In the context of demand response (DR), formulating rational electricity pricing (EP) and electricity pricing subsidy (EPS) strategies is crucial for the power grid when dealing with a high electricity user (EU), particularly an electrolytic aluminum enterprise (EAE) in an industrial park (IP). In addition, it is difficult to assess the response effectiveness of EU. This paper proposes a method to assess demand response willingness (DRW) by introducing indicators such as demand response economy and demand response potential, while taking into account carbon emission deviation. Then, the EPS is formulated based on the result of the DRW assessment. Second, this paper establishes a two-layer electricity supplier (ES)-EAE game model, in which the ES operates as the leader and EAE operates as the follower. The model takes into account the fluctuation and deviation of loads, constructs utility functions for both the leader and follower, selects dynamic EP scenarios at different time scales, and employs a large-scale global optimization particle swarm algorithm based on cooperative evolution for solving. Finally, the model's effectiveness is validated under three electricity pricing strategies: peak-valley pricing, critical peak pricing (CPP), and real-time pricing (RTP). According to the result of simulations, under the RTP strategy, the DRW of EAE has increased by 12.5% compared to the CPP strategy, and the DR load has increased by 82%. Additionally, there has been some reduction in costs of electricity consumption. This indicates that the ES can effectively guide the EU to reduce peak loads through EP, and the EU can also achieve a reasonable reduction in electricity costs.

1.
Y.
Rao
,
X.
Cui
,
X.
Zou
et al, “
Research on distributed energy storage planning-scheduling strategy of regional power grid considering demand response
,”
Sustainability
15
,
14540
(
2023
).
2.
Y.
Pezhmani
,
M. Z.
Oskouei
,
N.
Rezaei
, and
H.
Mehrjerdi
,
Sustainable Energy Grids Networks
31
,
100751
(
2022
).
3.
M.
Mohammadi
,
Y.
Noorollahi
, and
B.
Mohammadi-ivatloo
,
Sustainable Energy Technol. Assess.
37
,
100602
(
2020
).
4.
W.
Sun
,
H.
Song
,
Y.
Qin
et al, “
Energy storage system optimal allocation considering flexibility supply and demand uncertainty
,”
Power Syst. Technol.
44
,
4486
4497
(
2020
).
5.
Y.
Qi
,
S.
Ma
,
F.
Jin
et al, “
Optimal dispatch of concentrating solar thermal power (CSP)-wind combined power generation system
,”
J. Phys.: Conf. Ser.
2636
(
1
),
012045
(
2023
).
6.
H.
Liang
,
H.
Zhang
,
X.
Zhang
et al, “
Role of demand response in the decarbonization of China's power system
,”
Environ. Impact Assess. Rev.
104
,
107313
(
2024
).
7.
W.
Zhou
,
C.
Xu
,
D.
Yang
et al, “
Research on demand response strategy of electric vehicle group considering dynamic adjustment of willingness under P2P energy sharing
,” in
Proceedings of the CSEE
,
2023
.
8.
Z.
Guo
,
W.
Wei
,
L.
Chen
et al, “
Impact of energy storage on renewable energy utilization: A geometric description
,”
IEEE Trans. Sustainable Energy
12
(
2
),
874
885
(
2021
).
9.
L.
Ning
,
K.
Liang
,
B.
Zhang
et al, “
A two-layer optimal scheduling method for multi-energy virtual power plant with source-load synergy
,”
Energy Rep.
10
,
4751
4760
(
2023
).
10.
Y.
Qiu
,
Q.
Zhong
,
L.
Wang
et al, “
Multi-objective optimal sizing for grid-connected LVDC system with consideration of demand response of electric vehicles
,”
Electr. Power Syst. Res.
228
,
109991
(
2024
).
11.
S.
Bouckaert
,
V.
Mazauric
, and
N.
Maïzi
, “
Expanding renewable energy by implementing demand response
,”
Energy Procedia
61
,
1844
1847
(
2014
).
12.
P.
Nie
,
L.
