Resilience Enhancement Strategy of Combined Heat and Power-Virtual Power Plant Considering Thermal Inertia

doi:10.12096/j.2096-4528.pgt.23097

Abstract

Abstract:

The increasingly frequent extreme weather has a more serious impact on the electro-thermal coupling system. Resilience is a core indicator that measures the system’s ability to withstand extreme events, reduce the impact of failures,and recover quickly. To enhance the ability of the electro-thermal coupling system to withstand extreme disasters,a two-stage three-layer resilience enhancement strategy of combined heat and power-virtual power plant (CHP-VPP) considering thermal inertia was proposed. In the first stage, the system was reconstructed based on the minimum spanning tree theory with the goal of minimizing the cost of tie switches. In the second stage,aiming at minimizing the operating cost,the optimal decisions under the worst failure scenario was formulated based on distributionally robust optimization theory. The column and constraint generation algorithm was used for iterative solutions. A CHP-VPP test system was built based on an IEEE 33-bus system and a 6-node thermal system. The simulation results show that the proposed method can effectively enhance the resilience of CHP-VPP to cope with extreme disasters.

Key words: combined heat and power-virtual power plant (CHP-VPP), thermal inertia, distributionally robust, resilience enhancement, extreme disaster, electro-thermal coupling system

CLC Number:

TK 01

Songyuan YU, Junsong ZHANG, Zhiwei YUAN, Fang FANG. Resilience Enhancement Strategy of Combined Heat and Power-Virtual Power Plant Considering Thermal Inertia[J]. Power Generation Technology, 2023, 44(6): 758-768.

Figures/Tables 11

Fig. 1 Resilience enhancement strategy of CHP-VPP

Fig. 2 CHP-VPP test system framework

Fig. 3 Electric load, heat load, wind output, solar output and outdoor temperature

Fig. 4 State of interconnection switch based on the distributionally robust optimization

Fig. 5 CHP, HP and battery output based on distributionally robust optimization

Fig. 6 Conventional units output based on the distributionally robust optimization

Fig. 7 Partial enlarged view of power nodes

Tab. 1 Impact of thermal inertia on the cost of CHP-VPP

热惯性	CHP运行成本/元	HP运行成本/元	切负荷成本/元	总成本/元	弃风光率/%
有	4 516	556	25 523	30 595	5.02
无	4 723	603	32 132	37 458	12.62

Tab. 2 Total cost of the system at different confidence levels

$θ 1$	$θ ∞$
$θ 1$	0.5	0.8	0.99
0.2	28 923	29 035	29 381
0.4	28 927	29 119	29 572
0.6	29 012	29 290	29 966
0.8	29 014	29 317	30 923

Tab. 2 Total cost of the system at different confidence levels

$θ 1$	$θ ∞$
$θ 1$	0.5	0.8	0.99
0.2	28 923	29 035	29 381
0.4	28 927	29 119	29 572
0.6	29 012	29 290	29 966
0.8	29 014	29 317	30 923

Fig. 8 State of interconnection switch based on deterministic optimization method

Tab. 3 Unit operating costs and load shedding costs under three extreme scenarios

方法	故障线路	成本/元
方法	故障线路	CHP运行	HP运行	切负荷	总计
分布鲁棒优化	7—8	3 823	582	0	4 405
	16—17	4 516	556	25 523	30 595
	3—4	4 021	609	0	4 630
确定性优化	7—8	3 547	436	12 184	12 582
	16—17	4 399	624	37 347	37 849
	3—4	3 547	436	12 184	12 582

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