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MILP model for integrated expansion planning of multi-carrier active energy systems

IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第7期13卷 1177-1189页

作者： Ghasemi, Hosein Aghaei, Jamshid Gharehpetian, Gevork Babamalek Safdarian, Amir Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Amirkabir Univ Technol Elect Engn Dept Tehran Iran Sharif Univ Technol Elect Engn Dept Tehran Iran

In growing economic energy systems, the interdependency of various energy infrastructures has led to a change in countries' policies in their expansion planning of energy networks. In this study, a mixed-integer non-linear programming (MINLP) model is proposed for expansion planning of the multi-carrier systems including electricity and gas distribution networks. The optimal planning determines the best location, time, and alternative for network assets in order to minimise investment costs and reduce losses. Also, as another distinctive feature of this study, given the integration of electricity and gas distribution networks and the complexity of the problem, a new MILP model using linearisation methods is presented. In this planning model, several types of alternative plans and a set of candidates for the new placements or increase of the capacity of transformers, feeders, distributed generation, gas pipelines, and city gate stations are considered. The proposed MILP model provides the convergence of the problem to a global optimum response using the powerful commercial software. In addition, a solution based on Benders decomposition algorithm is finally proposed to reduce the solution time of the problem. Finally, the efficiency of the proposed model is evaluated by means of some simulation results.

关键词： natural gas technology investment power generation planning distribution networks integer programming power transmission planning pipelines linear programming nonlinear programming energy infrastructures countries energy networks mixed-integer nonlinear programming gas distribution networks optimal planning network assets electricity MILP model planning model alternative plans distributed generation gas pipelines integrated expansion planning multicarrier active energy systems economic energy systems

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Tiered Cloud Storage via Two-Stage, Latency-Aware Bidding

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2019年第1期16卷 176-191页

作者： Zhang, Yang Ghosh, Arnob Aggarwal, Vancet Lan, Tian Purdue Univ Sch Ind Engn W Lafayette IN 47907 USA George Washington Univ Dept Elect & Comp Engn Washington DC 20052 USA

In cloud storage, the digital data is stored in logical storage pools, backed by heterogeneous physical storage media and computing infrastructure that are managed by a cloud service provider (CSP). One of the key advantages of cloud storage is its elastic pricing mechanism, in which the users need only pay for the resources/services they actually use, e.g., depending on the storage capacity consumed, the number of file accesses per month, and the negotiated service level agreement. To balance the tradeoff between service performance and cost, CSPs often employ different storage tiers, for instance, cold storage and hot storage. Storing data in hot storage incurs high storage cost yet delivers low access latency, whereas cold storage is able to inexpensively store massive amounts of data and thus provides lower cost with higher latency. In this paper, we address a major challenge confronting the CSPs utilizing such tiered storage architecture-how to maximize their overall profit over a variety of storage tiers that offer distinct characteristics, as well as file placement and access request scheduling policies. To this end, we propose a scheme where the CSP offers a twostage auction process for: 1) requesting storage capacity and 2) requesting accesses with latency requirements. Our two-stage bidding scheme provides a hybrid storage and access optimization framework with the objective of maximizing the CSP's total net profit over four dimensions: file acceptance decision, placement of accepted files, file access decision and access request scheduling policy. The proposed optimization is a mixed-integer nonlinear program that is hard to solve. We propose an efficient heuristic to relax the integer optimization and to solve the resulting nonlinear stochastic programs. The algorithm is evaluated under different scenarios and with different storage system parameters, and insightful numerical results are reported by comparing the proposed approach with other profit-maxim

关键词： Cloud storage latency dependent pricing two-stage bidding mixed-integer nonlinear programming

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An income over feed cost nutritional grouping strategy

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JOURNAL OF DAIRY SCIENCE 2019年第5期102卷 4682-4693页

作者： Wu, Y. Liang, D. Shaver, R. D. Cabrera, V. E. Univ Wisconsin Madison Dept Dairy Sci Madison WI 53705 USA

