CN113937811A - 一种多能耦合配电系优化调度方法 - Google Patents
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Abstract
本发明公开了一种多能耦合配电系优化调度方法,该方法采用GA‑PSO算法进行可再生能源发电功率与区域负荷预测。本方法充分利用电动汽车的移动储荷特性,对电动汽车的储荷资源进行调度,实现快速、高效的配电网故障恢复,提升配电***的韧性,使得多能耦合的配电***在此规划下经济最优、稳定运行。同时本方法将遗传算法选择机制与交叉机制引入粒子群算法中,即在PSO算法的每次迭代后以一定的概率选择出待交叉的粒子放入杂交池中,杂交池中的粒子两两随机组合交叉后产生后代粒子,利用后代粒子取代双亲粒子,克服了传统PSO算法容易陷入局部最优的问题,提高全局搜索能力,从而实现多能耦合配电***的优化调度。
Description
技术领域
本发明涉及能源互联网技术领域,尤其是涉及一种多能耦合配电系优化调度方法。
背景技术
近年来频发的自然灾害和高比例的新能源装机容量,给配电***的稳定性带来了巨大的挑战。如2021年2月美国大停电,造成累计切除负荷约20000MW,影响人口约400万,同时带来巨大的经济损失。为了提升配电网应对极端情况如自然灾害、网络攻击、供应端与线路故障等情况下的恢复能力,引入规模化灵活的电动汽车移动储能单元为电-热配电***提供供需平衡。通过改进多能源耦合配电***调度方法能够满足***内部关键负荷的供电需求,甚至还能为邻近的配电网***提供电力。
发明内容
为了解决上述背景技术所存在的至少一技术问题,本发明提供一种多能耦合配电系优化调度方法。
为实现上述目的,本发明的技术方案是:
一种多能耦合配电系优化调度方法,所述方法采用GA-PSO算法进行可再生能源发电功率与区域负荷预测,包括如下步骤:
步骤1:输入历史数据,所述历史数据包括历史气象数据、历史风力发电数据、历史光伏发电数据、历史配电***负荷数据及历史电动汽车的充电数据,并对风力发电功率、光伏发电功率、区域内的负荷功率进行预测。
步骤2:确定算法参数,随机产生一个包含M个粒子的种群;初始化粒子:初始化光伏容量与风机容量以及热容量的初始值为PPV0,PWT0,PKw0;赋予每个粒子一个随机速度:设定风电机组容量和光伏阵列容量变化的步长,设置迭代次数为N;设定可再生能源消纳率最大化为目标函数;其中,M、N均为正整数,
步骤3:更新粒子的速度和位置,计算出粒子的数值;
步骤4:粒子交叉和变异计算,选择部分粒子作为需要进行交叉遗传计算的群体,并对其进行交叉和变异计算;
步骤5:计算粒子适应度值和约束条件,根据目标函数计算所有粒子维护费用,根据粒子维护费用的大小确定粒子的适应度值,并根据适应度值对粒子进行排序;判断粒子迭代次数是否已经完成,如果未达到最大迭代次数,则转到步骤3继续执行;如果达到最大迭代次数,则转到步骤6;
步骤6:获得风力发电功率、光伏发电功率、区域内的负荷功率预测曲线,并根据可再生能源波动与电动汽车充放电容量关系,确定电动汽车V2G价格弹性系数,获得电动汽车参与V2G的服务价格;对响应服务车辆进行充放电优化调度,获得车辆调度方案与服务价格组合方案;
步骤7:计算区域内电动汽车可调度容量,确保配电网能量供需平衡。
进一步地,步骤2中,所述目标函数为可再生能源消纳率的最大化:
式中,δre为可再生能源消纳率;Prc为平可再生能源实际消纳总量,Pgr为可再生能源发电总量。
进一步地,步骤3中,更新粒子的速度和位置按照如下式:
上式中,为粒子i(i=1,2,…,M)第k次迭代速度;为粒子i第k次迭代位置;为粒子i第k次迭代时的最优位置;为第k次迭代时群体最优位置;ω为惯性权重;c1和c2为学习因子;r1和r2为[0-1]之间的随机数。
进一步地,步骤5中,所述约束条件包括电量不足期望值EENS和能源可靠性指数EIR。
进一步地,所述电量不足期望值EENS在风光储互补发电***中的计算公式为
式中,Pi表示第i种容量情况的概率;Ei为负荷不能被满足的电量;N为不同容量的种类数。
进一步地,所述供电可靠性指数EIR的计算公式如下式所示:
式中,EL,i为第i个月内负荷需求的总电量;WPV,i为第i个月光伏发电容量;WWT,i为第i个月风力发电容量。
进一步地,所述步骤6的根据可再生能源波动与电动汽车充放电容量关系,确定电动汽车V2G价格弹性系数包括:
获取光伏发电功率、风力发电功率、常规发电功率及常规负荷功率,计算供需之间的功率缺口,根据功率缺口设定电动汽车调度价格弹性系数,对电动汽车发出V2G服务邀约,获取电动汽车用户响应V2G服务邀请结果,分析参与服务车辆SOC状态与其历史充放电数据;
