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Analytical FRTU deployment approach for reliability improvement of integrated cyber-physical distribution systems

IET GENERATION TRANSMISSION & DISTRIBUTION 2016年第11期10卷 2631-2639页

作者： Wang, Shouxiang Liang, Dong Ge, Leijiao Wang, Xudong Tianjin Univ Key Lab Smart Grid Minist Educ Tianjin Peoples R China Tianjin Elect Power Res Inst Tianjin Peoples R China

The increasing of distributed generators and high demand of customer service quality of recent years keep challenging the design and operation of electric power distribution systems. The future smart distribution grid is envisioned to be a large complex cyber-physical distribution system (CPDS), where physical grid components are monitored and controlled based on the interactions on the cyber space. As sensors and controllers, smart feeder remote terminal units (FRTUs) transfer necessary data to the control centre for situational awareness, and receive orders from the control centre for dynamic controlling. This study introduces an analytical FRTU deployment approach for the reliability improvement of integrated CPDSs. The FRTU deployment problem is modelled as a mixed-integer non-linear programming problem, which aims to minimise the total cost with the reliability index of average service available index as the constraint. Two kinds of distribution feeders (overhead and underground) are considered. The total cost includes the life cycle cost of FRTUs in addition to customer interruption cost. The proposed model can be solved by large-scale commercial solvers in an efficient manner. Test results of the RBTS-BUS4 distribution system and a China Southern Power Grid 62-bus distribution system validate the accuracy and effectiveness of the approach. Comparison with the genetic algorithm also shows its great efficiency.

关键词： power distribution reliability distributed power generation power grids cyber-physical systems nonlinear programming integer programming power distribution control 62-bus distribution system China southern power grid RBTS-BUS4 distribution system customer interruption life cycle cost underground distribution feeders overhead distribution feeders mixed-integer nonlinear programming problem dynamic control situational awareness FRTU deployment problem smart feeder remote terminal units cyber space physical grid components smart distribution grid electric power distribution systems distributed generators reliability improvement analytical FRTU deployment integrated cyber-physical distribution systems

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Joint energy-efficient power allocation and subcarrier pairing in orthogonal frequency division multiple-based multi-relay networks

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IET COMMUNICATIONS 2015年第5期9卷 649-657页

作者： Zhou Zhikang Zhu Qi Nanjing Univ Posts & Telecommun Sch Telecommun & Informat Engn Nanjing 210003 Jiangsu Peoples R China

In this study, a resource allocation scheme for energy-efficient communication in a cooperative orthogonal frequency division multiple (OFDM) network based on subcarrier pairing is proposed. Different from traditional cooperative OFDM networks, in the concerned network, one subcarrier pair can be forwarded by multiple relays, and the source is allowed to retransmit the signals sent in the first phase. The problem aimed at maximising the system energy efficiency (EE) under total power constraint and minimum spectral efficiency constraint is formulated into a mixed-integer nonlinear programming (MINLP) problem, which in general has intractable complexity because of its combinatorial nature. The authors simplify the optimising model as a typical fractional programming problem to solve the complex the MINLP problem. The simplified fractional programming problem is proved to be quasi-concave, thus Dinkelbach's method consisting of outer iterations and inner iterations can be used to find the optimal solution to the proposed MINLP problem.

关键词： frequency division multiple access OFDM modulation relay networks (telecommunication) cooperative communication energy conservation telecommunication power management nonlinear programming integer programming concave programming resource allocation joint energy-efficient power allocation subcarrier pairing cooperative orthogonal frequency division multiple-based multirelay network resource allocation scheme energy-efficient communication OFDM network signal retransmission energy efficiency maximisation EE maximisation power constraint spectral efficiency constraint mixed-integer nonlinear programming problem MINLP problem fractional programming problem Dinkelbach method quasiconcave programming

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Sustainable wastewater treatment plants design through multiobjective optimization

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COMPUTERS & CHEMICAL ENGINEERING 2020年 140卷

作者： Padron-Paez, Juan I. De-Leon Almaraz, Sofia Roman-Martinez, Alicia Univ Autonoma San Luis Potosi Fac Ciencias Quim Av Dr Manuel Nava 6 San Luis Potosi 78210 SLP Mexico Univ Toulouse UPS CNRS Lab Genie ChimUMR 5503INP 4 Allee Emile Monso F-31432 Toulouse 4 France

