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Combined cycle unit commitment in a changing electricity market scenario

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2015年 73卷 114-123页

作者： Casolino, G. M. Liuzzi, G. Losi, A. Univ Cassino & Lazio Merid Dipartimento Ingn Elettr & Informaz M Scarano Via G Di Biasio 43 I-03043 Cassino FR Italy CNR Ist Anal Sistemi & Informat A Ruberti I-00185 Rome Italy

For Generation Companies (GENCOs) one of the most relevant issue is the commitment of the units, the scheduling of them over a daily (or longer) time frame, with the aim of obtaining the best profit. It strongly depends on the plant operational generation costs, which depend in turn on the choices taken at the design stage;it follows that design technical choices should also aim at determining the best generation cost structure of generating units with respect to the market opportunities. In the paper the unit commitment (UC) problem has been considered, with highlights on changes in the market scenario. The paper analyzes the relevance of some design choices (structure, size, regulation type) on the economics of the operation of gas-steam combined cycle generating units. To solve the UC problem, a recently proposed method for mixed integer nonlinear programming problems, with the use of a derivative free algorithm to solve the continuous subproblems, has been considered. The results for two GENCOs are reported: one managing a single unit and the other managing three units. Numerical examples show the sensitivity of the UC solutions to the market conditions and to the design choices on the regulation type in the evolving scenario of the Italian Electricity Market. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Unit commitment Oligopolistic electricity markets Gas-steam generating units mixed integer non-linear programming

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Quantification and integration of Kano's model into QFD for optimising product design

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2014年第21期52卷 6335-6348页

作者： Ji, Ping Jin, Jian Wang, Ting Chen, Yizeng Hong Kong Polytech Univ Shenzhen Res Inst Shenzhen Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Kowloon Hong Kong Peoples R China Beijing Normal Univ Dept Informat Management Beijing 100875 Peoples R China Shanghai Univ Dept Management Sci & Engn Shanghai Peoples R China

With increasing concerns on customer needs in today's competitive market, the issue of incorporating customer requirements into product design arises the interest of both researchers and practitioners. Quality Function Deployment (QFD) is a well-known methodology for customer-driven product design. However, conventionally, QFD analysis has a major challenge in understanding customer needs accurately. Kano's model, which studies the nature of customer needs, provides a way for a better classification of customer needs. However, seldom research contributions are found in terms of integrating Kano's model with QFD quantitatively. In this research, a novel integration approach is proposed. At first, Kano's model is quantified by identifying relationship between customer needs and customer satisfaction (CS). Next, both qualitative and quantitative results from Kano's model are integrated into QFD. Finally, a mixed non-linear integer programming model is formulated to maximise CS under cost and technical constraints. In this research, an illustrative example associated with the design of notebook computers is also presented to demonstrate the availability of the proposed approach.

关键词： mixed integer non-linear programming QFD customer needs Kano's model

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Zeus: A resource allocation algorithm for the cloud of sensors

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 92卷 564-581页

作者： Santos, Igor L. Pirmez, Luci Delicato, Flavia C. Oliveira, Gabriel M. Farias, Claudio M. Khan, Samee U. Zomaya, Albert Y. Univ Fed Rio de Janeiro Programa Posgrad Informat PPGI BR-20001970 Rio De Janeiro RJ Brazil North Dakota State Univ Fac Elect & Comp Engn Fargo ND 58102 USA Univ Sydney Sch Informat Technol Ctr Distributed & High Performance Comp Sydney NSW 2006 Australia Ctr Fed Educ Tecnol Celso Suckow da Fonseca CEFET BR-20271110 Rio De Janeiro RJ Brazil

