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Reactive Power Planning for Regional Power Grids Based on Active and Reactive Power Adjustments of DGs

ENERGIES 2018年第6期11卷

作者： Shen, Yangwu Shen, Feifan Chen, Yaling Liang, Liqing Zhang, Bin Ke, Deping Sate Grid Hunan Elect Power Corp Res Inst Changsha 410007 Hunan Peoples R China Tech Univ Denmark Dept Elect Engn DK-2008 Copenhagen Denmark Hunan Univ Chinese Med Sch Humanities & Management Changsha 410208 Hunan Peoples R China Wuhan Univ Dept Elect Engn Wuhan 430000 Hubei Peoples R China

To deal with extreme overvoltage scenarios with small probabilities in regional power grids, the traditional reactive power planning model requires a huge VAR compensator investment. Obviously, such a decision that makes a large investment to cope with a small probability event is not economic. Therefore, based on the scenario analysis of power outputs of distributed generations and load consumption, a novel reactive power planning model considering the active and reactive power adjustments of distributed generations is proposed to derive the optimal allocation of VAR compensators and ensure bus voltages within an acceptable range under extreme overvoltage scenarios. The objective of the proposed reactive power planning model is to minimize the VAR compensator investment cost and active power adjustment cost of distributed generations. Moreover, since the proposed reactive power planning model is formulated as a mixed-integer nonlinear programming problem, a primal-dual interior point method-based particle swarm optimization algorithm is developed to effectively solve the proposed model. Simulation results were conducted with the modified IEEE 30-bus system to verify the effectiveness of the proposed reactive power planning model.

关键词： reactive power planning distributed generation active power adjustment mixed integer nonlinear programming

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Interactive nonlinear multiobjective optimal design of water distribution systems using Pareto navigator technique

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SUSTAINABLE CITIES AND SOCIETY 2021年 73卷

作者： Moazeni, Faegheh Khazaei, Javad Penn State Harrisburg Sch Sci Engn & Technol Civil & Environm Engn Dept 777 W Harrisburg Pike Middletown PA 17057 USA Penn State Harrisburg Elect Engn Dept 777 W Harrisburg Pike Middletown PA 17057 USA Penn State Univ Pk Architectural Engn Dept State Coll PA 16801 USA

Sustainable water distribution systems comprise complex combinations of critical components that are simultaneously optimized. The trade-offs and interactions that exist among these components, however, will introduce difficult challenges to optimization algorithms. In this paper, a multiobjective optimization formulation is proposed to scrupulously manage the demand, leakage, and age of water, as well as energy consumption of water distribution systems. Pareto navigator approach, a learning-oriented algorithm, is used to attain the final Pareto optimal solution for water network. The optimal power consumption, leakage, demand, and water age are used as inputs to the upper level energy resource allocation to optimize the energy demand and operational cost of integrated water-energy system. Numerical results show 76.6%, 95.4%, 13.14%, and 27.4% reduction in leakage, water age, water usage, and energy consumption of water network over 24 h, respectively, while the daily cost of electricity generation reduces by 3.7%. The energy system is designed based on isolated energy units, also known as microgrids, with minimum dependency on the fossil-fuel-based power plants. As such, the proposed multiobjective bi-level optimization model will also improve the carbon footprint of water distribution systems and their impact on climate change.

关键词： Multiobjective optimization Pareto optimal solution Water-energy nexus Bi-level optimization model mixed integer nonlinear programming Sustainable systems

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Critical Traffic Control Locations for Emergency Evacuation

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JOURNAL OF TRANSPORTATION ENGINEERING 2013年第10期139卷 1030-1038页

作者： Zhang, Xiongfei He, Rachel Shi, Qixin Ban, Jeff Ran, Bin Shenzhen Univ Coll Rail Transportat Shenzhen Key Lab Urban Rail Transit Shenzhen 518060 Peoples R China Santa Clara Univ Dept Civil Engn Santa Clara CA 95053 USA Tsinghua Univ Dept Civil Engn Beijing 100084 Peoples R China Rensselaer Polytech Inst Dept Civil & Environm Engn Troy NY 12180 USA Univ Wisconsin Dept Civil & Environm Engn Madison WI 53706 USA

Proper traffic control and guidance are imperative to reduce fatalities and property damage. When resources are scarce during emergency evacuation for any natural or artificial disasters, it is crucial to identify the most critical traffic control locations or intersections to deploy temporary control devices or to arrange manual traffic guidance so that the best system performance can be achieved. To answer the urgent theoretical and practical deployment problems, this study has proposed mixed integer nonlinear programming (MINLP) models for both static and dynamic situations. The proposed models aim to find the most crucial intersections, the optimal traffic control strategies for those intersections, and the best destination choices for evacuees to minimize the total system cost. The proposed models assume that evacuees will follow their past travel experience to safe zones unless they encounter the critical intersections. At those fully controlled critical intersections, evacuees will completely comply with the evacuation guidance to help achieve the minimum total system cost. The proposed MINLP models have also been expanded to include cell-based modules to capture the dynamic nature of evacuation flows, which is essential for real-time operation. Numerical tests on a large network with multiple origins and destinations have shown that the proposed models perform reasonably well.

