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Protection system planning in distribution networks with microgrids using a bi-level multi-objective and multi-criteria optimization technique

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ELECTRIC POWER SYSTEMS RESEARCH 2024年 228卷

作者： Reiz, Cleberton Leite, Jonatas B. Gouveia, Clara S. Javadi, Mohammad Sadegh Sao Paulo State Univ UNESP Dept Elect Engn Ilha Solteira SP Brazil Inst Syst & Comp Engn Technol & Sci INESC TEC Porto Portugal Sao Paulo State Univ UNESP BR-15385000 Ilha Solteira SP Brazil

Microgrids are able to improve several features of power systems, such as energy efficiencies, operating costs and environmental impacts. Nevertheless, microgrids' protection must work congruently with power distribution protection to safely take all advantages. This research contributes to enable their protection by proposing a bilevel method to simultaneously solve the allocation and coordination problems, where the proposed scheme also includes local protections of distributed energy resources. The uncertainties associated with generation and loads are categorized by the k-means method, as well. The non-dominated sorting genetic algorithm II is employed in the upper-level task to solve the protection and control devices allocation problem with two opposing objectives. In the lower-level task, a genetic algorithm ensures their coordination. Protection devices include reclosers and fuses from the network, and directional relays for the point of common coupling of microgrids, while control devices consist of remote-controlled switches. In contrast to related works, local devices installed at the point of coupling of distributed generation units are considered as well, such as voltage-restrained overcurrent relays and frequency relays. The optimal solution for the decision-maker is achieved by utilizing the compromise programming technique. Results show the importance of solving the allocation and coordination problems simultaneously, achieving up to $25,000 cost savings compared to cases that solve these problems separately. The integrated strategy allows the network operator to select the optimum solution for the protective system and avoid corrective actions afterward. The results also show the viability of the islanding operation depending on the decision maker's criteria.

关键词： Power distribution planning Microgrids Reliability Power system protection multi-objective programming Bi-level optimization

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Examining the potential of an irrigation work to improve sustainability in a rural area

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WATER SUPPLY 2021年第6期21卷 2959-2973页

作者： Ragkos, Athanasios Ambas, Vasileios Hellen Agr Org DIMITRA Agr Econ Res Inst Athens 11528 Greece Univ Western Macedonia Sch Agr Terma Kontopoulou 53100 Florina Greece

Irrigation works aim to increase the efficiency of water use and economic benefits for farmers. This study adopts a broader view and investigates their potential to contribute to the achievement of other sustainability objectives. In particular, the paper employs a multi-objective programming (MOP) model, which examines the possibilities to simultaneously achieve four conflicting objectives with the upgrade of an irrigation network in a rural area in Greece. The four objectives are maximization of economic result (economic sustainability) and of employment (social sustainability) as well as the minimization of agrochemical use and irrigation water consumption (environmental sustainability). The compromise is sought through different cropping patterns either by restructuring existing crops (Scenario 1) or by also introducing new crops (Scenario 2). The results show that solutions in Scenario 2 performs much better in all dimensions of sustainability, however large increases in economic performance and employment come with lower environmental gains. A Cost-Benefit Analysis shows that very few solutions yield positive Net Present Value and the investment could be halted if benefits relating to social and environmental sustainability are disregarded. Results are discussed in conjunction to the proposal of a new governance scheme, which could assume broader roles in supporting sustainable development.

关键词： cost-benefit analysis crop restructuring governance multi-objective programming public-private partnerships technical and economic data

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Solving multi-objective integer indefinite quadratic fractional programs

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ANNALS OF OPERATIONS RESEARCH 2021年第1-2期296卷 821-840页

作者： Mekhilef, Amal Moulai, Mustapha Drici, Wassila USTHB Fac Math Dept Operat Res BP 32 Algiers 16111 Algeria USTHB Fac Math LaROMaD Lab BP 32 Algiers 16111 Algeria

In this paper, we describe an exact algorithm for solving a multi-objective integer indefinite quadratic fractional maximization problem. The algorithm generates the whole set of efficient solutions of the above mentioned problem. We optimize at first one of the objective functions in the original feasible region;in an iterative way and through the introduction of auxiliary constraints (efficient cut or branching constraint), the same objective function is optimized over progressively restricted or separated parts of the original feasible region, each time we get a candidate solution for non dominated solution, the efficient set is updated, the process ends when there is no unexplored parts of the original domain. The proposed method is based on an efficient cut which allows to reduce the feasible set avoiding non efficient solutions, the simplex like algorithm to solve a mono objective quadratic fractional maximization problem, and the classical branch and bound technique for integer decision variables. We establish theoretical results which prove the effectiveness of this new exact method, for illustration, numerical experiments are reported.

