Skip to main content
SpringerLink
Log in

Energy Management in Microgrids

  • Chapter
  • First Online:
Microgrids Design and Implementation

Abstract

In this chapter the most significant characteristics and functionalities of an energy management system (EMS) for microgrids are introduced. For this, the definitions of hierarchical control layers are considered. First, the main concepts and modules of the hierarchical control structure of a generalized EMS are presented. Then, energy management function is represented as an optimization problem, described as the simultaneous solution of both, a unit commitment problem and an economic load dispatch problem. An extension of the energy management problem is also formulated based on an optimal power flow. Second, the advantages and disadvantages of using either a centralized or a decentralized EMS approach are discussed. Finally, since the energy management problem is represented as an optimization problem, the most common methodologies and solution algorithms used in the specialized literature are discussed, including metaheuristics, mixed-integer linear approximations, and nonlinear approaches, as well as software tools for implementing models and simulations.

Research grants FAPESP 2015/09136-8 and 2015/12564-1.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Note that Eqs. (7.5), (7.6), and (7.7) are equality constraints with linear and quadratic variables; hence, those equations are considered as convex quadratic constraints. However, as discussed in this section, the transformation of the terms \( {I}_{ij}^2 \) and \( {v}_i^2 \) into single variables will produce a linear equivalent expressions.

  2. 2.

    These kind of optimization problems are known as mixed-integer second-order cone programming problems (MISOCP).

References

  1. Olivares, D. E., Canizares, C. A., & Kazerani, M. (2011). A centralized optimal energy management system for microgrids. In 2011 I.E. Power and Energy Society General Meeting (pp. 1–6).

    Google Scholar 

  2. Katiraei, F., Iravani, R., Hatziargyriou, N., & Dimeas, A. (2008). Microgrids management. IEEE Power & Energy Magazine, 6(3), 54–65.

    Article  Google Scholar 

  3. Kanchev, H., Lu, D., Colas, F., Lazarov, V., & Francois, B. (2011). Energy management and operational planning of a microgrid with a PV-based active generator for smart grid applications. IEEE Transactions on Industrial Electronics, 58(10), 4583–4592.

    Article  Google Scholar 

  4. Guerrero, J. M., Vasquez, J. C., Matas, J., de Vicuña, L. G., & Castilla, M. (2011). Hierarchical control of droop-controlled AC and DC microgrids—A general approach toward standardization. IEEE Transactions on Industrial Electronics, 58(1), 158–172.

    Article  Google Scholar 

  5. Olivares, D. E., Mehrizi-Sani, A., Etemadi, A. H., Canizares, C. A., Iravani, R., Kazerani, M., Hajimiragha, A. H., Gomis-Bellmunt, O., Saeedifard, M., Palma-Behnke, R., Jimenez-Estevez, G. A., & Hatziargyriou, N. D. (2014). Trends in microgrid control. IEEE Transactions on Smart Grid, 5(4), 1905–1919.

    Article  Google Scholar 

  6. Bidram, A., & Davoudi, A. (2012). Hierarchical structure of microgrids control system. IEEE Transactions on Smart Grid, 3(4), 1963–1976.

    Article  Google Scholar 

  7. Basu, A. K., Chowdhury, S. P., Chowdhury, S., & Paul, S. (2011). Microgrids: Energy management by strategic deployment of DERs—A comprehensive survey. Renewable and Sustainable Energy Reviews, 15(9), 4348–4356.

    Article  Google Scholar 

  8. Molderink, A., Bakker, V., Bosman, M. G. C., Hurink, J. L., & Smit, G. J. M. (2010). Management and control of domestic smart grid technology. IEEE Transactions on Smart Grid, 1(2), 109–119.

    Article  Google Scholar 

  9. Chen, C., Duan, S., Cai, T., Liu, B., & Hu, G. (2011). Smart energy management system for optimal microgrid economic operation. IET Renewable Power Generation, 5(3), 258.

