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Exact approach for charging of PEVs with V2G capability to improve micro-grid reliability

IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第16期13卷 3690-3695页

作者： Shams, Hamed Sadeghfam, Amin Rostami, Naghi Tohidi, Sajjad Univ Tabriz Fac Elect & Comp Engn Tabriz Iran

Nowadays, the major proportion of fossil fuels is consumed in the transportation systems. Plug-in electric vehicles (PEVs) can play a significant role in reducing the petrol consumption. A large amount of additional electrical power demand needed for the charging of PEVs may adversely affect the electric grid reliability. Using the charging station in the micro-grids (MGs) to minimise the total system operation cost in the presence of PEVs with vehicle to grid (V2G) capability is becoming vitally important. In this work, a practical model for charging strategy of PEVs based on typical charge and discharge profiles given by the battery producers is considered. The problem is constrained by the loss of load probability index of reliability. The proposed strategy makes it possible to handle the generation shortage due to the outage of conventional units and intermittency of wind turbine units. Mixed Integer Non-linear programming is used for the optimal planning of MG in the presence of PEVs. Results show that an appropriate charge and discharge strategy may lead to a significant improvement in the reliability of the considered MG as well as a reduction in the operational cost of the total system.

关键词： power grids distributed power generation power generation scheduling probability battery powered vehicles nonlinear programming integer programming power generation dispatch power generation reliability wind turbines electric vehicle charging microgrid reliability transportation systems electric vehicles additional electrical power demand electric grid reliability charging station microgrids total system operation cost grid capability PEV charging V2G capability

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Iterative greedy user clustering algorithm for D2D-relay in vehicular communication systems

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IET MICROWAVES ANTENNAS & PROPAGATION 2019年第8期13卷 1087-1095页

作者： Wang, Zhenyu Zhou, Ting Xu, Tianheng Hu, Honglin ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China Chinese Acad Sci Shanghai Adv Res Inst Shanghai Peoples R China

Device-to-device (D2D) communications help in improving the performance of wireless services in cellular networks via enabling cooperation among mobile users. Further, compared to traditional pairwise cooperation, cluster-wise cooperation can achieve even higher spectral and power efficiency. D2D clustering certainly can be a great supporting technology for the future of intelligent transportation. In this study, the authors investigate the D2D-relay clustering network in the vehicular scenario and propose a new clustering scheme named iterative greedy user clustering (IGUC). By exploiting those vehicles with better channel state, system resources can be saved. Here, they mainly focus on the question which is by applying D2D-relay clustering among vehicles, how much bandwidth could be saved in the uplink. This problem is mathematically formulated into a non-linear binary programming problem with the goal being minimum spectrum usage. Due to the NP-hardness, the low-complexity suboptimal algorithm IGUC is introduced to tackle it. Simulation results show that compared to the non-cooperative uplink system, IGUC saves a considerable proportion of the spectrum. Furthermore, clusters in denser and further areas tend to produce a higher gain. Influence of the fast time-varying characteristic of vehicular wireless channels is also investigated, upon which an adaptive switch mechanism is proposed to further facilitate IGUC.

关键词： cooperative communication iterative methods resource allocation greedy algorithms computational complexity cellular radio wireless channels relay networks (telecommunication) vehicular ad hoc networks nonlinear programming vehicular wireless channels greedy user clustering algorithm vehicular communication systems wireless services cellular networks mobile users traditional pairwise cooperation cluster-wise cooperation intelligent transportation vehicular scenario clustering scheme system resources nonlinear binary programming problem device-to-device communications spectral power efficiency iterative greedy user clustering suboptimal algorithm IGUC D2D-relay clustering network noncooperative uplink system

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Deterministic allocation models for multi-period demand fulfillment in multi-stage customer hierarchies

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COMPUTERS & OPERATIONS RESEARCH 2019年 101卷 76-92页

作者： Cano-Belman, Jaime Meyr, Herbert Univ Hohenheim Dept Supply Chain Management 580C D-70593 Stuttgart Germany

