Commenced in January 2007
Paper Count: 31097
Dynamic Programming Based Algorithm for the Unit Commitment of the Transmission-Constrained Multi-Site Combined Heat and Power System
Abstract:High penetration of intermittent renewable energy sources (RES) such as solar power and wind power into the energy system has caused temporal and spatial imbalance between electric power supply and demand for some countries and regions. This brings about the critical need for coordinating power production and power exchange for different regions. As compared with the power-only systems, the combined heat and power (CHP) systems can provide additional flexibility of utilizing RES by exploiting the interdependence of power and heat production in the CHP plant. In the CHP system, power production can be influenced by adjusting heat production level and electric power can be used to satisfy heat demand by electric boiler or heat pump in conjunction with heat storage, which is much cheaper than electric storage. This paper addresses multi-site CHP systems without considering RES, which lay foundation for handling penetration of RES. The problem under study is the unit commitment (UC) of the transmission-constrained multi-site CHP systems. We solve the problem by combining linear relaxation of ON/OFF states and sequential dynamic programming (DP) techniques, where relaxed states are used to reduce the dimension of the UC problem and DP for improving the solution quality. Numerical results for daily scheduling with realistic models and data show that DP-based algorithm is from a few to a few hundred times faster than CPLEX (standard commercial optimization software) with good solution accuracy (less than 1% relative gap from the optimal solution on the average).
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1125951Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1250
 IEA (International Energy Agency). Cogeneration and renewables—Solutions for a lower carbon future, 2011.
 IEA (International Energy Agency). Cogeneration and district energy—Sustainable energy technologies for today and tomorrow, 2009.
 M. Jaradi, S.Riffat, “Trigeneration systems: Energy policies, prime movers, cooling techniques, configurations and operation strategies,” Renewable and Sustainable Energy Reviews, vol.32, pp.396-415, 2014.
 Z.Ouyang, M. Shahidehpour, “Heuristic multi-area unit commitment with economic dispatch,” IEE Proceedings-C, vol.138 (3), pp.242-252, 1991.
 D. Streiffert, “Multi-area economic dispatch with tie lines constraints,” IEEE Transactions on Power Systems, vol.10 (4), pp.1946-1951, 1995.
 Z. Li, M. Shahidehpour, W. Wu, B. Zeng, B. Zhang, W. Zheng, “Decentralized multiarea robust generation unit and tie-line scheduling under wind power uncertainty,” IEEE Transactions on Sustainable Energy, vol. 6(4), pp.1377-1388, 2015.
 A. Rong, R. Lahdelma, “Role of polygeneration in sustainable energy system development—Challenges and opportunities from optimization viewpoints,” Renewable and Sustainable Energy Reviews, vol.53, pp.363-372, 2016.
 P. Meibom, J. Kiviluoma, R. Barth, H. Brand, C. Weber, H.V. Larsen, “Value of electric boiler and heat bumps for wind power integration,” Wind Energy, vol.10, p.p. 321-337, 2007.
 D. Connolly, “Heat roadmap Europe—A low carbon heat and cooling strategy for Europe,” Summer School for District Heating and Cooling, Helsinki, August, 2014, Finland.
 C. L.Tseng, X. Guan, A.J. Svoboda, “Multi-area unit commitment for large scale power systems,” IEE Proceedings for Generation, Transmission and Distribution, vol. 145(4), pp. 415-421, 1998.
 M. Wang, B. Zhang, Y. Deng, “A novel unit commitment method considering various constraints,” 2000 IEEE Power Engineering Society Meeting, vol.3, pp.1778-1783.
 F.N. Lee, J. Huang, R. Adapa, “Multi-area unit commitment via sequential method and a DC power flow network model,” IEEE Transactions on Power Systems, vol. 9(1), pp. 279-287, 1994.
 A. Kargarian, Y. Fu, P. Liu, C. Wang, “A system of system engineering approach for unit commitment in multi-area power markets,” 2014 IEEE PES General Meeting, pp. 1-5, 2014.
 K. Venkatesan, C.C.A. Rajan, “A simulated annealing method for solving multi-area unit commitment problem in deregulated environment,” 2011 IEEE PES Innovative Smart Grid Technologies, pp.305-310, India, December, 2011.