Chen
, and
M.
Fukushima
, “
Dynamic programming approach to discrete time dynamic feedback Stackelberg games with independent and dependent followers
,”
Eur. J. Oper. Res.
169
(
1
),
310
328
(
2006
).
13.
Ö.
Erol
and
Ü.
Başaran Filik
, “
A Stackelberg game-based dynamic pricing and robust optimization strategy for microgrid operations
,”
Int. J. Electr. Power Energy Syst.
155
,
109574
(
2024
).
14.
N.
Liu
,
X.
Yu
,
C.
Wang
, and
J.
Wang
, “
Energy sharing management for microgrids with PV prosumers: A Stackelberg game approach
,”
IEEE Trans. Ind. Inf.
13
(
3
),
1088
1098
(
2017
).
15.
Y.
Zhang
,
H.
Zhao
,
B.
Li
, and
X.
Wang
, “
Research on dynamic pricing and operation optimization strategy of integrated energy system based on Stackelberg game
,”
Int. J. Electr. Power Energy Syst.
143
,
108446
(
2022
).
16.
Y.
Wan
,
J.
Qin
,
Y.
Shi
et al, “
Stackelberg–Nash game approach for price-based demand response in retail electricity trading
,”
Int. J. Electr. Power Energy Syst.
155
,
109577
(
2024
).
17.
N.
Liu
,
L.
Zhou
,
C.
Wang
et al, “
Heat-electricity coupled peak load shifting for multi-energy industrial parks: A Stackelberg game approach
,”
IEEE Trans. Sustainable Energy
11
(
3
),
1858
1869
(
2020
).
18.
S.
Yan
,
W.
Wang
,
X.
Li
et al, “
Stochastic optimal scheduling strategy of cross-regional carbon emissions trading and green certificate trading market based on Stackelberg game
,”
Renewable Energy
219
(
1
),
119268
(
2023
).
19.
M.
Samadi
,
S.
Ruj
,
H.
Schriemer
et al, “
Secure and robust demand response using Stackelberg game model and energy blockchain
,”
Sensors
23
(
20
),
8352
(
2023
).
20.
M.
Yu
and
S. H.
Hong
, “
Incentive-based demand response considering hierarchical electricity market: A Stackelberg game approach
,”
Appl. Energy
203
,
267
279
(
2017
).
21.
R.
Lu
and
S. H.
Hong
, “
Incentive-based demand response for smart grid with reinforcement learning and deep neural network
,”
Appl. Energy
236
,
937
949
(
2019
).
22.
B.
Xu
,
X.
Wang
,
M.
Guo
et al, “
A hybrid demand response mechanism based on real-time incentive and real-time pricing
,”
Energy
231
,
120940
(
2021
).
23.
H.
Zhang
,
J.
Li
,
Y.
Xu
et al, “
The economic relationship between DC power consumption and current efficiency in aluminum reduction
,”
Light Met.
12
,
1
3
(
2014
).
24.
X.
Tian
,
Y.
Chen
,
Z.
Liu
et al, “
Potential assessment of industrial load participating in capacity market under new power system
,”
Energy Conserv. Technol.
40
(
4
),
310
314
(
2022
).
25.
M.
Starke
,
N.
Alkadi
, and
O.
Ma
, “
Assessment of industrial load for demand response across U.S. Regions of the Western Interconnect
,”
Report No. ORNL/TM-2013/407
(
Oak Ridge National Laboratory Report
,
2013
).
26.
J.
Xu
,
S. Y.
Liao
,
Y. Z.
Sun
et al, “
An isolated industrial power system driven by wind-coal power for aluminum productions: A case study of frequency control
,”
IEEE Trans. Power Syst.
30
(
1
),
471
483
(
2015
).
27.
R.
Tang
,
Z.
Wu
, and
Y.
Fang
, “
Adaptive multi-context cooperatively coevolving particle swarm optimization for large-scale problems
,”
Soft Comput.
21
,
4735
4754
(
2017
).
You do not currently have access to this content.