关键词： User equilibrium Emergency evacuation mixed integer nonlinear programming Dynamic traffic assignment Cell transmission model System optimal

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Optimal durations of Weibull reliability tests based on failure counts

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 156卷

作者： Fernandez, Arturo J. Univ La Laguna Dept Matemat Estadist & Invest Operat Tenerife 38071 Canary Islands Spain

In industry, the durations of reliability experiments are usually fixed in advanced, which could lead to suboptimal results. A constrained optimization problem is stated in order to determine the best inspection time for reliability testing. The decision criterion is based on Weibull failure counts, whereas the constraints are related to the reliability and risk levels imposed by the producer and the consumer. The optimal reliability sampling plan provides the best strategy to determine the acceptability of lots and production processes. Minimum-cost inspection times, as well as the required number of test units and the maximum number of failures allowed, are derived by solving mixed integer nonlinear programming problems. An approximation of the minimum feasible acceptance number is first provided in closed-form. An efficient step-by-step procedure is then proposed in order to find the optimal reliability test plan for lot sentencing. In most cases, only a few iterations are needed to reach the optimal solution. For illustrative purposes, the suggested methodology is applied to the manufacturing of turbine engine combustors, systems of components and lawn mower motors.

关键词： Reliability Lot sampling inspection Constrained optimization mixed integer nonlinear programming Weibull distribution

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Multi-objective Optimization-Tool for the Universal Application in Chemical Process Design

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CHEMICAL ENGINEERING & TECHNOLOGY 2017年第10期40卷 1867-1875页

作者： Ernst, Philipp Zimmermann, Kristina Fieg, Georg Hamburg Univ Technol Inst Proc & Plant Engn Schwarzenberg Campus 4 D-21073 Hamburg Germany

A universally applicable procedure for multi-objective optimization of chemical processes is developed. A set of known methods and procedures is adapted, combined with newly developed concepts, and integrated into the developed optimization tool, namely, the Adv:ProcessOptimizer. It allows for efficient, comfortable, and robust optimization of a process which is modeled in one of the various linked commercial simulation tools. As a result, the application of the process design with an overlaid optimization is easily accessible for academia and process industry. The industrial styrene process was optimized in order to validate the method. The results show a very densely and mostly equally crowded Pareto front and considerable savings in investment as well as operating costs compared to two reference designs.

关键词： Chemical process design Genetic algorithm mixed integer nonlinear programming Multi-objective optimization

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mixed integer least squares optimization for flight and maintenance planning of mission aircraft

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NAVAL RESEARCH LOGISTICS 2012年第3-4期59卷 212-229页

作者： Kozanidis, George Gavranis, Andreas Kostarelou, Eftychia Univ Thessaly Dept Mech Engn Syst Optimizat Lab Volos Greece Hellen AF Combat Wing 111 N Aghialos Greece

We address the problem of generating a joint flight and maintenance plan for a unit of mission aircraft. The objective is to establish a balanced allocation of the flight load and the maintenance capacity to the individual aircraft of the unit, so that its long-term availability is kept at a high and steady level. We propose a mixed integer nonlinear model to formulate the problem, the objective function of which minimizes a least squares index expressing the total deviation of the individual aircraft flight and maintenance times from their corresponding target values. Using the model's special structure and properties, we develop an exact search algorithm for its solution. We analyze the computational complexity of this algorithm, and we present computational results comparing its performance against that of a commercial optimization package. Besides demonstrating the superiority of the proposed algorithm, these results reveal that the total computational effort required for the solution of the problem depends mainly on two crucial parameters: the size of the unit (i.e., the number of aircraft that comprise it) and the space capacity of the maintenance station. (c) 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2012

关键词： fleet availability flight and maintenance planning mission aircraft mixed integer nonlinear programming least squares optimization

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Conflict Analysis for MINLP

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INFORMS JOURNAL ON COMPUTING 2021年第2期33卷 421-435页

作者： Berthold, Timo Witzig, Jakob Fair Isaac Germany GmbH D-64625 Bensheim Germany Zuse Inst Berlin D-14195 Berlin Germany

