A State Transition MIP Formulation for the Unit Commitment Problem

In this paper, we present the state-transition formulation for the unit commitment (UC) problem. This formulation uses new decision variables that capture the state transitions of the generators, instead of their on/off statuses. We show that this new approach produces a formulation which naturally includes valid inequalities, commonly used to strengthen other formulations. We demonstrate the performance of the state-transition formulation and observe that it leads to improved solution times especially in longer time-horizon instances. As an important consequence, the new formulation allows us to solve realistic instances in less than 12 minutes on an ordinary desktop PC, leading to a speed-up of a factor of almost two, in comparison to the nearest contender. Finally, we demonstrate the value of considering longer planning horizons in UC problems.

[1]  Alper Atamtürk,et al.  A polyhedral study of production ramping , 2016, Math. Program..

[2]  David L. Woodruff,et al.  Toward scalable stochastic unit commitment , 2015 .

[3]  Jesús María Latorre Canteli,et al.  Tight and compact MILP formulation for the thermal unit commitment problem , 2013 .

[4]  S. Thomas McCormick,et al.  Integer Programming and Combinatorial Optimization , 1996, Lecture Notes in Computer Science.

[5]  A. Turgeon Optimal unit commitment , 1977 .

[6]  Jitka Dupacová,et al.  Scenario reduction in stochastic programming , 2003, Math. Program..

[7]  Yongpei Guan,et al.  A Polyhedral Study of the Integrated Minimum-Up/-Down Time and Ramping Polytope , 2016, 1604.02184.

[8]  Xu Andy Sun,et al.  Adaptive Robust Optimization for the Security Constrained Unit Commitment Problem , 2013, IEEE Transactions on Power Systems.

[9]  J. Birge,et al.  Using integer programming to refine Lagrangian-based unit commitment solutions , 2000 .

[10]  F. Albuyeh,et al.  Evaluation of Dynamic Programming Based Methods and Multiple area Representation for Thermal Unit Commitments , 1981, IEEE Transactions on Power Apparatus and Systems.

[11]  Dimitris Bertsimas,et al.  Dynamic resource allocation: A flexible and tractable modeling framework , 2014, Eur. J. Oper. Res..

[12]  Benjamin F. Hobbs,et al.  The Next Generation of Electric Power Unit Commitment Models , 2013 .

[13]  Lihua Yu,et al.  Decision Aids for Scheduling and Hedging (DASH) in deregulated electricity markets: a stochastic programming approach to power portfolio optimization , 2002, Proceedings of the Winter Simulation Conference.

[14]  M. Anjos,et al.  Tight Mixed Integer Linear Programming Formulations for the Unit Commitment Problem , 2012, IEEE Transactions on Power Systems.

[15]  N.P. Padhy,et al.  Unit commitment-a bibliographical survey , 2004, IEEE Transactions on Power Systems.

[16]  Zuyi Li,et al.  Market Operations in Electric Power Systems : Forecasting, Scheduling, and Risk Management , 2002 .

[17]  S. Sen,et al.  An Improved MIP Formulation for the Unit Commitment Problem , 2015 .

[18]  M. Carrion,et al.  A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem , 2006, IEEE Transactions on Power Systems.

[19]  John A. Muckstadt,et al.  An Application of Lagrangian Relaxation to Scheduling in Power-Generation Systems , 1977, Oper. Res..

[20]  S. Oren,et al.  Solving the Unit Commitment Problem by a Unit Decommitment Method , 2000 .

[21]  A. Conejo,et al.  Optimal response of a thermal unit to an electricity spot market , 2000 .

[22]  Deepak Rajan,et al.  IBM Research Report Minimum Up/Down Polytopes of the Unit Commitment Problem with Start-Up Costs , 2005 .

[23]  Claudio Gentile,et al.  A tight MIP formulation of the unit commitment problem with start-up and shut-down constraints , 2017, EURO J. Comput. Optim..

[24]  Philip M. Wolfe,et al.  Multiproject Scheduling with Limited Resources: A Zero-One Programming Approach , 1969 .

[25]  Ruiwei Jiang,et al.  Cutting planes for the multistage stochastic unit commitment problem , 2016, Math. Program..

[26]  M. Shahidehpour,et al.  Price-based unit commitment: a case of Lagrangian relaxation versus mixed integer programming , 2005, IEEE Transactions on Power Systems.

[27]  Werner Römisch,et al.  Stochastic Lagrangian Relaxation Applied to Power Scheduling in a Hydro-Thermal System under Uncertainty , 2000, Ann. Oper. Res..

[28]  Ross Baldick,et al.  The generalized unit commitment problem , 1995 .

[29]  François Margot,et al.  Min-up/min-down polytopes , 2004, Discret. Optim..

[30]  Jonathan F. Bard,et al.  Short-Term Scheduling of Thermal-Electric Generators Using Lagrangian Relaxation , 1988, Oper. Res..

[31]  L. L. Garver,et al.  Power Generation Scheduling by Integer Programming-Development of Theory , 1962, Transactions of the American Institute of Electrical Engineers. Part III: Power Apparatus and Systems.

[32]  D. P. Kothari,et al.  A solution to the unit commitment problem—a review , 2013 .