We prove that the Cohn–Elkies linear programming bound for sphere packing is not sharp in dimension 6. The proof uses duality and optimization over a space of modular forms, generalizing a construction of Cohn–Trian...
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In this letter, we discuss the problem of optimal control for affine systems in the context of data-driven linear programming. First, we introduce a unified framework for the fixed point characterization of the value ...
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Designing sustainable, cross-sectoral energy supply systems is a challenging task. A widespread and proven planning approach is mathematical optimization and in particular mixed-integer linear programming (MILP). Whil...
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Designing sustainable, cross-sectoral energy supply systems is a challenging task. A widespread and proven planning approach is mathematical optimization and in particular mixed-integer linear programming (MILP). While numerous MILP models have been presented in literature, there is no convention which level of detail is necessary to obtain reliable energy system designs. In this paper, a systematic performance comparison of 24 MILP models for designing multi-energy systems is conducted. The models include different combinations of five widely used model features: Piece-wise linear investment curves, multiple component resolution, minimum partload limitations, part-load efficiencies, and start-up costs. The operational performances of the optimal system designs are compared by using a unit commitment optimization with high level of detail. In a district heating case study, the total annualized costs of the unit commitment optimizations differ substantially from 391 to 481 kEUR and the computation times of the design optimizations range from 10 s to more than 10 h. Models that consider part-load efficiencies lead to the lowest system costs but the highest computation times. In addition, simple design heuristics are identified which lead in combination with fast-solving linear models to energy systems with low total annualized costs (410 kEUR, 5% cost increase).
The presence of wind farms in network makes short-circuit currents (SCC) seen by overcurrent relays (OCR) have not fixed magnitudes and contain transient states. The transient SCC changes real operating times of OCRs ...
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The presence of wind farms in network makes short-circuit currents (SCC) seen by overcurrent relays (OCR) have not fixed magnitudes and contain transient states. The transient SCC changes real operating times of OCRs compared with calculated operating times, in addition to, mis-coordination between primary and backup OCRs. In this paper, a new method based on the dynamic model of OCR is proposed for considering the effects of transient SCCs of wind farms on the coordination of OCRs, and determining proper settings for them. Since the proposed optimization problem is highly complex and nonlinear, and also uses large amount of data, the intelligent optimization algorithms have not sufficient ability for utilizing in the proposed method. Therefore, the proposed coordination method is formulated as a binary linear programming (BLP)-based approach. It is also shown that the speed of proposed BLP-based method can be increased by removing some unusable variables. It is found from simulation results and hardware tests that the proposed method has better performance in comparison with conventional coordination methods.
In order to make the color of image display more realistic, optimize the use of energy, and improve the light efficiency of the module through reasonable spectral distribution, this paper proposes a backlight spectral...
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In order to make the color of image display more realistic, optimize the use of energy, and improve the light efficiency of the module through reasonable spectral distribution, this paper proposes a backlight spectral optimization algorithm based on linear programming. With the goal of maximizing the backlight luminous efficiency, the theoretical maximum of the luminous efficiency of the backlight spectrum can be achieved by constructing a linear programming model. The research process is to obtain the optimal distribution of transmittance spectrum by linear programming method on the premise of ensuring the color gamut standard of display system. The results show that the light efficiency can be increased to 335.5lm/W, while the original light efficiency of the system is less than 150lm/W. With the goal of maximizing the light efficiency, light sources with narrow bandwidths such as lasers and quantum dot materials can be used to simulate and reconstruct these characteristic wavelengths. There will be easier to approach the ideal optimization spectrum and achieve the theoretical maximum luminous efficiency of 610.8lm/W.
Quantifying extra functions, herein referred to as outcome functions, over optimal solutions of an optimization problem can provide decision makers with additional information on a system. This bears more importance w...
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Quantifying extra functions, herein referred to as outcome functions, over optimal solutions of an optimization problem can provide decision makers with additional information on a system. This bears more importance when the optimization problem is subject to uncertainty in input parameters. In this paper, we consider linear programming problems where input parameters are described by real-valued intervals, and we address the outcome range problem which is the problem of finding the range of an outcome function over all possible optimal solutions of a linear program with interval data. We give a general definition of the problem and then focus on a special class of it where uncertainty occurs only in the right-hand side of the underlying linear program. We show that our problem is computationally hard to solve and also study some of its theoretical properties. We then develop two approximation methods to solve it: a local search algorithm and a super set based method. We test the methods on a set of randomly generated instances. We also provide a real case study on healthcare access measurement to show the relevance of our problem for reliable decision making. (C) 2020 Elsevier Ltd. All rights reserved.
Effective energy management has become more prominent with the population growth and increasing industrialization. In order to provide an effective planning, it is important to use all kinds of renewable energy source...
