A Two-Stage Optimization Model of Capacity Allocation and Regulation Operation for Virtual Power Plant

IntroductionThis study investigated the effectiveness of artificial intelligence-based instruction in improving second language (L2) speaking skills and speaking self-regulation in a natural setting. The research was conducted with 93 Chinese English as a foreign language (EFL) students, randomly assigned to either an experimental group receiving AI-based instruction or a control group receiving traditional instruction.MethodsThe AI-based instruction leveraged the Duolingo application, incorporating natural language processing technology, interactive exercises, personalized feedback, and speech recognition technology. Pre- and post-tests were conducted to assess L2 speaking skills and self-regulation abilities.ResultsThe results of the study demonstrated that the experimental group, which received AI-based instruction, exhibited significantly greater improvement in L2 speaking skills compared to the control group. Moreover, participants in the experimental group reported higher levels of self-regulation.DiscussionThese findings suggest that AI-based instruction effectively enhances L2 speaking skills and fosters self-regulatory processes among language learners, highlighting the potential of AI technology to optimize language learning experiences and promote learners’ autonomy and metacognitive strategies in the speaking domain. However, further research is needed to explore the long-term effects and specific mechanisms underlying these observed improvements.

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Assessment of Low-Carbon Flexibility in Self-Organized Virtual Power Plants Using Multi-Agent Reinforcement Learning

He,

Huang,

Huang

et al. 2024

Energies

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Virtual power plants (VPPs) aggregate a large number of distributed energy resources (DERs) through IoT technology to provide flexibility to the grid. It is an effective means to promote the utilization of renewable energy, and enable carbon neutrality for future power systems. This paper addresses the evaluation issue of DERs‘ low-carbon benefits, proposes a flexibility assessment model for self-organized VPP to quantify the low-carbon value of DERs’ response behavior in different time periods. Firstly, we introduce the definition of zero-carbon index based on the curve simultaneous rate of renewable energy and load demand. Then, we establish a multi-level self-organized aggregation method for virtual power plants, define the basic rules of DER, and characterize its self-organized aggregation as a Markov game process. Moreover, we use QMIX to achieve a bottom-up, hierarchical construction of VPP from simple to complex. Experimental results show that when users track the zero-carbon curve, they can achieve zero carbon emissions without reducing the overall load, significantly enhancing the grid’s regulation capabilities and the consumption of renewable energy. Additionally, self-organized algorithms can optimize the combinations of DERs to improve the coordination efficiency of VPPs in complex environments.

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A Two-Stage Optimization Model of Capacity Allocation and Regulation Operation for Virtual Power Plant

Cited by 3 publications

References 21 publications

Rural Energy Flexibility Resource Capacity Allocation Optimization Based on Virtual Power Plant Operation Optimization Model

Rural Energy Flexibility Resource Capacity Allocation Optimization Based on Virtual Power Plant Operation Optimization Model

Artificial intelligence-based language learning: illuminating the impact on speaking skills and self-regulation in Chinese EFL context

Assessment of Low-Carbon Flexibility in Self-Organized Virtual Power Plants Using Multi-Agent Reinforcement Learning

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