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A fuzzy-stochastic simulation-optimization model for planning electric power systems with considering peak-electricity demand: A case study of Qingdao, China

Yu, L., Li, Y.P., Huang, G.H.
Energy 2016 v.98 pp. 190-203
Monte Carlo method, case studies, decision making, electric power, electricity, electricity generation, models, planning, pollutants, prediction, risk, uncertainty, China
In this study, a FSSOM (fuzzy-stochastic simulation-optimization model) is developed for planning EPS (electric power systems) with considering peak demand under uncertainty. FSSOM integrates techniques of SVR (support vector regression), Monte Carlo simulation, and FICMP (fractile interval chance-constrained mixed-integer programming). In FSSOM, uncertainties expressed as fuzzy boundary intervals and random variables can be effectively tackled. In addition, SVR coupled Monte Carlo technique is used for predicting the peak-electricity demand. The FSSOM is applied to planning EPS for the City of Qingdao, China. Solutions of electricity generation pattern to satisfy the city's peak demand under different probability levels and p-necessity levels have been generated. Results reveal that the city's electricity supply from renewable energies would be low (only occupying 8.3% of the total electricity generation). Compared with the energy model without considering peak demand, the FSSOM can better guarantee the city's power supply and thus reduce the system failure risk. The findings can help decision makers not only adjust the existing electricity generation/supply pattern but also coordinate the conflict interaction among system cost, energy supply security, pollutant mitigation, as well as constraint-violation risk.