| 引用本文: | 张涛,董南江,王锐.全连接权重网络自适应的园区综合能源日内调度优化[J].控制理论与应用,2025,42(11):2231~2241.[点击复制] |
| ZHANG Tao,DONG Nan-jiang,WANG Rui.Self-adaptive fully connected weight network for intraday dispatch optimization in integrated park-level energy systems[J].Control Theory & Applications,2025,42(11):2231~2241.[点击复制] |
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| 全连接权重网络自适应的园区综合能源日内调度优化 |
| Self-adaptive fully connected weight network for intraday dispatch optimization in integrated park-level energy systems |
| 摘要点击 3181 全文点击 148 投稿时间：2025-03-26 修订日期：2025-11-07 |
| 查看全文 查看/发表评论 下载PDF阅读器 HTML |
| DOI编号 10.7641/CTA.2025.50115 |
| 2025,42(11):2231-2241 |
| 中文关键词 全连接权重网络 多目标优化 能源系统调度优化 参数自适应 |
| 英文关键词 fully connected weight network multi-objective optimization energy system dispatch optimisation pa rameter self-adaptation |
| 基金项目 湘江实验室重大专项/开放课题项目(22XJ02003),国家自然科学基金项目(62122093,72571279),湖南省科技创新计划项目(2023RC1002),国防 科技大学科研项目(ZK21–07)资助. |
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| 中文摘要 |
| 园区综合能源系统的日内调度需要根据风光自然因素、负荷的波动,对预调度方案进行快速调整,实现园
区能源系统的经济稳定运行.日内调度优化模型是一个包含半连续变量的非线性多目标的问题,同时对算法的时
效性要求较高,对现有的用于多目标智能优化算法的收敛速度提出挑战.为此,本文构建了包含两个时间粒度
(15min和5min)的日内调度模型及相应优化框架,针对日内调度优化时效性要求高的特点,在双层全连接权重网络
进化算法的基础上,设计了全连接全重网络模型自适应更新策略,记为S-TFCWNEA.通过模型参数标准差随优化
进程自适应调整的策略,平衡算法的全局和局部搜索,加速种群收敛.通过两个时间粒度的调度优化实现对日前调
度方案的逐步细化调整.仿真结果表明,引入全连接权重网络模型自适应更新策略能显著提高算法收敛速度,快速
响应风光、负荷的波动,在有限时间内快速调整调度方案. |
| 英文摘要 |
| The intraday dispatch optimization model is a nonlinear multi-objective problem involving semi-continuous
variables, while also imposing high requirements on computational efficiency. This poses a challenge to the convergence
speed of existing multi-objective intelligent optimization algorithms. To address this, this paper constructs an intraday
dispatch model with two time granularities (15-minute and 5-minute intervals) and a corresponding optimization frame
work. Given the high timeliness requirement of intraday dispatch optimization, a self-adaptive update strategy for the fully
connected weight network model is designed based on the two-fully-connected-weight-network evolutionary algorithm
(TFCWNEA), denoted as S-TFCWNEA. By adaptively adjusting the standard deviation of model parameters during the
optimization process, the strategy balances global and local search capabilities, accelerating population convergence. The
two-time-granularity dispatch optimization achieves a stepwise refinement of the day-ahead scheduling plan. Simulation
results demonstrate that the proposed self-adaptive update strategy for the fully connected weight network significantly
improves the algorithm’s convergence speed, enabling rapid response to fluctuations in renewable generation and load
demand. The method efficiently adjusts the dispatch plan within a limited time frame. |
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