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Tsinghua Science and Technology 2024, 29(2): 368-385 https://doi.org/10.26599/TST.2023.9010067
Open Access | Issue | Published: 22 September 2023

A Constraint Adaptive Multi-Tasking Differential Evolution Algorithm: Designed for Dispatch of Integrated Energy System in Coal Mine

Show Author's Information Canyun Dai1,  Xiaoyan Sun1( ),  Hejuan Hu1,  Yong Zhang1,  Dunwei Gong2
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China
Keywords:
differential evolution, integrated energy system, evolutionary multi-tasking, dispatch, coal mine, constraint
Cite this article:
Dai C, Sun X, Hu H, et al. A Constraint Adaptive Multi-Tasking Differential Evolution Algorithm: Designed for Dispatch of Integrated Energy System in Coal Mine. Tsinghua Science and Technology, 2024, 29(2): 368-385. https://doi.org/10.26599/TST.2023.9010067
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Abstract Full text About this article

The dispatch of integrated energy systems in coal mines (IES-CM) with mine-associated supplies is vital for efficient energy utilization and carbon emissions reduction. However, IES-CM dispatch is highly challenging due to its feature as multi-objective and strong multi-constraint. Existing constrained multi-objective evolutionary algorithms often fall into locally feasible domains with poorly distributed Pareto front, which greatly deteriorates dispatch performance. To tackle this problem, we transform the traditional dispatch model of IES-CM into two tasks: the main task with all constraints and the helper task with constraint adaptive. Then we propose a constraint adaptive multi-tasking differential evolution algorithm (CA-MTDE) to optimize these two tasks effectively. The helper task with constraint adaptive is developed to obtain infeasible solutions near the feasible domain. The purpose of this infeasible solution is to transfer guiding knowledge to help the main task move away from local search. Additionally, a dynamic dual-learning strategy using DE/current-to-rand/1 and DE/current-to-best/1 is developed to maintain task diversity and convergence. Finally, we comprehensively evaluate the performance of CA-MTDE by applying it to a coal mine in Shanxi Province, considering two IES-CM scenarios. Results demonstrate the feasibility of CA-MTDE and its ability to generate a Pareto front with exceptional convergence, diversity, and distribution.


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About this article

A Constraint Adaptive Multi-Tasking Differential Evolution Algorithm: Designed for Dispatch of Integrated Energy System in Coal Mine

Show Author's information Canyun Dai1, Xiaoyan Sun1( ), Hejuan Hu1, Yong Zhang1, Dunwei Gong2
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China

Abstract

The dispatch of integrated energy systems in coal mines (IES-CM) with mine-associated supplies is vital for efficient energy utilization and carbon emissions reduction. However, IES-CM dispatch is highly challenging due to its feature as multi-objective and strong multi-constraint. Existing constrained multi-objective evolutionary algorithms often fall into locally feasible domains with poorly distributed Pareto front, which greatly deteriorates dispatch performance. To tackle this problem, we transform the traditional dispatch model of IES-CM into two tasks: the main task with all constraints and the helper task with constraint adaptive. Then we propose a constraint adaptive multi-tasking differential evolution algorithm (CA-MTDE) to optimize these two tasks effectively. The helper task with constraint adaptive is developed to obtain infeasible solutions near the feasible domain. The purpose of this infeasible solution is to transfer guiding knowledge to help the main task move away from local search. Additionally, a dynamic dual-learning strategy using DE/current-to-rand/1 and DE/current-to-best/1 is developed to maintain task diversity and convergence. Finally, we comprehensively evaluate the performance of CA-MTDE by applying it to a coal mine in Shanxi Province, considering two IES-CM scenarios. Results demonstrate the feasibility of CA-MTDE and its ability to generate a Pareto front with exceptional convergence, diversity, and distribution.

Keywords: differential evolution, integrated energy system, evolutionary multi-tasking, dispatch, coal mine, constraint

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Publication history

Received: 26 April 2023
Revised: 10 June 2023
Accepted: 26 June 2023
Published: 22 September 2023
Issue date: April 2024

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© The author(s) 2024.

Acknowledgements

This work was supported by the National Key R&D Program of China (No. 2021YFE0199000) and the National Natural Science Foundation of China (No. 62133015).

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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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