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

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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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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Sustainable wastewater treatment plants design through multiobjective optimization

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COMPUTERS & CHEMICAL ENGINEERING 2020年 140卷 106850-106850页

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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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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Optimal Placement of Series Capacitive Compensation in Transmission Network Expansion Planning

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JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2020年第1期31卷 165-176页

作者： Gallego, Luis A. Garces, Lina P. Contreras, Javier Univ Estadual Londrina Dept Elect Engn Londrina PR Brazil Univ Fed Goias Elect Mech & Comp Engn Sch Goiania Go Brazil Univ Castilla La Mancha ETS Ingenieros Ind E-13071 Ciudad Real Spain

This paper proposes a novel mathematical model to solve the static and multistage transmission network expansion planning problems considering the optimal placement of series capacitive compensation (SCC) devices. This model jointly examines the construction of new transmission lines, transformers, and the optimal placement of SCC devices to minimize the total investment cost while satisfying the demand requirements in the planning horizon. The problem is formulated as a mixed-integer nonlinear programming problem, which is solved using a high-performance hybrid genetic algorithm. Simulations done in four electrical systems (IEEE 24-bus, South Brazilian 46-bus, North-Northeast Brazilian 87-bus, and Colombian 93-bus) show that inclusion of SCC devices in the planning model results in lower investment cost and a better redistribution of power flows through transmission components.

关键词： Transmission network expansion planning Optimal placement of series capacitive mixed-integer nonlinear programming problem High-performance hybrid genetic algorithm

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Dependable multi-population improved brain storm optimization with differential evolution for optimal operational planning of energy plants

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ELECTRICAL ENGINEERING IN JAPAN 2019年第4期207卷 25-35页

作者： Arai, Kiyo Fukuyama, Yoshikazu Iizaka, Tatsuya Matsui, Tetsuro Meiji Univ Sch Interdisciplinary Math Sci Dept Network Design Tokyo 1648525 Japan Fuji Elect Co Ltd Corp R&D Headquarters Adv Technol Lab Syst Technol Res CtrAdv Algorithm Res Dept Hino Tokyo Japan

This paper proposes dependable multi-population improved brain storm optimization with differential evolution for optimal operational planning of energy plants. The problem can be formulated as a mixed-integer nonlinear programming problem and various evolutionary computation techniques such as particle swarm optimization (PSO), differential evolutionary PSO (DEEPSO), multi-population DEEPSO (MP-DEEPSO), and brain storm optimization have been applied so far. When optimal operational planning of numbers of energy plants is calculated simultaneously in a data center, a challenge is to generate optimal operational planning as rapidly as possible considering control intervals and numbers of treated plants. One of the solutions for the challenge is speeding up by parallel and distributed computing. It utilizes numbers of processes and countermeasures for various faults of the distributed processes should be considered. Moreover, successive calculation at every control interval is required for keeping customer services. Therefore, sustainable (dependable) calculation keeping appropriate solution quality is required even if some of the calculation results cannot be returned from distributed processes. It is verified that total energy cost by the proposed dependable multi-population improved brain storm optimization with differential evolution strategy based method is lower than those by the compared methods, and higher quality of solutions can be kept even with high fault probabilities.

关键词： brain storm optimization with differential evolution dependability evolutionary computation mixed-integer nonlinear programming problem multi-population optimal operational planning of energy plant

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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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Joint node selection, flow routing, and cell coverage optimisation for network sum-rate maximisation in wireless sensor networks

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IET WIRELESS SENSOR SYSTEMS 2019年第6期9卷 424-437页

作者： Baidas, Mohammed W. Awad, Mohamad K. El-Amine, Ahmad Hassan, Omar A. Shen, Xuemin Sherman Kuwait Univ Coll Engn & Petr Dept Elect Engn Kuwait Kuwait Kuwait Univ Coll Engn & Petr Dept Comp Engn Kuwait Kuwait Huawei Technol Co Ltd Ottawa Res & Dev Ctr Markham ON Canada Univ Waterloo Dept Elect & Comp Engn Waterloo ON Canada

In this study, the problems of joint node selection, flow routing, and cell coverage optimisation in energy-constrained wireless sensor networks (WSNs) are considered. Due to the energy constraints on network nodes, maximising network sum-rate under target network lifetime, flow routing, cell coverage, and minimum rate constraints is of paramount importance in WSNs. To this end, a mixed-integer non-linear programming problem is formulated, where the aim is to optimally select which network nodes to act as sensors or relays while ensuring connectivity to the fusion centre optimised network flows, and full network coverage. The formulated problem happens to be NP-hard (i.e. computationally prohibitive). In turn, a solution procedure based on the branch and bound with the reformulation-linearisation technique (BB-RLT) is devised to provide a $\lpar 1 - {\rm \epsilon }\rpar $(1-epsilon)-optimal solution to the formulated problem. Simulation results are presented to validate the efficacy of the devised BB-RLT solution procedure. This work provides significant theoretical results on network sum-rate maximisation for WSNs under a variety of practical constraints.

关键词： optimisation integer programming linearisation techniques wireless sensor networks nonlinear programming telecommunication network routing cellular radio telecommunication power management communication complexity tree searching cell coverage optimisation network sum-rate maximisation joint node selection flow routing energy-constrained wireless sensor networks minimum rate constraints mixed-integer nonlinear programming problem fusion centre optimised network WSN NP-hard problem branch and bound with the reformulation-linearisation technique BB-RLT solution procedure

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

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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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