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Optimal planning of distributed hydrogen-based multi-energy systems

APPLIED ENERGY 2021年 281卷 116107-116107页

作者： Liu, Jinhui Xu, Zhanbo Wu, Jiang Liu, Kun Guan, Xiaohong Xi An Jiao Tong Univ MOEKLINNS Xian 710049 Peoples R China Tsinghua Univ TNLIST CFINS Dept Automat Beijing 100084 Peoples R China

As a clean and renewable energy, hydrogen has attracted increasing attention for the replacement of fossil fuels because it is an emerging way to address the uncertainties of the renewable energy. Besides, coordination of the energy storage units, such as hydrogen storage unit, hot water storage unit and chilled water storage unit, could improve energy efficiency and reduce system cost. Thus, the optimal planning of a distributed hydrogen-based multi-energy system is very important to build a hydrogen-based distributed energy system in the demand side. This paper focuses on the system planning problem, which is formulated as a mixed-integer linear programming problem and the objective is to minimize annual capital and operation expenditure of the system. The problem is solved by a commercial solver. The case studies are performed under different energy demand profiles and solar radiations obtained by EnergyPlus in twelve typical cities around the world. It is found that multi-energy storage units in the systems can significantly reduce system capital expenditure and operating expenses. Hot water storage unit enjoys the best benefits with an average system cost reduction rate being 64.6% with the planning horizon (8760 h). And the DHME system is environmental friendly, which can even drop over 100% carbon emission in high solar radiation regions compared with the conventional electricity-driven energy system. Furthermore, with optimistic hydrogen price target of USA Department of Energy, the developed DHME system will maximum reduce 60.0% OPEX compared with the conventional electricity-driven energy system.

关键词： Distributed hydrogen-based multi-energy system Optimal planning Storage devices mixed-integer linear programming

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MILPIBEA: Algorithm for Multi-objective Features Selection in (Evolving) Software Product Lines 1

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20th European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP)

作者： Saber, Takfarinas Brevet, David Botterweck, Goetz Ventresque, Anthony Univ Coll Dublin Sch Comp Sci Dublin Ireland Lab Informat Modelisat & Optimisat Syst Aubiere France Univ Limerick Limerick Ireland

ISBN: (数字)9783030436803

ISBN: (纸本)9783030436797;9783030436803

Software Product Lines Engineering (SPLE) proposes techniques to model, create and improve groups of related software systems in a systematic way, with different alternatives formally expressed, e.g., as Feature Models. Selecting the 'best' software system(s) turns into a problem of improving the quality of selected subsets of software features (components) from feature models, or as it is widely known, Feature Configuration. When there are different independent dimensions to assess how good a software product is, the problem becomes even more challenging - it is then a multi-objective optimisation problem. Another big issue for software systems is evolution where software components change. This is common in the industry but, as far as we know, there is no algorithm designed to the particular case of multi-objective optimisation of evolving software product lines. In this paper we present MILPIBEA, a novel hybrid algorithm which combines the scalability of a genetic algorithm (IBEA) with the accuracy of a mixed-integer linear programming solver (IBM ILOG CPLEX). We also study the behaviour of our solution (MILPIBEA) in contrast with SATIBEA, a state-of-the-art algorithm in static software product lines. We demonstrate that MILPIBEA outperforms SATIBEA on average, especially for the most challenging problem instances, and that MILPIBEA is the one that continues to improve the quality of the solutions when SATIBEA stagnates (in the evolving context).

