Cluster based wind-hydro-thermal unit commitment using GSA algorithm

Shukla, Abhishek; Singh, S. N.

doi:10.1109/pesgm.2014.6939113

Cited by 17 publications

(14 citation statements)

References 13 publications

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“…In recent years, based on forecasting wind power and the predicted errors, several scenario reduction methods are proposed such as scenario tree construction method [19, 20] and clustering‐based scenario reduction method [21, 22]. In [21], scenarios are used for clustering approach by identifying the area of maximum density according to a defined similarity criterion, and replacing each cluster by its more representative (focal) scenario.…”

Section: Introductionmentioning

confidence: 99%

“…This paper presents wind power forecasting uncertainty using wind scenarios generated by Monte Carlo simulation [22] and efficient simultaneous backward reduction (BR) technique based on probability metrics is used to decrease the number of scenarios to smaller scale. Weighted improved CPSO (WICPSO) technique is used to provide Pareto‐optimal solutions, which present the possible trade‐off between the cost and emission objectives considering hydro units, pump storage plant (PSP), wind farm and thermal units with and without ramp rate by utilising pseudo code algorithm to handle equality constraints.…”

Section: Introductionmentioning

confidence: 99%

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Multi‐objective unit commitment with renewable energy using hybrid approach

Shukla¹,

Singh²

2016

IET Renewable Power Generation

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi‐objective unit commitment with renewable energy using hybrid approach

Shukla¹,

Singh²

2016

IET Renewable Power Generation

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“…Other heuristic algorithms inspired by gravitational phenomena have been designed for clustering. For instance, a heuristic gravitational search algorithm (GSA) was proposed by Rashedi et al [32] and was applied in solving wind-hydro-thermal CO problem by Shukla and Singh [33]. Yin et al [34] designed a hybrid data clustering algorithm based on GSA.…”

Section: Related Work Of Gravity-based Clusteringmentioning

confidence: 99%

A Novel Hierarchical Clustering Approach Based on Universal Gravitation

Zhang

She

2020

Mathematical Problems in Engineering

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The target of the clustering analysis is to group a set of data points into several clusters based on the similarity or distance. The similarity or distance is usually a scalar used in numerous traditional clustering algorithms. Nevertheless, a vector, such as data gravitational force, contains more information than a scalar and can be applied in clustering analysis to promote clustering performance. Therefore, this paper proposes a three-stage hierarchical clustering approach called GHC, which takes advantage of the vector characteristic of data gravitational force inspired by the law of universal gravitation. In the first stage, a sparse gravitational graph is constructed based on the top k data gravitations between each data point and its neighbors in the local region. Then the sparse graph is partitioned into many subgraphs by the gravitational influence coefficient. In the last stage, the satisfactory clustering result is obtained by merging these subgraphs iteratively by using a new linkage criterion. To demonstrate the performance of GHC algorithm, the experiments on synthetic and real-world data sets are conducted, and the results show that the GHC algorithm achieves better performance than the other existing clustering algorithms.

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“…Several methods have been proposed to solve the UC problem such as Lagrangian relaxation (LR) approach (Merlin and Sandrin 1983), genetic algorithm (GA) (Bakirtzis and Petridis 1996), evolutionary programming (EP) (Juste et al 1999), simulated annealing (SA) (Mantawy et al 1998), particle swarm optimization (PSO) (Shukla and Singh 2013) and gravitational search algorithm (GSA) (Roy 2013;Shukla and Singh 2014), etc.…”

Section: G Tmentioning

confidence: 99%

Multi-Objective Unit Commitment With Renewable Energy Using GSA Algorithm

Shukla

Singh

2016

INAEL

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With increasing concerns of global warming and environmental change caused by the burning of fossil fuels, the operating paradigm of power system is shifting from minimum cost to minimum cost with minimum emission approach. Due to conflicting nature of economic and emission objectives, the generation scheduling becomes a multi-objective problem. In this paper, a multi-objective gravitational search algorithm (MOGSA) is used to provide Pareto optimal solutions which present the possible tradeoff between the cost and emission objectives of unit commitment (UC) problem. The MOGSA technique is used for solving the environmental and economic dispatch problem considering hydro, pump storage plant, wind farm and solar farm, with and without ramp rates. The simulation results demonstrate the effectiveness of the proposed approach in solving multi-objective UC problem.Cost coefficients of the ith generating unitEmission coefficients of the ith generating unitSystem load demand at hour t F T /E T Cost and emission over the scheduling horizon G t Forecast solar radiation at hour t GstSolar radiation in the standard environmentSchedule state of ith thermal unit at hour t NT/NH Number of thermal/hydro units Ph k (t)Generation of kth hydro unit at hour t P i (t)Generation of ith thermal unit at hour t P (max, min),iMax and min output power of unit i P (max, min),P,G Max and min output power of PSP P i max (t) Max generation of ith thermal unit at hour t P i min (t) Min generation of ith thermal unit at hour t P PG (t)Pumping and generation of PSP unit at hour t P Sn (t) Rated power output of the PV generator P WT (t)Total actual wind generation at hour t P WN Rated wind power output TO k (t)Turbine out flow for reservoir k during hour t TO k(max/min) Max/min turbine water out flow for reservoir k SR(t)

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Cluster based wind-hydro-thermal unit commitment using GSA algorithm

Cited by 17 publications

References 13 publications

Multi‐objective unit commitment with renewable energy using hybrid approach

Multi‐objective unit commitment with renewable energy using hybrid approach

A Novel Hierarchical Clustering Approach Based on Universal Gravitation

Multi-Objective Unit Commitment With Renewable Energy Using GSA Algorithm

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