Optimization on scheduling decision-making for wind/solar/hydrogen storage highway microgrid based on improved Pareto algorithm
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摘要: 针对公路能源微电网在高峰时段负荷大、突发性负荷波动频繁等问题，提出了一种基于改进Pareto算法的风/光/氢蓄储公路微电网日内调度决策优化模型，以实现微电网在日内运行周期中的电力削峰填谷，完成电力系统最大化消纳风/光机组出力，并达到不同类型能源间的互补和最优化利用；以微电网系统日内运行总成本最低、碳排放量最小、风/光消纳率最大为主要准则构建了目标函数，考虑电功率平衡、风/光能源出力、氢蓄储能以及与外电网交互等多种约束条件，按照优先消纳可再生能源政策制定了出力策略，并输出了区域内微电网最佳调度决策结果；为验证提出模型的有效性、精确度和实用性，以新疆S21公路吉力湖路段的气象数据和电力负荷数据为实例进行了分析。研究结果表明：基于提出的优化模型构建的风/光/氢蓄储公路微电网系统能有效提升风光能源的消纳，与传统Pareto算法和多目标粒子群优化等算法相比，改进后的Pareto算法可使系统在相同微电网结构下的日内运行总成本分别降低8.5%和3.7%，可再生能源消纳率分别提高3.6%和10.1%，碳排放量分别减少14.4%和23.9%。由此可见，基于改进Pareto算法的风/光/氢蓄储日内调度决策优化模型能够在确保公路微电网系统平稳运行的同时，提升风/光/氢蓄储系统的可靠性。Abstract: To address the challenges of large loads and frequent sudden load fluctuations during peak hours in highway energy microgrid, an intraday scheduling decision-making optimization model for wind/solar/hydrogen storage highway microgrid was proposed based on the improved Pareto algorithm, to ensure the power peak shaving and valley filling in the microgrid during the intraday operation cycle, help the power system maximize the consumption of wind/solar unit output, and achieve the complementary and optimal utilization of different types of energies. The objective function was constructed by using the lowest intraday operating cost, the lowest carbon emission, and the highest wind/solar consumption rate of the microgrid system as the main criteria. In view of various constraints such as the electric power balance, wind/solar energy output, hydrogen storage energy, and interaction with the external grid, the output strategy was formulated according to the policy of preferential consumption of renewable energy, and the optimal scheduling decision-making results of the microgrid in the region were output. To verify the validity, accuracy, and practicality of the proposed model, the meteorological data and electric load data of Jili Lake section of Xinjiang S21 Highway were analyzed. Research results show that the wind/solar/hydrogen storage highway microgrid system constructed based on the proposed optimization model can effectively improve the consumptions of wind/solar energies. Compared with the traditional Pareto algorithm and multi-objective particle swarm optimization algorithm, the improved Pareto algorithm reduces the total intraday operating cost of the system by 8.5% and 3.7% under the same microgrid structure, increases the renewable energy consumption rate by 3.6% and 10.1%, and lowers the carbon emission by 14.4% and 23.9%, respectively. Thus, the wind/solar/hydrogen storage intraday scheduling decision-making optimization model based on the improved Pareto algorithm can improve the reliability of the wind/solar/hydrogen storage system while ensuring the smooth operation of the highway microgrid system. 5 tabs, 5 figs, 30 refs.
