Long-term combined heat and power production and trade planning

Abstract

In this thesis a comprehensive framework for solving long-term combined heat and power (CHP) operations planning problems is developed. The framework has two main parts - the first is a modelling framework which allows for modelling arbitrary CHP plants and is aimed at the formulation of an optimization problem for CHP production and trade planning. The second is a solution algorithm which exploits the knowledge of the problem structure so that the problem is solved more efficiently. There exist very powerful stateof- the-art general-purpose solvers for mixed-integer linear programming (MILP) problems, such as Gurobi. However, even these solvers fail to find a feasible solution within reasonable time for production planning problems of large dimensions. An idea followed in this thesis is to achieve reasonable computation times of large problems by employing the knowledge of the special problem structure. For this purpose, a customized branch and bound (B&B) algorithm is proposed. The algorithm exploits the knowledge of the block-diagonal problem substructure, to obtain tight bounds. The bounds are much tighter than bounds produced by solving a linear relaxation of the solved MILP problem, which is the way of bound computation commonly used within general-purpose implementations of B&B. Besides an enhanced horizon cutting algorithm is developed, with the purpose of providing high-quality feasible solutions for the customized B&B algorithm. Efficiency of the proposed algorithm was evaluated based on 64 test cases using real-world data of three existing CHP plants. The performance of the proposed algorithm was compared to plain Gurobi usage. In most cases the proposed algorithm finds a certificate of near-optimality sooner than plain Gurobi does. More importantly, the proposed algorithm was able to find good feasible solutions for problems, for which Gurobi fails to find any feasible solution within the specified time limit.