Abstract
In the near future, Smart-Grid technologies will have an incredible impact on the economics of power systems and on environment. This will be possible thanks to the intelligent communication and computer systems, which would allow the system to accommodate much more energy from renewables by combining different technologies for energy storage, electric vehicles and demand response. The main contribution of this paper is the development of models for the different components of the Smart-Grid, which can be easily generalized for many different studies. The modelling framework includes energy storage, renewable energy sources, electric vehicles and demand response. We use the problem of distribution grid power balancing to illustrate the application of the models for improving the economic performance of a balancing group. The problem is formulated as a mixed-integer linear program and can help energy companies and custumers to make investment decisions for smart-grid.
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References
Dyson, M., Mandel, J.: The Economics of Demand Flexibility: How “flexiwatts” create quantifiable value for customers and the grid. Rocky Mountain Institute, Boulder (2015)
Battegay, A., Hadj-Saida, N., Roupiozd, G., Lhote, F.: Impacts of direct load control on reinforcement costs in distribution networks. Electr. Power Syst. Res. 150, 70–79 (2015)
Zhong, H., Xia, Q., Xia, Y., Kang, C.: Integrated dispatch of generation and load: A pathway towards smartgrids. Electr. Power Syst. Res. 120, 206–213 (2015)
Wen, Z., O’Neill, D., Maei, H.: Optimal demand response using device-based reinforcement learning. IEEE Trans. Smart Grid 8, 2312–2324 (2015)
Althaher, S., Mancarella, P., Mutale, J.: Automated demand response from home energy management system under dynamic pricing and power and comfort constraints. IEEE Trans. Smart Grid 6, 1874–1883 (2015)
Belhaiza, S., Baroudi, U.: A game theoretic model for smart grids demand management. IEEE Trans. Smart Grid 6, 1386–1393 (2015)
Li, N., Chen, L., Dahleh, M.A.: Demand response using linear supply function bidding. IEEE Trans. Smart Grid 6, 1827–1838 (2015)
Wei, W., Liu, F., Mei, S.: Energy pricing and dispatch for smart grid retailers under demand response and market price uncertainty. IEEE Trans. Smart Grid 6, 1364–1374 (2015)
Chen, Z., Wu, L., Fu, Y.: Real-Time price-based demand response management for residential appliances via stochastic optimization and robust optimization. IEEE Trans. Smart Grid 3, 1822–1831 (2012)
Zugno, M., Morales, J.M., Pinson, P., Madsen, H.: A bilevel model for electricity retailers’ participation in a demand response market environment. Energy Econ. 36, 182–197 (2013)
Acknowledgements
This paper is a results from project “Study of the electric power system stability and frequency control at a predominant share of renewable energy generation” grant ДН07/27/15.12.2016 from the Bulgarian National Science Fund.
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Nikolaev, N., Yordanov, S., Vasilev, R. (2018). An Optimization Algorithm for Simulating Smart-Grid Means for Distribution Grid Balancing. In: Abraham, A., Kovalev, S., Tarassov, V., Snasel, V., Vasileva, M., Sukhanov, A. (eds) Proceedings of the Second International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’17). IITI 2017. Advances in Intelligent Systems and Computing, vol 679. Springer, Cham. https://doi.org/10.1007/978-3-319-68321-8_37
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DOI: https://doi.org/10.1007/978-3-319-68321-8_37
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