Multi-Site Energy Use Management in the Absence of Smart Grids
Demand-side management (DSM) allows an energy load to be balanced across multiple consumers. Energy consumption fluctuations cause important costs based on the alternating energy price tariffs. DSM creates opportunities for consumers to reduce their energy consumption costs by smoothing the daily load curve. An MINLP model is constructed based on power consumption, which aligns with the production schedules of the industrial units. Then, these feasible schedules are used as an input for a cooperative Bayesian game that is designed to balance the hourly loads. A case study of three factories, where the demand-side manager tries to minimize the instability of purchasing electricity from the general grid through load balancing, is considered.
We would like to express our gratitude to the Hayat Kimya managers, who helped us to find a case study district and provided the necessary data and information to implement our model.
- Chen, C., Kishore, S., & Snyder, L. V. (2011). An innovative RTP-based residential power scheduling scheme for smart grids. In: IEEE book group authors (Eds.), Proceeding Book of the International Conference on Acoustics, Speech and Signal Processing, (pp. 5956–5959). New York: IEEE.Google Scholar
- Lampropoulos, I., Kling, W. L., Ribeiro, P. F., Berg van den, J. (2013). History of demand side management and classification of demand response control schemes. In: IEEE book group authors (Eds.), Proceeding Book of the Power and Energy Society General Meeting. New York: IEEE.Google Scholar
- Lasaulce, S., & Tembine, H. (2011). Game theory and learning for wireless networks. London: Elsevier.Google Scholar
- Mangiatordi, F., Pallotti, E., & Del Vecchio, P. (2013). A non-cooperative game theoretic approach for energy management in MV grid. In: IEEE Book Group Authors (Eds.), Proceeding book of the 13th international conference on environment and electrical engineering (pp. 266–271). New York: IEEE.Google Scholar
- Marzband, M., Sumper, A., Dominguez-Garcia, J. L., & Gumara-Ferret, R. (2013a). Experimental validation of a real time energy management system for microgrids in islanded mode using a local day-ahead electricity market and MINLP. Energy Conversion and Management, 76, 314–322.Google Scholar
- Marzband, M., Sumper, A., Ruiz-Alvarez, A., Dominguez-Garcia, J. L., & Tomoiaga, B. (2013b). Experimental evaluation of a real time energy management system for stand-alone microgrids in day-ahead markets. Applied Energy, 106, 365–376.Google Scholar
- Marzband, M., Ghadimi, M., Sumper, A., & Dominguez-Garcia, J. L. (2014). Experimental validation of a real-time energy management system using multi-period gravitational search algorithm for microgrids in islanded mode. Applied Energy, 128, 164–174.Google Scholar
- Marzband, M., Azarinejadian, F., Savaghebi, M., & Guerrero, J. M. (2015a). An optimal energy management system for islanded microgrids based on multiperiod artificial bee colony combined with markov chain. IEEE Systems Journal, 99, 1–11.Google Scholar
- Marzband, M., Parhizi, N., & Adabi, J. (2015b). Optimal energy management for stand-alone microgrids based on multi-period imperialist competition algorithm considering uncertainties: Experimental validation. International Transactions on Electrical Energy Systems.Google Scholar
- Marzband, M., Parhizi, N., Savaghebi, M., & Guerrero, J. M. (2015c). Distributed smart decision-making for a multimicrogrid system based on a hierarchical interactive architecture. IEEE Transactions on Energy Conversion, 99, 1–12.Google Scholar
- National Rates. Republic of Turkey Energy Market Regulatory Authority. (2015). http://www.epdk.org.tr/index.php/elektrik-piyasasi/tarifeler?id=133. Accessed July 27, 2015.
- Wu, C., Mohsenian-Rad, H., Huang, J., & Wang, A. Y. (2011). Demand side management for wind power integration in microgrid using dynamic potential game theory. In: IEEE Book Group Authors (Eds.), Proceeding book of the IEEE international workshop on smart grid communications and networks (pp. 1199–1204). New York: IEEE.Google Scholar
- Yang, P., Tang, G., & Nehorai, A. (2012). Optimal time-of-use electricity pricing using game theory. In: IEEE Book Group Authors (Eds.), Proceeding book of the international conference on acoustics, speech and signal processing (pp. 3081–3084). New York: IEEE.Google Scholar