A Novel Hierarchical Optimal Scheduling and Coordination Control Method for Microgrid Based on Multi-Energy Complementarity

Electronics, Journal Year: 2025, Volume and Issue: 14(9), P. 1829 - 1829

Published: April 29, 2025

To address the uncertainty of intermittent energy sources and enhance economic efficiency operational performance microgrids, this paper proposes a novel three-layer coupled microgrid scheduling model based on principles predictive control, optimized solved using an improved dung beetle algorithm. Firstly, by comprehensively considering time-varying electricity prices pollution protection costs, optimizes mitigates impact uncertain factors in day-ahead scheduling, thereby constructing new framework. Secondly, improvements to traditional algorithm, including population initialization, rolling behavior, foraging are validated through simulations, demonstrating enhanced accuracy convergence speed. Furthermore, algorithm is utilized optimize layer, determining optimal controls within control Finally, comparisons, analysis, effectiveness experiments, study demonstrates that proposed significantly improve environmental economics microgrids while enhancing system controllability stability.

Language: Английский

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High-precision air conditioning load forecasting model based on improved sparrow search algorithm

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: 92, P. 109809 - 109809

Published: June 1, 2024

Language: Английский

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Robust low-carbon scheduling optimization for energy hub amidst bilateral uncertainties in source-side and load-side conditions

Journal of Renewable and Sustainable Energy, Journal Year: 2024, Volume and Issue: 16(5)

Published: Sept. 1, 2024

In the era of burgeoning renewable integration, shift toward low-carbon energy hubs is a pivotal developmental trajectory. Amidst this paradigm, operational challenges posed by inherent uncertainty variable sources, such as wind and solar power, alongside stochastic load fluctuations, must be reckoned with. Herein, we present an innovative, economically viable strategy that embraces fuzzy opportunity constraints, thereby accommodating dual-sided impact on hubs. First, advanced optimization framework developed for hub holistically couples electricity, cooling, gas, heat sectors. Leveraging conversion technologies, it amplifies complementary interaction among diverse sources implements integrated demand response model to mitigate variability. Subsequently, ladder-type carbon trading green certificate mechanisms are incorporated, designed pare down both emissions expenditures. Addressing unpredictability grid-connected resources, introduces chance constraints. These transform rigid deterministic system limitations into more flexible constraints encapsulating variables employing trapezoidal parameters elucidate their nature. The robustness practical utility proposed substantiated through meticulous case analyses.

Language: Английский

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Fractional order calculus enhanced dung beetle optimizer for function global optimization and multilevel threshold medical image segmentation

The Journal of Supercomputing, Journal Year: 2024, Volume and Issue: 81(1)

Published: Oct. 28, 2024

Language: Английский

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Promotion strategy of low-carbon multimodal transportation considering government regulation and cargo owners’ willingness

Environment Development and Sustainability, Journal Year: 2024, Volume and Issue: unknown

Published: April 13, 2024

Language: Английский

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An enhanced snow ablation optimizer for UAV swarm path planning and engineering design problems

Heliyon, Journal Year: 2024, Volume and Issue: unknown, P. e37819 - e37819

Published: Sept. 1, 2024

Language: Английский

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A bi-objective data-driven chance-constrained optimization for sustainable urban medical waste management

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: unknown, P. 126213 - 126213

Published: Dec. 1, 2024

Language: Английский

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A fuzzy model for generalized transportation problems in China

Concurrency and Computation Practice and Experience, Journal Year: 2024, Volume and Issue: 36(17)

Published: April 24, 2024

Summary This study underscores the growing significance of multimodal transportation within cargo sector and its consequential environmental impacts. We present a novel mathematical model for operation scheduling, incorporating variables such as resource availability, customer service benchmarks, considerations. Our objective is to mitigate expenses reduce delivery delays. The proposed approach advocates LU decomposition with pivot strategy rapid resolution, adherence convergence criteria, optimization cost strategies, efficient utilization. Leveraging adaptive neural fuzzy inference system (ANFIS) genetic algorithm (GA), our methodology facilitates learning from past decisions enhance solutions, aligning supply, demand efficiently. evaluate financial implications across four scenarios, offering insights into economic advantages various modes—trains, ships, airplanes—compared truck transportation, specific focus on CO 2 emission Implementing ANFIS+GA in scenarios yield impressive results: minimal MAPE 0.17%, R 0.996, emissions 0.13%, 0.996. By identifying cost‐efficient routes optimizing allocations, enables informed regarding vehicle distribution, supplier selection, contract negotiations. Additionally, we use establish risk threshold, crucial comparing trade variances. Multimodal typically lower emissions, favoring buying allowances low selling them high. Notably, threshold affects low‐emission provider utilization, impacting emissions. With 0.12 an price 1.2, ANFIS+GA‐based achieves significant −20% deviation

Language: Английский

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Modeling a Green and Reliable Intermodal Routing Problem for Food Grain Transportation Under Carbon Tax and Trading Regulations and Multi-Source Uncertainty

Systems, Journal Year: 2024, Volume and Issue: 12(12), P. 547 - 547

Published: Dec. 9, 2024

This study addresses an intermodal routing problem encountered by transportation operator fulfilling the food grain order of agri-food company. To enhance environmental sustainability logistics, carbon tax and trading regulations have been employed to reduce emissions associated with transportation. Multi-source uncertainties, including company’s demand for grains various parameters related activities, are modeled via trapezoidal fuzzy numbers optimize comprehensive reliability solution. work incorporates wastage reduction lowering costs formulating a threshold constraint in routing. Accordingly, mixed-integer nonlinear programming model green reliable is proposed. overcome model’s insolvability difficulty finding global optimum solution optimization model, two-stage method developed, employing chance-constrained linearization technique reformulate initial model. A numerical case given verify feasibility proposed methods. Sensitivity analysis reveals influence confidence levels threshold, providing insights company balance economics, reliability, The also analyzes reducing emissions, concluding that consistently achieve greater reductions universally feasible. In contrast, depends on threshold. findings this could provide strong quantitative support operators companies seeking implement sustainable

Language: Английский

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Two non-linear programming models for the multi-stage multi-cycle smart production system with autonomation and remanufacturing in same and different cycles to reduce wastes

Journal of Industrial Information Integration, Journal Year: 2024, Volume and Issue: unknown, P. 100749 - 100749

Published: Dec. 1, 2024

Language: Английский

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