An improved TLBO based economic dispatch of power generation through distributed energy resources considering environmental constraints

Joshi, Prachi; Verma, H. K.

doi:10.1016/j.segan.2019.100207

Cited by 28 publications

(17 citation statements)

References 23 publications

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“…In [57]- [59], multiarea ELD was solved by SSA, FSA, and MFO algorithm. In [60], the authors used an improved version of TLBO for solving ELD problem considering distributed generation. A modified version of GWO was used for solving non-convex ELD for current power system scenario in [61].…”

Section: Related Workmentioning

confidence: 99%

“…VOLUME 4, 2016 ELD, ELD with valve point effect, CEED CTO [28] Dynamic ELD PSO TCO [29] ELD considering renewable sources Modified BA [30] ELD with losses QBA [31] Dynamic ELD considering renewable sources PSO [32] ELD with valve point effect QBA [33] ELD with losses PSO [34] Non convex ELD ACS [35] CEED Hybrid FA BA [74] ELD Jaya Algorithm [36] ELD with valve point effect, CEED MBA [37] ELD with valve point effect, ramp rates and prohibited zones Improved PSO [38] Convex and non-convex ELD SHO [39] ELD with ramp rates and prohibited zones WOA [40] Convex, non-convex and dynamic ELD MFO [41] ELD with valve point effect Hybrid GWO [42] ELD with losses Improved PSO [43] ELD with losses AEFA [44] Non-convex ELD ALO [45] ELD with losses ACSS [46] ELD with losses Adaptive PSO [47] ELD with losses Enhanced PSO [48] ELD with losses GWO [49] ELD with losses Enhanced BA [50] CEED QBA [51] Dynamic ELD, CEED Modified ABC [52] CEED Enhanced Cultural algorithm [53] Dynamic CEED Chaotic FA [54] Dynamic CEED TLBO [55] Smooth and non-smooth ELD WCA [56] ELD with renewable resources Hybrid BA [57] Multi-area ELD SSA [75] Multi-area ELD Hybrid Jaya TLBO [58] Multi-area ELD ISFS [76] CEED with renewable sources MOEA/D [59] Multi-area ELD MFO [60] CEED Improved TLBO [61] ELD with losses Modified GWO [62] Dynamic ELD GA PSO…”

Section: Economic Load Dispatch Problemmentioning

confidence: 99%

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Recent Methodology-Based Gradient-Based Optimizer for Economic Load Dispatch Problem

et al. 2021

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Economic load dispatch (ELD) in power system problems involves scheduling the power generating units to minimize cost and satisfy system constraints. Although previous works propose solutions to reduce CO2 emission and production cost, an optimal allocation needs to be considered on both cost and emission-leading to combined economic and emission dispatch (CEED). Metaheuristic optimization algorithms perform relatively well on ELD problems. The gradient-based optimizer (GBO) is a new metaheuristic algorithm inspired by Newton's method that integrates both the gradient search rule and local escaping operator. The GBO maintains a good balance between exploration and exploitation. Also, the possibility of the GBO getting stuck in local optima and premature convergence is rare. This paper tests the performance of GBO in solving ELD and CEED problems. We test the performance of GBO on ELD for various scenarios such as ELD with transmission losses, CEED, CEED with valve point effect, and other various test networks. The experimental results revealed that GBO has been obtained better results compared to eight other metaheuristic algorithms such as Slime mould algorithm (SMA), Elephant herding optimization (EHO), Monarch butterfly optimization (MBO), Moth search algorithm (MSA), Earthworm optimization algorithm (EWA), Artificial Bee Colony (ABC) Algorithm, Tunicate Swarm Algorithm (TSA) and Chimp Optimization Algorithm (ChOA). Therefore, the simulation results showed the competitive performance of GBO as compared to other benchmark algorithms.INDEX TERMS Gradient-Based Optimizer (GBO); Economic Load Dispatch (ELD); Combined Economic and Emission Dispatch (CEED); Metaheuristics; Optimization.

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Section: Related Workmentioning

confidence: 99%

Section: Economic Load Dispatch Problemmentioning

confidence: 99%

Recent Methodology-Based Gradient-Based Optimizer for Economic Load Dispatch Problem

et al. 2021

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show abstract

“…The conventional thermal power generation limits by including the ramp rate limits [17] can be expressed as,…”

Section: Power Generation Constraintsmentioning

confidence: 99%

Multi-objective based economic environmental dispatch with stochastic solar-wind-thermal power system

Salkuti

2020

IJECE

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This paper presents an evolutionary based technique for solving the multi-objective based economic environmental dispatch by considering the stochastic behavior of renewable energy resources (RERs). The power system considered in this paper consists of wind and solar photovoltaic (PV) generators along with conventional thermal energy generators. The RERs are environmentally friendlier, but their intermittent nature affects the system operation. Therefore, the system operator should be aware of these operating conditions and schedule the power output from these resources accordingly. In this paper, the proposed EED problem is solved by considering the nonlinear characteristics of thermal generators, such as ramp rate, valve point loading (VPL), and prohibited operating zones (POZs) effects. The stochastic nature of RERs is handled by the probability distribution analysis. The aim of proposed optimization problem is to minimize operating cost and emission levels by satisfying various operational constraints. In this paper, the single objective optimization problems are solved by using particle swarm optimization (PSO) algorithm, and the multi-objective optimization problem is solved by using the multi-objective PSO algorithm. The feasibility of proposed approach is demonstrated on six generator power system.

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“…9, No. 3, September 2020: 371 -378 372 based optimization (TLBO) [13], genetic algorithm (GA) [14] and adaptive charged system search algorithm (ACSS) [15] have been solved the complex ED problem. The performance of these algorithms is better than classical optimization methods in many aspects for instance fast, robust and easy to adjust according to the problem.…”

Section: Introductionmentioning

confidence: 99%

Optimal economic dispatch of power generation solution using lightning search algorithm

Nassar

Abdullah

Rahimoon

2020

IJ-AI

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Economic dispatch (ED) is the power demand allocating process for the committed units at minimum generation cost while satisfying system and operational constraints. Increasing cost of fuel price and electricity demand can increase the cost of thermal power generation. Therefore, robust and efficient optimization algorithm is required to determine the optimal solution for ED problem in power system operation and planning. In this paper the lightning search algorithm (LSA) is proposed to solve the ED problem. The system constraints such as power balance, generator limits, system transmission losses and valve-points effects (VPE) are considered in this paper. To verify the effectiveness of LSA in terms of convergence characteristic, robustness, simulation time and solution quality, the two case studies consists of 6 and 13 units have been tested. The simulation results show that the LSA can provide optimal cost than many methods reported in literature. Therefore, it has potential to solve many optimization problems in power dispatch and power system applications.

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An improved TLBO based economic dispatch of power generation through distributed energy resources considering environmental constraints

Cited by 28 publications

References 23 publications

Recent Methodology-Based Gradient-Based Optimizer for Economic Load Dispatch Problem

Recent Methodology-Based Gradient-Based Optimizer for Economic Load Dispatch Problem

Multi-objective based economic environmental dispatch with stochastic solar-wind-thermal power system

Optimal economic dispatch of power generation solution using lightning search algorithm

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