Fuzzy logic-based thermal generation scheduling strategy with solar-battery system using advanced quantum evolutionary method

This study presents a fuzzy logic-based strategy to solve thermal generation scheduling [namely unit commitment (UC)] problem integrated with an equivalent solar-battery system using quantum inspired evolutionary algorithm. Solar-battery system is included with this model as a measure of green-house effect. The crisp formulations of UC are modified utilising fuzzy logics, because of the inherent intermittency involved in solar energy integration and other uncertain variables. An evolutionary algorithm based on the concept and principle of quantum computation is applied to solve the resultant fuzzy logic-based UC problem. Conventional quantum evolutionary algorithm (QEA) is modified by incorporating a hierarchy-group oriented scheme to deal with the non-linear and multi-peak nature of the problem. QEA is further advanced facilitating some genetic algorithm operators and a new binary differential operator along with rotation operator with a redefined rotational angle look-up table. The probabilities of using such operators on individual solution(s) are fuzzified by defining membership function based on associated fitness of that individual. The fitness function is then determined by combining the objective function, penalty function and the aggregated fuzzy membership function. The proposed fuzzy logic-based QEA (FLQEA) is applied to UC problem in two different scaled power systems. Provided simulation results will show the effectiveness of FLQEA.