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Enhancing Hosting Capacity of Uncertain and Correlated Wind Power in Distribution Network With ANM Strategies

IEEE ACCESS 2020年 8卷 189115-189128页

作者： Xiao, Juanxia Li, Yong Qiao, Xuebo Tan, Yi Cao, Yijia Jiang, Lin Hunan Univ Coll Elect & Informat Engn Changsha 410082 Peoples R China Univ Liverpool Coll Elect Engn & Elect Liverpool L69 3GJ Merseyside England

To settle the large-scale integration of wind power, it is necessary to enhance the wind power hosting capacity of distribution network. To this end, this paper proposes a stochastic mixed-integer linear programming (MILP) model to enhance the hosting capacity of distribution network with active network management (ANM) strategies. The considered ANM strategies include power factor control, reactive power compensation and network reconfiguration. The coordination of these strategies enables the distribution network to accommodate more wind power without violating its constraints. The proposed model relaxes the original nonlinear power flow equations into linear format, so that a near optimal solution can be obtained with moderate computational burden. The wind power and load demand are inherently variable due to the influence of various factors. Moreover, the output powers of multiple wind turbines (WTs) located at adjacent sites of a wind farm are spatially correlated. To represent the uncertain loads, the roulette wheel mechanism is used to generate the load samples. In addition, a combined method of inverse transformation and Nataf transformation is established to deal with the uncertain and correlated wind power. Then, scenario combination and reduction are conducted to generate representative scenarios. Finally, the feasibility and accuracy of the proposed method are verified via numerical tests on the modified IEEE 33-bus test system.

关键词： Optimization Wind power generation Distribution networks Reactive power Stochastic processes Uncertainty Correlation Active network management hosting capacity mixed-integer linear programming stochastic optimization wind power correlation

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Robust chance-constrained gas management for a standalone gas supply system based on wind energy

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ENERGY 2020年 212卷

作者： Xu, Xiao Hu, Weihao Du, Yuefang Liu, Wen Liu, Zhou Huang, Qi Chen, Zhe Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu Peoples R China Univ Utrecht Copernicus Inst Sustainable Dev Princetonlaan 8a NL-3584 CB Utrecht Netherlands Aalborg Univ Dept Energy Technol Pontoppidanstraede 111 Aalborg Denmark

Remote areas are often rich in renewable energy resources, which can be used to produce synthetic natural gas (SNG) at power-to-gas conversion facilities. A balance between gas supply and demand is vital for the efficient and reliable operation of a standalone gas supply system. However, it is generally difficult to guarantee such a balance in a standalone gas supply system. Therefore, a transport tank vehicle is considered as an option for transporting SNG from other areas with sufficient natural gas. Existing studies in this domain have not considered a separate gas supply system for remote areas and fail to provide a robust gas management strategy in an uncertain environment. Thus, this study first introduces a chance-constrained gas management model for the standalone gas supply system. The objective function is to minimize the total operational cost of the gas supply system. An ambiguity set based on the historical wind power data is employed to address the uncertainty caused by the wind power generation. The chance-constrained gas management model is processed via distributionally robust optimization. Then, the gas management model can be reformulated as a mixed-integer linear programming problem. Finally, the proposed model is validated using a case study. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： Gas management Distributionally robust optimization Power-to-gas conversion mixed-integer linear programming

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Arterial Traffic Signal Coordination for General and Public Transport Vehicles Using Dedicated Lanes

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JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS 2020年第7期146卷

作者： Florek, Krzysztof Cracow Univ Technol Dept Transportat Syst Fac Civil Engn 24 Warszawska St PL-31155 Krakow Poland

Coordination of traffic signals is one of the most effective methods to improve the quality of traffic control on an artery. Properly coordinated traffic signals can effectively minimize delay and number of stops, increase travel speed, and reduce the number of collisions. MULTIBAND and AM-BAND are well known and widely used methods for arterial traffic control. The optimum solution provides the ability to travel without stops for most individual vehicles, whereas buses stopping at bus stops may experience excessive delays. This paper presents BUS-MULTIBAND and BUS-AM-BAND methods formulated as mixed-integer linear programs that solve the signal coordination problem for passenger cars and public transport vehicles simultaneously. The optimal signal coordination plans for general and transit vehicles were computed, simulated, and compared with signal plans generated for only general vehicles. Simulation results indicate that traffic signal coordination plans generated by the proposed methods can provide significant benefits for buses and, at most, small losses for general vehicles compared with plans generated by MULTIBAND and AM-BAND.