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图 1 风/光/氢蓄储公路微电网系统结构
Figure 1. Structure of wind/solar/hydrogen storage highway microgrid system
图 2 基于改进Pareto算法的多目标优化模型求解流程
Figure 2. Solution process of multi-objective optimization model based on improved Pareto algorithm
图 3 典型日内风/光机组出力功率预测曲线
Figure 3. Prediction curves of typical intraday wind/solar turbine units output powers
图 4 典型电负荷需求功率日内变化曲线
Figure 4. Intraday variation curves of typical electric load demand power
表 1 电负荷系统试验参数
Table 1. Test parameters for electric load system
能耗项目 设备名称 设备数量 平均额定功率/W 监控系统 LED可变信息标志(悬臂式) 12 900 LED可变信息标志(门架式) 14 1 900 高清网络摄像机 51 18 高速球型摄像机 35 40 微波雷达车辆检测 30 10 通信系统 光端机 70 5 交换机 6 500 英特尔处理器 30 270 路由器 10 300 收费系统 车道控制器 4 100 车道通行信号灯 4 50 车道检测线圈 4 10 自动栏杆机 4 60 内部有线紧急对讲及报警系统 4 70 费用额度显示器 4 40 车道摄像机 4 55 收费计算机、显示器 4 200 收费亭内配电机柜 1 3 000 收费亭内摄像机 4 55 收费天棚照明 12 100 广场高杆照明灯 8 1 500 服务生活系统 通风机 15 700 空调 20 580 电锅炉 2 2 200 台式电脑 20 200 电视机 5 135 中低杆照明灯具 80 300 高压钠灯 100 400 无极荧光灯 100 120 白光LED灯具 80 55 监控摄像 20 10 高清红外摄像 50 18 暖风机 10 1 800 电解槽 2 2 200 
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表 2 风/光/氢蓄储能公路微电网系统设备运行参数
Table 2. Operation parameters of wind/solar/hydrogen storage highway microgrid system equipment
设备名称 参数 数值 参数 数值 电转气 产气效率 0.55 容量上限/kW 200 损失系数 0.31 容量下限/kW 0 最大爬坡功率/kW 200 运维成本/(元·h-1) 4.2 储氢罐 储氢效率 0.92 容量上限/m3 500 放氢效率 0.85 容量下限/m3 30 最大放氢容量/m3 100 运维成本/(元·h-1) 1.5 氢燃料电池 氢气流率/(L·min-1) 100 裸堆功率密度/(kW·L-1) 4.7 电池效率 0.45 氢气利用率 0.64 最大放电功率/kW 20 运维成本/(元·h-1) 3.1 蓄电池 充电效率 0.85 容量上限/kW 500 放电效率 0.85 容量下限/kW 10 最大充、放功率/kW 100 运维成本/(元·h-1) 2.1 电动汽车充电站 充电效率 0.87 容量上限/kW 300 放电效率 0.87 容量下限/kW 30 最大充、放功率/kW 50 运维成本/(元·h-1) 2.8 风电机组 额定输出功率/kW 250 损失系数 0.55 轮毂高度/m 70 装机容量/kW 1 500 风电转换效率 0.45 运维成本/(元·h-1) 15.7 光伏机组 额定输出功率/kW 230 损失系数 0.78 光场面积/m2 4 600 装机容量/kW 1 000 光电转换效率 0.22 运维成本/(元·h-1) 11.8
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表 3 各典型日最优配置方案
Table 3. Optimal configuration schemes for each typical day
参数名称 极热日 极冷日 过渡日 风电机组数量 3 1 3 光伏电站数量 2 1 2 蓄电池组件数量 25 60 30 氢燃料电池组件数量 16 0 10 40L储氢罐数量 10 0 5 电动汽车充电数量 46 28 70 日内运行总成本/(元·d-1) 1 482.73 1 985.14 1 697.08 可再生能源消纳率/% 97.4 32.1 95.3 碳排放量/t 0.007 2 0.018 2 0.008 9
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表 4 分时电价
Table 4. Time-sharing tariffs
时段 购电电价/[元·(kW·h)-1] 售电电价/[元·(kW·h)-1] 0:00~6:00 0.48 0.25 6:00~10:00、12:00~16:00 0.65 0.44 10:00~12:00、16:00~18:00 0.83 0.58 18:00~00:00 1.15 0.95
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表 5 不同方案下的系统运行参数对比结果
Table 5. Comparison results of system operation parameters under different schemes
方案 优化算法 风/光发电 主网出力 氢储模块 蓄储模块 系统日内运行总成本/(元·d-1) 可再生能源消纳率/% 碳排放量/t 1 改进Pareto算法 √ 1 896.57 0.0 0.031 2 2 改进Pareto算法 √ √ 1 579.29 68.2 0.017 5 3 改进Pareto算法 √ √ √ 1 501.34 76.3 0.014 2 4 改进Pareto算法 √ √ √ √ 1 697.08 95.3 0.008 9 5 Pareto算法 √ √ √ √ 1 854.25 91.7 0.010 4 6 MOPSO算法 √ √ √ √ 1 761.92 85.2 0.011 7 7 SA-PSO算法 √ √ √ √ 1 743.41 89.4 0.009 2
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