获取车辆服务区域、期望价格分布、用户参与V2G概率,预计在线时段,及在线时长分布。
进一步地,,所述步骤6的对响应服务车辆进行充放电优化调度包括:
a:调整V2G激励措施参数;
b:获取电动汽车当前在线服务车辆数量、当前并网时长及用户响应程度;
c:从智能决策库选择提升车辆服务响应程度、并网时长的***运行优化策略;
d:执行电动汽车充放电调度程序,对比分析激励措施及对应的车辆调度方案,返回最佳V2G车辆充放电调度方案及可调度容量,满足区域内电动汽车消纳可再生能源的目标,配电***下能量平衡。
进一步地,所述V2G激励措施参数包括分时服务价格、补贴策略。
进一步地,所述提升车辆服务响应程度、并网时长的***运行优化策略包括电动汽车-充/放电桩资源优化分配策略、用户信用度筛选策略。
本发明与现有技术相比,其有益效果在于:
本方法充分利用电动汽车的移动储荷特性,对电动汽车的储荷资源进行调度,实现快速、高效的配电网故障恢复,提升配电***的韧性,使得多能耦合的配电***在此规划下经济最优、稳定运行。
附图说明
图1为本发明实施例提供的多能耦合配电系优化调度方法的流程图;
图2为对响应服务车辆进行充放电优化调度流程图。
具体实施方式
实施例:
为使本发明的目的、技术方案及优点更加清楚、明确,下面结合附图和具体实施方式对本发明的内容做进一步详细说明。可以理解的是,此处所描述的具体实施例仅仅用于解释本发明,而非对本发明的限定。另外还需要说明的是,为了便于描述,附图中仅示出了与本发明相关的部分而非全部内容。
下面结合附图和实施例对本发明的技术方案做进一步的说明。
对于多能耦合配电***的区域整体性调度模型的建立,首先与要考虑的是多能优化问题。因此对一个区域综合性的优化指标就变得十分重要。为此,本发明公开了一种多能耦合配电系优化调度方法。
如图1所示,本实施例所通过的一种多能耦合配电系优化调度方法,该方法采用GA-PSO算法进行可再生能源发电功率与区域负荷预测,包括如下步骤:
步骤1:输入历史数据,所述历史数据包括历史气象数据、历史风力发电数据、历史光伏发电数据、历史配电***负荷数据及历史电动汽车的充电数据,并对风力发电功率、光伏发电功率、区域内的负荷功率进行预测。
步骤2:确定算法参数,随机产生一个包含M个粒子的种群;初始化粒子:初始化光伏容量与风机容量以及热容量的初始值为PPV0,PWT0,PKw0;赋予每个粒子一个随机速度:设定风电机组容量和光伏阵列容量变化的步长,设置迭代次数为N;设定可再生能源消纳率最大化为目标函数;其中,M、N均为正整数,
步骤3:更新粒子的速度和位置,计算出光伏发电功率、风力发电功率等粒子的数值;
步骤4:粒子交叉和变异计算,选择部分粒子作为需要进行交叉遗传计算的群体,并对其进行交叉和变异计算;
步骤5:计算粒子适应度值和约束条件,根据目标函数计算所有粒子维护费用,根据粒子维护费用的大小确定粒子的适应度值,并根据适应度值对粒子进行排序;判断粒子迭代次数是否已经完成,如果未达到最大迭代次数,则转到步骤3继续执行;如果达到最大迭代次数,则转到步骤6;
步骤6:获得风力发电功率、光伏发电功率、区域内的负荷功率预测曲线,并根据可再生能源波动与电动汽车充放电容量关系,确定电动汽车V2G价格弹性系数,获得电动汽车参与V2G的服务价格;对响应服务车辆进行充放电优化调度,获得车辆调度方案与服务价格组合方案;
步骤7:计算区域内电动汽车可调度容量,确保配电网能量供需平衡。
由此可见,本方法充分利用电动汽车的移动储荷特性,对电动汽车的储荷资源进行调度,实现快速、高效的配电网故障恢复,提升配电***的韧性,使得多能耦合的配电***在此规划下经济最优、稳定运行。同时本方法将遗传算法选择机制与交叉机制引入粒子群算法中,即在PSO算法的每次迭代后以一定的概率选择出待交叉的粒子放入杂交池中,杂交池中的粒子两两随机组合交叉后产生后代粒子,利用后代粒子取代双亲粒子,这样所生成的后代粒子即继承了双亲粒子的优点,同时又加强了粒子相互间的搜索能力,克服了传统PSO算法容易陷入局部最优的问题,提高全局搜索能力,从而实现多能耦合配电***的优化调度。
具体地,步骤2中,所述目标函数为可再生能源消纳率的最大化:
式中,δre为可再生能源消纳率;Prc为平可再生能源实际消纳总量,Pgr为可再生能源发电总量。
具体地,步骤3中,更新粒子的速度和位置按照如下式:
上式中,为粒子i(i=1,2,…,M)第k次迭代速度;为粒子i第k次迭代位置;为粒子i第k次迭代时的最优位置;为第k次迭代时群体最优位置;ω为惯性权重;c1和c2为学习因子;r1和r2为[0-1]之间的随机数。