    Article  Google Scholar 

  10. Lujano-Rojas, J. M., Monteiro, C., Dufo-López, R., & Bernal-Agustín, J. L. (2012). Optimum load management strategy for wind/diesel/battery hybrid power systems. Renewable Energy, 44, 288–295.

    Article  Google Scholar 

  11. Palma-Behnke, R., Benavides, C., Lanas, F., Severino, B., Reyes, L., Llanos, J., & Saez, D. (2013). A microgrid energy management system based on the rolling horizon strategy. IEEE Transactions on Smart Grid, 4(2), 996–1006.

    Article  Google Scholar 

  12. Jaganmohan Reddy, Y., Pavan Kumar, Y. V., Sunil Kumar, V., & Padma Raju, K. (2012). Distributed ANNs in a layered architecture for energy management and maintenance scheduling of renewable energy HPS microgrids. In 2012 International Conference on Advances in Power Conversion and Energy Technologies (APCET) (No. 1, pp. 1–6).

    Google Scholar 

  13. Stluka, P., Godbole, D., & Samad, T. (2011). Energy management for buildings and microgrids. In IEEE Conference on Decision and Control and European Control Conference (pp. 5150–5157).

    Google Scholar 

  14. Kumar Nunna, H. S. V. S., & Doolla, S. (2013). Energy management in microgrids using demand response and distributed storage—A multiagent approach. IEEE Transactions on Power Delivery, 28(2), 939–947.

    Article  Google Scholar 

  15. Olivares, D. E., Canizares, C. A., & Kazerani, M. (2014). A centralized energy management system for isolated microgrids. IEEE Transactions on Smart Grid, 5(4), 1864–1875.

    Article  Google Scholar 

  16. Wu, X., Wang, X., & Qu, C. (2014). A hierarchical framework for generation scheduling of microgrids. IEEE Transactions on Power Delivery, 29(6), 2448–2457.

    Article  Google Scholar 

  17. Palma-Behnke, R., Benavides, C., Aranda, E., Llanos, J., & Saez, D. (2011). Energy management system for a renewable based microgrid with a demand side management mechanism. In 2011 I.E. Symposium on Computational Intelligence Applications In Smart Grid (CIASG) (pp. 1–8).

    Google Scholar 

  18. Sanseverino, E. R., Di Silvestre, M. L., Ippolito, M. G., De Paola, A., & Lo Re, G. (2011). An execution, monitoring and replanning approach for optimal energy management in microgrids. Energy, 36(5), 3429–3436.

    Article  Google Scholar 

  19. Hatziargyriou, N. D., Dimeas, A., Tsikalakis, A. G., Lopes, J. A. P., Karniotakis, G., & Oyarzabal, J. (2005). Management of microgrids in market environment. In 2005 International Conference on Future Power Systems (p. 7).

    Google Scholar 

  20. Oyarzabal, J., Jimeno, J., Ruela, J., Engler, A., & Hardt, C. (2005). Agent based micro grid management system. In 2005 International Conference on Future Power Systems (p. 6).

    Google Scholar 

  21. Zheng, W. D., & Cai, J. D. (2010). A multi-agent system for distributed energy resources control in microgrid. In 2010 5th International Conference on Critical Infrastructure (CRIS) (pp. 1–5).

    Google Scholar 

  22. Colson, C. M., & Nehrir, M. H. (2013). Comprehensive real-time microgrid power management and control with distributed agents. IEEE Transactions on Smart Grid, 4(1), 617–627.

    Article  Google Scholar 

  23. Jiang, Q., Xue, M., & Geng, G. (2013). Energy management of microgrid in grid-connected and stand-alone modes. IEEE Transactions on Power Apparatus and Systems, 28(3), 3380–3389.

    Article  Google Scholar 

  24. Strrelec, M., & Berka, J. (2013). Microgrid energy management based on approximate dynamic programming. In IEEE PES ISGT Europe 2013 (pp. 1–5).

