Short-term hydrothermal generation scheduling by a modified dynamic neighborhood learning based particle swarm optimization

Rasoulzadeh-akhijahani, Ali; Mohammadi‐Ivatloo, Behnam

doi:10.1016/j.ijepes.2014.12.011

Cited by 71 publications

(18 citation statements)

References 63 publications

(155 reference statements)

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“…QEA [25] 930,647.96 DE [25] 928,662.84 RCGA-AFSA [34] 927,899.872 RQEA [25] 926,068.33 DRQEA [25] 925,485.21 CRQEA [25] 925,403.1 RCCRO [41] 925,214.20 ACDE [42] 924,661.53 MAPSO [36] 924,636 TLBO [37] 924,550.78 RCGA [34] 923,966.285 RQEA [25] 923,634.53 DE [25] 923,234.56 MDE [33] 922,556.38 DRQEA [25] 922,526.73 HCRO-DE [35] 922,444.79 MAPSO [36] 922,421.66 MDNLPSO [15] 923,961 IDE [40] 923,016.29 TLBO [37] 922,373.39 RCGA-AFSA [34] 922,339.625 SPPSO [33] 922,336.31 SOHPSO_TVAC [38] 922,018.24 PSO [39] 921,920 Improved DE [40] 917,250.1 IDE [40] 917,237.7 FAPSO [39] 914,660.00 Proposed method 901,191.9735…”

Section: Optimization Methods Min Cost ($)mentioning

confidence: 99%

“…The obtained results are compared with those obtained by employing quantum-inspired evolutionary algorithm (QEA) [25], quantum-inspired evolutionary algorithm (WDA) [32], small population-based particle swarm optimization (SPSO) [33], real coded genetic algorithm (RCGA) [34], real-coded quantum-inspired evolutionary algorithm (RQEA) [25], DE [25], modified differential evolution (MDE) [33], differential real-coded quantum-inspired evolutionary algorithm (DRQEA) [25], hybrid chemical reaction optimization (HCRO)-DE [35], modified adaptive particle swarm optimization (MAPSO) [36], real-coded genetic algorithm and artificial fish swarm algorithm (RCGA-AFSA) [34], teaching learning-based optimization (TLBO) [37], smallpopulation-based particle swarm optimization (SPPSO) [33], self-organizing hierarchical particle swarm optimization technique with time-varying acceleration coefficients (SOHPSO_TVAC) [38], PSO [39], improved differential evolution (IDE) [40], fuzzy adaptive particle swarm optimization (FAPSO) [39], dynamic neighborhood learning based particle swarm optimization (DNLPSO) [15], and modified dynamic neighborhood learning based particle swarm optimization (MDNLPSO) [15], and is shown in Table 2. As it can be observed from this table, the best reported cost for this case is equal to $914,660, which is related to FAPSO [39], while total operational cost of the solution obtained by the proposed The obtained results are compared with those obtained by employing quantum-inspired evolutionary algorithm (QEA) [25], quantum-inspired evolutionary algorithm (WDA) [32], small population-based particle swarm optimization (SPSO) [33], real coded genetic algorithm (RCGA) [34], real-coded quantum-inspired evolutionary algorithm (RQEA) [25], DE [25], modified differential evolution (MDE) [33], differential real-coded quantum-inspired evolutionary algorithm (DRQEA) [25], hybrid chemical reaction optimization (HCRO)-DE ...…”

Section: Test Systemmentioning

confidence: 99%

“…Comparisons of simulation results for this case study are accomplished in Table 8. It can be observed that the obtained result using the proposed method outperform the results of QEA [25], ABC [43], DE [32], SPPSO [50], RQEA [25], DNLPSO [15], PSO [51], CSA [44], TLBO [33], SA-MOCDE [37], GSA [52], QOTLBO [33], MOCA-PSO [53], SHPSO-TAC [51], IDE [40], RCGA-AFSA [34], QABDEVT [16], ACDE [54]. Taking into account transmission losses and valve-point effects, the best reported solution is related to ACDE [54], which obtained total cost of $41,593.48.…”

Section: Test Systemmentioning

confidence: 99%

“…However, the proposed method provided the optimal solution with the total operational cost of $41,350.5574 which is better than other methods. 43,362.00 QOGSO [49] 43,560.35 DE [32] 42,801.04 SPPSO [50] 42,740.23 RQEA [25] 42,715.69 DNLPSO [15] 42,645 PSO [51] 42,474.00 CSA [44] 42,440.574 TLBO [33] 42,386.13 SA-MOCDE [37] 42,038.00 GSA [52] 42,032.35 QOTLBO [33] 42,187.49 MOCA-PSO [53] 42,001.00 SHPSO-TAC [51] 41,983.00 IDE [40] 41,856.5 RCGA-AFSA [34] 41,818.42 QABDEVT [16] 41,762.00 ACDE [54] 41,593.48 Proposed method 41,350.5574…”

