An interval-parameter chanced-constrained full-infinite mixed-integer programming (ICFMP) approach is proposed for planning energy systems under functional interval uncertainties. ICFMP cannot only deal with crisp intervals, functional intervals, and probability distributions, but also support the assessment of the reliability of satisfying (or the risk of violating) systems constraints. ICFMP can also facilitate capacity expansion planning for energy production facilities within a multi-period and multi-option context. Then, a real case study of energy systems planning in Beijing is applied to illustrate the applicability of the ICFMP, with an objective of minimizing system cost and under constraints of resources availability and environmental regulations. Various energy policies are incorporated within the modeling formulation, which can enhance the ICFMP's capability for planning municipal energy systems. The ICFMP is transformed into two deterministic submodels that correspond to the lower aid upper bounds for the desired objective function value. The results indicate that reasonable solutions for both binary and continuous variables have been generated under different levels of constraint-violation risk. The results are useful for making decisions of energy production and allocation as well as gaining insight into the tradeoffs between the system cost and the constraint-violation risk. (C) 2011 Elsevier Ltd. All rights reserved.