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5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)

作者： Li Lanqing Murata, Tomohiro Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka 8080135 Japan

ISBN: (纸本)9781467389853

With the rapid growth of electric energy consumption and environmental concerns, renewable energy resources, power quality and reliability, has become the fields of interest for many researchers. By integrating renewable energy resources such as wind and solar power into a power microgrid (PMG), we can give consideration to both sides of environmental issue and energy independency. A PMG is a localized grouping of electricity generation units, energy storage devices and loads, within which, power can be generated locally to reduce dependence on long distance transmission lines and cut transmission losses. The point of coupling with traditional centralized grid can be disconnected, so that PMG can function autonomously to ensure high reliability in case of blackouts in traditional grid. But wind and solar power are easily affected by weather condition so that uncertainty has been introduced into this economic optimal dispatching (EOD) problem. To deal with power shortage or violation of technical limitations, a PMG model containing battery as an energy buffer is built and a chance-constrained programming (CCP) based particle swarm optimization (PSO) method is proposed in this paper to solve the economic optimal dispatching problem.

关键词： power microgrid economic optimal dispatching particle swarm optimization chance-constrained programming uncertainty renewable energy

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Stochastic Multi-objective Distribution Feeder Reconfiguration Based on Point Estimation Considering N-1 Security and Net Loss

Stochastic Multi-objective Distribution Feeder Reconfigurati...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Liu, Jia Cheng, Haozhong Tian, Yuan Zeng, Pingliang Yao, Liangzhong Shanghai Jiao Tong Univ Dept Elect Engn Shanghai Peoples R China China Elect Power Res Inst Beijing Peoples R China

ISBN: (纸本)9781509041688

N-1 security criteria is a key principle for distribution operation. However, it is hardly taken into account in modern distribution feeder reconfiguration models, which cannot satisfy security margin demands. To address this fundamental problem, this paper develops a new stochastic reconfiguration method for distribution network considering N-1 security and net loss. Firstly, three indexes-average value, variance, variation coefficient-of security distance are proposed to express the N-1 security margin of the whole system based on distribution system security region (DSSR). Next, the reconfiguribution network is formulated into a chance-constrained programming with the aim of minimizing net loss and variation coefficient of security distance, which is then solved by normal boundary intersection-dynamic niche differential evolution algorithm (NBI-DNDEA) and stochastic power flow based on point estimation. Finally, Case study verifies the effectiveness of those techniques.

关键词： distribution feeder reconfiguration N-1 security chance-constrained programming point estimation NBI-DNDEA

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Multi-objective optimization operation considering environment benefits and economy based on ant colony optimization for isolated micro-grids

Multi-objective optimization operation considering environme...

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Applied Energy Symposium and Summit - Low-Carbon Cities and Urban Energy Systems (CUE)

作者： Li, Guoqing Zhai, Xiaojuan Li, Yang Feng, Bo Wang, Zhenhao Zhang, Mingjiang Northeast Dianli Univ Sch Elect Engn Jilin 132012 Peoples R China State Grid Heilongjiang Elect Power Res Inst Harbin 150030 Peoples R China

A new multi-objective optimization model considering environment benefits and economy is proposed for isolated micro-grids based on ant colony optimization in this paper. First, through fuzzy processing and satisfaction-maximizing scheme, the multi-objective programming problem is reformulated into a nonlinear mono-objective programming problem. Then, to cope with the uncertainty of photovoltaic and wind power generation, the sequence operation theory is introduced to transform such a chance-constrained programming problem into a corresponding deterministic equivalent problem. Next, an improved ant colony algorithm is used to solve the mono-objective mixed-integer linear programming problem. And finally, the test results demonstrate the effectiveness of the proposed model and algorithm. (C) 2016 The Authors. Published by Elsevier Ltd.

