234. doi:10.1016/j.electacta.2016.12.119Standard for Interconnecting Distributed Resources with Electric Power Systemshttp://fglongatt.org/OLD/Archivos/Archivos/SistGD/IEEE1547.pd

Smart Grid Technologies in Europe: An Overview

The old electricity network infrastructure has proven to be inadequate, with respect to modern challenges such as alternative energy sources, electricity demand and energy saving policies. Moreover, Information and Communication Technologies (ICT) seem to have reached an adequate level of reliability and flexibility in order to support a new concept of electricity network—the smart grid. In this work, we will analyse the state-of-the-art of smart grids, in their technical, management, security, and optimization aspects. We will also provide a brief overview of the regulatory aspects involved in the development of a smart grid, mainly from the viewpoint of the European Unio

Optimization of the operation of smart rural grids through a novel rnergy management system

The paper proposes an innovative Energy Management System (EMS) that optimizes the grid operation based on economic and technical criteria. The EMS inputs the demand and renewable generation forecasts, electricity prices and the status of the distributed storages through the network, and solves with an optimal quarter-hourly dispatch for controllable resources. The performance of the EMS is quantified through diverse proposed metrics. The analyses were based on a real rural grid from the European FP7 project Smart Rural Grid. The performance of the EMS has been evaluated through some scenarios varying the penetration of distributed generation. The obtained results demonstrate that the inclusion of the EMS from both a technical point of view and an economic perspective for the adopted grid is justified. At the technical level, the inclusion of the EMS permits us to significantly increase the power quality in weak and radial networks. At the economic level and from a certain threshold value in renewables’ penetration, the EMS reduces the energy costs for the grid participants, minimizing imports from the external grid and compensating the toll to be paid in the form of the losses incurred by including additional equipment in the network (i.e., distributed storage).Postprint (published version

Next-generation optical access seamless Evolution: concluding results of the European FP7 project OASE

Increasing bandwidth demand drives the need for next-generation optical access (NGOA) networks that can meet future end-user service requirements. This paper gives an overview of NGOA solutions, the enabling optical access network technologies, architecture principles, and related economics and business models. NGOA requirements (including peak and sustainable data rate, reach, cost, node consolidation, and open access) are proposed, and the different solutions are compared against such requirements in different scenarios (in terms of population density and system migration). Unsurprisingly, it is found that different solutions are best suited for different scenarios. The conclusions drawn from such findings allow us to formulate recommendations in terms of technology, strategy, and policy. The paper is based on the main results of the European FP7 OASE Integrated Project that ran between January 1, 2010 and February 28, 2013

Sub-agent elements for control methods in multi-agent energy management system

Increased penetration of generation and decentralised control are considered to be feasible and effective solution for reducing cost and emissions and hence efficiency associated with power generation and distribution. Distributed generation in combination with the multi-agent technology are perfect candidates for this solution. Pro-active and autonomous nature of multi-agent systems can provide an effective platform for decentralised control whilst improving reliability and flexibility of the grid