Modeling of Dynamic Pricing of Energy for a Smart Grid Using a Multi-agent Framework

The use of smart grids is being promoted to address issues such as energy independence, global warming and emergency resilience. A smart grid is a digitized form of the power grid and is comprised of an intelligent monitoring system that keeps track of the two-way digital communications in the system. A multi-agent system is a collection of interacting intelligent agents that can be used in problem solving for systems that are difficult or impossible to be solved by an individual agent. Applications of multi-agent systems can range from transportation, logistics, graphics, networking and mobile technologies to modeling real world scenarios to achieve automatic and dynamic load balancing, pricing, and disaster response. The goal of this project was to design and implement a multi-agent system to model dynamic pricing of electricity in a smart grid, thereby improving the overall efficiency of electricity consumption in a real world scenario. This project was accomplished by devising and implementing a multi-agent system for regulating automatic and dynamic pricing of electricity by monitoring power consumption periods and rising or falling prices accordingly. The system developed has the capability of rising and lowering the prices of electricity based on the availability of electricity from energy sources. This system will depict how much energy the consumers are using and how much it is actually costing them. We believe that the logistics analyzed above will help energy-consumption utilities and consumers to make better energy-efficient decisions

A multi-classifier approach to dialogue act classification using function words

This paper extends a novel technique for the classification of sentences as Dialogue Acts, based on structural information contained in function words. Initial experiments on classifying questions in the presence of a mix of straightforward and “difficult” non-questions yielded promising results, with classification accuracy approaching 90%. However, this initial dataset does not fully represent the various permutations of natural language in which sentences may occur. Also, a higher Classification Accuracy is desirable for real-world applications. Following an analysis of categorisation of sentences, we present a series of experiments that show improved performance over the initial experiment and promising performance for categorising more complex combinations in the future

The role of communication systems in smart grids: Architectures, technical solutions and research challenges

The purpose of this survey is to present a critical overview of smart grid concepts, with a special focus on the role that communication, networking and middleware technologies will have in the transformation of existing electric power systems into smart grids. First of all we elaborate on the key technological, economical and societal drivers for the development of smart grids. By adopting a data-centric perspective we present a conceptual model of communication systems for smart grids, and we identify functional components, technologies, network topologies and communication services that are needed to support smart grid communications. Then, we introduce the fundamental research challenges in this field including communication reliability and timeliness, QoS support, data management services, and autonomic behaviors. Finally, we discuss the main solutions proposed in the literature for each of them, and we identify possible future research directions

An intelligent self-configurable mechanism for distributed energy storage systems

