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Comprehensive Day-Ahead Marketing Model Considering N-1 Security and Renewables

JOURNAL OF ENERGY ENGINEERING 2023年第1期149卷 04022042-04022042页

作者： Tyroller, Karina Yalcinoz, Tankut Rudion, Krzysztof Univ Stuttgart Inst Power Transmiss & High Voltage Technol D-70569 Stuttgart Germany NewVation GmbH Landhausstr 32 D-70190 Stuttgart Germany Univ Stuttgart Inst Power Transmiss & High Voltage Technol D-70569 Stuttgart Germany

Integrating renewable energies into the electricity grid is a challenge that requires an in-depth investigation. This paper presents a model of the day-ahead electricity market, including wind and photovoltaic (PV) generators, and contingency analysis. The comprehensive day-ahead marketing model is designed as a modular structure so that the individual modules can be easily modified in the future. First, the day-ahead electricity marketing of an electricity grid with and without renewable energies is studied. A market model solves the problem of unit commitment and economic dispatch. A grid model was used to calculate the power flow of the branches and the grid losses. Congestions in the branches were handled by redispatching. For this purpose, the generators were redispatched using mixed-integer linear programming and the power transfer distribution factor. A contingency analysis, investigating the outage of one branch, was applied by using the line outage distribution factor. The redispatch process was operated with these calculated increased power flows, which need to be handled by the grid to achieve N-1 security. The simulations were carried out using 30-bus 118-bus systems. Historic wind speed and solar radiation data of Germany were used as input data for renewable energy production. The load data of the 118-bus system were generated based on a standard load profile from Germany. The results show that both systems were able to meet the demand without violation of the grid capacity during normal conditions, with and without renewable energies. However, when applying the contingency analysis, an outrageous amount of load curtailment is needed.

关键词： Day ahead Redispatch Contingency analysis Renewable energies Power transfer distribution factor mixed-integer linear programming

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Integrated planning framework for preventive maintenance grouping: A case study for a conveyor system in the Chilean mining industry

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY 2023年第5期237卷 1011-1028页

作者： Viveros, Pablo Mena, Rodrigo Zio, Enrico Miqueles, Leonardo Kristjanpoller, Fredy Univ Tecn Federico Santa Maria Dept Ind Engn Ave Espana Av 1680 Valparaiso 2390123 Chile PSL Res Univ CRC MINES ParisTech Sophia Antipolis France Politecn Milan Dept Energy Milan Italy

Increasingly complex production environments pose new challenges and require the development of new methodological approaches in the search for effective and cost-efficient maintenance planning, taking into account their applicability in real industrial environments. In this context, this article proposes an extension of the integrated framework for opportunistic preventive maintenance (PM) planning, proposed originally in Viveros et al. integrating non-negligible execution times of PM activities and time-window tolerances criterion for the generation of opportunistic grouping schemes. This work offers the implementation of the proposed framework in a practical case within the Chilean mining industry, expanding the analysis on scarce resource availability scenarios for PM tasks to be performed on conveyor belts in the grinding process. The proposed planning optimization model is formulated under the mixed-integer linear programming (MILP) paradigm, and searches minimizing the asset unavailability under several tolerance levels for the case study analyzed. The results show a 35% downtime reduction for a maximum tolerance factor of 10% considering an unconstrained maintenance resource scenario, which confirms the ability of the model to increase asset availability, minimizing system interruptions, improving its cost efficiency, and enhancing productivity levels in the mining industry.

