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Fuzzy satisfying stochastic multi-objective generation' scheduling by weightage pattern search methods

ELECTRIC POWER SYSTEMS RESEARCH 2004年第2-3期69卷 311-320页

作者： Bath, SK Dhillon, JS Kothari, DP GZS Coll Engn & Technol Dept Elect Engn Bathinda 151001 Punjab India Sant Longowal Inst Engn & Technol Dept Elect & Instrumentat Engn Sangrur 148106 Punjab India Indian Inst Technol Ctr Energy Studies New Delhi 110016 India

In the multi-objective framework, an interactive fuzzy satisfying method is presented to decide the generation schedule with explicit recognition of statistical uncertainties in system production cost data, NO, pollutant emission data and system load demand. In deciding the optimal schedule, four objectives viz. operating cost, NO, emission and risk due to variance of active and of reactive power generation mismatch are simultaneously minimized. Specific technique is put forth to convert the stochastic models into their respective deterministic equivalents. The weighting method is used to simulate the trade-off relationship between the conflicting objectives in the non-inferior domain. Generally, the weights are either simulated or searched in the non-inferior domain. In the paper, Hooke-Jeeves and evolutionary search techniques are implemented to search the 'preferred' weightage pattern in the non-inferior domain, which corresponds to the 'best' optimal solution. Fuzzy set theory has been exploited to decide the 'preferred' optimal operating point by interacting with the decision maker. The non-inferior solution, which attains maximum satisfaction level from the membership functions of the participating objectives, has been adjudged the 'best' solution. The goal/objectives being of fuzzy nature can be quantified by defining their membership functions. The validity of proposed method has been demonstrated on an 11-node IEEE system having five generators. The results obtained by searching weightage pattern using Hooke-Jeeves and evolutionary search techniques, are compared with the interactive method in which weightage patterns are simulated by giving suitable variation to weights in a specific manner. (C) 2003 Elsevier B.V. All rights reserved.

关键词： stochastic multi-objective optimization economic load dispatch fuzzy set membership function Hooke-Jeeves method evolutionary method weight simulation method

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Simulation-based multi-objective model for supply chains with disruptions in transportation

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ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING 2017年第0期43卷 39-49页

作者： Chavez, Hernan Castillo-Villar, Krystel K. Herrera, Luis Bustos, Agustin Univ Texas San Antonio Dept Mech Engn San Antonio TX 78249 USA Tecnol Monterrey Dept Ind & Syst Engn Mexico City 01389 DF Mexico Inst Mexicano Transporte Integrated Transport Res Ctr Sanfandila 76700 Queretaro Mexico

Unpredictable disruptions (e.g., accidents, traffic conditions, among others) in supply chains (SCs) motivate the development of decision tools that allow designing resilient routing strategies. The transportation problem, for which a model is proposed in this paper, consists of minimizing the stochastic transportation time and the deterministic freight rate. This paper extends a stochastic multi-objective minimum cost flow (SMMCF) model by proposing a novel simulation-based multi-objective optimization (SimMOpt) solution procedure. A real case study, consisting of the road transportation of perishable agricultural products from Mexico to the United States, is presented and solved using the proposed SMMCF-Continuous/SimMOpt solution framework. In this case study, time variability is caused by the inspection of products at the U.S.-Mexico border ports of entry. The results demonstrate that this framework is effective and overcomes the limitations of the multi-objective stochastic minimum cost flow problem (which becomes intractable for large-scale instances). (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Minimum cost flow Simulated annealing Simulation optimization stochastic multi-objective optimization Resilient supply chains

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multi-objective Sizing optimization Method of Microgrid Considering Cost and Carbon Emissions

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2024年第4期60卷 5565-5576页

作者： Zhu, Xiang Ruan, Guangchun Geng, Hua Liu, Honghai Bai, Mingfei Peng, Chao Tsinghua Univ Dept Automat Beijing 10084 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing 10084 Peoples R China MIT Lab Informat & Decis Syst LIDS Boston MA 02139 USA North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewable Beijing 102206 Peoples R China North China Elect Power Univ Sch Elect & Elect Engn Beijing 102206 Peoples R China Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China