Section: Test Systemmentioning

confidence: 99%

“…A modified dynamic neighborhood learning based particle swarm optimization (MDNLPSO) method is introduced in [15] to solve the STHTS problem. In this reference, the proposed approach is applied on two test systems with different characteristics.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Application of Dynamic Non-Linear Programming Technique to Non-Convex Short-Term Hydrothermal Scheduling Problem

et al. 2017

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Short-term hydro-thermal scheduling aims to obtain optimal generation scheduling of hydro and thermal units for a one-day or a one-week scheduling time horizon. The main goal of the problem is to minimize total operational cost considering a series of equality and inequality constraints. The problem is considered as a non-linear and complex problem involving the valve-point loading effect of conventional thermal units, the water transport delay between connected reservoirs, and transmission loss with a set of equality and inequality constraints such as power balance, water dynamic balance, water discharge, initial and end reservoir storage volume, reservoir volume limits and the operation limits of hydro and thermal plants. A solution methodology to the short-term hydro-thermal scheduling problem with continuous and non-smooth/non-convex cost function is introduced in this research applying dynamic non-linear programming. In this study, the proposed approach is applied to two test systems with different characteristics. The simulation results obtained in this paper are compared with those reported in recent research studies, which show the effectiveness of the presented technique in terms of total operational cost. In addition, the obtained results ensure the capability of the proposed optimization procedure for solving short-term hydro-thermal scheduling problem with transmission losses and valve-point effects.

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Section: Optimization Methods Min Cost ($)mentioning

confidence: 99%

Section: Test Systemmentioning

confidence: 99%

Section: Test Systemmentioning

confidence: 99%

Section: Test Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Application of Dynamic Non-Linear Programming Technique to Non-Convex Short-Term Hydrothermal Scheduling Problem

et al. 2017

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Application of chaos assisted sine cosine algorithm on wind–solar integrated hydrothermal scheduling problem

Dasgupta

Roy

Mukherjee

2023

Optim Control Appl Methods

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In recent times, renewable energy sources are essential for generating low‐cost, pollution‐free power. Since the last two decades, hydrothermal coordination system has been used to fulfill the demand power. The traditional generating model's two biggest flaws are high generation cost and excessive fossil fuel emission. In the proposed work, generation process of traditional hydrothermal scheduling (HTS) has been improved by integrating wind–solar energy. To overcome current issues of HTS, that is, excessive emission and generation cost, hydrothermal–solar–wind scheduling (HTSWS) problem has been introduced where two wind and two solar power generating units have been considered with four hydro and three thermal power generating units. Here, effective performance of low cost‐emission based proposed HTSWS has been examined by using chaos‐based sine cosine algorithm (SCA) (CSCA). Chaos has been used in sine cosine function of SCA to improve searching process for finding global optimal results. The proposed CSCA has been used to investigate three separate situations of standard HTS and recommended HTSWS problems including generation cost, emission, and combined cost‐emission. The cost of generation and emissions from conventional HTS can be reduced by 11% and 68%, respectively, by the suggested renewable energy source‐based generation model. The results of several case studies of proposed HTSWS and standard HTS reveal that wind–solar energy is effective in the HTS issue, and chaos assists in the SCA to improve its optimization process. In this study, effectiveness and robustness of the CSCA have been demonstrated by comparing the CSCA‐based results to existing methods for the challenges under consideration.

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A Modified Grey Wolf Optimizer Algorithm for Economic Scheduling of Hydrothermal Systems

Das

Bhowmik

Das

2020

Learning and Analytics in Intelligent Systems

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Short-term hydrothermal generation scheduling by a modified dynamic neighborhood learning based particle swarm optimization

Cited by 71 publications

References 63 publications

Application of Dynamic Non-Linear Programming Technique to Non-Convex Short-Term Hydrothermal Scheduling Problem

Application of Dynamic Non-Linear Programming Technique to Non-Convex Short-Term Hydrothermal Scheduling Problem

Application of chaos assisted sine cosine algorithm on wind–solar integrated hydrothermal scheduling problem

A Modified Grey Wolf Optimizer Algorithm for Economic Scheduling of Hydrothermal Systems

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