一个单一和多目标的强大的优化微电网在配电网络考虑不确定性风险的风险
Gholamreza Boroumandfar1, Alimorad Khajehzadeh2, Mahdiyeh Eslami3
1Department of Electrical Engineering, Kerman Branch, Islamic Azad University, Kerman, Iran.
Scientific reports
|November 15, 2024
概括
这项研究优化了使用可再生能源和电池存储的微电网 (MGs),使用强大的优化. 强大的方法提高了系统可靠性,以应对可再生能源生产和网络需求的不确定性.
科学领域:
- 电气工程 电气工程
- 可再生能源系统可再生能源系统
- 优化理论 优化理论
背景情况:
- 集成光伏 (PV) 和风力轮机 (WT) 源与电池存储的微电网 (MGs) 对现代电力系统至关重要.
- 可再生能源生产和网络需求的不确定性给MG运营和经济可行性带来了重大挑战.
- 需要强大的优化技术,以确保在不确定的条件下可靠和具有成本效益的MG性能.
研究的目的:
- 使用光伏,电热和电池存储,对微电网 (MG) 进行单一和多目标的强大优化.
- 尽量减少能源损失,电力购买成本,以及考虑到不确定性风险的MG的电力购买成本.
- 评估系统对可再生能源生产和网络需求预测错误的稳定性.
主要方法:
- 采用直流算法 (FDA) 实施了确定性和强大的优化方法.
- 利用信息差距决策理论 (IGDT) 采用风险回避策略进行强有力的优化.
- 分析了单个和多个目标案例,以确定最大不确定性半径 (MRU) 和系统稳定性.
主要成果:
- 确定性优化减少了网络损失并将总成本降至最低,超过了遗传算法 (GA) 和粒子群优化 (PSO).
- 强大的优化在各种不确定性预算下确定了系统强度水平,确定了敏感参数.
- 多目标强大的优化显示了不确定的参数灵敏度的平衡,超过40%的不确定性预算的约束未满足.
结论:
- 提出的强大的优化方法有效地提高了MG对不确定性的可靠性.
- 多目标优化在冲突的目标和参数灵敏度之间提供了平衡的权衡.
- 该研究证实了FDA基于决定性的方法的优越能力,并强调了强大的战略对MGs的重要性.
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