Microgrid serves as a promising solution to integrate and manage distributed renewable energy resources. In this paper, we establish a stochastic multi-objective sizing optimization (SMOS) model for microgrid planning, which fully captures the battery degradation characteristics and the total carbon emissions. The microgrid operator aims to simultaneously maximize the economic benefits and minimize carbon emissions, and the degradation of the battery energy storage system (BESS) is modeled as a nonlinear function of power throughput. A self-adaptive multi-objective genetic algorithm (SAMOGA) is proposed to solve the SMOSO model, and this algorithm is enhanced by pre-grouped hierarchical selection and self-adaptive probabilities of crossover and mutation. Several case studies are conducted to determine the microgrid size by analyzing Pareto frontiers, and the simulation results validate that the proposed method has superior performance over other algorithms on the solution quality of optimum and diversity.

关键词： Microgrids Batteries Degradation optimization stochastic processes Costs Genetic algorithms Distributed energy resource low carbon battery energy storage system renewable energy stochastic multi-objective optimization genetic algorithm

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Integration of logistics outsourcing decisions in a green supply chain design: A stochastic multi-objective multi-period multi-product programming model

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2016年 182卷 165-184页

作者： Ameknassi, Lhoussaine Ait-Kadi, Daoud Rezg, Nidhal Univ Laval Dept Mech Engn CIRRELT 2325 Rue Univ Quebec City PQ G1V 0A6 Canada Lorraine Univ LGPM F-57045 Metz 01 France

This paper develops a programming model, which combines logistics outsourcing decisions with some strategic Supply Chains' planning issues, such as the Security of supplies, the customer Segmentation, and the Extended Producer Responsibility. The purpose is to minimize both the expected logistics cost and the Green House Gas (GHG) emissions of the Supply Chain (SC) network, in the context of business environment uncertainty. First, we define a general structure of the closed-loop SC. Second, we provide constructive models to roughly estimate the insourcing and outsourcing logistics costs, and their corresponding GHG emissions. Third, we establish a stochastic plan based on a scenarios approach to capture the uncertainty od demand, capacity of facilities, quantity and quality of returns of used products, and the transportation, warehousing, and reprocessing costs. Fourth, we suggest a programming model, and an algorithm based on the Epsilon-constraint method to solve it. The result is a set of optimal non-dominant green SC configurations, which provide the decision' makers with optimal levels of logistics outsourcing integration within a decarbonized Supply Chain, before any further low-carbon investment. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Supply Chain Integration Third-Party Logistics Logistics costs Green House Gas emissions stochastic multi-objective optimization

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A multi-objective stochastic programming model to configure a sustainable humanitarian logistics considering deprivation cost and patient severity

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ANNALS OF OPERATIONS RESEARCH 2022年第1期319卷 1265-1300页

作者： Jamali, Amir Ranjbar, Amirhossein Heydari, Jafar Nayeri, Sina Univ Tehran Sch Ind Engn Coll Engn Tehran Iran

As the occurrence of disasters has increased frequently and has resulted in growing concern about their adverse effects on the environment, Sustainable Humanitarian Logistics (SHL) has received great attention recently. SHL aims to reduce disaster damages in an environmentally-friendly manner in the shortest possible time. The terms including 'environmentally-friendly' and 'shortest possible time' refer to the environmental and social aspects of sustainability. This research proposes a stochastic multi-objective mixed-integer programming model to configure an SHL network during the response phase. Having compared to the research literature, this is the first study that considers economic, social, and environmental aspects of sustainability by incorporating relief cost, deprivation cost, and carbon emissions, respectively. Then, the improved multi-choice goal programming approach is applied to solve the proposed multi-objective model. To indicate the validity of the proposed model, an earthquake that occurred in a region of Kermanshah, Iran, in 2017 is investigated as a real case study. Finally, sensitivity analysis is performed and several managerial and theoretical insights are provided. The results show that exerting environmental issues in humanitarian logistics does not necessarily increase the relief costs, but can be in contrast with the social aspect. Furthermore, a minor increase in the budget of the preparedness phase drastically decreases the response costs.