关键词： multi-objective programming Integer programming Quadratic programming Fractional programming Efficient cut Branch and cut

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Optimal allocation of regional water resources based on multi-objective dynamic equilibrium strategy

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APPLIED MATHEMATICAL MODELLING 2021年 90卷 1183-1203页

作者： Feng, Jianghong Jinan Univ Sch Management 601 Huangpu Ave West Guangzhou Peoples R China

Raising water demand, insufficient freshwater resources and uneven precipitation are the main causes of water conflicts in the river basin. The main challenges in water resources allocation are the following: first, the uncertainty of water supply caused by climate change;second, the dynamic allocation of water resources is not considered;third, there is a lack of equitable water allocation, which may lead to intensified conflicts among the water user sectors. Based on the above challenges, a multi-objective dynamic programming optimal water resources allocation model under uncertainties is established in this paper, which could be a resolution for water disputes as it addresses simultaneously economic, social and environmental benefits. The proposed model considers maximizing the minimum satisfaction degree of each water sector, maximizing the utilization efficiency of water resources in the basin, and maximizing economic benefits. Satisfaction degree function is introduced to optimize water allocation equality in water use sectors (agricultural, domestic, and industrial sectors), and economic efficiency loss acceptance is integrated into the model constraints to control the economic loss corresponding to variations in water availability. In addition, a case study demonstrated the practicality and rationality of the proposed model, and the model results proposed herein show significantly help for a single river basin. Most importantly, the results show that model with dynamic constraint ensures a greater overall economic return for the entire basin, and a larger variation in water resources of sub-area leads to a larger maximum economic efficiency loss. Moreover, the proposed model herein is flexible and can be used in other developing countries/regions only by modifying the parameters according to the actual situation. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Water allocation multi-objective programming Satisfaction degree Economic benefit multi-stage decision

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Adaptation to seasonal drought in irrigation districts of south China: A copula-based fuzzy-flexible stochastic multi-objective approach for precise irrigation planning

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JOURNAL OF HYDROLOGY 2023年第PartA期625卷

作者： Yue, Qiong Cui, Ningbo Zhang, Fan Guo, Shanshan Jiang, Shouzheng Yu, Xiuyun Zhu, Bin Hohai Univ Coll Agr Sci & Engn Nanjing 211100 Peoples R China Sichuan Univ State Key Lab Hydraul & Mt River Engn Chengdu 610065 Peoples R China Sichuan Univ Coll Water Resource & Hydropower Chengdu 610065 Peoples R China Beijing Forestry Univ Sch Soil & Water Conservat Beijing 100083 Peoples R China Tsinghua Univ Dept Hydraul Engn State Key Lab Hydrosci & Hydraul Engn Beijing 100084 Peoples R China

The occurrence of seasonal drought has led to periodic water shortages in irrigation districts of south China, posing significant agricultural challenges on water and food security. Consequently, there is a pressing need for high-efficient water resources management from a sustainability perspective. To address this issue, a copulabased fuzzy-flexible stochastic multi-objective programming (CFSMP) model was proposed to facilitate irrigation planning under the combined influence of seasonal dry and wet conditions. The model was aimed to simultaneously achieve maximum economic benefit, water productivity and green water use efficiency, considering the economic, social, and environmental spheres of the irrigation district. The CFSMP model represented an innovative solution that was capable of (1) precisely describing the response of crop yield to water supply at ten-day time scale, which can be matched with practical irrigation demand;(2) characterizing joint distributions of seasonal dry and wet conditions of precipitation during different crop growth periods based on the copula function;(3) reconciling conflicts on objectives among economic, social, and environmental spheres of the irrigation district in complex and uncertain environments. The proposed model was applied to the Dongfeng Reservoir Irrigation District in south China. The downscaled ten-day Jensen water production functions of main grain and cash crops were fitted to recognize precise distributions of crop water demand and water sensitivity index. The Frank copula, with the smallest value of squared Euclidean distribution, was selected to describe the joint distribution of seasonal precipitation and gain insights into actual seasonal water availability and water scarcity. The results showed that the proposed model produced more water-saving and yieldpromoting irrigation planning policies than the current quota. The average water allocation of rice, wheat, maize, rape, and citrus decreased by 352.6 mm, 10

关键词： Seasonal drought South China Irrigation planning Copula multi-objective programming

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multi-objective optimization for perishable product dispatch in a FEFO system for a food bank single warehouse