Next generation of smart grid technologies demand intel- ligent capabilities for communication, interaction, monitoring, storage, and energy transmission. Multiagent systems are envisioned to provide autonomic and adaptability features to these systems in order to gain advantage in their current environments. In this paper we present a mechanism for providing distributed energy storage systems (DESSs) with intelligent capabilities. In more detail, we propose a self-con gurable mechanism which allows a DESS to adapt itself according to the future environmental requirements. This mechanism is aimed at reducing the costs at which energy is purchased from the market.This work has been partially supported by projects TIN2012-36586-C03-01 and TIN2011-27652-C03-01.Alberola Oltra, JM.; Julian Inglada, VJ.; García-Fornes, A. (2014). An intelligent self-configurable mechanism for distributed energy storage systems. Cybernetics and Systems. 45(3):292-305. https://doi.org/10.1080/01969722.2014.894859S292305453Abbey , C. and G. Joos . “Coordination of Distributed Storage with Wind Energy in a Rural Distribution System.” Paper presented at Industry Applications Conference, 42nd IAS Annual Meeting, September 23–27, 2007, New Orleans, USA .Alberola , J. M. , V. Julian , and A. Garcia-Fornes . “Multi-Dimensional Transition Deliberation for Organization Adaptation in Multiagent Systems.” Paper presented at the 11th International Conference on Aut. Agents and MAS (AAMAS12), June 4–8, 2012, Valencia, Spain .Chouhan , N. S. and M. Ferdowsi . “Review of Energy Storage Systems.” Paper presented at North American Power Symposium (NAPS), October 4–6, 2009, Mississippi, USA.Conejo, A. J., Plazas, M. A., Espinola, R., & Molina, A. B. (2005). Day-Ahead Electricity Price Forecasting Using the Wavelet Transform and ARIMA Models. IEEE Transactions on Power Systems, 20(2), 1035-1042. doi:10.1109/tpwrs.2005.846054Costa , L. , F. Bourry , J. Juban , and G. Kariniotakis . “Management of Energy Storage Coordinated with Wind Power under Electricity Market Conditions.” Paper presented at 10th International Conference on Probabilistic Methods Applied to Power Systems, May 25–29, 2008, Rincón, Puerto Rico .Eyer , J. and G. Corey . “Energy Storage for the Electricity Grid: Benefits and Market Potential Assessment Guide.” Sandia National Laboratories, 2010. Technical Report .Jiang , Z. “Agent-Based Control Framework for Distributed Energy Resources Microgrids.” Paper presented at International Conference on Intelligent Agent Technology, December 18–22, 2006, Hong Kong .Karnouskos , S. and T. N. De Holanda . “Simulation of a Smart Grid City with Software Agents.” Paper presented at Third UKSim European Symposium on Computer Modeling and Simulation, November 25–27, 2009, Athens, Greece .Ketter, W., Collins, J., & Reddy, P. (2013). Power TAC: A competitive economic simulation of the smart grid. Energy Economics, 39, 262-270. doi:10.1016/j.eneco.2013.04.015Lakshman, A., & Malik, P. (2010). Cassandra. ACM SIGOPS Operating Systems Review, 44(2), 35. doi:10.1145/1773912.1773922Logenthiran, T., Srinivasan, D., Khambadkone, A. M., & Aung, H. N. (2012). Multiagent System for Real-Time Operation of a Microgrid in Real-Time Digital Simulator. IEEE Transactions on Smart Grid, 3(2), 925-933. doi:10.1109/tsg.2012.2189028Maly, D. K., & Kwan, K. S. (1995). Optimal battery energy storage system (BESS) charge scheduling with dynamic programming. IEE Proceedings - Science, Measurement and Technology, 142(6), 453-458. doi:10.1049/ip-smt:19951929Mihailescu , R. C. , M. Vasirani , and S. Ossowski . “Dynamic Coalition Formation and Adaptation for Virtual Power Stations in Smart Grids.” Paper presented at 2nd International Workshop on Agent Technologies for Energy Systems, May 2, 2011, Taipei, Taiwan .Mohd , A. , E. Ortjohann , A. Schmelter , N. Hamsic , and D. Morton . “Challenges in Integrating Distributed Energy Storage Systems into Future Smart Grid.” Paper presented at IEEE International Symposium on Industrial Electronics, June 30–July 2, 2008, Cambridge, UK .Mohsenian-Rad, A.