A Hybrid Multi-Objective Optimization Algorithm for Economic Emission Dispatch Considering Wind Power Uncertainty

Xia, Aiming; Wu, Xuedong

doi:10.1007/s40998-021-00436-7

Cited by 11 publications

(4 citation statements)

References 32 publications

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“…To overcome these difficulties, some multiobjective optimization techniques have been suggested for handling the EEDP. For example, Xia and Wu [22] have proposed a hybrid multiobjective optimization method combining the marine predator algorithm and DE for solving the EEDP, including wind turbines. In this method, a dynamic crowding distance has been used to generate a well-distributed optimal PF.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Chaotic-Based Multiobjective Differential Evolution Technique for Economic Emission Dispatch Problem

2023

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The economic emission dispatch problem (EEDP) is a nonconvex and nonsmooth multiobjective optimization problem in the power system field. Generally, fuel cost and total emissions of harmful gases are the problem objective functions. The EEDP decision variables are output powers of thermal generating units (TGUs). To make the EEDP problem more practical, valve point loading effects (VPLEs), prohibited operation zones (POZs), and power balance constraints should be included in the problem constraints. In order to solve this complex and constrained EEDP, a new multiobjective optimization technique combining the differential evolution (DE) algorithm and chaos theory is proposed in this study. In this new multiobjective optimization technique, a nondomination sorting principle and a crowding distance calculation are employed to extract an accurate Pareto front. To avoid being trapped in local optima and enhance the conventional DE algorithm, two different chaotic maps are used in its initialization, crossover, and mutation phases instead of random numbers. To overcome difficulties caused by the equality constraint describing the power balance constraint, a slack TGU is defined to compensate for the gap between the total generation and the sum of the system load and total power losses. Then, the optimal power outputs of all thermal units except the slack unit are determined by the suggested optimization technique. To assess the effectiveness and applicability of the proposed method for solving the EEDP, the six-unit and ten-unit systems are used. Moreover, obtained results are compared with other new optimization techniques already developed and tested for the same purpose. The superior performance of the ChMODE is also evaluated by using various metrics such as inverted generational distance (IGD), hyper-volume (HV), spacing metric (SM), and the average satisfactory degree (ASD).

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Section: Related Workmentioning

confidence: 99%

A Hybrid Chaotic-Based Multiobjective Differential Evolution Technique for Economic Emission Dispatch Problem

2023

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“…Te MPA algorithm was inspired by the motion of marine predators such as sunfsh and sharks. MPA has been employed to solve diferent optimisation problems, including solving the economic emission dispatch [51] and estimating photovoltaic module parameters [52].…”

Section: Overview Of the Marinementioning

confidence: 99%

Application of Metaheuristic Algorithms and ANN Model for Univariate Water Level Forecasting

Mohammed

Zubaidi

Al‐Ansari

et al. 2023

Advances in Civil Engineering

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With the rapid development of machine learning (ML) models, the artificial neural network (ANN) is being increasingly applied for forecasting hydrological processes. However, researchers have not treated hybrid ML models in much detail. To address these issues, this study herein suggests a novel methodology to forecast the monthly water level (WL) based on multiple lags of the Tigris River in Al-Kut, Iraq, over ten years. The methodology includes preprocessing data methods, and the ANN model optimises with a marine predator algorithm (MPA). In the optimisation procedure, to decrease uncertainty and expand the predicting range, the slime mould algorithm (SMA-ANN), constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithms (CPSOCGSA-ANN), and particle swarm optimisation (PSO-ANN) are applied to compare and validate the MPA-ANN model performance. Analysis of results revealed that the data pretreatment methods improved the original data quality and selected the ideal predictors’ scenario by singular spectrum analysis and mutual information methods, respectively. For example, the correlation coefficient of the first lag improved from 0.648 to 0.938. Depending on various evaluation metrics, MPA-ANN tends to forecast WL better than SMA-ANN, PSO-ANN, and CPSOCGSA-ANN algorithms with coefficients of determination of 0.94, 0.81, 0.85, and 0.90, respectively. Evidence shows that the proposed methodology yields excellent results, with a scatter index equal to 0.002. The research outcomes represent an additional step towards evolving various hybrid ML techniques, which are valuable to practitioners wishing to forecast WL data and the management of water resources in light of environmental shifts.

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“…In addressing the challenges associated with uncertainty, several studies have proposed effective solutions. [11] addressed the uncertainty associated with wind power generation. They tackled the economic dispatch of carbon emissions by employing a planning method that incorporates chance constraints and a multi-objective optimization approach.…”

Section: Introductionmentioning

confidence: 99%

Analysis of low-carbon comprehensive energy system scheduling considering multiple uncertainties

Wang,

Chen,

Luo

et al. 2024

Clean Techn Environ Policy

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Carbon capture systems and the utilization of renewable energy are key ways to reduce carbon emissions, but their uncertainty seriously affects the stable operation and economic efficiency of power systems. To tackle this challenge, a low-carbon economic scheduling model for microgrid electric-thermal integrated energy systems(IES) considering uncertainty is proposed in this paper. The model suggests an ideal dispatch of the IES system that accounts for the disruption of the carbon capture system's integration into the grid belt and plans for the carbon capture plant's low carbon features. Coupling of wind energy with carbon capture power plants employing incentives for system operation and dispatch to incorporate load-side demand response resources. The system's low carbon performance is increased through cooperative source-load resource optimization. A well-established industrial automation optimal dispatch model is developed using YALMIP and CPLEX. The results reveal that the method contributes to the resolution of the system adaptability problem and encourages a balance between low carbon and economics for large loads, taking into account that uncertainty raises the cost by around 3%, thereby boosting system robustness to an acceptable degree.

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A Hybrid Multi-Objective Optimization Algorithm for Economic Emission Dispatch Considering Wind Power Uncertainty

Cited by 11 publications

References 32 publications

A Hybrid Chaotic-Based Multiobjective Differential Evolution Technique for Economic Emission Dispatch Problem

A Hybrid Chaotic-Based Multiobjective Differential Evolution Technique for Economic Emission Dispatch Problem

Application of Metaheuristic Algorithms and ANN Model for Univariate Water Level Forecasting

Analysis of low-carbon comprehensive energy system scheduling considering multiple uncertainties

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