Managing Risk in Electric Distribution Networks

  • Marco R. M. Cruz
  • Desta Z. FitiwiEmail author
  • Sergio F. Santos
  • Miadreza Shafie-khah
  • Joao P. S. Catalao
Part of the Power Systems book series (POWSYS)


This book chapter explores existing and emerging flexibility options that can facilitate the integration of large-scale variable renewable energy sources (vRESs) in next-gen electric distribution networks while minimizing their side-effects and associated risks. Nowadays, it is widely accepted that integrating vRESs is highly needed to solve a multitude of global concerns such as meeting an increasing demand for electricity, enhancing energy security, reducing heavy dependence on fossil fuels for energy production and the overall carbon footprint of power production. As a result, the scale of vRES development has been steadily increasing in many electric distribution networks. The favorable agreements of states to curb greenhouse gas emissions and mitigate climate change, along with other technical, socio-economic and structural factors, is expected to further accelerate the integration of renewables in electric distribution networks. Many states are now embarking on ambitious “clean” energy development targets. Distributed generations (DGs) are especially attracting a lot of attention nowadays, and planners and policy makers seem to favor more on a distributed power generation to meet the increasing demand for electricity in the future. And, the role of traditionally centralized power production regime is expected to slowly diminish in future grids. This means that existing electric distribution networks should be readied to effectively handle the increasing penetration of DGs, vRESs in particular, because such systems are not principally designed for this purpose. It is because of all this that regulators often set a maximum RES penetration limit (often in the order of 20%) which is one of the main factors that impede further development of the much-needed vRESs.

The main challenge is posed by the high-level variability as well as partial unpredictability of vRESs which, along with traditional sources of uncertainty, leads to several technical problems and increases operational risk in the system. This is further exacerbated by the increased uncertainty posed by the continuously changing and new forms of energy consumption such as power-to-X and electric vehicles. All these make operation and planning of distribution networks more intricate. Therefore, there is a growing need to transform existing systems so that they are equipped with adequate flexibility mechanisms (options) that are capable of alleviating the aforementioned challenges and effectively managing inherent technical risk. To this end, the main focus of this chapter is on the optimal management of distribution networks featuring such flexibility options and vRESs. This analysis is supported by numerical results from a standard network system. For this, a reasonably accurate mathematical optimization model is developed, which is based on a linearized AC network model. The results and analysis in this book chapter have policy implications that are important to optimally design ad operate future grids, featuring large-scale variable energy resources. In general, based on the analysis results, distribution networks can go 100% renewable if various flexibility options are adequately deployed and operated in a more efficient manner.


Demand response Electric distribution networks Energy storage systems Flexibility options Mixed integer linear programming Network reconfiguration Stochastic programing Variable renewable energy sources 



This publication has emanated from research supported in part by a research grant from Science Foundation Ireland (SFI) under the SFI Strategic Partnership Programme Grant number SFI/15/SPP/E3125. The opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Science Foundation Ireland.


