Here, an intelligent optimization method for optimal switching in reconfigurable microgrids is proposed to reduce the cost of microgrids with the presence of distributed generation resources and electrical vehicles. T...
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Here, an intelligent optimization method for optimal switching in reconfigurable microgrids is proposed to reduce the cost of microgrids with the presence of distributed generation resources and electrical vehicles. The suggested model deploys the point estimation to manage the uncertainties in the microgrid. The uncertainties are due to load demand, market price, electric vehicle charging and discharging, wind turbine production capacity and photovoltaic cells. The formulation structure is in the form of a nonlinear and single-objective problem that minimizes the total microgrid asking price by observing the constraints in the problem. In this research, an improved algorithm is used to solve the optimization problem to advance the performance of the process. The capability and efficiency of the proposed method have been investigated on the IEEE 33-Bus standard test network and the simulation results have shown that optimal switching can be useful in more fitting power distribution and reduction of microgrid cost with the presence of electric vehicle and various scattered products.
In this study, single-objective (SOP) and multi-objective (MOP) design optimization problems have been solved for mechanical forced-draft counter-flow wet-cooling towers using an enhanced-search variant of a recently ...
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In this study, single-objective (SOP) and multi-objective (MOP) design optimization problems have been solved for mechanical forced-draft counter-flow wet-cooling towers using an enhanced-search variant of a recently proposed swarm-intelligence based metaheuristic, artificial hummingbird algorithm (AHA). Incorporating the oppositional rule to ensure a faster convergence by avoiding unnecessary search of the space, and with chaos-embedded sequences to obtain more diversifying search-population towards more accuracy in the obtained solutions, the proposed oppositional chaotic artificial hummingbird algorithm (OCAHA) has been implemented for effective design optimizations of cooling towers. In SOP, six number cases of literature have been algorithmically experimented for minimizing the total annual cost TAC as the single-objective function with mass flow rate of cooling air and cross-sectional area of tower fill as the two decision variables and with fourteen number of design inequality constraints based on the process temperatures and enthalpies. Merkel's method has been used for deriving the tower geometrical dimensions from empirical correlations of overall mass transfer-coefficient and loss-coefficient for the specified type of tower fill-packing. In MOP, range, tower characteristic ratio, effectiveness are the three objective functions, which have been maximized simultaneously with minimizing the water evaporation rate as the fourth objective for the problem. Mass flow rates of cooling air and circulating water are the two decision variables with the required input parameters of recent literature have been considered for multi-objectiveproblem. The obtained designs through SOP and MOP have been analyzed with the competing designs, and a superior performance of OCAHA in both the optimizations have been validated.
In order to reduce hardware cost and system complexity, sub-array partition is necessary for large array antennas. Traditional sub-array partition methods are mainly aiming at the single performance optimisation of be...
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In order to reduce hardware cost and system complexity, sub-array partition is necessary for large array antennas. Traditional sub-array partition methods are mainly aiming at the single performance optimisation of beampattern. Therefore, this study proposes a multiple performance parameters optimisation method based on particle swarm optimisation (PSO). In the proposed method, the multi-objective optimisation problem is converted into a single-objective problem, and then dynamic inertia weights and learning factors are used in the algorithm. Based on the proposed method, the signal processing performances can be improved compared with the traditional methods. Through the simulation of the division of the linear array, the effectiveness of the proposed method is verified.
The design of a frequency notched antenna for 5G base stations is presented in this paper. In particular, given the considerable number of constraints on the antenna performance, the problem can be defined as a multi-...
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ISBN:
(纸本)9781538632840
The design of a frequency notched antenna for 5G base stations is presented in this paper. In particular, given the considerable number of constraints on the antenna performance, the problem can be defined as a multi-objectiveproblem. The reduction of such complex problem into a single-objective one allows the use of well known optimization strategies like evolutionary algorithms, capable of determine a set of valuable tradeoff solutions. Preliminary numerical results are discussed in order to validate the proposed design procedure.
In this paper, we investigate the Energy Efficiency (EE)- Spectrum Efficiency (SE) tradeoff issue in an OFDM-based cognitive radio (CR) network. A multi-objective resource allocation problem is formulated, where we tr...
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ISBN:
(纸本)9781479935130
In this paper, we investigate the Energy Efficiency (EE)- Spectrum Efficiency (SE) tradeoff issue in an OFDM-based cognitive radio (CR) network. A multi-objective resource allocation problem is formulated, where we try to maximize the EE and the SE simultaneously. The Pareto optimal set of the formulated problem is characterized by analyzing the relationship between the EE and the SE. To find a unique globally optimal solution, we proposed a unified EE-SE tradeoff metric, based on which the original optimization task is transformed into a single-objective problem that has a D.C. (Difference of two Convex functions/sets) structure. Then an efficient barrier method is developed, where we speeds up the time-consuming computation of Newton step by exploiting the structure of the D.C. programming problem. Simulation results validate the effectiveness and efficiency of the proposed algorithm. Our general problem formulation sheds some insights on how to design an energy- and spectrum-efficient CR system.
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