Nowadays, an adequate design of wastewater treatment plants taking into consideration all sustainability dimensions- economic, environmental and social- is fundamental. This can be achieved by implementing systematic methodologies where conceptual and mathematical tools can be used together. This contribution proposes a framework that uses total cost, consumed energy, and reclaimed wastewater as sustainability metrics. A mixed-integer nonlinear programming problem arises from a general superstructure for wastewater treatment plants. A case study from Mexico City is solved by a hybrid multiobjective optimization approach that combines lexicographic and s-constraint methods. Solutions are provided in the form of a Pareto front. A modified technique for order of preference by similarity to ideal solution (MTOPSIS) analysis is used as a multiple criteria decision-making tool to find the best trade-off solution. The optimal sustainable configuration resulted consists of three levels of treatment and 100% of treated water reuse. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Wastewater treatment plants Socio-eco-efficient sustainability Multiobjective optimization mixed-integer nonlinear programming problem Modified technique for order of preference by similarity to ideal solution Multiple criteria decision making

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Q-learning based computation offloading for multi-UAV-enabled cloud-edge computing networks

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IET COMMUNICATIONS 2020年第15期14卷 2481-2490页

作者： Wang, Meng Shi, Shuo Gu, Shushi Gu, Xuemai Qin, Xue Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China Harbin Inst Technol Shenzen Sch Elect & Informat Engn Shenzhen 518055 Peoples R China Texas A&M Univ Sch Comp Sci Corpus Christi TX 78412 USA

Unmanned aerial vehicles (UAVs) have been recently considered as a flying platform to provide wide coverage and relaying services for mobile users (MUs). Mobile edge computing (MEC) is developed as a new paradigm to improve quality of experience of MUs in future networks. Motivated by the high flexibility and controllability of UAVs, in this study, the authors study a multi-UAV-enabled MEC system, in which UAVs have computation resources to offer computation offloading opportunities for MUs, aiming to reduce MUs' total consumptions in terms of time and energy. Considering the rich computation resource in the remote cloud centre, they propose the MUs-Edge-Cloud three-layer network architecture, where UAVs play the role of flying edge servers. Based on this framework, they formulate the computation offloading issue as a mixed-integer non-linear programming problem, which is difficult to obtain an optimal solution in general. To address this, they propose an efficientQ-learning based computation offloading algorithm (QCOA) to reduce the complexity of optimisation problem. Numerical results show that the proposed QCOA outperforms benchmark offloading policies (e.g. random offloading, traversal offloading). Furthermore, the proposed three-layer network architecture achieves a 5% benefits compared with the traditional two-layer network architecture in terms of MUs' energy and time consumptions.

关键词： cloud computing integer programming cellular radio nonlinear programming autonomous aerial vehicles mobile computing learning (artificial intelligence) relay networks (telecommunication) multi-robot systems optimisation problem complexity reduction QCOA quality of experience MUs-Edge-Cloud three-layer network architecture MU energy consumption two-layer network architecture traversal offloading random offloading efficient Q-learning based computation offloading algorithm mixed-integer nonlinear programming problem edge servers remote cloud centre computation resources multiUAV-enabled MEC system controllability mobile edge computing mobile users relaying services flying platform UAV unmanned aerial vehicles multiUAV-enabled cloud-edge computing networks

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Throughput enhancement of IEEE 802.11ad through space-time division multiple access scheduling of multiple co-channel networks

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IET COMMUNICATIONS 2016年第4期10卷 425-434页

作者： Feng, Wei Niu, Yong Li, Yong Gao, Bo Su, Li Jin, Depeng Wu, Dapeng Oliver Tsinghua Univ TNLIST Dept Elect Engn State Key Lab Microwave & Digital Commun Beijing 100084 Peoples R China Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA

60-GHz millimetre-wave (mm-wave) communication is gradually becoming a promising candidate for the next generation wireless system to meet the demands of mobile applications. To compensate for high path loss, directional links are established in mm-wave communication system, which adds opportunity for spatial reuse. As one of its most promising protocols for commercial production, IEEE 802.11ad standard provides a mechanism to support space-time division multiple access (STDMA) within a single network. However, the interference level among different co-channel networks is much lower than that inside a network, which provides greater potential for spatial reuse. In this study, based on the architecture and timing structure of IEEE 802.11ad, the authors propose a spatial reuse strategy among multiple co-channel networks. They formulate the problem as a mixed-integer non-linear programming problem, and then propose an inter-network STDMA scheduling algorithm, which considers clustering frame structure in IEEE 802.11ad, and combines greedy principle and mutual interference avoidance strategy. Extensive simulation results have shown that the proposed scheme enlarges total throughput in comparison with STDMA inside each network, as well as non-STDMA scheme. Meanwhile, it achieves the lowest packet loss rate under heavy traffic load.