The cloud of sensors (CoS) paradigm brings together cloud computing, edge computing and wireless sensor and actuator networks (WSAN) in the form of a three-tier CoS architecture. By employing CoS virtualization, a set of virtual nodes (VNs) is made available to applications, decoupling them from the CoS infrastructure. Assigning VNs to application requests, in order to timely and efficiently meet application requirements, gives rise to the challenge of resource allocation in CoS. In this work, we formulate the problem of resource allocation in CoS and propose Zeus, a hybrid (partly-decentralized) algorithm for solving it. Zeus has two key features. First, it is able to identify requests that are common for multiple applications and perform only once their required tasks, sharing the results among the applications for saving resources. Second, its hybrid approach leveraging edge computing, makes Zeus scalable in terms of the number of VNs and applications supported, and suitable for meeting delay-sensitive applications. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Cloud of sensors Edge computing Resource allocation mixed integer non-linear programming Data provisioning Data freshness

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Grid-Specific Co-Optimization of Incentive for Generation Planning in Power Systems With Renewable Energy Sources

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2020年第2期11卷 947-957页

作者： Parizy, Ehsan Saeidpour Choi, Seungdeog Bahrami, Hamid Reza Univ Akron Dept Elect & Comp Engn Akron OH 44325 USA Case Western Reserve Univ Dept Elect Engn & Comp Sci Cleveland OH 44106 USA Mississippi State Univ Dept Elect & Comp Engn Starkville MS 39762 USA

Enhancement in efficiency of renewable energy sources, raised concern about greenhouse gas emission, significant power losses within transmission and distribution networks, unpredictable and unreliable fossil fuel cost, and expansion of reliable communication infrastructure offered by smart grids have all contributed to an increasing interest in deployment of renewable energy sources in energy mix. However, the widespread utilization of renewable energy sources in an open energy market without proper grid-based incentives for aimed contribution levels is uncertain. This article proposes a co-optimization algorithm to find the minimum incentives that result in the desired level of renewable energy source penetration in energy supply chain and optimal distribution of power sources in the electric power grid. The algorithm first finds the proper incentive based on the power-flow losses, then it uses a novel optimization approach to solve a mixed integer non-linear programming optimization of generation planning to minimize the energy cost. Unlike other incentive design methodologies, the proposed algorithm is not only supply specific, but also it depends on the demand and the structures of the grid. The method finds an exclusively designed set of incentives for electric energy supplies that do not emit greenhouse gases or heat to the atmosphere.

关键词： Power systems Renewable energy sources Optimization Power generation Planning Device-to-device communication Generators Incentive design renewable energy sources and grid integration economic impact of sustainable energy sources generation expansion planning mixed integer non-linear programming

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Procurement planning in oil refining industries considering blending operations

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COMPUTERS & CHEMICAL ENGINEERING 2013年 58卷 1-13页

作者： Oddsdottir, Thordis Anna Grunow, Martin Akkerman, Renzo Tech Univ Denmark Dept Engn Management DK-2800 Lyngby Denmark Tech Univ Munich TUM Sch Management D-80290 Munich Germany

This paper addresses procurement planning in oil refining, which has until now only had limited attention in the literature. We introduce a mixed integer nonlinear programming (MINLP) model and develop a novel two-stage solution approach, which aims at computational efficiency while addressing the problems due to discrepancies between a non-linear and a linearized formulation. The proposed model covers realistic settings by allowing the blending of crude oil in storage tanks, by modeling storage tanks and relevant processing units individually, and by handling more crude oil types and quality parameters than in previous literature. The developed approach is tested using historical data from Statoil A/S as well as through a comprehensive numerical analysis. The approach generates a feasible procurement plan within acceptable computation time, is able to quickly adjust an existing plan to take advantage of individual procurement opportunities, and can be used within a rolling time horizon scheme. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Procurement planning Oil refining industry mixed integer non-linear programming Solution approach Crude oil scheduling Decision support

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Optimal operation of water distribution networks with intermediate storage facilities

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COMPUTERS & CHEMICAL ENGINEERING 2018年 119卷 215-227页

