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Erschienen in:
Enhanced Multiobjective Population-Based Incremental Learning with Applications in Risk Treaty Optimization Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Optimization of Operation and Control Strategies for Battery Energy Storage Systems by Evolutionary Algorithms

verfasst von : Jan Müller, Matthias März, Ingo Mauser, Hartmut Schmeck

Erschienen in: Applications of Evolutionary Computation

Verlag: Springer International Publishing

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Abstract

To support the utilization of renewable energies, an optimized operation of energy systems is important. Often, the use of battery energy storage systems is stated as one of the most important measures to support the integration of intermittent renewable energy sources into the energy system. Additionally, the complexity of the energy system with its many interdependent entities as well as the economic efficiency call for an elaborate dimensioning and control of these storage systems. In this paper, we present an approach that combines the forward-looking nature of optimization and prediction with the feedback control of closed-loop controllers. An evolutionary algorithm is used to determine the parameters for a closed-loop controller that controls the charging and discharging control strategy of a battery in a smart building. The simulation and evaluation of a smart residential building scenario demonstrates the ability to improve the operation and control of a battery energy storage system. The optimization of the control strategy allows for the optimization with respect to variable tariffs while being conducive for the integration of renewable energy sources into the energy system.

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Vorheriges Kapitel Electrical Load Pattern Shape Clustering Using Ant Colony Optimization
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Fußnoten
1
The overall research environment is more closely described in [4].
 