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OPERATIONS RESEARCH PERSPECTIVES 2024年 12卷

作者： Suarez, Carlos Anibal Guano, Walter A. Perez, Cinthia C. Roa-Lopez, Heydi ESPOL Escuela Super Politecn Litoral Fac Nat Sci & Math Campus Gustavo GalindoKm 30-5 Via Perimetral Guayaquil 090902 Ecuador ESPOL Escuela Super Politecn Litoral Fac Mech Engn & Prod Sci Campus Gustavo GalindoKm 30-5 Via Perimetral Guayaquil 090902 Ecuador ESPOL Escuela Super Politecn Litoral Pacific Int Ctr Disaster Risk Reduct Campus Gustavo GalindoKm 30-5 Via Perimetral Guayaquil 090902 Ecuador

One of the main challenges of food bank warehouses in developing countries is to determine how to allocate perishable products to beneficiary agencies with different expiry dates while ensuring food safety, meeting nutritional requirements, and minimizing the shortage. The contribution of this research is to introduce a new multi-objective, multi-product, and multi-period perishable food allocation problem based on a single warehouse management system for a First Expired-First Out (FEFO) policy. Moreover, it incorporates the temporal aspect, guaranteeing the dispatch of only those perishable products that meet the prescribed minimum quality standards. A weighted sum approach converts the multi-objective problem of minimizing a vector of objective functions into a scalar problem by constructing a weighted sum of all the objectives. The problem can then be solved using a standard constrained optimization procedure. The proposed mixed integer linear model is solved by using the CPLEX solver. The solution obtained from the multi-objective problem allows us to identify days and products experiencing shortages. In such cases, when there is insufficient available inventory, the total quantity of product to be dispatched is redistributed among beneficiaries according to a pre-established prioritization. These redistributions are formulated as integer programming problems using a score-based criterion and solved by an exact method based on dynamic programming. Computational results demonstrate the applicability of the novel model for perishable items to a real-world study case.

关键词： Linear optimization multi-objective programming Perishable products FEFO Dynamic programming

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Optimization of above-ground environmental factors in greenhouses using a multi-objective adaptive annealing genetic algorithm

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HELIYON 2024年第12期10卷 e33036页

作者： Zhang, Ning Tan, Qinyue Song, Wancong Li, Qiuying Northwest A&F Univ Coll Water Resources & Architectural Engn Yangling 712100 Shaanxi Peoples R China China JILIANG Univ Coll Opt & Elect Technol Hangzhou 310018 Zhejiang Peoples R China

The greenhouse environment represents a dynamic, nonlinear system characterized by hysteresis and is influenced by a myriad of interacting environmental parameters, posing a complex multi- variable optimization challenge. This study proposes a multi-objective adaptive annealing genetic algorithm to optimize above-ground environmental factors in greenhouses, addressing the challenges of variable environmental conditions and extensive heating and humidity infrastructure. Initially, after analyzing the multi-objective model of greenhouse above-ground environmental factors, including temperature, relative humidity, and CO2 2 concentration, a comprehensive multi- objective, multi-constraint model was developed to encapsulate these factors in greenhouse environments. Subsequently, the model optimization incorporated multi-parameter coding of decision variables, a fitness function, and an annealing dynamic penalty factor. Validation conducted at Yangling Agricultural Demonstration Park revealed that the application of multi- objective adaptive annealing genetic algorithms (schemes 1 and 2) significantly outperformed the single-objective genetic algorithm (scheme 3) and the traditional genetic algorithm (scheme 4). Specifically, the improvements included a reduction in average temperature rise by 2.64 degrees C and 5.29 degrees C for schemes 1 and 2, respectively, equating to 20 % and 34 % decreases. Additionally, average humidification reductions of 2.39 % and 3.9 % were observed, alongside decreases in the total lengths of heating and humidification pipes by up to 2.99 km and 0.443 km, respectively, with a maximum reduction of 14 % in heating pipes. The integration of an annealing dynamic penalty factor enhanced the adaptive climbing ability of schemes 1 and 2, improving static stability and robustness. Furthermore, the number of iterations required to achieve convergence was reduced by approximately 170-240 times compared to schemes 3 and 4. This reduction in iterations

关键词： Greenhouse Environmental factors multi-objective programming Genetic algorithm Adaptive optimization

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Joint chance-constrained multi-objective multi-commodity minimum cost network flow problem with copula theory

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COMPUTERS & OPERATIONS RESEARCH 2023年第1期156卷