作者： Kurian, Varghese Chinnusamy, Saravanan Natarajan, Ashok Narasimhan, Sridharakumar Narasimhan, Shankar Indian Inst Technol Madras Dept Chem Engn Madras Tamil Nadu India Tamil Nadu Water Investment Co Madras Tamil Nadu India

The nexus between water and energy reveals that transporting water for end use is a highly energy intensive operation. In this work we consider the optimal operation of a water distribution network consisting of pumps delivering water to different reservoirs, with each reservoir catering to a time varying demand. Pumps and ON/OFF valves are used as manipulated variables to minimize energy consumption while meeting the demand. Due to the nonlinear nature of the pump operating curve and the hydraulics, this results in a mixed integer nonlinear Program (MINLP). We propose a three step decomposition approach to solve this problem efficiently. The applicability of this technique is demonstrated on a water network proposed for a municipality in India and the potential advantages are reported. We also compare the solution times required for the proposed technique and a standard solver and demonstrate the efficiency of the proposed approach. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Water distribution networks Energy minimization Scheduling mixed integer non-linear programming

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Design a bi-objective mathematical model for cellular manufacturing systems considering variable failure rate of machines

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2014年第24期52卷 7401-7415页

作者： Yadollahi, Mohammad Sadegh Mahdavi, Iraj Paydar, Mohammad Mahdi Jouzdani, Javid Mazandaran Univ Sci & Technol Dept Ind Engn Babol Sar Iran Babol Univ Technol Dept Ind Engn Babol Sar Iran Iran Univ Sci & Technol Dept Ind Engn Tehran Iran

Current business environment compels manufacturers to produce high-quality products at low cost with the shortest possible delivery time. Cellular Manufacturing Systems (CMSs), utilised to equip the producers to deal with this predicament, have been a point of attraction to both researchers and practitioners. Machine reliability is one of key performance measures of a CMS and the reason is that high machine failure rates lead to due date collision and loss of customers. Therefore, this paper presents a bi-objective mathematical model for a CMS considering the sequence data, alternative process plans, candidate locations for machines, maximum capacity for each machine and variable failure rate of each machine. In the proposed model, the variable failure rate is considered as a dependent variable of 'number of setups' and 'total processing time' in a regression equation. The first objective of this model is minimising the purchase cost of machines, intra-cellular movements (forward and backward) and the inter-cellular movement costs of materials while the second one is to minimise the total repair time for failed machines. To illustrate the performance of the proposed model, a numerical example is solved in the Generalized Algebraic Modeling Systems software using augmented epsilon-constraint method. The results of the numerical examples show that the proposed approach is promising.

关键词： variable failure rate mixed integer non-linear programming augmented epsilon-constraint method cellular manufacturing system alternative process plan

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Signal optimization at an isolated intersection under cyclic vehicle arrivals using spatially sparse trajectory data

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2024年 163卷

作者： Wan, Lijuan Ma, Wanjing Lo, Hong K. Yu, Chunhui Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Hong Kong Peoples R China Tongji Univ Key Lab Rd & Traff Engn Minist Educ 4800 Caoan Rd Shanghai 201804 Peoples R China

Existing trajectory -based signal timing studies either assume time -invariant vehicle arrivals or require high penetration rates of connected vehicles (CVs). However, vehicle arrivals at an urban intersection typically follow a cyclic pattern in the real world due to the impacts of traffic signals at upstream signalized intersections. And the penetration rates of CVs are likely to remain at low levels in the near future. This study develops two time -slot based signal optimization models for isolated intersections under cyclic vehicle arrivals using spatially sparse CV trajectories. The number of sub -cycles within a generalized cycle, along with phase sequence, cycle length, and green splits in each sub -cycle, is optimized. The minimization of the weighted sum of oversaturated phase numbers and total vehicle delays is used as the objective function taking into consideration both under- and over -saturated traffic. The consideration of the over -saturated phase number is to increase vehicle throughput and reduce queue length. The cyclicity of vehicle arrivals is identified by Autocorrelation Function with the aid of the aggregation of spatially sparse trajectories. An aggregated -trajectory -based mixed -integer linear programming model is formulated to estimate the shockwaves under the initial signal timing plan. The evolution of shockwaves with varying signal timings is predicted based on the estimated profiles of queueing shockwave speeds under the cyclic demand as well as total vehicle delay and residual queue length. Two mixed integer non-linear programming models are formulated to optimize the time -slot based signal schemes with a fixed and a flexible cycle structure. Aggregated -trajectoryinformed Monte Carlo Tree Search (ATI-MCTS) and Dynamic programming (DP) algorithms are designed for solutions, respectively. Numerical studies validate the advantages of the proposed models over the one in Synchro Studio, vehicle -actuated control, and longest -queue -fi

