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Analysis of renewable generation's integration using multi-objective fashion for multistage distribution network expansion planning

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2019年 106卷 301-310页

作者： Ugranli, Faruk Bartin Univ Elect Elect Engn Dept TR-74110 Merkez Bartin Turkey

In this paper, the integration of intermittent distributed generation (wind and solar) is dealt with by proposing a multi-objective multistage distribution network expansion planning formulation based on epsilon-constraint method. This approach allows the planners to obtain pareto-optimal solutions based on the two objective functions: (1) installation costs of distributed generation, substations, feeders, maintenance costs of assets, costs of energy purchased from substations, cost of unserved energy and (2) utilization of renewable distributed generation. To represent the intermittent nature of load and renewable distributed generation, several time blocks are utilized, and its effectiveness is compared with an existing approach. The overall problem is formulated as a mixed-integer linear programming and a linear equation-based substation price is utilized within the optimization framework to reduce the size of the problem. The proposed method is applied to the modified 24-nodes test network for different cases to analyse the utilization of renewable distributed generation from different point of views and it is inferred from the results that the proposed model yields a variety of planning solutions which are better to increase the utilization of renewable distributed generation.

关键词： Distribution network expansion planning mixed-integer linear programming Multi-objective fashion Intermittent distributed generation Renewable energy Clustering

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A multi-stage supply chain network design problem with in-house production and partial product outsourcing

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APPLIED MATHEMATICAL MODELLING 2019年 70卷 572-594页

作者： Cortinhal, M. J. Lopes, M. J. Melo, M. T. Univ Inst Lisbon ISCTE IUL Av Forcas Armadas P-1649026 Lisbon Portugal Univ Lisbon Fac Ciencias CMAF CIO P-1749016 Campo Grande Portugal Saarland Univ Appl Sci Business Sch Waldhausweg 14 D-66123 Saarbrucken Germany

We propose a mixed-integer linear programming model for a novel multi-stage supply chain network design problem. Our model integrates location and capacity choices for plants and warehouses with supplier and transportation mode selection, and the distribution of multiple products through the network. The aim is to identify the network configuration with the least total cost subject to side constraints related to resource availability, technological conditions, and customer service level requirements. In addition to in-house manufacturing, end products may also be purchased from external sources and consolidated in warehouses. Therefore, our model identifies the best mix between in-house production and product outsourcing. To measure the impact of this strategy, we further present two additional formulations for alternative network design approaches that do not include partial product outsourcing. Several classes of valid inequalities tailored to the problems at hand are also proposed. We test our models on randomly generated instances and analyze the trade-offs achieved by integrating partial outsourcing into the design of a supply chain network against a pure in-house manufacturing strategy, and the extent to which it may not be economically attractive to provide full demand coverage. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Supply chain network design In-house production Product outsourcing Transportation mode selection Single-assignment mixed-integer linear programming

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A two-stage flow-shop scheme for the multi-satellite observation and data downlink scheduling problem considering weather uncertainties

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2019年 188卷 263-275页

作者： Xiao, Yiyong Zhang, Siyue Yang, Pei You, Meng Huang, Jiaoying Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China

An Earth observation satellite (EOS) is a sort of low earth orbit (LEO) satellite that is equipped with high resolution cameras for observing various types of target objects scattered across the surface of the Earth. The available time windows of multiple EOSs for observing a given target object and for downloading the acquired image/video data from satellites to ground receiver stations are scarce resources that should to be utilized efficiently. This problem is hereby named as the multi-satellite observation and data-downlink scheduling problem (MSODSP). We developed a two-stage flow shop scheme for the MSODSP in order to optimize the observation scheduling (at stage 1) and data-downlink scheduling (at stage 2) concurrently and get truly optimized results. A mixed-integer linear programming (MILP) model is developed for the MSODSP with three objective functions. The effects of weather uncertainties on the tasks' success are considered in the MILP model, which allow us to conduct a reliability-maximized task arrangement of EOSs. Computational experiments were conducted on the simulated data of a real LEO satellite to verify the proposed MILP model. The results showed that it was able to solve real-world instances of the MSODSP for up to 20 tasks over 8 days.

关键词： Satellite earth observation Scheduling mixed-integer linear programming Optimization

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Sharing Energy Storage Between Transmission and Distribution

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IEEE TRANSACTIONS ON POWER SYSTEMS 2019年第1期34卷 152-162页

作者： Elliott, Ryan T. Fernandez-Blanco, Ricardo Kozdras, Kelly Kaplan, Josh Lockyear, Brian Zyskowski, Jason Kirschen, Daniel S. Univ Washington Dept Elect Engn Seattle WA 98195 USA Univ Malaga Dept Appl Math Malaga 29076 Spain Doosan GridTech Seattle WA 98104 USA Snohomish Cty PUD Everett WA 98206 USA