关键词： micro-grids distributed generation multi-objective optimization fuzzy modeling chance-constrained programming improved ant colony algorithm

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Distributional Assumptions in chance-constrained programming Models of Stochastic Water Pollution

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ENVIRONMENTAL MODELING & ASSESSMENT 2010年第4期15卷 273-281页

作者： Kataria, Mitesh Elofsson, Katarina Hasler, Berit Max Planck Inst Econ Strateg Interact Grp D-07745 Jena Germany Swedish Univ Agr Sci Dept Econ S-75007 Uppsala Sweden Aarhus Univ Natl Environm Res Inst DK-4000 Roskilde Denmark

In the water management literature both the normal and log-normal distribution are commonly used to model stochastic water pollution. The normality assumption is usually motivated by the central limit theorem, while the log-normality assumption is often motivated by the need to avoid the possibility of negative pollution loads. We utilize the truncated normal distribution as an alternative to these distributions. Using probabilistic constraints in a cost-minimization model for the Baltic Sea, we show that the distribution assumption bias is between 1% and 60%. Simulations show that a greater difference is to be expected for data with a higher degree of truncation. Using the normal distribution instead of the truncated normal distribution leads to an underestimation of the true cost. On the contrary, the difference in cost when using the normal versus the log-normal can be positive as well as negative.

关键词： Cost effectiveness Water pollution chance-constrained programming Log-normal distribution Truncated normal distribution

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Hybrid Wind-PV System Capacity Limit Calculation of Based on Constraint Transformer

Hybrid Wind-PV System Capacity Limit Calculation of Based on...

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China International Conference on Electricity Distribution (CICED)

作者： Guo, Yifei Gao, Houlei Wu, Zhigang Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Peoples R China State Grid Shandong Elect Power Co Jinan 250001 Peoples R China

ISBN: (纸本)9781467390682

Aiming at the hybrid wind-PV system planning problem, a optimization model of installed capacity is proposed in this paper with the constraint of main transformer. Firstly', to account for the stochastic nature of wind and solar resource, by use of ARMA model and Fuzzy C-means(FCM) algorithm, a full-year time series models of wind power, photovoltaic(PV) power are presented. Then, using main transformer utilization, the complementarity of wind and solar energy resource is quantified. And based on the chance-constrained programming, the calculation models are respectively built for two common kinds of planning problem. With an actual case in Shandong province, the problems of wind and solar accommodation and the optimal proportion are calculated and analyzed. The results demonstrate the effectiveness of the proposed method in improving the renewable energy resource utilization.

关键词： wind-PV system chance-constrained programming curtailment probability optimal proportion

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A coupled random fuzzy two-stage programming model for crop area optimization - A case study of the middle Heihe River basin, China

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AGRICULTURAL WATER MANAGEMENT 2015年 155卷 53-66页

作者： Li, Mo Guo, Ping China Agr Univ Ctr Agr Water Res China Beijing 100083 Peoples R China

Crop area optimization is essential for agricultural water management. However, decision makers are challenged by the complexity of fluctuating stream condition and irrigation quota, varying economic profits and crop yield, as well as grayness and errors in the estimated modeling parameters. A random-fuzzy-variable-based inexact two-stage stochastic chance-constrained programming (RFV-ITSCCP) model is developed for crop area optimization in response to such complexities in more efficient and sustainable ways. The model is capable of tackling parameters' dual uncertainties of both randomness and fuzziness and meanwhile reflecting uncertainties expressed as intervals and probability distributions. Moreover, the developed model is helpful for managers in gaining insight into the tradeoffs between the system benefit and the constraint-violation risk. The RFV-ITSCCP model is applied in crop area optimization in the middle reaches of Heihe River basin, northwest of China. The results provide crop area allocation under various flow levels with the maximum system benefit under uncertainty, minimizing the penalty due to water deficit especially in arid and semi-arid area. The results show that the system efficiency (benefit per unit area) increases by 13.2% than conventional linear programming (LP) model with the most credible fuzzy number values under the same condition of water supply, irrigation regimes and socio-economic conditions, which can demonstrate the feasibility and applicability of the developed RFV-ITSCCP model. Sensitivity analysis is also conducted to analyze the impacts of key model parameters on the results. The obtained results are valuable for supporting the adjustment of the existing crop area patterns and identifying desired crop area schemes under complex uncertainties. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Random fuzzy variable chance-constrained programming Inexact two-stage stochastic programming Crop area optimization Uncertainty

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Planning of water resources management and pollution control for Heshui River watershed, China: A full credibility-constrained programming approach

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SCIENCE OF THE TOTAL ENVIRONMENT 2015年 524卷 280-289页

