Layout Optimization for Photovoltaic Panels in Solar Power Plants via a
MINLP Approach
Abstract
Photovoltaic (PV) technology is one of the most popular means of
renewable generation, whose applications range from commercial and
residential buildings to industrial facilities and grid infrastructures.
The problem of determining a suitable layout for the PV arrays, on a
given deployment region, is generally non-trivial and has a crucial
importance in the planning phase of solar plants design and development.
In this paper, we provide a mixed integer non-linear programming
formulation of the PV arrays’ layout problem.
First, we define the astronomical and geometrical models, considering
crucial factors such as self-shadowing and irradiance variability,
depending on the geographical position of the solar plant and yearly
time window. Subsequently, we formalize the mathematical optimization
problem, whose constraints’ set is characterized by non-convexities. In
order to propose a computationally tractable approach, we provide a
tight parametrized convex relaxation. The resulting optimization
resolution procedure is tested numerically, using realistic data, and
benchmarked against the traditional global resolution approach, showing
that the proposed methodology yields near-optimal solutions in lower
computational time.
Note to Practitioners:
The paper is motivated by the need for efficient algorithmic procedures
which can yield near-optimal solutions to the PV arrays layout problem.
Due to the strong non-convexity of even simple instances, the existing
methods heavily rely on global or stochastic solvers, which are
computationally demanding, both in terms of resources and run-time. Our
approach acts as a baseline, from which practitioners can derive more
elaborate instances, by suitably modifying both the objective function
and/or the constraints. In fact, we focus on the minimum set of
necessary geometrical (e.g., arrays position model), astronomical (e.g.,
irradiance variation), and operational (e.g., power requirements)
constraints which make the overall problem hard. The Appendices provide
a guideline for suitably choosing the optimization parameters. All data
and simulation code are available on a public
repository.
This preprint has been accepted for publication in IEEE
Transactions on Automation Science and Engineering.
This work has been submitted to the IEEE for possible
publication. Copyright may be transferred without notice, after which
this version may no longer be accessible.