CN114091350A - 一种基于预测模型的电力调度方法 - Google Patents
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Abstract
一种基于预测模型的电力调度方法,属于电力调度领域。为了解决未考虑动态随机因素对能量调度管理的影响,导致能量管理能力差的问题。本发明方法具体为利用构建的光伏预测模型生成光伏能源预测数据,并将其光伏能源预测数据送至电力调度平台;还利用风机发电预测模型生成风机能源预测数据,并将其风机能源预测数据送至电力调度平台;还利用构建的负荷预测模型生成负荷预测数据,并将其负荷预测数据送至电力调度平台;最后利用电力调度平台对其所接收的光伏能源预测数据、风机能源预测数据和负荷预测数据间进行能量交易调度处理,从而完成电力调度。本发明主要用于实现电力调度。
Description
技术领域
本发明属于电力调度领域。
背景技术
电力调度是为了保证电网安全稳定运行、对外可靠供电、各类电力生产工作有序进行而采用的一种有效的管理手段。电力调度的具体工作内容是依据各类信息采集设备反馈回来的数据信息,或监控人员提供的信息,结合电网实际运行参数,如电压、电流、频率、负荷等,综合考虑各项生产工作开展情况,对电网安全、经济运行状态进行判断,通过电话或自动系统发布操作指令,指挥现场操作人员或自动控制系统进行调整,如调整发电机出力、调整负荷分布、投切电容器、电抗器等,从而确保电网持续安全稳定运行。
而随着经济的快速发展及环境的加剧污染,风电装机容量在世界范围内迅速增长,其并网运行有效的缓解了负荷需求对电网的压力,但其间歇性和不确定性对风电消纳并网和电力系统的安全稳定运行带来了巨大的挑战。为了供电可靠性,缺电微电网会向电力系统购电以满足负荷维持微电网正常运行。多个微电网互联构成微电网群系统可以进一步促进微电网间的能量交换,系统内单个微电网之间可以进行互联互供满足区域供电需求。
如何满足子微电网内部功率平衡,不考虑动态随机因素时,按经验值得出的发电量和负荷值会使功率计算发生误差。而未考虑动态随机因素的确定性模型下计算净电量,会导致净电量计算值偏离实际值,且能量调度管理不是真正的最优。因此,考虑动态随机下的能量调度管理方法十分必要,因此,以上问题亟需解决。
发明内容
本发明目的是为了解决未考虑动态随机因素对能量调度管理的影响,导致能量管理能力差的问题,本发明提供了一种基于预测模型的电力调度方法。
一种基于预测模型的电力调度方法,该方法包括步骤:
S1、构建光伏预测模型、风机发电预测模型、负荷预测模型;
S2、利用构建的光伏预测模型生成光伏能源预测数据,并将其光伏能源预测数据送至电力调度平台;
还利用风机发电预测模型生成风机能源预测数据,并将其风机能源预测数据送至电力调度平台;
还利用构建的负荷预测模型生成负荷预测数据,并将其负荷预测数据送至电力调度平台;
S3、电力调度平台对其所接收的光伏能源预测数据、风机能源预测数据和负荷预测数据间进行能量交易调度处理,从而完成电力调度。
优选的是,S1中、构建光伏预测模型、风机发电预测模型、负荷预测模型的实现方式均采用贝塔分布和威布尔分布实现的。
优选的是,S3中、进行能量交易调度处理采用粒子群优化算法实现。
优选的是,S3中、电力调度平台对其所接收的光伏能源预测数据、风机能源预测数据和负荷预测数据间进行能量交易调度处理的实现方式为:
当负荷预测数据大于光伏能源预测数据,且负荷预测数据大于风机能源预测数据时,利用供电电网给负载供电,并以日整体运行费用最低为经济目标进行能量交易调度;
当负荷预测数据大于光伏能源预测数据,且负荷预测数据小于或等于风机能源预测数据时,利用风机能源给负载供电,并以日整体运行费用最低为经济目标进行能量交易调度;
当负荷预测数据小于或等于光伏能源预测数据,且负荷预测数据大于风机能源预测数据时,利用光伏能源给负载供电,并以日整体运行费用最低为经济目标进行能量交易调度。
优选的是,日整体运行费用最低的经济目标函数为:
式中,minF1表示日整体运行费用最低;
n为变量。
本发明带来的有益效果:
本发明提出一种基于预测模型的电力调度方法,建立基于光伏、风机及负荷的动态随机性模型,实现微电网间的能量交易和管理,使能量进行实时的调度,最终,实现微电网群的经济优化运行。
具体应用时,利用构建的光伏预测模型、风机发电预测模型、负荷预测模型分别生成相应的光伏能源预测数据、风机能源预测数据和负荷预测数据,并利用电力调度平台对其所接收的光伏能源预测数据、风机能源预测数据和负荷预测数据间进行能量交易调度处理,从而完成电力调度。
附图说明
图1是本发明所述一种基于预测模型的电力调度方法的原理示意图。
具体实施方式
下面将结合本发明实施例中的附图,对本发明实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例仅仅是本发明一部分实施例,而不是全部的实施例。基于本发明中的实施例,本领域普通技术人员在没有作出创造性劳动的前提下所获得的所有其他实施例,都属于本发明保护的范围。
