Economic Load Dispatch with Emission and Line Constraints using Biogeography Based Optimization Technique

Goyal, Sunil Kumar; Kanwar, Neeraj; Singh, Jitendra; Shrivastava, Manish; Saraswat, Amit; Mahela, Om Prakash

doi:10.1109/iciem48762.2020.9160266

Cited by 11 publications

(6 citation statements)

References 25 publications

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“…5. Unlike traditional models with exponential dimensionality (i.e., 𝒪 (2 ) for 𝑛 generating units), the proposed algorithm has linear dimensionality (i.e., 𝒪(2𝑛) ). This characteristic underscores its scalability and better performance to handle large-scale problems compared to existing methods.…”

Section: Emission Objective Functionmentioning

confidence: 99%

“…Power system operators utilize a set of generating units to achieve this equilibrium, each with its own technological and operational constraints. The cost of power generation also varies among generating units, and it is not directly proportional to the power output [2]. Economic cost dispatch (ECD) forms the foundational aspect of power scheduling, representing a single-objective optimization problem.…”

Section: Introductionmentioning

confidence: 99%

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Reinforcement Learning-Based Multi-Objective Optimization for Generation Scheduling in Power Systems

Ebrie,

Kim

2024

Systems

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Multi-objective power scheduling (MOPS) aims to address the simultaneous minimization of economic costs and different types of environmental emissions during electricity generation. Recognizing it as an NP-hard problem, this article proposes a novel multi-agent deep reinforcement learning (MADRL)-based optimization algorithm. Within a custom multi-agent simulation environment, representing power-generating units as collaborative types of reinforcement learning (RL) agents, the MOPS problem is decomposed into sequential Markov decision processes (MDPs). The MDPs are then utilized for training an MADRL model, which subsequently offers the optimal solution to the optimization problem. The practical viability of the proposed method is evaluated across several experimental test systems consisting of up to 100 units featuring bi-objective and tri-objective problems. The results demonstrate that the proposed MADRL algorithm has better performance compared to established methods, such as teaching learning-based optimization (TLBO), real coded grey wolf optimization (RCGWO), evolutionary algorithm based on decomposition (EAD), non-dominated sorting algorithm II (NSGA-II), and non-dominated sorting algorithm III (NSGA-III).

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Section: Emission Objective Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning-Based Multi-Objective Optimization for Generation Scheduling in Power Systems

Ebrie,

Kim

2024

Systems

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“…The equation for the fuel consumption of every unit is represented by the quadratic function in Equation ( 4), considering the generator output power P i , given as $/h, as in [4]:…”

Section: Mathematical Model Of Cost Of Thermal Plants (F 1 )mentioning

confidence: 99%

“…The standard economic load dispatch (ELD) solution seeks to assign the total power demand among all generators used to achieve the minimum fuel cost [4]. Deterministic methods applied in the ELD solution are difficult to apply because of the non-continuous, non-convex, and nonlinear nature of the problem [5,6].…”

Section: Introductionmentioning

confidence: 99%

Rational Application of Electric Power Production Optimization through Metaheuristics Algorithm

Santos

Nunes

Nascimento³

et al. 2022

Energies

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The aim of this manuscript is to introduce solutions to optimize economic dispatch of loads and combined emissions (CEED) in thermal generators. We use metaheuristics, such as particle swarm optimization (PSO), ant lion optimization (ALO), dragonfly algorithm (DA), and differential evolution (DE), which are normally used for comparative simulations, and evaluation of CEED optimization, generated in MATLAB. For this study, we used a hybrid model composed of six (06) thermal units and thirteen (13) photovoltaic solar plants (PSP), considering emissions of contaminants into the air and the reduction in the total cost of combustibles. The implementation of a new method that identifies and turns off the least efficient thermal generators allows metaheuristic techniques to determine the value of the optimal power of the other generators, thereby reducing the level of pollutants in the atmosphere. The results are presented in comparative charts of the methods, where the power, emissions, and costs of the thermal plants are analyzed. Finally, the comparative results of the methods were analyzed to characterize the efficiency of the proposed algorithm.

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“…By lowering the weight and dimensions of the output filters in grid-connected inverters, removing or detaching the galvanic transformer and increasing the number of inverter output voltage levels can also aid in the achievement of a high power density design [3]. In light of the aforementioned, transformer-less grid-tied photovoltaic systems are regarded as an effective, small, and well-liked option in recent years, which has led to the widespread release of numerous commercialized versions of recently created inverters [4], [5].…”

mentioning

confidence: 99%

To design a controller for the solar power Inverter to nullifying the temperature effect

Modi,

Kumar,

Jain

et al. 2024

IJAEMS

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The output waveform of sine triangle pulse width modulation at different modulation index is a crisp waveform. This waveform can be completed by adding another waveform. It is called a clamp waveform. This concept is called C to C concept or clipper clamper concept. The single-phase controller circuit is shown below. According to this circuit, anti-parallel diodes with constant DC supply connect to the grid supply. The extension of this circuit i.e. the 3-φ clamper circuit has been shown in The output of controller circuit is added up with NPC inverter.

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Economic Load Dispatch with Emission and Line Constraints using Biogeography Based Optimization Technique

Cited by 11 publications

References 25 publications

Reinforcement Learning-Based Multi-Objective Optimization for Generation Scheduling in Power Systems

Reinforcement Learning-Based Multi-Objective Optimization for Generation Scheduling in Power Systems

Rational Application of Electric Power Production Optimization through Metaheuristics Algorithm

To design a controller for the solar power Inverter to nullifying the temperature effect

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