  1. 1.
    I.T. Papaioannou, A. Purvins, E. Tzimas, Demand shifting analysis at high penetration of distributed generation in low voltage grids. Int. J. Electr. Power Energy Syst. 44(1), 540–546 (2013)CrossRefGoogle Scholar
  2. 2.
    M. Junjie, W. Yulong, L. Yang, Size and location of distributed generation in distribution system based on immune algorithm. Syst. Eng. Procedia 4, 124–132 (2012)CrossRefGoogle Scholar
  3. 3.
    P.D. Lund, J. Lindgren, J. Mikkola, J. Salpakari, Review of energy system flexibility measures to enable high levels of variable renewable electricity. Renew. Sustain. Energy Rev. 45, 785–807 (2015)CrossRefGoogle Scholar
  4. 4.
    P. Crespo Del Granado, Z. Pang, S.W. Wallace, Synergy of smart grids and hybrid distributed generation on the value of energy storage. Appl. Energy 170, 476–488 (2016)CrossRefGoogle Scholar
  5. 5.
    R. Tulabing et al., Modeling study on flexible load’s demand response potentials for providing ancillary services at the substation level. Electr. Power Syst. Res. 140, 240–252 (2016)CrossRefGoogle Scholar
  6. 6.
    E. Cutter, C.K. Woo, F. Kahrl, A. Taylor, Maximizing the value of responsive load. Electr. J. 25(7), 6–16 (2012)CrossRefGoogle Scholar
  7. 7.
    E. Lannoye, D. Flynn, M. O’Malley, Assessment of power system flexibility: a high-level approach, in Power and Energy Society General Meeting, 2012 IEEE (2012), pp. 1–8Google Scholar
  8. 8.
    J.L. Mathieu, M.G. Vayá, G. Andersson, Uncertainty in the flexibility of aggregations of demand response resources, in Industrial Electronics Society, IECON 2013–39th Annual Conference of the IEEE (2013), pp. 8052–8057Google Scholar
  9. 9.
    V. Calderaro, G. Conio, V. Galdi, G. Massa, A. Piccolo, Active management of renewable energy sources for maximizing power production. Int. J. Electr. Power Energy Syst. 57, 64–72 (2014)CrossRefGoogle Scholar
  10. 10.
    A. Ulbig, G. Andersson, Analyzing operational flexibility of electric power systems, in Power Systems Computation Conference (PSCC), 2014 (Wroclaw, Poland, 2014)Google Scholar
  11. 11.
    A. Ulbig, G. Andersson, Analyzing operational flexibility of electric power systems. Int. J. Electr. Power Energy Syst. 72, 155–164 (2015)CrossRefGoogle Scholar
  12. 12.
    S.G. Task Force, Regulatory Recommendations for the Deployment of Flexibility, EG3 Report, Jan 2015Google Scholar
  13. 13.
    J.R. Aguero, E. Takayesu, D. Novosel, R. Masiello, Modernizing the grid: challenges and opportunities for a sustainable future. IEEE Power Energy Mag. 15(3), 74–83 (2017)CrossRefGoogle Scholar
  14. 14.
    A. Kumar et al., A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renew. Sustain. Energy Rev. 69, 596–609 (2017)CrossRefGoogle Scholar
  15. 15.
    J.L. Christensen, D.S. Hain, Knowing where to go: the knowledge foundation for investments in renewable energy. Energy Res. Soc. Sci. 25, 124–133 (2017)CrossRefGoogle Scholar
  16. 16.
    Y. Fernando, S. Yahya, Challenges in implementing renewable energy supply chain in service economy era. Procedia Manuf. 4, 454–460 (2015)CrossRefGoogle Scholar
  17. 17.
    M. Bhattacharya, S.R. Paramati, I. Ozturk, S. Bhattacharya, The effect of renewable energy consumption on economic growth: evidence from top 38 countries. Appl. Energy 162, 733–741 (2016)CrossRefGoogle Scholar
  18. 18.
    P. Blechinger, C. Cader, P. Bertheau, H. Huyskens, R. Seguin, C. Breyer, Global analysis of the techno-economic potential of renewable energy hybrid systems on small islands. Energy Policy 98, 674–687 (2016)CrossRefGoogle Scholar
  19. 19.
    A. Botelho, L.M.C. Pinto, L. Lourenço-Gomes, M. Valente, S. Sousa, Social sustainability of renewable energy sources in electricity production: an application of the contingent valuation method. Sustain. Cities Soc. 26, 429–437 (2016)CrossRefGoogle Scholar
