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multiobjective Calibration of Reservoir Water Quality Modeling Using multiobjective particle swarm optimization (MOPSO)

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WATER RESOURCES MANAGEMENT 2013年第7期27卷 1931-1947页

作者： Afshar, Abbas Shojaei, Nasim Sagharjooghifarahani, Mahdi Iran Univ Sci & Technol Dept Civil Engn Tehran 16844 Iran Portland State Univ Dept Civil Engn Portland OR 97207 USA Maseeh Coll Engn & Comp Sci NW Ctr Engn Sci & Technol Portland OR 97201 USA

Water resource management encounters large variety of multi objective problems that require powerful optimization tools in order to fully characterize the existing tradeoffs between various objectives that can be minimizing difference between forecasted physical, chemical, and biological behaviors of model and measured data. Calibration of complex water quality models for river and reservoir systems may include conflicting objectives addressed by various combinations of interacting calibration parameters. Calibration of the two dimensional CE-QUAL-W2 water quality and hydrodynamic model is an excellent example where the model must be calibrated for both hydrodynamic and water quality behavior. The aim of the present study is to show how multiobjective particle swarm optimization (MOPSO) can be implemented for automatic calibration of water quality and hydrodynamic parameters of a 2-dimensional, hydrodynamic, and water quality models (CEQUAL-W2) to predict physical, chemical, and biological behaviors of a water body, and then focus on a relevant case study. So MOPSO is utilized to generate Pareto optimal solutions for two conflicting calibration objectives. A combined measure of thermal and reservoir water level is considered as the first calibration objective. The second objective is formulated to forecast the best physical, chemical, and biological behavior of the model. Realizing the strong interactions between water quality and hydrodynamic issues of water bodies and their dependencies on the same set of calibration parameters, the proposed multiobjective approach may provide a wide version of all possible calibration solutions for better decision making to select best solution from pareto front.

关键词： multiobjective particle swarm optimization algorithm multiobjective calibration Water quality modeling Karkheh Reservoir CE-QUAL-W2

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Probabilistic optimal planning in active distribution networks considering non-linear loads based on data clustering method

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IET GENERATION TRANSMISSION & DISTRIBUTION 2022年第4期16卷 686-702页

作者： Ebrahimi, Hasan Rezaeian-Marjani, Saeed Galvani, Sadjad Talavat, Vahid Urmia Univ Fac Elect & Comp Engn Dept Power Engn Orumiyeh Iran

Renewable energies have a significant portion in supplying energy demands in modern distribution networks. Due to the wide use of power electronic devices, these networks may have power quality problems. The unpredictable nature of renewable energies, besides the effect of non-linear loads brings out serious planning and operating challenges for distribution systems. Basicly, harmonic distortion is a severe problem for both electric efficiency and power energy customers. This study proposes an optimal scheduling strategy for wind turbine's integrated distribution networks with non-linear loads using a multi-objective individualized instruction mechanism teaching-learning-based optimization algorithm and the best solution is selected via the TOPSIS technique. In the proposed strategy, energy storage systems are optimally scheduled besides wind turbines, and reactive power compensators. Also, to use the distribution network more efficiently, an optimal network reconfiguration is applied. The wind turbine's output and load demands have probabilistic nature. The proposed scheme reduces the total harmonic distortion as well as total costs. The efficacy of the proposed management scheme is investigated using the IEEE standard 33 bus distribution network. Also, the performance of the multi-objective individualized instruction mechanism teaching-learning-based optimization algorithm is compared with the multi-objective particle swarm optimization algorithm.

关键词： energy storage systems power system harmonics particle swarm optimisation reactive power multiobjective particle swarm optimization algorithm harmonic distortion renewable energies TOPSIS technique total harmonic distortion multiobjective individualized instruction mechanism distribution systems wind turbines power quality problems energy demands reactive power compensators teaching-learning-based optimization algorithm probabilistic optimal planning optimal network reconfiguration probabilistic nature electric efficiency data clustering method optimal scheduling strategy power energy customers nonlinear loads TOPSIS power supply quality power distribution planning power electronic devices IEEE standard 33 bus distribution network active distribution networks

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Stochastic scheduling of energy storage systems in harmonic polluted active distribution networks

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IET GENERATION TRANSMISSION & DISTRIBUTION 2022年第23期16卷 4689-4709页

作者： Ebrahimi, Hasan Rezaeian-Marjani, Saeed Farhadi-Kangarlu, Mohammad Galvani, Sadjad Urmia Univ Fac Elect & Comp Engn Dept Power Engn Orumiyeh Iran

Harmonics originating from non-linear loads in distribution systems will cause undesired effects and malfunctions of equipment. It is important to use efficient techniques for eliminating the voltage and current harmonic components. However, the uncertain nature of the active distribution networks (ADN) makes it difficult to apply these techniques, efficiently. Renewable energy sources (RESs), such as wind turbines (WTs) besides load fluctuations are the most important uncertainty sources. This study employs energy storage systems (ESS) to reduce total harmonic distortion (THD) as well as the ADN operating costs. In addition to the ESS, network structure reconfiguration and reactive power scheduling is presented for a more efficient operation of ADN. Also, the uncertainty of the network is modelled by the K-means and LHS methods. This methodology is conducted under a multi-objective optimization problem, which is handled by a multi-objective particle swarm optimization (MOPSO) algorithm. Furthermore, a technique for order of preference by similarity to ideal solution (TOPSIS) approach is applied to obtain the best compromise between two objectives. The efficacy of the proposed methodology is investigated in the IEEE 33 bus test system. Also, for validating the results, a large-scale network, the IEEE 118 bus test system is surveyed, too.

