An adaptive particle swarm optimization algorithm for reservoir operation optimization

Zhang, Zhongbo; Jiang, Yunzhong; Zhang, Shuanghu; Geng, Simin; Wang, Hao; Sang, Guoqing

doi:10.1016/j.asoc.2014.01.034

Cited by 81 publications

(28 citation statements)

References 24 publications

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“…Two other independent versions of APSO where proposed in subsequent papers, APSO-12 [33] and APSO-14 [32]. In APSO-12 the parameters w, c 1 , c 2 are changed following a simple rule, based on current and the maximum number of iterations.…”

Section: Particle Swarm Optimisation (Pso)mentioning

confidence: 99%

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Swarm intelligence algorithms for macroscopic traffic flow model validation with automatic assignment of fundamental diagrams

Poole

Kotsialos

2016

Applied Soft Computing

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Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractThis paper is concerned with the problem of macroscopic road traffic flow model calibration and verification. Thoroughly validated models are necessary for both control system design and scenario evaluation purposes. Here, the second order traffic flow model METANET was calibrated and verified using real data.A powerful optimisation problem formulation is proposed for identifying a set of model parameters that makes the model fit to measurements. For the macroscopic traffic flow model validation problem, this set of parameters characterise the aggregate traffic flow features over a road network. In traffic engineering, one of the most important relationships whose parameters need to be determined is the fundamental diagram of traffic, which models the non-linear relationship between vehicular flow and density. Typically, a real network does not exhibit the same traffic flow aggregate behaviour everywhere and different fundamental diagrams are used for covering different network areas. As a result, one of the initial steps of the validation process rests on expert engineering opinion assigning the spatial extension of fundamental diagrams. The proposed optimisation problem formulation allows for automatically determining the number of different fundamental diagrams to be used and their corresponding spatial extension over the road network, simplifying this initial step. Although the optimisation problem suffers from local minima, good solutions which generalise well were obtained.The design of the system used is highly generic and allows for a number of evolutionary and swarm intelligence algorithms to be used. Two UK sites have been used for testing it. Calibration and verification results are Preprint submitted to Applied Soft Computing September 18, 2015 discussed in detail. The resulting models are able to capture the dynamics of traffic flow and replicate shockwave propagation. A total of ten different algorithms were considered and compared with respect to their ability to converge to a solution, which remains valid for different sets of data. Particle Swarm Optimisation (PSO) algorithms have proven to be particularly effective and provide the best results both in terms of speed of convergence and solution generalisation. An interesting result reported is that more recently proposed PSO algorithms were outperformed by older variants, both in terms of speed of convergence and model error minimisation.

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Section: Particle Swarm Optimisation (Pso)mentioning

confidence: 99%

“…A gravitational search is conducted to optimise a fuzzy servo controller in [31]. Particle swarm optimisation has been used for reservoir optimisation [32], and hydrothermal scheduling [33].…”

Section: Introductionmentioning

confidence: 99%

Swarm intelligence algorithms for macroscopic traffic flow model validation with automatic assignment of fundamental diagrams

Poole

Kotsialos

2016

Applied Soft Computing

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“…The new method can achieve global optimal results with general applicability. [24][25][26] A. PSO-BIA PSO has been successfully adopted in electric power system. Recently, in order to avoid trapping in local optimum, 24 Adaptive Mutation PSO (AMPSO) has been proposed in many Refs.…”

Section: Solution Methodsmentioning

confidence: 99%

Bilevel economic operation of distribution networks with microgrid integration

Zhang

Zhao

et al. 2015

Journal of Renewable and Sustainable Energy

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Traditionally, the economic operation of a microgrid (MG) is independent with that of a distribution network (DN), and the electricity trade between them is simply based on contract prices or wholesale electricity market prices. In order to coordinate the benefits of DNs and MGs more effectively, a new economic operation model for DNs with MG is proposed in this paper modeled as a bilevel programming problem (BLPP). At the upper level, electric power from the gird and electricity price of MGs are control variables. A DN seeks the least operation cost by optimizing these control variables. At the lower level, control variables are electric power of dispatchable distributed generations (DGs) and the DN. Based on the electricity price got from the upper level and the operation costs of dispatchable DGs, a MG minimizes its hourly operation cost by optimizing the control variables. Furthermore, electricity price constraints are considered in this BLPP model, and a method to dynamically determine the constraints is proposed. They are decided by the load power demand profile in the DN and the price differential between the wholesale electricity market and dispatchable DGs. A comprehensive real-time pricing for residential consumers is also considered in the operation model to study the impact from demand response. The proposed BLPP model is solved by particles swarm optimization based on bilevel iterative algorithm. The demonstration is performed on a modified IEEE 33-node radial distribution system. V C 2015 AIP Publishing LLC. [http://dx.

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“…The mathematical model is expressed by Eqs. (32) and (33). Currently, optimization methods for this problem include DEDS (different evolution with dynamic stochastic selection) (84), HEAA (hybrid evolutionary algorithm and adaptive constrainthandling technique) (85), and SBO (social based optimization algorithm) (86).…”

Section: Ide-pso In Real-world Applicationmentioning

confidence: 99%

A novel improved particle swarm optimization algorithm based on individual difference evolution

Gou

Lei

Guo

et al. 2017

Applied Soft Computing

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An adaptive particle swarm optimization algorithm for reservoir operation optimization

Cited by 81 publications

References 24 publications

Swarm intelligence algorithms for macroscopic traffic flow model validation with automatic assignment of fundamental diagrams

Swarm intelligence algorithms for macroscopic traffic flow model validation with automatic assignment of fundamental diagrams

Bilevel economic operation of distribution networks with microgrid integration

A novel improved particle swarm optimization algorithm based on individual difference evolution

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