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面向高比例新能源电网的重大耗能企业需求响应调度

李远征 倪质先 段钧韬 徐磊 杨涛 曾志刚

李远征, 倪质先, 段钧韬, 徐磊, 杨涛, 曾志刚. 面向高比例新能源电网的重大耗能企业需求响应调度. 自动化学报, 2022, 45(x): 1−15 doi: 10.16383/j.aas.c220034
引用本文: 李远征, 倪质先, 段钧韬, 徐磊, 杨涛, 曾志刚. 面向高比例新能源电网的重大耗能企业需求响应调度. 自动化学报, 2022, 45(x): 1−15 doi: 10.16383/j.aas.c220034
Li Yuan-Zheng, Ni Zhi-Xian, Duan Jun-Tao, Xu Lei, Yang Tao, Zeng Zhi-Gang. Demand response scheduling of major energy-consuming enterprises based on a high proportion of renewable energy power grid. Acta Automatica Sinica, 2022, 45(x): 1−15 doi: 10.16383/j.aas.c220034
Citation: Li Yuan-Zheng, Ni Zhi-Xian, Duan Jun-Tao, Xu Lei, Yang Tao, Zeng Zhi-Gang. Demand response scheduling of major energy-consuming enterprises based on a high proportion of renewable energy power grid. Acta Automatica Sinica, 2022, 45(x): 1−15 doi: 10.16383/j.aas.c220034

面向高比例新能源电网的重大耗能企业需求响应调度

doi: 10.16383/j.aas.c220034
基金项目: 国家自然科学基金(61991403, 62133003, 62073148)资助
详细信息
    作者简介:

    李远征:华中科技大学人工智能与自动化学院副教授.主要研究方向为人工智能及其在智能电网中的应用, 深度学习, 强化学习和大数据分析. E-mail: Yuanzheng_Li@hust.edu.cn

    倪质先:华中科技大学中欧清洁与可再生能源学院硕士研究生.2019年获得武汉理工大学自动化专业学士学位. 主要研究方向为含大规模可再生能源综合电力系统规划、优化及调度. E-mail: Zhixian_Ni@hust.edu.cn

    段钧韬:华中科技大学人工智能与自动化学院硕士研究生.主要研究方向为智能电网控制调度, 分布式控制与优化. E-mail: duanjuntao1@outlook.com

    徐磊:东北大学流程工业综合自动化国家重点实验室博士研究生.主要研究方向为分布式控制及优化, 网络化系统和马尔可夫跳变系统. E-mail: 2010345@stu.neu.edu.cn

    杨涛:东北大学流程工业综合自动化国家重点实验室教授.主要研究方向为工业人工智能, 信息物理系统, 分布式协同控制和优化. 本文通信作者. E-mail: yangtao@mail.neu.edu.cn

    曾志刚:华中科技大学人工智能与自动化学院院长.主要研究切换系统控制理论与应用, 计算智能, 系统稳定性和联想记忆. E-mail: zgzeng@hust.edu.cn

Demand Response Scheduling of Major Energy-consuming Enterprises Based on a High Proportion of Renewable Energy Power Grid

Funds: Supported by National Natural Science Foundation of China (61991403, 62133003, 62073148)
More Information
    Author Bio:

    LI Yuan-Zheng Associate professor at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. His research interest covers artificial intelligence and its application in smart grid, deep learning, reinforcement learning, and big data analysis

    NI Zhi-Xan Master student at China-EU Institute for Clean and Renewable Energy, Huazhong University of Science and Technology. He received his bachelor degree in automation from Wuhan University of Technology in 2019. His main research interest is planning, optimization and scheduling of integrated power systems containing large-scale renewable energy sources

    DUAN Jun-Tao Master student at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. His research interest covers smart grid control and scheduling, distributed control and optimization

    XU Lei Ph.D. candidate of the State Laboratory at Synthetical Automation for Process Industries, Northeastern University. His research interest covers distributed control and optimization, networked systems and Markovian jump systems

    YANG Tao Professor at the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University. His research interest covers industrial artificial intelligence, information physical systems, distributed cooperative control and optimization. Corresponding author of this paper

    ZENG Zhi-Gang Dean and Professor at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. His research interest covers switching system control theory and application, computational intelligence, system stability, and associative memory