    Google Scholar 

  25. Wu, X., Wang, X., & Bie, Z. (2012). Optimal generation scheduling of a microgrid. In 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe) (pp. 1–7).

    Google Scholar 

  26. Chakraborty, S., & Simoes, M. G. (2008). PV-microgrid operational cost minimization by neural forecasting and heuristic optimization. In 2008 I.E. Ind. Appl. Soc. Annu. Meet. (pp. 1–8), Oct 2008.

    Google Scholar 

  27. Paudyal, S., Canizares, C. A., & Bhattacharya, K. (2011). Optimal operation of distribution feeders in smart grids. IEEE Transactions on Industrial Electronics, 58(10), 4495–4503.

    Article  Google Scholar 

  28. Macedo, L. H., Franco, J. F., Rider, M. J., & Romero, R. (2015). Optimal operation of distribution networks considering energy storage devices. IEEE Transactions on Smart Grid, 6(6), 2825–2836.

    Article  Google Scholar 

  29. Shi, W., Xie, X., Chu, C.-C., & Gadh, R. (2015). Distributed optimal energy management in microgrids. IEEE Transactions on Smart Grid, 6(3), 1137–1146.

    Article  Google Scholar 

  30. Franco, J. F., Rider, M. J., & Romero, R. (2015). A mixed-integer linear programming model for the electric vehicle charging coordination problem in unbalanced electrical distribution systems. IEEE Transactions on Smart Grid, 6(5), 2200–2210.

    Article  Google Scholar 

  31. Garces, A. (2016). A linear three-phase load flow for power distribution systems. IEEE Transactions on Power Systems, 31(1), 827–828.

    Article  MathSciNet  Google Scholar 

  32. Cespedes, R. G. (1990). New method for the analysis of distribution networks. IEEE Transactions on Power Delivery, 5(1), 391–396.

    Article  MathSciNet  Google Scholar 

  33. Baran, M. E., & Wu, F. F. (1989). Optimal capacitor placement on radial distribution systems. IEEE Transactions on Power Delivery, 4(1), 725–734.

    Article  Google Scholar 

  34. Gan, L., Li, N., Topcu, U., & Low, S. H. (2015). Exact convex relaxation of optimal power flow in radial networks. IEEE Transactions on Automatic Control, 60(1), 72–87.

    Article  MathSciNet  Google Scholar 

  35. Alsac, O., Bright, J., Prais, M., & Stott, B. (1990). Further developments in LP-based optimal power flow. IEEE Transactions on Power Systems, 5(3), 697–711.

    Article  Google Scholar 

  36. Stott, B., Jardim, J., & Alsac, O. (2009). DC power flow revisited. IEEE Transactions on Power Systems, 24(3), 1290–1300.

    Article  Google Scholar 

  37. Franco, J. F., Rider, M. J., Lavorato, M., & Romero, R. (2013). A mixed-integer LP model for the reconfiguration of radial electric distribution systems considering distributed generation. Electric Power Systems Research, 97, 51–60.

    Article  Google Scholar 

  38. Cavalcante, P. L., Lopez, J. C., Franco, J. F., Rider, M. J., Garcia, A. V., Malveira, M. R. R., Martins, L. L., & Direito, L. C. M. (2016). Centralized self-healing scheme for electrical distribution systems. IEEE Transactions on Smart Grid, 7(1), 145–155.

    Article  Google Scholar 

  39. Fortuny-Amat, J., & McCarl, B. (1981). A representation and economic interpretation of a two-level programming problem. Journal of the Operational Research Society, 32(9), 783.

    Article  MathSciNet  Google Scholar 

  40. Fourer, R., Gay, D. M., & Kernighan, B. W. (2003). AMPL: A modeling language for mathematical programming (2nd ed.). Pacific Grove, CA: Brooks/Cole-Thomson Learning.