This study introduces a new nutritional grouping method, OptiGroup, which maximizes milk income over feed cost (IOFC) using a mixed-integer nonlinear programming optimization algorithm. Analyses compared the OptiGroup with the cluster method, the current state-of-the-art nutritional grouping technique. Analyses were performed using cow-level data from 7 Wisconsin dairy farms. Consistently, the OptiGroup and the cluster were constrained to group cows simultaneously into 2 (low and high nutrient requirements) and 3 (low, medium, and high nutrient requirements) same-size groups. Each diet satisfied the net energy (NEL) and crude protein (CP) requirements of approximately 83% of the cows in each group by using lead factors based on nutrient density. A control treatment (1-group scenario) was used as a baseline for comparisons. The IOFC, dietary nutrient densities (NEL and CP), and dry matter intake with both methods were computed and compared. The percentage of cows grouped differently and the percentages of primiparous cows and late-lactation (> 200 d in milk) cows in each group were also analyzed. Results were as follows: (1) average extra IOFC of $8/cow per yr (2-group) and $12/cow per yr (3-group) by switching from cluster to OptiGroup method;(2) difference between dietary nutrient densities of the groups were reduced under OptiGroup method compared with cluster (i.e., NEL differences in 2 groups were 0.20 Mcal/kg for the cluster vs. 0.11 Mcal/kg for OptiGroup);(3) dry matter intake decreased with increasing group numbers within a grouping method, and decreased from cluster to OptiGroup method with constant group numbers;(4) percentage of primiparous cows was greater in the low group of cluster and in the high group of OptiGroup;and (5) proportion of late-lactation cows tended to be greater in the low group in both grouping strategies. Results indicated that the OptiGroup performed economically better than the cluster because of nutrient savings, even with high feed

关键词： cluster diet formulation precision feeding mixed-integer nonlinear programming

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A generalized short-term unit commitment approach for analyzing electric power and natural gas integrated systems

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ELECTRIC POWER SYSTEMS RESEARCH 2019年 172卷 63-76页

作者： Tovar-Ramirez, C. A. Fuerte-Esquivel, C. R. Martinez Mares, A. Sanchez-Garduno, J. L. Univ Michoacana Morelia Michoacan Mexico

This paper proposes a generalized short-term unit commitment approach for optimizing the hourly thermal generation scheduling by considering the existing interdependence between natural gas and electricity infrastructures. This generalized approach is formulated as a mixed-integer nonlinear optimization problem where a multi-period optimal gas and alternating current power flow model is incorporated into the thermal unit commitment problem. Contrary to all other proposals that ignore the reactive power dispatch and alternating current electricity network constraints for solving the unit commitment problem that considers the integrated electricity-natural gas system, the proposed solution approach simultaneously co-optimizes the active and reactive power scheduling and dispatch, while taking into account the set of physical and operational constraints associated with each infrastructure. This set of constraints includes reactive power generation limits, nodal voltage magnitude limits and dynamic gas flow constraints. The solution is obtained by decomposing this generalized optimization problem into two mutually connected optimization subproblems: one spatially separable related to the unit commitment problem and the other temporally separable associated with the unified co-optimization of both energy systems, which are sequentially solved until the same hourly generation dispatch in both subproblems is obtained. Hence, this solution is optimal for the unit commitment problem and the optimal gas and power flow problem. Study cases on two multi-energy systems are presented to numerically illustrate the suitability and main characteristics of the proposed approach.

关键词： Integrated gas and electricity networks mixed-integer nonlinear programming Power systems operation Security-constrained unit commitment

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Heat integration and heat recovery steam cycle optimization for a low carbon lignite/biomass-to-jet fuel demonstration project

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APPLIED ENERGY 2019年 239卷 1322-1342页

作者： Elsido, Cristina Martelli, Emanuele Kreutz, Thomas Politecn Milan Dept Energy Via Lambruschini 4 I-20156 Milan Italy Princeton Univ Andlinger Ctr Energy & Environm 86 Olden St Princeton NJ 08544 USA

This paper reports the heat integration study for a demonstration plant to co-process lignite and woody biomass into jet fuel with CO2 capture and storage. Since all the main process reactions are exothermic and convert approximately 65% of the feedstock chemical energy into heat, designing an efficient heat recovery steam cycle and heat exchanger network is essential for the overall thermo-economic performance. Different integration options for the plant's heat recovery steam cycle are analyzed and compared, considering costs and the key technical limitations. The design of the heat recovery steam cycle and heat exchanger network is optimized with an energy targeting methodology, a sequential synthesis method and a recently proposed simultaneous methodology. Given the high specific costs of the units caused by the novelty and small size and of the demonstration plant, the techno-economic optimization returns solutions with considerably lower efficiency (up to -5% percentage points) and power output (up to -18%) compared to the energy targeting methodology. The difference in optimal HRSC designs and performance are minor (less than -2% power output) for full-scale plants based on mature technologies.

