Robust Optimal Scheduling Strategy for Virtual Power Plant Participation in Electric Energy and Demand Response Markets Under Multiple Uncertainties

doi:10.12096/j.2096-4528.pgt.23073

Abstract

Abstract:

Objectives To address the uncertainties of renewable energy output and load faced by virtual power plant (VPP) when participating in electric energy and demand response markets, a robust optimal scheduling strategy considering multiple uncertainties was proposed to reduce the conservativeness of robust optimization and improve the economic benefits of VPP. Methods A polyhedral uncertainty set based on conditional value at risk (CVaR) was constructed. On this basis, considering the uncertainties of wind power, photovoltaic output and load, a day-ahead two-stage robust optimization model of VPP participating in electric energy and demand response markets was established. Then, using a column-and-constraint generation (C&CG) algorithm and Lagrangian dual theory, the model was divided into a master problem and a sub-problem that can be solved by a solver. Finally, Monte Carlo method was used to generate a large number of wind power, photovoltaic and load data. The proposed strategy was simulated and analyzed, and compared with the optimization results of other schemes. Results The proposed strategy adopting a polyhedral uncertainty set based on CVaR can make full use of historical data. Compared with the scheme using traditional uncertainty set, the total cost of VPP is reduced by about 2%. Conclusions The proposed strategy can significantly reduce the conservativeness of robust optimization results and enhance the economy of VPP participation in the market under multiple uncertainties.

Key words: virtual power plant (VPP), wind power, photovoltaic, electric energy, demand response market, condition value at risk (CVaR), robust optimization, scheduling strategy

CLC Number:

TK 01

Yushen WANG, Haoyong CHEN, Yuxiang HUANG, Xiaobin WU, Yanjin ZHU, Jianbin ZHANG. Robust Optimal Scheduling Strategy for Virtual Power Plant Participation in Electric Energy and Demand Response Markets Under Multiple Uncertainties[J]. Power Generation Technology, 2024, 45(6): 1173-1185.

Figures/Tables 13

Fig. 1 Framework for VPP participation in electricity energy and demand response markets

Fig. 2 Topology of VPP

Fig. 3 Power output characteristics of wind turbines

Fig. 4 Output characteristics of photovoltaic units

Fig. 5 Load characteristics

Fig. 6 Day-ahead price

Tab. 1 Operating parameters of virtual power plant

项目	参数	数值
燃气轮机	$k g t$ /[元/(MW∙h)]	575
燃气轮机	$θ$ /元	485.9
储能系统	充电效率/%	95
	放电效率/%	95
	$k e s s$ /[元/(MW∙h)]	75
可转移负荷	$k D R$ /[元/(MW∙h)]	2 800
可转移负荷	$D D R$ /MW	2.96
置信水平	$β$	0.95

Tab. 1 Operating parameters of virtual power plant

项目	参数	数值
燃气轮机	$k g t$ /[元/(MW∙h)]	575
燃气轮机	$θ$ /元	485.9
储能系统	充电效率/%	95
	放电效率/%	95
	$k e s s$ /[元/(MW∙h)]	75
可转移负荷	$k D R$ /[元/(MW∙h)]	2 800
可转移负荷	$D D R$ /MW	2.96
置信水平	$β$	0.95

Fig. 7 Scheduling plan for energy storage in general

Fig. 8 Bidding programs for the markets

Fig. 9 Scheduling plan for distributed generation

Fig. 10 Scheduling plan for transferable load

Tab. 2 Comparison of optimization results under different schemes

方案	日前调度成本	实时平衡成本	合计
本文优化策略	68 315.12	3 134.76	71 449.88
方案1	89 213.34	3 425.75	92 639.09
方案2	85 406.13	3 672.13	89 078.26
方案3	70 016.41	2 841.54	72 857.95

Tab. 3 Costs of virtual power plant day-ahead dispatch at different confidence levels

置信水平	日前调度成本/元
1.00	70 016.41
0.98	69 423.74
0.95	68 315.12
0.90	67 653.14
0.85	66 846.68
0.80	66 012.76
0.75	65 782.33
0.70	64 678.78

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