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10th IFAC Symposium on Control of Power and Energy Systems (CPES)

作者： Konneh, David Abdul Lotfy, Mohamed E. Shigenobu, Ryuto Senjyu, Tomonobu Univ Ryukyus Elect & Elect Engn Dept Okinawa 9032013 Japan

The need for Sierra Leone, like every other country in Sub-Saharan Africa, to improve its electricity supply in order to enhance energy access for all thereby improving economic growth has become imminent. With a small population of about 7 million, the daily supply is not guaranteed and is far below the demand. With the huge renewable energy potential of Solar (1600kWh/m2/year), hydro (more than 2500MW for 27 sites), and biomass, (about 2500 GWh/year) that the country boasts of for its small population, urgent environmentally friendly and cost effective measures have to be taken to improve on the current socio economic conditions. In this work, multi -objective particle swarm optimization (MOPSO) technique was used to optimally size governmental rooftop and ground-mounted grid connected Photovoltaic (PV) panels and Battery Energy Storage System (BESS) in a bid to reduce the supply deficit in the capital, Freetown. The objectives considered in this contribution are maximizing daily generation and minimizing daily hybrid generation cost. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Operation Analysis Distributed Generation (DG) Sizing Grid-connected Control Photovoltaic multi-objective particle swarm optimization Freetown

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multi-objective optimization of Acoustic Performances of Polyurethane Foam Composites

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POLYMERS 2018年第7期10卷 788-788页

作者： Chen, Shuming Zhu, Wenbo Cheng, Yabing Jilin Univ State Key Lab Automot Simulat & Control Changchun 130022 Jilin Peoples R China Jilin Univ Sch Mech & Aerosp Engn Changchun 130022 Jilin Peoples R China

Polyurethane (PU) foams are widely used as acoustic package materials to eliminate vehicle interior noise. Therefore, it is important to improve the acoustic performances of PU foams. In this paper, the grey relational analysis (GRA) method and multi-objective particle swarm optimization (MOPSO) algorithm are applied to improve the acoustic performances of PU foam composites. The average sound absorption coefficient and average transmission loss are set as optimization objectives. The hardness and content of Ethylene Propylene Diene Monomer (EPDM) and the content of deionized water and modified isocyanate (MDI) are selected as design variables. The optimization process of GRA method is based on the orthogonal arrays L-9 (3(4)), and the MOPSO algorithm is based on the Response Surface (RS) surrogate model. The results show that the acoustic performances of PU foam composites can be improved by optimizing the synthetic formula. Meanwhile, the results that were obtained by GRA method show the degree of influence of the four design variables on the optimization objectives, and the results obtained by MOPSO algorithm show the specific effects of the four design variables on the optimization objectives. Moreover, according to the confirmation experiment, the optimal synthetic formula is obtained by MOPSO algorithm when the weight coefficient of the two objectives set as 0.5.

关键词： grey relational analysis multi-objective particle swarm optimization acoustic performances Ethylene Propylene Diene Monomer polyurethane foam composites

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multi-objective optimal power management and sizing of a reliable wind/PV microgrid with hydrogen energy storage using MOPSO

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2017年第3期32卷 1753-1773页

作者： Baghaee, Hamid Reza Mirsalim, Mojtaba Gharehpetian, Gevork B. Amirkabir Univ Technol Dept Elect Engn Tehran Iran Amirkabir Univ Technol Dept Elect Engn Elect Machines & Transformer Res Lab Tehran Iran

In this paper, a multi-objective algorithm is presented for optimal power management and design of a hybrid Wind/Photovoltaic/ generation system with hydrogen energy storage system including electrolyzer, fuel cell and hydrogen tank to supply power demand in a microgrid system. The generation units are intrinsically non-dispatchable and moreover, the major components of the system i.e. wind turbine generators, photovoltaic arrays and DC/AC converter may be subjected to failure. Also, solar radiation, wind speed and load data are assumed to be entirely deterministic. The goal of this design is to use a novel multi-objective optimization algorithm to minimize the objective functions i.e. annualized cost of the system, loss of load expected and loss of energy expected and provide optimal energy management in the microgrid. The system costs involve investment, replacement and operation and maintenance costs. Prices are all empirical and components are commercially available. The simulation results for different cases reveal the impact of components outage on the reliability and cost of the system. So, they are directly depends on component's reliabilities, i.e. outages lead to need for a larger and more expensive generation system to supply the load with the acceptable level of reliability. In addition, an approximate method for reliability evaluation of hybrid system is presented which lead to reduce computation time. Simulation results show the effectiveness of proposed multi-objective algorithm to solve optimal sizing problem in contrast with traditional single objective methods.

