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Hybrid energy system optimization model: Electrification of Ontario's residential space and water heating case study

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ENERGY AND CLIMATE CHANGE 2022年 3卷

作者： Sanongboon, P. Pettigrew, T. Canadian Nucl Labs Chalk River ON Canada

Energy systems are becoming more complex as new energy sources are introduced in support of clean energy goals. These hybrid energy systems can be configured for cogeneration to account for multiple energy uses, including not only electricity but also space heating, water heating, and industrial process heat. Variable renewable energy systems are increasingly being added to hybrid systems to mitigate climate change and reduce greenhouse gas (GHG) emissions. This often creates additional challenges to meet energy demands due to variability associated with renewable generation. In support of energy planning for the new clean economy, the Hybrid Energy System Optimization (HESO) model has been developed to study the feasibility and benefits of nuclear-renewable hybrid energy systems. The model is formulated, as a mixed-integer linear programming (MILP) algorithm, to determine the best energy mix by minimizing annual cost. Because electrification will play a significant role in realizing a clean economy, this study explores the potential economic viability of electrification of residential water and space heating in Ontario. Different energy scenarios have been analyzed to understand the challenges associated with electrification and determine which energy sources will significantly reduce greenhouse gas emissions, while also maintaining competitive energy costs. The results show that electrification of residential water heating can be a viable alternative to natural gas heaters;reducing GHG emissions and energy cost. However, electrification of residential space heating is more challenging due to the large seasonal temperature variations that create significant energy demand fluctuations. Additional nuclear and wind generating capacity, as well as storage systems, are all important elements to support Ontario's transition to a low carbon economy through electrification.

关键词： Hybrid energy system mixed-integer linear programming Electrification Greenhouse gas emissions Levelized cost of energy

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AutoMoG: Automated data-driven Model Generation of multi-energy systems using piecewise-linear regression

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COMPUTERS & CHEMICAL ENGINEERING 2021年 145卷

作者： Kaemper, Andreas Leenders, Ludger Bahl, Bjoern Bardow, Andre Rhein Westfal TH Aachen Inst Tech Thermodynam D-52062 Aachen Germany Forschungszentrum Julich Inst Energy & Climate Res Energy Syst Engn IEK 10 D-52425 Julich Germany Swiss Fed Inst Technol Energy & Proc Engn Dept Mech & Proc Engn CH-8092 Zurich Switzerland

Operational optimization of multi-energy systems requires a mathematical model that is accurate and computationally efficient. A model can be generated in a data-driven way if measured data is available. Commonly, data is then used to model each component of the multi-energy system independently. However, independent modeling of each component may lead to models that are unnecessarily complicated and, thus, inefficient in practice. In this work, we propose the method AutoMoG for Automated data-driven Model G eneration of multi-energy systems using piecewise-linear regression. AutoMoG provides mixed-integer linear programming models of multi-energy systems. To accurately model the overall multi-energy system, AutoMoG balances the errors caused by each component. Model accuracy is measured in terms of operating cost. In a case study, AutoMoG provides a multi-energy system model with less linear sections than single-component regression Still, AutoMoG retains high accuracy. Thereby, AutoMoG enables efficient data-driven modeling as the basis for multi-energy system optimization. (C) 2020 The Authors. Published by Elsevier Ltd.

关键词： Regression analysis mixed-integer linear programming Energy system optimization Information criterion

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Sustainable reverse logistic network design for end-of-life use-case study

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RAIRO-OPERATIONS RESEARCH 2021年第1期55卷 S503-S521页

作者： Cinar, Suna Wichita State Univ Dept Ind & Mfg Engn Wichita KS 67260 USA

Due to the increased interests in environmental issues along with stringent environmental legislation and regulations, companies start taking a fresh look at the impact on their reverse logistic activties on the environment. This paper is an example of the recovery of valuable material that can be recycled/recovered or remanufactured at the end of product useful life by designing an effective reverse logistics network. In this study, a mixed integer linear programming (MILP) model is proposed to determine a long-term strategy for end-of-life (EOL). The mathematical model not only takes into account the minimization of system operating costs, but also considered minimization of carbon emissions related to the transportation and processing of used products. Therefore, the objective in this model was to minimize the transportation and operating cost as well as minimizing environmental effects these activities. The results of this study show the trade-off between the costs and carbon emissions, and cost effectiveness for improving environmental performance, all of which have great practical implication on decision-making of network configurations a reverse logistics system. The proposed model is validated by examining a case study from wind turbine (WT) sector.