关键词： Traffic signal coordination MULTIBAND AM-BAND mixed-integer linear programming Traffic simulation

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Hourly pricing and day-ahead dispatch setting in Brazil: The dessem model

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ELECTRIC POWER SYSTEMS RESEARCH 2020年 189卷

作者： Santos, T. N. Diniz, A. L. Saboia, C. H. Cabral, R. N. Cerqueira, L. F. CEPEL Elect Energy Res Ctr Rio De Janeiro RJ Brazil UERJ State Univ Rio de Janeiro Av Horacio Macedo 354 BR-22220040 Rio De Janeiro Brazil

Power generation planning and price setting in hydrothermal systems is a complex task, usually performed by a chain of optimization models, ranging from long to short-term time horizons. In Brazil, the weekly hydrothermal dispatch and spot pricing has been officially set since 2002 with two optimization models: NEWAVE and DECOMP. This work briefly presents the main features of a third model in this chain (DESSEM) which has been validated since 2017 by the Brazilian ISO and the Market Operator to determine the hourly dispatch and energy prices starting in 2020/2021. This model considers a very comprehensive set of thermal unit commitment constraints, nonconvex security constraints for the electrical network and a very detailed operation of the hydro plants. We apply CPLEX solver to the resulting MILP problem, with smart ad-hoc iterative procedures to reduce the computational burden. Results are presented for the large scale Brazilian system.

关键词： Hydrothermal scheduling Energy pricing mixed-integer linear programming Unit commitment

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Program File Placement Strategies for Machine-to-Machine Service Network Platform in Dynamic Scenario

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IEICE TRANSACTIONS ON COMMUNICATIONS 2020年第11期E103B卷 1353-1366页

作者： Sato, Takehiro Oki, Eiji Kyoto Univ Grad Sch Informat Kyoto 6068501 Japan

The machine-to-machine (M2M) service network platform that accommodates and controls various types of Internet of Things devices has been presented. This paper investigates program file placement strategies for the M2M service network platform that achieve low blocking ratios of new task requests and accommodate as many tasks as possible in the dynamic scenario. We present four strategies for determining program file placement, which differ in the computation method and whether the relocation of program files being used by existing tasks is allowed or not. Simulation results show that a strategy based on solving a mixed-integer linear programming model achieves the lowest blocking ratio, but a heuristic algorithm-based strategy can be an attractive option by allowing recomputation of the placement when the placement cannot be obtained at the timing of new task request arrival.

关键词： machine to machine placement problem mixed-integer linear programming heuristic algorithm online algorithm

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Online offset optimization for urban traffic network with distributed model predictive control ⁎

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IFAC-PapersOnLine 2020年第2期53卷 6604-6609页

作者： Yunwen Xu Na Wu Dewei Li Yugeng Xi Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China

This paper proposes a distributed control framework to optimize the offset for a path in a traffic network with arbitrary topology. Each intersection along the target path applies the model predictive control to optimize their own phase sequence and green splits with the objective of minimizing the sum of queue lengths. The first intersection on this path is regarded as the main intersection and responsible for optimizing the start green time and duration of the first phase on this path with a weighted objective according to the real-time traffic information, while the other intersections take the constraints of offset imposed by intersections ahead into consideration. The signal cycles of these intersections are fixed but allowed to be different. For computation efficiency, the nonlinear optimization problem is approximately reformulated as a mixed-integer linear programming problem. Numerical experiments on a calibrated network of Caohejing District in Shanghai indicate that our proposed method can effectively decrease delay time and waiting time especially at medium and high traffic loads.

关键词： Traffic signal control Online offset Model predictive control Distributed control framework mixed-integer linear programming

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N-1 Multi-contingency transient stability constrained AC optimal power flow with volt/var controllers

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ELECTRIC POWER SYSTEMS RESEARCH 2020年 188卷

作者： Cruz, John W. Lopez, Juan Camilo Dotta, Daniel Rider, Marcos J. Univ Estadual Campinas Sch Elect & Comp Engn Dept Syst & Energy UNICAMP BR-13083852 Campinas SP Brazil

In order to guarantee the continuous operation of electric power systems (EPS), a proper planning of these networks in steady and transient state must be performed. This paper presents an N - 1 multi-contingency AC optimal power flow (OPF) that embeds, in the same mathematical optimization model, a set of transient stability constraints (TSC) that guarantee rotor angle and angular velocity variation within technical limits, at given N - 1 contingencies. Furthermore, the proposed model considers the operation of volt/var controllers, such as shunt elements and OLTC transformers, to further improve the operation of the EPS, under given levels of demand and generation. Taking advantage of the classical first-order transient stability model of synchronous machines and the implicit trapezoidal integration rule, the proposed model can be formulated as a stand-alone mixed-integer nonlinear programming (MINLP) model. Then, through well-established linearization techniques, the initially proposed MINLP model is transformed into a new mixed-integer linear programming (MILP) model, which can be implemented via algebraic programming languages, such as AMPL, and solved using convex optimization solvers, such as CPLEX. Three systems with dissimilar number of synchronous machines and nodes have been used for tests (i.e., the 9-Bus/3-Generator Western System Coordinating Council (WSCC) system, the 39-Bus/10Generator New England system and the 68-Bus/16-Generator IEEE system). The efficiency of the proposed linearization techniques and the stability requirements of the solutions have been validated using an exact AC power flow and the transient stability analysis program PSAT-Matlab. Results show the ability of the proposed MILP model to provide stable operating points under N - 1 contingencies, at minimum production cost.

