Game-Theoretical Energy Management for Residential User and Micro Grid for Optimum Sizing of Photo Voltaic Battery Systems and Energy Prices
There is emerging trend in power system, i.e., energy internet that provides energy production, transmission, storage and utilization. Which is used to manage and control energy centrally by using information and communication technologies. In this paper, coordinated management of renewable and traditional energy is focused. In proposed work, storage system is embedded with renewable resources in microgrid, so that after satisfying users energy requirement, surplus energy can be stored in battery. Energy management is performed with storage capacity includes cost of renewable resources, depreciation cost of battery and bidirectional energy transmission. User and microgrid are two players that are involved in non cooperative game theory. In order to maximize the payoff of user as well as microgrid, the two stage non cooperative game theoretic method optimizes battery capacity and prices. Which are charged by micro grid from user and optimize user energy consumption. The distributed algorithm is proposed to explain nash equilibrium which ensures Pareto optimality in terms of increasing pay off of both stakeholder. Furthermore, forecasting algorithm back propagation (BP), Support Vector Machine (SVM) and Stacked Auto Encoder (SAE) are used for forecasting historical data related to solar power generation. Predicted data is, thus used by microgrid in defining energy prices and battery storage capacity.
KeywordsGame theory Renewable energy resources Microgrid User Nash equilibrium
- 6.Bingtuan, G.A.O., Xiaofeng, L.I.U., Cheng, W.U., Yi, T.A.N.G.: Game-theoretic energy management with storage capacity optimization in the smart grids. J. Mod. Power Syst. Clean Energ. 1–12 (2018)Google Scholar
- 10.Darivianakis, G., Georghiou, A., Smith, R.S., Lygeros, J.: A stochastic optimization approach to cooperative building energy management via an energy hub. In: 2015 IEEE 54th Annual Conference on Decision and Control (CDC), pp. 7814–7819. IEEE, December 2015Google Scholar
- 14.Khalid, R., Javaid, N., Rahim, M.H., Aslam, S., Sher, A.: Fuzzy energy management controller and scheduler for smart homes. Sustain. Comput. Inf. Syst. 21, 103–118 (2019)Google Scholar
- 23.Yin, S., Gao, X., Karimi, H.R., Zhu, X.: Study on support vector machine-based fault detection in Tennessee Eastman process. Abstr. Appl. Anal. 2014, 8 (2014)Google Scholar
- 24.Dai, Y.M., Gao, Y.: Real-time pricing strategy with multi-retailers based on demand-side management for the smart grid. Proc. Chin. Soc. Electric. Eng. 34(25), 4244–4249 (2014)Google Scholar