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检索条件"主题词=Mixed integer linear programming"

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Multi-Uncertainty Capacity Planning of Integrated Energy System Considering Low-Carbon and Flexible Transformation of Coal-Fired Power Plants

Multi-Uncertainty Capacity Planning of Integrated Energy Sys...

引用

IEEE General Meeting Power& Energy Society

作者： Xinglei Liu Jun Liu Yin Yang Kezheng Ren Yu Zhao School of Electrical Engineering Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

Driven by the growing utilization of renewable energies and the pursuit of carbon neutrality goals, traditional coal-fired power plants (CFPPs) require greater flexibility in energy output dispatch and a reduction in carbon emissions. Meanwhile, in the long-term capacity planning of integrated energy systems (IESs) incorporating CFPPs, uncertainties at both the investment and operation stages cannot be ignored. Therefore, a novel low-carbon and flexible transformation structure for CFPPs is proposed in this paper, with its operation characteristics being modelled and thoroughly analyzed. Furthermore, to address multiple uncertainties, a capacity planning model for IESs based on conditional value at risk and information gap decision theory (CVaR-IGDT) is developed. The proposed model can be converted to a mixed-integer linear programming (MILP) problem to facilitate the computation. Numerical case analyses validate the effectiveness of the proposed low-carbon and flexible transformation structure for CFPPs, as well as the overall economy and reliability of the capacity planning scheme with multiple uncertainties.

关键词： Renewable energy sources Uncertainty Computational modeling Mathematical models Numerical models Planning mixed integer linear programming Power generation Capacity planning Investment

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Improved discrete JAYA algorithms for hot rolling scheduling considering equipment performance degradation

Improved discrete JAYA algorithms for hot rolling scheduling...

引用

IEEE International Conference on Automation Science and Engineering (CASE)

作者： Peitang Zhong Kun Zou Ziyan Zhao Shixin Liu College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Hot rolling is a typical industrial process with equipment performance degradation. Slabs to be rolled have specific equipment performance requirements and would cause different degrees of equipment performance degradation. Quantifying equipment performance to explore its impact on production scheduling has rarely been considered in existing work. In this paper, a new hot-rolling scheduling problem is tackled where the quantifying roller performance is considered. It aims to minimize the maximum completion time and the performance renewal cost. A mixed integer linear programming model is formulated for the problem. An improved discrete JAYA algorithm is proposed to effectively solve it. Experimental results show that the proposed algorithm performs well in solving the concerned problem, betters competitive peers, and shows great potential to be used in practice.

关键词： Degradation Job shop scheduling Costs Computer aided software engineering Automation Production mixed integer linear programming Slabs

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A Distributed Algorithm for Large-Scale Multi-Agent MINLPs

A Distributed Algorithm for Large-Scale Multi-Agent MINLPs

引用

IEEE Conference on Decision and Control

作者： Sheng Dong Weiguo Xia Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education and School of Control Science and Engineering Dalian University of Technology China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we focus on the optimization of large-scale multi-agent systems, where agents collaboratively optimize the sum of local objective functions through their own continuous and/or discrete decision variables, subject to global coupling constraints and local constraints. The resulting mixed-integer Nonlinear programmings (MINLPs) are NP-hard, non-convex, and large-scale. Therefore, this paper aims to design distributed algorithms to find feasible suboptimal solutions with a guaranteed bound. To this end, considering dual decomposition as an effective method to decompose large-scale constraint-coupled optimization problems, we first show, based on the convexification effects of large-scale MINLPs, that the primal solutions from the dual are near-optimal under certain conditions. This expands recent results in mixed-integer linear programmings (MILPs) to the nonlinear case but requires additional efforts on the proof. Utilizing this result to tighten the coupling constraints, we develop a fully distributed algorithm for the tightened problem, based on dual decomposition and consensus protocols. The algorithm is guaranteed to provide feasible solutions for the original MINLP. Moreover, asymptotic suboptimality bounds are established for the obtained solution. Finally, the efficacy of the method is verified through numerical simulations.

关键词： Couplings programming Numerical simulation linear programming mixed integer linear programming Consensus protocol Distributed algorithms Optimization Standards Multi-agent systems

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Integration of FDIR Practices in Distribution Optimal Network Reconfiguration Routines

Integration of FDIR Practices in Distribution Optimal Networ...

