Development of a Self-Healing Strategy With Multiagent Systems for Distribution Networks

Leite, Jônatas Boás; Mantovani, José Roberto Sanches

doi:10.1109/tsg.2016.2518128

Cited by 53 publications

(34 citation statements)

References 18 publications

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“…In the presence of scale, dynamism, uncertainty, and elasticity, it becomes more complex for engineers to effectively allocate resources for cloud‐based software services. The self‐adaptive capabilities, including self‐configuring, self‐optimizing, and self‐healing, can cope with the complexity of resource allocation in this scenario.…”

Section: Related Workmentioning

confidence: 99%

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Self‐learning and self‐adaptive resource allocation for cloud‐based software services

Chen

Lin

et al. 2018

Concurrency and Computation

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Summary In the presence of scale, dynamism, uncertainty, and elasticity, cloud engineers face several challenges when allocating resources for cloud‐based software services. They should allocate appropriate resources in order to guarantee good quality of services as well as low cost of resources. Self‐adaptive ability is needed in this process because engineers' intervention is difficult. Traditional self‐adaptive resource allocation methods are policy‐driven. Thus, cloud engineers usually have to develop separate sets of rules for each systems in order to allocate resources effectively, which leads to high administrative cost and implementation complexity. Machine learning has made great achievements in many fields, and it can be also applied to resource allocation. In this paper, we present a self‐learning and self‐adaptive approach to resource allocation for cloud‐based software services. For a given cloud‐based software service, its QoS model is firstly trained on history data, which is capable to predict the QoS value as output by using the information on workload and allocated resources as inputs. Then, on‐line decision‐making on resource allocation can be carried out automatically based on genetic algorithm, which is aimed to search reasonable resource allocation plan by using the QoS model. We evaluate our approach on RUBiS benchmark, demonstrating the accuracy of the QoS model over 90% and the improvement of resource utilization by 10%‐30%.

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Section: Related Workmentioning

confidence: 99%

“…The self-adaptive capabilities, including self-configuring, 24 self-optimizing, 25 and self-healing, 26 can cope with the complexity of resource allocation in this scenario.…”

Section: Related Workmentioning

confidence: 99%

Self‐learning and self‐adaptive resource allocation for cloud‐based software services

Chen

Lin

et al. 2018

Concurrency and Computation

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“…Another important issue to consider in this article is the use of backup feeders for system restoration. Backup feeders are only used in References for the restoration problem. In Reference , load shedding and islanding state are not considered for restoration.…”

Section: Introductionmentioning

confidence: 99%

“…In Reference , load shedding and islanding state are not considered for restoration. In References , load shedding is not considered for restoration. The cost of distributed generation rescheduling (DGR) is another important issue that is overlooked in the above literature.…”

Section: Introductionmentioning

confidence: 99%

Self‐healing optimization in active distribution network to improve reliability, and reduction losses, switching cost and load shedding

Choopani

Hedayati

Effatnejad

2020

Int Trans Electr Energ Syst

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Summary Self‐healing refers to the specific ability of the smart grid that takes precautionary actions before the fault. It also performs fault location, isolation, and service restoration to minimize network damage after the fault. This process is performed with the help of communications infrastructure and remote control. The main components of this article include the use of graph theory and binary particle swarm optimization algorithm for optimal switching, the use of particle swarm optimization algorithm and forward‐backward sweep load flow for load shedding and distributed generation rescheduling, investigating the effect of self‐healing on improved reliability, power losses reduction, and load shedding reduction and the present a new approach for self‐healing optimization and a new mathematical model. The problem of self‐healing due to nonlinear and unbounded constraints is one of the nonconvex mixed integer nonlinear programming optimization problems. The optimization problem becomes a convex mixed integer linear programming optimization problem using the proposed scheme. Due to the nonlinearity and the nonconvexity of the problem, commercial solvers are not able to solve this problem. Even the Baron solver, which is used to solve nonconvex nonlinear problems, cannot achieve the optimal global solution. However, the proposed method is easily able to achieve the optimal solution by eliminating the nonlinear and nonconvex element. A modified RBTS‐4 distribution system is used as a test system. The IEEE 33‐bus distribution system is used to validate the proposed method. The results of case studies demonstrate the effectiveness of the proposed methodology.

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“…Actually, they cannot be generalized for any network topology [3], [6][7]. Moreover, in all the published works using these methods, the feasibility of the solution concerning the technical constraints (ex., voltage and current limits) is either ignored [10] or checked at the last stage by a load flow simulation but with a scarce feedback on the modification of the identified restoration solution [9]. Indeed, if the solution is not feasible, it is removed from the search space and the process is repeated to find a new solution.…”

Section: Introductionmentioning

confidence: 99%

Service Restoration in DG-Integrated Distribution Networks Using an Exact Convex OPF Model

Sekhavatmanesh

Nick

Paolone

et al. 2018

2018 Power Systems Computation Conference (PSCC)

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The ever-increasing requirements for reliability and quality of supply suggest to enable the self-healing features of modern distribution networks. Within the context of Active Distribution Networks (ADNs), new, fast and efficient restoration strategies can be developed relying not only on switching devices to realize load transfer operation, but also on the contribution of Distributed Generators (DGs) while controlling their set points. In this paper, a global optimization method is proposed for the restoration problem in a DG-integrated distribution network. The objective is to restore a maximum of loads with a minimum number of switching operations. In order to check the technical constraints of the resulting configuration (ex., line current and bus voltage constraints), a recently published method for exact convex formulation of the OPF problem is incorporated making the restoration problem robust particularly in the case of high nodal injections. The proposed restoration model is formulated as a mixed-integer second-order cone programming (MISOCP) problem. Two test cases are used to quantify the DG effects on the quality of the restoration strategy and to demonstrate the feasibility of the identified solution in cases where general relaxation methods for OPF problem leads to inexact solutions.

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Development of a Self-Healing Strategy With Multiagent Systems for Distribution Networks

Cited by 53 publications

References 18 publications

Self‐learning and self‐adaptive resource allocation for cloud‐based software services

Self‐learning and self‐adaptive resource allocation for cloud‐based software services

Self‐healing optimization in active distribution network to improve reliability, and reduction losses, switching cost and load shedding

Service Restoration in DG-Integrated Distribution Networks Using an Exact Convex OPF Model

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