    MATH  Google Scholar 

  41. Rosenthal, R. E. (2012). GAMS—A user’s guide. Washington D.C.: GAMS Development Corporation.

    Google Scholar 

  42. The Language Reference—AIMMS.

    Google Scholar 

  43. CPLEX optimization subroutine library guide and reference. (2008). Inclines Village, NV: ILOG Inc.

    Google Scholar 

  44. GUROBI 5.6, Gurobi Optimization, Inc, user’s manual. (2013).

    Google Scholar 

  45. XPRESS 25, FICO, user’s manual. (2013).

    Google Scholar 

  46. Bonami, P., Biegler, L. T., Conn, A. R., Cornuéjols, G., Grossmann, I. E., Laird, C. D., Lee, J., Lodi, A., Margot, F., Sawaya, N., & Wächter, A. (2008). An algorithmic framework for convex mixed integer nonlinear programs. Discrete Optimization, 5(2), 186–204.

    Article  MathSciNet  Google Scholar 

  47. KNITRO documentation. (2012). Evanston, IL: Ziena Optimization LLC.

    Google Scholar 

  48. Solanki, B. V., Raghurajan, A., Bhattacharya, K., & Cañizares, C. A. (2017). Including smart loads for optimal demand response in integrated energy management systems for isolated microgrids. In IEEE Transactions on Smart Grid, 8(4), 1739–1748.

    Google Scholar 

  49. Olivares, D. E., Lara, J. D., Canizares, C. A., & Kazerani, M. (2015). Stochastic-predictive energy management system for isolated microgrids. IEEE Transactions on Smart Grid, 6(6), 2681–2693.

    Article  Google Scholar 

  50. Farivar, M., & Low, S. H. (2013). Branch flow model: relaxations and convexification—Part I. IEEE Transactions on Power Systems, 28(3), 2554–2564.

    Article  Google Scholar 

  51. Jabr, R. A. (2006). Radial distribution load flow using conic programming. IEEE Transactions on Power Systems, 21(3), 1458–1459.

    Article  MathSciNet  Google Scholar 

  52. Wei, H., & Bai, X. (2009). Semi-definite programming-based method for security-constrained unit commitment with operational and optimal power flow constraints. IET Generation Transmission and Distribution, 3(2), 182–197.

    Article  Google Scholar 

  53. Andersen, E. D., & Andersen, K. D. (2000). The Mosek interior point optimizer for linear programming: An implementation of the homogeneous algorithm. In: Frenk H., Roos K., Terlaky T., Zhang S. (Eds) High Performance Optimization. Applied Optimization (Vol. 33). Springer: Boston, MA.

    Google Scholar 

  54. Boyd, S., & Vandenberghe, L. (2009). Convex optimization (2nd ed.). Cambridge: Cambridge University Press.

    MATH  Google Scholar 

  55. Li, N., Chen, L., & Low, S. H. (2012). Exact convex relaxation of OPF for radial networks using branch flow model. In 2012 I.E. Third International Conference on Smart Grid Communications (SmartGridComm) (pp. 7–12).

    Google Scholar 

  56. Lavaei, J. (2011). Zero duality gap for classical opf problem convexifies fundamental nonlinear power problems. In Proceedings of the 2011 American Control Conference (pp. 4566–4573).

    Google Scholar 

  57. Lavaei, J., & Low, S. H. (2012). Zero duality gap in optimal power flow problem. IEEE Transactions on Power Systems, 27(1), 92–107.

    Article  Google Scholar 

  58. Phan, D. T. (2012). Lagrangian duality and branch-and-bound algorithms for optimal power flow. Operations Research, 60(2), 275–285.

    Article  MathSciNet  Google Scholar 

  59. Meng, L., Sanseverino, E. R., Luna, A., Dragicevic, T., Vasquez, J. C., & Guerrero, J. M. (2016). Microgrid supervisory controllers and energy management systems: A literature review. Renewable and Sustainable Energy Reviews, 60, 1263–1273.

