Time-Adaptive Unit Commitment

The short-term operation of a power system is usually planned by solving a day-ahead unit commitment problem. Because of historical reasons, the commitment of power generating units is decided over a time horizon typically consisting of the 24 hourly periods of a day. In this paper, we show that, as a result of the increasing penetration of intermittent renewable generation, this somewhat arbitrary and artificial division of time may prove to be significantly suboptimal and counterproductive. Instead, we propose a time-adaptive day-ahead unit commitment formulation that better captures the net-demand variability throughout the day. The proposed formulation provides the commitment and dispatch of thermal generating units over a set of 24 time periods too, but with different duration. To do that, we use a clustering procedure to select the duration of those adaptive time periods taking into account the renewable generation and demand forecasts. Numerical results show that, without increasing the computational burden, the proposed time-adaptive unit commitment allows us for a more efficient use of the system flexibility, which translates into a lower operating cost and a higher penetration of renewable production than those achieved by a conventional hourly unit commitment problem.

[1]  Jianhui Wang,et al.  Stochastic Optimization for Unit Commitment—A Review , 2015, IEEE Transactions on Power Systems.

[2]  Andres Ramos,et al.  Tight and Compact MILP Formulation of Start-Up and Shut-Down Ramping in Unit Commitment , 2013, IEEE Transactions on Power Systems.

[3]  Albert Moser,et al.  Development of adaptive time patterns for multi-dimensional power system simulations , 2017, 2017 14th International Conference on the European Energy Market (EEM).

[4]  Juan M. Morales,et al.  Chronological Time-Period Clustering for Optimal Capacity Expansion Planning With Storage , 2018, IEEE Transactions on Power Systems.

[5]  Gerald B. Sheblé,et al.  A profit-based unit commitment GA for the competitive environment , 2000 .

[6]  Brian Ó Gallachóir,et al.  The impact of sub-hourly modelling in power systems with significant levels of renewable generation , 2014 .

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

[8]  Pandelis N. Biskas,et al.  Multiple Time Resolution Stochastic Scheduling for Systems With High Renewable Penetration , 2017, IEEE Transactions on Power Systems.

[9]  Pierluigi Siano,et al.  Evaluating the impact of sub-hourly unit commitment method on spinning reserve in presence of intermittent generators , 2016 .

[10]  Gerald B. Sheblé,et al.  Unit commitment literature synopsis , 1994 .

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

[12]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[13]  R. Sioshansi,et al.  Economic Consequences of Alternative Solution Methods for Centralized Unit Commitment in Day-Ahead Electricity Markets , 2008, IEEE Transactions on Power Systems.

[14]  Pandelis N. Biskas,et al.  Multiple Time Resolution Unit Commitment for Short-Term Operations Scheduling Under High Renewable Penetration , 2014, IEEE Transactions on Power Systems.

[15]  Benjamin F. Hobbs,et al.  Hidden power system inflexibilities imposed by traditional unit commitment formulations , 2017 .

[16]  J. Franklin,et al.  The elements of statistical learning: data mining, inference and prediction , 2005 .

[17]  Daniel S. Kirschen,et al.  Comparison of state-of-the-art transmission constrained unit commitment formulations , 2013, 2013 IEEE Power & Energy Society General Meeting.

[18]  C. R. Philbrick Wind integration and the need for advanced decision support tools , 2011, 2011 IEEE Power and Energy Society General Meeting.

[19]  J. H. Ward Hierarchical Grouping to Optimize an Objective Function , 1963 .

[20]  B. Hobbs,et al.  Value of Price Responsive Load for Wind Integration in Unit Commitment , 2014, IEEE Transactions on Power Systems.

[21]  Daniel S. Kirschen,et al.  Effect of time resolution on unit commitment decisions in systems with high wind penetration , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.