Study on Robust Chance Constrained Optimization of Multi-energy Supply System Based on Wind and Solar Power Combined Output Simulation

doi:10.16182/j.issn1004731x.joss.23-0848

Abstract

Abstract:

In order to effectively avoid potential imbalance between supply and demand caused by the uncertainty of wind and solar power outputs, and promote the sustained development of the multi-energy supply system, a robust chance-constrained optimization model is developed for identifying optimal operation strategies under complexities and uncertainties through incorporating Copula theory, chance-constrained programming, and robust programming within a general framework. The results show that this model can not only accurately characterize the distribution probability of combined outputs of wind and solar power and formulate the operational strategies under low default risk conditions, but also reduce the proportion of high-risk energy output by adjusting the energy output structure on the supply side, and generate a robust operation scheme for the multi-energy supply system, which significantly improves the stability of the system operation, and reduces the economic risks caused by the uncertainty of wind and light.

Key words: multi-energy supply system, uncertainty, Copula theory, chance-constrained programming, robust optimization

CLC Number:

TK01

Bao Zhe, Li Wei, Zhang Xiaofang, An Zongyuan, Xu Ye. Study on Robust Chance Constrained Optimization of Multi-energy Supply System Based on Wind and Solar Power Combined Output Simulation[J]. Journal of System Simulation, 2024, 36(8): 1895-1913.

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主要经济性参数	值
燃气轮机维护成本单价/(元/kWh)	0.03
吸收式制冷机组维护成本单价/(元/MJ)	0.000 2
电锅炉维护成本单价/(元/MJ)	0.000 6
电制冷机组维护成本单价/(元/MJ)	0.000 5
弃风、弃光成本/(元/kW)	0.2
风、光出力违约成本/(元/kW)	0.4
天然气价格/(元/m³)	2.12
主要工程参数	值
燃气轮机额定功率/MW	2.5
光伏额定功率/MW	1
风电额定功率/MW	2
储能设备额定功率/MW	1
储能设备出力系数	0.4
余热锅炉效率	0.776
电锅炉效率	0.95
天然气低位发热量/(MJ/m³)	35.175
吸收式制冷机组制冷系数	1.2
电制冷机组制冷系数	3.6

典型日	数据类别	Kendall	Spearman	典型日	数据类别	Kendall	Spearman	典型日	数据类别	Kendall
过渡季	原始数据	0.052	0.076	夏季	原始数据	0.038	0.055	冬季	原始数据	0.258
	Clayton	0.057	0.086		Clayton	0.015	0.022		0.372	Clayton
	Frank	0.053	0.079		Frank	0.038	0.057		0.212	0.312
	Gumbel	0.046	0.069		Gumbel	0.050	0.075		Frank	0.252

经济性指标	过渡季典型日			夏季典型日			冬季典型日
经济性指标	情景I	情景II	情景III	情景I	情景II	情景III	情景I	情景II	情景III
优化运行成本	42 713	43 909	51 949	63 148	64 365	69 377	93 560	95 390	104 760
优化不确定性风险成本	1 167	1 152	548	613	593	419	1 678	1 073	297
随机模拟不确定性风险最小成本	1 829	1 804	260	778	750	356	1 992	1 383	116
随机模拟不确定性风险平均成本	2 160	2 130	347	942	907	293	2 306	1 692	176
随机模拟不确定性风险最大成本	2 591	2 554	414	1 106	1 074	230	2 620	2 005	237
最小风险成本	662	652	-288	164	157	-188	314	309	-182
平均风险成本	993	978	-201	329	314	-125	628	618	-121
最大风险成本	1 424	1 402	-134	493	481	-63	942	932	-61

违约水平	经济性指标	过渡季典型日			夏季典型日			冬季典型日
违约水平	经济性指标	情景I	情景II	情景III	情景I	情景II	情景III	情景I	情景II	情景III
0.01~0.1	运行成本	42 713	43 909	51 949	63 148	64 365	69 377	93 560	95 390	104 760
0.01~0.1	不确定性风险成本	1 167	1 152	548	613	593	419	1 678	1 073	297
0.05~0.15	运行成本	42 145	42 847	50 251	62 298	63 730	66 870	92 162	94 841	101 832
0.05~0.15	不确定性风险成本	1 132	1 156	656	631	602	510	1 896	1 332	580
0.2~0.5	运行成本	37 674	38 268	41 035	55 452	56 262	60 767	89 131	90 124	91 131
0.2~0.5	不确定性风险成本	3 267	3 123	1 341	928	879	689	3 890	3 675	2 364

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