关键词： Microgrid wind energy photovoltaic fuel cell reliability optimal power management and sizing multi-objective particle swarm optimization

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Economic Analysis of Hybrid Wind/PV/Diesel/ESS System on a Large Oil Tanker

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2017年第7期45卷 705-714页

作者： Wen, Shuli Lan, Hai Dai, Jinfeng Hong, Ying-Yi Yu, David C. Yu, Lijun Harbin Engn Univ Coll Automat Harbin 150001 Peoples R China Chung Yuan Christian Univ Dept Elect Engn Taoyuan Taiwan Univ Wisconsin Dept Elect Engn & Comp Sci Milwaukee WI 53201 USA

Due to the global concern on the increasing amount of fossil energy consumed by traditional ships, the application of renewable energy into a ship power system provides a new solution to improve the energy efficiency and to reduce the greenhouse gas emissions. This study proposes a stand-alone power system on a large oil tanker including wind generation system, photovoltaic generation system, the diesel generator, and the energy storage system (ESS). Unlike on land, the wind generation on the shipboard not only relies on the natural wind speed but also the ship's course and speed. The installation of the wind turbines on the board is optimally designed, which takes the relative speed into account. In order to mitigate the intermittence of the renewable energy generation, a lead-acid battery serves as the ESS to enhance the stability of the ship power system, and the size is optimized by the multi-objective particle swarm optimization to minimize the whole system cost and CO2 emissions. Additionally, variations of the ship loads are considered with respect to the different operational conditions. Various cases are compared in detail to demonstrate the applicability of the proposed algorithm.

关键词： wind power generation system photovoltaic generation system energy storage system oil tanker multi-objective particle swarm optimization

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Applying Control Scheme Function of PV Inverter for Minimizing Voltage Fluctuation and Imbalance with Consideration of Maximum PV Generation

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2017年第Sup1期12卷 S42-S53页

作者： Chanhome, Anuwat Chaitusaney, Surachai Chulalongkorn Univ Dept Elect Engn Fac Engn Bangkok 10330 Thailand

At present, connections of photovoltaic (PV) systems to low-voltage (LV) distribution systems are growing rapidly because of the compliance with government policies, drop in the prices of PV technologies, and environmental awareness. Unfortunately, the high penetration of solar PV systems, which suffers from the intermittence of sunlight, leads to voltage fluctuation and voltage imbalance, thereby deteriorating the power quality. To cope with this problem, this paper proposes a control strategy of the PV inverter to improve the limiting and balancing of voltage profiles in an unbalanced, three-phase, four-wire LV distribution system. The control strategy is based on the real power limitation and the reactive power adjustment through a control scheme function that is embedded in all PV inverters for supporting high penetration of PV systems. However, real power limitation leads to less utilization of solar energy. Then, the concern on PV generation (real power) regarding voltage fluctuation and imbalance is optimally analyzed by multi-objective particle swarm optimization. The optimal solution of the control scheme function is numerically demonstrated in a modified 29-node LV distribution system. (C) 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： low-voltage distribution system multi-objective particle swarm optimization photovoltaic system reactive power real power

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Aeroacoustic and aerodynamic optimization of a MW class HAWT using MOPSO algorithm

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ENERGY 2017年第Part1期140卷 1198-1215页

作者： Kaviani, H. Nejat, A. Univ Tehran Sch Mech Engn POB 14395-515 Tehran Iran

In this paper, results of WindPACT 1.5 MW baseline horizontal axes wind turbine aeroacoustic and aerodynamic optimization are presented. For this purpose, the blade twist, the chord distribution, the airfoils for all sections, and the rotational speed are optimized with multi-objective particle swarm optimization (MOPSO) algorithm. The geometric class/shape function transformation technique and Bezier curve are used for geometry parameterization. Improved blade element momentum theory and Brooks, Pope and Marcolini semi-empirical methods are implemented for wind turbine power and aeroacoustic noise calculation in the optimization procedure. MOPSO parametric study is conducted to increase both robustness and speed of the optimization cycle. optimization objective functions are power output and overall average sound pressure level. After optimization, improved delayed detached eddy simulation is employed to verify the new wind turbine output power and noise sources. The noise propagation to the far field is calculated with the Ffowcs Williams and Hawkings acoustic analogy. Results show about 1 dB noise reduction as well as 6% power increment for the optimized WindPACT wind turbine. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Aeroacoustic Aerodynamic Horizontal axis wind turbine multi-objective particle swarm optimization

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An Effective Application of Bacteria Quorum Sensing and Circular Elimination in MOPSO

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017年第1期14卷 56-63页

作者： Cheng, Shan Zhao, Long-Long Jiang, Xiao-Yu China Three Gorges Univ Hubei Key Lab Cascaded Hydropower Stat Operat & C Yichang Hubei Peoples R China China Three Gorges Univ Hubei Prov Collaborat Ctr New Energy Microgrid Yichang Hubei Peoples R China China Three Gorges Univ Coll Elect Engn & New Energy Yichang Hubei Peoples R China

In this paper, an approach that incorporates a turbulence mechanism and a circular elimination strategy is presented to strengthen the performance of multi-objective particle swarm optimization (MOPSO). For convergence enhancement, the turbulence mechanism derived from bacteria quorum sensing behavior is introduced to MOPSO to preserve the swarm diversity. Meanwhile, the circular elimination strategy is used to select particles for next iteration for better distribution of the Pareto-optimal solutions. The improved MOPSO algorithm has been tested on a set of benchmark functions and compared with representative multi-objective optimization algorithms. Simulation results illustrate that the algorithm outperforms the other algorithms on convergence while keep good spread performance, and could be used as an effective global optimization tool.

