Optimal Operation Strategy of Power Systems in the Presence of Smart Grids and Electric Vehicles

  • alireza sedaghati shahabdanesh university
Keywords: Operation of power plants, Smart Grid, Fleet of electric vehicles


Abstract: Due to the dramatic growth in the electric power industry and the large gap between small and large loads and loads economic crisis that has gripped most of the world, an issue critical to the operation of power plants has become. Also growing use of traditional sources of energy and lack of response to this need has created a lot of problems around the world. Including that they can reduce fossil fuel resources, and the environmental impact of greenhouse gas increases noted. This problem has led to concerns of environmentally friendly technologies such as electric cars get more attention. Considering to the capability of bi-directional exchange power in vehicles, electric vehicles are a significant number of network connections To coordinated the management and intelligent control of an entity causing the network to be connected together, Considering In this type of electric vehicles as well as hardware that is installed in the parking lot, as they can quickly set up a small virtual power plant set-up costs are too high and behave. The main focus of this thesis is to develop a model in order to exploit the electrical grid in the smart grid the intelligent power network operation in the presence of electric vehicles can be connected to the network has been studied. This paper presents an optimal load management strategy for residential consumers that utilizes the communication infrastructure of the future smart grid. The strategy considers predictions of electricity prices, energy demand, renewable power production, and power-purchase of energy of the consumer in determining the optimal relationship between hourly electricity prices and the use of different household appliances and electric vehicles in a typical smart house. The proposed strategy is illustrated using two study cases corresponding to a house located in Zaragoza (Spain) for a typical day in summer. Results show that the proposed model allows users to control their diary energy consumption and adapt their electricity bills to their actual economical situation.is Abstract.


[1] Kim, J., Shcherbakova, A., Common failures of demand response. Energy 36, 873–88, 2011.
[2] Albadi, M.H., El-Saadany, E.F., 2008. A summary of demand response in electricity markets. Electric Power Systems Research 1989–1990.
[3] Eissa, M.M., 2011. Demand side management program evaluation based on industrial and commercial field data. Energy Policy 39, 5961–5969.
[4] Molderink, A., Bakker, V., Bosman, M.G.C., Hurink, J.L., Smit, G.J.M., 2010. Management and control of domestic smart grid technology. IEEE Transactions on Smart Grid 1, 109–119.
[5] Houwing, M., Negenborn, R.R., De Schutter, B., 2011. Demand response with micro-CHP systems. Proceedings of the IEEE 99, 200–213.
[6] Mohsenian-Rad, A.H., Leon-Garcia, A., 2010. Optimal residential load control with price prediction in real-time electricity pricing environments. IEEE Transactions on Smart Grid 1, 120–133.
[7] Mohsenian-Rad, A.H., Wong, V.W.S., Jatkevich, J., Schober, R., Leon-Garcia, A., 2010. Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE Transactions on Smart Grid 1, 320–331.
[8] Conejo, A.J., Morales, J.M., Baringo, L., 2010. Real-time demand response model. IEEE Transactions on Smart Grid 1, 236–242.
[9] Sianaki, O.A., Hussain, O., Tabesh, A.R., 2010. A knapsack problem approach for achieving efficient energy consumption in smart grid for end-users’ life style. In: Proceedings of the IEEE conference CITRES, pp. 159–164.
[10] Tanaka, K., Yoza, A., Ogimi, K., Yona, A., Senjyu, T., Funabashi, T., Kim, C.H., 2011. Optimal operation of DC smart house system by controllable loads based on smart grid topology. Renewable Energy 39, 132–139.
[11] Bartusch, C., Wallin, F., Odlare, M., Vassileva, I., Wester, L., 2011. Introducing a demand-based electricity distribution tariff in the residential sector: demand response and customer perception. Energy Policy 39, 5008–5025.
[12] Gyamfi, S., Krumdieck, S., 2011. Price, environment and security: exploring multimodal motivation in voluntary residential peak demand response. Energy Policy 39, 2993–3004.
[13] Wang, W., Xu, Y., Khanna, M., 2011. A survey on the communication architectures in smart grid. Computer Networks 55, 3604–3629.
[14] Wang, J., Liu, C., Ton, D., Zhou, Y., Kim, J., Vyas, A., 2011. Impact of plug-in hybrid electric vehicle on power systems with demand response and wind power. Energy Policy 39, 4016–4021.
[15] Bradley, T.H., Frank, A.A., 2009. Design, demonstrations and sustainability impact assessments for plug-in hybrid electric vehicles. Renewable and Sustainable Energy Reviews 13, 115–128.
[16] Copetti, J.B., Chenlo, F., 1994. Lead/acid batteries for photovoltaic applications. Test results and modeling. Journal of Power Sources 47, 109–118.
[17] Copetti, J.B., Lorenzo, E., Chenlo, F., 1993. A general battery model for PV system simulation. Progress in Photovoltaic 1, 283–292.
[18] Paoli, C., Voyant, C., Muselli, M., Nivet, M.L., 2010. Forecasting of preprocessed daily solar radiation time series using neural networks. Solar Energy 84, 2146–2160.
[19] Kleissl, J., 2010. Current state of art in solar forecasting. California Institute for Energy and Environment. Available at: /http://uc-ciee.org/all-documents/a/ 457/113/nestedS.
[20] Aggarwal, S.K., Saini, L.M., Kumar, A., 2009. Electricity price forecasting in deregulated markets: a review and evaluation. Electrical Power and Energy Systems 31, 13–22.
[21] Abdel-Aal, R.E., 2004a. Hourly temperature forecasting using abductive networks. Engineering Applications of Artificial Intelligence 17, 543–556.
[22] Abdel-Aal, R.E., 2004b. Short-term hourly load forecasting using abductive networks. IEEE Transactions on Power Systems 19, 164–173.
[23] Fan, S., Chen, L., Lee, W.J., 2009. Short-term load forecasting using comprehensive combination based on multimeteorological information. IEEE Transactions on Industry Applications 45, 1460–1466.
[24] Goldberg, D.E., 1989. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Professional.
[25] Foley, A.M., Leahy, P.G., Marvuglia, A., McKeogh, E.J., 2012. Current methods and advances in forecasting of wind power generation. Renewable Energy 37, 1–8.
[26] Hahn, H., Meyer-Nieberg, S., Pickl, S., 2009. Electric load forecasting methods: tools for decision making. European Journal of Operational Research 199, 902–907.
[27] Lei, M., Shiyan, L., Chuanwen, J., Hongling, L., Yan, Z., 2009. A review on the forecasting of wind speed and generated power. Renewable and Sustainable Energy Reviews 13, 915–920.
How to Cite
sedaghati, alireza. (2018). Optimal Operation Strategy of Power Systems in the Presence of Smart Grids and Electric Vehicles. Majlesi Journal of Mechatronic Systems, 6(4). Retrieved from http://journals.iaumajlesi.ac.ir/ms/index/index.php/ms/article/view/381