RSE
A A
Login
login name
password
login
GSE
ITA english

Presentation RSE 14003906

Mathematical Programming bounds for Large-Scale Unit Commitment Problems in Medium-Term Energy System Simulations

Presentation

4th Student Conference on Operational Research (SCOR’14) 2-4 , Maggio-2014.

Request Document (927.91 KB, .pdf)

A. Ceselli (Università degli studi di Milano), A. Gelmini (RSE SpA) , G. Righini (Università degli studi di Milano), A. Taverna (Università degli studi di Milano)

EVOLRETE 2014 - Evolution and planning of the national electric system

We present algorithms and mathematical models developed to solve the dispatching and commitment problems of thermoelectric plants, which represent a mixed-integer linear problem (MILP) whose size makesit impractical to be solved directly by a commercial MILP solver.

We consider a large-scale unit commitment problem arising in medium-term simulation of energy networks, stemming from a joint project between the University of Milan and a major energy research centre in Italy. Optimal plans must be computed for a set of thermal and hydroelectric power plants, located in one or more countries, over a time horizon spanning from a few months to one year, with a hour-by-hour resolution. We propose a mixed-integer linear programming model for the problem. Since the complexity of this unit commitment problem and the size of real-world instances make it impractical to directly optimise this model using general purpose solvers, we devise ad-hoc heuristics and relaxations to obtain approximated solutions and quality estimations.

We exploit an incremental approach: at first, a linear relaxation of an aggregated model is solved. Then, the model is disaggregated and the full linear relaxation is computed. Finally, a tighter linear relaxation of an extended formulation is obtained using column generation. At each stage, matheuristics are run to obtain good integer solutions. Experimental tests on real-world data reveal that accurate results can be obtained by our framework in affordable time, making it suitable for efficient scenario simulations.

Related Links

Topics