Abstract
As distributed energy resources continue to be connected to the grid, the supply side and demand side of the power system are becoming increasingly uncertain in both directions. At the same time, many flexible loads have emerged. We can take advantage of their adjustable characteristics, which can be considered virtual storage to cut peaks and fill valleys for the grid. Data centers and buildings are gradually becoming a hot topic in recent years due to their substantial annual energy consumption. In this paper, we considered a flexible energy aggregator considering virtual energy storage. The cold energy from the data center and the heat energy from the ground source heat pump system(GSHP), i.e., the building, are incorporated as broad energy storage to participate in the aggregator’s dispatch. In this case, the data center(DC) and GSHP hybrid systems have outstanding performance. Then, a two-stage optimal scheduling strategy was used to minimize total cost, which includes day-ahead cost and real-time cost and determine appropriate real-time temperatures for data centers and buildings. We performed a detailed numerical comparison to prove the economy and validity of the proposed model.
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References
Cui, H.T., Li, F.X., Hu, Q.R., et al.: Day-ahead coordinated operation of utility-scale electricity and natural gas networks considering demand response based virtual power plants. Appl. Energy 176, 183–195 (2016)
Xu, Y., Hu, Q.R., Li, F.X.: Probabilistic model of payment cost minimization considering wind power and its uncertainty. IEEE Trans. Sustain. Energy 4(3), 716–724 (2013)
Peralta, D., Canizares, C.A., Bhattacharya, K.: Ground source heat pump modeling, operation, and participation in electricity markets. IEEE Trans. Smart Grid. 13(2), 1126–1138 (2022)
Radovanovic, A., Koningstein, R., Schneider, I., et al.: Carbon-aware computing for datacenters. IEEE Trans. Power Syst. 1–1 (2022)
Rao, L., Liu, X., Xie, L., et al.: Hedging against uncertainty: a tale of internet data center operations under smart grid environment. IEEE Trans. Smart Grid 2(3), 555–563 (2011)
Zhang, Y.W., Wang, Y.F., Wang, X.R., et al.: Capping the electricity cost of cloud-scale data centers with impacts on power markets. In: 20th international symposium on high performance distributed computing 271–272 (2011)
Wang, H., Huang, J.W., Lin, X.J., et al.: Proactive demand response for data centers: a win-win solution. IEEE Trans. Smart Grid 7(3), 1584–1596 (2016)
Huang, W.J., Zhang, X., Li, K.P., et al.: Resilience oriented planning of urban multi-energy systems with generalized energy storage sources. IEEE Trans. Power Syst. 37(4), 2906–2918 (2022)
Huang, W.J., Zhang, N., Yang, J.W., et al.: Optimal Configuration Planning of Multi-Energy Systems Considering Distributed Renewable Energy. IEEE Transactions on Smart Grid. 10(2), 1452–1464 (2019)
Zhu, X., Yang, J., Liu, Y., et al.: Optimal Scheduling Method for a Regional Integrated Energy System Considering Joint Virtual Energy Storage. IEEE Access. 7, 138260–138272 (2019)
Jing, W.L., Lai, C.H., Wong, S.H.W., et al.: Battery-supercapacitor hybrid energy storage system in standalone DC microgrids: a review. IET Renew. Power Gener. 11(4), 461–469 (2017)
Werth, A., Kitamura, N., Tanaka, K.: Conceptual study for open energy systems: distributed energy network using interconnected DC nanogrids. IEEE Trans. Smart Grid 6(4), 1621–1630 (2015)
Ding, H.H., Hu, Q.R., Hou, K., et al.: The coordinated operation of dual batteries energy storage system for cold areas. Energy Rep. 7, 84–91 (2021)
Ge, P.D., Hu, Q.R., Wu, Q.W., et al.: Increasing operational flexibility of integrated energy systems by introducing power to hydrogen. IET Renew. Power Gener. 14(3), 372–380 (2020)
Chen, T., Cui, Q.S., Gao, C.W., et al.: Optimal demand response strategy of commercial building-based virtual power plant using reinforcement learning. IET Gener. Transm. Distrib. 15(16), 2309–2318 (2021)
Acknowledgments
This work is supported by Key Research and Development Program of Jiangsu Province, China (BE2020081–2).
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Liang, Z., Ge, Z., Chen, S., Ding, H., Liang, Y., Hu, Q. (2023). Optimal Dispatch Strategy of a Flexible Energy Aggregator Considering Virtual Energy Storage. In: Sun, F., Yang, Q., Dahlquist, E., Xiong, R. (eds) The Proceedings of the 5th International Conference on Energy Storage and Intelligent Vehicles (ICEIV 2022). ICEIV 2022. Lecture Notes in Electrical Engineering, vol 1016. Springer, Singapore. https://doi.org/10.1007/978-981-99-1027-4_55
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DOI: https://doi.org/10.1007/978-981-99-1027-4_55
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