Towards Sustainable Energy Management: Analyzing AI-Based Solutions for PV Systems with Battery in Energy Communities

Dávid Holecska, A. Dineva
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Abstract

This paper addresses the pressing issue of meeting energy demand sustainably, which has become increasingly challenging in recent years due to the rising prices and limited supply of fossil fuels. In response, the use of distributed renewable energy generation systems has emerged as a potential solution. The European Union has shifted its regulatory focus towards promoting renewable energy communities, as opposed to centralized fossil fuel production. To overcome the variability and unpredictability of renewable sources, electrical energy storage devices are often used, typically Li-ion batteries. Proper sizing and control of batteries and the entire system is crucial for optimal performance. The objective of this paper is to develop a simulation framework suitable for developing AI-based energy management solutions for a grid-connected system with solar cells and a shared battery energy storage that serves the energy needs of multiple residential consumers. The simulation is conducted using actual solar radiation and load data in the Matlab Simulink environment. Finally, the study aims to investigate the impact of various consumption profiles and seasonal variation in solar energy production on battery utilization.
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迈向可持续能源管理:分析基于人工智能的能源社区电池光伏系统解决方案
本文讨论了可持续地满足能源需求的紧迫问题,近年来由于化石燃料的价格上涨和供应有限,这一问题变得越来越具有挑战性。因此,使用分布式可再生能源发电系统已成为一种潜在的解决方案。欧盟已将其监管重点转向促进可再生能源社区,而不是集中的化石燃料生产。为了克服可再生能源的可变性和不可预测性,经常使用电能存储设备,典型的是锂离子电池。电池和整个系统的适当尺寸和控制是最佳性能的关键。本文的目标是开发一个模拟框架,适用于为太阳能电池和共享电池储能并网系统开发基于人工智能的能源管理解决方案,以满足多个住宅消费者的能源需求。在Matlab Simulink环境下,利用实际太阳辐射和负载数据进行仿真。最后,本研究旨在调查太阳能生产的各种消费概况和季节变化对电池利用率的影响。
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