关键词： Heat recovery steam cycle Heat exchanger network Polygeneration mixed-integer nonlinear programming Utility Fischer-Tropsch

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An improved series-parallel optimization approach for cooling water system

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APPLIED THERMAL ENGINEERING 2019年 154卷 368-379页

作者： Liu, Shiqi Song, Jinchun Shi, Jia Yang, Bin Northeastern Univ Sch Mech Engn & Automat Shenyang 110819 Liaoning Peoples R China Shanghai Baosteel Co Shanghai 201900 Peoples R China

Conventional cooling networks are usually operated in a parallel configuration. Although this configuration is easy operated, it results in inefficient use of water. A series configuration can effectively reduce water flow rate, while its arrangement is relatively complex, incurring more operating and capital cost. This paper proposes a water and energy conservation strategy to save energy cost and to simplify the network structure. Heat exchangers are arranged in series-parallel configuration to reduce the water flow rate for water conservation, and two kinds of installation conditions with or without static pressure drop are taken into account for energy conservation. A main-auxiliary pump structure is applied to reduce the operating consumption. The number of reuse branch for the economic performance is analyzed. mixed-integer nonlinear programming based on a superstructure description is formulated by considering the configuration of pumps and heat exchanger networks simultaneously. Two case studies are employed to demonstrate the effectiveness of the proposed approach. The results show that the improved series-parallel configuration approach can save 10.9% of the total cost compared with the two-step sequential optimization and 4.7% compared with the simultaneous optimization method.

关键词： Cooling water system Series-parallel configuration Static pressure drop Reuse branch mixed-integer nonlinear programming

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Reformulation, linearization, and decomposition techniques for balanced distributed operating room scheduling

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2020年第0期93卷 102043-000页

作者： Roshanaei, Vahid Luong, Curtiss Aleman, Dionne M. Urbach, David R. Univ Toronto Rotman Sch Management 105 St George Toronto ON M5S 3E6 Canada Univ Toronto Dept Mech & Ind Engn 5 Kings Coll Rd Toronto ON M5S 3G8 Canada Univ Toronto Inst Hlth Policy Management & Evaluat 155 Coll StSuite 425 Toronto ON M5S 3E3 Canada Univ Hlth Network Techna Inst 124-100 Coll St Toronto ON M5G 1P5 Canada Univ Hlth Network Toronto Gen Hosp Div Gen Surg 200 Elizabeth St Toronto ON M5G 2C4 Canada

We study the balanced distributed operating room (OR) scheduling (BDORS) problem as a location allocation model, encompassing two levels of balancing decisions: (i) daily macro imbalance among collaborating hospitals in terms of the number of allocated ORs and (ii) daily micro imbalance among open ORs in each hospital in terms of the total caseload assigned. BDORS is formulated as a novel mixed integer nonlinear programming (MINLP) in which the macro and micro imbalance are penalized using absolute value and quadratic functions. We develop various reformulation-linearization techniques (RLTs) for the MINLP models, leading to three mathematical modelling variants: (i) a mixed-integer quadratically constrained program (MIQCP) and (ii) two mixed-integer programs (MIPs) for the absolute value penalty function and an MIQCP for the quadratic penalty function. Two novel exact techniques based on reformulation-decomposition techniques (RDTs) are developed to solve these models: a uni- and a bi-level logic-based Benders decomposition (LBBD). We motivate the LBBD methods with an application to BDORS in the University Health Network (UHN), consisting of three collaborating hospitals: Toronto General Hospital, Toronto Western Hospital, and Princess Margaret Cancer Centre in Toronto, Ontario, Canada. The uni-level LBBD method decomposes the model into a surgical suite location, OR allocation, and macro balancing master problem (MP) and micro OR balancing sub-problems (SPs) for each hospital-day. The bi-level approach uses a relaxed MP, consisting of a surgical suite location and relaxed allocation/macro balancing MP and two optimization SPs. The primary SP is formulated as a bin-packing problem to allocate patients to open operating rooms to minimize the number of ORs, while the secondary SP is the uni-level micro balancing SP. Using UHN datasets consisting of two datasets, hard MP/easy SPs and easy MP/hard SPs, we show that both LBBD approaches and both MIP models solved via Gu

关键词： Healthcare Operating room scheduling Balanced location-allocation Large scale optimization Multi-level decomposition Logic-based Benders balancing cuts mixed-integer nonlinear programming

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Evaluating the Economic Benefits of a Smart-Community Microgrid with Centralized Electrical Storage and Photovoltaic Systems

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ENERGIES 2020年第7期13卷 1764页

作者： Arkhangelski, Jura Siano, Pierluigi Mahamadou, Abdou-Tankari Lefebvre, Gilles Univ Paris Est Univ Res Inst Creteil Vitry Ctr Studies & Thermal Environm & Syst Res 61 Gen Gaulle Ave F-94000 Creteil France Univ Salerno Dept Management & Innovat Syst I-84084 Fisciano Italy

