Forecasting Recharging Demand to Integrate Electric Vehicle Fleets in Smart Grids

Electric vehicle fleets and smart grids are two growing technologies. These technologies provided new possibilities to reduce pollution and increase energy efficiency. In this sense, electric vehicles are used as mobile loads in the power grid. A distributed charging prioritization methodology is proposed in this paper. The solution is based on the concept of virtual power plants and the usage of evolutionary computation algorithms. Additionally, the comparison of several evolutionary algorithms, genetic algorithm, genetic algorithm with evolution control, particle swarm optimization, and hybrid solution are shown in order to evaluate the proposed architecture. The proposed solution is presented to prevent the overload of the power grid

European White Book on Real-Time Power Hardware in the Loop Testing : DERlab Report No. R- 005.0

The European White Book on Real-Time-Powerhardware-in-the-Loop testing is intended to serve as a reference document on the future of testing of electrical power equipment, with specifi c focus on the emerging hardware-in-the-loop activities and application thereof within testing facilities and procedures. It will provide an outlook of how this powerful tool can be utilised to support the development, testing and validation of specifi cally DER equipment. It aims to report on international experience gained thus far and provides case studies on developments and specifi c technical issues, such as the hardware/software interface. This white book compliments the already existing series of DERlab European white books, covering topics such as grid-inverters and grid-connected storag

Power systems with high renewable energy sources: A review of inertia and frequency control strategies over time

Traditionally, inertia in power systems has been determined by considering all the rotating masses directly connected to the grid. During the last decade, the integration of renewable energy sources, mainly photovoltaic installations and wind power plants, has led to a significant dynamic characteristic change in power systems. This change is mainly due to the fact that most renewables have power electronics at the grid interface. The overall impact on stability and reliability analysis of power systems is very significant. The power systems become more dynamic and require a new set of strategies modifying traditional generation control algorithms. Indeed, renewable generation units are decoupled from the grid by electronic converters, decreasing the overall inertia of the grid. ‘Hidden inertia’, ‘synthetic inertia’ or ‘virtual inertia’ are terms currently used to represent artificial inertia created by converter control of the renewable sources. Alternative spinning reserves are then needed in the new power system with high penetration renewables, where the lack of rotating masses directly connected to the grid must be emulated to maintain an acceptable power system reliability. This paper reviews the inertia concept in terms of values and their evolution in the last decades, as well as the damping factor values. A comparison of the rotational grid inertia for traditional and current averaged generation mix scenarios is also carried out. In addition, an extensive discussion on wind and photovoltaic power plants and their contributions to inertia in terms of frequency control strategies is included in the paper.This work was supported by the Spanish Education, Culture and Sports Ministry [FPU16/04282]

Novel Conceptual Architecture for the Next-Generation Electricity Markets to Enhance a Large Penetration of Renewable Energy

