A novel distributed particle swarm optimization algorithm for the optimal power flow problem

Gionfra, Nicolò; Sandou, Guillaume; Siguerdidjane, Houria; Loevenbruck, Philippe; Faille, Damien

doi:10.1109/ccta.2017.8062537

Cited by 3 publications

(3 citation statements)

References 14 publications

(41 reference statements)

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“…This solution is similar to [14] as it allows a system supervisor to coordinate distributed controllers over a star communications network to cooperate in the solution search. The modular distributed framework exists in [16], [17]. In ADPSO, the solution search requires only the exchange of the state variable between agents and the supervisor completely in asynchronous manner [20], meaning that there is no need for synchronization between distributed controllers and the supervisor does not need to wait for every controller to progress in order to update the state variable.…”

Section: B Contributionsmentioning

confidence: 99%

“…In [16], an optimal power flow problem is formulated and solved by Distributed Control Units in a modular framework; the lack of need for powerful computers for these units is remarked. The adoption of a modular framework is also present in [17] where a PSO algorithm to solve general optimization problems cooperatively by sharing the optimization variable and performing a finite-time average consensus algorithm for each step is presented. In [15], a Neurodynamic-Based Distributed Optimal Control Algorithm is used for the distributed optimization of economic system operation in a multienergy system with combined heat and power, and conventional generators.…”

Section: Introductionmentioning

confidence: 99%

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Microgrid Energy Management With Asynchronous Decentralized Particle Swarm Optimization

et al. 2021

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Controlling distributed energy resources (DERs) in low voltage microgrids is a challenging task for operators. The simultaneous operation of independent small-scale DER owners could compromise the operator's hierarchical and centralized control to reach system stability and cost optimization. Recent Decentralized Energy Management (DEM) approaches provide flexibility for DERs control, but several existing solutions depend on powerful and expensive computer clusters and their ability to deal with a high burden of data in the communication channel. This work is motivated towards a DEM framework that involves independent DER owners while microgrid operator still maintains a hierarchical control philosophy. The framework must include a method to reduce the need of powerful computer clusters and depend on low bandwidth communications channel. Here, a multi-layered framework for every DER, consisting of physical, control, and agent layers for DEM is approached, where the agent layer participates in the energy management task. An Asynchronous Decentralized PSO (ADPSO) algorithm is proposed for the agent layer based on its primal characteristic: it can reach a consensus state between networked computing units by exchanging asynchronously only the state variable through the communications channel. The proposed solution allows the integration of DEM capabilities within the physical controller of the DERs, distinguishing it from other decentralized solutions. Easiness of implementation and low computational requirements are shown by performing DEM tests on single board computers. The tests show improved convergence rate, improved swarm diversity behavior and fast consensus reaching of DEM optimization.

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Section: B Contributionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Microgrid Energy Management With Asynchronous Decentralized Particle Swarm Optimization

et al. 2021

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“…A modified consensus technique is employed to estimate the sum of local cost functions at each step of PSO. Unfortunately such estimation may fail to be sufficiently accurate to guarantee proper convergence of the algorithm when the agents do share a common variable, [38]. Authors of [39] propose a distributed primal-dual optimization method, where the primal variable update, usually provided by sub-gradient methods, is replaced by the PSO algorithm.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Wind farm distributed PSO-based control for constrained power generation maximization

et al. 2019

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A novel distributed approach to treat the wind farm (WF) power maximization problem accounting for the wake interaction among the wind turbines (WTs) is presented. Power constraints are also considered within the optimization problem. These are either the WTs nominal power or a maximum allowed power injection, typically imposed by the grid operator. The approach is model-based. Coupled with a distributed architecture it allows fast convergence to a solution, which makes it exploitable for real-time operations. The WF optimization problem is solved in a cooperative way among the WTs by introducing a new distributed particle swarm optimization algorithm, based on cooperative co-evolution techniques. The algorithm is first analyzed for the unconstrained case, where we show how the WF problem can be distributed by exploiting the knowledge of the aerodynamic couplings among the WTs. The algorithm is extended to the constrained case employing Deb's rule. Simulations are carried out on different WFs and wind conditions, showing good power gains and fast convergence of the algorithm.

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Optimization of Peak Load Shaving in STS Group Cranes Based on PSO Algorithm

Kermani

Parise

Martirano

et al. 2018

2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Syst

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A novel distributed particle swarm optimization algorithm for the optimal power flow problem

Cited by 3 publications

References 14 publications

Microgrid Energy Management With Asynchronous Decentralized Particle Swarm Optimization

Microgrid Energy Management With Asynchronous Decentralized Particle Swarm Optimization

Wind farm distributed PSO-based control for constrained power generation maximization

Optimization of Peak Load Shaving in STS Group Cranes Based on PSO Algorithm

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