关键词： AC optimal power flow mixed-integer linear programming N-1 multi-contingency efficiency Transient stability constraints Volt/var controllers

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A heuristic approach for optimal integrated airline schedule design and fleet assignment with demand recapture

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APPLIED SOFT COMPUTING 2020年 96卷

作者： Khanmirza, Esmaeel Nazarahari, Milad Haghbeigi, Morteza Iran Univ Sci & Technol Sch Mech Engn Tehran Iran Univ Alberta Dept Mech Engn Neuromuscular Control & Biomech Lab Edmonton AB Canada

Flight schedule design and fleet assignment are the two main elements of the airline scheduling process, which have the highest effect on cost and revenue. Although mixed-integer linear programming models were developed for integrated schedule design and fleet assignment, it has been shown that this approach was not efficient for large-scale models. Therefore, this paper aimed at developing a parallel master-slave Genetic Algorithm (PMS-GA) for solving the integrated flight schedule design and fleet assignment problem with demand recapture, particularly for large-scale problems. The integrated schedule design and fleet assignment problem was solved by the master GA, while the slave GA nested inside the master GA solved passenger flow adjustment problem. Considering the complexities of a large-scale integrated problem, we (1) proposed an innovative approach for creating feasible suboptimal initial population, (2) developed customized genetic operators to improve the performance of the PMS-GA compared to the conventional GAs, and (3) implemented migration and repopulation to prevent premature convergence. PMS-GA was tested on seven models with small-, medium-, and large-scales, and the results were compared with the gold-standard mixed-integer linear programming in terms of cost and runtime. The comparative study showed that the PMS-GA achieved suboptimal solutions with costs only 1.8% to 3.0% different than the optimal solution for medium- and large-scale models. However, these solutions were obtained in significantly shorter runtimes (over 500% to 1000%) compared to the mixed-integer linear programming. Also, the results showed that in contrast to the mixed-integer linear programming approach, runtimes of the proposed PMS-GA are highly predictable as a function of the problem size. Our results showed the importance of PMS-GA for integrated schedule design and fleet assignment, particularly for solving large-scale re-scheduling problems in a short time. (C) 2020 Else

关键词： Airline flight scheduling Airline fleet assignment Passenger flow adjustment Parallel master-slave genetic algorithm mixed-integer linear programming

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Cargo routing and scheduling problem in deep-sea transportation: Case study from a fertilizer company

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COMPUTERS & OPERATIONS RESEARCH 2020年 119卷

作者： Giavarina dos Santos, Pietro Tiaraju Kretschmann, Endel Borenstein, Denis Guedes, Pablo Cristini Univ Fed Rio Grande do Sul Management Sch BR-90010460 Porto Alegre RS Brazil Yara Brasil Global HR Operat Porto Alegre RS Brazil

This study presents a mixed-integer linear programming (MILP) model and a solution method for a maritime cargo routing and shipping problem faced by a chemical company in Brazil. This problem is associated with the operational planning of multiple raw materials, collected from European ports and delivered to Brazilian ones to supply mixing production plants. First, the problem is modeled as a pickup and delivery problem with time windows, incorporating several constraints and operational requisites of the specific problem. In order to solve large real-world instances, a matheuristic was developed, employing a modified relax-and-fix strategy, a relaxation procedure, and repair and polishing routines for MILP solutions. The matheuristic was evaluated using real-life instances provided by the company, demonstrating the efficiency and efficacy of the developed solution method. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Cargo routing and scheduling Maritime transportation Pickup and delivery Heterogeneous fleet mixed-integer linear programming Matheuristic

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Optimal Dispatch of Integrated Energy Campus Microgrids Considering the Time-Delay of Pipelines

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IEEE ACCESS 2020年 8卷 178782-178795页

作者： Tang, Zhiqiang Lin, Shunjiang Liang, Weikun Xie, Yuquan Song, Yunong Wang, Jiayu Liu, Mingbo South China Univ Technol Sch Elect Power Engn Guangzhou 510640 Peoples R China

Energy transmission in cooling/heating and gas pipelines has time-delay, which impacts the power balance between various sources and loads in the optimal dispatch of an integrated energy campus microgrid (IECM). This article proposed an optimal dispatch model for IECMs, which considered the time-delay of energy transmission in cooling/heating and gas pipelines. In this model, the electricity and natural gas purchase cost of the IECM was made into an objective function. Partial differential equations (PDEs) were used to describe the time-delay in pipelines, and the discrete variables that described the switching number of absorption chillers and heat exchange units were included in the constraints. The proposed model was essentially a mixed-integer nonlinear programming (MINLP) model with PDE constraints. Orthogonal collocation on finite elements (OCFE) in the two-dimensional domain was used to transform PDEs into algebraic equations (AEs). Several linearization methods, including piecewise linearization and the big M method, were used to transform the initial MINLP model into a mixed-integer linear programming (MILP) model to reduce computational complexity. OCFE was compared with the first-order finite difference method, and simulation results were used to demonstrate the accuracy and efficiency of the proposed algorithm. The impact of the time-delay of pipelines on IECM was analyzed through comparison with a steady-state model.

关键词： Water heating Pipelines Resistance heating Natural gas Mathematical model Generators Power systems Integrated energy campus microgrids optimal dispatch time-delay orthogonal collocation on finite elements mixed-integer linear programming

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