Long-term combined heat and power production and trade planning
Type of document
disertační práceAuthor
Dvořák, Michal
Supervisor
Havel, Petr
Field of study
Řídicí technika a robotikaStudy program
Elektrotechnika a informatikaInstitutions assigning rank
České vysoké učení technické v Praze. Fakulta elektrotechnická. Katedra řídicí technikyMetadata
Show full item recordAbstract
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.
Collections
- Disertační práce - 13000 [697]
The following license files are associated with this item: