A practical algorithm for distribution state estimation including renewable energy sources

Niknam, Taher; Firouzi, Bahman Bahmani

doi:10.1016/j.renene.2009.03.005

Cited by 63 publications

(27 citation statements)

References 23 publications

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“…The optimum location parameters are used as the inputs of the GA second phase. Here, the fuel cell capacity [25] is randomly generated and applied for the obtained optimum location. From the applied capacities, we can find the optimum capacity of fuel cell by using the multi-objective function, i.e., minimization of both power loss and voltage deviation.…”

Section: Fuel Cell Capacity Optimization Using Ga Second Phasementioning

confidence: 99%

Optimal Location for Fixing Fuel Cells in a Distributed Generation Environment using Hybrid Technique

Subramanyam¹,

Ram²,

Subrahmanyam³

2016

ijeei

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The paper proposes optimal location for fixing fuel cells in a distribution system using an innovative hybrid technique. The novelty of the proposed method is the combined performance of the Genetic Algorithm (GA) and Recurrent Neural Network (RNN) technique, thereby integrating GA first phase, RNN technique and GA second phase. The optimum location for fixing the fuel cell is attained by using the GA first phase. Here, the GA first phase utilizes the load flow data at different loading conditions for determining the optimum location. The RNN is aptly trained by the target fuel cell size and the corresponding inputs such as load variation and bus number. During the testing time, the RNN provides the fuel cell capacity according to the load variation and bus number. By using the attained fuel cell capacities, the GA second phase optimizes the fuel cell capacity to minimize the power loss and the voltage deviation. The objective function mainly helps to improve the bus voltage profile and the power loss reduction. Thus, the proposed hybrid technique is implemented in the MATLAB/simulink platform and its effectiveness is analyzed by comparing it with the GA, PSO and other hybrid PSO techniques. The comparison results unequivocally demonstrate the superiority of the proposed approach and confirm its sterling potential to solve the problem.

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Section: Fuel Cell Capacity Optimization Using Ga Second Phasementioning

confidence: 99%

Optimal Location for Fixing Fuel Cells in a Distributed Generation Environment using Hybrid Technique

Subramanyam¹,

Ram²,

Subrahmanyam³

2016

ijeei

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show abstract

“…At the same time, a number of optimization modeling methods were proposed for supporting the management of GHG emission mitigation particularly through the adoption of renewable energies [54,[125][126][127][128][129][130][131][132][133][134][135][136][137][138][139][140][141][142][143]. In the middle of the 1970s, Duff (1975) presented an optimization model to design solar thermal energy systems in accordance with the minimum system cost [144].…”

Section: Optimization Of Ghg Emission Mitigationmentioning

confidence: 99%

A Review on Optimization Modeling of Energy Systems Planning and GHG Emission Mitigation under Uncertainty

et al. 2011

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Abstract:Energy is crucial in supporting people's daily lives and the continual quest for human development. Due to the associated complexities and uncertainties, decision makers and planners are facing increased pressure to respond more effectively to a number of energy-related issues and conflicts, as well as GHG emission mitigation within the multiple scales of energy management systems (EMSs). This quandary requires a focused effort to resolve a wide range of issues related to EMSs, as well as the associated economic and environmental implications. Effective systems analysis approaches under uncertainty to successfully address interactions, complexities, uncertainties, and changing conditions associated with EMSs is desired, which require a systematic investigation of the current studies on energy systems. Systems analysis and optimization modeling for low-carbon energy systems planning with the consideration of GHG emission reduction under uncertainty is thus comprehensively reviewed in this paper. A number of related methodologies and applications related to: (a) optimization modeling of GHG emission mitigation; (b) optimization modeling of energy systems planning under uncertainty; and (c) model-based decision support tools are examined. Perspectives of effective management schemes are investigated, demonstrating many demanding areas for enhanced research efforts, which include issues of data availability and reliability, concerns in OPEN ACCESSEnergies 2011, 4 1625 uncertainty, necessity of post-modeling analysis, and usefulness of development of simulation techniques.

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“…They found that the proposed method can be applicable for PSSE. A practical algorithm for DSE including renewable energy sources was presented in [58]. The proposed algorithm is based on the combination of Nelder-Mead simplex search and PSO algorithms, called PSO-NM.…”

Section: Introductionmentioning

confidence: 99%

Power system state estimation solution using modified models of PSO algorithm: Comparative study

2016

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A practical algorithm for distribution state estimation including renewable energy sources

Cited by 63 publications

References 23 publications

Optimal Location for Fixing Fuel Cells in a Distributed Generation Environment using Hybrid Technique

Optimal Location for Fixing Fuel Cells in a Distributed Generation Environment using Hybrid Technique

A Review on Optimization Modeling of Energy Systems Planning and GHG Emission Mitigation under Uncertainty

Power system state estimation solution using modified models of PSO algorithm: Comparative study

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