Learning-based Optimization of Active Distribution System Dispatch in Industrial Park Considering the Peak Operation Demand of Power Grid
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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.Abstract: The dynamic economic dispatch problem of the active distribution system combined of photovoltaic (PV), vanadium redox battery (VRB) energy storage device and multiple types of flexible load in industrial parks with uncertain renewable sources and demands is focused in this paper. First, the random dynamic variations of photovoltaic, multiple loads demand and peak operation demand are described as continuous Markov processes, and the VRB energy storage system is modeled considering its charge-discharge characteristics. Then, decision epoch, outputs level of photovoltaic, multiple load demands level, peak operation demands level and state of charge (SOC) level of VRB are defined as states of the system, the adjustment level of VRB and multiple types of flexible load are set as the actions. Based on relevant restrictions including the power balance constraint, the dynamic optimal dispatch problem for the system was described as a stochastic dynamic programming model, which aims to meet the peak operation demand of power grid and realize economic operation of the system. Finally, a reinforcement learning method is adopted to obtain the optimal policy. Simulation results show that the operational efficiency is significantly enhanced and the peak operation demand of power grid is partly satisfied by the optimal policy.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 Institute2) 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
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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 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 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 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 ${{C_{con}}}$ 
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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
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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
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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
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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%
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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)
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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
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