引用

International Conference on Environment and Electrical Engineering (EEEIC)

作者： Roberto Cometa Mohammad Rajabi Nasab Francesco Lorusso Angelo Velini Massimo La Scala Sergio Bruno DEI Politecnico di Bari Bari Italy

ISBN: (数字)9798350355185

ISBN: (纸本)9798350355192

The adoption of optimal network reconfiguration (ONR) tools is crucial in enhancing the operational efficiency, security and reliability of smart distribution grids, since ONR can be used to ensure grid integrity and quality of supply among end-users. However, in order to improve ONR applicability in a SCADA/DMS framework, the actual practices in fault detection, isolation, and recovery (FDIR) must be taken into account. For this reason, this paper proposes a ONR formulation, based on mixed-integer linear programming, aimed at optimizing quality of supply indicators, while respecting connectivity and radiality constraints, together with other constraints on selectivity and coordination of protection devices. In order to validate its feasibility in a realistic scenario, the proposed approach has been tested on the model of an actual primary distribution grid which supplies energy to about 60,000 inhabitants and 30,000 customers in the South of Italy.

关键词： Distribution networks Reliability engineering Real-time systems Planning Timing Power systems mixed integer linear programming Logic Security Protection

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Cooperative Schedule Optimization with Rolling Traversal Method for Connected and Automated Vehicles in a Highway Interchange

Cooperative Schedule Optimization with Rolling Traversal Met...

引用

International Conference on Intelligent Transportation

作者： Pengfei Li Yihe Chen Jia Shi Haoyu Yang Keqiang Li Yugong Luo School of Vehicle and Mobility Tsinghua University Beijing China

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Over the past few decades, lots of highway interchanges have been constructed with the development of transportation. However, both off-ramp diverging and on-ramp merging in an interchange are typical bottlenecks in highways, and growing commuting demand frequently leads to traffic breakdown in the rush hour. With advanced communication technology, connected and automated vehicles (CAVs) and cloud control system (CCS) emerge to serve as a promising solution. Among existing research, there is relatively little research on the traffic congestion issue in highway interchanges, and most of them try to address the problem from a macro perspective in traffic flow. In the field of CAVs, there are many studies addressing congestion issue within a single bottleneck. However, they can only solve local traffic congestion and might lead to downstream congestion instead. In this study, passing sequences of vehicles in the diverging area and the merging area are comprehensively considered in a mixed-integer nonlinear programming (MINLP) problem. A rolling traversal (RT) method was proposed to decompose the MINLP problem into several small-scale solvable mixed-integer linear programming (MILP) problems. We build a simulation platform in python, in which simulations at different traffic flow rate are conducted. Results suggest that compared to individually optimize the off-ramp diverging and on-ramp merging, the proposed RT method successfully reduces the overall travel delay while guaranteeing safety, especially at high traffic flow rate.

关键词： Schedules Simulation Merging Transportation Optimal scheduling programming Safety mixed integer linear programming Traffic congestion Python

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Towards Enhanced Reliability of Active Distribution Systems with Adaptive Control of Inverters

Towards Enhanced Reliability of Active Distribution Systems ...

引用

IEEE General Meeting Power& Energy Society

作者： Hadis Hosseinpour Mohammed Benidris Joydeep Mitra Department of Electrical and Computer Engineering Michigan State University East Lansing MI

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

Electricity distribution companies are actively working to improve grid operational efficiency and reliability to meet the evolving expectations of customers. Achieving these objectives necessitates the utilization of advanced control methods for distributed power generation while emphasizing grid reliability improvements. This paper proposes an approach to integrate adaptive control systems of inverter-based resources (IBRs) with reliability evaluation of distribution systems to improve the impact of IBRs on overall system reliability. This approach effectively utilizes adaptive droop control of inverter-based distributed energy resources, leading to substantial enhancements in system reliability. The proposed approach leverages mixed-integer linear programming optimization and incorporates detailed linear AC optimal power flow to find efficient solutions. Notably, this study is pioneering in its incorporation of adaptive droop control into reliability assessments, demonstrating significant improvements in critical reliability indicators like the System Average Interruption Duration and Frequency Indices (SAIDI) and (SAIFI) in distribution systems. The effectiveness of the proposed approach is validated through a series of diverse case studies conducted on the IEEE 33-bus test system.

关键词： Control systems Inverters Power system reliability Distributed power generation mixed integer linear programming Reliability Voltage control Adaptive control Optimization Load flow

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Economic Dispatch Method with Chance Constraints for Power Systems Incorporating Pumped Storage, Wind Power Curtailment and Load Shedding

Economic Dispatch Method with Chance Constraints for Power S...