 M. Carrion, J.M. Arroyo, “A computationally efficient mixed integer linear formulation for the thermal unit commitment problem,” IEEE Transactions on Power Systems, vol. 21(3), pp.1371-1378, 2016.
 J. Ostrowski, M.F. Anjos, A. Vanneli, “Tight mixed integer linear programming frmulations for the unit commitment problem,” IEEE Transactions on Power Systems, vol. 27(1), pp. 39-46, 2012.
 G. Morales-Espana, J.M. Latorre, A. Ramos, “Tight and compact MILP formulation for the thermal unit commitment problem,” IEEE Transactions on Power Systems, vol.28 (4), pp. 4897-4908, 2013.
 E. Thorin, H. Brand, C. Weber, “Long term optimization of cogeneration systems in a competitive market environment,” Applied Energy, vol. 81, pp. 152-169, 2005.
 A.L. Facci, L. Andreassi, S. Ubertini, “Optimization of CHCP(combined heat, power and cooling) systems operation strategy using dynamic programming,” Energy, vol.66, pp. 387-400, 2014.
 A. Rong, H. Hakonen, R. Lahdelma, “A variant of dynamic programming algorithm for unit commitment of combined heat and power systems. European Journal of Operational Research, vol. 190, pp.741-755, 2008.
 A. Rong, R. Lahdelma, M. Grunow, “An improved unit decommitment for combined heat and power systems,” European Journal of Operational Research, vol. 195, pp.552-562, 2009.
 A. Rong, H. Hakonen, R. Lahdelma, “A dynamic regrouping based sequential dynamic programming algorithm for unit commit of combined heat and power systems,” Energy Conversion and Management, vol.50, pp.1108-1115, 2009.
 N.H. Kjeldsen, M. Chiarandini, “Heuristic solutions to long-term unit commitment problem with cogeneration plants,” Computers & Operations Research, vol.39, pp. 269-282, 2012.
 H. Gopalakrishnan, D. Kosanovic, “ Operational planning of combined heat and power plants through genetic algorithms for mixed 0-1 nonlinear programming,” Computers & Operations Research, vol.56, pp. 51-67,2015.
 R.Bellman. Dynamic programming, Princeton University Press, NJ, USA, 1957.
 A. Rong, R. Lahdelma, “An efficient model and algorithm for the transmission-constrained multi-site combined heat and power system,” paper in submission.
 R. Lahdelma, H. Hakonen, “An efficient linear programming algorithm for combined heat and power production,” European Journal of Operational Research, vol. 148, pp. 141-151, 2003.
 A. Rong, H. Hakonen, R. Lahdelma, “An efficient linear model and optimization algorithm for multi-site combined heat and power production, European Journal of Operational Research, vol.168, pp. 612-632, 2006.
 S. Makkonen, R. Lahdelma, “Non-convex power plant modeling in energy optimization,” European Journal of Operational Research, vol.171, pp.1113-1126, 2006.
 A. Rong, R. Lahdelma, “An efficient envelope-based Branch and Bound algorithm for non-convex combined heat and power production planning,” European Journal of Operational Research, vol. 183, pp. 412-431, 2007.
 G. Dantzig, Linear programming and extension, Princeton University Press, Princeton. NJ, 1963.
 R.K. Ahuja, T.L. Magnanti, J.B. Olin, Network flows—theory, algorithms and applications, Prentice Hall, Upper Saddle River, NJ, 1993.
 P.K. Singhal, R.N. Sharma, “Dynamic programming approach for solving power generating unit commitment problem,” International Conference on Computer & Communication Technology, pp. 298-303, 2011.
 IBM ILOG CPLEX Optimization Studio 12.5. http://ibm-ilog-cplex-optimization-studio.software.informer.com/12.5/.
 Nordic power market. www.nordpoolspot.com.
 M.A. Hozouri, A. Abbaspour, M. Fotuhi-Firuzabad, M. Moeini-Aghtaie, “On the use of pumped storage for wind energy maximization in transmission-constrained power system,” IEEE Transactions on Power Systems, vol.30 (2), pp. 1017-1025, 2015.