The generalization of mixed integer program (MIP) techniques to deal with nonlinear, potentially nonconvex, constraints has been a fruitful direction of research for computational mixed integer nonlinear programs (MINLPs) in the last decade. In this paper, we follow that path in order to extend another essential subroutine of modern MIP solvers toward the case of nonlinear optimization: the analysis of infeasible subproblems for learning additional valid constraints. To this end, we derive two different strategies, geared toward two different solution approaches. These are using local dual proofs of infeasibility for LP-based branch-and-bound and the creation of nonlinear dual proofs for NLP-based branch-and-bound, respectively. We discuss implementation details of both approaches and present an extensive computational study, showing that both techniques can significantly enhance performance when solving MINLPs to global optimality. Summary of Contribution: This original article concerns the advancement of exact general-purpose algorithms for solving one of the largest and most prominent problem classes in optimization, mixed integer nonlinear programs (MINLPs). It demonstrates how methods for conflict analysis that learn from infeasible subproblems can be transferred to nonlinear optimization. Further, it develops theory for how nonlinear dual infeasibility proofs can be derived from a nonlinear relaxation. This paper features a thoroughly computational study regarding the impact of conflict analysis techniques on the overall performance of a state-of-the-art MINLP solver when solving MINLPs to global optimality.

关键词： mixed integer nonlinear programming infeasibility analysis conflict analysis branch-and-bound

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A Multi-Objective Optimization Model for the Design of Biomass Co-Firing Networks Integrating Feedstock Quality Considerations

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ENERGIES 2019年第12期12卷

作者： San Juan, Jayne Lois G. Aviso, Kathleen B. Tan, Raymond R. Sy, Charlle L. De La Salle Univ Ind Engn Dept Manila 0922 Philippines De La Salle Univ Chem Engn Dept Manila 0922 Philippines De La Salle Univ Ctr Engn & Sustainable Dev Res Manila 0922 Philippines

The growth in energy demand, coupled with declining fossil fuel resources and the onset of climate change, has resulted in increased interest in renewable energy, particularly from biomass. Co-firing, which is the joint use of coal and biomass to generate electricity, is seen to be a practical immediate solution for reducing coal use and the associated emissions. However, biomass is difficult to manage because of its seasonal availability and variable quality. This study proposes a biomass co-firing supply chain optimization model that simultaneously minimizes costs and environmental emissions through goal programming. The economic costs considered include retrofitting investment costs, together with fuel, transport, and processing costs, while environmental emissions may come from transport, treatment, and combustion activities. This model incorporates the consideration of feedstock quality and its impact on storage, transportation, and pre-treatment requirements, as well as conversion yield and equipment efficiency. These considerations are shown to be important drivers of network decisions, emphasizing the importance of managing biomass and coal blend ratios to ensure that acceptable fuel properties are obtained.

关键词： biomass co-firing biomass quality network optimization goal programming mixed integer nonlinear programming

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Development of a Diversity-Preserving Strategy for the Pareto Optimization in Chemical Process Design

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CHEMIE INGENIEUR TECHNIK 2017年第10期89卷 1297-1305页

作者： Zimmermann, Kristina Fieg, Georg Hamburg Univ Technol Inst Proc & Plant Engn Schwarzenberg Campus 4 D-21073 Hamburg Germany

The simulation-based design of chemical processes with an overlaid multi-objective optimization is of growing importance. The quality of the determined optimization results, referred to as the Pareto front, influences directly the variety of solutions given to the decision maker. In case of the frequently used genetic algorithms, a lot of studies have been done for a precise approximation of the Pareto front. However, most of the developed methods are inconvenient and/or time-consuming and, thus, inappropriate for industrial applications. A novel approach, the two-step optimization, was successfully tested in order to overcome these challenges.

关键词： Computer-aided process engineering Diversity Genetic algorithm mixed integer nonlinear programming Multi-objective optimization

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Production/Distribution System Design with Inventory Considerations

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NAVAL RESEARCH LOGISTICS 2012年第2期59卷 172-195页

作者： Keskin, Burcu B. Uester, Halit Texas A&M Univ Dept Ind & Syst Engn College Stn TX 77843 USA Univ Alabama Dept Informat Syst Stat & Management Sci Tuscaloosa AL 35487 USA

This study addresses the design of a three-stage production/distribution system where the first stage includes the set of established retailers and the second and third stages include the sets of potential distribution centers (DCs) and potential capacitated suppliers, respectively. In this problem, in addition to the fixed location/operating costs associated with locating DCs and suppliers, we consider the coordinated inventory replenishment decisions at the located DCs and retailers along with the appropriate inventory costs explicitly. In particular, we account for the replenishment and holding costs at the retailers and selected DCs, and the fixed plus distance-based transportation costs between the selected plants and their assigned DCs, and between the selected DCs and their respective retailers, explicitly. The resulting formulation is a challenging mixed-integer nonlinear programming model for which we propose efficient heuristic solution approaches. Our computational results demonstrate the performance of the heuristic approaches as well as the value of integrated decision-making by verifying that significant cost savings are realizable when the inventory decisions and costs are incorporated in the production distribution system design. (C) 2012 Wiley Periodicals, Inc. Naval Research Logistics 59: 172-195, 2012

关键词： production/distribution inventory mixed integer nonlinear programming heuristics

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