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Optimisation of indoor hybrid PLC/VLC/RF communication systems

IET COMMUNICATIONS 2020年第1期14卷 117-126页

作者： Sreenivasa Reddy, Yeduri Panda, Meenakshi Dubey, Ankit Kumar, Abhinav Panigrahi, Trilochan Rabie, Khaled M. NIT Goa Dept Elect & Commun Engn Ponda 403401 India IIT Jammu Dept Elect Engn Jammu 181221 India IIT Hyderabad Dept Elect Engn Hyderabad 502285 Telangana India Manchester Metropolitan Univ Sch Engn Manchester M15 6BH Lancs England

In this study, the authors propose a hybrid power line communication (PLC)/visible light communication (VLC)/radio frequency (RF) fronthaul with a fibre-based wired backhaul system to support massive number of smart devices (SDs). Since a signal-to-noise ratio-based access point (AP) association and bandwidth (BW) allocation for each SD do not necessarily improve the system capacity, they propose novel and efficient AP association and BW allocation strategies to maximise the sum rate capacity (SRC) of the hybrid system under consideration. An optimisation problem is formulated for the SRC with the AP association and BW allocation as the optimisation parameters and a hierarchical decomposition method is used to convert the non-linear optimisation problem into a set of convex optimisation problems. Then, the proposed strategies are used to solve the optimisation problem in an iterative manner until the SRC converges to an optimal value. Further, an analytical approximation for the BW allocated to each SD for a given AP association is derived using the Lagrangian multiplier method. The performance of the proposed system is evaluated through extensive numerical results. Moreover, the effect of the increased number of SDs on the optimal SRC is analysed.

关键词： carrier transmission on power lines convex programming iterative methods approximation theory free-space optical communication bandwidth allocation indoor communication nonlinear programming AP association fibre-based wired backhaul system system capacity optimisation parameters nonlinear optimisation problem convex optimisation problems indoor hybrid PLC-VLC-RF communication system optimization hybrid power line communication visible light communication radio frequency fronthaul signal-to-noise ratio-based access point association smart devices bandwidth allocation BW allocation strategies sum rate capacity maximization hierarchical decomposition method analytical approximation Lagrangian multiplier method optimal SRC

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IMPLEMENTING A SMOOTH EXACT PENALTY FUNCTION FOR EQUALITY-CONSTRAINED nonlinear OPTIMIZATION

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SIAM JOURNAL ON SCIENTIFIC COMPUTING 2020年第3期42卷 A1809-A1835页

作者： Estrin, Ron Friedlander, Michael P. Orban, Dominique Saunders, Michael A. Stanford Univ Inst Computat & Math Engn Stanford CA 94305 USA Univ British Columbia Dept Comp Sci Vancouver BC V6T 1Z4 Canada Ecole Polytech GERAD Montreal PQ H3C 3A7 Canada Ecole Polytech Dept Math & Ind Engn Montreal PQ H3C 3A7 Canada Stanford Univ Dept Management Sci & Engn Syst Optimizat Lab Stanford CA 94305 USA

We develop a general equality-constrained nonlinear optimization algorithm based on a smooth penalty function proposed by Fletcher in 1970. Although it was historically considered to be computationally prohibitive in practice, we demonstrate that the computational kernels required are no more expensive than other widely accepted methods for nonlinear optimization. The main kernel required to evaluate the penalty function and its derivatives is solving a structured linear system. We show how to solve this system efficiently by storing a single factorization at each iteration when the matrices are available explicitly. We further show how to adapt the penalty function to the class of factorization-free algorithms by solving the linear system iteratively. The penalty function therefore has promise when the linear system can be solved efficiently, e.g., for PDE-constrained optimization problems where efficient preconditioners exist. We discuss extensions including handling simple constraints explicitly, regularizing the penalty function, and inexact evaluation of the penalty function and its gradients. We demonstrate the merits of the approach and its various features on some nonlinear programs from a standard test set, and some PDE-constrained optimization problems.

