Multi-objective membrane search algorithm: A new solution for economic emission dispatch

Lai, Wenhao; Zheng, Xiaoliang; Song, Qi; Hu, Feng; Tao, Qiong; Chen, Hualiang

doi:10.1016/j.apenergy.2022.119969

Cited by 20 publications

(3 citation statements)

References 89 publications

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“…It is an optimization problem with strongly real-time requirements (Li et al, 2020). Mathematical methods for solving it include nonlinear and linear programming methods (Lai et al, 2022). Meta-heuristic algorithms are employed to solve it.…”

Section: Introductionmentioning

confidence: 99%

Data-driven method of solving computationally expensive combined economic/emission dispatch problems in large-scale power systems: an improved kriging-assisted optimization approach

Lin,

Liang,

Pang

et al. 2023

Front. Energy Res.

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Combined economic/emission dispatch (CEED) is generally studied using analytical objective functions. However, for large-scale, high-dimension power systems, CEED problems are transformed into computationally expensive CEED (CECEED) problems, for which existing approaches are time-consuming and may not obtain satisfactory solutions. To overcome this problem, a novel data-driven surrogate-assisted method is introduced firstly. The fuel cost and emission objective functions are replaced by improved Kriging-based surrogate models. A new infilling sampling strategy for updating Kriging-based surrogate models online is proposed, which improves their fitting accuracy. Through this way, the evaluation time of the objective functions is significantly reduced. Secondly, the optimization of CECEED is executed by an improved non-dominated sorting genetic algorithm-II (NSGA-II). The above infilling sampling strategy is also used to reduce the number of evaluations for original mathematic fitness functions. To improve their local convergence ability and global search abilities, the individuals that exhibit excellent performance in a single objective are cloned and mutated. Finally, information about the Pareto front is used to guide individuals to search for better solutions. The effectiveness of this optimization method is demonstrated through simulations of IEEE 118-bus test system and IEEE 300-bus test system.

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Section: Introductionmentioning

confidence: 99%

Data-driven method of solving computationally expensive combined economic/emission dispatch problems in large-scale power systems: an improved kriging-assisted optimization approach

Lin,

Liang,

Pang

et al. 2023

Front. Energy Res.

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“…Refs. [11][12][13] proposed a multi-objective mathematical programming approach to solve the economic and emission dispatch in energy markets. A new method was developed for planning energy and environmental systems under the various uncertainties to find optimal energy resource allocation and ideal policies for greenhouse gas reduction [14,15].…”

Section: Introductionmentioning

confidence: 99%

The Feasibility Assessment of Power System Dispatch with Carbon Tax Considerations

Lin

et al. 2022

Processes

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“…This method integrated leader selection and guidance as learning methodologies to enhance the uniformity and diversity of the Pareto frontier. Furthermore, for the coupled EED problem, a multi-objective membrane search approach was proposed [27]. The evaluation of this algorithm across multiple test cases demonstrated its high spatial detection capability and ability to generate improved solutions for the EED problem.…”

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confidence: 99%

Optimizing Economic Dispatch with Renewable Energy and Natural Gas Using Fractional-Order Fish Migration Algorithm

Aldosary

2024

Fractal Fract

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This work presents a model for solving the Economic-Environmental Dispatch (EED) challenge, which addresses the integration of thermal, renewable energy schemes, and natural gas (NG) units, that consider both toxin emission and fuel costs as its primary objectives. Three cases are examined using the IEEE 30-bus system, where thermal units (TUs) are replaced with NGs to minimize toxin emissions and fuel costs. The system constraints include equality and inequality conditions. A detailed modeling of NGs is performed, which also incorporates the pressure pipelines and the flow velocity of gas as procedure limitations. To obtain Pareto optimal solutions for fuel costs and emissions, three optimization algorithms, namely Fractional-Order Fish Migration Optimization (FOFMO), Coati Optimization Algorithm (COA), and Non-Dominated Sorting Genetic Algorithm (NSGA-II) are employed. Three cases are investigated to validate the effectiveness of the proposed model when applied to the IEEE 30-bus system with the integration of renewable energy sources (RESs) and natural gas units. The results from Case III, where NGs are installed in place of two thermal units (TUs), demonstrate that the economic dispatching approach presented in this study significantly reduces emission levels to 0.4232 t/h and achieves a lower fuel cost of 796.478 USD/MWh. Furthermore, the findings indicate that FOFMO outperforms COA and NSGA-II in effectively addressing the EED problem.

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Multi-objective membrane search algorithm: A new solution for economic emission dispatch

Cited by 20 publications

References 89 publications

Data-driven method of solving computationally expensive combined economic/emission dispatch problems in large-scale power systems: an improved kriging-assisted optimization approach

Data-driven method of solving computationally expensive combined economic/emission dispatch problems in large-scale power systems: an improved kriging-assisted optimization approach

The Feasibility Assessment of Power System Dispatch with Carbon Tax Considerations

Optimizing Economic Dispatch with Renewable Energy and Natural Gas Using Fractional-Order Fish Migration Algorithm

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