Multi-objective optimisation framework for standalone DC-microgrids with direct load control in demand-side management

被引:1
作者
Jayasinghe, Hasith [1 ]
Gunawardane, Kosala [1 ]
Zamora, Ramon [2 ]
机构
[1] Univ Technol Sydney, Sch Elect & Data Engn, Ultimo, NSW, Australia
[2] Auckland Univ Technol, Dept Elect & Elect Engn, Auckland, New Zealand
关键词
demand side management; energy management systems; energy storage;
D O I
10.1049/ell2.13290
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Renewable energy-powered DC microgrids have emerged as a sustainable alternative for standalone power systems in remote locations, which were traditionally reliant on diesel generators (DIG) only. To ensure power quality and reliability, energy storage systems (ESS) and demand-side management (DSM) techniques are employed, addressing the intermittent nature of renewable energy sources (RES). This manuscript presents a novel multi-objective optimisation framework to determine the equipment sizing, depth of discharge (DoD) of ESS, and share of controllable loads contributing to DSM in a standalone DC microgrid incorporated with RES as a primary energy source and a backup DIG. The proposed optimisation strategy utilises genetic algorithm with the objectives of minimizing lifecycle cost and carbon footprint. A novel battery energy storage system (BESS) management criterion is introduced, which accounts for battery degradation in the lifecycle cost calculation. The minimum allowable DoD of the BESS is considered a decision variable in the optimisation problem to assess the impact of higher DoD on lifecycle cost improvement. MATLAB simulation results demonstrate that the proposed optimisation model significantly reduces the levelized cost of electricity and per unit carbon footprint compared to previous models. Additionally, it identifies an optimal range of DoD for the BESS to enhance the lifecycle cost of a standalone DC microgrid. Renewable energy sources have emerged as a sustainable alternative to diesel generators in standalone power systems. Energy storage, coupled with demand-side management, addresses the intermittency issues of renewable energy when integrating into standalone DC microgrids. This study presents a novel methodology of a multi-objective optimization framework for integrating demand-side management and a novel battery management methodology to minimize the cost, carbon footprint, and energy curtailment of standalone DC microgrids. image
引用
收藏
页数:5
相关论文
共 37 条
[21]   Voltage Regulation, Power Balancing and Battery Storage Discharge Control by Smart Demand Side Management and Multi-Objective Decision Making [J].
Shahnia, Farhad ;
Wishart, Michael T. ;
Ghosh, Arindam .
2013 AUSTRALASIAN UNIVERSITIES POWER ENGINEERING CONFERENCE (AUPEC), 2013,
[22]   EOLD: A reinforcement learning-based energy-optimised load disaggregation framework for demand-side energy management [J].
Wei, Ying'an ;
Fan, Jingjing ;
Meng, Qinglong ;
Debnath, Kumar Biswajit ;
Yang, Yuqin ;
Liu, Jiao ;
Lei, Yu .
RENEWABLE ENERGY, 2025, 252
[23]   Probabilistic Pricing for Collaborative Demand-Side Management With Coordinated Operation of Energy Storage Systems for Optimal Peak Load Control in Smart Grids [J].
Masoumi-Anaraki, Mohsen ;
Hooshmand, Rahmat-Allah ;
Kabiri-Renani, Yahya .
IET Generation, Transmission and Distribution, 2025, 19 (01)
[24]   A novel multi-objective optimization approach for resilience enhancement considering integrated energy systems with renewable energy, energy storage, energy sharing, and demand-side management [J].
Shafiei, Kasra ;
Seifi, Ali ;
Hagh, Mehrdad Tarafdar .
JOURNAL OF ENERGY STORAGE, 2025, 115
[25]   Multi-objective optimal scheduling of household appliances for demand side management using a hybrid heuristic algorithm [J].
Liu, Youquan ;
Li, Huazhen ;
Zhu, Jiawei ;
Lin, Yishuai ;
Lei, Weidong .
ENERGY, 2023, 262
[26]   Integrating renewable energy sources for optimal demand-side management using decentralized multi-agent control [J].
Ikram, Muhammad ;
Aslam, Muhammad ;
Aurangzeb, Khursheed ;
Ahmed, Salman ;
Marwat, Safdar Nawaz Khan ;
Haider, Syed Irtaza ;
Alhussein, Musaed .
SUSTAINABLE ENERGY GRIDS & NETWORKS, 2023, 36
[27]   Load Frequency Control via Multi-Agent Reinforcement Learning and Consistency Model for Diverse Demand-Side Flexible Resources [J].
Yu, Guangzheng ;
Li, Xiangshuai ;
Chen, Tiantian ;
Liu, Jing .
PROCESSES, 2025, 13 (06)
[28]   Demand Side Management using Model-Free Fuzzy Controller in a Direct Load Control Program [J].
Yazdkhasti, Pegah ;
Diduch, Chris P. .
2020 IEEE ELECTRIC POWER AND ENERGY CONFERENCE (EPEC), 2020,
[29]   A novel approach for Direct Load Control of residential air conditioners for Demand Side Management in developing regions [J].
Curiel, Jose Adrian Rama ;
Thakur, Jagruti .
ENERGY, 2022, 258
[30]   Multi-Objective Generation Scheduling of Hydro-Thermal System Incorporating Energy Storage With Demand Side Management Considering Renewable Energy Uncertainties [J].
Jena, Chitralekha ;
Guerrero, Josep M. ;
Abusorrah, Abdullah ;
Al-Turki, Yusuf ;
Khan, Baseem .
IEEE ACCESS, 2022, 10 :52343-52357