Deep reinforcement learning approach for hybrid renewable energy systems optimization

IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Inoussa Legrene , Tony Wong , Louis-A. Dessaint
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Abstract

The sizing of hybrid renewable energy systems (HRES) is a major challenge faced in contemporary energy research. The optimal configuration based on the specific consumption requirements is essential for strategic energy planning. Effective sizing must balance the investment costs, reliability, environmental impacts, and greenhouse gas emissions while satisfying the expected energy requirements. This study proposes a novel multi-criteria sizing approach based on deep reinforcement learning (DRL). The DRL agent is guided by a reward function that integrates three essential performance metrics: energy cost (LCOE), renewable energy fraction (REF), and the loss of power supply probability (LPSP). A penalty function is also included to consider the reliance on external sources, such as diesel generators and the public grid, promoting greater autonomy and renewable usage. The DRL-based approach was implemented and tested on three distinct demand profiles, using hourly data for one year. A comparative analysis was conducted against three established methods: particle swarm optimization (PSO), multi-objective PSO (MOPSO), and non-dominated sorted genetic algorithm (NSGA-II). The results indicate that DRL significantly outperforms all the benchmark methods in terms of economic efficiency. DRL achieves a significant reduction in the energy costs, ranging from 21.33 % to 30.09 % when compared with PSO, 27.89 %–30.27 % when compared with MOPSO, and 27.63 %–28.47 % when compared with NSGA-II. These findings demonstrate that DRL presents a robust and adaptive framework for the sizing and operational control of HRES. DRL presents more autonomous, cost-effective, and scalable renewable energy solutions by minimizing the energy costs while maintaining the system reliability.

Abstract Image

混合可再生能源系统优化的深度强化学习方法
混合可再生能源系统(HRES)的规模是当代能源研究面临的主要挑战。基于特定消费需求的优化配置对于战略性能源规划至关重要。有效的分级必须在满足预期能源需求的同时平衡投资成本、可靠性、环境影响和温室气体排放。本研究提出了一种基于深度强化学习(DRL)的新型多准则分级方法。DRL代理由一个奖励函数指导,该函数集成了三个基本性能指标:能源成本(LCOE)、可再生能源分数(REF)和电力供应损失概率(LPSP)。还包括一个惩罚函数,以考虑对外部资源的依赖,如柴油发电机和公共电网,促进更大的自主权和可再生能源的使用。基于drl的方法在三个不同的需求剖面上进行了实施和测试,使用了一年的每小时数据。对粒子群优化(PSO)、多目标粒子群优化(MOPSO)和非支配排序遗传算法(NSGA-II)三种已建立的算法进行了对比分析。结果表明,在经济效率方面,DRL显著优于所有基准方法。与PSO相比,DRL的能源成本降低幅度为21.33% ~ 30.09%,与MOPSO相比为27.89% ~ 30.27%,与NSGA-II相比为27.63% ~ 28.47%。这些发现表明,DRL为HRES的规模和操作控制提供了一个鲁棒性和适应性的框架。DRL通过最大限度地降低能源成本,同时保持系统可靠性,提供了更加自主、经济、可扩展的可再生能源解决方案。
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来源期刊
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence 工程技术-工程:电子与电气
CiteScore
9.60
自引率
10.00%
发文量
505
审稿时长
68 days
期刊介绍: Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.
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