  20. 20.
    M. Engelken, B. Römer, M. Drescher, I.M. Welpe, A. Picot, Comparing drivers, barriers, and opportunities of business models for renewable energies: a review. Renew. Sustain. Energy Rev. 60, 795–809 (2016)CrossRefGoogle Scholar
  21. 21.
    C.-A. Gabriel, What is challenging renewable energy entrepreneurs in developing countries? Renew. Sustain. Energy Rev. 64, 362–371, Out (2016)Google Scholar
  22. 22.
    M. Jamil, F. Ahmad, Y.J. Jeon, Renewable energy technologies adopted by the UAE: prospects and challenges—a comprehensive overview. Renew. Sustain. Energy Rev. 55, 1181–1194 (2016)CrossRefGoogle Scholar
  23. 23.
    A. Seetharaman, L.L. Sandanaraj, M.K. Moorthy, A.S. Saravanan, Enterprise framework for renewable energy. Renew. Sustain. Energy Rev. 54, 1368–1381 (2016)CrossRefGoogle Scholar
  24. 24.
    A. Zyadin, P. Halder, T. Kähkönen, A. Puhakka, Challenges to renewable energy: a bulletin of perceptions from international academic arena. Renew. Energy 69, 82–88 (2014)CrossRefGoogle Scholar
  25. 25.
    W. D’haeseleer, L. de Vries, C. Kang, E. Delarue, Flexibility challenges for energy markets: fragmented policies and regulations lead to significant concerns. IEEE Power Energy Mag. 15(1), 61–71 (2017)CrossRefGoogle Scholar
  26. 26.
    M. Engelken, B. Römer, M. Drescher, I.M. Welpe, A. Picot, Comparing drivers, barriers, and opportunities of business models for renewable energies: a review. Renew. Sustain. Energy Rev. 60, 795–809 (2016)CrossRefGoogle Scholar
  27. 27.
    M.S. Hossain, N.A. Madlool, N.A. Rahim, J. Selvaraj, A.K. Pandey, A.F. Khan, Role of smart grid in renewable energy: an overview. Renew. Sustain. Energy Rev. 60, 1168–1184 (2016)CrossRefGoogle Scholar
  28. 28.
    A. Zyadin, P. Halder, T. Kähkönen, A. Puhakka, Challenges to renewable energy: a bulletin of perceptions from international academic arena. Renew. Energy 69, 82–88 (2014)CrossRefGoogle Scholar
  29. 29.
    G. Papaefthymiou, K. Grave, K. Dragoon, Flexibility options in electricity systems, Ecofys 2014 by order of: European Copper Institute (2014)Google Scholar
  30. 30.
    Union internationale d’électrothermie and Electric load management in industry working group, in Electric Load Management In Industry (Paris-La Défense, UIE 1996)Google Scholar
  31. 31.
    M.H. Shoreh, P. Siano, M. Shafie-khah, V. Loia, J.P.S. Catalão, A survey of industrial applications of demand response. Electr. Power Syst. Res. 141, 31–49 (2016)CrossRefGoogle Scholar
  32. 32.
    E. Union, Research Challenges to Increase the Flexibility of Power Systems (Belgium, 2014)Google Scholar
  33. 33.
    K. Knezovic, S. Martinenas, P.B. Andersen, A. Zecchino, M. Marinelli, Enhancing the role of Electric Vehicles in the power grid: field validation of multiple ancillary services. IEEE Trans. Transp. Electrification 3(1), 201–209 (2017)CrossRefGoogle Scholar
  34. 34.
    D.T. Hoang, P. Wang, D. Niyato, E. Hossain, Charging and discharging of Plug-In Electric Vehicles (PEVs) in Vehicle-to-Grid (V2G) systems: a cyber insurance-based model. IEEE Access 5, 732–754 (2017)CrossRefGoogle Scholar
  35. 35.
    S.I. Vagropoulos, G.A. Balaskas, A.G. Bakirtzis, An investigation of plug-in electric vehicle charging impact on power systems scheduling and energy costs. IEEE Trans. Power Syst. 32(3), 1902–1912 (2017)CrossRefGoogle Scholar
  36. 36.
    N. Neyestani, M.Y. Damavandi, M. Shafie-khah, A.G. Bakirtzis, J.P.S. Catalao, Plug-In Electric Vehicles parking lot equilibria with energy and reserve markets. IEEE Trans. Power Syst. 32(3), 2001–2016 (2017)CrossRefGoogle Scholar
