IEEE Power & Energy Magazine - July/August 2021 - 73

1) The precondition for implementing cloud-based energy
storage is that there must be a stable and long-lasting
policy or market that can fully realize economic
value for power systems. First, in recent years, with
grid flexibility becoming increasingly needed, several
policies have been introduced in China to encourage
investment in flexible resources, e.g., creating special
subsidies and organizing (relatively) simple markets.
However, such policies are not long-term solutions.
Second, storage brings multiple benefits, e.g., peak
shaving, frequency regulation, congestion management,
and reliability improvements. In most situations,
only one or two kinds of benefits receive the
right supporting policy for them to be realized. Others
are unpaid, hence making it difficult for stakeholders
to fully recover investments in storage resources. At
present, the most mature business model for cloudbased
energy storage is for a grid company to directly
aggregate distributed storage resources.
2) The cybersecurity aspects of distributed energy storage
aggregation need to be addressed. In China, the
power system uses dedicated communication networks
that are separate from public ones. But for distributed
energy, the required communication cannot
avoid the use of public networks because of the expense
to build private systems for each small-scale
resource. In terms of cybersecurity requirements,
then a cloud-based energy storage operator usually
acts as a protective screen between distributed
resources and a power system operator. For cloudbased
energy storage to seamlessly become part of
business as usual, addressing the cybersecurity problem
is therefore critical.
3) The business model for cloud-based energy storage
that serves small consumers, such as wind farms and
factories, still needs further research and trials. There
are huge needs for storage for such consumers, e.g., to
deal with wind farm forecast errors and reduce energy
bills. The cloud-based energy storage concept can
provide services at a much lower cost than installing
dedicated facilities. Therefore, there is great market
potential. However, several key business model issues
need to be addressed. How do we set the customized
storage price for each consumer, according to its demand
pattern? How do we develop a storage " sharing
benefit " scheme among different distributed energy
resources, cloud-based energy storage operators, grid
companies, and consumers? How do we motivate distributed
energy resources to provide reliable service?
Acknowledgments
This work was supported in part by the S&T Major Project
of the Inner Mongolia Autonomous Region of China (grant
2020ZD0018), International (Regional) Joint Research Projjuly/august
2021
ect of the National Natural Science Foundation of China
(grant 52061635101), and Tsinghua University Initiative Scientific
Research Program (grant 20193080026).
For Further Reading
J. Yang, N. Zhang, A. Botterud, and C. Kang, " On an equivalent
representation of the dynamics in district heating networks
for combined electricity-heat operation, " IEEE Trans.
Power Syst., vol. 35, no. 1, pp. 560-570, Jan. 2020. doi:
10.1109/TPWRS.2019.2935748.
J. Liu, N. Zhang, C. Kang, D. S. Kirschen, and Q. Xia,
" Cloud energy storage for residential and small commercial
consumers: A business case study, " Appl. Energy, vol. 188, pp.
226-236, Feb. 2017. doi: 10.1016/j.apenergy.2016.11.120.
M. Yazdani-Damavandi, N. Neyestani, G. Chicco, M.
Shafie-Khah, and J. P. S. Catalao, " Aggregation of distributed
energy resources under the concept of multi-energy
players in local energy systems, " IEEE Trans. Sustain.
Energy, vol. 8, no. 4, pp. 1679-1693, 2017. doi: 10.1109/
TSTE.2017.2701836.
S. Mhanna, A. C. Chapman, and G. Verbic, " A fast distributed
algorithm for large-scale demand response aggregation, "
IEEE Trans. Smart Grid, vol. 7, no. 4, pp. 2094-2107,
2016. doi: 10.1109/TSG.2016.2536740.
F. L. Muller, J. Szabo, O. Sundstrom, and J. Lygeros, " Aggregation
and disaggregation of energetic flexibility from distributed
energy resources, " IEEE Trans. Smart Grid, vol. 10, no.
2, pp. 1205-1214, 2019. doi: 10.1109/TSG.2017.2761439.
Biographies
Ning Zhang is with the State Key Laboratory of Power Systems,
Department of Electrical Engineering, Tsinghua University,
Beijing, 100084, China.
Haiyang Jiang is with the State Key Laboratory of Power
Systems, Department of Electrical Engineering, Tsinghua
University, Beijing, 100084, China.
Yaowang Li is with the State Key Laboratory of Power
Systems, Department of Electrical Engineering, Tsinghua
University, Beijing, 100084, China.
Pei Yong is with the State Key Laboratory of Power Systems,
Department of Electrical Engineering, Tsinghua University,
Beijing, 100084, China.
Mingxuan Li is with the State Key Laboratory of Power
Systems, Department of Electrical Engineering, Tsinghua
University, Beijing, 100084, China.
Huan Zhu is with State Grid Jiangsu Electric Power,
Nanjing, 210024, China.
Song Ci is with the State Key Laboratory of Power Systems,
Department of Electrical Engineering, Tsinghua University,
Beijing, 100084, China.
Chongqing Kang is with the State Key Laboratory of
Power Systems, Department of Electrical Engineering, Tsinghua
University, Beijing, 100084, China.
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