The high penetration of intermittent renewable generation has prompted the development of Stochastic Hydrothermal Unit Commitment (SHUC) models, which are more difficult to be solved than their thermal-based counterparts due to hydro generation constraints and inflow uncertainties. This work presents a SHUC model applied in centralized cost-based dispatch. The SHUC is represented by a two-stage stochastic model, formulated as a large-scale mixed-binary linear programming problem. The solution strategy is divided into two steps. The first step is the Lagrangian Relaxation (LR) approach, which is applied to solve the dual problem and generate a lower bound for SHUC. The second step is given by a Primal Recovery where we use the solution of the LR dual problem with heuristics based on Benders’ Decomposition to obtain the primal-feasible solution. Both steps benefit from each other, exchanging information over the iterative process. We assess our approach in terms of the quality of the solutions and running times on space and scenario LR decompositions. The computational instances use various power systems, considering the different configuration of plants (capacity and number of units). The results show the advantage of our primal recovery technique compared to solving the problem via MILP solver. This is true already for the deterministic case, and the advantage grows as the problem’s size (number of plants and/or scenarios) does. The space decomposition provides better solutions, although scenario one provides better lower bounds, but the main idea is to encourage researchers to explore LR decompositions and heuristics in other relevant problems.

Solving Stochastic Hydrothermal Unit Commitment with a New Primal Recovery Technique Based on Lagrangian Solutions

Antonio Frangioni
2021-01-01

Abstract

The high penetration of intermittent renewable generation has prompted the development of Stochastic Hydrothermal Unit Commitment (SHUC) models, which are more difficult to be solved than their thermal-based counterparts due to hydro generation constraints and inflow uncertainties. This work presents a SHUC model applied in centralized cost-based dispatch. The SHUC is represented by a two-stage stochastic model, formulated as a large-scale mixed-binary linear programming problem. The solution strategy is divided into two steps. The first step is the Lagrangian Relaxation (LR) approach, which is applied to solve the dual problem and generate a lower bound for SHUC. The second step is given by a Primal Recovery where we use the solution of the LR dual problem with heuristics based on Benders’ Decomposition to obtain the primal-feasible solution. Both steps benefit from each other, exchanging information over the iterative process. We assess our approach in terms of the quality of the solutions and running times on space and scenario LR decompositions. The computational instances use various power systems, considering the different configuration of plants (capacity and number of units). The results show the advantage of our primal recovery technique compared to solving the problem via MILP solver. This is true already for the deterministic case, and the advantage grows as the problem’s size (number of plants and/or scenarios) does. The space decomposition provides better solutions, although scenario one provides better lower bounds, but the main idea is to encourage researchers to explore LR decompositions and heuristics in other relevant problems.
2021
Reolon Scuzziato, Murilo; Cristian Finardi, Erlon; Frangioni, Antonio
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1063004
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