  37. 37.
    S. Hers, M. Afman, S. Cherif, F. Rooijers, Potential for Power-to-Heat in the Netherlands (CE Delft, Delft, Netherlands, 2015)Google Scholar
  38. 38.
    D. Böttger, M. Götz, N. Lehr, H. Kondziella, T. Bruckner, Potential of the Power-to-Heat technology in district heating grids in Germany. Energy Procedia 46, 246–253 (2014)CrossRefGoogle Scholar
  39. 39.
    M. Götz et al., Renewable Power-to-Gas: a technological and economic review. Renew. Energy 85, 1371–1390 (2016)CrossRefGoogle Scholar
  40. 40.
    F.D. Meylan, F.-P. Piguet, S. Erkman, Power-to-gas through CO2 methanation: assessment of the carbon balance regarding EU directives. J. Energy Storage 11, 16–24 (2017)CrossRefGoogle Scholar
  41. 41.
    U. Mukherjee, S. Walker, A. Maroufmashat, M. Fowler, A. Elkamel, Development of a pricing mechanism for valuing ancillary, transportation and environmental services offered by a power to gas energy system. Energy 128, 447–462 (2017)CrossRefGoogle Scholar
  42. 42.
    X. Liang, Emerging power quality challenges due to integration of renewable energy sources. IEEE Trans. Ind. Appl. 53(2), 855–866 (2017)CrossRefGoogle Scholar
  43. 43.
    C.I. Ossai, Optimal renewable energy generation—approaches for managing ageing assets mechanisms. Renew. Sustain. Energy Rev. 72, 269–280 (2017)CrossRefGoogle Scholar
  44. 44.
    T. Strasser et al., A review of architectures and concepts for intelligence in future electric energy systems. IEEE Trans. Ind. Electron. 62(4), 2424–2438 (2015)CrossRefGoogle Scholar
  45. 45.
    S. Islam, Challenges and opportunities in grid connected commercial scale PV and wind farms, in Electrical and Computer Engineering (ICECE), 2016 9th International Conference on, 2016, pp. 1–7Google Scholar
  46. 46.
    M.I. Alizadeh, M. Parsa Moghaddam, N. Amjady, P. Siano, M.K. Sheikh-El-Eslami, Flexibility in future power systems with high renewable penetration: a review. Renew. Sustain. Energy Rev. 57, 1186–1193 (2016)CrossRefGoogle Scholar
  47. 47.
    J. Cochran et al., Flexibility in 21st century power systems (National Renewable Energy Laboratory (NREL), Golden, CO, 2014)CrossRefGoogle Scholar
  48. 48.
    J.A. Schachter, P. Mancarella, A critical review of real options thinking for valuing investment flexibility in smart grids and low carbon energy systems. Renew. Sustain. Energy Rev. 56, 261–271 (2016)CrossRefGoogle Scholar
  49. 49.
    M. Wang, J. Zhong, A novel method for distributed generation and capacitor optimal placement considering voltage profiles, in Power and Energy Society General Meeting, 2011 IEEE, 2011, pp. 1–6Google Scholar
  50. 50.
    S.F. Santos, D.Z. Fitiwi, M. Shafie-khah, A.W. Bizuayehu, C.M.P. Cabrita, J.P.S. Catalao, New multi-stage and stochastic mathematical model for maximizing RES hosting capacity—part II: numerical results. IEEE Trans. Sustain. Energy (2016), pp. 1–1Google Scholar
  51. 51.
    D.Z. Fitiwi, L. Olmos, M. Rivier, F. de Cuadra, I.J. Pérez-Arriaga, Finding a representative network losses model for large-scale transmission expansion planning with renewable energy sources. Energy 101, 343–358 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Marco R. M. Cruz
    • 1
  • Desta Z. Fitiwi
    • 2
    • 4
    Email author
  • Sergio F. Santos
    • 2
  • Miadreza Shafie-khah
    • 2
  • Joao P. S. Catalao
    • 3
  1. 1.Department of Electrical and Computer EngineeringUniversity of Beira InteriorCovilhãPortugal
  2. 2.Department of Electromechanical EngineeringUniversity of Beira InteriorCovilhãPortugal
  3. 3.Department of Electrical and Computer EngineeringUniversity of PortoPortoPortugal
  4. 4.The Economic and Social Research InstituteDublinIreland

Personalised recommendations