关键词： nonlinear programming exact penalty smooth penalty factorization-free iterative methods trust-region

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Coordinated optimal dispatch of VPPs in unbalanced ADNs

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第8期14卷 1430-1437页

作者： Xu, Tong Wu, Wenchuan Wang, Zhen-Yi Zhu, Tao Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Shenzhen Environm Sci & New Energy Technol Engn L Shenzhen Peoples R China Tsinghua Univ Dept Elect Engn State Key Lab Power Syst Beijing Peoples R China Yunnan Power Grid Co Ltd Power Dispatch & Control Ctr Kunming Yunnan Peoples R China Kunming Elect Power Supply Bur Power Dispatch & Control Ctr Kunming Yunnan Peoples R China

To manage and control massive distributed energy resources (DERs) integrated into active distribution networks (ADNs), dispersed DERs are aggregated as virtual power plants (VPPs). This study introduces a distributed optimal dispatch strategy of VPPs considering operational constraints in unbalanced ADN. First, a practical multi-VPP coordination optimal dispatch model considering P-Q coupling characteristics in unbalanced ADN is proposed. Then, the primal-dual interior-point algorithm is employed to transform the original model into a Karush-Kuhn-Tucker equation, where afterwards distributed quasi-Newton method is developed to solve the equation. Under the proposed distributed strategy, each VPP only communicates limited boundary information with utility grid. With the advantage of quasi-second-order information, the proposed method outperforms the popular distributed gradient-based algorithm, alternating direction method of multipliers. The distributed method provides merits such as efficient calculation and privacy protection for multi-VPP operation in ADN. Numerical tests evaluate the efficiency and accuracy of the proposed method.

关键词： Newton method energy resources distributed power generation power grids distribution networks power generation dispatch nonlinear programming power system management power plants primal-dual interior-point algorithm distributed quasiNewton method quasisecond-order information distributed method VPP unbalanced ADN massive distributed energy resources active distribution networks virtual power plants distributed optimal dispatch strategy multiVPP coordination optimal dispatch model dispersed DER Karush-Kuhn-Tucker equation DER management DER control operational constraints P-Q coupling characteristics privacy protection

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Supporting legacy and RF-energy harvesting devices in multi-cells OFDMA networksInspec keywordsOther keywords

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IET COMMUNICATIONS 2020年第22期14卷 3967-3976页

作者： Sarwar, Muhammad Zeeshan Chin, Kwan-Wu Univ Wollongong Sch Elect Comp & Telecommun Engn Wollongong NSW Australia

A multi-cell network that uses orthogonal frequency division multiplexing access (OFDMA) is studied here. Unlike prior works, the proposed network consists of both (i) legacy data users that do not have energy harvesting capability and have a minimum data rate requirement and (ii) radio frequency (RF)-energy harvesting devices with a minimum energy requirement. It studies sub-band allocation to users and transmits power allocation at base stations. The authors formulate a mixed-integer non-linear program and also present two heuristics to assign sub-band to base stations. Numerical results show that RF energy harvesting devices will not affect network capacity if legacy data users require a high data rate. In addition, the results obtained from the two proposed heuristics are similar to 95% of the optimal solution.

关键词： energy harvesting cellular radio nonlinear programming frequency division multiple access integer programming OFDM modulation telecommunication power management mixed‐ integer nonlinear program base stations RF energy harvesting devices network capacity legacy data users RF‐ energy harvesting devices multicell network orthogonal frequency division multiplexing access minimum data rate requirement minimum energy requirement multicells OFDMA networks

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Joint optimization of pricing and seat allocation with multistage and discriminatory strategies in high-speed rail networks

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COMPUTERS & INDUSTRIAL ENGINEERING 2020年 148卷 106690-106690页

作者： Hu, Xinlei Shi, Feng Xu, Guangming Qin, Jin Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Hunan Peoples R China

Pricing and seat allocation are complementary strategies of revenue management (RM) for railway industry, but these two strategies were typically considered as isolated problems in most of existing studies. To extend the literature, a nonlinear programming model is proposed for the joint optimization of pricing and seat allocation in high-speed rail (HSR) networks. Meanwhile, multistage and discriminatory pricing strategies are employed to attract more passengers and improve total revenue. Following the Davidon-Fletcher-Powell method, we design an efficient solution algorithm for large-scale joint HSR pricing and seat allocation problems. The sensitivity analysis is performed to capture the uncertain impacts of model inputs on the outputs. The advantages of joint RM strategy are demonstrated by a trade study where different RM strategies are compared. The results of a large-scale instance in the real HSR network show that the proposed model and solution algorithm can provide decision support for railway companies in their daily operation and management.

