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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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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

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.

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年第Oct.期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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4PL routing optimization under emergency conditions

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KNOWLEDGE-BASED SYSTEMS 2015年第Nov.期89卷 126-133页

作者： Huang, Min Ren, Liang Lee, Loo Hay Wang, Xingwei Northeastern Univ State Key Lab Synthet Automat Proc Ind Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China Natl Univ Singapore Dept Ind & Syst Engn Singapore 119260 Singapore

Routing optimization is one of important problems in fourth party logistics (4PL) management. Under emergency conditions, effectively describing uncertain parameters for routing optimization entails major challenges because of a lack of historical data. Hence, this paper studies a fourth party logistics routing optimization problem (4PLROP) with uncertain delivery time under emergency conditions. A novel 4PLROP uncertain programming model (UPM) under emergency conditions is proposed by describing the uncertain delivery time as an uncertain variable based on the uncertainty theory (UT) first. Then, to justify the advantage of UPM for addressing this problem with little historical data, UPM is compared with the stochastic programming model (SPM) in which the uncertain delivery time is described as a stochastic variable based on probability theory. The comparison results show that the UPM solution can satisfy the belief degree constraint, which is used to describe the customer delivery time requirement, whereas the SPM solution cannot. Finally, numerical examples are used to verify the effectiveness of the proposed method. The results also suggest the proposed model's advantages. (C) 2015 Elsevier B.V. All rights reserved.

关键词： 4PLROP Emergency logistics 3PL Uncertainty theory chance-constrained programming Cumulative prospect theory

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Economic Optimal Dispatching Method of Power Microgrid under Environmental Uncertainties

Economic Optimal Dispatching Method of Power Microgrid under...

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IIAI International Congress on Advanced Applied Informatics

作者： Li Lanqing Tomohiro Murata Graduate School of Information Production and Systems Waseda University

ISBN: (纸本)9781467389860

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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Optimal Siting and Sizing of Intermittent Distributed Generators in Distribution System

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2015年第6期10卷 628-635页

作者： Zhang, Shenxi Cheng, Haozhong Li, Ke Bazargan, Masoud Yao, Liangzhong Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China

Intermittent distributed generators (IDGs), such as distributed wind turbine generator (WTG) and photovoltaic generator (PVG), have been developing rapidly in recent years. The output power of WTG and PVG highly depends on the wind speed and illumination intensity, respectively. There always exist correlations among the wind speed, illumination intensity, and bus load, which could have significant influence on the determination of siting and sizing of IDGs in distribution system. Given this background, a chance-constrained-programming-based IDGs planning model, which can take into account the correlations, is developed in this paper. Latin hypercube sampling technique and Cholesky decomposition are introduced to handle the correlations. A Monte Carlo simulation-embedded multi-population differential evolution algorithm is employed to solve the developed model. Case studies carried out on the Baran & Wu 33-bus distribution system verify the feasibility of the developed model and effectiveness of the proposed solving methodology. (C) 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： intermittent distributed generators siting and sizing chance-constrained programming correlation Monte Carlo simulation multi-population differential evolution algorithm

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A convex chance-constrained model for reactive power planning

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2015年 71卷 403-411页

作者： Lopez, Julio Pozo, David Contreras, Javier Mantovani, J. R. S. Sao Paulo State Univ UNESP Dept Elect Engn Ilha Solteira SP Brazil Pontificia Univ Catolica Chile Ind & Syst Engn Dept Santiago Chile Univ Castilla La Mancha Escuela Tecn Super Ingenieros Ind E-13071 Ciudad Real Spain

This paper presents a new approach for long-term reactive power investment planning and operation using a multiperiod mixed-integer stochastic convex model, where load uncertainty is also included. The risk of not meeting the load with a certain level of confidence due to a reactive power deficit is represented by chance constraints. Tap settings of under-load tap-changing transformers are modeled as a set of mixed-integer linear equations. Existing and new discrete and continuous reactive power sources are modeled. These type of problem is challenging and have never been studied before. The proposed model is applied to the CIGRE-32 electric power system. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： chance-constrained programming Convex model Load shedding Reactive power deficit Reactive power planning Tap settings

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Nonpoint-Source Water Quality Management Under Uncertainty Through an Inexact Double-Sided chance-constrained Model

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WATER RESOURCES MANAGEMENT 2015年第9期29卷 3079-3094页

