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考虑电网调峰需求的工业园区主动配电系统调度学习优化

唐昊 刘畅 杨明 汤必强 许丹 吕凯

唐昊, 刘畅, 杨明, 汤必强, 许丹, 吕凯. 考虑电网调峰需求的工业园区主动配电系统调度学习优化. 自动化学报, 2021, 47(10): 2449−2463 doi: 10.16383/j.aas.c190079
引用本文: 唐昊, 刘畅, 杨明, 汤必强, 许丹, 吕凯. 考虑电网调峰需求的工业园区主动配电系统调度学习优化. 自动化学报, 2021, 47(10): 2449−2463 doi: 10.16383/j.aas.c190079
Tang Hao, Liu Chang, Yang Ming, Tang Bi-Qiang, Xu Dan, Lv Kai. Learning-based optimization of active distribution system dispatch in industrial park considering the peak operation demand of power grid. Acta Automatica Sinica, 2021, 47(10): 2449−2463 doi: 10.16383/j.aas.c190079
Citation: Tang Hao, Liu Chang, Yang Ming, Tang Bi-Qiang, Xu Dan, Lv Kai. Learning-based optimization of active distribution system dispatch in industrial park considering the peak operation demand of power grid. Acta Automatica Sinica, 2021, 47(10): 2449−2463 doi: 10.16383/j.aas.c190079

考虑电网调峰需求的工业园区主动配电系统调度学习优化

doi: 10.16383/j.aas.c190079
基金项目: 国家重点研发计划项目(2017YFB0902600), 国家电网公司科技项目(SGJS0000DKJS1700840)资助
详细信息
    作者简介:

    唐昊:合肥工业大学电气与自动化工程学院教授. 2002年获得中国科技大学博士学位. 主要研究方向为离散事件动态系统, 随机决策与优化理论, 智能电网调度与控制方法. 本文通信作者. E-mail: htang@hfut.edu.cn

    刘畅:合肥工业大学电气与自动化工程学院硕士研究生. 2016年获得合肥工业大学学士学位. 主要研究方向为源荷不确定电力系统的调度学习优化. E-mail: cliu@mail.hfut.edu.cn

    杨明:高级工程师. 主要研究方向为电力系统仿真与经济调度. E-mail: yangming@epri.sgcc.com.cn

    汤必强:研究员级高级工程师. 主要研究方向为智能电网调度, 电力系统仿真. E-mail: tangbiqiang@epri.sgcc.com.cn

    许丹:高级工程师. 主要研究方向为电力系统节能经济调度. E-mail: xudan@epri.sgcc.com.cn

    吕凯:合肥工业大学电气与自动化工程学院博士研究生. 2012年获得辽宁大学学士学位. 主要研究方向为人工智能及其在电网调度优化中的应用. E-mail: kail@mail.hfut.edu.cn

Learning-based Optimization of Active Distribution System Dispatch in Industrial Park Considering the Peak Operation Demand of Power Grid

Funds: Supported by the National Key R&D of China (2017YFB0902600) and the State Grid Corporation of China Project (SGJS0000DKJS1700840)
More Information
    Author Bio:

    TANG Hao Professor at the School of Electric and Automatization Engineering, Hefei University of Technology. He received his Ph.D. degree from University of Science and Technology of China in 2002. His research interest covers discrete event dynamic system, stochastic decision and optimization theory, smart grid dispatching and control method. Corresponding author of this paper

    LIU Chang Master student at the School of electrical and Automation Engineering, Hefei University of Technology. She received her bachelor degree from Hefei University of Technology in 2016. Her research interest covers learning-based optimization of the power system dispatch with uncertain sources and loads

    YANG Ming Senior engineer. His research interest covers power system simulation and economic dispatch

    TANG Bi-Qiang Senior engineer. His research interest covers smart grid dispatching and power system simulation

    XU Dan Senior engineer. His research interest covers economic dispatch of energy saving in power system

    LV Kai Ph.D. candidate at the School of electrical and Automation Engineering, Hefei University of Technology. He received his bachelor degree from Liaoning University in 2012. His research interest covers artificial intelligence and its application in power grid scheduling optimization

