Enhanced demand side management for solar-based isolated microgrid system: Load prioritisation and energy optimisation

IF 2.4 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IET Smart Grid Pub Date : 2023-12-18 DOI:10.1049/stg2.12151
Yaju Rajbhandari, Anup Marahatta, Ashish Shrestha, Anand Gachhadar, Anup Thapa, Francisco Gonzalez-Longatt, Petr Korba
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Abstract

A novel control mechanism is presented for rural microgrids, standing out in the current literature with its advanced approach to load prioritisation and energy allocation. The system's main goal is to maximise energy supply to essential loads while effectively managing available resources. Distinct from traditional methods, this mechanism dynamically classifies loads according to user-defined priorities, adjustable based on the control system's computational power and complexity. A critical feature is the utilisation of the Particle Swarm Optimisation (PSO) algorithm to optimise demand side management (DSM). This innovative approach leverages day-ahead load and generation forecasts to ensure optimal energy distribution across load levels, maintaining continuous power supply to high-priority loads and reducing blackout risks due to generation and load fluctuations. Analyses under stochastic scenarios demonstrate the robustness of the control action, with percentile-based day-ahead forecasting allowing for adaptation to significant variations in renewable energy generation patterns. The implementation results are significant, maintaining 100% supply continuity to essential loads throughout the day, even with generation fluctuations up to -20%. This marks a considerable improvement in load satisfaction, increasing it from 83% to 96%. A significant advancement in microgrid control is contributed, providing an adaptive, user-centric approach that enhances load management and energy distribution, and facilitates more resilient and efficient microgrid systems in the face of highly variable renewable energy sources (RESs).

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加强太阳能隔离微电网系统的需求侧管理:负载优先和能源优化
本文介绍了一种适用于农村微电网的新型控制机制,其先进的负载优先排序和能源分配方法在现有文献中脱颖而出。该系统的主要目标是在有效管理可用资源的同时,最大限度地为基本负载提供能源。与传统方法不同,该机制根据用户定义的优先级对负载进行动态分类,并可根据控制系统的计算能力和复杂程度进行调整。它的一个重要特点是利用粒子群优化(PSO)算法来优化需求侧管理(DSM)。这种创新方法利用日前负荷和发电量预测,确保在不同负荷水平上实现最佳能源分配,维持对高优先级负荷的持续供电,降低发电量和负荷波动造成的停电风险。随机情景下的分析表明了控制行动的稳健性,基于百分位数的日前预测可适应可再生能源发电模式的显著变化。实施效果显著,即使在发电量波动高达 -20% 的情况下,也能全天保持对基本负荷 100% 的连续供电。这标志着负荷满意度大幅提高,从 83% 提高到 96%。这是微电网控制领域的一大进步,提供了一种自适应的、以用户为中心的方法,加强了负荷管理和能源分配,并促进了微电网系统在面对高度可变的可再生能源(RES)时更具弹性和效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IET Smart Grid
IET Smart Grid Computer Science-Computer Networks and Communications
CiteScore
6.70
自引率
4.30%
发文量
41
审稿时长
29 weeks
期刊最新文献
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