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

doi:10.12204/j.issn.1000-7229.2022.04.013

Abstract

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

CLC Number:

TM734

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.

Figures/Tables 19

Fig.1 Energy conversion structure of CSP plant

Fig.2 Schematic diagram of VPP structure

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

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

Table 1 Relevant parameters of CSP plant

Fig.5 DNI predicted value and real-time value

Table 2 Relevant parameters of energy storage battery

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

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

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

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

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

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

Table 4 VPP revenue and cost without energy storage battery

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

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

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

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

Table 5 VPP revenue and cost with energy storage battery

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