关键词： Maintenance planning opportunistic grouping mining process mixed-integer linear programming multi-component systems

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Fair Least Core: Efficient, Stable and Unique Game-Theoretic Reward Allocation in Energy Communities by Row-Generation

IEEE Transactions on Energy Markets, Policy and Regulation

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IEEE Transactions on Energy Markets, Policy and Regulation 2025年第2期3卷 170-181页

作者： Fioriti, Davide Bigi, Giancarlo Frangioni, Antonio Passacantando, Mauro Poli, Davide University of Pisa Department of Energy Systems Territory and Construction Engineering Pisa56122 Italy Dipartimento di Informatica University of Pisa PI Pisa56127 Italy Department of Business and Law University of Milano-Bicocca MI Milan20126 Italy

Energy Communities are increasingly proposed as a tool to boost renewable penetration and maximize citizen participation in energy matters. These policies enable the formation of legal entities that bring together power system members, enabling collective investment and operation of energy assets. However, designing appropriate reward schemes is crucial to fairly foster individuals to join, as well to ensure collaborative and stable aggregation, maximizing community benefits. Cooperative Game Theory, emphasizing coordination among members, has been extensively proposed for ECs and microgrids;however, it is still perceived as obscure and difficult to compute due to its exponential computational requirements. This study proposes a novel framework for stable fair benefit allocation, named Fair Least Core, that provides uniqueness, reproducibility, stability and fairness. A novel row-generation algorithm is also proposed that allows to efficiently compute the imputations for coalitions of practical size. A case study of ECs with up to 100 members shows the stability, reproducibility, fairness and efficiency properties of proposed model. The results also highlight how the market power of individual users changes as the community grows larger, which can steer the development of practical reliable, robust and fair reward allocations for energy system applications. © 2024 IEEE.

关键词： mixed-integer linear programming

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Learning-Assisted Variables Reduction Method for Large-Scale MILP Unit Commitment

IEEE OPEN ACCESS JOURNAL OF POWER AND ENERGY

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IEEE OPEN ACCESS JOURNAL OF POWER AND ENERGY 2023年 10卷 245-258页

作者： Shekeew, Mohamed Ibrahim Abdelaziz Venkatesh, Bala Toronto Metropolitan Univ Dept Elect Comp & Biomed Engn Toronto ON M5B 2K3 Canada

The security-constrained unit commitment (SCUC) challenge is solved repeatedly several times every day, for operations in a limited time. Typical mixed-integer linear programming (MILP) formulations are intertemporal in nature and have complex and discrete solution spaces that exponentially increase with system size. Improvements in the SCUC formulation and/or solution method that yield a faster solution hold immense economic value, as less time can be spent finding the best-known solution. Most machine learning (ML) methods in the literature either provide a warm start or convert the MILP-SCUC formulation to a continuous formulation, possibly leading to sub-optimality and/or infeasibility. In this paper, we propose a novel ML-based variables reduction method that accurately determines the optimal schedule for a subset of trusted generators, shrinking the MILP-SCUC formulation and dramatically reducing the search space. ML indicators sets are created to shrink the MILP-SCUC model, leading to improvement in the solution quality. Test results on IEEE systems with 14, 118, and 300 busses, the Ontario system, and Polish systems with 2383 and 3012 busses report significant reductions in solution times in the range of 48% to 98%. This is a promising tool for system operators to solve the MILP-SCUC with a lower optimality gap in a limited-time operation, leading to economic benefits.

关键词： Generators Power systems Costs Spinning Machine learning programming Power transmission lines mixed-integer linear programming UC variables reduction unit commitment

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Routing a mixed fleet of conventional and electric vehicles for urban delivery problems: considering different charging technologies and battery swapping

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE-OPERATIONS & LOGISTICS 2023年第1期10卷 Article: 2191804页

作者： Amiri, Afsane Zolfagharinia, Hossein Amin, Saman Hassanzadeh Toronto Metropolitan Univ Dept Mech & Ind Engn Toronto ON Canada Toronto Metropolitan Univ Ted Rogers Sch Management Toronto ON M5B 2K3 Canada

We present a vehicle routing problem with load capacity and time windows for a fleet of electric vehicles (EVs) and internal combustion vehicles (ICVs). Different charging technologies, including Level 1, 2, and 3 chargers and swapping batteries, are considered in this research. Given the location of the depot, the existing customers, and the set of charging stations, this problem aims to minimise the overall cost of constructing the routes over the vertices that need to be visited by either an ICV or EV. We develop a mixed-integer linear programming (MILP) model for this problem, and we solve small samples using a CPLEX solver. In addition, we develop two metaheuristic solution approaches by combining Adaptive Large Neighbourhood Search (ALNS) with Simulated Annealing (SA) and Tabu Search (TS). Using a set of locations from Scarborough, Ontario, Canada, we investigate the delivery routing problem with a fleet of ICVs and EVs. By solving the problem for different scenarios, we observed that EVs often require partial recharging and faster chargers (Level 3) when traveling in the city.