In manufacturing environments with heterogeneous (e.g., because of different profit margins) customers the optimal matching of available supply with dynamic demand is a challenging task as soon as supply becomes scarce. In Make-to-Stock systems, Demand Fulfillment first allocates these scarce resources (in form of Available-to-Promise - ATP) to customer segments on basis of their forecasted demand. The resulting quotas are then consumed ("promised") when real customer orders arrive. In a multi-stage sales hierarchy, this allocation process often has to be executed level by level, on basis of decentral, aggregate information only. Decentral, multi-period, deterministic linear and non-linear programming models are proposed approximating the first-best benchmark of a central, multi-period allocation planning with full information. Roll over simulations have been performed to obtain insights about the behavior of the proposed decentral models and to demonstrate their benefits in comparison with common quantity-based methods, especially for high levels of customer heterogeneity and high shortage rates. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Supply chain management Demand fulfillment Profit-based allocation planning Real-time order promising Multi-stage customer hierarchies Linear programming nonlinear programming Central/decentral planning

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Flexible, reliable, and renewable power system resource expansion planning considering energy storage systems and demand response programs

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IET RENEWABLE POWER GENERATION 2019年第11期13卷 1862-1872页

作者： Hamidpour, Hamidreza Aghaei, Jamshid Pirouzi, Sasan Dehghan, Shahab Niknam, Taher Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Islamic Azad Univ Semirom Branch Power Syst Grp Semirom Iran Islamic Azad Univ Qazvin Branch Fac Elect Biomed & Mechatron Engn Qazvin Iran

This study presents a flexible, reliable, and renewable power system resource planning approach to coordinate generation, transmission, and energy storage (ES) expansion planning in the presence of demand response (DR). The flexibility and reliability of the optimal resource expansion planning are ensured by means of appropriate constraints incorporated into the proposed planning tool where thermal generation units, ES systems, and DR programs are considered as flexibility resources. The proposed planning tool is a mixed-integer non-linear programming (MINLP) problem due to the non-linear and non-convex constraints of AC power flow equations. Accordingly, to linearise the proposed MINLP problem, the AC nodal power balance constraints are linearised by means of the first-order expansion of Taylor's series and the line flow equations are linearised by means of a polygon. Additionally, the stochastic programming is used to characterise the uncertainty of loads, a maximum available power of wind farms, forecasted energy price, and availability/unavailability of generation units and transmission lines by means of a sufficient number of scenarios. The proposed planning tool is implemented on the IEEE 6-bus and the IEEE 30-bus test systems under different conditions. Case studies illustrate the effectiveness of the proposed approach based on both flexibility and reliability criteria.

关键词： integer programming load flow wind power plants power generation planning stochastic programming nonlinear programming power transmission planning demand side management thermal power stations power transmission lines power generation economics power transmission economics concave programming energy storage AC power flow equations AC nodal power balance constraints first-order expansion stochastic programming maximum available power planning tool IEEE 30-bus test systems reliability criteria flexible power system resource expansion planning renewable power system resource expansion planning energy storage systems energy storage expansion planning demand response optimal resource expansion planning thermal generation units ES systems DR programs flexibility resources mixed-integer nonlinear programming nonconvex constraints demand response programs reliable power system resource expansion planning

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Interpretable clustering via multi-polytope machines

arXiv

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arXiv 2021年

作者： Lawless, Connor Kalagnanam, Jayant Nguyen, Lam M. Phan, Dzung Reddy, Chandra Cornell Univeristy Operations Research and Information Engineering IthacaNY14850 United States IBM Research Thomas J. Watson Research Center Yorktown HeightsNY10598 United States

Clustering is a popular unsupervised learning tool often used to discover groups within a larger population such as customer segments, or patient subtypes. However, despite its use as a tool for subgroup discovery and description-few stateof-the-art algorithms provide any rationale or description behind the clusters found. We propose a novel approach for interpretable clustering that both clusters data points and constructs polytopes around the discovered clusters to explain them. Our framework allows for additional constraints on the polytopes-including ensuring that the hyperplanes constructing the polytope are axis-parallel or sparse with integer coefficients. We formulate the problem of constructing clusters via polytopes as a Mixed-Integer Non-Linear Program (MINLP). To solve our formulation we propose a two phase approach where we first initialize clusters and polytopes using alternating minimization, and then use coordinate descent to boost clustering performance. We benchmark our approach on a suite of synthetic and real world clustering problems, where our algorithm outperforms state of the art interpretable and non-interpretable clustering algorithms. Copyright © 2021, The Authors. All rights reserved.

