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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. [ABSTRACT FROM AUTHOR]

This paper presents an intelligent economic operation of smart grid environment facilitating an advanced quantum evolutionary method. The proposed method models the wind generation (WG) and photovoltaic (PV) generation as renewable power generation sources as a measure of global warming effect. Thermal generators (TGs) are included in this model to provide the maximum amount of energy to meet consumers' demand. On the other hand, plug-in hybrid electric vehicles (PHEVs) are capable of reducing CO_2, NO_x, and gradually becoming an integral part of smart-grid infrastructure. Such integration introduces uncertainties into the system that are addressed by fuzzy-logic-based formulations. Demanded load, wind speed, solar radiation, and number of involved PHEVs are taken under fuzzy formulations. An intelligent quantum inspired evolutionary algorithm (IQEA) is proposed and applied in this model to perform the intelligent economic scheduling operation concerning scheduling and dispatching TG, WG, PV, and PHEV. IQEA features intelligent operators such as sophisticated rotation operator, differential operator, etc. The method is tested on a hypothetical power system with 10 thermal units, equivalent number of PHEVs, equivalent solar and wind farm. The simulation results will show the effectiveness of IQEA that provides excellent operational resource scheduling while reducing the production cost and emission. [ABSTRACT FROM PUBLISHER]

Abstract: This paper presents a fuzzy controlled and multi-population based binary clustered particle swarm optimization (BCPSO) algorithm to solve short term thermal generation scheduling problem. In order to incorporate the uncertainties regarding forecasted load demand, the spinning reserve requirement and the total production cost, the formulations are modified by employing fuzzy logics. Each of these uncertain entities are associated with fuzzy membership functions which determine the degree of acceptance. The aggregated membership function, which combines the individual membership functions of fuzzified variables, is incorporated with the fitness value to provide the acceptability measurement of a particular candidate schedule. Typically, generation scheduling is a highly non-linear, multi-peak combinatorial optimization problem. In this method, the potential candidate schedules (or individuals) are distributed among several clusters based on their acceptance values. Each individual of a particular cluster then flies through to its cluster-space towards the cluster best while improving its personal best position. Gradually, as the population grows, the cluster space is also increased to ensure the global convergence. Therefore, this algorithm explores a larger search space and thus reduces the probability of local trapping. A dynamic probabilistic mutation operator is applied on the individual solutions based on their associated fitness values. Simulation result is provided to show the effectiveness of BCPSO while considering two different power system configurations. [Copyright &y& Elsevier]