Stochastic economic emission load dispatch
ABSTRACT The economic emission load dispatch (EELD) problem is a multiple non-commensurable objective problem that minimizes both cost and emission together. In the paper a stochastic EELD problem is formulated with consideration of the uncertainties in the system production cost and nature of the load demand, which is random. In addition, risk is considered as another conflicting objective to be minimized because of the random load and uncertain system production cost. The weighted minimax technique is used to simulate the trade-off relation between the conflicting objectives in the non-inferior domain. Once the trade-off has been obtained, fuzzy set theory helps the power system operator to choose the optimal operating point over the trade-off curve and adjust the generation levels in the most economic manner associated with minimum emission and risk. The validity of the method is demonstrated by analysing a sample system comprising six generators.
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ABSTRACT: A multi-objective power unit commitment problem is framed to consider simultaneously the objectives of minimizing the operation cost and minimizing the emissions from the generation units. To find the solution of the optimal schedule of the generation units, a memetic evolutionary algorithm is proposed, which combines the non-dominated sorting genetic algorithm-II (NSGA-II) and a local search algorithm. The power dispatch sub-problem is solved by the weighed-sum lambda-iteration approach. The proposed method has been tested on systems composed by 10 and 100 generation units for a 24-hour demand horizon. The Pareto-optimal front obtained contains solutions of different trade off with respect to the two objectives of cost and emission, which are superior to those contained in the Pareto-front obtained by the pure NSGA-II. The solutions of minimum cost are shown to compare well with recent published results obtained by single-objective cost optimization algorithms.IEEE Transactions on Power Systems 01/2013; 28(3):2660-2669. · 2.92 Impact Factor
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ABSTRACT: Multiple energy carriers (MECs) have been broadly engrossing power system planners and operators toward a modern standpoint in power system studies. Energy hub, though playing an undeniable role as the intermediate in implementing the MECs, still needs to be put under examination in both modeling and operating concerns. Since wind power continues to be one of the fastest-growing energy resources worldwide, its intrinsic challenges should be also treated as an element of crucial role in the vision of future energy networks. In response, this paper aims to concentrate on the online economic dispatch (ED) of MECs for which it provides a probabilistic ED optimization model. The presented model is treated via a robust optimization technique, i.e., multiagent genetic algorithm (MAGA), whose outstanding feature is to find well the global optima of the ED problem. ED once constrained by wind power availability, in the cases of wind power as one of the input energy carriers of the hub, faces an inevitable uncertainty that is also probabilistically overcome in the proposed model. Efficiently approached via MAGA, the presented scheme is applied to test systems equipped with energy hubs and as expected, introduces its applicability and robustness in the ED problems. Index Terms—Economic dispatch (ED), energy hub, multiagent genetic algorithm (MAGA), multiple energy carriers (MECs), probabilistic modeling, wind power.IEEE Transactions on Sustainable Energy 01/2013; 1. · 3.84 Impact Factor
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ABSTRACT: Detection of intermittent faults in sensor nodes is an important issue in sensor networks. This requires repeated application of test since an intermittent fault will not occur consistently. Optimization of inter test interval and maximum number of tests required is crucial. In this paper, the intermittent fault detection in wireless sensor networks is formulated as an optimization problem. The two objectives, i.e., detection latency and energy overhead are taken into consideration. Tuning of detection parameters based on two-lbests based multi-objective particle swarm optimization (2LB-MOPSO) algorithm is proposed here and compared with that of non-dominated sorting genetic algorithm (NSGA-II) and multiobjective evolutionary algorithm based on decomposition (MOEA/D). A comparative study of the performance of the three algorithms is carried out, which show that the 2LB-MOPSO is a better candidate for solving the multiobjective problem of intermittent fault detection. A fuzzy logic based strategy is also used to select the best compromised solution on the Pareto front.Swarm and Evolutionary Computation. 06/2013;