The economic dispatch (ED) problem is one of the important optimization problems in power system operation. Recently the power system has stressed the need for reliable, nonpolluting, and economic operation. Hence, 3 conflicting functions of reliability, emission, and fuel cost are considered in the objective function of the proposed ED problem. The problem is formulated as a nonsmooth and nonconvex problem when the valve-point effects of thermal units are considered in the proposed reliable emission and economic dispatch (REED) problem. This paper presents a multiobjective optimization methodology for solving the newly developed REED problem using a fuzzified artificial bee colony algorithm. The artificial bee colony algorithm is used to schedule the optimal dispatch and fuzzy membership approach is used to find the best compromise solution from the Pareto optimal set. The methodology is validated on an IEEE 30-bus system and 3-, 6-, 10-, 26-, and 40-unit systems and the results are compared with the existing literature. The results clearly show that the proposed method is able to produce well-distributed Pareto optimal solutions when compared with other methods reported in the literature.
Maximum power point tracking (MPPT) is a basic and indispensable requirement for photovoltaic (PV) systems under normal irradiance and under partial shading conditions (PSCs) Although the simple perturb and observe algorithm is quite effective under normal conditions, it fails to recognize the global maximum operating point (GMOP) under the PSC This paper explores the fast determination of GMOP under PSCs using a proposed high-speed MPPT module, which operates in conjunction with a boost converter With this high-speed MPPT module, the tracking time of the MPPT controller is considerably reduced The tracking accuracy and efficiency are significantly better than other techniques in the literature The concept is first implemented on a PV system simulation model and the results are further validated with a prototype implementation of a 300 W PV-fed boost converter operated under various PSCs The enthusiastic results were finally verified in an installation of a 25 kW PV system The results demonstrate that the proposed high-speed MPPT controller outperforms both duty sweep and particle swarm optimization based MPPTs
DOI : 10.1109/TIE.2018.2833036 Anahtar Kelimeler :
Maximum power point trackers, Inductors, Capacitors, Switching circuits, Steady-state, Integrated circuit modeling, Mathematical model, maximum power point trackers, particle swarm optimisation, photovoltaic power systems, high-speed maximum power point tracking module, photovoltaic systems, normal irradiance, partial shading conditions, PSCs, global maximum operating point, GMOP, high-speed MPPT module, tracking time, tracking accuracy, PV system simulation model, high-speed MPPT controller, PV-fed boost converter, power 2.5 kW, power 3. W
ISSN: 0278-0046 Cilt: 66 Sayı: 2 Sayfa: 1119-1129
Global maximum operating point (GMOP) tracking is an important requirement of solar photovoltaic (PV) systems under partial shading conditions (PSCs). Though the perturb and observe algorithm is simple and effective, it fails to recognize the GMOP. This paper explores the application of the firefly algorithm (FA) to the maximum power point tracking (MPPT) problem of PV systems. In order to determine the shortest path to reach the GMOP under various PSCs, a new fast convergence firefly algorithm (FA) is proposed. Additionally, the change in firefly position is limited to a maximum value identified based on the characteristics of the PSC. The fast convergence method is guaranteed to find the GMOP, avoiding the local operating point obstacle through a repeated space search technique. Using MATLAB, the algorithm is implemented on a model PV system. An experimental 300-W PV system is developed to validate the operating point of the PV system under various PSCs. The proposed method is tested on a 5-kW solar power plant. The results demonstrate that the proposed MPPT algorithm outperforms particle swarm optimization, FA-based MPPTs, and other methods available in the literature.
This paper proposes an improved firefly (FF) algorithm with multiple workers for solving the unit commitment (UC) problem of power systems. The UC problem is a combinatorial optimization problem that can be posed as minimizing a quadratic objective function under system and unit constraints. Nowadays, highly developed computer systems are available in plenty, and proper utilization of these systems will reduce the time and complexity of combinatorial optimization problems with large numbers of generating units. Here, multiple workers are assigned to solve a UC problem as well as the subproblem, namely economic dispatch (ED) in distributed memory models. The proposed method incorporates a group search in a FF algorithm and thereby a global search is attained through the local search performed by the individual workers, which fine tune the search space in achieving the final solution. The execution time taken by the processor and the solution obtained with respect to the number of processors in a cluster are thoroughly discussed for different test systems. The methodology is validated on a 100 unit system, an IEEE 118 bus system, and a practical Taiwan 38 bus power system and the results are compared with the available literature.