  • 摘要: 本文针对含光伏(Photovoltaic, PV)、全钒液流电池(Vanadium redox battery, VRB)储能装置与多类型柔性负荷的工业园区主动配电系统, 研究在考虑源荷随机性情况下该系统的动态经济调度问题. 首先, 将PV出力、多类型负荷需求和电网调峰需求的随机动态变化近似描述为连续马尔科夫过程, 并根据系统内VRB的充放电特性对储能系统进行建模; 然后, 以各决策时刻下PV出力、负荷需求、调峰需求以及储能荷电状态(State of charge, SOC)的离散等级为状态, 以储能充放电及多类型柔性负荷调整方案为行动, 在系统功率平衡等相关约束下, 以应对电网调峰需求和提高系统经济运行水平为目标, 将工业园区主动配电网系统动态经济调度优化问题建立成随机动态规划模型; 最后, 引入强化学习方法进行策略求解. 算例仿真结果表明所得策略可有效提高系统经济运行效益, 并在一定程度上满足电网调峰需求.
    1)   收稿日期 2019-02-01 录用日期 2019-06-02 Manuscript received February 1, 2019; accepted June 2, 2019 国家重点研发计划项目 (2017YFB0902600), 国家电网公司科技项目(SGJS0000DKJS1700840) 资助 Supported by National Key Research and Development Program of China (2017YFB0902600) and the State Grid Corporation of China Project (SGJS0000DKJS1700840) 本文责任编委 诸兵 Recommended by Associate Editor ZHU Bing 1. 合肥工业大学 电气与自动化工程学院 合肥 230009    2. 国网江苏省电力公司电力科学研究院 南京 211103    3. 中国电力科学研究院 (南京)  南京 210003    4. 中国电力科学研究院(北京) 北京 100192 1. Electrical Engineering and Automation Hefei University of Technology, Hefei 230009    2. Electric Power Research Institute
    2)  of State Grid Jiangsu Electric Power Company, Nanjing 211103 3. China Electric Power Research Institute (Nanjing), Nanjing 210003 4. Editorial China Electric Power Research Institute (Beijing), Beijing 100192
  • 图  1  工业园区主动配电系统结构模型

    Fig.  1  Structure model of active distribution system in industrial park

    图  2  VRB等效电路图

    Fig.  2  Equivalent circuit model of VRB

    图  3  采用三段式充放电策略时VRB的充电曲线

    Fig.  3  Charging curve using strategy three-phase of VRB

    图  4  工业园区内PV出力与总负荷需求预测曲线

    Fig.  4  Prediction curves of PV and loads demand in industrial parks

    图  5  工业园区内各类柔性负荷典型日曲线

    Fig.  5  Prediction curves of multi-type flexible loads in industrial parks

    图  6  不同学习优化算法下的系统总学习优化过程曲线

    Fig.  6  The optimal curve of the system under different learning optimization algorithm

    图  7  系统学习优化过程的策略性能曲线

    Fig.  7  The strategic performance curve of the system learning optimization process

    图  8  调峰学习优化曲线

    Fig.  8  The optimal curve of peak operation

    图  10  末代价学习优化曲线

    Fig.  10  The optimal curve of final cost

    图  9  各时段电网调峰指令完成情况示意图

    Fig.  9  The completion of peak adjustment instruction

    图  11  不同初始荷电状态下各时段SOC变化情况

    Fig.  11  Changing process of SOC under different initial values

    图  12  学习优化前后系统负荷曲线

    Fig.  12  The curves of load before and after learning optimization in the system

    图  13  学习优化后各时段典型柔性负荷调整量

    Fig.  13  Adjustment of multi-type flexible loads after optimization

    图  14  不同调度模式下的调峰效果比较图

    Fig.  14  Comparison effect of peak operation under different dispatching modes

    图  15  不同调度模式下负荷优化结果比较图

    Fig.  15  Comparison diagram of load optimal results under different dispatching modes

    图  16  不同储能容量占比下的各项代价比较图

    Fig.  16  Comparison of costs under different proportion of energy storage capacity

    图  17  不同柔性负荷占比下的各项代价比较图

    Fig.  17  Comparison of costs under different proportion of flexible load

    图  18  不同方案下的调峰需求未完成量比较图

    Fig.  18  Unfinished amount of peak operation demand under different projects

    图  19  优化策略下各时段行动选取情况

    Fig.  19  Choice of action pairs under optimal policy

    图  20  优化策略下不同模式的调峰需求未完成量比较图

    Fig.  20  Unfinished amount of peak operation demand under different modes in optimal policy