具体地,步骤5中,所述约束条件包括电量不足期望值EENS和能源可靠性指数EIR。该电量不足期望值EENS在风光储互补发电***中的计算公式为:
式中,Pi表示第i种容量情况的概率;Ei为负荷不能被满足的电量;N为不同容量的种类数。
该供电可靠性指数EIR的计算公式如下式所示:
式中,EL,i为第i个月内负荷需求的总电量;WPV,i为第i个月光伏发电容量;WWT,i为第i个月风力发电容量。
具体地,所述步骤6的根据可再生能源波动与电动汽车充放电容量关系,确定电动汽车V2G(Vehicle-to-grid,车辆到电网)价格弹性系数包括:
获取光伏发电功率、风力发电功率、常规发电功率及常规负荷功率,计算供需之间的功率缺口,根据功率缺口设定电动汽车调度价格弹性系数,对电动汽车发出V2G服务邀约,获取电动汽车用户响应V2G服务邀请结果,分析参与服务车辆SOC(State of Charge,荷电状态,也叫剩余电量)状态与其历史充放电数据;
获取车辆服务区域、期望价格分布、用户参与V2G概率,预计在线时段,及在线时长分布。
具体地,如图2所示,所述步骤6的对响应服务车辆进行充放电优化调度包括:
a:调整V2G激励措施参数;
b:获取电动汽车当前在线服务车辆数量、当前并网时长及用户响应程度;
c:从智能决策库选择提升车辆服务响应程度、并网时长的***运行优化策略;
d:执行电动汽车充放电调度程序,对比分析激励措施及对应的车辆调度方案,返回最佳V2G车辆充放电调度方案及可调度容量,满足区域内电动汽车消纳可再生能源的目标,配电***下能量平衡。
如此,通过上述步骤即可以获得最佳车辆调度方案与服务价格组合。
具体地,上述的V2G激励措施参数包括分时服务价格、补贴策略。上述的提升车辆服务响应程度、并网时长的***运行优化策略包括电动汽车-充/放电桩资源优化分配策略、用户信用度筛选策略。
上述实施例只是为了说明本发明的技术构思及特点,其目的是在于让本领域内的普通技术人员能够了解本发明的内容并据以实施,并不能以此限制本发明的保护范围。凡是根据本发明内容的实质所做出的等效的变化或修饰,都应涵盖在本发明的保护范围内。
Claims (10)
1.一种多能耦合配电系优化调度方法,其特征在于,所述方法采用GA-PSO算法进行可再生能源发电功率与区域负荷预测,包括如下步骤:
步骤1:输入历史数据,所述历史数据包括历史气象数据、历史风力发电数据、历史光伏发电数据、历史配电***负荷数据及历史电动汽车的充电数据,并对风力发电功率、光伏发电功率、区域内的负荷功率进行预测;
步骤2:确定算法参数,随机产生一个包含M个粒子的种群;初始化粒子:初始化光伏容量与风机容量以及热容量的初始值为PPV0,PWT0,PKw0;赋予每个粒子一个随机速度:设定风电机组容量和光伏阵列容量变化的步长,设置迭代次数为N;设定可再生能源消纳率最大化为目标函数;其中,M、N均为正整数,
步骤3:更新粒子的速度和位置,计算出粒子的数值;
步骤4:粒子交叉和变异计算,选择部分粒子作为需要进行交叉遗传计算的群体,并对其进行交叉和变异计算;
步骤5:计算粒子适应度值和约束条件,根据目标函数计算所有粒子维护费用,根据粒子维护费用的大小确定粒子的适应度值,并根据适应度值对粒子进行排序;判断粒子迭代次数是否已经完成,如果未达到最大迭代次数,则转到步骤3继续执行;如果达到最大迭代次数,则转到步骤6;
步骤6:获得风力发电功率、光伏发电功率、区域内的负荷功率预测曲线,并根据可再生能源波动与电动汽车充放电容量关系,确定电动汽车V2G价格弹性系数,获得电动汽车参与V2G的服务价格;对响应服务车辆进行充放电优化调度,获得车辆调度方案与服务价格组合方案;
步骤7:计算区域内电动汽车可调度容量,确保配电网能量供需平衡。
4.如权利要求1所述的多能耦合配电系优化调度方法,其特征在于,步骤5中,所述约束条件包括电量不足期望值EENS和能源可靠性指数EIR。
7.如权利要求1所述的多能耦合配电系优化调度方法,其特征在于,所述步骤6的根据可再生能源波动与电动汽车充放电容量关系,确定电动汽车V2G价格弹性系数包括:
获取光伏发电功率、风力发电功率、常规发电功率及常规负荷功率,计算供需之间的功率缺口,根据功率缺口设定电动汽车调度价格弹性系数,对电动汽车发出V2G服务邀约,获取电动汽车用户响应V2G服务邀请结果,分析参与服务车辆SOC状态与其历史充放电数据;
获取车辆服务区域、期望价格分布、用户参与V2G概率,预计在线时段,及在线时长分布。
8.如权利要求1所述的多能耦合配电系优化调度方法,其特征在于,所述步骤6的对响应服务车辆进行充放电优化调度包括:
