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Solver-Based mixed integer linear programming (MILP) Based Novel Approach for Hydroelectric Power Generation Optimization

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IEEE ACCESS 2020年 8卷 174880-174892页

作者： Kumar, Jasvinder Khalil, Ihsan Ullah Ul Haq, Azhar Perwaiz, Aqib Mehmood, Khalid Neelam Jhelum Hydropower Co Water & Power Dev Author Muzaffarabad 13100 Pakistan Natl Univ Sci & Technol NUST Coll Elect & Mech Engn Rawalpindi 44000 Pakistan De Montfort Univ Emerging Technol Res Ctr EMTERC Leicester LE1 9BH Leics England

In Pakistan, hydroelectric power is one of the reliable sources of electricity with a capacity of 8,713 MW, which is 29% of the total energy mix. Hence, with such a vast resource capacity of hydroelectric power, its optimization and dispatch planning will have a great significance. This research work discusses the importance of hydroelectric power generation planning for both storage and run-of-river (ROR) hydropower plants, as well a;a solver-based optimization technique is proposed for the first time to resolve the intricate job of generation planning for hydroelectric power plants in MATLAB. A mathematical-optimization model is also developed, which uses a mixed-integer linear-programming (MILP) algorithm, based on the objective function of profit maximization, which considers a random varying revenue plan as model input. Three hydropower generators of different capacities and efficiencies are considered for the optimization problem. MILP based solution is proposed for both storage and ROR hydropower plants with two dispatch schedules, i.e., Normal dispatch schedule and optimum dispatch schedule. The objective functions are solved, and the profit (in dollars) from each dispatch schedule is calculated and compared. The preliminary optimization results show an increase of $22,000 and $29,130 in the profit for storage and ROR hydropower plants, respectively, which is 19% more than average income. Hence, ensuring the credibility of the proposed algorithm for maximizing the revenue ($), is aimed to facilitate and assist better planning for electric power producers.

关键词： Power systems Optimization Hydroelectric power generation Planning Uncertainty Reliability Power generation planning hydroelectric power system MATLAB mixed integer linear programming mathematical optimization model water storage dams run-of-river

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Comparative Assessment of Priority Listing and mixed integer linear programming Unit Commitment Methods for Non-Interconnected Island Systems

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ENERGIES 2019年第4期12卷 657-657页

作者： Psarros, Georgios N. Papathanassiou, Stavros A. NTUA Sch Elect & Comp Engn Athens 15780 Greece

The generation management concept for non-interconnected island (NII) systems is traditionally based on simple, semi-empirical operating rules dating back to the era before the massive deployment of renewable energy sources (RES), which do not achieve maximum RES penetration, optimal dispatch of thermal units and satisfaction of system security criteria. Nowadays, more advanced unit commitment (UC) and economic-dispatch (ED) approaches based on optimization techniques are gradually introduced to safeguard system operation against severe disturbances, to prioritize RES participation and to optimize dispatch of the thermal generation fleet. The main objective of this paper is to comparatively assess the traditionally applied priority listing (PL) UC method and a more sophisticated mixed integer linear programming (MILP) UC optimization approach, dedicated to NII power systems. Additionally, to facilitate the comparison of the UC approaches and quantify their impact on systems security, a first attempt is made to relate the primary reserves capability of each unit to the maximum acceptable frequency deviation at steady state conditions after a severe disturbance and the droop characteristic of the unit's speed governor. The fundamental differences between the two approaches are presented and discussed, while daily and annual simulations are performed and the results obtained are further analyzed.

关键词： autonomous power systems frequency constraints islands isolated grids mixed integer linear programming priority listing unit commitment

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New mixed integer linear programming models and an iterated local search for the clustered traveling salesman problem with relaxed priority rule

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JOURNAL OF COMBINATORIAL OPTIMIZATION 2023年第1期46卷 1-1页

作者： Doan, Thanh Tan Bostel, Nathalie Ha, Minh Hoang Nguyen, Vu Hoang Vuong Univ Nantes CNRS LS2N F-44000 Nantes France Phenikaa Univ Fac Comp Sci ORLab Hanoi 12116 Vietnam VNU Univ Engn & Technol Fac Informat Technol ORLab Hanoi Vietnam