关键词： Electric vehicle routing problem mixed-integer linear programming mixed-fleet battery swapping metaheuristics

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MILP-based model predictive control for home energy management systems: A real case study in Algarve, Portugal

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ENERGY AND BUILDINGS 2023年第1期281卷

作者： Gomes, I. L. R. Ruano, M. G. Ruano, A. E. Univ Algarve Fac Sci & Technol Faro Portugal Univ Lisbon IDMEC Inst Super Tecn Lisbon Portugal Univ Evora ICT Evora Portugal Univ Coimbra CISUC Coimbra Portugal

This paper addresses the development of an innovative home energy management system (HEMS). The presented HEMS relies on a mixed-integer linear programming (MILP)-based model predictive control. The system takes advantage of the powerful formulation capabilities of a MILP-based mathematical pro-gramming problem with the capabilities of model predictive control to optimize, at each sample instant the HEMS operation using a receding-horizon formulation. The system is designed for a residence located in Algarve, Portugal. The results of the presented system are compared with the real experimental results obtained by a commercial PV-battery management system. Additionally, an analysis of the system???s per-formance is conducted, in terms of operation planning for 2021 market prices compared to 2022 prices, where there was a significant rise of buying price due to the energy world context. In all simulations per-formed, it is verified that the MILP-based model predictive control presents better results, with statistical relevance. CO 2023 Elsevier B.V. All rights reserved.

关键词： Home energy management system HEMS Model predictive control mixed-integer linear programming Energy storage Renewable energy Demand response

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Single machine scheduling with release dates: A distributionally robust approach

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第1期308卷 19-37页

作者： Lu, Haimin Pei, Zhi Zhejiang Univ Technol Coll Mech Engn Hangzhou 310023 Peoples R China

In the present study, a single machine scheduling problem with job release dates is considered, where the job processing time is depicted by an ambiguity set, with the mean, the mean absolute deviation, and the support set information given. To the best of our knowledge, this is the first time that a distribution -ally robust optimization method is applied to the single machine scheduling with release dates. Based on the risk attitude of the decision maker, two distributionally robust optimization models are proposed, namely, the risk-neutral and risk-averse scheduling. The two models concern with the worst-case per-formance and the robust conditional value-at-risk with respect to the total flow time, respectively. Since the proposed robust optimization models are intractable in nature, we reformulate them as two separate mixed-integer linear programming models, which can then be solved via off-the-shelf solvers. Interest-ingly, it is proved that the mean absolute deviation can be ignored in the risk-neutral robust model. Thus a more succinct reformulation is constructed, which can be rapidly solved to optimum. Furthermore, ex-tensive numerical experiments are performed with the stochastic programming model as a benchmark. And it is observed that the risk-averse model outperforms the others on both the average and worst-case metrics in most cases. Meanwhile, the risk-neutral model is more preferable on the average metric when the jobs are sparsely released.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Scheduling Release date Distributionally robust optimization CVaR mixed-integer linear programming

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Optimal sizing and dispatch of solar power with storage

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OPTIMIZATION AND ENGINEERING 2023年第4期24卷 2579-2617页

作者： Cox, John Hamilton, William T. T. Newman, Alexandra M. M. Martinek, Janna Colorado Sch Mines 1500 Illinois St Golden CO 80401 USA Natl Renewable Energy Lab Thermal Sci Grp 15013 Denver West Pkwy Golden CO 80401 USA