[EN] A transition to a sustainable energy system is essential. In this context, smart grids represent the future of power systems for efficiently integrating renewable energy sources and active consumer participation. Recently, different studies were performed that defined the conceptual architecture of power systems and their agents. However, these conceptual architectures do not overcome all issues for the development of new electricity markets. Thus, a novel conceptual architecture is proposed. The transactions of energy, operation services, and economic flows among the agents proposed are carefully analysed. In this regard, the results allow setting their activities' boundaries and state their relationships with electricity markets. The suitability of implementing local electricity markets is studied to enforce competition among distributed energy resources by unlocking all the potential that active consumers have. The proposed architecture is designed to offer flexibility and efficiency to the system thanks to a clearly defined way for the exploitation of flexible resources and distributed generation. This upgraded architecture hereby proposed establishes the characteristics of each agent in the forthcoming markets and studies to overcome the barriers to the large deployment of renewable energy sources.This work was supported by the Ministerio de Economia, Industria, y Competitividad (Spanish Government) under research project ENE-2016-78509-C3-1-P, and EU FEDER funds. The authors received funds from these grants for covering the costs to publish in open access. This work was also supported by the Spanish Ministry of Education under the scholarship FPU16/00962.Rodríguez-García, J.; Ribó-Pérez, DG.; Álvarez, C.; Peñalvo-López, E. (2019). Novel Conceptual Architecture for the Next-Generation Electricity Markets to Enhance a Large Penetration of Renewable Energy. Energies. 12(13):1-23. https://doi.org/10.3390/en12132605S1231213Gabaldón, A., Guillamón, A., Ruiz, M. C., Valero, S., Álvarez, C., Ortiz, M., & Senabre, C. (2010). Development of a methodology for clustering electricity-price series to improve customer response initiatives. IET Generation, Transmission & Distribution, 4(6), 706. doi:10.1049/iet-gtd.2009.0112Weitemeyer, S., Kleinhans, D., Vogt, T., & Agert, C. (2015). Integration of Renewable Energy Sources in future power systems: The role of storage. Renewable Energy, 75, 14-20. doi:10.1016/j.renene.2014.09.028Albano, M., Ferreira, L. L., & Pinho, L. M. (2015). Convergence of Smart Grid ICT Architectures for the Last Mile. IEEE Transactions on Industrial Informatics, 11(1), 187-197. doi:10.1109/tii.2014.2379436Goncalves Da Silva, P., Ilic, D., & Karnouskos, S. (2014). The Impact of Smart Grid Prosumer Grouping on Forecasting Accuracy and Its Benefits for Local Electricity Market Trading. IEEE Transactions on Smart Grid, 5(1), 402-410. doi:10.1109/tsg.2013.2278868Ipakchi, A., & Albuyeh, F. (2009). Grid of the future. IEEE Power and Energy Magazine, 7(2), 52-62. doi:10.1109/mpe.2008.931384Coelho, V. N., Weiss Cohen, M., Coelho, I. M., Liu, N., & Guimarães, F. G. (2017). Multi-agent systems applied for energy systems integration: State-of-the-art applications and trends in microgrids. Applied Energy, 187, 820-832. doi:10.1016/j.apenergy.2016.10.056Logenthiran, T., Srinivasan, D., & Khambadkone, A. M. (2011). Multi-agent system for energy resource scheduling of integrated microgrids in a distributed system. Electric Power Systems Research, 81(1), 138-148. doi:10.1016/j.epsr.2010.07.019Radhakrishnan, B. M., & Srinivasan, D. (2016). A multi-agent based distributed energy management scheme for smart grid applications. Energy, 103, 192-204. doi:10.1016/j.energy.2016.02.117Yoo, C.-H., Chung, I.-Y., Lee, H.-J., & Hong, S.-S. (2013). Intelligent Control of Battery Energy Storage for Multi-Agent Based Microgrid Energy Management. Energies, 6(10), 4956-4979. doi:10.3390/en6104956Zhao, B., Xue, M., Zhang, X., Wang, C., & Zhao, J. (2015). An MAS based energy management system for a stand-alone microgrid at high altitude. Applied Energy, 143, 251-261. doi:10.1016/j.apenergy.2015.01.016Ringler, P., Keles, D., & Fichtner, W. (2016). Agent-based modelling and simulation of smart electricity grids and markets – A literature review. Renewable and Sustainable Energy Reviews, 57, 205-215. doi:10.1016/j.rser.2015.12.169Wang, Q., Zhang, C., Ding, Y., Xydis, G., Wang, J., & Østergaard, J. (2015). Review of real-time electricity markets for integrating Distributed Energy Resources and Demand Response. Applied Energy, 138, 695-706. doi:10.1016/j.apenergy.2014.10.048Pandžić, H., Kuzle, I., & Capuder, T. (2013). Virtual power plant mid-term dispatch optimization. Applied Energy, 101, 134-141. doi:10.1016/j.apenergy.2012.05.039Pandžić, H., Morales, J. M., Conejo, A. J., & Kuzle, I. (2013). Offering model for a virtual power plant based on stochastic programming. Applied