Virtual power plant (VPP) with a high percentage of flexibility resources has issues that need to be addressed, such as high source-load volatility and limited scope to participate in multi-market bids. Therefore, this paper proposes a VPP standby capacity setting method based on normal distribution framework and Bayesian parameter optimization. Through the marginal revenue and expenditure of standby capacity analysis, this paper constructs a two-stage optimization strategy for VPP trading in multi-market considering double uncertainty, which is solved by the Improved Multi-Objective Squirrel Search Algorithm (IMSSA). Compared to the traditional program, the VPP's participation in the day-ahead spot bidding increased by 5.97% and 2.48%, respectively, total revenue increased by 17.41% and 12.97%, respectively, reliability increased by 0.21%, and overall energy efficiency increased by 10%. Compared to Squirrel Search Algorithm and Particle Swarm Optimization Algorithm, IMSSA improves the optimal revenue by 1.03% and 1.91%, and the convergence speed by 24.24% and 38.01%, respectively.

1.
L.
Peng
,
W.
Jiahao
,
L.
Canbing
et al, “
Co-optimization of integrated energy systems in parks taking into account source-load uncertainty and variable operating conditions of equipment
,”
Chin. J. Electr. Eng.
43
(
20
),
7802
7812
(
2023
).
2.
L.
Wang
,
L.
Dong
,
W.
Yufeng
et al, “
Low-carbon economic dispatch of power system considering source-load uncertainty and user response behavior
,”
Chin. J. Electr. Eng.
44
(
03
),
905
918
(
2024
).
3.
L.
Yan
,
L.
Jingyuan
,
W.
Da
et al, “
Master-slave game optimization strategy for multi-microgrid market considering source-load uncertainty and multiple energy storage sharing
,”
J. Power Syst. Autom.
36
(
11
),
121
129+137
(
2024
).
4.
R.
Hao
,
T.
Lu
,
Q.
Ai
et al, “
Distributed online learning and dynamic robust standby dispatch for networked microgrids
,”
Appl. Energy
274
,
115256
(
2020
).
5.
C.
Zhi
,
D.
Qiang
,
C.
Hui
et al, “
Sub-provincial reserve capacity retention scheme for regional power grids (II):an optimization model
,”
Chin. J. Electr. Eng.
43
(
03
),
1037
1048
(
2023
).
6.
G.
Chong
,
D.
Yao
,
C.
La
et al, “
Cooperative optimal scheduling method of active distribution network considering dispatchable backup energy storage and intelligent soft switch for 5G base station
,”
J. Shanghai Jiao Tong Univ.
(published online) (2024).
7.
Q.
Li
,
D.
Mo
,
X.
Kong
et al, “
Intelligent optimal scheduling strategy of IES with considering the multiple flexible loads
,”
Energy Rep.
9
,
1983
1994
(
2023
).
8.
J.
Xu
and
Y.
Yi
, “
Multi-microgrid low-carbon economy operation strategy considering both source and load uncertainty: A Nash bargaining approach
,”
Energy
263
,
125712
(
2023
).
9.
C.
Huilai
,
Z.
Haibo
, and
W.
Zhaolin
, “
A review of research on parameter aggregation algorithms for different types of virtual power plant markets and scheduling characteristics
,”
Chin. J. Electr. Eng.
43
(
01
),
15
28
(
2023
).
10.
Z.
Yizhou
,
W.
Junzhao
,
S.
Guoqiang
et al, “
A two-stage robust trading strategy for virtual power plants oriented to multilevel coupled electricity-carbon markets
,”
Power Syst. Autom.
48(18), 38–46 (2024).
11.
S.
Zhengyu
,
Z.
Kaixiang
,
W.
Can
et al, “
A bidding strategy for virtual power plant day-ahead electricity market considering carbon trading
,”
Power Eng. Technol.
43(05) 58–68+149 (2024).
12.
P.
Chaoyi
,
X.
Suyue
,
G.
Huijie
et al, “
Research on bidding strategy of virtual power plant participating in multi-competitive market based on master-slave game
,”
Power Syst. Prot. Control
52
(
07
),
125
137
(
2024
).
13.
N. T.
Kalantari
,
A.
Abdolahi
,
S. H.
Mousavi
et al, “
Strategic decision making of energy storage owned virtual power plant in day-ahead and intra-day markets
,”
J. Energy Storage
73
,
108839
(
2023
).
14.
J.
Liu
,
S. S.
Yu
,
H.