This paper addresses the problem of how best to coordinate, or "stack," energy storage services in systems that lack centralized markets. Specifically, its focus is on how to coordinate transmission-level congestion relief with local, distribution-level objectives. We describe and demonstrate a unified communication and optimization framework for performing this coordination. The congestion relief problem formulation employs a weighted l(1)-norm objective. This approach determines a set of corrective actions, i.e., energy storage injections and conventional generation adjustments, that minimize the required deviations from a planned schedule. To exercise this coordination framework, we present two case studies. The first is based on a 3-bus test system, and the second on a realistic representation of the Pacific Northwest region of the United States. The results indicate that the scheduling methodology provides congestion relief, cost savings, and improved renewable energy integration. The large-scale case study informed the design of a live demonstration carried out in partnership with the University of Washington, Doosan GridTech, Snohomish County PUD, and the Bonneville Power Administration. The goal of the demonstration was to test the feasibility of the scheduling framework in a production environment with real-world energy storage assets. The demonstration results were consistent with computational simulations.

关键词： Distribution system operator energy storage system mixed-integer linear programming state of charge transmission congestion transmission system operator unit commitment

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Data-driven distributionally robust optimization of shale gas supply chains under uncertainty

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AICHE JOURNAL 2019年第3期65卷 947-963页

作者： Gao, Jiyao Ning, Chao You, Fengqi Cornell Univ Robert Frederick Smith Sch Chem & Biomol Engn Ithaca NY 14853 USA

This article aims to leverage the big data in shale gas industry for better decision making in optimal design and operations of shale gas supply chains under uncertainty. We propose a two-stage distributionally robust optimization model, where uncertainties associated with both the upstream shale well estimated ultimate recovery and downstream market demand are simultaneously considered. In this model, decisions are classified into first-stage design decisions, which are related to drilling schedule, pipeline installment, and processing plant construction, as well as second-stage operational decisions associated with shale gas production, processing, transportation, and distribution. A data-driven approach is applied to construct the ambiguity set based on principal component analysis and first-order deviation functions. By taking advantage of affine decision rules, a tractable mixed-integer linear programming formulation can be obtained. The applicability of the proposed modeling framework is demonstrated through a small-scale illustrative example and a case study of Marcellus shale gas supply chain. Comparisons with alternative optimization models, including the deterministic and stochastic programming counterparts, are investigated as well. (c) 2018 American Institute of Chemical Engineers AIChE J, 65: 947-963, 2019

关键词： data-driven distributionally robust optimization shale gas supply chain uncertainty mixed-integer linear programming

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Two continuous-time assignment-based models for the multi-mode resource-constrained project scheduling problem

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 129卷 346-353页

作者： Gnaegi, Mario Rihm, Tom Zimmermann, Adrian Trautmann, Norbert Univ Bern Dept Business Adm Schuetzenmattstr 14 CH-3012 Bern Switzerland

In the multi-mode resource-constrained project scheduling problem, a set of precedence-related project activities and, for each activity, a set of alternative execution modes are given. Each activity requires some time and some scarce resources during execution;these requirements depend on the selected execution mode. Sought is a project schedule, i.e, a start time and an execution mode for each activity, such that the project makespan is minimized. In the literature, besides a large variety of specific solution approaches, several mixed-integer linear programming (MILP) models have been proposed for this problem. We present two novel MILP models that are based on mode-selection, resource-assignment and sequencing variables;we enhance the performance of the models by eliminating some symmetric solutions from the search space and by adding some redundant sequencing constraints for pairs and for triples of activities that cannot be processed in parallel. In a comparison with reference models from the literature, it turned out that the advantages of the novel models are a simple structure, an enhanced flexibility, and a superior performance when the range of the activities' durations is relatively large.

关键词： Operations research mixed-integer linear programming Multi-mode resource-constrained project scheduling

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Optimal mathematical programming for the warehouse location problem with Euclidean distance linearization

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 136卷 70-79页

作者： You, Meng Xiao, Yiyong Zhang, Siyue Yang, Pei Zhou, Shenghan Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China

The warehouse location problem (WLP) involves determining one (or multiple) locations as the materials/products collecting/distributing centers for serving a group of customers scattered geographically in a region, at a minimum total transportation cost. The most conventional and widely used approach for solving the WLP is the weighted k-means algorithm. However, this is not a global approach, because it always traps into local optima and is sensitive to the initial settings. Our numeric examples demonstrated that the solutions obtained by the weighted k-means could depart from the optimal values by as much as 16.8% on average. In this paper, we present an optimal programming approach based on mixed-integer linear programming (MILP) for the WLP, which is irrelative to the initial solution and can be optimally solved by commercial solvers. For large-sized datasets, we developed an MILP-based dynamic iterative partial optimization (MILP-DIPO) to search for the near-optimal results with controllable computational time. Experiments on 14 datasets, including 6 small-sized synthesized datasets and 8 variants of the known benchmark datasets in the UEF repository, were performed to validate the proposed model and heuristics. The computational results confirm that improvements with the proposed method could be as great as -22.9% (-14.0% on average) for small-sized datasets. For the eight benchmark datasets, the MILP-DIPO algorithm delivered near-optimal solutions in a reasonable computational time, with up to - 8.0% (- 2.6% on average) improvement compared to the results obtained by the conventional weighted k-means algorithm.

