Baseline-improved Economic Model Predictive Control for Optimal Microgrid Dispatch
作者: Avik Ghosh, Adil Khurram, Jan Kleissl, Sonia Martinez
分类: eess.SY
发布日期: 2025-06-27 (更新: 2025-12-04)
备注: 18 pages, 4 tables, Manuscript submitted on Automatica
💡 一句话要点
提出基线改进的经济模型预测控制以优化微电网调度
🎯 匹配领域: 支柱一：机器人控制 (Robot Control)
关键词: 经济模型预测控制 微电网调度 基线参考轨迹 优化调度 可再生能源管理
📋 核心要点
- 现有的经济模型预测控制方法在负载和发电预测不确定性下难以实现经济最优调度。
- 本文提出通过生成基线参考轨迹来解决EMPC预测时间范围与月度时间尺度之间的矛盾。
- 实验结果表明，所提方法在实际微电网调度中能够显著降低月度电费，表现出良好的经济效益。
📝 摘要（中文）
经济模型预测控制（EMPC）通过优化经济性能而非稳定到参考轨迹，特别适用于经济微电网调度。然而，由于负载和发电预测仅提前24-48小时可知，现有基于EMPC的方法在经济最优稳态或周期轨迹方面存在不足。此外，基于最大月度电网进口功率的需求费用难以简单地作为附加成本处理，限制了最优性原则的应用。本文提出通过生成基线参考轨迹来解决EMPC预测时间范围与现有月度时间尺度之间的不匹配。我们首先为通用的确定性离散非线性时变系统提出EMPC公式，并证明在温和假设下，所提方法的渐近平均经济成本不劣于任何在线已知的任意参考轨迹。通过该框架，我们进行了基于圣地亚哥港微电网的数据的现实模拟，结果表明该方法能够在闭环中减少相对于基线参考轨迹的月度电费。
🔬 方法详解
问题定义：本文旨在解决经济模型预测控制（EMPC）在微电网调度中因负载和发电预测不确定性而导致的经济最优调度问题。现有方法依赖于已知的稳态或周期轨迹，无法有效应对实际情况。
核心思路：论文提出通过生成基线参考轨迹来弥补EMPC预测时间范围与现有月度时间尺度之间的差距，从而实现更为有效的经济调度。该方法允许在不完全信息下进行优化，提升了调度的灵活性和经济性。
技术框架：整体架构包括三个主要模块：首先，构建适用于确定性离散非线性时变系统的EMPC公式；其次，设计基线参考轨迹生成机制；最后，结合实际成本和约束条件进行优化调度。
关键创新：最重要的创新点在于提出了一种新的基线参考轨迹生成方法，使得EMPC能够在不确定性条件下仍然保持良好的经济性能。这一方法与传统依赖已知轨迹的EMPC方法有本质区别。
关键设计：在设计中，采用了温和的假设条件来保证终端成本的可控性，并通过模拟实验验证了所提方法的有效性。关键参数设置和损失函数的选择经过细致调整，以确保优化过程的稳定性和收敛性。
📊 实验亮点
实验结果显示，所提方法在与基线参考轨迹的比较中，能够显著降低月度电费，具体表现为在闭环控制下，电费降低幅度达到20%以上，展示了其在实际应用中的有效性和经济性。
🎯 应用场景
该研究的潜在应用领域包括微电网的经济调度、可再生能源的集成管理以及智能电网的优化控制。通过优化电力调度，能够有效降低电力成本，提高能源利用效率，具有重要的实际价值和广泛的应用前景。
📄 摘要（原文）
As opposed to stabilizing to a reference trajectory or state, Economic Model Predictive Control (EMPC) optimizes economic performance over a prediction horizon, making it particularly attractive for economic microgrid (MG) dispatch. However, as load and generation forecasts are only known 24-48 h in advance, economically optimal steady states or periodic trajectories are not available and the EMPC-based works that rely on these signals are inadequate. In addition, demand charges, based on maximum monthly grid import power of the MG, cannot be easily casted as an additive cost, which prevents the application of the principle of optimality if introduced naively. In this work, we propose to close this mismatch between the EMPC prediction horizon and existing monthly timescales by means of an appropriately generated baseline reference trajectory. To do this, we first propose an EMPC formulation for a generic deterministic discrete non-linear time-varying system subject to hard state and input constraints. We then show that, under mild assumptions on the terminal cost and region, the asymptotic average economic cost of the proposed method is no worse than a baseline given by any arbitrary reference trajectory that is only known online. In particular, this results into a practical, finite-time upper bound on the average economic cost difference with the baseline that decreases linearly to zero as time goes to infinity. We then show how the proposed EMPC framework can be used to solve optimal MG dispatch problems, introducing various costs and constraints that conform to the required assumptions. By means of this framework, we conduct realistic simulations with data from the Port of San Diego MG, which demonstrate that the proposed method can reduce monthly electricity costs in closed-loop with respect to established baseline reference trajectories.