A HOLISTIC MODELING AND SIMULATION APPROACH TO OPTIMIZE A SMART COMBINED GRID SYSTEM OF DIFFERENT RENEWABLE ENERGIES

Abstract

In this contribution, a model-based method for analyzing and designing energy systems comprising the electrical, thermal and chemical domains is presented. Beside the energy generation and consumption, the bidirectional coupling between all energy domains is considered, as well. This method is an adapted variant of the so called Hardware-in-the-Loop simulation where virtual energy components are combined with geographically distributed real energy components. In order to integrate the real components with minimal instrumentation efforts, measured quantities are included as information flows, only, while the physical power flows are connected to local available grid structures. This virtual coupling has the further advantage of a simple scalability so that existing real components can be used for different applications. The virtual energy components are represented by real-time capable models describing their physical behavior. In this contribution, a CHP unit is described as a first virtual energy component. The modeling approach is based on a time domain approach using state variables of the multiple domains to describe the dynamic behavior. Furthermore, the model is scalable regarding the modeling depth and the power ratings which allows an application for different simulation scenarios. Besides the modeling of a standalone CHP unit, its integration into a simulated electrical grid is discussed as well. Afterwards, the overall model is parameterized and validated with data of a medium size CHP unit. Finally, the model is used for simulations of an exemplary electrical grid.

Keywords

• HIL simulation; combined grid system; CHP model

References

1. Alanne, K. and Saari, A., 2006. Distributed energy generation and sustainable development. Renewable and Sustainable Energy Reviews 10, pp. 539-558.
2. Garrity, T. F., 2009. Innovation and trends for future electric power systems. Power Systems Conference, pp. 1- 8.
3. Sterner, M., 2009. Bioenergy and renewable power methane in integrated 100% renewable energy systems: Limiting global warming by transforming energy systems. Vol. 14, kassel university press GmbH.
4. Grimm, C., Neumann, P. and Mahlknecht, S., 2013. Embedded Systems for Smart Appliances and Energy Management. Springer.
5. Rohjans, S., Lehnhoff, S., Steinbrink, C. and Velásquez, J., 2014. The smart energy and automation lab (SESA). 7th Real-Time International User Conference.
6. Panwar, M., Lundstrom, B., Langston, J., Suryanarayanan, S. and Chakraborty, S., 2013. An overview of real time hardware-in-the-loop capabilities in digital simulation for electric microgrids. In Proc. IEEE North American Power Symposium (NAPS), pp. 1-6.
7. Steurer, M., 2006. PEBB based high-power hardware-in- loop simulation facility for electric power systems. In Proc. IEEE Power Engineering Society General Meeting, 3 pp.
8. Oldewurtel, F., et al., 2010. Energy efficient building climate control using stochastic model predictive control and weather predictions. In American control conference (ACC), IEEE, pp. 5100-5105.

9. Bui, T. D., 1979. Some A-stable and L-stable methods for the numerical integration of stiff ordinary differential equations. Journal of the ACM (JACM) 26(3), pp. 483- 493.
10. Beausoleil-Morrison, I. and Kelly, N. J., 2007. Specifications for modelling fuel cell and combustion- based residential cogeneration device within whole- building simulation programs. IEA/ECBCS Annex 42.
11. Beausoleil-Morrison, I. and Ferguson, A., 2007. Inter- model comparative testing and empirical validation of Annex 42 models for residential cogeneration devices. IEA/ECBCS Annex 42.
12. Rosato, A. and Sibilio, S., 2012. Calibration and validation of a model for simulating thermal and electric performance of an internal combustion engine-based micro-cogeneration device. Applied Thermal Engineering 45, pp. 79-98.
13. Lee, H., Bush, J., Hwang, Y. and Radermacher, R., 2013. Modeling of micro-CHP (combined heat and power) unit and evaluation of system performance in building application in United States. Energy 58, pp. 364-375.
14. De, S., Kaiadi, M., Fast, M. and Assadi, M., 2007. Development of an artificial neural network model for the steam process of a coal biomass cofired combined heat and power (CHP) plant in Sweden. Energy 32 (11), pp. 2099-2109.
15. Fast, M. and Palme, T., 2010. Application of artificial neural networks to the condition monitoring and diagnosis of a combined heat and power plant. Energy 35 (2), pp. 1114-1120.
16. Griese, M., Pawlik, T., Schulte, T. and Maas, J., 2014. Scalable model of a CHP unit for HIL simulation of a smart combined grid system. II. International Energy Technologies Conference, ENTECH ‘14, pp. 189-200.
17. Droste-Franke, B., Paal, B. P., Rehtanz, C., Sauer, D. U., Schneider, J. P., Schreurs, M. and Ziesemer, T., 2012. Technologies for Balancing Electrical Energy and Power. Balancing Heidelberg, pp. 83-142. Electricity. Springer Berlin
18. Guzzella, L. and Onder, C. H., 2004. Introduction to modeling and control of internal combustion engine systems. Springer.
19. Kundur, P., 1994. Power system stability and control. N. J. Balu, & M. G. Lauby (Eds.), Vol. 7, New York: McGraw- hill.
20. Woschni, G., 1967. A universally applicable equation for the instantaneous heat transfer coefficient in the internal combustion engine. SAE Technical paper, No. 670931.
21. Uudrill, J. M., 1968. Dynamic stability calculations for an arbitrary number of interconnected synchronous machines. IEEE Transactions on Power Apparatus and Systems (3), pp. 835-844.