关键词： Software product line Feature selection Multi-objective optimisation Evolutionary algorithm mixed-integer linear programming

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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 120343-120343页

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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Development of a Framework for Activation of Aggregator Led Flexibility

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ENERGIES 2021年第16期14卷 4950页

作者： O'Connell, Sarah Keane, Marcus Martin Inst Technol Carlow Fac Engn R93 V960 Carlow Ireland Natl Univ Ireland Galway Informat Res Unit Sustainable Engn Ryan Inst Galway H91 TK33 Ireland

This paper presents a novel framework architecture for an online, real-time flexibility assessment and activation platform targeted at unlocking the flexibility potential of commercial buildings and smaller industrial sites, thereby enabling greater levels of renewable grid integration. Renewable integration targets in Europe of up to 40% of power generation from renewable sources by 2030 and over 90% by 2050 aim to decarbonize the electrical grid and increase electrification of transport, industry, and buildings. As renewable integration targets increase, participation in flexibility programs will be required from a much greater range of buildings and sites to balance grids hosting high levels of renewable generation. In this paper, an online implementation of a standardized flexibility assessment methodology, previously developed for offline contract negotiations between stakeholders, is modified to automate the assessment. The automated assessment is then linked to an aggregator-based multi-building or site optimization stage, enabling increased participation of multiple buildings and sites. To implement the assessment, models for individual flexible systems were reviewed, selected, and adapted, including physics-based, data-driven, and grey-box models. A review of optimization for flexibility found mixed-integer linear programming to be the optimal approach for the selection of flexible systems for demand response events.

关键词： energy flexibility demand response aggregator data-driven models mixed-integer linear programming

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Look-ahead risk-constrained scheduling for an energy hub integrated with renewable energy

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APPLIED ENERGY 2021年 297卷 117109-117109页

作者： Xu, Xiao Hu, Weihao Liu, Wen Du, Yuefang Huang, Rui Huang, Qi Chen, Zhe Univ Elect Sci & Technol China Sch Mech & Elect Engn Sichuan Prov Key Lab Power Syst Wide Area Measure Chengdu Peoples R China Univ Utrecht Copernicus Inst Sustainable Dev Princetonlaan 8a NL-3584 CB Utrecht Netherlands Southwest Univ Polit Sci & Law Chongqing Inst Higher Learning Ctr Forens Sci Eng Chongqing Peoples R China Aalborg Univ Dept Energy Technol Pontoppidanstr 111 Aalborg Denmark

Energy hubs or multi-energy systems facilitate an improvement in their efficiency and flexibility for different energy supplies. However, the application of energy hubs results in scheduling challenges for the entire system due to the mutual impact of various energy forms and the increase in the number of uncertain variables. One of the parameters of an energy storage system, which is an indispensable component of an energy hub, is the stateof-charge at the end of the first day of scheduling. This parameter is crucial because its final state represents the initial state-of-charge for the second day, which has an effect on the operational cost of the second day. Based on a look-ahead risk-constrained technique, this work investigates the optimal scheduling of an energy hub for two days with the aim of minimizing the total operational cost. The state-of-charges of an energy storage system at the end of the first day is optimized by considering the scheduling result on the second day. The uncertainties of the two days are demonstrated via various scenarios. Additionally, a demand response program is adopted to reduce the total operational cost and increase the system flexibility. The results validate the feasibility of using look-ahead risk-constrained scheduling in an energy hub and indicate that a reduction in the total operational cost is achieved.

关键词： Risk-constrained scheduling Energy hub Demand response program mixed-integer linear programming

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The Multi-Period Workforce Scheduling and Routing Problem *

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2021年 102卷 102302-102302页

作者： Guastaroba, G. Cote, J. -F. Coelho, L. C. Univ Brescia Dept Econ & Management Brescia Italy CIRRELT Quebec City PQ Canada Univ Laval Quebec City PQ Canada

