| 引用本文: | 熊文静,宋啸宇,袁 亮,等.基于事件触发自适应MPC的电解铝工业园区孤网多时间尺度优化调度[J].电力系统保护与控制,2025,53(22):77-88.[点击复制] |
| XIONG Wenjing,SONG Xiaoyu,YUAN Liang,et al.Multi-timescale optimal scheduling for isolated electrolytic aluminum industrial parks based on event-triggered adaptive MPC[J].Power System Protection and Control,2025,53(22):77-88[点击复制] |
|
| 摘要: |
| 新型绿色电解铝工业园区孤立电网具有负荷大、新能源占比高、调节能力有限等特点，其运行易受转折性天气及阳极效应等不确定因素影响，导致光伏出力与电解铝负荷剧烈波动，威胁电网安全。因此，提出一种日前、日内两阶段多时间尺度优化调度方法。日前阶段以系统经济性最优为目标，优化火电与储能的出力计划。日内阶段，兼顾系统经济性最优与调整量最小，采用模型预测控制(model predictive control, MPC)滚动修正调度计划。同时利用电解铝的虚拟电池特性参与调节，平抑源荷功率波动和预测误差。针对传统MPC固定时间步长难以平衡优化精度与计算效率的问题，提出基于事件触发的自适应变步长MPC策略，依据功率波动和预测误差动态调整调度步长。算例表明，所提方法能有效降低弃光、减轻火电调节压力，提升电解铝工业园区的灵活性、经济性和绿色性。 |
| 关键词: 孤立电网 多时间尺度优化调度 电解铝虚拟电池 模型预测控制 事件触发驱动 自适应变步长 |
| DOI：10.19783/j.cnki.pspc.250056 |
| 投稿时间：2025-01-16修订日期：2025-05-22 |
| 基金项目:国家重点研发计划项目资助(2024YFE0202600)；国家自然科学基金项目资助(52307156，52377202) |
|
| Multi-timescale optimal scheduling for isolated electrolytic aluminum industrial parks based on event-triggered adaptive MPC |
| XIONG Wenjing1,SONG Xiaoyu1,YUAN Liang1,ZHANG Jian2,LIU Ruiping1,SU Mei1 |
| (1. School of Automation, Central South University, Changsha 410083, China;
2. China Electric Power Research Institute, Beijing 100192, China) |
| Abstract: |
| Isolated power grids in new green electrolytic aluminum industrial parks are characterized by large loads, high proportion of renewable energy, and limited regulating capability. Their operation is easily affected by uncertainties such as sudden weather changes and anode effects, resulting in severe fluctuations in photovoltaic output and electrolytic aluminum load that threaten grid security. To address these issues, this paper proposes a two-stage multi-timescale optimal scheduling method consisting of day-ahead and intra-day phases. In the day-ahead phase, the objective is to minimize system operating cost by optimizing thermal power and storage output plans. In the intra-day phase, both economic efficiency and minimal adjustment effort are considered. A rolling optimization strategy based on model predictive control (MPC) is adopted to update the schedule, while leveraging the virtual battery characteristics of electrolytic aluminum load to mitigate source-load power fluctuations and forecasting errors. To overcome the limitation of conventional fixed-step MPC, which struggles to balance optimization accuracy and computational efficiency, an event-triggered adaptive variable-step MPC strategy is proposed. This strategy dynamically adjusts the scheduling step size according to real-time power fluctuations and prediction errors. Case studies show that the proposed method effectively reduces photovoltaic curtailment, alleviates thermal unit regulation pressure, and enhances the flexibility, economy, and sustainability of electrolytic aluminum industrial park operations. |
| Key words: isolated grid multi-timescale optimal scheduling electrolytic aluminum virtual battery model predictive control event-triggered mechanism adaptive variable step size |
