A multi-objective genetic algorithm for unit commitment with significant wind penetration

This paper addresses day-ahead unit commitment with significant wind penetration as a multi-objective optimization problem in an uncertain environment considering system operation cost and reliability as the multiple conflicting objectives. The uncertainties occurring due to thermal unit outage, load forecast error and wind forecast error are efficiently incorporated using expected energy not served (EENS) reliability index while EENS cost is used to reflect the reliability objective. A multi-objective genetic algorithm is proposed to solve the aforementioned scheduling problem. The algorithm is implemented on a 20 unit test system and the effect of wind penetration level, value of lost load and load forecast uncertainty is analyzed. It is demonstrated and validated through simulation studies that optimum system spinning reserve is the amount for which the sum of system operation cost and expected energy not served cost i.e., total cost is minimum. Amongst the trade-off optimal solutions obtained, a single optimum solution is highlighted which can be most important to system operators.