Motivated by the problem faced by a company that provides installation and maintenance services of electrical infrastructures, we study the Multi-Period Workforce Scheduling and Routing Problem (MPWSRP). A set of jobs requiring a service is given. A given set of workers, each proficient in a number of skills, is available to serve the jobs. Each job requires a team of workers providing the required skills, and is associated with a given priority level. A service time, which might span over multiple time periods, is associated with each job, and depends on the number of workers that have been assigned. The MPWSRP calls for determining an optimal dispatch plan to serve the given set of jobs by routing a number of teams over a finite planning horizon. The MPWSRP is a highly complex combinatorial optimization problem where routing, scheduling, and assignment decisions have to be taken simultaneously. We cast the MPWSRP as a mixed-integer linear Program (MILP). As an off-the-shelf solver can solve only small-size instances of the proposed MILP in reasonable computing times, we devise an Adaptive Large Neighborhood Search (ALNS) heuristic and a three-phase Decomposition Algorithm (DA) to solve largesize instances. Extensive computational experiments are conducted on a large set of instances comprising up to 100 jobs, 20 workers, and 20 time periods. The results show that ALNS is competitive with the solver on the small-size instances, producing solutions of similar average quality in considerably shorter computing times. In addition, they point out that ALNS consistently outperforms the DA in terms of solution quality. Further computational experiments are conducted applying our ALNS and DA on a set of large-size instances. Finally, a case study using real data sets is provided. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： Workforce routing and scheduling Multi-period planning mixed-integer linear programming Adaptive large neighborhood search Matheuristic

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A Genetic Algorithm Approach for a Truck and Trailer Routing Problem in a Loading/Unloading Bays Application 19th

A Genetic Algorithm Approach for a Truck and Trailer Routing...

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19th Mexican International Conference on Artificial Intelligence (MICAI)

作者： Bricia Galindo-Muro, Ana Mora-Vargas, Jaime Gaston Cedillo-Campos, Miguel Regis-Hernandez, Fabiola Tecnol Monterrey Monterrey Estado De Mexic Mexico Inst Mexicano Transporte Queretaro Mexico

ISBN: (纸本)9783030608842;9783030608835

Nowadays urban mobilities represent a necessity more than a challenge. Urban centers have a large vehicle congregation in the streets, causing difficulties in the last mile operations for urban freight. Further to the traffic problems, authorities imposed strict regulations in the cities for freight vehicles. As a consequence of both of these limitations, the freight vehicles can't optimally execute their activities. In this research, a Genetic Algorithm is developed for the resolution of the Truck and Trailer Routing Problem (TTRP). Urban freight dynamics for loading/unloading bays are represented through mixed-integer linear programming (MILP) model. The obtained results for instances up to 100 customers shows that the approach presented provides competitive solutions with the best known in the area.

关键词： Loading bays Truck and Trailer mixed-integer linear programming Genetic algorithm

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Motion Planning and Task Allocation for a Jumping Rover Team

Motion Planning and Task Allocation for a Jumping Rover Team

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Tan, Kai Chuen Jung, Myungjin Shyu, Isaac Wan, Changhuang Dai, Ran Ohio State Univ Mech & Aerosp Engn Dept Columbus OH 43210 USA

ISBN: (纸本)9781728173955

This paper presents a cooperative robotic team composed of unmanned ground vehicles (UGVs) with hybrid operational modes to tackle the multiple traveling salesman problem (mTSP) with obstacles. The hybrid operational modes allow every UGV in the team to not only travel on a ground surface but also jump over obstacles. We name these UGVs jumping rovers. The jumping capability provides a flexible form of locomotion by leaping and landing on top of obstacles instead of navigating around obstacles. To solve the mTSP, an optimal path between any two objective points in an mTSP is determined by the optimized rapidly-exploring random tree method, named RRT*, and is further improved through a refined RRT* algorithm to find a smoother path between targets. We then formulate the mTSP as a mixed-integer linear programming (MILP) problem to search for the most cost-effective combination of paths for multiple UGVs. The effectiveness of the hybrid operational modes and optimized motion with assigned tasks is verified in an indoor, physical experimental environment using the customized jumping rovers.

关键词： Jumping Robots Multiple Traveling Salesman Problem Path Planning Rapidly-exploring Random Tree mixed-integer linear programming

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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 107162-107162页

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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