关键词： mixed-integer linear programming optimization reverse logistic end-of-life use carbon emissions environmental policy climate control

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A two-stage sequential approach for scheduling with lot-sizing decisions in the context of plastic injection systems

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 151卷

作者： Cervantes-Sanmiguel, K., I Vargas-Flores, M. J. Ibarra-Rojas, O. J. Univ Autonoma Nuevo Leon Fac Ciencias Fis Matemat Av Univ S-N San Nicolas De Los Garza 66455 Nuevo Leon Mexico Ctr Invest Matemat Alianza Ctr 502 Monterrey 66629 NL Mexico

This study addresses a planning problem for plastics injection systems, where final products are assembled of compatible plastic pieces. Additionally, the production of pieces requires the use of dedicated molds that are installed on compatible injection machines (leading to long setup times). Then, our Manufacturing Planning Problem determines the lot-size of each product in terms of the number of cycles for each piece-mold-machine combination and the scheduling of molds for each injection machine in a single planning period to maximize the total profit. We consider constraints for machines' available time and avoid mold overlapping on different machines. Moreover, we define a mixed-integer linear program for our problem, and we obtain high-quality solutions for medium-size instances using a commercial solver in minutes. Still, the convergence to optimal solutions for large instances is slower. In response, we propose a two-stage sequential approach that takes advantage of the mathematical formulation to obtain better solutions for large instances in short computational times.

关键词： Scheduling Lot-sizing Integrated formulation mixed-integer linear programming Sequential approach

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Robustness within the optimal economic polygeneration system for a dairy industry

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JOURNAL OF CLEANER PRODUCTION 2021年 314卷

作者： Lobo Correia, Victor Hugo de Abreu, Renata Portela Carvalho, Monica Univ Fed Paraiba Grad Program Mech Engn Joao Pessoa Paraiba Brazil Univ Fed Paraiba Dept Renewable Energy Engn Joao Pessoa Paraiba Brazil

This study synthesizes and optimizes a polygeneration system for a dairy industry located in Northeast Brazil. Based on the energy demands (steam, hot water, electricity, and chilled water), a superstructure was built to represent all the possible processes and connections of the energy supply system, using commercially available equipment and locally available energy sources. The optimization model is based on mixed-integer linear programming and its objective function is to minimize the total annual costs. The solution of the optimization problem determines the optimal combination between pieces of equipment, connections, and energy resources within the superstructure. A reference system is established, which does not include the possibilities of cogeneration nor biomass. The annual cost of the reference system is R$ 2,780,550/year. When the optimization model is freely solved, the optimal economic system yields an annual cost of R$ 2,074,793/year (25% lower). This reduction is reached by using diesel and natural gas cogeneration modules to substitute electricity from the grid at peak hours. Only 10% of the cogenerated heat is employed within the system (due to low thermal energy demands), and the remainder is evacuated by a cooling tower, Sensitivity analyses were carried out to assess the system behavior under variations of energy demands, fuel costs, and capital recovery factor, and the optimal economic solution presented good performance under fuel price variations, demonstrating robustness in most scenarios.

关键词： Milk processing mixed-integer linear programming Optimization Energy system

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A hierarchical optimization approach to robust design of energy supply systems based on a mixed-integer linear model

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ENERGY 2021年 229卷

作者： Yokoyama, Ryohei Kamada, Hiroki Shinano, Yuji Wakui, Tetsuya Osaka Prefecture Univ Dept Mech Engn Naka Ku 1-1 Gakuen Cho Sakai Osaka 5998531 Japan Zuse Inst Berlin Dept Optimizat Takustr 7 D-14195 Berlin Germany

In designing energy supply systems, designers should heighten the robustness in performance criteria against the uncertainty in energy demands. In this paper, a robust optimal design method using a hierarchical mixed-integer linear programming (MILP) method is proposed to maximize the robustness of energy supply systems under uncertain energy demands based on a mixed-integer linear model. A robust optimal design problem is formulated as a three-level min-max-min MILP one by expressing uncertain energy demands by intervals, evaluating the robustness in a performance criterion based on the minimax regret criterion, and considering relationships among integer design variables, uncertain energy demands, and integer and continuous operation variables. This problem is solved by evaluating upper and lower bounds for the minimum of the maximum regret of the performance criterion repeatedly outside, and evaluating lower and upper bounds for the maximum regret repeatedly inside. Different types of optimization problems are solved by applying a hierarchical MILP method developed for ordinary optimal design problems without and with its modifications. In a case study, the proposed approach is applied to the robust optimal design of a cogeneration system. Through the study, its validity and effectiveness are ascertained, and some features of the obtained robust designs are clarified. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Energy supply Robust design Minimax regret criterion mixed-integer linear programming Hierarchical optimization

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An integrated optimization approach using a collaborative strategy for sustainable cities freight transportation: A Case study

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SUSTAINABLE CITIES AND SOCIETY 2021年 75卷

作者： Aloui, Aymen Hamani, Nadia Delahoche, Laurent Univ Picardie Jules Verne Lab Innovat Technol LTI EA 3899 F-80025 Amiens France