关键词： Electromagnetic compatibility and interference space-time division multiple access scheduling spatial reuse strategy frequency 60 GHz mm-wave communication internetworking Optimisation techniques high path loss nonlinear programming time division multiple access mixed-integer nonlinear programming problem cochannel interference next generation wireless system multiple co-channel networks Computer networks and techniques directional links Mobile radio systems greedy principle Computer communications mobile applications heavy traffic load millimetre-wave communication Multiple access communication mobile radio pattern clustering telecommunication scheduling IEEE 802.11ad standard throughput enhancement wireless LAN clustering frame structure internetwork STDMA scheduling algorithm mutual interference avoidance strategy radio links integer programming millimetre waves next generation networks space division multiple access telecommunication traffic lowest packet loss rate interference suppression

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Adaptive planning approach for customer DG installations in smart distribution networks

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IET RENEWABLE POWER GENERATION 2018年第1期12卷 81-89页

作者： Alkaabi, Sultan S. Zeineldin, Hatem H. Khadkikar, Vinod Khalifa Univ Sci & Technol Masdar Inst Sci & Technol Dept Elect Engn & Comp Sci EECS Abu Dhabi U Arab Emirates ADWEA AADC Abu Dhabi U Arab Emirates

The planning of active network management (ANM) schemes for distribution systems with distributed generation (DG) does not consider the random participation of customer-owned DGs. This study analyses and addresses the impact of the integration of customer-owned DG on the planning of ANM schemes and maximum DG penetration limits. The random customer DG installations are incorporated by considering sets of events where in each event the number, size and location of DG units are randomly generated. The events, in a set, are chronologically ordered and used to represent a possible scenario of customer-owned DG installations. A two-phase planning approach is proposed where the problem is formulated as a mixed-integer non-linear programming problem with an objective of determining the optimal ANM scheme for maximising the utility DG penetration considering customer DG installations. Several case studies are conducted on a generic 33kV UK distribution network, including a comparison with the one-time planning approach. The results show that the optimal ANM scheme will vary with the number, locations and sizes of customer DGs and thus for utilities to achieve maximum DG penetration, it is recommended to adaptively control and equip DG units with the capability of switching between various ANM schemes.

关键词： power distribution planning distributed power generation nonlinear programming integer programming smart distribution networks adaptive planning approach active network management distributed generation random customer DG installations customer-owned DG installations two-phase planning approach mixed-integer nonlinear programming problem UK distribution network optimal ANM scheme adaptive control voltage 33 kV

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Approximated MILP model for AC transmission expansion planning: global solutions versus local solutions

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IET GENERATION TRANSMISSION & DISTRIBUTION 2016年第7期10卷 1563-1569页

作者： Akbari, Tohid Bina, Mohammad Tavakoli KN Toosi Univ Technol Fac Elect Engn Tehran Iran

This study presents a novel linear approximated methodology for AC-transmission expansion planning (AC-TEP). The AC-TEP can provide more precise picture of active and reactive power flow, along with the voltage profile of buses compared with DC-transmission expansion planning. While the AC-TEP is inherently a mixed-integer non-linear programming problem, this can be transformed into a mixed-integer linear programming framework by the proposed model which is based upon the binary expansion theory. The global optimality of the solution for the approximated model can be guaranteed by existing algorithms. The presented mathematical model has been successfully applied to several power systems and the results are compared with the exact AC-TEP model. Simulation results are promising and show that the proposed method is effective, reliable and accurate.

关键词： binary expansion theory approximated MILP model DC-transmission expansion planning power transmission planning integer programming load flow voltage profile mixed-integer nonlinear programming problem Power system planning and layout AC-TEP AC transmission expansion planning Optimisation techniques linear approximated methodology reactive power flow a.c. transmission nonlinear programming

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An analytical approach for reducing k-line failure analysis and load shed computation

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IET GENERATION TRANSMISSION & DISTRIBUTION 2022年第13期16卷 2623-2641页

作者： Sarkar, Ranadhir Univ Cent Florida Elect & Comp Engn Orlando FL 32816 USA

In this paper, the problem of identification of critical k-line contingencies that fail one after another in quick succession that render large load shed in the power system is addressed. The problem is formulated as a mixed-integer non-linear programming problem (MINLP) that determines total demand that cannot be satisfied under various k-line removal scenarios. Due to the large search space of the problem, the solution through enumeration is intractable. Two algorithms are proposed using a proposed power flow sensitivity and a topological metric to identify a reduced number of k-line contingencies that initiate cascading overload failure and islanding of power system respectively, that are used to solve the MINLP iteratively for identification of critical k-line contingencies. The algorithms identify a reduced number of k-line contingencies in linear time as compared to the exponential time complexity of brute-force search for solutions of the MINLP. Case studies show that the proposed algorithms significantly reduce the search space and the computation time of the MINLP problem to find the most critical k-line contingency in the IEEE 30 and 118 bus systems at 2 <= k <= 4$2 \le k \le 4$ that are also obtained in the list of k-line contingencies identified using the proposed algorithms.