The Traveling Salesman Problem (TSP) is a well known problem in operations research with various studies and applications. In this paper, we address a variant of the TSP in which the customers are divided into several priority groups and the order of servicing groups can be flexibly changed with a rule called the d-relaxed priority rule. The problem is called the Clustered Traveling Salesman Problem with Relaxed Priority Rule (CTSP-d). We propose two new mixed integer linear programming (MILP) models for the CTSP-d and a metaheuristic based on Iterated Local Search (ILS) with operators designed for or adapted to the problem. The experimental results obtained on the benchmark instances show that two new models performs better than previous ones, and ILS also proves its performance with 13 new best known solutions found and significant stability compared to existing metaheuristics.

关键词： Traveling Salesman Problem d-relaxed priority rule mixed integer linear programming Iterated local search

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Improving the manufacturability of highly materially restricted topology-optimized designs with mixed integer linear programming

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ENGINEERING STRUCTURES 2023年第1期284卷

作者： Carstensen, Josephine, V Kim-Tackowiak, Hajin Liang, Mia Y. MIT Dept Civil & Environm Engn 77 Massachusetts Ave Cambridge MA 02139 USA

This paper presents a new free-form topology optimization framework for highly materially restricted design situations. Highly materially restricted design situations are herein defined as being limited significantly by the combined demands on material use and manufacturability. The new framework uses the ground structure approach with frame elements and casts the design problem as a mixed integer linear Program (MILP), ensuring a solution with a manufacturable discrete material distribution. This paper also presents an extension to hybrid mesh ground structures containing both frame and thin solid elements. In both cases, the user can easily tailor the resultant design to discrete fabrication requirements as e.g. relevant when using an extrusion -based Additive Manufacturing (AM) process that requires the design features to adhere to a discrete number of bead depositions. The new framework is numerically demonstrated on topology optimization benchmark examples. In addition, numerical and experimental comparisons are made to the conventional density-based topology optimization approach (with continuous density variables). When the design is highly restricted, the new MILP framework is in most tested cases found to generate solutions with improved numerical predictions on the compliance. For the experimentally validated case study, the numerical prediction is seen to significantly underestimate the experimentally observed performance gain. The experimental investigation additionally cements the high performance of density-based topology optimization in design situations with low levels of design restrictions. In these cases (6 out of 7 tested herein), the solutions generated with density-based topology optimization have comparable or preferable performance.

关键词： Topology optimization Manufacturability Additive Manufacturing Frame structures Hybrid mesh mixed integer linear programming

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Integral techno-economic design & operational optimization for district heating networks with a mixed integer linear programming strategy

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ENERGY 2024年 308卷

作者： Rojer, Jim Janssen, Femke van der Klauw, Thijs van Rooyen, Jacobus TNO Oudewaalsdorperweg 63 NL-2597 AK The Hague Zuid Holland Netherlands TNO Leeghwaterstr 44 NL-2628 CA Delft Zuid Holland Netherlands

The Netherlands, with over 90% of homes heated by natural gas is currently in the early phases of the heat transition to find alternative solutions towards 2050. According to ambitions of the Dutch government up to 50% of future implemented heating systems in the built environment will use District Heat Networks. The investment-heavy nature for District Heating System (DHS) makes it challenging to establish viable business- cases as supply-side parties require security of demand. However, resident participation is lacking as there is no integral estimate for the cost of heat over the lifetime in the early planning phase. This paper proposes a network integral techno-economic optimization with minimal a-priori assumptions. An integral network cost optimization enables to achieve a considerably more reliable cost of heat in the early planning phase. Both the investments and operational strategy are optimized with a mixed integer linear programming approach that captures the physics as well as the financial choices. linearization of the physics are chosen to have a conservative impact on the costs estimates. The end scenario network is designed where the placement and size of sources, storage components and pipes are optimized together with the operational strategy, e.g. thermal allocation time-series, for all assets. The workflow was applied to a greenfield network for the Dutch municipality of Rijswijk. It was shown that a 18% reduction in Total Cost of Ownership in the primary grid could be achieved by introducing decentralized sources, decentralized storages and seasonal storage.