关键词： Joint optimization Pricing Seat allocation High-speed rail Multistage decision nonlinear programming

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Efficient sensitivity based approach for AC transmission expansion planning

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第12期14卷 2378-2388页

作者： Yasasvi, Puvvada Naga Mohapatra, Abheejeet Srivastava, Suresh C. IIT Kanpur Dept Elect Engn Kanpur 208016 Uttar Pradesh India

AC transmission expansion planning (TEP) is a complex problem to solve due to its large scale, mixed-integer, non-linear and non-convex characteristics. This paper presents an efficient sensitivity based approach which employs the primal-dual interior point method (PDIPM), for tackling integer variables in AC TEP. PDIPM's Hessian is used to evaluate the sensitivities of the objective function with respect to the line candidates, which are then used to find a feasible AC TEP solution. The proposed approach solves this non-convex mixed integer non-linear programming problem, without resorting to any use of relaxations/approximations in the AC TEP model. Also, the need and superiority of the AC TEP over the conventional DC TEP is established by comparing both using the proposed approach. Solutions of AC TEP with reactive power planning and an integrated multistage AC TEP are also obtained to demonstrate the performance of the proposed approach. Superior solution quality and lesser computation time of the proposed approach are confirmed by comparing the same with that of the branch and bound/cut approach, constructive heuristic algorithm, merit function-based approach, conic programming and particle swarm optimisation. The studies performed for the various test cases prove the efficacy and computational efficiency of the proposed approach.

关键词： particle swarm optimisation power transmission planning power grids power transmission economics concave programming power transmission reliability reactive power integer programming linear programming nonlinear programming AC transmission expansion planning mixed-integer nonconvex characteristics efficient sensitivity based approach primal-dual interior point method integer variables feasible AC TEP solution nonconvex mixed integer nonlinear programming problem AC TEP model conventional DC TEP reactive power planning integrated multistage AC TEP merit function based approach

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Integrated active/reactive power scheduling of interdependent microgrid and EV fleets based on stochastic multi-objective normalised normal constraint

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第11期14卷 2055-2064页

作者： Saffari, Mohammadali Kia, Mohsen Vahidinasab, Vahid Mehran, Kamyar Shahid Beheshti Univ Dept Elect Engn Tehran Iran Islamic Azad Univ Fac Engn Dept Elect Engn Pardis Branch Tehran Iran Newcastle Univ Sch Engn Newcastle Upon Tyne Tyne & Wear England Queen Mary Univ London Sch Elect Engn & Comp Sci London England

This study proposes an integrated framework for coordinated optimisation of the interdependent microgrid (MG) and electric vehicle (EV) fleet entities using the normalised normal constraint approach. By considering the active/reactive power management option of the bidirectional charger enabled EVs in the proposed model, the authors investigate the effectiveness of EV's integration in the presence of the techno-economical objective functions. This work concentrates on the trade-off analysis of two conflicting objectives, including the economic objective of the MG's operation cost minimisation and the technical objective of the MG's voltage deviation. Besides, they consider several uncertainty sources, e.g. wind, EV and solar panel (PV) power provision, as well as market price fluctuations in the proposed model affecting the aforementioned techno-economic trade-off solution. The proposed model is a stochastic multi-objective mixed-integer non-linear programming problem where the authors apply the designed integrated framework on a modified IEEE 18-bus test case in GAMS software. Through numerical results, they demonstrate MG optimal operation changes due to different MGO priorities and study the positive effects of EVs integrated energy management on the bi-objective operation.

关键词： optimisation distributed power generation linear programming nonlinear programming Pareto optimisation reactive power electric vehicles power markets integer programming EVs integrated energy management bi-objective operation interdependent microgrid stochastic multiobjective coordinated optimisation electric vehicle fleet entities normalised normal constraint approach bidirectional charger EV's integration techno-economical objective functions trade-off analysis economic objective MG's operation cost minimisation technical objective MG's voltage deviation solar panel power provision market price fluctuations aforementioned techno-economic trade-off multiobjective mixed-integer nonlinear programming problem designed integrated framework MG optimal operation changes

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Q-Law Aided Direct Trajectory Optimization of Many-Revolution Low-Thrust Transfers

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JOURNAL OF SPACECRAFT AND ROCKETS 2020年第4期57卷 672-682页

作者： Shannon, Jackson L. Ozimek, Martin T. Atchison, Justin A. Hartzell, Christine M. Univ Maryland Dept Aerosp Engn College Pk MD 20740 USA Johns Hopkins Univ Appl Phys Lab Space Explorat Sect 11101 Johns Hopkins Rd Laurel MD 20723 USA