关键词： failure analysis line contingency search space line failure analysis power system load flow mixed-integer nonlinear programming problem line removal scenarios MINLP problem computational complexity integer programming linear programming nonlinear programming

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Optimal allocation of distributed generation for planning master-slave controlled microgrids

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第16期13卷 3704-3712页

作者： Pasha, Aisha M. Zeineldin, Hatem H. El-Saadany, Ehab F. Alkaabi, Sultan S. Khalifa Univ Dept Elect & Comp Engn Abu Dhabi U Arab Emirates Cairo Univ Fac Engn Dept Elect Power & Machines Giza 12316 Egypt Univ Waterloo ECE Dept Waterloo ON Canada Al Ain Distribut Co Abu Dhabi Water & Elect Author Al Ain U Arab Emirates

This study proposes a novel problem formulation for a planning distributed generation (DG) allocation for microgrids, using the master-slave approach. In the previous planning studies, all DGs have the same operating mode (e.g. operate at unity power factor). For master-slave controlled microgrid, DGs have two possible operating modes: master (non-unity power factor operation) and slave (unity power factor operation). For planning a master-slave controlled microgrid, in addition to DG siting, the optimal DG operating mode is determined by including a new set of constraints in the planning problem. Thus, the proposed formulation is capable of determining the optimal location of the master and slave DGs with the main objective of minimizing the microgrid's energy losses. The proposed model is formulated as a mixed-integer non-linear programming problem;incorporated into an optimal power flow framework and tested on the IEEE 38-bus systems considering a variable load profile. In addition to this, sensitivity analysis is carried for case studies with different load types and reactive power injection by the slave DGs in the system (e.g. operate at fixed non-unity power factor). The proposed approach can serve as an efficient tool for utility operators for planning microgrids.

关键词： power generation control power factor power generation reliability nonlinear programming integer programming power distribution control optimisation power distribution reliability distributed power generation sensitivity analysis linear programming load flow planning problem slave DGs mixed-integer nonlinear programming problem optimal power flow framework fixed nonunity power factor utility operators planning microgrids optimal allocation planning master-slave controlled microgrids novel problem formulation planning distributed generation master-slave approach optimisation problem previous planning studies master-slave controlled microgrid possible operating modes nonunity power factor operation unity power factor operation optimal DG operating mode

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Probabilistic decomposition-based security constrained transmission expansion planning incorporating distributed series reactor

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第17期14卷 3478-3487页

作者： Yuan, Zhi Wang, Weiqing Wang, Haiyun Ghadimi, Noradin Xinjiang Univ Engn Res Ctr Renewable Energy Power Generat & Gri Minist Educ Urumqi 830047 Xinjiang Peoples R China Islamic Azad Univ Ardabil Branch Young Researchers & Elite Club Ardebil Iran

This study presents a probabilistic transmission expansion planning model incorporating distributed series reactors, which are aimed at improving network flexibility. Although the whole problem is a mixed-integer non-linear programming problem, this study proposes an approximation method to linearise it in the structure of the Benders decomposition (BD) algorithm. In the first stage of the BD algorithm, optimal number of new transmission lines and distributed series reactors are determined. In the second stage, the developed optimal power flow problem, as a linear sub-problem, is performed for different scenarios of uncertainties and a set of probable contingencies. The Benders cuts are iteratively added to the first stage problem to decrease the optimality gap below a given threshold. The proposed model utilises the Monte Carlo simulation method to take into account uncertainty of wind generations and demands. Several case studies on three test systems are presented to validate the efficacy of the proposed approach.

关键词： power transmission planning power system security linear programming load flow Monte Carlo methods probability power generation planning integer programming power transmission reliability nonlinear programming BD algorithm optimal number transmission lines distributed series reactors developed optimal power flow problem linear sub-problem stage problem optimality gap probabilistic decomposition-based security transmission expansion planning incorporating distributed series reactor probabilistic transmission expansion planning model network flexibility mixed-integer nonlinear programming problem approximation method Benders decomposition algorithm

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