作者： Zhang, Y. M. Huang, G. Lu, H. W. He, Li North China Elect Power Univ Suzhou Inst Res Suzhou 215123 Peoples R China North China Elect Power Univ Resources & Environm Res Acad Beijing 102206 Peoples R China North China Elect Power Univ Coll Renewable Energy Beijing 102206 Peoples R China

A key issue facing integrated water resources management and water pollution control is to address the vague parametric information. A full credibility-based chance-constrained programming (FCCP) method is thus developed by introducing the new concept of credibility into the modeling framework. FCCP can deal with fuzzy parameters appearing concurrently in the objective and both sides of the constraints of the model, but also provide a credibility level indicating how much confidence one can believe the optimal modeling solutions. The method is applied to Heshui River watershed in the south-central China for demonstration. Results from the case study showed that groundwater would make up for the water shortage in terms of the shrinking surface water and rising water demand, and the optimized total pumpage of groundwater from both alluvial and karst aquifers would exceed 90% of its maximum allowable levels when credibility level is higher than or equal to 0.9. It is also indicated that an increase in credibility level would induce a reduction in cost for surface water acquisition, a rise in cost from groundwater withdrawal, and negligible variation in cost for water pollution control. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Credibility chance-constrained programming Water resources management Water pollution control Agricultural development

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A chance-constrained Optimization Model for Determining Renewables Penetration Limit in Power Systems

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2016年第7期44卷 701-712页

作者： Xiang, Yue Liu, Youbo Liu, Junyong Yang, Wei Sichuan Univ Sch Elect Engn & Informat 24 South Sect 1Yihuan Rd Chengdu 610065 Peoples R China

In this article, an optimization model determining renewables penetration limit in power systems is presented. The penetration limit is defined as the enabling renewables output with quantified maximum capacity avoiding the violation of power system operation constraints. Thus, an optimal power flow (OPF)-based model with chance constraints is built and a framework including a Monte Carlo-based genetic algorithm is designed. Moreover, a transient stability verification and correction strategy based on trajectory sensitivity is proposed and modularized in the extended framework. The feasibility of the proposed methodology is verified using several test scenarios, and some related factors are investigated as well. The results indicate that renewables penetration limit can be increased by improving those studied factors.

关键词： renewable generation penetration limit chance-constrained programming transient stability constraint optimization

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Multi-objective Optimization Operation Considering Environment Benefits and Economy Based on Ant Colony Optimization for Isolated Micro-grids

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Energy Procedia 2016年 104卷 21-26页

作者： Guoqing Li Xiaojuan Zhai Yang Li Bo Feng Zhenhao Wang Mingjiang Zhang School of Electrical Engineering Northeast Dianli University Jilin 132012 P.R. China State Grid Heilongjiang Electric Power Research Institute Harbin 150030 P.R. China

关键词： micro-grids distributed generation multi-objective optimization fuzzy modeling chance-constrained programming improved ant colony algorithm

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Sensitivity-based chance-constrained Generation Expansion Planning

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ELECTRIC POWER SYSTEMS RESEARCH 2015年 127卷 32-40页

作者： Manickavasagam, Monishaa Anjos, Miguel F. Rosehart, William D. Univ Calgary Dept Elect & Comp Engn Calgary AB Canada Ecole Hautes Etud Commerciales Gerad Montreal PQ Canada Ecole Polytech Montreal PQ H3C 3A7 Canada

A Generation Expansion Planning problem with load uncertainty is formulated based on joint chance-constrained programming (CCP) and is solved by incorporating sensitivity into iterative algorithms. These algorithms exploit the characteristics of the system and its response to load variations. Sensitivities help to classify buses according to stress level, and sensitivity-based iterative algorithms distinguish each bus based on its contribution to the overall system reliability. The use of sensitivity overcomes some of the mathematical obstacles to solving joint CCP problems and, in addition, leads to optimal expansion solutions because uncertain loads are correctly estimated. The IEEE 30- and 118-bus test systems are used to demonstrate the proposed algorithms, and the results Of these algorithms are compared with those of other algorithms for solving the joint CCP problem. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Generation Expansion Planning chance-constrained programming Sensitivity

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