【机组组合】基于数据驱动的模型预测控制电力系统机组组合优化【IEEE24节点】（Matlab代码实现）

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📋📋📋本文目录如下：🎁🎁🎁

目录

💥1 概述

📚2 运行结果

2.1 UC_original

 2.2 UC_compact

2.3 SCi结果 

🎉3 参考文献

🌈4 Matlab代码、数据、文章讲解

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💥1 概述

文献来源：

 作为电力系统运营和电力市场清算中的重要应用，网络约束机组组合（NCUC）问题通常由独立系统运营商（ISO）在开环预测后优化（O-PO）过程中执行，其中上游预测（例如，可再生能源（RES）和负载）和下游NCUC在队列中执行。但是，在O-PO框架中，统计上更准确的预测不一定会导致相对于实际RES和负载实现的更高NCUC经济性。为此，本文提出了一个闭环预测和优化（C-PO）框架，用于改善NCUC经济学。具体而言，C-PO利用NCUC模型的结构（即约束和目标）和相关特征数据来训练面向成本的RES预测模型，其中通过诱导的NCUC成本而不是统计预测误差来评估预测质量。因此，预测和优化之间的循环是闭合的，以便为NCUC优化提供面向成本的RES功率预测。采用拉格朗日松弛来加速训练过程，使C-PO适用于现实世界的系统。基于IEEE RTS 24节点系统和ISO尺度5655节点系统的实实数据，结果表明，与传统的O-PO相比，所提出的C-PO可以有效提高NCUC的经济性。本文主要做的是一个基于数据驱动的电力系统机组组合调度模型，相比于以往的基于开环模型预测控制的方法，采用闭环模型预测控制方法，通过样本训练、日前调度以及实时调度等步骤，实现了基于数据驱动的闭环模型预测控制电力系统机组组合问题的求解，模型整体创新度非常高，难度也较大。

文献直接下载：
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提取码：U6E2

原文摘要：

Abstract:

As an important application in the power system operation and electricity market clearing, the network-constrained unit commitment (NCUC) problem is usually executed by Independent System Operators (ISO) in an open-looped predict-then-optimize (O-PO) process, in which an upstream prediction (e.g., on renewable energy sources (RES) and loads) and a downstream NCUC are executed in a queue. However, in the O-PO framework, a statistically more accurate prediction may not necessarily lead to a higher NCUC economics against actual RES and load realizations. To this end, this paper presents a closed-loop predict-and-optimize (C-PO) framework for improving the NCUC economics. Specifically, the C-PO leverages structures (i.e., constraints and objective) of the NCUC model and relevant feature data to train a cost-oriented RES prediction model, in which the prediction quality is evaluated via the induced NCUC cost instead of the statistical forecast errors. Therefore, the loop between the prediction and the optimization is closed to deliver a cost-oriented RES power prediction for NCUC optimization. Lagrangian relaxation is adopted to accelerate the training process, making the C-PO applicable for real-world systems. Case studies on an IEEE RTS 24-bus system and an ISO-scale 5655-bus system with real-world data show that the proposed C-PO can effectively improve the NCUC economics as compared to the traditional O-PO.

📚2 运行结果

2.1 UC_original

 2.2 UC_compact

2.3 SCi结果 

部分代码：

%% -------------------------Constraints: general------------------------ %%
CC_General = [UC_A_ineq*x <= UC_b_ineq];
%
%% -------------------------Constraints: special------------------------ %%
CC_Special = [];
% CC_Special_01: Load shedding limit
Load_RUM      = Data_load_city{24*(Day_1st-1)+1:24*Day_end, :};
Country_Load  = sum(Load_RUM,2);
CC_Special    = CC_Special + [Decision_L_s(:) + Decision_L_r(:) == Load_RUM(:)];
% CC_Special_02: RES curtailment limit
RES_DAF     = Data_RES_DAF{(24*(Day_1st-1)+1:24*Day_end), :};
Country_RES = sum(RES_DAF,2);
CC_Special  = CC_Special + [Decision_W_s(:) + Decision_W_r(:) == RES_DAF(:)];
% CC_Special_03: Provided reseve
CC_Special = CC_Special...
           + [Decision_R_load_req == R_for_load*Country_Load]...
           + [Decision_R_RES_req  == R_for_RES*Country_RES]...
           + [Decision_R_load_req + Decision_R_RES_req == Decision_R_all_req];
%
%% ---------------------------Constraints: all-------------------------- %%
CC = CC_General + CC_Special;
%
%% ------------------------------Objective------------------------------ %%
Cost_UC = UC_c'*x;
%% -------------------------------Solve it------------------------------ %%
ops = sdpsettings('solver', 'gurobi'); 

🎉3 参考文献

部分理论来源于网络，如有侵权请联系删除。

[1]X. Chen, Y. Yang, Y. Liu and L. Wu, "Feature-Driven Economic Improvement for Network-Constrained Unit Commitment: A Closed-Loop Predict-and-Optimize Framework," in IEEE Transactions on Power Systems, vol. 37, no. 4, pp. 3104-3118, July 2022, doi: 10.1109/TPWRS.2021.3128485.

🌈4 Matlab代码、数据、文章讲解