关键词： Humanitarian logistics Deprivation cost Sustainable disaster management stochastic multi-objective optimization

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Security Constrained stochastic multi-objective Optimal Power Dispatch

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INTERNATIONAL JOURNAL OF EMERGING ELECTRIC POWER SYSTEMS 2007年第1期8卷 1-20页

作者： Bath, Sarbjeet Kaur Dhillon, Jaspreet Singh Kothari, D. P. GZS Coll Engn & Technol Bathinda Punjab India St Longowal Inst Engn & Technol Sangrur Punjab India IIT New Delhi India

A stochastic multi-objective line security constrained problem is formulated to minimize non-commensurable objectives viz. operating cost, polluting gaseous emission and variance of active power generation and reactive power generation, with explicit recognition of statistical uncertainties in the thermal power generation cost coefficients, gaseous emission coefficients, power demands and hence power generations and bus voltages, which are considered random variables. Specific technique is put forth to convert the stochastic models into their respective deterministic equivalents. Fuzzy set theory has been exploited to evaluate the different objectives that are quantified by defining their membership functions. Security of transmission lines with respect to expected active power flow is considered in the form of fuzzy objective function. The solution set of such formulated problems is non-inferior due to contradictions among the objectives undertaken. The weighting method is used to simulate the trade-off relationship between the conflicting objectives in the non-inferior domain. Generally, the weights are either simulated or searched in the non-inferior domain. In the paper Evolutionary search technique is implemented to search the 'preferred' weightage pattern in the non-inferior domain, which corresponds to the 'best' compromised solution. Among the non-inferior solutions, the system operator selects the 'preferred' optimal operating point that provides maximum satisfaction level of the most under achieved objective in terms of membership function and is termed as fitness function. The validity of the proposed method has been demonstrated on an IEEE system comprising of 11-nodes, 17-lines and 5-generators.

关键词： decision maker (DM) evolutionary optimization fuzzy goals membership function stochastic multi-objective optimization weight pattern

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multi-objective optimal design of a solar absorption cooling and heating system under life-cycle uncertainties

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2015年 11卷 92-105页

作者： Xu, Donghao Qu, Ming Hang, Yin Zhao, Fu Purdue Univ W Lafayette IN 47907 USA Areva Solar Inc Palo Alto CA 94303 USA

SACH systems, as a type of renewable energy systems, meet building cooling and heating demand through solar energy and therefore can reduce energy consumption and, in turn, mitigate the environmental impacts associated with building operation. Many researchers have evaluated and optimized the design of SACH systems under deterministic schemes;however, deterministic approach is subject to uncertain parameters in the evaluation or objective functions such as energy prices in the future and the results subsequently may not hold true. To enhance the design robustness and validate the use of deterministic approaches, a stochastic multi-objective optimization (SMOO) model is developed that incorporates these uncertainties. In this paper, a SACH system is optimized with respects to life-cycle energy, economic, and environment performance. The uncertain parameters considered include the life span of SACH systems and the fluctuations of energy markets and financial conditions. A case study follows to investigate the relationship among uncertainty and different designs. The comparison between deterministic and stochastic optimization shows that deterministic optimization can be used by designers who are moderate or not risk-averse at all. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Solar absorption cooling and heating system stochastic multi-objective optimization Life-cycle uncertainty Design guideline

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stochastic Day-Ahead Generation Scheduling With Pumped-Storage Stations and Wind Power Integrated

Stochastic Day-Ahead Generation Scheduling With Pumped-Stora...

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International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

作者： Zheng, J. H. Quan, X. Y. Jing, Z. X. Wu, Q. H. South China Univ Technol Sch Elect Engn Guangzhou 510641 Guangdong Peoples R China Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ Merseyside England

ISBN: (纸本)9781509019700

With more and more uncertain wind power generation integrated in power systems, it is significant to enhance the resilience of generation scheduling to avoid imbalance charges. This paper proposes a stochastic day-ahead generation scheduling (SDAGS) with pumped-storage (PS) stations and wind power (WP) integrated in power systems to tackle the variability of wind power for the purpose of reliability and economy of system operation. Considering the uncertainties of load and wind power generation, Latin hypercube sampling with Cholesky decomposition (LIIS-CD) is utilized to generate several scenarios. multi-objective group search optimizer with adaptive covariance and Levy flights (MGSO-ACL) is applied to optimize the SDAGS over 24-hour period, aiming at reaching a compromise between the minimization of expectation and variance of total cost of the SDAGS. Furthermore, a decision making method based on evidential reasoning (ER) approach is utilized to determine a final optimal solution considering expected carbon dioxide emission and expected polluted gas emission. Simulation studies are conducted on two different power systems with PS stations and WP integrated to verify the efficiency of the SDAGS.