关键词： nonlinear programming

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Direct Solution of Multi-Objective Optimal Control Problems Applied to Spaceplane Mission Design

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2019年第1期42卷 30-46页

作者： Ricciardi, Lorenzo A. Maddock, Christie Alisa Vasile, Massimiliano Univ Strathclyde Aerosp Ctr Excellence Mech & Aerosp Engn 75 Montrose St Glasgow G1 1XJ Lanark Scotland

This paper presents a novel approach to the solution of multiphase multi-objective optimal control problems. The proposed solution strategy is based on the transcription of the optimal control problem with Finite Elements in Time and the solution of the resulting multi-objective nonlinear programming (MONLP) problem with a memetic strategy that extends the Multi Agent Collaborative Search algorithm. The MONLP problem is reformulated as two nonlinear programming problems: a bilevel and a single-level problem. The bilevel formulation is used to globally explore the search space and generate a well spread set of nondominated decision vectors, whereas the single-level formulation is used to locally converge to Pareto efficient solutions. Within the bilevel formulation, the outer level selects trial decision vectors that satisfy an improvement condition based on Chebyshev weighted norm, whereas the inner level restores the feasibility of the trial vectors generated by the outer level. The single-level refinement implements a Pascoletti-Serafini scalarization of the MONLP problem to optimize the objectives while satisfying the constraints. The approach is applied to the solution of three test cases of increasing complexity: an atmospheric reentry problem, an ascent and abort trajectory scenario, and a three-objective system and trajectory optimization problem for spaceplanes.

关键词： Trajectory Optimization Spaceplane nonlinear programming Ascent Abort Search Algorithm Propellant Flight Path Angle Aerodynamic Efficiency Aerodynamic Coefficients Wing Loading

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Indefinite multi-constrained separable quadratic optimization: Large-scale efficient solution

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第1期278卷 49-63页

作者： Edirisinghe, Chanaka Jeong, Jaehwan Rensselaer Polytech Inst Lally Sch Management Troy NY 12180 USA Radford Univ Coll Business & Econ Radford VA 24142 USA

Multi-constrained indefinite separable quadratic optimization occurs in many practical applications. However, it is an NP-hard problem and its solution even for problems of moderate size is computationally tedious. Extending our previous work on singly constrained problems, we develop the necessary theory and computational procedures for problems with multiple linear constraints, by employing iterative constraint aggregation, known as surrogation. The surrogate dual solution is obtained using a cutting plane technique over the multiplier search space, which then is used to develop a monotonic sequence of upper bounds that yields a near-global optimal solution. A detailed numerical analysis indicates the method is extremely efficient for quadratic programs with hundreds of thousands of variables. While the number of constraints affects the computational efficiency, it is still an order of magnitude superior, both in speed and quality, compared to leading commercial global optimization software. Preliminary results with application to mixed integer quadratic indefinite optimization further reveal the performance superiority of the proposed methodology relative to the standard techniques. (C) 2019 Elsevier B.V. All rights reserved.

关键词： nonlinear programming Large scale optimization Integer programming Global optimization Constraint aggregation

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Flexibility of controllable power transformers for managing wind uncertainty using robust adjustable linearised optimal power flow

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IET RENEWABLE POWER GENERATION 2019年第2期13卷 262-272页

作者： Nikoobakht, Ahmad Aghaei, Jamshid Farahmand, Hossein Lakshmanan, Venkatachalam Korpas, Magnus Higher Educ Ctr Eghlid Dept Elect Engn Eghlid Iran Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Norwegian Univ Sci & Technol NTNU Dept Elect Power Engn NO-7491 Trondheim Norway

As renewable energy sources (RESs) penetration increases in the power system, the transmission system operators face new challenges to ensure system reliability and flexibility while ensuring high utilisation of uncertain RES generation. Controllable transformers with on-load tap changers and phase shifting capability are the promising flexibility tools to keep the system acceptable security and flexibility levels by controlling the voltage levels and energy flow. The AC optimal power flow (AC OPF) with detailed modelling considerations such as the bus voltage magnitude by including these devices is challenging. This study develops the AC OPF model to propose a robust flexibility optimisation framework for daily scheduling problem with uncertain wind energy sources. Nevertheless, the proposed formulation representation is an intractable mixed integer nonlinear programming (MINLP) while it includes AC grid constraints and the augmented modelling of the mentioned transformers. Accordingly, the proposed MINLP problem has been converted into a mixed-integer linear program where a certain level of solution accuracy can be achieved for the available time budget. The effectiveness of the proposed method is demonstrated using a modified 6-bus and IEEE 118-bus test systems.