作者： Khezri, Somayeh Khodayifar, Salman Inst Adv Studies Basic Sci IASBS Dept Math Zanjan *** Iran

This study focuses on a specific problem in network optimization, namely the minimum cost multi-commodity network flow (MCNF) problem. The problem is complicated by the presence of uncertain parameters, including various types of costs associated with each arc in the network. The study presents a multi-objective approach to solving this problem, where the coefficients of the capacity constraints are modelled as random variables with a normal distribution, and the dependence between them is modelled using an Archimedean copula. The capacity constraints are presented as joint chance constraints, and a multi-objective problem is formulated to deal with the uncertainty. This uncertain multi-objective problem is then converted into a certain multi-objective problem using fuzzy programming. The resulting certain multi-objective MCNF problem is converted to a certain single objective problem using second-order cone programming (SOCP), which is solved using either piecewise tangent approximation or piecewise linear approximation methods. The proposed ap- proaches are tested using numerical examples and experimental tests to demonstrate their effectiveness in solving large-scale network problems efficiently. The results show that the proposed approaches are a useful tool for solving uncertain multi-objective MCNF problems in real-world applications.

关键词： Minimum cost network flow problem multi-objective programming Joint chance-constrained optimization Copula theory

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Location selection of agricultural Machinery sheds for improved scheduling and efficiency under sustainability goals

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ECOLOGICAL INDICATORS 2023年 155卷

作者： Liu, Yang Ma, Xu Jiang, Mingjun Huang, Weilun Ren, Haisong Bengbu Univ Sch Econ & Management Bengbu Anhui Peoples R China Wenzhou Business Coll Sch Management Wenzhou Peoples R China Wenzhou Business Coll Sch Finance & Trade Wenzhou Peoples R China

The storage of agricultural machinery (AM) poses a critical operational challenge that requires attention to attain China's "Carbon Peaking and Carbon Neutrality" objectives, due to the carbon emissions and fuel consumption associated with AM operations. Nevertheless, this matter has not garnered adequate consideration. Existing studies have rarely focused on optimizing the parking locations for AM to mitigate emissions and advance sustainability. This study employs the TOPSIS-MCGP method to explore sustainable location selection for Chinese AM sheds. The principal factors influencing location selection include carbon emissions, fuel consumption, operational distance, total cost, and timeliness, which were thoroughly examined for their impact. A weighted selection approach was utilized to ascertain key factors and their optimization implications. The AM shed location within the AM dispatching system was developed through the following steps: (1) Evaluation of influential factors for sustainable AM dispatching, encompassing carbon emissions, fuel consumption rate, AM dispatching distance, cost, and timeliness. (2) Quantification of the manager's experience in the AM service center as an index weight using the Fuzzy Delphi Method (FDM). (3) Adoption of the TOPSIS-MCGP model to establish the relationship between operation points and AM shed locations during sustainable AM operations. A preliminary investigation was carried out in a company located in Harbin City, yielding an optimal solution encompassing a 13,500 mu working area, 3,300 total building area, and a 28-million RMB investment. The outcomes underscore the model's compatibility and its utility for optimizing sustainable AM service operations. In conclusion, this study underscores the significance of addressing AM storage to foster sustainability and emissions reduction. The TOPSIS-MCGP approach proves effective in selecting sustainable AM shed locations. By considering diverse factors like carbon emissions, f

关键词： Agricultural machinery TOPSIS-MCGP Sustainable dispatch Emissions multi-objective programming

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An integrated two dimensional cutting stock and lot sizing problem with two criteria

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EUROPEAN JOURNAL OF INDUSTRIAL ENGINEERING 2023年第5期17卷 766-+页

作者： Sanan, Ayse Burcu Azizoglu, Meral METU Dept IE TR-06800 Ankara Turkiye

In this study, we consider an integrated two dimensional cutting stock and lot sizing problem arising in an aircraft manufacturing plant. The items are to be cut from steel panels of identical size to satisfy all periodic demands over a specified planning horizon. Two objectives, minimising the number of panels cut and the total inventory carrying cost of the items, are defined and all non-dominated objective vectors concerning the defined objectives are generated. To generate each non-dominated objective vector, we propose a mixed integer linear programming model whose efficiency is improved by optimality properties and bounding mechanisms. The results of our experiments have revealed that the instances with few items can be solved for up to 14 periods and the instances with more items can be solved for up to seven periods, in two hours. [Submitted: 29 March 2022;Accepted: 7 August 2022]

关键词： two dimensional cutting stock problems 2DCSPs lot sizing problems integrated problems multi-objective programming

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