关键词： Circular elimination crowding distance multi-objective particle swarm optimization quorum sensing

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Meta-Model Assisted 2D Hydrodynamic and Thermal Simulation Model (CE-QUAL-W2) in Deriving Optimal Reservoir Operational Strategy in Selective Withdrawal Scheme

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WATER RESOURCES MANAGEMENT 2017年第9期31卷 2729-2744页

作者： Saadatpour, Motahareh Afshar, Abbas Edinger, John Eric Iran Univ Sci & Technol Sch Civil Engn Tehran Iran Univ Calif Davis Dept Land Air & Water Resources Davis CA 95616 USA

Reconciling simultaneous water quantity and quality aspects in drawing optimal reservoir operational strategy involves extensive computational burdens. Surrogate based optimization techniques (SBOTs) are common approaches to overcome computational bottlenecks of numerical hydrodynamic simulation models coupling evolutionary algorithm in simulation-optimization approaches. In this study, the reservoir high resolution CE-QUAL-W2 model is replaced by the lower resolution CE-QUAL-W2 and/or static ANN to speed up the optimization process. These surrogate models could consider the complex relationships to emulate the main dynamics of HR CE-QUAL-W2 model due to various reservoir operational strategies. The performance of various SBOTs, based on adaptive and sequential surrogate models coupled with particle swarm optimization algorithm, are evaluated in deriving optimal reservoir operational strategies. Then adaptive surrogate model, as the more efficient and accurate one, is applied to derive long term optimal reservoir operational strategy in the selective withdrawal scheme. The results show application of the proposed approach could enhance downstream water temperature, water demand satisfactions, and hydropower peak energy generation compared with the standard operation policy (SOP) in Karkheh reservoir during 15-year-time horizon.

关键词： CE-QUAL-W2 multi-objective particle swarm optimization Reservoir operational strategy Selective withdrawal Surrogate model

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Optimizing the lattice design of a diffraction-limited storage ring with a rational combination of particle swarm and genetic algorithms

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Chinese Physics C 2017年第2期41卷 166-176页

作者：焦毅徐刚 Key Laboratory of Particle Acceleration Physics and Technology Institute of High Energy Physics Chinese Academy of Sciences Beijing 100049 China

In the lattice design of a diffraction-limited storage ring（DLSR） consisting of compact multi-bend achromats（MBAs）, it is challenging to simultaneously achieve an ultralow emittance and a satisfactory nonlinear performance, due to extremely large nonlinearities and limited tuning ranges of the element parameters. Nevertheless, in this paper we show that the potential of a DLSR design can be explored with a successive and iterative implementation of the multi-objective particle swarm optimization（MOPSO） and multi-objective genetic algorithm（MOGA）. For the High Energy Photon Source, a planned kilometer-scale DLSR, optimizations indicate that it is feasible to attain a natural emittance of about 50 pm·rad, and simultaneously realize a sufficient ring acceptance for on-axis longitudinal injection, by using a hybrid MBA lattice. In particular, this study demonstrates that a rational combination of the MOPSO and MOGA is more effective than either of them alone, in approaching the true global optima of an explorative multi-objective problem with many optimizing variables and local optima.

关键词： diffraction-limited storage ring High Energy Photon Source multi-objective particle swarm optimization multi-objective genetic algorithm lattice design

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multi-objective evolutionary optimization of prediction intervals for solar energy forecasting with neural networks

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INFORMATION SCIENCES 2017年 418卷 363-382页

作者： Galvan, Ines M. Valls, Jose M. Cervantes, Alejandro Aler, Ricardo Univ Carlos III Madrid Avda Univ 30 Leganes 28911 Spain

In the context of forecasting for renewable energy, it is common to produce point forecasts but it is also important to have information about the uncertainty of the forecast. To this extent, instead of providing a single measure for the prediction, lower and upper bound for the expected value for the solar radiation are used (prediction interval). This estimation of optimal prediction intervals requires the simultaneous optimization of two objective measures: on one hand, it is important that the coverage probability of the interval is as close as possible to a given target value. On the other, in order to bound uncertainty, intervals must be narrow;this means that there is a trade-off between both objectives, as narrow intervals reduce the coverage probability for those solutions, as the actual value of solar radiation is more likely to fall outside the predicted margins. In this work we propose a multi-objective evolutionary approach that is able to optimize both goals simultaneously. The proposal uses neural networks to create complex non-linear models whose outputs are the upper and lower limits of the prediction intervals. Results have been compared with a single-objective optimization of similar neural network architectures and a baseline algorithm (quantile estimation with gradient boosting). Results show that the neural network is able to provide accurate results. Also, the multi-objective approach is able to obtain the best results and has also the advantage that a single run is able to obtain prediction intervals for any target coverage probability. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Estimation of prediction intervals Neural networks multi-objective particle swarm optimization Solar energy forecasting

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