Energy, 105, 282-292. doi:10.1016/j.apenergy.2012.12.077Rahimiyan, M., & Baringo, L. (2016). Strategic Bidding for a Virtual Power Plant in the Day-Ahead and Real-Time Markets: A Price-Taker Robust Optimization Approach. IEEE Transactions on Power Systems, 31(4), 2676-2687. doi:10.1109/tpwrs.2015.2483781Mnatsakanyan, A., & Kennedy, S. W. (2015). A Novel Demand Response Model with an Application for a Virtual Power Plant. IEEE Transactions on Smart Grid, 6(1), 230-237. doi:10.1109/tsg.2014.2339213Bartolucci, L., Cordiner, S., Mulone, V., & Santarelli, M. (2019). Ancillary Services Provided by Hybrid Residential Renewable Energy Systems through Thermal and Electrochemical Storage Systems. Energies, 12(12), 2429. doi:10.3390/en12122429Cucchiella, F., D’Adamo, I., Gastaldi, M., & Stornelli, V. (2018). Solar Photovoltaic Panels Combined with Energy Storage in a Residential Building: An Economic Analysis. Sustainability, 10(9), 3117. doi:10.3390/su10093117Dupont, B., De Jonghe, C., Olmos, L., & Belmans, R. (2014). Demand response with locational dynamic pricing to support the integration of renewables. Energy Policy, 67, 344-354. doi:10.1016/j.enpol.2013.12.058Comparison of Actual Costs to Integrate Commercial Buildings with the Grid; Jun. 2016https://www.semanticscholar.org/paper/Comparison-of-Actual-Costs-to-Integrate-Commercial-Piette-Black/b953cfef9716b1f87c759048ef714e8c70e19869/Alfonso, D., Pérez-Navarro, A., Encinas, N., Álvarez, C., Rodríguez, J., & Alcázar, M. (2007). Methodology for ranking customer segments by their suitability for distributed energy resources applications. Energy Conversion and Management, 48(5), 1615-1623. doi:10.1016/j.enconman.2006.11.006Rodríguez-García, J., Álvarez-Bel, C., Carbonell-Carretero, J.-F., Alcázar-Ortega, M., & Peñalvo-López, E. (2016). A novel tool for the evaluation and assessment of demand response activities in the industrial sector. Energy, 113, 1136-1146. doi:10.1016/j.energy.2016.07.146Morales, D. X., Besanger, Y., Sami, S., & Alvarez Bel, C. (2017). Assessment of the impact of intelligent DSM methods in the Galapagos Islands toward a Smart Grid. Electric Power Systems Research, 146, 308-320. doi:10.1016/j.epsr.2017.02.003Derakhshan, G., Shayanfar, H. A., & Kazemi, A. (2016). The optimization of demand response programs in smart grids. Energy Policy, 94, 295-306. doi:10.1016/j.enpol.2016.04.009Söyrinki, S., Heiskanen, E., & Matschoss, K. (2018). Piloting Demand Response in Retailing: Lessons Learned in Real-Life Context. Sustainability, 10(10), 3790. doi:10.3390/su10103790McPherson, M., & Tahseen, S. (2018). Deploying storage assets to facilitate variable renewable energy integration: The impacts of grid flexibility, renewable penetration, and market structure. Energy, 145, 856-870. doi:10.1016/j.energy.2018.01.002Hornsdale Power Reserve, Year 1 Technical and Market Impact Case Studyhttps://www.aurecongroup.com/markets/energy/hornsdale-power-reserve-impact-study/Burger, S., Chaves-Ávila, J. P., Batlle, C., & Pérez-Arriaga, I. J. (2017). A review of the value of aggregators in electricity systems. Renewable and Sustainable Energy Reviews, 77, 395-405. doi:10.1016/j.rser.2017.04.014Niesten, E., & Alkemade, F. (2016). How is value created and captured in smart grids? A review of the literature and an analysis of pilot projects. Renewable and Sustainable Energy Reviews, 53, 629-638. doi:10.1016/j.rser.2015.08.069Calvillo, C. F., Sánchez-Miralles, A., Villar, J., & Martín, F. (2016). Optimal planning and operation of aggregated distributed energy resources with market participation. Applied Energy, 182, 340-357. doi:10.1016/j.apenergy.2016.08.117Lopes, A. J., Lezama, R., & Pineda, R. (2011). Model Based Systems Engineering for Smart Grids as Systems of Systems. Procedia Computer Science, 6, 441-450. doi:10.1016/j.procs.2011.08.083Lüth, A., Zepter, J. M., Crespo del Granado, P., & Egging, R. (2018). Local electricity market designs for peer-to-peer trading: The role of battery flexibility. Applied Energy, 229, 1233-1243. doi:10.1016/j.apenergy.2018.08.004Kabalci, Y. (2016). A survey on smart metering and smart grid communication. Renewable and Sustainable Energy Reviews, 57, 302-318. doi:10.1016/j.rser.2015.12.114Alahakoon, D., & Yu, X. (2016). Smart Electricity Meter Data Intelligence for Future Energy Systems: A Survey. IEEE Transactions on Industrial Informatics, 12(1), 425-436. doi:10.1109/tii.2015.2414355Luthander, R., Widén, J., Nilsson, D., & Palm, J. (2015). Photovoltaic self-consumption in buildings: A review. Applied Energy, 142, 80-94. doi:10.1016/j.apenergy.2014.12.028Jha, M., Blaabjerg, F., Khan, M. A., Bharath Kurukuru, V. S., & Haque, A. (2019). Intelligent Control of Converter for Electric Vehicles Charging Station. Energies, 12(12), 2334. doi:10.3390/en12122334Full Report Australian Energy Storagehttps://www.smartenergy.org.au/resources/australian-energy-storage-market-analysis

