考虑分时电价及光热电站参与的多能源虚拟电厂优化调度

doi:10.12204/j.issn.1000-7229.2022.04.013

电力建设 ›› 2022, Vol. 43 ›› Issue (4): 119-129.doi: 10.12204/j.issn.1000-7229.2022.04.013

考虑分时电价及光热电站参与的多能源虚拟电厂优化调度

赵玲霞(), 王兴贵(), 丁颖杰(), 郭永吉(), 李锦健()

兰州理工大学电气工程与信息工程学院,兰州市 730050

收稿日期:2021-11-11 出版日期:2022-04-01 发布日期:2022-03-24
通讯作者: 赵玲霞 E-mail:zhaolingxia@163.com;wangxg689@126.com;dingyj0820@126.com;guoyj0605@163.com;lijinjian0326@163.com
作者简介:王兴贵(1963),男,教授,博士生导师,主要研究方向为可再生能源发电系统与控制、电力电子与电力传动,E-mail: wangxg689@126.com;
丁颖杰(1992),女,博士研究生,主要研究方向为可再生能源发电系统与控制,E-mail: dingyj0820@126.com;
郭永吉(1976),男,副教授,主要研究方向为电力电子与电力传动,E-mail: guoyj0605@163.com;
李锦键(1995),男,博士研究生,主要研究方向为可再生能源发电系统及控制,E-mail: lijinjian0326@163.com。
基金资助:
甘肃省自然科学基金项目(21JR7RA205)

Optimal Dispatch of Multi-energy Virtual Power Plant Considering Time-of-Use Electricity Price and CSP Plant

ZHAO Lingxia(), WANG Xinggui(), DING Yingjie(), GUO Yongji(), LI Jinjian()

College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China

Received:2021-11-11 Online:2022-04-01 Published:2022-03-24
Contact: ZHAO Lingxia E-mail:zhaolingxia@163.com;wangxg689@126.com;dingyj0820@126.com;guoyj0605@163.com;lijinjian0326@163.com
Supported by:
Natural Science Foundation of Gansu Province(21JR7RA205)

摘要/Abstract

摘要：

针对分布式风电、光伏并网对电网调度运行的影响,利用光热电站(concentrating solar power plant,CSP)出力可调的特点,联合储能电池构建包含风电、光伏、光热及储能电池的多能源虚拟电厂(virtual power plant,VPP)。根据光热及储能电池特性,基于分时电价制定运行策略,以各时段净收益最大为目标,建立虚拟电厂两阶段优化调度模型。在日前调度中,综合风光预测出力,考虑分时电价和光照因素,优化光热出力,制定申报出力计划;在实时调度中,光热通过储热装置充放热对风光出力偏差进行修正,储能电池辅助光热对偏差进行调节,并采用自适应粒子群算法优化各单元出力。最后,通过仿真验证所建模型合理性及运行策略可行性。结果表明采用光热和储能电池联合调节,可有效降低虚拟电厂实时出力跟踪申报出力偏差,提高经济效益。

关键词: 虚拟电厂(VPP), 光热发电, 分时电价, 自适应粒子群算法, 优化调度

Abstract:

In view of the impact of distributed photovoltaic and wind power grid-connection on power grid dispatching and operation, a multi-energy virtual power plant (VPP) including photovoltaic, solar thermal power, wind power and energy storage batteries is constructed by combining energy storage batteries with the flexibility of concentrating solar power plant (CSP). According to the characteristics of CSP and energy storage battery, the operation strategy is formulated according to time-of-use (TOU) electricity price, and a two-stage optimal dispatching model of virtual power plant is established with the goal of maximizing the net income in each period. In the day-ahead scheduling, it optimizes the CSP output and formulates the declaration and output plan by considering the TOU electricity price and sunlight factors. In the real-time scheduling, the deviation of wind power and photovoltaic output is corrected by using the charge and discharge of thermal storage system, CSP is assisted by energy storage battery to adjust the deviation, and the adaptive particle swarm optimization algorithm is used to optimize the output of each unit. Finally, the rationality of the model and the feasibility of the operation strategy are verified by simulation. The results show that the combined regulation of CSP and energy storage battery can effectively reduce the deviation of real-time output tracking declared output of virtual power plant and improve economic benefits.

Key words: virtual power plant(VPP), concentrating solar power, time-of-use electricity price, adaptive particle swarm optimization, optimal scheduling

中图分类号:

TM734

赵玲霞, 王兴贵, 丁颖杰, 郭永吉, 李锦健. 考虑分时电价及光热电站参与的多能源虚拟电厂优化调度[J]. 电力建设, 2022, 43(4): 119-129.

ZHAO Lingxia, WANG Xinggui, DING Yingjie, GUO Yongji, LI Jinjian. Optimal Dispatch of Multi-energy Virtual Power Plant Considering Time-of-Use Electricity Price and CSP Plant[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(4): 119-129.

图/表 19

图1 CSP电站能量转化结构

Fig.1 Energy conversion structure of CSP plant

图2 VPP结构简化示意

Fig.2 Schematic diagram of VPP structure

图3 多能源VPP调度优化流程

Fig.3 Flow chart of multi-energy VPP scheduling optimization

图4 风电、光伏预测出力及实时出力曲线

Fig.4 Predicted and real-time output curves of photovoltaic and wind power

表1 光热电站相关参数

Table 1 Relevant parameters of CSP plant

图5 DNI预测值及实时值

Fig.5 DNI predicted value and real-time value

表2 储能电池相关参数

Table 2 Relevant parameters of energy storage battery

表3 电网峰谷平各时段分时电价

Table 3 Time-of-use electricity price for peak, valley, flat periods of power grid

图6 日前申报时2种不同情景下的出力曲线

Fig.6 Output curves under two different scenarios at the stage of day-ahead declaration

图7 实时调度情景3下光热在不同储热容量下的出力曲线

Fig.7 In real-time scheduling scenario 3, the output of CSP under different thermal storage capacity

图8 实时调度情景3下光热储能装置充放热功率及储热量变化情况

Fig.8 In real-time scheduling scenario 3, changes of charging and discharging heat power and heat storage of CSP thermal storage device

图9 VPP日前-实时(情景3)两阶段出力情况

Fig.9 Output of VPP under day-ahead real-time (scenario 3) two stage

图10 两种不同申报模式下VPP日前-实时(情景3)申报偏差功率

Fig.10 Declaration deviation power of day-ahead real-time (scenario 3) two stage of VPP under different declaration modes

表4 不含储能电池时的VPP收益及成本

Table 4 VPP revenue and cost without energy storage battery

图11 实时调度情景4下光热在不同储热容量下的出力曲线

Fig.11 In real-time scheduling scenario 4, the output of CSP under different thermal storage capacity

图12 实时调度情景4下储能电池充放电功率及储电量

Fig.12 In real-time scheduling scenario 4, charging and discharging power and storage capacity of energy storage battery

图13 VPP日前-实时(情景4)两阶段出力情况

Fig.13 Output of VPP under day-ahead real-time (scenario 4) two stage

图14 两种不同申报模式下VPP日前-实时(情景4)申报偏差功率

Fig.14 Declaration deviation power of day-ahead real-time(scenario 4) two stage of VPP under different declaration modes

表5 含储能电池时的VPP收益及成本

Table 5 VPP revenue and cost with energy storage battery

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考虑分时电价及光热电站参与的多能源虚拟电厂优化调度

Optimal Dispatch of Multi-energy Virtual Power Plant Considering Time-of-Use Electricity Price and CSP Plant

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