Designers of utility-scale solar plants with storage, seeking to maximize some aspect of plant performance, face multiple challenges. In many geographic locations, there is significant penetration of photovoltaic generation, which depresses energy prices during the hours of solar availability. An energy storage system affords the opportunity to dispatch during higher-priced time periods, but complicates plant design and dispatch decisions. Solar resource variability compounds these challenges, because determining optimal system sizes requires simultaneously considering how the plant will be operated under the imposed market and weather conditions. We develop an approach to analyze the economic performance of hybrid and single-technology solar power plants, which incorporates optimal dispatch, and considers the expected electricity market and weather conditions. We utilize the System Advisor Model software package to simulate the operation of multiple renewable generation and energy storage technologies, in conjunction with hourly-fidelity generation decisions determined by a revenue-maximizing, mixed-integer linear program. We show that, under our assumed market and weather conditions, the lifetime benefit-to-cost ratio can be improved by 6 to 19 percent, relative to a baseline design without optimizing, and that a concentrating solar power with thermal energy storage design produces significantly more energy per year, but is less profitable under our cost assumptions.

关键词： Dispatch optimization Concentrating solar power Photovoltaics mixed-integer linear programming Hybrid renewable systems Black-box optimization

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A decomposition approach for the periodic consistent vehicle routeing problem with an application in the cleaning sector

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2023年第22期61卷 7727-7748页

作者： Messaoudi, Bilal Oulamara, Ammar Salhi, Said Univ Lorraine LORIA Lab Campus Sci BP 239 F-54506 Vandoeuvre Les Nancy France Antsway SA ARTEM 92 Rue Sergent Blandan F-54000 Nancy France Univ Kent Kent Business Sch Ctr Logist & Heurist Optimisat CLHO Canterbury CT2 7FS Kent England

This study is inspired by a challenging logistic problem encountered in the cleaning service sector. The company wishes to solve the consistent vehicle routeing problem over a three-month planning horizon. The company has a heterogeneous vehicle fleet to guarantee multiple frequencies of visits to its customers. The objective is to minimise the number of vehicles used and the total distance travelled. This problem is a generalisation of the periodic vehicle routeing problem. We decompose the problem into two sub-problems, namely, the planning and routeing optimisation sub-problems. We construct a mathematical model for the former and a large neighbourhood search for the latter. We evaluate the performance of our approach using the results of the industrial partner and instances from the literature on problems that are closely related to our case study. Our approach is found to be effective and robust. Our results outperform the existing company's plan in terms of solution quality, and staff convenience, and speed. We also discovered new best solutions on some of the instances from the literature.

关键词： Routing driver consistency mixed-integer linear programming large neighbourhood search Decomposition methods

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Multimodal Vaccine Distribution Network Design with Drones

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TRANSPORTATION SCIENCE 2023年第4期57卷 1068-1095页

作者： Enayati, Shakiba Li, Haitao Campbell, James F. Pan, Deng Univ Missouri St Louis Coll Business Adm Supply Chain & Analyt Dept St Louis MO 63121 USA

Childhood vaccines play a vital role in social welfare, but in hard-to-reach regions, poor transportation, and a weak cold chain limit vaccine availability. This opens the door for the use of vaccine delivery by drones (uncrewed aerial vehicles, or UAVs) with their fast transportation and reliance on little or no infrastructure. In this paper, we study the problem of strategic multimodal vaccine distribution, which simultaneously determines the locations of local distribution centers, drone bases, and drone relay stations, while obeying the cold chain time limit and drone range. Two mathematical optimization models with complementary strengths are developed. The first model considers the vaccine travel time at the aggregate level with a compact formulation, but it can be too conservative in meeting the cold chain time limit. The second model is based on the layered network framework to track the vaccine flow and travel time associated with each origin destination (OD) pair. It allows the number of transshipments and the number of drone stops in a vaccine flow path to be limited, which reflects practical operations and can be computationally advantageous. Both models are applied for vaccine distribution network design with two types of drones in Vanuatu as a case study. Solutions with drones using our parameter settings are shown to generate large savings, with differentiated roles for large and small drones. To generalize the empirical findings and examine the performance of our models, we conduct comprehensive computational experiments to assess the sensitivity of optimal solutions and performance metrics to key problem parameters.

关键词： vaccine distribution drone delivery cold chain limit network design multimodal and intermodal network design mixed-integer linear programming

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