Intelligent control of battery energy storage for microgrid energy management using ANN

In this paper, an intelligent control strategy for a microgrid system consisting of Photovoltaic panels, grid-connected, and li-ion battery energy storage systems proposed. The energy management based on the managing of battery charging and discharging by integration of a smart controller for DC/DC bidirectional converter. The main novelty of this solution are the integration of artificial neural network (ANN) for the estimation of the battery state of charge (SOC) and for the control of bidirectional converter. The simulation results obtained in the MATLAB/Simulink environment explain the performance and the robust of the proposed control technique

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

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

Methods for Optimal Microgrid Management

Abstract During the last years, the number of distributed generators has grown significantly and it is expected to become higher in the future. Several new technologies are being de-veloped for this type of generation (including microturbines, photovoltaic plants, wind turbines and electrical storage systems) and have to be integrated in the electrical grid. In this framework, active loads (i.e., shiftable demands like electrical vehicles, intelligent buildings, etc.) and storage systems are crucial to make more flexible and smart the dis-tribution system. This thesis deals with the development and application of system engi-neering methods to solve real-world problems within the specific framework of microgrid control and management. The typical kind of problems that is considered when dealing with the manage-ment and control of Microgrids is generally related to optimal scheduling of the flows of energy among the various components in the systems, within a limited area. The general objective is to schedule the energy consumptions to maximize the expected system utility under energy consumption and energy generation constraints. Three different issues related to microgrid management will be considered in detail in this thesis: 1. The problem of Nowcasting and Forecasting of the photovoltaic power production (PV). This problem has been approached by means of several data-driven techniques. 2. The integration of stations to charge electric vehicles in the smart grids. The impact of this integration on the grid processes and on the demand satisfaction costs have been analysed. In particular, two different models have been developed for the optimal integration of microgrids with renewable sources, smart buildings, and the electrical vehicles (EVs), taking into account two different technologies. The first model is based on a discrete-time representation of the dynamics of the system, whereas the second one adopts a discrete-event representation. 3. The problem of the energy optimization for a set of interconnencted buildings. In ths connection, an architecture, structured as a two-level control scheme has been developed. More precisely, an upper decision maker solves an optimization problem to minimize its own costs and power losses, and provides references (as 3 regars the power flows) to local controllers, associated to buildings. Then, lower level (local) controllers, on the basis of a more detailed representation of each specific subsystem (the building associated to the controller), have the objective of managing local storage systems and devices in order to follow the reference values (provided by the upper level), to contain costs, and to achieve comfort requirements

Smart grid architecture for rural distribution networks: application to a Spanish pilot network

This paper presents a novel architecture for rural distribution grids. This architecture is designed to modernize traditional rural networks into new Smart Grid ones. The architecture tackles innovation actions on both the power plane and the management plane of the system. In the power plane, the architecture focuses on exploiting the synergies between telecommunications and innovative technologies based on power electronics managing low scale electrical storage. In the management plane, a decentralized management system is proposed based on the addition of two new agents assisting the typical Supervisory Control And Data Acquisition (SCADA) system of distribution system operators. Altogether, the proposed architecture enables operators to use more effectively—in an automated and decentralized way—weak rural distribution systems, increasing the capability to integrate new distributed energy resources. This architecture is being implemented in a real Pilot Network located in Spain, in the frame of the European Smart Rural Grid project. The paper also includes a study case showing one of the potentialities of one of the principal technologies developed in the project and underpinning the realization of the new architecture: the so-called Intelligent Distribution Power Router.Postprint (published version

Agent-based homeostatic control for green energy in the smart grid

With dwindling non-renewable energy reserves and the adverse effects of climate change, the development of the smart electricity grid is seen as key to solving global energy security issues and to reducing carbon emissions. In this respect, there is a growing need to integrate renewable (or green) energy sources in the grid. However, the intermittency of these energy sources requires that demand must also be made more responsive to changes in supply, and a number of smart grid technologies are being developed, such as high-capacity batteries and smart meters for the home, to enable consumers to be more responsive to conditions on the grid in real-time. Traditional solutions based on these technologies, however, tend to ignore the fact that individual consumers will behave in such a way that best satisfies their own preferences to use or store energy (as opposed to that of the supplier or the grid operator). Hence, in practice, it is unclear how these solutions will cope with large numbers of consumers using their devices in this way. Against this background, in this paper, we develop novel control mechanisms based on the use of autonomous agents to better incorporate consumer preferences in managing demand. These agents, residing on consumers' smart meters, can both communicate with the grid and optimise their owner's energy consumption to satisfy their preferences. More specifically, we provide a novel control mechanism that models and controls a system comprising of a green energy supplier operating within the grid and a number of individual homes (each possibly owning a storage device). This control mechanism is based on the concept of homeostasis whereby control signals are sent to individual components of a system, based on their continuous feedback, in order to change their state so that the system may reach a stable equilibrium. Thus, we define a new carbon-based pricing mechanism for this green energy supplier that takes advantage of carbon-intensity signals available on the internet in order to provide real-time pricing. The pricing scheme is designed in such a way that it can be readily implemented using existing communication technologies and is easily understandable by consumers. Building upon this, we develop new control signals that the supplier can use to incentivise agents to shift demand (using their storage device) to times when green energy is available. Moreover, we show how these signals can be adapted according to changes in supply and to various degrees of penetration of storage in the system. We empirically evaluate our system and show that, when all homes are equipped with storage devices, the supplier can significantly reduce its reliance on other carbon-emitting power sources to cater for its own shortfalls. By so doing, the supplier reduces the carbon emission of the system by up to 25% while the consumer reduces its costs by up to 14.5%. Finally, we demonstrate that our homeostatic control mechanism is not sensitive to small prediction errors and the supplier is incentivised to accurately predict its green production to minimise costs

A review of microgrid development in the United States – A decade of progress on policies, demonstrations, controls, and software tools

Microgrids have become increasingly popular in the United States. Supported by favorable federal and local policies, microgrid projects can provide greater energy stability and resilience within a project site or community. This paper reviews major federal, state, and utility-level policies driving microgrid development in the United States. Representative U.S. demonstration projects are selected and their technical characteristics and non-technical features are introduced. The paper discusses trends in the technology development of microgrid systems as well as microgrid control methods and interactions within the electricity market. Software tools for microgrid design, planning, and performance analysis are illustrated with each tool's core capability. Finally, the paper summarizes the successes and lessons learned during the recent expansion of the U.S. microgrid industry that may serve as a reference for other countries developing their own microgrid industries