A Novel Hybrid Algorithm of Max-Min Ant System with Quadratic Programming to Solve the Unit Commitment Problem (original) (raw)
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Evolving ant colony optimization based unit commitment
Applied Soft Computing, 2011
Ant colony optimization (ACO) was inspired by the observation of natural behavior of real ants' pheromone trail formation and foraging. Ant colony optimization is more suitable for combinatorial optimization problems. ACO is successfully applied to the traveling salesman problem. Multistage decision making of ACO gives an edge over other conventional methods. This paper proposes evolving ant colony optimization (EACO) method for solving unit commitment (UC) problem. The EACO employs genetic algorithm (GA) for finding optimal set of ACO parameters, while ACO solves the UC problem. Problem formulation takes into consideration the minimum up and down time constraints, startup cost, spinning reserve, and generation limit constraints. The feasibility of the proposed approach is demonstrated on two different systems. The test results are encouraging and compared with those obtained by other methods.
Unit Commitment by Evolving Ant Colony Optimization
Innovations and Developments of Swarm Intelligence Applications, 2012
Ant Colony Optimization is more suitable for combinatorial optimization problems. ACO is successfully applied to the traveling salesman problem, and multistage decision making of ACO has an edge over other conventional methods. In this paper, the authors propose the Evolving Ant Colony Optimization (EACO) method for solving unit commitment (UC) problem. The EACO employs Genetic Algorithm (GA) for finding optimal set of ACO parameters, while ACO solves the UC problem. Problem formulation takes into consideration the minimum up and down time constraints, start up cost, spinning reserve, and generation limit constraints. The feasibility of the proposed approach is demonstrated on the systems with number of generating units in the range of 10 to 60. The test results are encouraging and compared with those obtained by other methods.
Solution Economic Power Dispatch Problems By An Ant Colony Optimization Approach
2014
The objective of the Economic Dispatch(ED) Problems<br> of electric power generation is to schedule the committed generating<br> units outputs so as to meet the required load demand at minimum<br> operating cost while satisfying all units and system equality and<br> inequality constraints. This paper presents a new method of ED<br> problems utilizing the Max-Min Ant System Optimization.<br> Historically, traditional optimizations techniques have been used,<br> such as linear and non-linear programming, but within the past<br> decade the focus has shifted on the utilization of Evolutionary<br> Algorithms, as an example Genetic Algorithms, Simulated Annealing<br> and recently Ant Colony Optimization (ACO). In this paper we<br> introduce the Max-Min Ant System based version of the Ant System.<br> This algorithm encourages local searching around the best solution<br> found in each iteration. To show its effici...
Ant Colony Optimization (ACO) Technique in Economic Power Dispatch Problems
Lecture Notes in Electrical Engineering, 2009
Most of electrical power utilities in the world are required to ensure that electrical energy requirement from the customer is served smoothly in accordance to the respective policy of the country. Despite serving the power demands of the country, the power utility has also to ensure that the electrical power is generated within minimal cost. Thus, the total demand must be appropriately shared among the generating units with an objective to minimize the total generation cost for the system in order to satisfy the economic operation of the system. Economic dispatch is a procedure to determine the electrical power to be generated by the committed generating units in a power system so that the total generation cost of the system is minimized, while satisfying the load demand simultaneously. This paper presents the economic power dispatch problems solved using Ant Colony Optimization (ACO) technique. ACO is a meta-heuristic approach for solving hard combinatorial optimization problems. In this study, the proposed technique was tested using the standard IEEE 26-Bus RTS and the results revealed that the proposed technique has the merit in achieving optimal solution for addressing the problems. Comparative studies with other optimization technique namely the artificial immune system (AIS) were also conducted in order to highlight the strength of the proposed technique.
Optimal Unit Commitment Problem Solution Using Real-Coded Particle Swarm Optimization Technique
This paper present real-coded particle swarm optimization RPSO is proposed to solve unit commitment problem UCP. The unit commitment is the problem to determining the schedule of generating units subject to device and operating constraints. The problem is decomposed in two sub-problem are unit commitment and economic dispatch that are solved by RPSO. The UCP is formulated as the minimization of the performance index, which is the sum of objectives (fuel cost, startup cost and shutdown cost) and some constraints (power balance, generation limits, spinning reserve, minimum up time and minimum down time). The RPSO technique is tested and validated on 10 generation units system for 24 hour scheduling horizon.