    表  1  部分变量符号

    Table  1  Partial variable symbols

    参数/变量 符号
    $t$时刻与电网交互功率 $P_{grid}^t$
    $t$时刻光伏出力 $P_{pv}^t$
    $t$时刻刚性负荷功率 $P_{rl}^t$
    $t$时刻可削减负荷功率 $P_{cu}^t$
    $t$时刻可转移负荷功率 $P_{sh}^t$
    $t$时刻电网调峰需求 $P_{peak}^t$
    $t$时刻电网调峰需求未完成量 $P_{unf}^t$
    $t$时刻储能装置充放电功率 $P_{vrb}^t$
    $t$时刻储能装置功率上/下限 ${P_{vrbmax}^t/P_{vrbmin}^t}$
    调度周期始/末时刻 ${t_{beg}}/{t_{end}}$
    储能装置充/放电电流 ${I_d^{charge}/I_d^{discharge}}$
    储能装置充/放电电压 ${U_d^{charge}/U_d^{discharge}}$
    储能装置端电压上/下限 ${U_d^{max}U_d^{min}}$
    储能装置额定电流 ${I_d^{max}}$
    储能装置涓流充放电电流 ${I_d^{min}}$
    储能装置SOC上/下限 ${SOC_{vrb}^{max}/SOC_{vrb}^{min}}$
    始末时刻荷电状态期望值 ${{C_{con}}}$
    下载: 导出CSV

    表  2  VRB模型参数设置表

    Table  2  Parameters of VRB

    VRB本体参数名称 数值 VRB模型参数名称 数值
    能量 30 kWh $R_1$ 0.045 Ω
    容量 630 Ah $R_2$ 0.03 Ω
    额定功率 5 kW $R_f$ 13.889 Ω
    端电压 42 ~ 60 V $C_e$ 0.154 F
    额定电流 105 A $I_p$ 5 A
    下载: 导出CSV

    表  3  学习优化前后系统总负荷特征

    Table  3  The characteristic of load before and after learning optimization in the system

    类型 峰值(kW) 谷值(kW) 峰谷差(kW)
    优化前 5 289 2 600 2 689
    优化后 4 995 2 460 2 535
    下载: 导出CSV

    表  4  不同调度模式下的相关指标

    Table  4  Related indexes under different dispatching modes

    总体代价(元) 调峰代价(元) 购电代价(元) VRB充放代价(元) 柔性负荷补偿金额(元/d)
    模式1 44 500 1 421 37 910 342 4 743
    模式2 48 870 7 483 40 997 362 0
    模式3 46 260 1 845 37 986 0 6 432
    模式4 55 160 12 780 42 380 0 0
    下载: 导出CSV

    表  5  不同方案下的相关指标

    Table  5  Related indexes under different projects

    总体代价(元) 调峰代价(元) 调峰完成度
    方案1 44 500 1421 88.9%
    方案2 45 120 1772.9 86.1%
    下载: 导出CSV

    表  6  优化策略下部分状态行动

    Table  6  Partial state-action pairs under optimal policy

    状态编号 407 8832 18549 24075 25533 33491 38955 42845
    决策时刻 0时 4时 9时 12时 13时 17时 20时 22时
    各类负荷状态 (1, 1, 0, 2) (1, 1, 2, 1) (1, 1, 1, 0) (2, 1, 1, 0) (2, 1, 1, 0) (0, 1, 2, 0) (1, 0, 1, 0) (1, 0, 2, 0)
    储能装置动作 充电 充电 放电 放电 闲置 放电 放电 闲置
    柔性负荷动作 (0, 1, 0) (0, 1, 1) (1, 0, 0) (0, −1, 0) (2, −1, 0) (2, 0, 0) (1, 0, 0) (0, 1, 0)
    下载: 导出CSV

    表  7  优化策略下不同模式的相关指标

    Table  7  Related indexes under different modes in optimal policy

    总体代价(元) 调峰代价(元) 购电代价(元) VRB充放代价(元) 柔性负荷补偿金额(元/d)
    模式1 42 370 1 125 35 800 389 5 056
    模式2 50 856 8 266 42 049 350 0
    模式3 47 555 1 566 39 867 0 6 122
    模式4 55 297 13 131 42 166 0 0
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-02-01
  • 网络出版日期:  2020-01-02
  • 刊出日期:  2021-10-20

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