关键词： Warehouse location problem mixed-integer linear programming Clustering Optimization

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Optimization model for long-distance integrated transmission of wind farms and pumped-storage hydropower plants

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APPLIED ENERGY 2019年 242卷 285-293页

作者： Su, Chengguo Cheng, Chuntian Wang, Peilin Shen, Jianjian Wu, Xinyu Dalian Univ Technol Inst Hydropower & Hydroinformat Dalian 116024 Peoples R China Yellow River Conservancy Tech Inst Sch Hydraul Engn Kaifeng 475004 Peoples R China

Long-distance and large-capacity wind power transmission from western and northern China to load centers through ultra-high voltage (UHV) transmission lines is an important and effective measure for large-scale wind power accommodation in China. This paper proposes a novel practical integrated transmission mode for a wind farm (WF) and a pumped-storage hydropower plant (PSHP). In order to cope with additional imbalance costs caused by deviations from the submitted day-ahead schedule and increase the total operational profit, an optimization model for the integrated transmission of a WF and a PSHP has been formulated. The forecast errors for wind power generation are modeled by a scenario analysis technique. Hydraulic constraints and the head effect on the performance of each pumped-storage hydropower unit (PSHU) in both generating and pumping modes have been considered. Through incorporating the nonlinearities, including constraints on the operation status of units and turbine performance curves, the model is converted into a mixed-integer linear programming (MILP) formulation using piecewise linear approximation techniques. Case studies demonstrate that the integrated transmission of the WF and PHSP can efficiently alleviate the negative effect of wind power fluctuations and significantly increase profit. Sensitivity analyses have been carried out to evaluate the effect of the penalty factor for energy imbalance and the electricity price mechanism in the receiving-end power grid on the overall performance of the WF-PSHP union.

关键词： Integrated transmission Wind farm Pumped-storage hydropower plant Wind power forecast errors mixed-integer linear programming

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Tackling perishability in multi-level process industries

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2019年第17期57卷 5604-5623页

作者： Wei, Wenchao Amorim, Pedro Guimaraes, Luis Almada-Lobo, Bernardo Beijing Jiaotong Univ Sch Econ & Management Beijing Peoples R China Beijing Jiaotong Univ Beijing Key Lab Logist Management & Technol Beijing Peoples R China Univ Porto Fac Engn INESC TEC Rua Doutor Roberto Frias 378 P-4200465 Porto Portugal

The classical multi-level lot-sizing and scheduling problem formulations for process industries rarely address perishability issues, such as limited shelf lives of intermediate products. In some industries, ignoring this specificity may result in severe losses. In this paper, we start by extending a classical multi-level lot-sizing and scheduling problem formulation (MLGLSP) to incorporate perishability issues. We further demonstrate that with the objective of minimising the total costs (purchasing, inventory and setup), the production plans generated by classical models are often infeasible under a setting with perishable products. The model distinguishes different perishability characteristics of raw materials, intermediates and end products according to various industries. Finally, we provide quantitative insights on the importance of considering perishability for different production settings when solving integrated production planning and scheduling problems.

关键词： Multi-level production process perishability production modelling lot-sizing and scheduling mixed-integer linear programming

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A strategy for transmission network expansion planning considering multiple generation scenarios

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ELECTRIC POWER SYSTEMS RESEARCH 2019年 172卷 22-31页

作者： Freitas, Patricia F. S. Macedo, Leonardo H. Romero, Ruben Sao Paulo State Univ Dept Elect Engn Ave Brasil 56 BR-15385000 Ilha Solteira SP Brazil

Transmission network expansion planning (TNEP) is subject to several constraints that are driven by new market rules, such as generation uncertainty, demand growth, seasonal climate changes and technological advances. When considering multiple generation scenarios, due to different operational conditions, it is necessary to find an expansion plan that allows the expanded system to operate properly for each scenario. This paper presents a strategy that provides several expansion plans that operate efficiently in all the previously defined generation scenarios and that keep reasonable values for the expansion costs. Thus, the developed mathematical model allows us to find adequate expansion plans with expansion costs that can be controlled, with small infeasibilities in the operation of the system that may not be significant in the long-term planning. The proposed approach is applied to the 24-bus system and the Colombian 93-bus system. The results indicate that the plans obtained using the proposed strategy present much lower expansion costs compared to the conventional TNEP model with multiple generation scenarios, with very low infeasibilities in the operation of the system, which can be corrected in the short-term expansion planning.

关键词： Generation scenarios mixed-integer linear programming Power systems optimisation Transmission network expansion planning

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