A new heuristic method to solve unit commitment by using a time-variant acceleration coefficients particle swarm optimization algorithm (original) (raw)
Related papers
—This paper presents a new approach via hybrid particle swarm optimization (HPSO) scheme to solve the unit commitment (UC) problem. HPSO proposed in this paper is a blend of binary particle swarm optimization (BPSO) and real coded particle swarm optimization (RCPSO). The UC problem is handled by BPSO, while RCPSO solves the economic load dispatch problem. Both algorithms are run simultaneously, adjusting their solutions in search of a better solution. Problem formulation of the UC takes into consideration the minimum up and down time constraints, start-up cost, and spinning reserve and is defined as the minimization of the total objective function while satisfying all the associated constraints. Problem formulation, representation , and the simulation results for a ten generator-scheduling problem are presented. Results clearly show that HPSO is very competent in solving the UC problem in comparison to other existing methods. Index Terms—Hybrid particle swarm optimization (HPSO), industrial power system, optimization methods, power generation dispatch.
A New Heuristic Approach for Unit Commitment Problem Using Particle Swarm Optimization
Arabian Journal for Science and Engineering, 2012
In this paper, a new method is proposed to solve unit commitment problem using particle swarm optimization. This method limits the search space considering demand information and the generation history of the generating units to solve the UC problem in an hourly basis. An expert system is utilized to generate the search space combinations of the generating units and a particle swarm optimization algorithm is used to optimize generation levels according to each combination. The results are more accurate and get lower operation costs in comparison with other traditional methods.
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 New Approach for Solving the Unit Commitment Problem by Enhanced Particle Swarm Optimization
IFAC Proceedings Volumes, 2012
This paper proposes an Enhanced Particle Swarm Optimization (EPSO) approach to the Unit Commitment Problem (UCP). The Binary Particle Swarm Optimization (BPSO) algorithm for on/off decision and the Classical Particle Swarm Optimization (PSO) algorithm for the economic load dispatch problem are enhanced to find the optimal solution and reduce the overall computation time. The proposed technique is tested on 10 to 100 unit systems with a 24-h scheduling horizon. The results of the EPSO on the benchmark datasets are comparable with the results of other heuristic approaches found in the literature. This preliminary experimental study shows that EPSO is suitable for the UCP and the promising results promote further study.
Solving Unit Commitment Problem Using Hybrid Particle Swarm Optimization
Journal of Heuristics, 2003
This paper presents a Hybrid Particle Swarm Optimization (HPSO) to solve the Unit Commitment (UC) problem. Problem formulation of the unit commitment takes into consideration the minimum up and down time constraints, start up cost and spinning reserve, which is defined as the minimization of the total objective function while satisfying all the associated constraints. Problem formulation, representation and the simulation results for a 10 generator-scheduling problem are presented. Results shown are acceptable at this early stage.
The State of Art in Particle Swarm Optimization Based Unit Commitment: A Review
Processes
Unit Commitment (UC) requires the optimization of the operation of generation units with varying loads, at every hour, under different technical and environmental constraints. Many solution techniques were developed for the UC problem, and the researchers are still working on improving the efficiency of these techniques. Particle swarm optimization (PSO) is an effective and efficient technique used for solving the UC problems, and it has gotten a considerable amount of attention in recent years. This study provides a state-of-the-art literature review on UC studies utilizing PSO or PSO-variant algorithms, by focusing on research articles published in the last decade. In this study, these algorithms/methods, objectives, constraints are reviewed, with focus on the UC problems that include at least one of the wind and solar technologies, along with thermal unit(s). Although, conventional PSO is one of the most effective techniques used in solving UC problem, other methods were also dev...
International Journal of Renewable Energy Research, 2019
This paper introduces a Hybrid Particle Swarm Optimization with Sine Cosine Acceleration Coefficients (H-PSO-SCAC) for solving the Unit Commitment (UC) problem of grid connected Microgrid (MG). The optimal set point of MG’s generation units is determined for a Day Ahead (DA) power market to supply the required demand. The studied MG consists of one Wind Turbine (WT) generator, one Photovoltaic (PV) panel and three Diesel Generators (DGs). The new algorithm is employed to minimize the fuel cost of DGs and the transaction costs of transferable power trade whilst taking into consideration load balance constraint and MG’s generation units constraints. The performance of the new H-PSO-SCAC is examined by comparing with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The effectiveness of these methods is analyzed by using different criteria of the objective function. MATLAB environment is used to code H-PSO-SCAC, PSO, GA, and the system under study. The simulation result...
Optimal unit commitment of a power plant using particle swarm optimization approach
International Journal of Electrical and Computer Engineering (IJECE), 2020
Economic load dispatch among generating units is very important for any power plant. In this work, the economic load dispatch was made at Egbin Thermal Power plant supplying a total load of 600MW using six generating units. In carrying out this study, transmission losses were assumed to be included into the load supplied. Also, three different combinations in the form of 6, 5-and 4-units commitment were considered. In each case, the total load was optimally dispatched between committed generating units using Particle Swarm Optimization (PSO). Similarly, the generation cost for each generating unit was determined. For case 1, the six generators were committed and the generation cost is 2,100,685.069$/h. For case 2, five generators were committed and the generation cost is 2,520,861.947$/h. For case 3, four generators were committed and the generation cost is 3,150,621.685$/h. From all considered cases, it was found that, the minimum generation cost was achieved when all six generating units were committed and a total of 420,178.878$/h was saved.