Online Load-Scheduling Strategy and Sizing Optimization for a Stand-Alone Hybrid System

Mena, Antonio José Gil; Bouakkaz, Abderraouf; Haddad, Salim

doi:10.1061/(asce)ey.1943-7897.0000725

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…This nature makes the Markov decision process computable. Dynamic programming and other methods can be used to solve the optimal strategy [16][17]. The probability of a system transitioning from one state to another is defined as the state transition matrix, as shown in Eq.…”

Section: A Directed Acyclic Graph Task Scheduling In Heterogeneous En...mentioning

confidence: 99%

Design of Big Data Task Scheduling Optimization Algorithm Based on Improved Deep Q-Network

Chen,

2024

IJACSA

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Big data analysis can provide valuable insights not easily obtained from traditional data scales. However, addressing scheduling issues in big data can be challenging due to the vast amount and diverse nature of the data. To overcome this, a scheduling model based on Markov decision process is proposed. The deep Q-network algorithm is used for directed acyclic graph task scheduling. To improve this model further, the gradient strategy algorithm is introduced. From the results, when the dataset size was about 500, the hybrid algorithm achieved a recall rate of 0.96, outperforming the gradient strategy algorithm (0.83), deep Q-network algorithm (0.79), and estimated earliest completion time algorithm (0.63). Although the estimated earliest completion time algorithm had longer training times under different dataset sizes, the hybrid algorithm's training time was slightly longer than the gradient strategy algorithm and slightly shorter than the deep Q-network algorithm. Overall, the proposed algorithm exhibits superior performance and significant value in solving engineering problems.

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Section: A Directed Acyclic Graph Task Scheduling In Heterogeneous En...mentioning

confidence: 99%

Design of Big Data Task Scheduling Optimization Algorithm Based on Improved Deep Q-Network

Chen,

2024

IJACSA

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show abstract

“…For a system with and without load control, only the investment costs of the HES components and the cost of energy are shown, while other economic and environmental indicators are not considered. A paper [24], apart from HES sizing optimization, the scheduling of particular household loads is addressed. In this work, optimal switching intervals of several appliances were found, and the size of the HES was optimized after this.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Size of Autonomous Hybrid Microgrids with Regard to Load Shifting

2021

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The article proposes a method of multipurpose optimization of the size of an autonomous hybrid energy system consisting of photovoltaic, wind, diesel, and battery energy storage systems, and including a load-shifting system. The classical iterative Gauss–Seidel method was applied to optimize the size of a hybrid energy system in a remote settlement on Sakhalin Island. As a result of the optimization according to the minimum net present value criterion, several optimal configurations corresponding to different component combinations were obtained. Several optimal configurations were also found, subject to a payback period constraint of 5, 6, and 7 years. Optimizing the size of the hybrid power system with electric load shifting showed that the share of the load not covered by renewable energy sources decreases by 1.25% and 2.1%, depending on the parameters of the load shifting model. Net present cost and payback period also decreased, other technical and economic indicators improved; however, CO2 emissions increased due to the reduction in the energy storage system.

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Analysis and optimisation of collective self-consumption in residential buildings in Spain

Mena

Medina

Bouakkaz

et al. 2023

Energy and Buildings

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Online Load-Scheduling Strategy and Sizing Optimization for a Stand-Alone Hybrid System

Cited by 4 publications

References 26 publications

Design of Big Data Task Scheduling Optimization Algorithm Based on Improved Deep Q-Network

Design of Big Data Task Scheduling Optimization Algorithm Based on Improved Deep Q-Network

Optimizing the Size of Autonomous Hybrid Microgrids with Regard to Load Shifting

Analysis and optimisation of collective self-consumption in residential buildings in Spain

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