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Stochastic Unit Commitment Problem, Incorporating Wind Power and an Energy Storage System

doi: 10.3390/su122310100
This paper presents a modified formulation for the wind-battery-thermal unit commitment problem that combines battery energy storage systems with thermal units to compensate for the power dispatch gap caused by the intermittency of wind power generation. The uncertainty of wind power is described by a chance constraint to escape the probabilistic infeasibility generated by classical approximations of wind power. Furthermore, a mixed-integer linear programming algorithm was applied to solve the unit commitment problem. The uncertainty of wind power was classified as a sub-problem and separately computed from the master problem of the mixed-integer linear programming. The master problem tracked and minimized the overall operation cost of the entire model. To ensure a feasible and efficient solution, the formulation of the wind-battery-thermal unit commitment problem was designed to gather all system operating constraints. The solution to the optimization problem was procured on a personal computer using a general algebraic modeling system. To assess the performance of the proposed model, a simulation study based on the ten-unit power system test was applied. The effects of battery energy storage and wind power were deeply explored and investigated throughout various case studies.
- University of Sfax Tunisia
- University of Hail Saudi Arabia
Environmental effects of industries and plants, TJ807-830, modeling, stochastic optimization, wind power, simulation, TD194-195, Renewable energy sources, Environmental sciences, energy storage systems, GE1-350, unit commitment
Environmental effects of industries and plants, TJ807-830, modeling, stochastic optimization, wind power, simulation, TD194-195, Renewable energy sources, Environmental sciences, energy storage systems, GE1-350, unit commitment
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).21 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
