In this paper, Multiobjective Cuckoo Search Algorithm (MOCSA) is developed to solve Economic Load Dispatch (ELD) problem. The main goal of the ELD is to meet the load demand at minimum operating cost by determining the output of the committed generating unit while satisfying system equality and inequality constraints. The problem formulation is based on a multiobjective model in which the multiobjective are defined as fuel cost minimization and carbon emission minimization. MOCSA is based on the inspiration from the brooding parasitism of cuckoo species in nature. Three cases are considered to test the effectiveness of the proposed technique which are fuel cost minimization, carbon emission minimization and multiobjective function with fixed weighted sum. The effectiveness of the MOCSA’s performances are illustrated through comparative study with other techniques such as Multiobjective Genetic Algorithm (MOGA) and Multiobjective Particle Swarm Optimization (MOPSO) in terms of fitness functions. The proposed study was conducted on three generating unit system at various loading condition. The result proved that MOCSA provide better solution in minimizing fuel cost and carbon emission usage as compared to other techniques.
<p>The energy growth in Malaysia is rapidly increasing as the country moves forward with the advancement of industrial revolution. Peak hours require more energy generation, thus cost is also more expensive than during off-peak. Due to this reason, Demand Side Management (DSM) through Demand Response (DR) technique is introduced to modify the demand profile by implementing different strategies of measures. The objective of this study is to optimize the energy profile for commercial sector, as well as analyse the significance of electricity cost reduction by using the optimization technique. A Meta-heuristic technique called as Evolutionary Algorithm (EA) has been implemented in this study to optimize the load profile of a commercial installation. Significant testing shows that the proposed optimization technique has the ability to reform the Maximum Demand from peak zone to off-peak zone to reduce electricity cost. The test results have been validated through 4 cases, which are conventional method for C1 ETOU, C2 ETOU, and C1 ETOU with Optimization technique, and C2 ETOU with optimization technique tariff, respectively. The impact of the EP has been analysed, while the performance of six-time segmentation of C1 and C2 ETOU tariff indicate that the electricity cost for the medium voltage of installation has been reduced. It is hoped that the results from this study can benefit consumers by giving them the flexibility to rearrange their own energy consumption profile, so that the demand side will enjoy significant reduction of electricity cost in the future.</p><p> </p>
This paper presents load shifting strategy for cost reduction on manufacturing electricity demand side, by which a real test load profile had been used to prove the concept. Superior bio-inspired algorithm, Ant Colony Optimization (ACO) had been implemented to optimize the upright load profile of load shifting strategy in the Malaysia Enhance Time of Use (ETOU) tariff condition. Subsequently, significant simulation results of operation profit gain through 24 hours electricity consumption had been analyzed properly. The proposed method had shown reduction of approximately 6% of the electricity cost at peak and mid peak zones, when 20%, 40%, 60%, 80% and 100% load shifting weightages were applied to the identified 10% controlled loads consequently. It is hoped that the finding of this study can help poise the manufacturers to switch to ETOU tariff as well as support the national Demand Side Management (DSM) program
<span>Machine learning application have been widely used in various sector as part of reducing work load and creating an automated decision making tool. This has gain the interest of power industries and utilities to apply machine learning as part of the operation. Fault identification and classification based machine learning application in power industries have gain significant accreditation due to its great capability and performance. In this paper, a machine-learning algorithm known as Support Vector Machine (SVM) for fault type classification in distribution system has been developed. Eleven different types of faults are generated with respect to actual network. A wide range of simulation condition in terms of different fault impedance value as well as fault types are considered in training and testing data. Right setting parameters are important to learning results and generalization ability of SVM. Gaussian radial basis function (RBF) kernel function has been used for training of SVM to accomplish the most optimized classifier. Initial finding from simulation result indicates that the proposed method is quick in learning and shows good accuracy values on faults type classification in distribution system. The developed algorithm is tested on IEEE 34 bus and IEEE 123 bus test distribution system. </span>
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