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

Distributed Coordination and Optimisation of Network-Aware Electricity Prosumers

Electricity networks are undergoing a transformation brought on by new technologies, market pressures and environmental concerns. This includes a shift from large centralised generators to small-scale distributed generators. The dramatic cost reductions in rooftop solar PV and battery storage means that prosumers (houses and other entities that can both produce and consume electricity) will have a large role to play in future networks. How can networks be managed going forward so that they run as efficiently as possible in this new prosumer paradigm? Our vision is to treat prosumers as active participants by developing a mechanism that incentivises them to help balance power and support the network. The whole process is automated to produce a near-optimal outcome and to reduce the need for human involvement. The first step is to design an autonomous energy management system (EMS) that can optimise the local costs of each prosumer in response to network electricity prices. In particular, we investigate different optimisation strategies for an EMS in an uncertain household environment. We find that the uncertainty associated with weather, network pricing and occupant behaviour can be effectively handled using online optimisation techniques using a forward receding horizon. The next step is to coordinate the actions of many EMSs spread out across the network, in order to minimise the overall cost of supplying electricity. We propose a distributed algorithm that can efficiently coordinate a network with thousands of prosumers without violating their privacy. We experiment with a range of power flow models of varying degrees of accuracy in order to test their convergence rate, computational burden and solution quality on a suburb-sized microgrid. We find that the higher accuracy model, although non-convex, converges in a timely manner and produces near-optimal solutions. We also develop simple but effective techniques for dealing with residential shiftable loads which require discrete decisions. The final part of the problem we explore is prosumer manipulation of the coordination mechanism. The receding horizon nature of our algorithm is great for managing uncertainty, but it opens up unique opportunities for prosumers to manipulate the actions of others. We formalise this form of receding horizon manipulation and investigate the benefits manipulative agents can obtain. We find that indeed strategic agents can harm the system, but only if they are large enough and have information about the behaviour of other agents. For the rare cases where this is possible, we develop simple privacy-preserving identifiers that monitor agents and distinguish manipulation from uncertainty. Together, these components create a complete solution for the distributed coordination and optimisation of network-aware electricity prosumers

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