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Effective energy management has become more prominent with the population growth and increasing industrialization. In order to provide an effective planning, it is important to use all kinds of renewable energy sources efficiently. The main renewable energy sources are solar, geothermal, biomass, hydropower and wind. Agricultural, forest, animal and municipal solid wastes are considered as potential resources for biomass. However, the uncertainty in the obtained amount of biomass complicates the related facility investment decisions. Fuzzy linear programming is quite useful to handle impreciseness in resource availability constraints. Therefore, in this paper, the most suitable locations for waste-to energy power plants in Central Anatolia Region of Turkey are investigated by using a fuzzy linear programming model. It is concluded that Ankara, Konya, Kayseri, Eskisehir, Sivas and Aksaray are the most suitable cities for the investment. (c) 2021 Elsevier Ltd. All rights reserved.
Optimization algorithms are tools used in the planning and operations of renewable energy-based distributed power systems. Mixed integer linear programming as a classical optimization method is considered in the liter...
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Optimization algorithms are tools used in the planning and operations of renewable energy-based distributed power systems. Mixed integer linear programming as a classical optimization method is considered in the literature for sizing nanogrid systems due to simplicity and speed. However, the method has limited capabilities in implementing multi-configurational analysis and requires large formulations. In this paper, nested integer linear programming is proposed to decompose the large formulations and simplify the multi-configurational sizing of residential nanogrid in a semiarid zone. The proposed method is aimed at optimal sizing for energy cost reduction and increased supply availability. The method is implemented in multi-stage hybridization of relaxation and integer methods of linear programming to achieve optimal sizes of the nanogrid components using photovoltaics, wind turbines, and battery. The method realizes $72,343 net present cost and 0.3755 $/kWh levelized cost of energy indicating 33% and 11% reductions compared to mixed integer linear programming and particle swarm optimization. System availability of 99.97% is envisaged to achieve 6677-7782 kWh per capita electricity consumption in residential buildings against the existing 150 kWh. Three configurations analyzed indicated the robustness of the proposed method and the multi-configurational designs clarify options against factors such as space, logistics, or policies.
Classical planning in Artificial Intelligence is a computationally expensive problem of finding a sequence of actions that transforms a given initial state of the problem to a desired goal situation. Lack of informati...
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Classical planning in Artificial Intelligence is a computationally expensive problem of finding a sequence of actions that transforms a given initial state of the problem to a desired goal situation. Lack of information about the initial state leads to conditional and conformant planning that is more difficult than classical one. A parallel plan is the plan in which some actions can be executed in parallel, usually leading to decrease of the plan execution time but increase of the difficulty of finding the plan. This paper is focused on three planning problems which are computationally difficult: conditional, conformant and parallel conformant. To avoid these difficulties a set of transformations to linear programming Problem (LPP), illustrated by examples, is proposed. The results show that solving LPP corresponding to the planning problem can be computationally easier than solving the planning problem by exploring the problem state space. The cost is that not always the LPP solution can be interpreted directly as a plan.
作者：
Jeong, DaeinYoshioka, KatsuheiJeong, HoonyoungMin, BaehyunSchlumberger
Reservoir & Prod Engn Team Digital & Integrat Chuo Ku 6th Floor3-9 Nihonbashi 3 Chome Tokyo 1030027 Japan SODECO
Minato Ku Toranomon 2 Chome Tower2-3-17 Toranomon Tokyo 1050001 Japan Seoul Natl Univ
Dept Energy Resources Engn Gwanak Ro 1 Seoul 08826 South Korea Seoul Natl Univ
Res Inst Energy & Resources Seoul 08826 South Korea Seoul Natl Univ
Inst Engn Res Seoul 08826 South Korea Ewha Womans Univ
Dept Climate & Energy Syst Engn Off 404Res Cooperat Bldg52 Ewhayeodae Gil Seoul 03760 South Korea
The present study examines the sequential optimization of total lifting-gas allocation into multiple producing wells via linear programming for the maximization of oil production in a mature field located in Russia. B...
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The present study examines the sequential optimization of total lifting-gas allocation into multiple producing wells via linear programming for the maximization of oil production in a mature field located in Russia. Because the injection rate of lifting gas depends on the well production status, the ten-year gas lift schedule is determined by performing the gas lift optimization on a sequential, bi-weekly basis. For each short-term production optimization, the linear programming is solved by considering four operating parameters (e.g., tubing head pressure, bottom hole pressure, oil production rate, and gas lifting rate) which are calculated via high-fidelity reservoir simulation runs. Compared to the constant rate injection case, the optimal injection case increases the cumulative oil production by 14% at the same expenditure. These numerical results indicate that the sequential short-term optimization of the gas lift can be a practical approach towards boosting the asset value of a mature oil field with consideration for the well production status.
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