Abstract To correctly investigate the effects of wind power, it is necessary to embed wind turbines in a wider power system and to take a look at the overall picture. It is not possible to isolate wind turbines and their impact from the rest of the power system; they interact with the electricity generation of the entire power system. This paper presents a simulation tool that models wind power and its unpredictability properly, and allows determining the effects wind power has on the cost of electricity generation and on CO 2 emissions. The simulation model uses mixed integer linear programming (MILP) and has the characteristics of an advanced unit commitment (UC) model. The model takes into account a wide set of technical constraints of power plants. To take wind power and its limited unpredictable character into account properly, a specific algorithm has been developed. In a first step, a regular day-ahead UC optimization is performed, applying with a certain forecast of wind power. In a second step, the real-time dispatch is executed. Each hour of the day, the activated plants are dispatched, now taking the actual wind power output into account. Spinning reserves can be used to overcome incorrect wind power forecasts. The method is applied to a case study for Belgium (having an interesting diverse generation mix). High detailed technical information of power plants, demand profiles and empirical data of several wind sites is used in the simulations. Accurate estimates are made concerning the beneficial impact of wind power on a yearly basis. Fuel cost reductions are situated around 56 k EUR per MW of installed wind power capacity per year, while CO 2 emission reduction would reach a level of 1.26 kton of CO 2 avoided, again per MW of installed wind power capacity per year. Wind speed forecast errors do not seem to have a significant effect on this cost and CO 2 reduction.
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