关键词： Generation scheduling wind power penetration pumped-storage stations stochastic multi-objective optimization decision making

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stochastic multi-objective scheduling of a wind farm integrated with high-temperature heat and power storage in energy market

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2021年 132卷 107194-107194页

作者： Lasemi, Mohammad Ali Arabkoohsar, Ahmad Hajizadeh, Amin Aalborg Univ Dept Energy Technol Aalborg Denmark

In spite of significant opportunities created by wind energy, the high uncertainty of wind farms is problematic for their operators, making their participation in the energy market quite challenging. The combination of Air-based High-Temperature Heat and Power Storage (HTHPS), which is known as a novel energy storage technology, with a wind farm as an integrated system can make new opportunities for a Generation Company (GenCo) to overcome the wind generation challenges. This paper proposes a novel stochastic multi-objective optimization (SMOO) problem to determine the optimal charging and discharging scheduling and the best bidding strategy in the day-ahead energy market for an integrated energy system including a wind farm and HTHPS unit. The proposed model presents a comprehensive and coherent approach from historical data analysis as input to final decision making as output, that it can maximize Genco's profit by avoiding incorrect commitment and energy offering. In the phase of uncertainty handling, via a scenario-based approach, the wind uncertainty is modeled by Monte-Carlo Simulation (MCS) method based on wind speed forecast errors. In the optimization phase, the Pareto optimal set is obtained by Non-dominated Sorting Genetic Algorithm-II (NSGA-II), and then, a new method is also provided to choose the best possible solution based on the entropy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method and the minimax regret criterion. The historical data related to wind generation and energy prices provided by the Danish energy market in West Denmark is used in simulation analysis, and the results demonstrate pleasantly the effectiveness of the proposed model.

关键词： Wind farm Energy storage system Energy market Uncertainty analysis stochastic multi-objective optimization multi-criteria decision making

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A Proposed multi-objective, multi-Stage stochastic Programming With Recourse Model for Reservoir Management and Operation

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WATER RESOURCES RESEARCH 2021年第10期57卷 e2020WR029200-e2020WR029200页

作者： Li, Jinshu Zhang, Wei Yeh, William W-G Univ Calif Los Angeles Dept Civil & Environm Engn Los Angeles CA 90095 USA China Three Gorges Corp Beijing Peoples R China Wuhan Univ Wuhan Peoples R China

We present a multi-objective, multi-stage stochastic programming with recourse model for reservoir management and operation, where we use utility theory to select the best compromise solution from the Pareto front. A multi-stage streamflow scenario tree is generated first by the neural gas method. Then the Pareto front at each stage is produced by a modified constrained NSGA-II. A single best compromise solution on the Pareto front must be selected for the immediate stage and the model moves forward one stage and is re-optimized over a moving planning horizon of fixed duration. A key contribution of this study is the selection of the best compromise solution, which is achieved by a proposed linear spline utility function allied with regression. Our proposed utility function has the following advantages: (a) It satisfies the law of diminishing marginal rate of substitution, (b) it does not rely on the pre-specified weight or goal, and (c) it selects the best compromise solution that is likely to fall in the "knee regions" of the Pareto front. We apply the proposed optimization model to the Three Gorges Reservoir in China. The two conflicting objectives are (a) maximizing the total expected energy output in the planning horizon, and (b) maximizing the average expected ecological benefits in the planning horizon. The results show that the proposed model produces the optimal water release policy successfully under different hydrological scenarios, considering both the inflow uncertainty and the tradeoff between the two conflicting objectives.

关键词： stochastic multi-objective optimization stochastic programming with recourse reservoir operation utility function Three Gorges Reservoir

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