a:调整V2G激励措施参数;
b:获取电动汽车当前在线服务车辆数量、当前并网时长及用户响应程度;
c:从智能决策库选择提升车辆服务响应程度、并网时长的***运行优化策略;
d:执行电动汽车充放电调度程序,对比分析激励措施及对应的车辆调度方案,返回最佳V2G车辆充放电调度方案及可调度容量,满足区域内电动汽车消纳可再生能源的目标,配电***下能量平衡。
9.如权利要求8所述的多能耦合配电系优化调度方法,其特征在于,所述V2G激励措施参数包括分时服务价格、补贴策略。
10.如权利要求8所述的多能耦合配电系优化调度方法,其特征在于,所述提升车辆服务响应程度、并网时长的***运行优化策略包括电动汽车-充/放电桩资源优化分配策略、用户信用度筛选策略。
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CN117200176A (zh) * | 2023-07-26 | 2023-12-08 | 国网安徽省电力有限公司芜湖供电公司 | 一种基于新能源多负荷协同控制的需求侧控制方法及*** |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106887841A (zh) * | 2017-03-23 | 2017-06-23 | 东北大学 | 一种含电动汽车微电网容量配置的多种群遗传粒子群优化方法 |
US20170337646A1 (en) * | 2016-05-19 | 2017-11-23 | Hefei University Of Technology | Charging and discharging scheduling method for electric vehicles in microgrid under time-of-use price |
CN107834601A (zh) * | 2017-11-17 | 2018-03-23 | 燕山大学 | 一种考虑柔性负荷的独立微电网***容量优化配置方法 |
WO2019196375A1 (zh) * | 2018-04-13 | 2019-10-17 | 华南理工大学 | 基于需求侧响应的微电网最优机组及分时电价的优化方法 |
-
2021
- 2021-10-14 CN CN202111199625.1A patent/CN113937811B/zh active Active
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20170337646A1 (en) * | 2016-05-19 | 2017-11-23 | Hefei University Of Technology | Charging and discharging scheduling method for electric vehicles in microgrid under time-of-use price |
CN106887841A (zh) * | 2017-03-23 | 2017-06-23 | 东北大学 | 一种含电动汽车微电网容量配置的多种群遗传粒子群优化方法 |
CN107834601A (zh) * | 2017-11-17 | 2018-03-23 | 燕山大学 | 一种考虑柔性负荷的独立微电网***容量优化配置方法 |
WO2019196375A1 (zh) * | 2018-04-13 | 2019-10-17 | 华南理工大学 | 基于需求侧响应的微电网最优机组及分时电价的优化方法 |
Non-Patent Citations (1)
Title |
---|
张江林;庄慧敏;刘俊勇;刘友波;向月;高红均;张里;: "基于CS-PSO算法的电池储能***多目标优化运行策略", 工程科学与技术, no. 04 * |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN117200176A (zh) * | 2023-07-26 | 2023-12-08 | 国网安徽省电力有限公司芜湖供电公司 | 一种基于新能源多负荷协同控制的需求侧控制方法及*** |
CN117200176B (zh) * | 2023-07-26 | 2024-04-30 | 国网安徽省电力有限公司芜湖供电公司 | 一种基于新能源多负荷协同控制的需求侧控制方法及*** |
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