关键词： District heating network Design and operational optimization Asset sizing Seasonal thermal energy storage Decentralized thermal energy storage mixed integer linear programming

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A mixed integer linear programming support vector machine for cost-effective feature selection

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KNOWLEDGE-BASED SYSTEMS 2020年 203卷 106145-106145页

作者： Lee, In Gyu Zhang, Qianqian Yoon, Sang Won Won, Daehan SUNY Binghamton Dept Syst Sci & Ind Engn Binghamton NY 13902 USA Waseda Univ Fac Commerce Tokyo Japan

In the era of big data, feature selection is indispensable as a dimensional reduction technique to lower data complexity and enhance machine learning performances. However, traditional feature selection methods mainly focus on classification performances, while they exclude the impact of associated feature costs;e.g., price, risk, and computational complexity for feature acquisition. In this research, we extend the l(1) norm support vector machine (l(1)-SVM) to address the feature costs, by incorporating a budget constraint to preserve classification accuracy with the least expensive features. Furthermore, we formulate its robust counterpart to address the uncertainty of the feature costs. To enhance computational efficiency, we also develop an algorithm to tighten the bound of the weight vector in the budget constraint. Through the experimental study on a variety of benchmark and synthetic datasets, our proposed mixed integer linear programming (MILP) models show that they can achieve competitive outcomes in terms of predictive and economic performances. Also, the algorithm that tightens the budget constraint helps to curtail computational complexity. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Feature selection Support vector machine mixed integer linear programming Robust optimization Feature cost Cost uncertainty

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Optimal Electrification Using Renewable Energies: Microgrid Installation Model with Combined Mixture k-Means Clustering Algorithm, mixed integer linear programming, and Onsset Method

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ENERGIES 2024年第12期17卷 3022页

作者： Kabe, Moyeme Bokovi, Yao Sedzro, Kwami Senam Takouda, Pidename Lare, Yendoube Univ Lome CERME Ctr Excellence Reg Maitrise Elect Elect Engn 01 BP 1515 Lome Togo Univ Lome Lab Solar Energy 01 BP 1515 Lome Togo CERME Dept Elect Engn Lab Res Engn Sci LARSI 01 BP Lome 1515 Togo Natl Renewable Energy Lab Golden CO 80401 USA EPL Ecole Polytech Lome 01 BP 1515 Lome Togo

Optimal planning and design of microgrids are priorities in the electrification of off-grid areas. Indeed, in one of the Sustainable Development Goals (SDG 7), the UN recommends universal access to electricity for all at the lowest cost. Several optimization methods with different strategies have been proposed in the literature as ways to achieve this goal. This paper proposes a microgrid installation and planning model based on a combination of several techniques. The programming language Python 3.10 was used in conjunction with machine learning techniques such as unsupervised learning based on K-means clustering and deterministic optimization methods based on mixed linear programming. These methods were complemented by the open-source spatial method for optimal electrification planning: onsset. Four levels of study were carried out. The first level consisted of simulating the model obtained with a cluster, which is considered based on the elbow and k-means clustering method as a case study. The second level involved sizing the microgrid with a capacity of 40 kW and optimizing all the resources available on site. The example of the different resources in the Togo case was considered. At the third level, the work consisted of proposing an optimal connection model for the microgrid based on voltage stability constraints and considering, above all, the capacity limit of the source substation. Finally, the fourth level involved a planning study of electrification strategies based mainly on microgrids according to the study scenario. The results of the first level of study enabled us to obtain an optimal location for the centroid of the cluster under consideration, according to the different load positions of this cluster. Then, the results of the second level of study were used to highlight the optimal resources obtained and proposed by the optimization model formulated based on the various technology costs, such as investment, maintenance, and operating costs, which w

关键词： microgrids optimization k-means clustering mixed integer linear programming onsset

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Target setting with minimum improving costs in data envelopment analysis: A mixed integer linear programming approach

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EXPERT SYSTEMS 2019年第4期36卷

作者： Ang, Sheng An, Qingxian Yang, Feng Ji, Xiang Univ Sci & Technol China Sch Management Hefei Anhui Peoples R China Cent South Univ Sch Business Changsha 410083 Hunan Peoples R China

