26,901 research outputs found

    Battery storage systems in smart grid optimised buildings

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    The building sector is responsible for a significant proportion of the consumed energy and the consequent carbon emissions. Currently, electricity and natural gas are the most popular fuels used in the UK Services sector and the industry. Furthermore, buildings constitute a key component of the power network, in both its current conventional form and its evolution, the smart grid. The smart grid is expected to integrate energy storage, distributed generation and buildings into the network. This paper introduces the concept of Smart Grid Optimised Buildings (SGOBs), recognising the importance of energy storage to establish a dynamic interaction between the building and the smart grid. SGOBs are expected to be fully electric, make the best use of the available resources and utilise their embedded battery storage systems to respond to notifications issued by the smart grid and to dynamic electricity prices. Assuming that buildings have access to the day-ahead electricity market, initial results show that battery storage can be successfully used to change a building’s electricity profile and perform load-shifting (arbitrage) and peak-shaving while the excess electricity is exported back to grid to take advantage of the price difference and relieve pressure on the infrastructure

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

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    [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.RodrĂ­guez-GarcĂ­a, J.; RibĂł-PĂ©rez, DG.; Álvarez, C.; Peñalvo-LĂłpez, 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., & GuimarĂŁes, 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.2014.10.048PandĆŸić, H., Kuzle, I., & Capuder, T. (2013). Virtual power plant mid-term dispatch optimization. Applied Energy, 101, 134-141. doi:10.1016/j.apenergy.2012.05.039PandĆŸić, 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., PĂ©rez-Navarro, A., Encinas, N., Álvarez, C., RodrĂ­guez, J., & AlcĂĄzar, 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.2006.11.006RodrĂ­guez-GarcĂ­a, J., Álvarez-Bel, C., Carbonell-Carretero, J.-F., AlcĂĄzar-Ortega, M., & Peñalvo-LĂłpez, 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., & PĂ©rez-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., SĂĄnchez-Miralles, A., Villar, J., & MartĂ­n, 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.2011.08.083LĂŒth, 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., WidĂ©n, 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

    biomass early stage combustion in a small size boiler experimental and numerical analysis

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    Abstract The increment in the world energy consumption and the necessity for a sustainable industrial production, indicate that renewable resources may be key actors for future development. In this scenario, biomass appears fundamental for the smooth transition from fossil fuels to lower carbon footprint technologies, and as a moderator agent within the renewable market. The small size biomass combustion application appears as suitable for smart grid and distributed generation applications, but it is necessary to improve the design tools capabilities and the experimental knowledge of these systems. The present work aims at investigating the thermal behaviour of a 140 kW fixed-bed boiler sited at the Biomass to Energy Research Centre (CRIBE) of the University of Pisa and fed with woodchips. Experimental activities were conducted in order to acquire thermal and chemical data. Moreover, a computational fluid dynamic model was developed and validated. Attention was paid to the fixed bed analysis, and the results showed a good model prediction capability, with respect to the reduced computational demand required

    Smart Grid Technologies in Europe: An Overview

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    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

    Service Orientation and the Smart Grid state and trends

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    The energy market is undergoing major changes, the most notable of which is the transition from a hierarchical closed system toward a more open one highly based on a “smart” information-rich infrastructure. This transition calls for new information and communication technologies infrastructures and standards to support it. In this paper, we review the current state of affairs and the actual technologies with respect to such transition. Additionally, we highlight the contact points between the needs of the future grid and the advantages brought by service-oriented architectures.

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

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    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.
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