Pre-trained solution methods for unit commitment

This thesis aims to improve the solution methods for the unit commitment problem, a short-term planning problem in the energy industry. In particular, we focus on Dantzig-Wolfe decomposition with a column generation procedure. Special emphasis is placed on approaches based on machine learning, which is of interest when one needs to solve the unit commitment problem repeatedly. Firstly, an initialisation method of the column generation procedure based on a neural network is studied. After offline training, for each unit commitment problem, the method outputs dual values which can be used to warmstart the solution method, leading to a significant saving of computational time. The training is done efficiently by exploiting the decomposable structure of the problem. Secondly, primal heuristics are discussed. Two novel primal heuristics are proposed: one based on the decomposition and another based on machine learning. Both of them fix a subset of the binary variable to reduce the problem size. The remaining variable is optimised quickly by an optimisation solver, which gives primal feasible solutions with small suboptimality in a short time. Finally, the column generation procedure is extended to handle incremental generation of columns. Instead of generating columns for all the components (power plants in the unit commitment problem) in each iteration, our method generates a subset of them and update the dual variable using the partially updated restricted master problem. Convergence analysis of the method is given under various conditions as well as numerical experiments to show the performance of the method. By combining the above enhancements, we obtain a fast solution method to solve the unit commitment problem to small tolerances down to 0.1%

Robust optimization for energy transactions in multi-microgrids under uncertainty

Independent operation of single microgrids (MGs) faces problems such as low self-consumption of local renewable energy, high operation cost and frequent power exchange with the grid. Interconnecting multiple MGs as a multi-microgrid (MMG) is an effective way to improve operational and economic performance. However, ensuring the optimal collaborative operation of a MMG is a challenging problem, especially under disturbances of intermittent renewable energy. In this paper, the economic and collaborative operation of MMGs is formulated as a unit commitment problem to describe the discrete characteristics of energy transaction combinations among MGs. A two-stage adaptive robust optimization based collaborative operation approach for a residential MMG is constructed to derive the scheduling scheme which minimizes the MMG operating cost under the worst realization of uncertain PV output. Transformed by its KKT optimality conditions, the reformulated model is efficiently solved by a column-and-constraint generation (C&CG) method. Case studies verify the effectiveness of the proposed model and evaluate the benefits of energy transactions in MMGs. The results show that the developed MMG operation approach is able to minimize the daily MMG operating cost while mitigating the disturbances of uncertainty in renewable energy sources. Compared to the non-interactive model, the proposed model can not only reduce the MMG operating cost but also mitigate the frequent energy interaction between the MMG and the grid

Adaptive Robust Optimization with Dynamic Uncertainty Sets for Multi-Period Economic Dispatch under Significant Wind

The exceptional benefits of wind power as an environmentally responsible renewable energy resource have led to an increasing penetration of wind energy in today's power systems. This trend has started to reshape the paradigms of power system operations, as dealing with uncertainty caused by the highly intermittent and uncertain wind power becomes a significant issue. Motivated by this, we present a new framework using adaptive robust optimization for the economic dispatch of power systems with high level of wind penetration. In particular, we propose an adaptive robust optimization model for multi-period economic dispatch, and introduce the concept of dynamic uncertainty sets and methods to construct such sets to model temporal and spatial correlations of uncertainty. We also develop a simulation platform which combines the proposed robust economic dispatch model with statistical prediction tools in a rolling horizon framework. We have conducted extensive computational experiments on this platform using real wind data. The results are promising and demonstrate the benefits of our approach in terms of cost and reliability over existing robust optimization models as well as recent look-ahead dispatch models.Comment: Accepted for publication at IEEE Transactions on Power System