Hu
et al, “
A combinatorial auction energy trading approach for VPPs consisting of interconnected microgrids in demand-side ancillary services market
,”
Electr. Power Syst. Res.
224
,
109694
(
2023
).
15.
Y.
Dongmin
,
W.
Xiaopeng
,
S.
Qinfei
et al, “
Multi-objective multi-timescale optimal scheduling based on VPP carbon flow calculation
,”
Intell. Power
52
(
01
),
30
38
(
2024
).
16.
Z.
Chong
,
K. Y.
Ping
,
Y. S.
Hai
et al, “
Multi-objective optimal operation of virtual power plant in distribution network based on improved AUGMECON
,”
Electr. Demand Side Manage.
26
(
01
),
42
47
(
2024
).
17.
H.
Jianhong
,
H.
Ting
,
X.
Qiuming
et al, “
Multi-objective optimal scheduling strategy for virtual power plants containing multiple energy suppliers
,”
Electr. Supply Use
40
(
12
),
32
42
(
2023
).
18.
I. M. A. N.
Taheri S
,
M.
Davoodi
, and
M.
Hasan Ali
, “
A modified modeling approach of virtual power plant via improved federated learning
,”
Int. J. Electr. Power Energy Syst.
158
,
109905
(
2024
).
19.
H.
Wei
,
W.
Wang
, and
X.
Kao
, “
A novel approach to hybrid dynamic environmental-economic dispatch of multi-energy complementary virtual power plant considering renewable energy generation uncertainty and demand response
,”
Renewable Energy
219
,
119406
(
2023
).
20.
X.
Yan
,
C.
Gao
,
J.
Meng
et al, “
An analytical target cascading method-based two-step distributed optimization strategy for energy sharing in a virtual power plant
,”
Renewable Energy
222
,
119917
(
2024
).
21.
P.
Sulc
,
S.
Backhaus
et al, “
Optimal distributed control of reactive power via the alternating direction method of multipliers
,”
IEEE Trans. Energy Convers.
29
(
4
),
968
977
(
2014
).
22.
L.
Meng
,
X.
Yang
,
J.
Zhu
et al, “
Network partition and distributed voltage coordination control strategy of active distribution network system considering photovoltaic uncertainty
,”
Appl. Energy
362
,
122846
(
2024
).
23.
H.
Nantian
,
C.
Qingzhu
,
C.
Guowei
et al, “
Fault diagnosis of bearing in wind turbine gearbox under actual operating conditions driven by limited data with noise labels
,”
IEEE Trans. Instrum. Meas.
70
,
1
10
(
2021
).
24.
H.
Shuai
,
H.
Mingming
, and
L.
Yuejun
, “
Seismic performance analysis of a wind turbine with a monopile foundation affected by sea ice based on a simple numerical method
,”
Eng. Appl. Comput. Fluid Mech.
15
(
1
),
1113
1133
(
2021
).
25.
J.
Li
,
Y.
Xiao
, and
S.
Lu
, “
Optimal configuration of multi microgrid electric hydrogen hybrid energy storage capacity based on distributed robustness
,”
J. Energy Storage
76
,
109762
(
2024
).
26.
M. A.
Ortega-Vazquez
and
D. S.
Kirschen
, “
Estimating the spinning reserve requirements in systems with significant wind power generation penetration
,”
IEEE Trans. Power Syst.
24
(
1
),
114
124
(
2009
).
27.
G.
Liu
and
K.
Tomsovic
, “
Quantifying spinning reserve in systems with significant wind power penetration
,”
IEEE Trans. Power Syst.
27
(
4
),
2385
2393
(
2012
).
28.
S.
Zhao
,
Y.
Fang
, and
Z.
Wei
, “
Stochastic optimal dispatch of integrating concentrating solar power plants with wind farms
,”
Int. J. Electr. Power Energy Syst.
109
,
575
583
(
2019
).
29.
Y.
Liu
,
C.
Jiang
,
J.
Shen
et al, “
Cost allocation of spinning reserve based on risk contribution
,”
IEE J. Trans. Electr. Electron. Eng.
10
(
6
),
664
673
(
2015
).
30.
N.
Srinivas
, “
Information-theoretic regret bounds for gaussian process optimization in the bandit setting
,”
IEEE Trans. Inf. Theory
58
(
5
),
3250
3265
(
2012
).
31.
Y.
Wang
,
L.
Guo
,
Y.
Wang
et al, “
Bi-level programming optimization method of rural integrated energy system based on coupling coordination degree of energy equipment
,”
Energy
298
,
131289
(
2024
).
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