关键词： Time-slot based signal scheme Spatially sparse trajectory data Cyclic vehicle arrival Traffic shockwave mixed integer non-linear programming

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A multistage stochastic programming formulation to evaluate feedstock/process development for the chemical process industry

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CHEMICAL ENGINEERING SCIENCE 2018年 187卷 223-244页

作者： Christian, Brianna Cremaschi, Selen Auburn Univ Dept Chem Engn Samuel Ginn Coll Engn 345 W Magnolia Ave Auburn AL 36849 USA

Industry trends suggest that the feedstock and product portfolios along with utilized technologies of chemical process industry (CPI) may grow and be quite different compared to today's in the near future. Incorporation of new feedstocks and technologies into the existing CPI infrastructure may require significant amounts of investments. Determining the investment decisions, i.e., how much to invest, which technologies to invest in, and when to invest in each technology for research and development and for capacity expansion, is a challenging task, because there are often many emerging technologies and the future performances of these technologies are uncertain. Here, we present a multistage stochastic programming (MSSP) formulation accounting for both endogenous and exogenous uncertain parameters associated with the new technology investment planning (NTIP) problem. The MSSP formulation is used to determine investment decisions for four case studies, including a biomass to ethylene production network. The solution for the biomass to ethylene case study suggests that, given the current market conditions, the investment in biomass technologies is not financially advantageous. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Multistage stochastic programming Endogenous uncertainty Exogenous uncertainty Investment planning mixed integer non-linear programming

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A multi-objective optimization and multi-criteria evaluation integrated framework for distributed energy system optimal planning

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ENERGY CONVERSION AND MANAGEMENT 2018年 166卷 445-462页

作者： Jing, Rui Zhu, Xingyi Zhu, Zhiyi Wang, Wei Meng, Chao Shah, Nilay Li, Ning Zhao, Yingru Xiamen Univ Coll Energy Xiamen Peoples R China CLP Power Hong Kong Ltd East & West Reg Hong Kong Hong Kong Peoples R China Imperial Coll London Dept Chem Engn London England

This study proposes an integrated framework for planning distributed energy system with addressing the multi-objective optimization and multi-criteria evaluation issues simultaneously. The framework can be decomposed into two stages. At the optimization stage, the system design and dispatch are optimized considering multiple objectives by E-constraint method. Three decision making approaches are applied to identify the Pareto optimal solution. At the evaluation stage, a combined Analytic Hierarchy Process and Gray Relation Analysis method is proposed to evaluate and rank various optimal solutions when different objectives and cases are considered. Two stages of work are integrated by introducing the baseline conditions. As an illustrative example, an optimal planning model for a solar-assisted Solid Oxide Fuel Cell distributed energy system is proposed by mixed integer non-linear programming approach firstly. Then, the system is applied to different cases considering two types of buildings located in three climate zones. The obtained optimal solutions are further evaluated by the proposed multi-criteria evaluation method. Therefore, the overall optimal system design and dispatch strategy, as well as the best demonstration site can be identified comprehensively considering multiple objectives. In general, the results have verified the effectiveness of the proposed framework.

关键词： Distributed energy system mixed integer non-linear programming Solid Oxide Fuel Cell Multi-objective optimization Decision making Multi-criteria evaluation

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