An optimization decision support approach for risk analysis of carbon emission trading in electric power systems

Concerns over dramatic increasing electricity demand, exacerbating power shortage and changing climatic condition are emerging associated with municipal electric power systems (EPS). In this study, a risk-explicit mixed-integer full-infinite programming (RMFP) approach is developed for planning carbon emission trading (CET) in EPS. RMFP-CET has advantages in risk reflection and policy analysis, particularly when the input parameters are provided as crisp and functional intervals as well as probabilistic distributions. The developed method is applied to a real case study of CET planning of EPS in Beijing. Various electricity policies are incorporated within the modeling formulation for enhancing the RMFP-CET's capability. The results indicate that reasonable solutions have been generated, which are useful for making decisions of electricity production and supply as well as gaining insight into the tradeoffs among electricity supply risk, system cost, and CO2 mitigation strategy. An RMFP method is developed for solving risks under uncertainties.The method then applied to managing risks in carbon emission trading.The RMFP model can tackle trading problems among electric power plants.Trade-offs occurs between system risk and CO2 mitigation in Beijing.

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