259 research outputs found

    Heuristic-based fireļ¬‚y algorithm for bound constrained nonlinear binary optimization

    Get PDF
    Fireļ¬‚y algorithm (FA) is a metaheuristic for global optimization. In this paper,we address the practical testing of aheuristic-based FA (HBFA) for computing optimaof discrete nonlinear optimization problems,where the discrete variables are of binary type. An important issue in FA is the formulation of attractiveness of each ļ¬reļ¬‚y which in turn affects its movement in the search space. Dynamic updating schemes are proposed for two parameters, one from the attractiveness term and the other from the randomization term. Three simple heuristics capable of transforming real continuous variables into binary ones are analyzed. A new sigmoid ā€˜erfā€™ function is proposed. In the context of FA, three different implementations to incorporate the heuristics for binary variables into the algorithm are proposed. Based on a set of benchmark problems, a comparison is carried out with other binary dealing metaheuristics. The results demonstrate that the proposed HBFA is efļ¬cient and outperforms binary versions of differential evolution (DE) and particle swarm optimization (PSO). The HBFA also compares very favorably with angle modulated version of DE and PSO. It is shown that the variant of HBFA based on the sigmoid ā€˜erfā€™ function with ā€˜movements in continuous spaceā€™ is the best, both in terms of computational requirements and accuracy.FundaĆ§Ć£o para a CiĆŖncia e a Tecnologia (FCT

    Reliability Constrained Unit Commitment Considering the Effect of DG and DR Program

    Get PDF
    Due to increase in energy prices at peak periods and increase in fuel cost, involving Distributed Generation (DG) and consumption management by Demand Response (DR) will be unavoidable options for optimal system operations. Also, with high penetration of DGs and DR programs into power system operation, the reliability criterion is taken into account as one of the most important concerns of system operators in management of power system. In this paper, a Reliability Constrained Unit Commitment (RCUC) at presence of time-based DR program and DGs integrated with conventional units is proposed and executed to reach a reliable and economic operation. Designated cost function has been minimized considering reliability constraint in prevailing UC formulation. The UC scheduling is accomplished in short-term so that the reliability is maintained in acceptable level. Because of complex nature of RCUC problem and full AC load flow constraints, the hybrid algorithm included Simulated Annealing (SA) and Binary Particle Swarm Optimization (BPSO) has been proposed to optimize the problem. Numerical results demonstrate the effectiveness of the proposed method and considerable efficacy of the time-based DR program in reducing operational costs by implementing it on IEEE-RTS79

    Wind Integrated Thermal Unit Commitment Solution Using Grey Wolf Optimizer

    Get PDF
    The augment of ecological shield and the progressive exhaustion of traditional fossil energy sources have increased the interests in integrating renewable energy sources into existing power system. Wind power is becoming worldwide a significant component of the power generation portfolio. Profuse literature have been reported for the thermal Unit Commitment (UC) solution. In this work, the UC problem has been formulated by integrating wind power generators along with thermal power system. The Wind Generator Integrated UC (WGIUC) problem is more complex in nature, that necessitates a promising optimization tool. Hence, the modern bio-inspired algorithm namely, Grey Wolf Optimization (GWO) algorithm has been chosen as the main optimization tool and real coded scheme has been incorporated to handle the operational constraints. The standard test systems are used to validate the potential of the GWO algorithm. Moreover, the ramp rate limits are also included in the mathematical WGIUC formulation. The simulation results prove that the intended algorithm has the capability of obtaining economical resolutions with good solution quality

    A Binary differential search algorithm for the 0-1 multidimensional knapsack problem

    Get PDF
    The multidimensional knapsack problem (MKP) is known to be NP-hard in operations research and it has a wide range of applications in engineering and management. In this study, we propose a binary differential search method to solve 0-1 MKPs where the stochastic search is guided by a Brownian motion-like random walk. Our proposed method comprises two main operations: discrete solution generation and feasible solution production. Discrete solutions are generated by integrating Brownian motion-like random search with an integer-rounding operation. However, the rounded discrete variables may violate the constraints. Thus, a feasible solution production strategy is used to maintain the feasibility of the rounded discrete variables. To demonstrate the efficiency of our proposed algorithm, we solved various 0-1 MKPs using our proposed algorithm as well as some existing meta-heuristic methods. The numerical results obtained demonstrated that our algorithm performs better than existing meta-heuristic methods. Furthermore, our algorithm has the capacity to solve large-scale 0-1 MKPs

    Optimal Sizing and Placement of Solar Cell Distributed Generator Suitable for Integrated Power System Environment

    Get PDF
    A novel fuzzified Clustered Gravitational Search Algorithm (CGSA) has been employed for solving multi-objective problem formulated for solar based distributed generation. Optimal sizing and placement of solar distributed generation is considered. High solar penetration can lead to high-risk level in power system reliability. In order to maintain the system reliability, solar power dispatch is usually restricted based on the reliability level of the system. Two conflicting objective functions such as power loss and reliability level of the system are also considered for solving optimal placement of solar distributed generation (SDG). Binary coded CGSA is employed for solving optimal placement of SDG and sizing is determined using real coded CGSA. The fuzzy membership function for each objective is designed and multi-objective optimal placement problem has been presented. The proposed method is validated on IEEE standard 69-bus radial distribution networks. The efficiency of the proposed optimization technique is validated by comparing the results with other results available in the existing articles

    Unit commitment by a fast and new analytical non-iterative method using IPPD table and ā€œĪ»-logicā€ algorithm

    Get PDF
    Many different methods have been presented to solve unit commitment (UC) problem in literature with different advantages and disadvantages. The need for multiple runs, huge computational burden and time, and poor convergence are some of the disadvantages, where are especially considerable in large scale systems. In this paper, a new analytical and non-iterative method is presented to solve UC problem. In the proposed method, improved pre-prepared power demand (IPPD) table is used to solve UC problem, and then analytical ā€œĪ»-logicā€ algorithm is used to solve economic dispatch (ED) sub-problem. The analytical and non-iterative nature of the mentioned methods results in simplification of the UC problem solution. Obtaining minimum cost in very small time with only one run is the major advantage of the proposed method. The proposed method has been tested on 10 unit and 40-100 unit systems with consideration of different constraints, such as: power generation limit of units, reserve constraints, minimum up and down times of generating units. Comparing the simulation results of the proposed method with other methods in literature shows that in large scale systems, the proposed method achieves minimum operational cost within minimum computational time

    Multiā€“dimensional firefly algorithm based on local search for solving unit commitment problem

    Get PDF
    The Unit Commitment problem (UC) is a complex mixed-integer nonlinear programming problem, so the main challenge faced by many researchers is obtaining the optimal solution. Therefore, this dissertation proposes a new methodology combining the multi-dimensional firefly algorithm with local search called LS-MFA and utilizes it to solve the UC problem. In addition, adaptive adjustment, tolerance mechanism, and pit-jumping random strategy help to improve the optimal path and simplify the redundant solutions. The experimental work of unit commitment with the output of 10ā€“100 machines in the 24-hour period is carried out in this paper. And it shows that compared with the previous UC artificial intelligence algorithms, the total cost obtained by LS-MFA is less and the results are excellent
    • ā€¦
    corecore