Energy dispatch of wind-photovoltaic-thermal-storage considering the coupling characteristics of thermal power and molten salt heat storage

doi:10.3969/j.issn.2097-0706.2024.12.007

Abstract

Abstract:

The construction of a new power system， with renewable energy as the main component， has become an inevitable trend in China's energy development.Using a large-scale energy base as the simulation object， a multi-energy complementary system operation revenue model incorporating molten salt heat storage was established. This system enabled energy storage and off-peak utilization by coupling thermal power units with molten salt heat storage systems， thereby improving the absorption rate of renewable energy. The model considered the multi-level peak-shaving benefits and ramping cost of thermal power plants， the operating cost of energy storage systems， and the penalty cost for curtaining renewable energy. The model was optimized with the objective of maximizing net system operation revenue， exploring the impact of thermal power unit heat storage retrofitting on renewable energy absorption and system operating economics. After integration of the molten salt heat storage system， the overall economic efficiency and utilisation rate of renewable energy in the system were significantly improved. In scenario 2， the system's daily net revenue reached 8.381 4 million yuan， an increase of 473 600 yuan compared to scenario 1. The wind energy utilization rate in Scenario 2 increased from 93.91% to 97.33%， while the photovoltaic utilization rate increased from 95.51% to 98.44%.

Key words: new power system, wind-photovoltaic-thermal-storage, thermal power plant, molten salt heat storage, coupling characteristics, energy dispatch, multi-energy complementary system, absorption of renewable energy, peak-shaving

CLC Number:

TK01：TP23：TP29

LI Bo, CAO Yue, XU Jingyi, SI Fengqi. Energy dispatch of wind-photovoltaic-thermal-storage considering the coupling characteristics of thermal power and molten salt heat storage[J]. Integrated Intelligent Energy, 2024, 46(12): 55-63.

Figures/Tables 14

Fig.1

Fig.2

Table 1

Fig.3

Fig.4

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Table 2

Simulation conditions of the model

变量	取值	变量	取值
$k c o a l$ /[元·(kW·h)^-1]	0.35	$d 1$ /[元·(MW·h)^-1]	10
$γ T E S$ /[元·(kW·h)^-1]	0.05	$d 2$ /[元·(MW·h)^-1]	200
$c c o a l$ /(元·t^-1)	750	$d 3$ /[元·(MW·h)^-1]	600
$C N$ /台	1	$P m a x T E S$ /MW	70
$T N$ /台	1	$P m i n T E S$ /MW	-80
$L$ /%	12	$E m i n T E S$	0.1 $E m a x T E S$
$F$ /%	5	$E m a x T E S$ /(MW·h)	500
$G$ /%	5	( $P m a x d o w n$ /P_THA)/(%·min^-1)	1
( $P m a x u p$ /P_THA)/(%·min^-1)	1	$P m i n T N$ /MW	120
$P m i n C N$ /MW	180	a₂	0.26
$P m a x T N$ /MW	600	b₂	1.9×10^-5
$a 1$	0.28	c₂	11.83
$b 1$	1.7×10^-5	c₁	11.46

Table 2

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Fig.9

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Table 3

Table 4

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调峰区间	电负荷范围/MW	调峰负荷ΔP/MW	调峰补偿系数ζ/[元·（kW·h）^-1]
一档	[240,300）	（0，60]	0.01
二档	[180,240)	(60,120]	0.20
三档	[120,180)	(120,180]	0.60

机组类型	场景	平均发电煤耗/[g·(kW·h)^-1]	发电收益/（万元）	煤耗成本/（万元）	调峰收益/（万元）	净收益/（万元）	储能成本/（万元）
^#1 机组	1	339.44	166.21	64.48	25.77	127.50	0
^#1 机组	2	335.06	182.07	69.72	20.12	132.46	0
^#2 机组	1	302.30	302.29	104.44	13.49	211.34	0
^#2 机组	2	319.78	275.48	100.68	53.62	221.36	7.06
风光机组	1		504.77			475.09
风光机组	2		515.58			507.46

场景	风能利用率/%	光伏利用率/%	系统运行成本/（万元）	净收益/（万元）	可再生能源发电占比/%
1	93.91	95.51	221.75	790.78	51.86
2	97.33	98.44	200.61	838.14	52.98