-H., & Leon-Garcia, A. (2010). Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments. IEEE Transactions on Smart Grid, 1(2), 120-133. doi:10.1109/tsg.2010.2055903Momoh , J. A. “Smart Grid Design for Efficient and Flexible Power Networks Operation and Control.” Paper presented at IEEE PES Power Systems Conference and Exposition, March 15–18, 2009, Seattle, USA .Nguyen, C. P., & Flueck, A. J. (2012). Agent Based Restoration With Distributed Energy Storage Support in Smart Grids. IEEE Transactions on Smart Grid, 3(2), 1029-1038. doi:10.1109/tsg.2012.2186833Nourai , A. “Installation of the First Distributed Energy Storage System (DESS) At American Electric Power.” Sandia National Laboratories, 2007. Technical Report.Oyarzabal , J. , J. Jimeno , J. Ruela , A. Engler , and C. Hardt . “Agent Based Micro Grid Management System.” Paper presented at International Conference on Future Power Systems, November 16–18, 2005, Amsterdam, Netherlands .Pinson, P., Chevallier, C., & Kariniotakis, G. N. (2007). Trading Wind Generation From Short-Term Probabilistic Forecasts of Wind Power. IEEE Transactions on Power Systems, 22(3), 1148-1156. doi:10.1109/tpwrs.2007.901117Pipattanasomporn , M. , H. Feroze , and S. Rahman . “Multi-agent Systems in a Distributed Smart Grid: Design and Implementation.” Paper presented at IEEE/PES Power Systems Conference and Exposition, March 15–18, 2009, Seattle, USA .Reddy , P. P. and M. M. Veloso . “Factored Models for Multiscale Decision Making in Smart Grid Customers.” Paper presented at the Twenty-sixth AAAI Conference on Artificial Intelligence, July 22–26, 2012, Toronto, Canada .Ribeiro, P. F., Johnson, B. K., Crow, M. L., Arsoy, A., & Liu, Y. (2001). Energy storage systems for advanced power applications. Proceedings of the IEEE, 89(12), 1744-1756. doi:10.1109/5.975900Schutte , S. and M. Sonnenschein . “Mosaik-Scalable Smart Grid Scenario Specification.” Paper presented at Proceedings of the 2012 Winter Simulation Conference (WSC), December 9–12, 2012, Berlin, Germany .Sioshansi, R., Denholm, P., Jenkin, T., & Weiss, J. (2009). Estimating the value of electricity storage in PJM: Arbitrage and some welfare effects. Energy Economics, 31(2), 269-277. doi:10.1016/j.eneco.2008.10.005Szkuta, B. R., Sanabria, L. A., & Dillon, T. S. (1999). Electricity price short-term forecasting using artificial neural networks. IEEE Transactions on Power Systems, 14(3), 851-857. doi:10.1109/59.780895Van Dam, K. H., Houwing, M., Lukszo, Z., & Bouwmans, I. (2008). Agent-based control of distributed electricity generation with micro combined heat and power—Cross-sectoral learning for process and infrastructure engineers. Computers & Chemical Engineering, 32(1-2), 205-217. doi:10.1016/j.compchemeng.2007.07.012Vosen, S. (1999). Hybrid energy storage systems for stand-alone electric power systems: optimization of system performance and cost through control strategies. International Journal of Hydrogen Energy, 24(12), 1139-1156. doi:10.1016/s0360-3199(98)00175-xVytelingum , P. , T. D. Voice , S. Ramchurn , A. Rogers , and N. R. Jennings . “Agent-Based Micro-Storage Management for the Smart Grid.” Paper presented at Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems, May 10–14, 2010a, Toronto, Canada .Vytelingum , P. , T. D. Voice , S. Ramchurn , A. Rogers , and N. R. Jennings . “Intelligent Agents for the Smart Grid.” Paper presented at the 9th International Conference on Autonomous Agents and Multiagent Systems, May 10–14, 2010b, Toronto, Canada