关键词： energy storage systems power system harmonics particle swarm optimisation reactive power multiobjective particle swarm optimization algorithm harmonic distortion employs energy storage systems total harmonic distortion ADN operating costs wind turbines IEEE standards IEEE 33 bus test system large-scale network current harmonic components K-means reactive power scheduling stochastic scheduling load fluctuations distributed power generation distribution networks stochastic processes network structure reconfiguration harmonics originating RES nonlinear loads power generation scheduling IEEE 118 bus test system TOPSIS harmonic polluted active distribution networks renewable energy sources THD LHS methods TOPSIS approach MOPSO

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State Operation optimization in Electrical Networks

State Operation Optimization in Electrical Networks

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1st BRICS Countries Congress on Computational Intelligence / 11th Brazilian Congress on Computational Intelligence (BRICS-CCI and CBIC)

作者： Pereira, Paulo Leitao, Sergio Solteiro Pires, E. J. Univ Tras Os Montes & Alto Douro Escola Ciencias & Tecnol P-5000811 Vila Real Portugal

ISBN: (纸本)9781479931941

This paper makes a study about optimal supply of the energy service, using simulations of network operation scenarios, in order to optimize resources and minimize the variables: operation cost, energy losses, generation cost and consumers shedding. These simulations create optimal operation models of the network, allowing the system operator obtain knowledge to take pre-established procedures that must be performed in situations of contingency in order to forecast and minimize drawbacks. The simulations were performed using a multiobjective particle swarm optimization algorithm. The algorithm was applied to the IEEE 14 Bus network where the optimal power flow was evaluated by MATPOWER tool to establish an optimal electrical working model to minimize the associated costs.

关键词： Optimal Power Flow multiobjective particle swarm optimization algorithm multiobjective Problem

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Permanent Magnet Synchronous Machine Design for Hybrid Traction Applications: Impact of Magnetic Laminations Materials

Permanent Magnet Synchronous Machine Design for Hybrid Tract...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Nedjar, B. Hlioui, S. Vido, L. Gabsi, M. Amara, Y. Hoang, E. Miraoui, A. UniverSud CNRS ENS Cachan SATIE 61 Av President Wilson F-94230 Cachan France GREAH F-76063 Le Havre France UTBM L2ES FLab Belfort F-90010 Belfort France

ISBN: (纸本)9781424463916

This paper examines the influence of lamination material on the performance of a flux focusing permanent magnet machine. This machine is designed for the traction of hybrid vehicles. The design optimization is performed by coupling a multi-objective particle swarm optimization algorithm with the finite element method. The optimization procedure is performed for three different lamination materials. Solutions from optimization procedure are then compared.

关键词： FEM electric vehicles finite element analysis finite element method hybrid traction applications hybrid vehicles laminations magnetic laminations materials multiobjective particle swarm optimization algorithm particle swarm optimisation permanent magnet machines permanent magnet synchronous machine design synchronous machines traction

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Optimal Operation Point in Electrical Grids using a MOPSO algorithm 6

Optimal Operation Point in Electrical Grids using a MOPSO Al...

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6th World Congress on Nature and Biologically Inspired Computing (NaBIC)

作者： Pereira, Paulo Leitao, Sergio Solteiro Pires, E. J. INESC TEC INESC Technol & Sci Oporto Portugal Univ Tras Os Montes & Alto Douro Escola Ciencias & Tecnol P-5000811 Vila Real Portugal

ISBN: (纸本)9781479959372

The paper presents a study about optimal supply of the energy service, using simulations of network operation scenarios, in order to optimize resources and minimize the variables: operation cost, energy losses, generation cost and consumers shedding. These simulations create optimal operation models of the network, allowing the system operator obtain knowledge to take pre-established procedures that must be performed in situations of contingency in order to forecast and minimize drawbacks. The simulations were performed using a multiobjective particle swarm optimization algorithm. The algorithm was applied to the IEEE 14 Bus network where the optimal power flow was evaluated by the MATPOWER tool to establish an optimal electrical working model to minimize the associated costs.

关键词： Optimal Power Flow multiobjective particle swarm optimization algorithm multiobjective Problem

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Surrogate-Based Multiperiod, multiobjective Reservoir Operation optimization for Quality and Quantity Management

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2020年第8期146卷

作者： Saadatpour, Motahareh Afshar, Abbas Solis, Samuel Sandoval Iran Univ Sci & Technol Sch Civil Engn Tehran *** Iran Univ Calif Davis Dept Land Air & Water Resources Davis CA 95616 USA

Deriving optimal reservoir operation rules in a selective withdrawal framework (SWF) considering quality and quantity objectives is a challenging problem due to its computational burdens. To overcome the computational bottleneck, two surrogate models have been developed and coupled with the evolutionary algorithm in an adaptive-recursive framework to form the surrogate-based multiobjective optimization technique (SBMOOT). SBMOOT is used to derive the optimal reservoir operating strategies and the set of nondominated optimal solutions to enhance reservoir outflow water quality and maximize water supply and hydropower energy generation. The most desirable scenarios of the Pareto front have been identified to derive monthly operating rules in the SWF. The operating rules focusing on water supply, hydropower energy, and water quality objectives are estimated using polynomial regression technique. The performances of the operating rules, with and without regarding the uncertainty of inflows, have been compared with the historical operating strategy in Karkheh Reservoir, Khuzestan, Iran. The results show that water quality measure may be enhanced while maintaining desirable water supply and/or hydropower energy compared with the historical operating strategy of Karkheh Reservoir.

关键词： CE-QUAL-W2 Computationally expensive Large-scale multiobjective particle swarm optimization algorithm Multiperiod Reservoir operating rule Surrogate model

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