Combined Economic and Emission Dispatch with Valve-point loading of Thermal Generators using Modified NSGA-II

Rajkumar, M.; Mahadevan, K.; Kannan, S.; Baskar, S.

doi:10.5370/jeet.2013.8.3.490

Cited by 12 publications

(4 citation statements)

References 27 publications

(49 reference statements)

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“…The scientific model of SAR is discussed below. Figure 2 𝑆𝐷 𝑖 = (𝑋 𝑖 − 𝐶 𝑘 ), 𝑘 ≠ 𝑖 (7) Where X_i, C_k, and 〖SD〗_i are the position of the ith human, the position of the kth clue, and the search direction for the ith human, respectively. k is a arbitrary integer value between 1 and 2N.…”

Section: Search and Rescue Optimization Algorithmmentioning

confidence: 99%

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Search and rescue optimization algorithm for combined economic and emission load dispatch

Prakash¹,

Vamsi²,

Anjaneyulu³

et al. 2022

sajet

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Economic-emission load dispatch minimizes fuel cost factors and gas emissions to provide optimal performance in generation units in a power plant, ensuring demand supply. The first variable is definitive to maintain business continuity, and the second to assure compliance with environmental legislation and no environmental harm. This work investigates the application of a new computational optimization technique known as the Search and Rescue Optimization Algorithm (SAR), which improves searching capability by employing specified parameters. This algorithm has three effective parameters and works with all generators to minimize the cost and emission while satisfying the defined demand. Simulations were run on multiple IEEE test systems with varying demand values. The obtained findings were compared to each other as well as to the results of other procedures mentioned in the literature. The acquired results appear to be superior to those achieved by other metaheuristics algorithms.

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Section: Search and Rescue Optimization Algorithmmentioning

confidence: 99%

“…Fast and stable computation times make this method ideal in terms of speed and efficiency for high-speed online applications [6]. Solving the CEED valve point loading problem with NSGA and MNSGA, two non-dominant sorting genetic algorithms, are employed in [7]. MNSGA is an enhanced version of NSGA.…”

Section: Introductionmentioning

confidence: 99%

Search and rescue optimization algorithm for combined economic and emission load dispatch

Prakash¹,

Vamsi²,

Anjaneyulu³

et al. 2022

sajet

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show abstract

“…Evolutionary multi-objective optimisation algorithms named non-dominated sorting genetic algorithm-II (NSGA-II) (Deb, 2001;Deb et al, 2002) (EMOAs) proposed by Deb et al is extensively used to solve various multi-objective engineering optimisation problems (Panda, 2010(Panda, , 2011Kalaivani et al, 2013;Rajkumar et al, 2014). Modified non-dominated sorting genetic algorithm-II (MNSGA-II) which incorporates the control elitism and dynamic crowding distance (DCD) features to ensure better convergence and diversity (Jeyadevi et al, 2011), is also widely used to optimise engineering problems (Narayanan et al, 2012;Rajkumar et al, 2013). In this proposed work, multi-objective algorithms are used for optimal tuning of UPFC damping controller parameters to improve the damping performance of SMIB system under different loading conditions.…”

Section: Introductionmentioning

confidence: 99%

UPFC damping controller design using multi-objective evolutionary algorithms

Kannayeram¹,

Manoharan²,

Iruthayarajan³

et al. 2017

IJBIDM

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In this paper, modified non-dominated sorting genetic algorithm-II (MNSGA-II)-based optimal damping control of unified power flow controller (UPFC) has been designed to enhance the damping of low frequency oscillations in power systems. The robust damping of UPFC controller design is formulated as a multi-objective optimisation problem, thereby minimising the integral squared error (ISE) of speed deviation and input control signal (u) under a wide range of operating conditions. The effectiveness of the proposed controller is confirmed through nonlinear time domain simulation and Eigen value analysis. The results are compared with NSGA-II and conventional method. Simulation result reveals that the obtained Pareto-front using MNSGA-II-based UPFC controllers are better and uniformly distributed due to the controlled elitism and dynamic crowding distance concepts. The proposed modulation index of shunt inverter (m E)-based damping controller is superior to the other damping controllers under different loading conditions and improves the stability of system.

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“…With the vigorous development of evolutionary algorithm, optimizing these conflicting objectives concurrently is the recent research trend. Many scholars presented a lot of research around this problem, and putted forward many advanced optimization algorithm and obtained a certain effect [10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

A Study of Multi-Objective Load Optimal Dispatch in Thermal Power Unit Based on Improved Particle Swarm Optimization Algorithm

Wang

Pan

Wang

2013

AMR

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In order to fit in with the demands of the development of electricity market in China, a multi-objective optimization mathematical model is presented to dispatch load within the units, taking economy, environmental protection and quick responsiveness to dispatching commands into consideration at the same time. And take the minimal whole plants power-supply coal cost rate, the minimal pollutant emissions and the minimal load adjusting time as these three objective functions respectively. The four constraint conditions are unit power balance constraint, load bound constraint, ramping constraint and pollution discharge standards constraint. An improved particle swarm optimization algorithm is used to get the Pareto solution set. The optimal solution was obtained by using the method of multi-attribute decision making, through sequencing the solution set by comprehensive evaluation. A case study based on a power plant with 4×600MW units was carried out. The result shows that the method can solve the multi-objective optimal load distribution problem accurately and quickly, and get the good effect in energy conservation and emissions reduction.

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Combined Economic and Emission Dispatch with Valve-point loading of Thermal Generators using Modified NSGA-II

Cited by 12 publications

References 27 publications

Search and rescue optimization algorithm for combined economic and emission load dispatch

Search and rescue optimization algorithm for combined economic and emission load dispatch

UPFC damping controller design using multi-objective evolutionary algorithms

A Study of Multi-Objective Load Optimal Dispatch in Thermal Power Unit Based on Improved Particle Swarm Optimization Algorithm

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