In this paper, an innovative method for managing a smart-community microgrid (SCM) with a centralized electrical storage system (CESS) is proposed. The method consists of day-ahead optimal power flow (DA OPF) for day-ahead SCM managing and its subsequent evaluation, considering forecast uncertainties. The DA OPF is based on a data forecast system that uses a deep learning (DL) long short-term memory (LSTM) network. The OPF problem is formulated as a mathematical mixed-integer nonlinear programming (MINLP) model. Following this, the developed DA OPF strategy was evaluated under possible operations, using a Monte Carlo simulation (MCS). The MCS allowed us to obtain potential deviations of forecasted data during possible day-ahead operations and to evaluate the impact of the data forecast errors on the SCM, and that of unit limitation and the emergence of critical situations. Simulation results on a real existing rural conventional community endowed with a centralized community renewable generation (CCRG) and CESS, confirmed the effectiveness of the proposed operation method. The economic analysis showed significant benefits and an electricity price reduction for the considered community if compared to a conventional distribution system, as well as the easy applicability of the proposed method due to the CESS and the developed operating systems.

关键词： microgrid deep learning optimal power flow mixed-integer nonlinear programming long short-term memory Monte Carlo simulation centralized electrical storage

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Decarbonizing existing coal-fired power stations considering endogenous technology learning: A Turkish case study

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JOURNAL OF CLEANER PRODUCTION 2020年 261卷 121100-121100页

作者： Aliabadi, Danial Esmaeili Sabanci Univ Fac Engn & Nat Sci Istanbul Turkey Sabanci Univ Syst Modelling & Decis Analyt Lab Istanbul Turkey Istanbul Int Ctr Energy & Climate IICEC Istanbul Turkey

Despite environmental threats, coal is expected to remain a remarkable energy source as the share of intermittent renewable sources in the energy mix grows;hence, managing pollutants such as carbon dioxide (CO2) is critical while pushing for clean alternatives. Carbon capture, utilization, and storage (CCUS) is a set of methods that removes CO2 from emissions and stores it safely for the long-term. As it stands now, CCUS is prohibitively expensive;however, learning-by-doing is a recognized phenomenon that assists novel technologies to become more affordable as employed continuously. Thus, for accurate long-term planning, this study investigates the impact of early adopters on CCUS market diffusion considering endogenous technology learning (ETL). A mixed-integer nonlinear programming model (MINLP) is developed which determines the optimal capacity and deployment time of post-combustion carbon capture units and the optimal sourceesink matching in the CO2 supply chain, taking into account techno-economic constraints and regulations. The MINLP model is transformed into a series of mixed-integer linear programming models using a Stackelberg game approach and solved with a diagonalization method. Equilibrium solutions of the proposed model were obtained for independent and coordinated actions of players. The proposed model is applied to the Turkish energy market with multiple CO2 sources and sinks, where a cap-and-trade program is presumed to be in place. While the study demonstrates the interplay between CCUS and ETL in Turkey, the proposed model is universal, and it can be applied to other countries if the inputs are available. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Decarbonization Post-combustion Endogenous technology learning Source-sink matching mixed-integer nonlinear programming

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Additive manufacturing capacity allocation problem over a network

A I I E Transactions

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A I I E Transactions 2023年第8期55卷

作者： Taner Cokyasar Mingzhou Jin a Department of Industrial and Systems Engineering The University of Tennessee Knoxville TN USAb Transportation and Power Systems Division Argonne National Laboratory Lemont IL USAc Department of Industrial Engineering Tarsus University Takbas Mersin Turkey a Department of Industrial and Systems Engineering The University of Tennessee Knoxville TN USA

The use of Additive Manufacturing (AM) for low demand volumes, such as spare parts, has recently attracted considerable attention from researchers and practitioners. This study defines the AM Capacity Allocation Problem (AMCAP) to design an AM supply network and choose between printing upon demand and sourcing through an alternative option for each part in a given set. A mixed-integer nonlinear program was developed to minimize the production, transportation, alternative sourcing, and lead time costs. We developed a cut generation algorithm to find optimal solutions in finite iterations by exploring the convexity of the nonlinear waiting time for AM products at each AM facility. Numerical experiments show the effectiveness of the proposed algorithm for the AMCAP. A case study was conducted to demonstrate that the optimal AM deployment can save almost 20% of costs over situations that do not use any AM. The case also shows that AM can realize its maximum benefits when it works in conjunction with an alternative option, e.g., inventory holding, and its capacity is strategically deployed. Since AM is a new technology and is rapidly evolving, this study includes a sensitivity analysis to see the effects of improved AM technology features, such as machine cost and build speed. When the build speed increases, the total cost decreases quickly, but the number of AM machines will increase first then decrease later when more parts are assigned to the AM option.

关键词： Additive manufacturing capacity planning network optimization mixed-integer nonlinear programming

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