Low-thrust spacecraft trajectory optimization for the many-revolution orbital transfer problem is especially challenging due to the high problem dimension and perturbing accelerations that prevent or complicate accurate analytical solutions. This analysis seeks to simplify the calculation of the nonaveraged spiral trajectory through the use of the well-known Q-Law guidance algorithm. Here, Q-Law is used to seed phases of the direct trajectory optimization problem. We explore the trade space between the fast calculation speed of Q-Law and the time or mass optimality of the full optimization problem using the common example of a geostationary transfer orbit to geostationary transfer. We find that a computationally efficient, near-optimal solution can be achieved by using Q-Law for an initial fraction of a spiral trajectory and direct optimization for the remainder of the trajectory. This reduces the direct optimization problem to a lower dimension and enables the spacecraft to reach a specific final state. Q-Law is particularly effective for portions of the trajectory where there are many eclipses, which can challenge direct optimization methods.

关键词： Trajectory Optimization Thrust Partial Eclipse Geostationary Transfer Orbit Orbital Transfer Guidance Algorithms Spacecraft Trajectory Optimization nonlinear programming Propellant Multi Objective Genetic Algorithm

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Flexible two-stage robust model for moving the transmission and reactive power infrastructures expansion planning towards power system integration of renewables

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IET RENEWABLE POWER GENERATION 2020年第11期14卷 1921-1932页

作者： Olfatinezhad, Nader Vahidinasab, Vahid Ahmadian, Mohammad Arasteh, Hamidreza Aghaei, Jamshid Mehran, Kamyar Shahid Beheshti Univ Abbaspour Sch Engn Dept Elect Engn Tehran *** Iran Niroo Res Inst Power Syst Planning & Operat Grp Tehran Iran Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Queen Mary Univ London Sch Elect Engn & Comp Sci London England

This study proposes a two-stage robust optimisation framework to model uncertainties in construction cost, load and renewable power generations (RPGs) in the coordinated transmission and reactive power infrastructure expansion planning problem. The proposed planning model, based on the network AC model and its voltage constraints, is represented as a mixed-integer linear programing problem with the aim of minimising: (i) the construction costs of lines, (ii) installation cost of reactive power resources and (iii) active load shedding as RPGs curtailment. Using the suggested robust optimisation approach, the best expansion plan is obtained even in the worst case of the defined uncertainty sets and the plan robustness level is controlled via parameter definition for the individual uncertainty sets. The Benders decomposition algorithm is used to solve the two-stage problem where the plan security level is also taken into consideration using theN-1 contingency criterion. The standard Garver 6-bus system and the IEEE 24-bus reliability test system are used to validate the proposed robust framework.

关键词： load shedding nonlinear programming power system security power transmission planning integer programming reactive power power generation planning power generation reliability power transmission reliability power system integration renewable power generations coordinated transmission network AC model mixed-integer nonlinear programing problem installation cost robust optimisation approach plan security level uncertainty sets reactive power infrastructure expansion planning Benders decomposition algorithm load shedding RPG curtailment Garver 6-bus system

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Simple algorithm for L1-norm regularisation-based compressed sensing and image restoration

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IET IMAGE PROCESSING 2020年第14期14卷 3405-3413页

作者： Qin, Shun Georg August Univ Gottingen Inst Phys 3 Friedrich Hund Pl 1 Gottingen Germany

L1-norm regularisation plays an important role in compressed sensing reconstruction and image restoration. However, the discontinuity of L1-norm function makes solving the involved optimisation problem very challenging with traditional optimisation methods. In this article, a simple but efficient algorithm is proposed for the L1-norm regularised compressed sensing and image restoration problem. In the proposed algorithm, the L1-norm regularised optimisation problem is converted to a non-linear optimisation problem with L1-norm approximation by a smoothening function, which then can be solved by existing powerful non-linear optimisation methods. The simulation results show that the proposed algorithm is more efficient and results in a higher accurate solution. Compared to existing methods, the proposed algorithm is very easy to implement and promising for applications in medical and biological imaging.

关键词： image reconstruction compressed sensing approximation theory image restoration nonlinear programming smoothing methods image restoration compressed sensing reconstruction traditional optimisation methods L1-norm regularised compressed sensing L1-norm regularised optimisation problem nonlinear optimisation problem L1-norm approximation nonlinear optimisation methods medical imaging biological imaging L1-norm regularisation-based compressed sensing L1-norm function discontinuity smoothening function

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