引用

Power System and Green Energy Conference (PSGEC)

作者： Zuwang Yu Xiong Wu Guodong Guo Dong Liu Yawei Xue Xi’an Jiaotong University Xi’an China State Grid Economic and Technological Research Institute Co. Ltd Beijing China

ISBN: (数字)9798350365573

ISBN: (纸本)9798350365580

The paper presents an economic dispatch method with chance constraints for electricity systems, specifically tackling issues related to the unpredictable nature of wind energy generation. The primary goal of this proposed approach is to minimize wind curtailment and load shedding by taking into account the variability of wind power output and reserve shortages. By creating dispatch plans that incorporate pumped storage, the system can operate economically while reducing wind curtailment. Furthermore, a method for reconstructing chance constraints is introduced using conditional and overall risk values, along with dual theory. This approach transforms a bilinear problem into a mixed integer linear programming problem by employing a piecewise linear strategy. Numerical experiments confirm the practicality and efficiency of this method.

关键词： Wind energy generation Optical wavelength conversion Transforms Load shedding Wind power generation Convex functions Power systems Numerical models mixed integer linear programming Load modeling

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Unit Commitment in a System Integrated with Adiabatic Compressed Air Energy Storage

Unit Commitment in a System Integrated with Adiabatic Compre...

引用

Energy, Power and Grid (ICEPG), International Conference on

作者： Yu Zhou Xuelin Zhang Guangkuo Li Linrui Ma Yazhou Wang Tong Zhang Xiaodai Xue Department of Electrical Engineering Tsinghua University Beijing China School of Energy and Electrical Engineering Qinghai University Xining China

ISBN: (数字)9798350377798

ISBN: (纸本)9798350377804

Adiabatic compressed air energy storage (A-CAES) has grown rapidly in China recently. The paper investigates the unit commitment (UC) problem for a hybrid system containing thermal plants and an A-CAES plant. The time-varying charging characteristics of A-CAES are analyzed and then modeled with a pressure-based state of charge (SOC). The hybrid system’s UC model is constructed and finally reformulated in a mixed-integer linear programming (MILP) way. A modified IEEE-30 bus system is used to verify the model’s effectiveness. Numerical results demonstrate that integrating A-CAES could reduce the number of unit startups and shutdowns and lower the system operating costs. Finally, the effect of the initial state of A-CAES on the UC results is studied.

关键词： Analytical models Costs Atmospheric modeling linear programming Numerical models mixed integer linear programming State of charge Energy storage

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

Learning-Assisted Variables Reduction Method for Large-Scale...

引用

IEEE General Meeting Power& Energy Society

作者： Mohamed Ibrahim Abdelaziz Shekeew Bala Venkatesh Toronto Metropolitan University Centre for Urban Energy

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

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： Economics Optimal scheduling Machine learning Generators mixed integer linear programming

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Enhancing the Resilience of Urban Energy Systems in Multi-Energy Micro-grid Via Preventive and Corrective Intelligent Strategies

Enhancing the Resilience of Urban Energy Systems in Multi-En...

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Power and Energy Systems Engineering (CPESE), International Conference on

作者： Qinghe Sun Xi Zhang Qiong Wang Jiawei Li Heng Hu Meng Hou State Grid China Electric Power Research Institute Beijing China State Grid Beijing Municipal Electric Power Company Beijing China

ISBN: (数字)9798350377439

ISBN: (纸本)9798350377446

With the increase of occurrence frequency and severity of power outages, the urban micro-grid is gradually established to resist the impact of emergency events. The multienergy micro-grid (MEMG) can further enhance resilience of urban energy systems, however, the cooperative operation of multi-energy sectors to enhance the resilience needs further optimization in the MEMG. In this paper, the normal operating mode of the multi-energy micro-grid and island operation mode under disaster conditions have been modeled, aiming to minimize costs through the coordinated optimization of operating equipment such as distributed generation (DG), combined heat and power (CHP), storage and so on. Furthermore, we have designed an intelligent overall operational strategy that includes preventive and corrective controls. This strategy optimizes the dispatch of local resources, reducing the curtailment of essential loads during disasters, thereby enhancing the resilience of urban energy systems.

关键词： Cogeneration Disasters Simulation Resists Minimization Power system reliability mixed integer linear programming Optimization Resilience Intelligent control

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