This study addresses a problem called cost-minimizing target setting in data envelopment analysis (DEA) methodology. The problem is how to make an inefficient decision-making unit efficient by allocating to it as few organizational resources as possible, assuming that the marginal costs of reducing inputs or increasing outputs are known and available, which is different from previous furthest and closest DEA targets setting methods. In this study, an existed cost minimizing target setting heuristics approach based on input-oriented model is examined to show that there exist some limitations. This study develops a simple mixed integer linear programming to determine the desired targets on the strongly efficient frontier based on non-oriented DEA model considering the situation in the presence of known marginal costs of reducing inputs and increasing outputs simultaneously. Some experiments with the simulated datasets are conducted, and results show that the proposed model can obtain more accurate projections with lower costs compared with those from furthest and closest target setting approaches. Besides, the proposed model can be realistic and efficient in solving cost-minimizing target setting problem.

关键词： closest targets data envelopment analysis mixed integer linear programming non-oriented model

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Genset Scheduling in a Redundant Electric Power System for an Offshore Vessel Using mixed integer linear programming

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JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING-TRANSACTIONS OF THE ASME 2021年第6期143卷 061401-061401页

作者： Thorat, Laxminarayan Skjetne, Roger Osen, Ottar L. Norwegian Univ Sci & Technol NTNU Ctr Autonomous Marine Operat & Syst AMOS Dept Marine Technol N-7491 Trondheim Norway Norwegian Univ Sci & Technol NTNU Dept ICT & Nat Sci N-6009 Alesund Norway

This paper proposes several schemes for optimal scheduling of power producers in a shipboard power system for a typical offshore supply vessel, having a multiple number of possibly varying capacity gensets. The proposed scheduling methods are illustrated using four alternative power system configurations, ranging from a few large gensets to many gensets of smaller ratings. mixed integer linear programming is used to formulate the optimization problems. Three formulations are presented: one for minimizing online capacity without further objectives, one that includes a redundancy constraint for loosing a group of gensets (to account for a worst-case failure scenario), and one to also balance running hours and minimizing connect/disconnect of the gensets. These different objectives can be combined and weighted based on importance, with or without redundancy constraints. Simulations are carried out to demonstrate the properties of the three different scheduling methods. The first method, minimizing online capacity only, is also used to illustrate the differences between the four genset configurations. This shows, for instance, that using more small gensets ensures generally a lower online available power (the connected capacity matches better the prevailing load) and near optimal loading of each genset around 80% (assuming equal loadsharing), at the same time as resilience to genset failures is preserved.

关键词： marine vessels marine power systems optimal genset scheduling mixed integer linear programming

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Non-iterative division circuit design with accuracy and performance trade-off based on mixed integer linear programming approach

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MICROELECTRONICS JOURNAL 2022年 127卷

作者： Wu, Lei Jong, Ching Chuen Nanyang Technol Univ Sch Elect & Elect Engn 50 Nanyang Ave Singapore 659798 Singapore

A wide range of applications such as image/video processing involve arithmetic division and demand high computing performance but can tolerate some degree of errors or allow small deviations from their theoretical results. This paper presents an approach for non-iterative arithmetic division circuit design, leveraging computation accuracy for high performance. The quotient is modeled as a curved surface and the surface is partitioned into many small rectangular planar stripes for linearity. mixed integer linear programming is adopted to optimize the worst case error due to approximation in each stripe. As a result, an architecture for the division circuits consisting of simple additions and a small look-up table is achieved. The accuracy achieved in terms of the maximum absolute error percentage (MAEP) ranges from 0.44% to 0.11%. Comparing the proposed designs with the existing counterparts with similar MAEP, the area-delay product (ADP) is reduced by more than 40%. Compared to the counterpart with the closest ADP by the single stage logarithmic method, the proposed circuit has a smaller MAEP with 45% reduction. Comparing with the existing non-iterative division circuit by the curve fitting method, the proposed circuit has lowered MAEP and ADP by 21% and 27% respectively.

关键词： Non-iterative divider mixed integer linear programming Approximation

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