作者： Ji, Yao Huang, Guo H. Sun, Wei North China Elect Power Univ SC Resources & Environm Res Acad MOE Key Lab Reg Energy & Environm Syst Optimizat Beijing 102206 Peoples R China Univ Regina Fac Engn & Appl Sci Environm Syst Engn Program Regina SK S4S 0A2 Canada Univ Regina Inst Energy Environm & Sustainable Communities Regina SK S4S 0A2 Canada

This study proposed an inexact double-hand-side chance-constrained programming (IDCCP) method for nonpoint-source water quality management within an agricultural system. The IDCCP model can express interval parameters and double-hand-side random variables (e.g. nitrogen requirement of crop and nitrogen content of manure) simultaneously. To handle the double-sided random variables, a linear form of sufficient conditions for the IDCCP was deduced and proved. The performance of IDCCP was testified in the water quality management study case at three individual probabilities (theta (i) = 0.1, 0.05 and 0.01), and compared with the corresponding ILP model. The results demonstrated that, the net benefit of the agriculture system decrease with a decreasing violating probability (theta(i) ), an increasing satisfaction of constraint (1-theta(i) ), or a lower risk of the system. Although it is the first application of the IDCCP method to nonpoint-resource water quality management, the IDCCP could also be applied to other environmental issues under such uncertainties.

关键词： Double-hand-side chance-constrained programming Uncertainty Water quality Agricultural system

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Reliability analysis of evacuation routes under capacity uncertainty of road links

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IIE TRANSACTIONS 2015年第1期47卷 50-63页

作者： Lim, Gino J. Rungta, Mukesh Baharnemati, M. Reza Univ Houston Dept Ind Engn Houston TX 77204 USA Amer Air Liquide Newark DE 19702 USA Innovat Scheduling Inc Gainesville FL 32641 USA

This article presents a reliability-based evacuation route planning model that seeks to find the relationship between the clearance time, number of evacuation paths, and congestion probability during an evacuation. Most of the existing models for network evacuation assume deterministic capacity estimates for road links without taking into account the uncertainty in capacities induced by myriad external conditions. Only a handful of models exist in the literature that account for capacity uncertainty of road links. A dynamic network-based evacuation model is extended by incorporating probabilistic arc capacity constraints and a minimum-cost network flow problem is formulated that finds a lower bound on the clearance time within the framework of a chance-constrained programming technique. Network breakdown minimization principles for traffic flow in evacuation planning problem are applied and a path-based evacuation routing and scheduling model is formulated. Given the horizon time for evacuation, the model selects the evacuation paths and finds flows on the selected paths that result in the minimum congestion in the network along with the reliability of the evacuation plan. Numerical examples are presented and the effectiveness of the stochastic models in evacuation planning is discussed. It is shown that the reliability-based evacuation plan is conservative compared with plans made using a deterministic model. Stochastic models guarantee that congestion can be avoided with a higher confidence level at the cost of an increased clearance time.

关键词： stochastic capacity Dynamic network flow problem emergency evacuation reliability chance-constrained programming

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Reactive power planning under conditional-value-at-risk assessment using chance-constrained optimisation

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IET GENERATION TRANSMISSION & DISTRIBUTION 2015年第3期9卷 231-240页

作者： Lopez, Julio Contreras, Javier Mantovani, Jose R. S. UNESP Univ Estadual Paulista Fac Engn Ilha Solteira Dept Engn Eletr Sao Paulo Brazil Univ Castilla La Mancha ETS Ingn Ind E-13071 Ciudad Real Spain

This study presents a risk-assessment approach to the reactive power planning problem. chance-constrained programming is used to model the random equivalent availability of existing reactive power sources for a given confidence level. Load shedding because of random equivalent availability of those reactive power sources is implemented through conditional-value-at-risk. Tap settings of under-load tap-changing transformers are considered as integer variables. Active and reactive demands are considered as probability distribution functions. The proposed mathematical formulation is a two-stage stochastic, multi-period mixed-integer convex model. The tradeoff between risk mitigation and investment cost minimisation is analysed. The proposed methodology is applied to the CIGRE-32 electric power system, using the optimisation solver CPLEX in AMPL.

关键词： reactive power risk management load shedding power transformers minimisation investment reactive power planning conditional-value-at-risk assessment chance-constrained optimisation chance-constrained programming random equivalent availability reactive power sources confidence level load shedding under-load tap-changing transformers integer variables reactive demands probability distribution functions mathematical formulation two-stage stochastic model multiperiod mixed-integer convex model risk mitigation investment cost minimisation CIGRE-32 electric power system optimisation solver CPLEX AMPL

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