Intelligent Agents for the Smart Grid

Meeting the challenge of cutting global greenhouse gas emissions by 50% by 2050, and ensuring energy security in the face of dwindling oil and gas reserves, requires a radical change in the way energy (and particularly electricity) is generated, distributed and consumed. Central to delivering this change, is the vision of a smart electrical distribution network (the Smart Grid) within which micro-generation and storage capabilities are ubiquitous, where intelligent sensing devices allow users to make informed choices about the control of devices in their home, and where producers and consumers are connected via a series of dynamically negotiated supply contracts. In this article, we describe why we believe intelligent agents are essential to delivering on the vision of a Smart Grid, and describe some results applying agents to the problem of coordinating micro-storage within a model of the smart grid

A Self-configurable agent-based System for Intelligent Storage in Smart Grid

[Otros] Next generation of smart grid technologies demand intelligent capabilities for communication, interaction, monitoring, storage, and energy transmission. Multiagent systems are envisioned to provide autonomic and adaptability features to these systems in order to gain advantage in their current environments. In this paper we present a mechanism for providing distributed energy storage systems (DESSs) with intelligent capabilities. In more detail, we propose a self-configurable mechanism which allows a DESS to adapt itself according to the future environmental requirements. This mechanism is aimed at reducing the costs at which electricity is purchased from the marketThis work has been partially supported by projects TIN2012-36586-C03-01 and TIN2011-27652-C03-01Alberola Oltra, JM.; Julian Inglada, VJ.; García-Fornes, A. (2013). A Self-configurable agent-based System for Intelligent Storage in Smart Grid. Springer. 240-250. https://doi.org/10.1007/978-3-642-38061-7_24S240250Momoh, J.A.: Smart grid design for efficient and flexible power networks operation and control. In: IEEE PES Power Systems Conference and Exposition, pp. 15–18 (2009)Pipattanasomporn, M., Feroze, H., Rahman, S.: Multi-agent systems in a distributed smart grid: Design and implementation. In: IEEE/PES Power Systems Conference and Exposition, pp. 1–8 (2009)Vytelingum, P., Voice, T.D., Ramchurn, S., Rogers, A., Jennings, N.R.: Agent-based micro-storage management for the Smart Grid. In: Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems, pp. 39–46 (2010)Vytelingum, P., Voice, T.D., Ramchurn, S., Rogers, A., Jennings, N.R.: Intelligent agents for the smart grid. In: Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems, pp. 1649–1650 (2010)Van Dam, K.H., Houwing, M., Bouwmans, I.: Agent-based control of distributed electricity generation with microcombined heat and power-cross-sectoral learning for process and infrastructure engineers. Computers & Chemical Engineering 32, 205–217 (2008)Oyarzabal, J., Jimeno, J., Ruela, J., Engler, A., Hardt, C.: Agent based Micro Grid Management System. In: International Conference on Future Power Systems, vol. 18(8) (2005)Reddy, P.P., Veloso, M.M.: Factored Models for Multiscale Decision Making in Smart Grid Customers. In: Proceedings of AAAI 2012, the Twenty-Sixth AAAI Conference on Artificial Intelligence (2012)Mihailescu, R.C., Vasirani, M., Ossowski, S.: Dynamic coalition formation and adaptation for virtual power stations in smart grids. In: Proc. of the 2nd Int. Workshop on Agent Technologies for Energy Systems, pp. 85–88 (2011)Nourai, A.: Installation of the First Distributed Energy Storage System (DESS) at American Electric Power (AEP). Technical report, Sandia National Laboratories (2007)Eyer, J., Corey, G.: Energy Storage for the Electricity Grid: Benefits and Market Potential Assessment Guide. Technical report, Sandia National Laboratories (2010)Mohd, A., Ortjohann, E., Schmelter, A., Hamsic, N., Morton, D.: Challenges in integrating distributed Energy storage systems into future smart grid. In: IEEE International Symposium on Industrial Electronics, pp. 1627–1632 (2008)Costa, L., Bourry, F., Juban, J., Kariniotakis, G.: Management of energy storage coordinated with wind power under electricity market conditions. In: 10th International Conference on Probabilistic Methods Applied to Power Systems, pp. 259–266 (2008)Pinson, P., Chevallier, C., Kariniotakis, G.N.: Trading Wind Generation From Short-Term Probabilistic Forecasts of Wind Power. IEEE Transactions on Power Systems 22(3), 1148–1156 (2007)Maly, D.K., Kwan, K.S.: Optimal battery energy storage system (BESS) charge scheduling with dynamic programming. IEE Proceedings-Science, Measurement and Technology 142(6), 453–458 (1995)Alberola, J.M., Julian, V., Garcia-Fornes, A.: Multi-Dimensional Adaptation in MAS Organizations. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics (in press, 2013)Alberola, J.M., Julian, V., Garcia-Fornes, A.: Multi-dimensional Transition Deliberation for Organization Adaptation in Multiagent Systems. In: Proc. 11th Int. Conf. on Aut. Agents and MAS, AAMAS 2012, pp. 1379–1380 (2012)Conejo, A.J., Plazas, M.A., Espinola, R., Molina, A.B.: Day-Ahead Electricity Price Forecasting Using the Wavelet Transform and ARIMA Models. IEEE Transactions on Power Systems 20(2), 1035–1042 (2005)Mohsenian, A.H., Leon-Garcia, A.: Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments. IEEE Trans. Smart Grid 1(2), 120–133 (2010)Szkuta, B., Sanabria, L., Dillon, T.: Electricity price short-term forecasting using artificial neural networks. IEEE Transactions on Power Systems 14(3), 851–857 (1999