In recent years, the increasing freight transportation activities has caused extensive economic, environmental and social impacts on society. Therefore, it becomes necessary for logistics companies to find high-performance strategies to enhance their sustainability. Collaboration between companies appears as one of the promising solution that improves the freight transport efficiency of modern cities. Existing studies on collaborative trans-port did not sufficiently account for sustainability aspects and they mainly discuss the independent planning of decision-making problems with economic objectives. However, collaboration an integrated decision-making process can significantly ameliorate the effectiveness of urban transportation systems. This study addresses the issue of sustainable freight transportation planning by formulating and solving a collaborative and integrated two-echelon inventory, location and routing problem (2E-CILRP). The 2E-CILRP is formulated as a multi-objective integer programming model that minimizes logistics costs, CO2 emissions and accident rate. A hybrid heuristic based on machine learning is also introduced to solve the studied problem. The proposed approach combines k-means clustering and genetic algorithms. Comparison of the obtained results with those provided with exact method reveals that the developed approach is more efficient and more practical for designing integrated and collaborative networks with large-sized data. A case study of four agri-food SMEs in France is investigated in order to demonstrate the sustainable benefits of the developed optimization approach. The obtained results suggest that the developed approach can significantly reduce the level of CO2 emissions, logistics costs and accident rate caused by transportation in cities.

关键词： Collaborative city logistics Integrated planning Sustainable freight transport mixed-integer linear programming Heuristic Collaborative inventory-location-routing problem

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Energy-Water-CO₂ Synergetic Optimization Based on a mixed-integer linear Resource Planning Model Concerning the Demand Side Management in Beijing's Power Structure Transformation

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ENERGIES 2021年第11期14卷

作者： Liu, Yuan Tan, Qinliang Han, Jian Guo, Mingxin North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China North China Elect Power Univ Beijing Key Lab Renewable Elect Power & Low Carbo Beijing 102206 Peoples R China North China Elect Power Univ Res Ctr Beijing Energy Dev Beijing 102206 Peoples R China

Studies on the energy-water-CO2 synergetic relationship is an effective way to help achieve the peak CO2 emission target and carbon neutral goal in global countries. One of the most valid way is to adjust through the electric power structure transformation. In this study, a mixed-integer linear resource planning model is proposed to investigate the energy-water-CO2 synergetic optimization relationship, concerning the uncertainties in the fuel price and power demand prediction process. Coupled with multiple CO2 emissions and water policy scenarios, Beijing, the capital city of China, is chosen as a case study. Results indicate that the demand-side management (DSM) level and the stricter environmental constraints can effectively push Beijing's power supply system in a much cleaner direction. The energy-water-CO2 relationship will reach a better balance under stricter environmental constraints and higher DSM level. However, the achievement of the energy-water-CO2 synergetic optimization will be at an expense of high system cost. Decision makers should adjust their strategies flexibly based on the practical planning situations.

关键词： energy-water-carbon synergetic optimization mixed-integer linear programming uncertainty DSM power structure transformation

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A Novel linear Optimal Power Flow Model for Three-Phase Electrical Distribution Systems 3

A Novel Linear Optimal Power Flow Model for Three-Phase Elec...

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3rd International Conference on Smart Energy Systems and Technologies (SEST)

作者： Giraldo, Juan S. Vergara, Pedro P. Lopez, Juan Camilo Nguyen, Phuong H. Paterakis, Nikolaos G. Eindhoven Univ Technol Dept Elect Engn Eindhoven Netherlands Univ Estadual Campinas Sch Elect & Comp Engn Campinas SP Brazil

ISBN: (纸本)9781728147017

This paper presents a new linear optimal power flow model for three-phase unbalanced electrical distribution systems considering binary variables. The proposed formulation is a mixed-integer linear programming problem, aiming at minimizing the operational costs of the network while guaranteeing operational constraints. Two new linearizations for branch current and nodal voltage magnitudes are introduced. The proposed branch current magnitude linearization provides a discretization of the Euclidean norm through a set of intersecting planes, while the bus voltage magnitude approximation uses a linear combination of the L1 and the L norm. The proposed approach is compared to a nonlinear power flow for an unbalanced distribution system with fixed power injections. The obtained results showed errors of less than 4% for currents and 0.005% for voltages, demonstrating that satisfactory accuracy may be obtained using the proposed linearizations.

关键词： linear optimal power flow unbalanced three-phase electrical distribution systems mixed-integer linear programming

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Incoherence measures and relations between coherence conditions for pairwise comparisons

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DECISIONS IN ECONOMICS AND FINANCE 2020年第2期43卷 613-635页

作者： Brunelli, Matteo Cavallo, Bice Univ Trento Dept Ind Engn Trento Italy Univ Naples Federico II Dept Architecture Naples Italy

Coherence of preferences, and the measurement of its violation, has been a long-standing issue in decision analysis. This paper continues the inquiry into coherence conditions for pairwise comparisons following a distance-based approach, in which the deviations from coherence conditions are measured on a continuous scale. Firstly, we consider eight coherence conditions already introduced in the literature and provide a complete study on their inclusion relations. Then, we consider four of these conditions and introduce optimization problems to quantify the extent of their violation.

关键词： Pairwise comparisons Preference relations Coherence mixed-integer linear programming

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