需要说明的是,在不冲突的情况下,本发明中的实施例及实施例中的特征可以相互组合。
实施例1:
下面结合图1说明本实施方式,本实施方式所述一种基于预测模型的电力调度方法,该方法包括步骤:
S1、构建光伏预测模型、风机发电预测模型、负荷预测模型;
S2、利用构建的光伏预测模型生成光伏能源预测数据,并将其光伏能源预测数据送至电力调度平台;
还利用风机发电预测模型生成风机能源预测数据,并将其风机能源预测数据送至电力调度平台;
还利用构建的负荷预测模型生成负荷预测数据,并将其负荷预测数据送至电力调度平台;
S3、电力调度平台对其所接收的光伏能源预测数据、风机能源预测数据和负荷预测数据间进行能量交易调度处理,从而完成电力调度。
本实施方式中,建立基于光伏、风机及负荷的动态随机性预测模型,实现微电网间的能量交易和管理,并通过电力调度平台对其所接收的光伏能源预测数据、风机能源预测数据和负荷预测数据间进行能量交易调度处理,从而完成电力调度。
进一步的,S1中、构建光伏预测模型、风机发电预测模型、负荷预测模型的实现方式均采用贝塔分布和威布尔分布实现的。
更进一步的,S3中、进行能量交易调度处理采用粒子群优化算法实现。
更进一步的,S3中、电力调度平台对其所接收的光伏能源预测数据、风机能源预测数据和负荷预测数据间进行能量交易调度处理的实现方式为:
当负荷预测数据大于光伏能源预测数据,且负荷预测数据大于风机能源预测数据时,利用供电电网给负载供电,并以日整体运行费用最低为经济目标进行能量交易调度;
当负荷预测数据大于光伏能源预测数据,且负荷预测数据小于或等于风机能源预测数据时,利用风机能源给负载供电,并以日整体运行费用最低为经济目标进行能量交易调度;
当负荷预测数据小于或等于光伏能源预测数据,且负荷预测数据大于风机能源预测数据时,利用光伏能源给负载供电,并以日整体运行费用最低为经济目标进行能量交易调度。
更进一步的,日整体运行费用最低的经济目标函数为:
式中,minF1表示日整体运行费用最低;
n为变量。
具体应用时,日前调度计划:可从日前角度,以1小时为尺度,基于可再生能源及负荷日前预测和实时电价信息,在满足系统约束条件的前提下,以微电网日整体经济成本最低为目标,进行能量调度。然而在微电网实际运行中,由于可再生能源及负荷功率的随机性,日前预测往往误差较大,故而设置分级调度的方式,进行能量调度。
虽然在本文中参照了特定的实施方式来描述本发明,但是应该理解的是,这些实施例仅仅是本发明的原理和应用的示例。因此应该理解的是,可以对示例性的实施例进行许多修改,并且可以设计出其他的布置,只要不偏离所附权利要求所限定的本发明的精神和范围。应该理解的是,可以通过不同于原始权利要求所描述的方式来结合不同的从属权利要求和本文中所述的特征。还可以理解的是,结合单独实施例所描述的特征可以使用在其他所述实施例中。
Claims (5)
1.一种基于预测模型的电力调度方法,其特征在于,该方法包括步骤:
S1、构建光伏预测模型、风机发电预测模型、负荷预测模型;
S2、利用构建的光伏预测模型生成光伏能源预测数据,并将其光伏能源预测数据送至电力调度平台;
还利用风机发电预测模型生成风机能源预测数据,并将其风机能源预测数据送至电力调度平台;
还利用构建的负荷预测模型生成负荷预测数据,并将其负荷预测数据送至电力调度平台;
S3、电力调度平台对其所接收的光伏能源预测数据、风机能源预测数据和负荷预测数据间进行能量交易调度处理,从而完成电力调度。
2.根据权利要求1所述的一种基于预测模型的电力调度方法,其特征在于,S1中、构建光伏预测模型、风机发电预测模型、负荷预测模型的实现方式均采用贝塔分布和威布尔分布实现的。
3.根据权利要求1所述的一种基于预测模型的电力调度方法,其特征在于,S3中、进行能量交易调度处理采用粒子群优化算法实现。
4.根据权利要求1所述的一种基于预测模型的电力调度方法,其特征在于,S3中、电力调度平台对其所接收的光伏能源预测数据、风机能源预测数据和负荷预测数据间进行能量交易调度处理的实现方式为:
当负荷预测数据大于光伏能源预测数据,且负荷预测数据大于风机能源预测数据时,利用供电电网给负载供电,并以日整体运行费用最低为经济目标进行能量交易调度;
当负荷预测数据大于光伏能源预测数据,且负荷预测数据小于或等于风机能源预测数据时,利用风机能源给负载供电,并以日整体运行费用最低为经济目标进行能量交易调度;
当负荷预测数据小于或等于光伏能源预测数据,且负荷预测数据大于风机能源预测数据时,利用光伏能源给负载供电,并以日整体运行费用最低为经济目标进行能量交易调度。
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CN115102237B (zh) * | 2022-08-25 | 2022-11-29 | 华能山西综合能源有限责任公司 | 一种基于风电光伏系统的运行调度方法 |
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