  • 摘要: 随着国家“双碳”重大战略的提出, 高比例新能源并网将成为我国电力能源转型的重要态势. 针对火电机组、配电网和需求侧关联的系列运行约束制约了电网对高比例新能源的有效消纳这一问题, 本文提出重大耗能企业这一主要电力负荷参与网需求响应(Demand response, DR)的研究思路, 通过重大耗能企业与电网协调调度促进新能源消纳, 并获得经济补偿以减少运行成本. 研究首先基于混合需求侧响应机制, 提出以重大耗能企业、新能源、火电机组为核心的协调调度方法, 并根据新能源预测值-预测误差的信息依存顺序提出了两步调度策略. 在此基础上, 进行生产过程行为建模以实现重大耗能企业需求侧响应决策描述, 并建立高比例新能源并网的重大耗能企业需求响应与电网协调调度优化模型. 最后, 基于烟台电网实际系统进行算例分析, 验证了重大耗能企业通过需求响应参与电网协调调度以及两步调度策略的有效性.
    1)  1 由于所证明的命题与时段$ t $无关, 因此为了简化标记将下标$ t $省略.
  • 图  1  基于混合需求侧响应的协调调度方法示意图

    Fig.  1  The chart of hybrid demand response based coordination scheduling method

    图  2  两步调度策略流程图

    Fig.  2  Structure of stepwise scheduling

    图  3  生产过程架构

    Fig.  3  Structure of PMP system

    图  4  分步协调调度第一步模型

    Fig.  4  Scheduling model of step 1

    图  5  第二步调度模型

    Fig.  5  Scheduling model of step 2

    图  6  两步调度优化模型的求解算法

    Fig.  6  Solution algorithm of two-step optimization model

    图  7  预测风电功率以及负荷需求信息

    Fig.  7  Forecast power outputs of wind and power demand of local loads

    图  8  两步调度中PMP系统的调度甘特图

    Fig.  8  Gant figures of PMP system in stepwise scheduling

    图  9  算例1求解结果中各时段功率信息

    Fig.  9  Power information from solution of case 1

    图  10  算例2求解结果中各时段功率信息

    Fig.  10  Power information from solution of case 2

    图  11  在不同权重作用下算例3目标函数值比较图

    Fig.  11  Objective values comparison of subcases mentioned in case 3 with different weights

    A1  烟台电网拓扑图

    A1  Topology of Yantai power grid topology

    表  1  发电机组相关参数

    Table  1  Related coefficients of thermal generators

    节点 最小时间(h) 耗量系数
    启动 停止 $a$ $b$ $c$ $\alpha$ $\beta$ $\gamma$
    1 0 0 0.077 242.20 759.49 129.0 129.0 2
    4 0 0 0.084 242.91 761.81 129.0 129.0 2
    9 0 0 0.181 167.25 159.70 64.5 64.5 1
    21 0 0 0.187 168.09 160.54 64.5 64.5 1
    25 5 3 0.032 68.95 920.61 967.5 967.5 6
    下载: 导出CSV

    表  2  PMP网络结构参数

    Table  2  Parameters of PMP system

    单元编号 最大加工容量(个) 功率(kW/个)
    $M_1$ 2 48
    $B_1$ 4 4
    $M_2$ 2 32
    $B_2$ 4 4
    $M_3$ 2 12
    $B_3$ 4 4
    $M_4$ 2 36
    $B_4$ 4 4
    $M_5$ 2 32
    $B_5$ 4 4
    $M_6$ 2 21
    下载: 导出CSV

    表  3  分时定价策略表

    Table  3  Time-of-use price strategy

    用电场景 用电时段 消耗电价($\yen$/kWh) 需求电价($\yen$/kWh) 固定费用($\yen$)
    高峰期 2am, 7-12am, 5-7pm 1.08296 121.26 331.66
    低峰期 0-1am, 3-6am, 1-4pm, 8-12pm 0.53367 0
    下载: 导出CSV

    表  4  三个算例对应目标函数值及相关功率

    Table  4  Objective values and corresponding power information of three cases

    算例 调度步骤 发电成本($\yen$) 生产成本($\yen$/单位) 风电消纳量(MWh) 系统负荷(MWh) 火电发电量(MWh) 外部备用(MWh)
    1,2,3 208110.02 3528.41 2531.54 5083.35 2551.81
    1 244436.30 3478.16 2738.45 5038.90 2454.23 268.00
    2 256888.21 3528.41 2826.61 5087.74 2479.80 333.46
    3-1 187535.17 3493.58 2528.95 5031.14 2446.30 55.89
    3-2 187862.76 3492.16 2527.15 5032.68 2448.78 56.75
    3-3 243154.81 3469.97 2871.31 5027.01 2445.36 401.30
    3-4 187717.58 3492.03 2529.73 5033.88 2447.81 56.34
    3-5 242932.61 3469.97 2871.31 5035.34 2448.59 398.39
    3-6 242928.22 3469.97 2871.31 5035.15 2448.35 398.55
    下载: 导出CSV
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