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A multi-objective optimization framework for risk-controlled integration of renewable generation into electric power systems

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  • Mena, Rodrigo
  • Hennebel, Martin
  • Li, Yan-Fu
  • Zio, Enrico

Abstract

We introduce a MOO (multi-objective optimization) framework for the integration of renewable DG (distributed generation) into electric power networks. The framework searches for the optimal size and location of different DG technologies, taking into account uncertainties related to primary renewable resources availability, components failures, power demands and bulk-power supply. A non-sequential MCS-OPF (Monte Carlo simulation and optimal power flow) computational model is developed to emulate the network operation by generating random scenarios from the diverse sources of uncertainty, and assess the system performance in terms of CG (global cost). To measure uncertainty in the system performance, we introduce the DCVaR (conditional value-at-risk deviation) which, due to its axiomatic relation to the CVaR (conditional value-at-risk), allows the conjoint control of risk. A MOO strategy can, then, be adopted for the concurrent minimization of the ECG (expected global cost) and the associated deviation DCVaR(CG). In our work this is operatively implemented by a heuristic search engine based on differential evolution (MOO-DE). An example of application of the proposed framework is given with regards to the IEEE 30 bus test system, where in DCVaR is shown capable of enabling and controlling tradeoffs between optimal expected economic performance, uncertainty and risk.

Suggested Citation

  • Mena, Rodrigo & Hennebel, Martin & Li, Yan-Fu & Zio, Enrico, 2016. "A multi-objective optimization framework for risk-controlled integration of renewable generation into electric power systems," Energy, Elsevier, vol. 106(C), pages 712-727.
    • Handle: RePEc:eee:energy:v:106:y:2016:i:c:p:712-727
    DOI: 10.1016/j.energy.2016.03.056
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    Cited by:

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    4. Ioannou, Anastasia & Angus, Andrew & Brennan, Feargal, 2017. "Risk-based methods for sustainable energy system planning: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 602-615.
    5. Zeinolabedin Sadeghi & Yahya Hatami & Seyed Abdolmajid Jalaei & Amir Abdollahi, 2020. "Generation capacity expansion economic-environmental planning under uncertainty of demand and supply," Environment Systems and Decisions, Springer, vol. 40(4), pages 544-559, December.
    6. Cui, Yunfei & Geng, Zhiqiang & Zhu, Qunxiong & Han, Yongming, 2017. "Review: Multi-objective optimization methods and application in energy saving," Energy, Elsevier, vol. 125(C), pages 681-704.
    7. Rocchetta, Roberto & Zio, Enrico & Patelli, Edoardo, 2018. "A power-flow emulator approach for resilience assessment of repairable power grids subject to weather-induced failures and data deficiency," Applied Energy, Elsevier, vol. 210(C), pages 339-350.
    8. Ji, Ling & Huang, Guohe & Xie, Yulei & Zhou, Yong & Zhou, Jifang, 2018. "Robust cost-risk tradeoff for day-ahead schedule optimization in residential microgrid system under worst-case conditional value-at-risk consideration," Energy, Elsevier, vol. 153(C), pages 324-337.
    9. Vinel, Alexander & Mortaz, Ebrahim, 2019. "Optimal pooling of renewable energy sources with a risk-averse approach: Implications for US energy portfolio," Energy Policy, Elsevier, vol. 132(C), pages 928-939.

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