A Novel Binary Ant Colony Optimization: Application to the Unit Commitment Problem of Power Systems
Journal of Electrical Engineering and Technology, 2011
This paper proposes a novel binary ant colony optimization (NBACO) method. The proposed NBACO is based on the concept and principles of ant colony optimization (ACO), and developed to solve the binary and combinatorial optimization problems. The concept of conventional ACO is similar to Heuristic Dynamic Programming. Thereby ACO has the merit that it can consider all possible solution sets, but also has the demerit that it may need a big memory space and a long execution time to solve a large problem. To reduce this demerit, the NBACO adopts the state probability matrix and the pheromone intensity matrix. And the NBACO presents new updating rule for local and global search. The proposed NBACO is applied to test power systems of up to 100-unit along with 24-hour load demands.
A modified binary artificial bee colony algorithm for ramp rate constrained unit commitment problem
International Transactions on Electrical Energy Systems, 2015
This paper proposes a new approach based on modified binary artificial bee colony (MBABC) algorithm and dynamic economic dispatch (DED) method for unit commitment problem (UCP). MBABC algorithm is used for committing/decommitting the thermal units, while optimum dispatch solution is determined using DED method. Proposed MBABC algorithm utilizes a new mechanism based on the measure of dissimilarity between binary strings for generating the new binary solutions for UCP. Moreover, in MBABC algorithm, an intelligent scout bee phase is proposed that replaces the abandoned solution with the global best solution. The solution quality achieved by MBABC is enhanced by hybridizing the genetic crossover (GC) that provides the diversified search space. Performance of the proposed MBABC-GC algorithm is tested up to 300 thermal units over 24-hour time interval. The comparison of obtained results with other methods in the literature has confirmed the superiority of the MBABC-GC algorithm in terms of production cost and robustness.
Scheduling in manufacturing systems using the ant colonies optimization algorithm
This paper introduces a new approach for decentralized scheduling in a parallel machine environment based on the ant colonies optimization algorithm. The algorithm extends the use of the traveling salesman problem for scheduling in one single machine, to a multiple machine problem. The results are presented using simple and illustrative examples, and show that the algorithm is able to optimize the different scheduling problems. Using the same parameters, the completion time of the tasks is minimized and the processing time of the parallel machines is balanced.
International Journal of Engineering Research and Technology (IJERT), 2012
https://www.ijert.org/ant-colony-search-algorithm-for-solving-multi-area-unit-commitment-problem-with-import-and-export-constraints https://www.ijert.org/research/ant-colony-search-algorithm-for-solving-multi-area-unit-commitment-problem-with-import-and-export-constraints-IJERTV1IS8486.pdf This paper presents a new approach to solve the multi area unit commitment problem (MAUCP) using a Ant Colony Search Algorithm (ACSA). The objective of this paper is to determine the optimal or a near optimal commitment schedule for generating units located in multiple areas that are interconnected via tie lines. The Ant Colony Search Algorithm is used to solve multi area unit commitment problem, allocated generation for each area and find the operating cost of generation for each hour. Joint operation of generation resources can result in significant operational cost savings. Power transfer between the areas through the tie lines depends upon the operating cost of generation at each hour and tie line transfer limits. The tie line transfer limits were considered as a set of constraints during optimization process to ensure the system security and reliability. The overall algorithm can be implemented on an IBM PC, which can process a fairly large system in a reasonable period of time. Case study of four areas with different load pattern, each containing 26 units connected via tie lines has been taken for analysis. Numerical results showed comparing the operating cost using Ant Colony Search method with conventional evolutionary programming (EP) and dynamic programming (DP) method. Experimental results shows that the application of this Ant Colony Search method have the potential to solve multi area unit commitment problem with lesser computation time.
Journal of Engineering and Management in Industrial System, 2019
In this paper, we studied ant colony optimization for solving integrated scheduling of production and distribution problems. We improved the ant colony optimization by adding double selections, there are roulette wheel and elitism selections. Roulette wheel selection is used to determine the path where ants pass through before knowing pheromone information in that path. Meanwhile, elitism selection is used to keep the best solution before the more optimum solution obtained. Then, ant colony optimization and improved ant colony optimization are implemented in solving integrated scheduling of production and distribution problem in PT. BFPI. The aim of this paper is to achieve optimum production and distribution schedule in order to minimize the total cost of production and distribution. We also compare the performance of both applied methods and draw a conclusion. The results show that the method we proposed has more advantage.