Frequency support characteristics of grid-interactive power converters based on the synchronous power controller

Grid-interactive converters with primary frequency control and inertia emulation have emerged and are promising for future renewable generation plants because of the contribution in power system stabilization. This paper gives a synchronous active power control solution for gridinteractive converters , as a way to emulate synchronous generators for inerita characteristics and load sharing. As design considerations, the virtual angle stability and transient response are both analyzed, and the detailed implementation structure is also given without entailing any difficulty in practice. The analytical and experimental validation of frequency support characteristics differentiates the work from other publications on generator emulation control. The 10 kW simulation and experimental frequency sweep tests on a regenerative source test bed present good performance of the proposed control in showing inertia and droop characteristics, as well as the controllable transient response.Peer ReviewedPostprint (author's final draft

Scenarios for the development of smart grids in the UK: literature review

Smart grids are expected to play a central role in any transition to a low-carbon energy future, and much research is currently underway on practically every area of smart grids. However, it is evident that even basic aspects such as theoretical and operational definitions, are yet to be agreed upon and be clearly defined. Some aspects (efficient management of supply, including intermittent supply, two-way communication between the producer and user of electricity, use of IT technology to respond to and manage demand, and ensuring safe and secure electricity distribution) are more commonly accepted than others (such as smart meters) in defining what comprises a smart grid. It is clear that smart grid developments enjoy political and financial support both at UK and EU levels, and from the majority of related industries. The reasons for this vary and include the hope that smart grids will facilitate the achievement of carbon reduction targets, create new employment opportunities, and reduce costs relevant to energy generation (fewer power stations) and distribution (fewer losses and better stability). However, smart grid development depends on additional factors, beyond the energy industry. These relate to issues of public acceptability of relevant technologies and associated risks (e.g. data safety, privacy, cyber security), pricing, competition, and regulation; implying the involvement of a wide range of players such as the industry, regulators and consumers. The above constitute a complex set of variables and actors, and interactions between them. In order to best explore ways of possible deployment of smart grids, the use of scenarios is most adequate, as they can incorporate several parameters and variables into a coherent storyline. Scenarios have been previously used in the context of smart grids, but have traditionally focused on factors such as economic growth or policy evolution. Important additional socio-technical aspects of smart grids emerge from the literature review in this report and therefore need to be incorporated in our scenarios. These can be grouped into four (interlinked) main categories: supply side aspects, demand side aspects, policy and regulation, and technical aspects.

Toward sustainable data centers: a comprehensive energy management strategy

Data centers are major contributors to the emission of carbon dioxide to the atmosphere, and this contribution is expected to increase in the following years. This has encouraged the development of techniques to reduce the energy consumption and the environmental footprint of data centers. Whereas some of these techniques have succeeded to reduce the energy consumption of the hardware equipment of data centers (including IT, cooling, and power supply systems), we claim that sustainable data centers will be only possible if the problem is faced by means of a holistic approach that includes not only the aforementioned techniques but also intelligent and unifying solutions that enable a synergistic and energy-aware management of data centers. In this paper, we propose a comprehensive strategy to reduce the carbon footprint of data centers that uses the energy as a driver of their management procedures. In addition, we present a holistic management architecture for sustainable data centers that implements the aforementioned strategy, and we propose design guidelines to accomplish each step of the proposed strategy, referring to related achievements and enumerating the main challenges that must be still solved.Peer ReviewedPostprint (author's final draft