    Article  Google Scholar 

  60. Abido, M. A. (2003). Environmental/economic power dispatch using multiobjective evolutionary algorithms. IEEE Transactions on Power Systems, 18(4), 1529–1537.

    Article  Google Scholar 

  61. Colson, C. M., Nehrir, M. H., & Pourmousavi, S. A. (2010). Towards real-time microgrid power management using computational intelligence methods. In IEEE PES General Meeting (pp. 1–8).

    Google Scholar 

  62. Takeuchi, A., Hayashi, T., Nozaki, Y., & Shimakage, T. (2012). Optimal scheduling using metaheuristics for energy networks. IEEE Transactions on Smart Grid, 3(2), 968–974.

    Article  Google Scholar 

  63. Haupt, R. L., & Haupt, S. E. (2004). Practical genetic algorithms (2nd ed.). New York: Wiley.

    MATH  Google Scholar 

  64. Narkhede, M. S., Chatterji, S., & Ghosh, S. (2012). Trends and challenges in optimization techniques for operation and control of Microgrid—A review. In 2012 1st International Conference on Power and Energy in NERIST (ICPEN) (pp. 1–7).

    Google Scholar 

  65. Vergara, P. P., Torquato, R., & da Silva, L. C. P. (2015). Towards a real-time Energy Management System for a Microgrid using a multi-objective genetic algorithm. In 2015 I.E. Power & Energy Society General Meeting (pp. 1–5).

    Google Scholar 

  66. Reference Manual: LocalSolver. 2016.

    Google Scholar 

  67. Zhao, B., Zhang, X., Chen, J., Wang, C., & Guo, L. (2013). Operation optimization of standalone microgrids considering lifetime characteristics of battery energy storage system. IEEE Transactions on Sustainable Energy, 4(4), 934–943.

    Article  Google Scholar 

  68. Corso, G., Di Silvestre, M. L., Ippolito, M. G., Riva, E., & Zizzo, G. (2010). Multi-objetive long term optimal dispatch of distributed energy resources in Microgrids. In 2010 45th Int. Univ. Power Eng. Conf. (pp. 1–5).

    Google Scholar 

  69. OpenDSS, Repository of source code for OpenDSS. 2013.

    Google Scholar 

  70. Chassin, D. P., Schneider, K., & Gerkensmeyer, C. (2008). GridLAB-D: An open-source power systems modeling and simulation environment. In 2008 IEEE/PES Transmission and Distribution Conference and Exposition (pp. 1–5).

    Google Scholar 

  71. IEEE draft standard for the specification of microgrid controllers. In IEEE P2030.7/D10 (pp. 1–49).

    Google Scholar 

  72. Communication networks and systems for power utility automation—Part 7-420: Basic communication structure—Distributed energy resources logical nodes. IEC Standard 61850-7-420. 2009.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Campinas (UNICAMP), Campinas, Brazil

    Pedro P. Vergara, Juan C. López, Luiz C. P. da Silva & Marcos J. Rider

  2. Universidad Industrial de Santander (UIS), Bucaramanga, Colombia

    Juan M. Rey

Authors
  1. Pedro P. Vergara
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Juan C. López
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Juan M. Rey
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Luiz C. P. da Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Marcos J. Rider
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Juan M. Rey .

Editor information

Editors and Affiliations

  1. Institute of Electrical Systems and Energy, Federal University of Itajubá, Itajubá, Minas Gerais, Brazil

    Antonio Carlos Zambroni de Souza

  2. Electronic Engineering Department, Technical University of Catalonia, Vilanova i la Geltrú, Spain

    Miguel Castilla

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Vergara, P.P., López, J.C., Rey, J.M., da Silva, L.C.P., Rider, M.J. (2019). Energy Management in Microgrids. In: Zambroni de Souza, A., Castilla, M. (eds) Microgrids Design and Implementation. Springer, Cham. https://doi.org/10.1007/978-3-319-98687-6_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-98687-6_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98686-9

  • Online ISBN: 978-3-319-98687-6

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics

Search

Navigation