Multiobjective multi verse optimization algorithm to solve dynamic economic emission dispatch problem with transmission loss prediction by an artificial neural network

Sundaram, Arunachalam

doi:10.1016/j.asoc.2022.109021

Cited by 30 publications

(15 citation statements)

References 50 publications

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“…The integration of the machine learning model into the DEED problem is not widely explored in the literature. The authors were the first to attempt the integration of ANN into the DEED formulation using multi-objective multi-verse optimization (MOMVO) [29].…”

Section: Contributionmentioning

confidence: 99%

“…Solving the complex power flow equations during each iteration to find the power loss dramatically increases the computational efforts, increasing the convergence time. Various other approaches, such as modeling using the quadratic equation in [28], loss prediction using an ANN in [29], and other different methods are investigated in detail in [30]. This research tries to integrate a machine learning-based algorithm to predict the transmission loss without increasing the computational complexity.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Objective Stochastic Paint Optimizer for Solving Dynamic Economic Emission Dispatch with Transmission Loss Prediction Using Random Forest Machine Learning Model

Sundaram,

Alkhaldi

2024

Energies

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Dynamic economic emission dispatch problems are complex optimization tasks in power systems that aim to simultaneously minimize both fuel costs and pollutant emissions while satisfying various system constraints. Traditional methods often involve solving intricate nonlinear load flow equations or employing approximate loss formulas to account for transmission losses. These methods can be computationally expensive and may not accurately represent the actual transmission losses, affecting the overall optimization results. To address these limitations, this study proposes a novel approach that integrates transmission loss prediction into the dynamic economic emission dispatch (DEED) problem. A Random Forest machine learning model was offline-trained to predict transmission losses accurately, eliminating the need for repeated calculations during each iteration of the optimization process. This significantly reduced the computational burden of the algorithm and improved its efficiency. The proposed method utilizes a powerful multi-objective stochastic paint optimizer to solve the highly constrained and complex dynamic economic emission dispatch problem integrated with random forest-based loss prediction. A fuzzy membership-based approach was employed to determine the best compromise Pareto-optimal solution. The proposed algorithm integrated with loss prediction was validated on widely used five and ten-unit power systems with B-loss coefficients. The results obtained using the proposed algorithm were compared with seventeen algorithms available in the literature, demonstrating that the multi-objective stochastic paint optimizer (MOSPO) outperforms most existing algorithms. Notably, for the Institute of Electrical and Electronics Engineers (IEEE) thirty bus system, the proposed algorithm achieves yearly fuel cost savings of USD 37,339.5 and USD 3423.7 compared to the existing group search optimizer algorithm with multiple producers (GSOMP) and multi-objective multi-verse optimization (MOMVO) algorithms.

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Section: Contributionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Objective Stochastic Paint Optimizer for Solving Dynamic Economic Emission Dispatch with Transmission Loss Prediction Using Random Forest Machine Learning Model

Sundaram,

Alkhaldi

2024

Energies

Self Cite

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“…Infinite solutions can be obtained for different combinations of good performance of objectives. solutions [27][28][29]. A Pareto optimal is a feasible solution X that satisfies the following condition: we cannot find another feasible solution Y that f i (Y) ≤ f i (X) for all objectives (i = 1, 2, .…”

Section: (C) Nom For Multi-objective Optimizationmentioning

confidence: 99%

Neural optimization machine: a neural network approach for optimization and its application in additive manufacturing with physics-guided learning

Chen,

Liu

2023

Phil. Trans. R. Soc. A.

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Neural networks (NNs) are increasingly used in design to construct the objective functions and constraints, which leads to the needs of optimization of NN models with respect to design variables. A Neural Optimization Machine (NOM) is proposed for constrained single/multi-objective optimization by appropriately designing the NN architecture, activation function and loss function. The NN's built-in backpropagation algorithm conducts the optimization and is seamlessly integrated with the additive manufacturing (AM) process-property model. The NOM is tested using several numerical optimization problems. It is shown that the increase in the dimension of design variables does not increase the computational cost significantly. Next, a brief review of the physics-guided machine learning model for fatigue performance prediction of AM components is given. Finally, the NOM is applied to design processing parameters in AM to optimize the mechanical fatigue properties through the physics-guided NN under uncertainties. One novel contribution of the proposed methodology is that the constrained process optimization is integrated with physics/knowledge and the data-driven AM process-property model. Thus, a physics-compatible process design can be achieved. Another significant benefit is that the training and optimization are achieved in a unified NN model, and no separate process optimization is needed. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 1)'.

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“…In the process of power supply, the power system will inevitably have some power loss, including the energy loss caused by transmission lines and the power loss caused by transformers and other power (Sundaram, 2022). In addition, there is also the loss of transmission from the grid to the onshore power supply.…”

Section: Power Lossmentioning

confidence: 99%

Carbon emission reduction of shore power from power energy structure in China

et al. 2022

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With the construction of China’s ecological civilization and the proposal of carbon peaking and carbon neutrality goals, shore power has been vigorously developed as an important technology for the future green development of ports. However, China’s electricity is still mostly coal-fired, which produces many carbon emissions. Coupled with regional differences, shore power is by no means certain to lower carbon emissions compared with fuel throughout China. Considering the power energy structure in different regions, this paper establishes a carbon emission correlation model between fuel and shore power during ship berthing, calculates the feasibility and actual emission reduction effect of shore power in coastal ports, and studies the restriction condition of starting time for the use of shore power for ships attached to ports according to the national policy of mandatory use of shore power. The results show that only a small part of coastal provinces and cities are suitable for using shore power, and it is limited by the berthing time of the ship. However, this condition is not related to the size of ships but related to the proportion of power generation. Therefore, the government should develop shore power according to local conditions, and vigorously increase the proportion of clean energy, so that the shore power truly achieve zero carbon emissions.

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Multiobjective multi verse optimization algorithm to solve dynamic economic emission dispatch problem with transmission loss prediction by an artificial neural network

Cited by 30 publications

References 50 publications

Multi-Objective Stochastic Paint Optimizer for Solving Dynamic Economic Emission Dispatch with Transmission Loss Prediction Using Random Forest Machine Learning Model

Multi-Objective Stochastic Paint Optimizer for Solving Dynamic Economic Emission Dispatch with Transmission Loss Prediction Using Random Forest Machine Learning Model

Neural optimization machine: a neural network approach for optimization and its application in additive manufacturing with physics-guided learning

Carbon emission reduction of shore power from power energy structure in China

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