关键词： wind power plants wind power power generation control optimal control load flow control robust control on load tap changers power generation reliability power system security power generation scheduling integer programming nonlinear programming power grids power control voltage control controllable power transformers wind uncertainty management robust adjustable linearised optimal power flow renewable energy sources penetration power system transmission system operators system reliability system flexibility uncertain RES generation on-load tap changers phase shifting capability system acceptable security level system acceptable flexibility level voltage level control energy flow control AC optimal power flow model AC OPF model bus voltage magnitude robust flexibility optimisation framework daily scheduling problem uncertain wind energy sources intractable mixed integer nonlinear programming MINLP problem AC grid constraints modified 6-bus test system modified IEEE 118-bus test system

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Decentralised self-healing model for gas and electricity distribution network

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第19期13卷 4451-4463页

作者： Vazinram, Farzad Effatnejad, Reza Hedayati, Mahdi Hajihosseini, Payman Islamic Azad Univ Dept Elect Engn Karaj Branch Karaj Iran

With regard to the global environmental challenges, low cost of the natural gas and fast developing of new gas-fired power generation technologies, conduce to unwanted integration of gas and electricity network. Moreover, one of the benefits of emerging distribution network as microgrids (MGs) would be self-healing capability. So, in this research a model has been formulated as Mixed Integer Linear programming (MILP) problem which determines optimally the power generation of micro turbines (MTs), Energy Storages (ESs) and Combined Heat and Power (CHP) units during the normal operation of MGs with consideration of combined gas and electricity network. In case of fault occurrence in MG/MGs and need to compensate the power generation shortage the model determines the power transferring rate of all MGs with normal operation mode, adjacent to the faulty MG/MGs, according to the cost-based optimization. In critical situations load shedding procedure will be applied to the faulty MG/MGs. It would be considered that CHPs supply both heat and electricity loads and the remaining heat demands will be supplied by natural gas network. The proposed model has been applied to the test system with six MGs as three scenarios and also verified by applying to the network with 30 MGs.

关键词： cogeneration distributed power generation nonlinear programming optimisation steam power stations natural gas technology distribution networks load shedding smart power grids integer programming linear programming electricity network electricity loads natural gas network 30 MGs self-healing model electricity distribution network global environmental challenges gas-fired power generation technologies natural gas fuel electricity networks emerging distribution network power units combined gas power generation shortage power-transferring rate normal operation mode

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A matrix-free line-search algorithm for nonconvex optimization

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OPTIMIZATION METHODS & SOFTWARE 2019年第1期34卷 1-24页

作者： Zhou, W. Akrotirianakis, I. G. Yektamaram, S. Griffin, J. D. SAS Inst Cary NC 27513 USA Lehigh Univ Dept Ind & Syst Engn Bethlehem PA 18015 USA

In this paper, we have developed a new algorithm for solving nonconvex large-scale problems. The new algorithm performs explicit matrix modifications adaptively, mimicing the implicit modifications used by trust-region methods. Thus, it shares the equivalent theoretical strength of trust-region approaches, without needing to accommodate an explicit step-size constraint. We show that the algorithm is well suited for solving very large-scale nonconvex problems whenever Hessian-vector products are available. The numerical results on the CUTEr problems demonstrate the effectiveness of this approach in the context of a line-search method for large-scale unconstrained nonconvex optimization. Moreover, applications in deep-learning problems further illustrate the usefulness of this algorithm. It does not share any of the prohibitive traits of popular matrix-free algorithms such as truncated conjugate gradient (CG) due to the difficult nature of deep-learning problems. Thus the proposed algorithm serves to bridge the gap between the needs of data-mining community and existing state-of-the-art approaches embraced foremost by the optimization community. Moreover, the proposed approach can be realized with minimal modification to the CG algorithm itself with negligible storage and computational overhead.

关键词： nonlinear programming nonconvex large-scale problems trust-region methods conjugate gradient method Hessian-free methods machine learning

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