Literatur
1.
International Energy Agency (ed.): Tracking Clean Energy Progress 2015. International Energy Agency (2015)
2.
von Appen, J., Stetz, T., Braun, M., Schmiegel, A.: Local voltage control strategies for PV storage systems in distribution grids. IEEE Trans. Smart Grid 5(2), 1002–1009 (2014)CrossRef
3.
Wirth, H., Schneider, K.: Recent Facts about Photovoltaics in Germany. Fraunhofer Institute for Solar Energy Systems (2015)
4.
Becker, B., Kern, F., Lösch, M., Mauser, I., Schmeck, H.: Building energy management in the FZI house of living labs. In: Gottwalt, S., König, L., Schmeck, H., Steber, D., Bazan, P., German, R., Pruckner, M., Dethlefs, T., et al. (eds.) EI 2015. LNCS, vol. 9424, pp. 95–112. Springer, Heidelberg (2015). doi:10.​1007/​978-3-319-25876-8_​9 CrossRef
5.
Mauser, I., Müller, J., Allerding, F., Schmeck, H.: Adaptive building energy management with multiple commodities and flexible evolutionary optimization. Renew. Energ. 87(Part 2), 911–921 (2016)CrossRef
6.
Ahmadi, P., Rosen, M.A., Dincer, I.: Multi-objective exergy-based optimization of a polygeneration energy system using an evolutionary algorithm. Energy 46(1), 21–31 (2012)CrossRef
7.
Kavvadias, K., Maroulis, Z.: Multi-objective optimization of a trigeneration plant. Energ. Policy 38(2), 945–954 (2010)CrossRef
8.
Weidlich, A., Veit, D.: A critical survey of agent-based wholesale electricity market models. Energ. Econ. 30(4), 1728–1759 (2008)CrossRef
9.
Henning, H.M., Palzer, A.: A comprehensive model for the German electricity and heat sector in a future energy system with a dominant contribution from renewable energy technologies - Part I: Methodology. Renew. Sustain. Energ. Rev. 30, 1003–1018 (2014)CrossRef
10.
van der Waaij, B., Wijbrandi, W., Konsman, M.: White Paper Energy Flexibility Platform and Interface (EF-Pi) - How the Developments in the Energy Flexibility Market can be Accelerated by the Energy Flexibility Platform and Interface. Technical report, TNO, NRG, Alliander, and Technolution (2015)
11.
Toersche, H., Hurink, J., Konsman, M.: Energy management with triana on FPAI. In: 2015 IEEE Eindhoven PowerTech, pp. 1–6 (2015)
12.
Nestle, D., Ringelstein, J., Waldschmidt, H.: Open energy gateway architecture for customers in the distribution grid. IT - Inf. Technol. 52(2), 83–88 (2010)
13.
Ha, D.L., Joumaa, H., Ploix, S., Jacomino, M.: An optimal approach for electrical management problem in dwellings. Energ. Build. 45, 1–14 (2012)CrossRef
14.
Chen, Z., Wu, L., Fu, Y.: Real-time price-based demand response management for residential appliances via stochastic optimization and robust optimization. IEEE Trans. Smart Grid 3(4), 1822–1831 (2012)MathSciNetCrossRef
15.
Soares, A., Antunes, C.H., Oliveira, C., Gomes, Á.: A multi-objective genetic approach to domestic load scheduling in an energy management system. Energy 77, 144–152 (2014)CrossRef
16.
Wright, A., Firth, S.: The nature of domestic electricity-loads and effects of time averaging on statistics and on-site generation calculations. Appl. Energ. 84(4), 389–403 (2007)CrossRef
17.
Mohsenian-Rad, 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)CrossRef
18.
Sou, K.C., Weimer, J., Sandberg, H., Johansson, K.H.: Scheduling smart home appliances using mixed integer linear programming. In: 2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC), pp. 5144–5149 (2011)
19.
Shirazi, E., Jadid, S.: Optimal residential appliance scheduling under dynamic pricing scheme via HEMADAS. Energ. Build. 93, 40–49 (2015)CrossRef
20.
Mesghouni, K., Hammadi, S., Borne, P.: Evolutionary algorithms for job-shop scheduling. Int. J. Appl. Math. Comput. Sci. 14(1), 91–104 (2004)MathSciNetMATH
21.
Pedrasa, M., Spooner, T., MacGill, I.: Coordinated scheduling of residential distributed energy resources to optimize smart home energy services. IEEE Trans. Smart Grid 1(2), 134–143 (2010)CrossRef
22.
Doyle, M.: Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. J. Electrochem. Soc. 140(6), 1526 (1993)CrossRef
23.
Chen, M., Rincon-Mora, G.: Accurate electrical battery model capable of predicting runtime and i-v performance. IEEE Trans. Energ. Convers. 21(2), 504–511 (2006)CrossRef
24.
Manwell, J.F., McGowan, J.G.: Lead acid battery storage model for hybrid energy systems. Sol. Energ. 50(5), 399–405 (1993)CrossRef
25.
Boaro, M., Fuselli, D., Angelis, F., Liu, D., Wei, Q., Piazza, F.: Adaptive dynamic programming algorithm for renewable energy scheduling and battery management. Cogn. Comput. 5(2), 264–277 (2013)CrossRef
26.
Huang, T., Liu, D.: Residential energy system control and management using adaptive dynamic programming. In: The 2011 International Joint Conference on Neural Networks (IJCNN), pp. 119–124 (2011)
27.
Morais, H., Kdr, P., Faria, P., Vale, Z.A., Khodr, H.: Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming. Renew. Energ. 35(1), 151–156 (2010)CrossRef
28.
Lu, B., Shahidehpour, M.: Short-term scheduling of battery in a grid-connected pv/battery system. IEEE Trans. Power Syst. 20(2), 1053–1061 (2005)CrossRef
29.
von Appen, J., Braun, M.: Grid integration of market-oriented PV storage systems. In: ETG Congress 2015: Die Energiewende - Blueprint for the new energy age, VDE in press (2015)
30.
Zeh, A., Witzmann, R.: Operational strategies for battery storage systems in low-voltage distribution grids to limit the feed-in power of roof-mounted solar power systems. Energ. Procedia 46, 114–123 (2014)CrossRef
31.
Castillo-Cagigal, M., Caamaño-Martín, E., Matallanas, E., Masa-Bote, D., Gutiérrez, A., Monasterio-Huelin, F., Jiménez-Leube, J.: PV self-consumption optimization with storage and active DSM for the residential sector. Sol. Energ. 85(9), 2338–2348 (2011)CrossRef
32.
Allerding, F., Schmeck, H.: Organic smart home: architecture for energy management in intelligent buildings. In: Proceedings of the 2011 Workshop on Organic computing, pp. 67–76. ACM (2011)
33.
Mauser, I., Feder, J., Müller, J., Schmeck, H.: Evolutionary optimization of smart buildings with interdependent devices. In: Mora, A.M., Squillero, G. (eds.) EvoApplications 2015, pp. 239–251. Springer, Heidelberg (2015)
34.
Durillo, J., Nebro, A., Alba, E.: The jMetal framework for multi-objective optimization: Design and architecture. CEC 2010, 4138–4325 (2010)
35.
Mauser, I., Dorscheid, M., Allerding, F., Schmeck, H.: Encodings for evolutionary algorithms in smart buildings with energy management systems. In: IEEE Congress on Evolutionary Computation (CEC), pp. 2361–2366. IEEE (2014)
Metadaten
Titel
Optimization of Operation and Control Strategies for Battery Energy Storage Systems by Evolutionary Algorithms
verfasst von
Jan Müller
Matthias März
Ingo Mauser
Hartmut Schmeck
Copyright-Jahr
2016
Buch
Applications of Evolutionary Computation

Print ISBN: 978-3-319-31203-3

Electronic ISBN: 978-3-319-31204-0

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-31204-0_33

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