###### Electric Vehicles as Electricity Storages in Electric Power Systems

electric utilities.” Transportation Research Part D: Transport and Environment vol. 2, pp. 157–175 (1997). DOI: 10.1016/S1361-9209(97)00001-1. 4. AF-Mercados EMI (Energy Market International): About electricity markets. https://www.esmap.org/sites/esmap.org/files/Session1-About%20Electricity%20Markets.pdf (2011). Accessed 31 January 2017 5. Guille, C., & Gross, G.: A conceptual framework for the vehicle-to-grid (V2G) implementation. Energy Policy 37, 4379–4390 (2009). DOI: 10.1016/j.enpol.2009.05.053 6. Ela, E., Milligan, M., Kirby, B.: Operating

###### Utilization of Electric Vehicles Connected to Distribution Substations for Peak Shaving of Utility Network Loads

## Abstract

Use of modern electric vehicles and their effective integration into power grids depends on the technologies applied around distribution substations. Distribution substations equipped with energy storing and V2G capability enable peak load shaving and demand response, which will reduce the need to make new investments into building new power sources or power grids to meet peak demand. This paper presents a distribution substation topology for utilizing electric vehicles as energy resource units for peak shaving of utility network loads. The topology allows bidirectional energy exchange among electric vehicles, battery pack energy storage devices and utility networks. The substation acts as a service provider in a microgrid. Functions of a microgrid application were simulated with MATLAB. The evaluation of the results has shown that electric vehicles can be effectively utilized for peak shaving of utility network loads. The results of the modelling and simulation were used for the development of a microgrid prototype. Assessment of capacities of electric vehicles showed that electric vehicles can provide short term support for the utility network.

###### The Role of the Latvian District Heating System in the Development of Sustainable Energy Supply

. Integration of Renewable Energy into the Transportation and Electricity Sectors through V2G// Energy Policy.-2008.-36(9).-3578-3587 Lund, H., Salgi, G. The Role of Compressed Air Energy Storage (CAES) in Future Sustainable Energy Systems// Energy Conversion and Management.-2009.-50(5).-1172-1179 Liu, W., Lund, H., Mathiesen, B. V. Large-scale integration of wind power into the existing Chinese energy system// Energy.-2011.-article in press Lund, H., Mathiesen, B. V. Energy system analysis of

###### Effective business models for electric vehicles

). Integration of renewable energy into the transport and electricity sectors through V2G, Energy Policy, 36 (9), 3578-3587. Popper, K., (1934). Logica cercetării”, Editura Științifică, București, 1981.

###### A spatially explicit assessment of middle and low voltage grid requirements in Bavaria until 2050

://bookshop.eu-ropa.eu/uri?target=EUB:NOTICE:LBNA27019:EN:HTML > Campana PE, Quan SJ, Robbio FI, Lundblad A, Zhang Y, Ma T, Yan J (2016) Spatial Optimization of Residential Urban District—Energy and Water Perspectives. Energy Procedia 88: 38–43. Child M, Nordling A, Breyer C (2018) The Impacts of High V2G Participation in a 100% Renewable Åland Energy System. Energies 11(9): 2206. Dijkstra L, Poelman H (2014) A harmonised definition of cities and rural areas: The new degree of urbanisation. 28. GDAL Development Team (2016) GDAL-Geospatial Data Abstraction Library. Available from:< http

###### Research and Application of Rule Updating Mining Algorithm for Marine Water Quality Monitoring Data

Performance Evaluation using Genetic Algorithm, IEEE Latin America Transactions, Vol. 13, No. 10, pp. 3439-3446, 2015. 14. K. Thirugnanam, M. Singh, and P. Kumar, Mathematical Modeling of Li-Ion Battery using Genetic Algorithm Approach for V2G Applications, IEEE Transactions on Energy Conversion, Vol. 29, No. 2, pp. 332-343, 2014. 15. X. K. Wei, W. Shao, and C. Zhang, Improved Self-Adaptive Genetic Algorithm with Quantum Scheme for Electromagnetic Optimisation, IET Microwaves, Antennas and Propagation, Vol. 8, No. 12, pp. 965-972, 2014

###### Is a 100% Renewable Energy Economy Possible in the Light of Wind Silence Occurrences?

.08.2018). Sinn, H. W. (2017). Buffering volatility: A study on the limits of Germany’s energy revolution. European Economic Review 99 , p. 130–150. doi: 10.1016/j. euroecorev.2017.05.007 Sovacool, B. K., Hirsh, R. F. (2009). Beyond batteries: An examination of the benefits and barriers to plug-in hybrid electric vehicles (PHEVs) and a vehicle-to-grid (V2G) transition. Energy Policy 37 , p. 1095–1103. doi: 10.1016/j. enpol.2008.10.005 The British Wind Energy Association (BWEA). (2005). Wind Turbine Technology . Retrieved from: https

###### Black-Box Accumulation: Collecting Incentives in a Privacy-Preserving Way

architecture for v2g networks in smart grid. IEEE Trans. Smart Grid, 2(4):697-706, 2011.

######
Vertex PI*
*_{v}
Topological Index of Titania Carbon Nanotubes *TiO*
_{2}
*(m,n)*

_{v}

}[m,n]) = 2(m + 1) + (m + 1) \end{array}$$ and n v 1 ( g 1 | T i O 2 [ m , n ] ) = ( 6 n + 4 ) ( m + 1 ) − 3 ( m + 1 ) = ( 6 n + 1 ) ( m + 1 ) $$\begin{array}{} \displaystyle {n_{v1}}({g_1}|Ti{O_2}[m,n]) = (6n + 4)(m + 1) - 3(m + 1) = (6n + 1)(m + 1) \end{array}$$ For g 2 = u 2 v 2: n u 2 ( g 2 | T i O 2 [ m , n ] ) = 3 × 2 ( m + 1 ) + 2 ( m + 1 ) + ( m + 1 ) = 9 ( m + 1 ) $$\begin{array}{} \displaystyle {n_{u2}}({g_2}|Ti{O_2}[m,n]) = 3 \times 2(m + 1) + 2(m + 1) + (m + 1) = 9(m + 1) \end{array}$$ and n v 2 ( g 2 | T i O 2 [ m , n ] ) = ( 6 n

###### Computing the two first probability density functions of the random Cauchy-Euler differential equation: Study about regular-singular points

− 1 ∫ min [ a 2 , 1 , b 2 4 ] min [ a 2 , 2 , b 2 4 ] f X 0 , C , B , A 2 ( v 1 g ¯ R ( s 2 ) − v 2 g ¯ R ( s 1 ) g ¯ R ( s 2 ) h ¯ R ( s 1 ) − g ¯ R ( s 1 ) h ¯ R ( s 2 ) , ( v 2 h ¯ R ( s 1 ) − v 1 h ¯ R ( s 2 ) g ¯ R ( s 2 ) h ¯ R ( s 1 ) − g ¯ R ( v 1 ) h ¯ R ( v 2 ) ) s 0 , b + 1 , a 2 ) × s 0 | g ¯ R ( s 2 ) h ¯ R ( s 1 ) − g ¯ R ( s 1 ) h ¯ R ( s 2 ) | d a 2 d b , f ¯ 2 C ( v 1 , s 1 ; v 2 , s 2 ) = ∫ b 1 − 1 b 2 − 1 ∫ max [ a 2 , 1 , b 2 4 ] max [ a 2 , 2 , b 2 4 ] f X 0 , C , B , A 2 ( v 1 g ¯ C ( s 2 ) − v 2 g ¯ C ( s 1 ) g ¯ C ( s 2 ) h ¯ C