A robust possibilistic mixed-integer programming method for planning municipal electric power systems

• An innovative robust method is proposed for addressing ambiguous uncertainties.
• It can capture recourse actions of deviation of objective function and constraint.
• The method is applied to a real case of municipal electric systems planning.
• Cost-effective and sustainable electricity generation schemes are identified.
• Results are helpful for formulating policies of electricity generation.

In this study, a robust possibilistic mixed-integer programming (RPMP) method is developed for planning municipal electric power systems (EPS) under uncertainty. RPMP incorporates the concept of robustness within a possibilistic mixed-integer programming framework to handle ambiguous uncertainties in the objective function and constraints. It is superior to existing fuzzy possibilistic programming method by accounting for recourse actions of deviation of objective function with imprecise parameters from its optimal value, as well as economic penalties as corrective measures of possible violation for constraints with imprecise parameters. A RPMP-based electric power system (RPMP-EPS) model is then formulated for planning EPS of the City of Shenzhen, China, while cost-effective and sustainable electricity generation schemes can be achieved through analyzing city’s electricity consumption mix, electricity balance condition, as well as energy self-sufficiency. Results demonstrate that (i) power export contracts based on national and regional energy policies bring significant effects on the municipal EPS, particularly in energy supply schemes and electricity consumption mix; (ii) although city can be basically self-sufficient in power supply if nuclear power is not enforced for export, import dependency of fuels remains extremely high, leading to the insecure fuel supply and vulnerable EPS; (iii) uncertainties have significant effects on the city’s energy source supply as well as the relevant electricity-generation scheme. The findings are helpful for formulating policies of electricity generation as well as analyzing interactions among system cost, environmental objective, and electricity supply security.