Ard

Dig into wind farm design.

An ard is a type of simple and lightweight plow, used through the single-digit centuries to prepare a farm for planting. The intent of Ard is to be a modular, full-stack multi-disciplinary optimization tool for wind farms.

The problem with wind farms is that they are complicated, multi-disciplinary objects. They are aerodynamic machines, with complicated control systems, power electronic devices, social and political objects, and the core value (and cost) of complicated financial instruments. Moreover, the design of one of these aspects affects all the rest!

Ard seeks to make plant-level design choices that can incorporate these different aspects and their interactions to make wind energy projects more successful.

Installation instructions

Ard is currently in pre-release and is only available as a source-code installation. The source can be cloned from github using the following command in your preferred location:

git clone [email protected]:WISDEM/Ard.git

Once downloaded, you can enter the Ard root directory using

cd Ard

From here, installation can be handled by pip:

pip install .

will install Ard in its most basic and static configuration. For development (and really for everyone during pre-release), we recommend a full development installation:

pip install -e .[dev,docs]

which will install in "editable mode" (-e), such that changes made to the source will not require re-installation, and with additional optional packages for development and documentation ([dev,docs]).

There can be some hardware-software mis-specification issues with WISDEM installation from pip for MacOS 12 and 13 on machines with Apple Silicon. In the event of issues, WISDEM can be installed manually or using conda without issues, then pip installation can proceed.

For user information, in pre-release, we are using some co-developed changes to the FLORIS library.

Current capabilities

For the alpha pre-release of Ard, we have concentrated on optimization of wind plants, starting from a structured layout and optimizing to minimize the levelized cost of energy, or LCOE. This capability is demonstrated in examples/LCOE_stack and tested in an abridged form in test/system/LCOE_stack/test_LCOE_stack.py. In the alpha pre-release stage, the constituent subcomponents of these problems are known to work and fully tested; any capabilities not touched in the layout-to-LCOE stack should be treated as experimental.

These cases start from a four parameter farm layout, compute landuse area, make FLORIS AEP estimates, compute turbine capital costs, balance-of-station (BOS), and operational costs using WISDEM components, and finally give summary estimates of plant finance figures. The components that achieve this can be assembled to either run a single top-down analysis run, or run an optimization.

Roadmap to future capabilities

The future development of Ard is centered around two user cases:

1. systems energy researchers who are focusing on one specific subdiscipline (e.g. layout strategies, social impacts, or aerodynamic modeling) but want to be able to easily keep track of how it impacts the entire value chain down to production, cost, and/or value of energy or even optimize with respect to it, and
2. private industry researchers who are interested in how public-sector research results change when proprietary analysis tools are dropped in and coupled the other tools in a systems-level simulation.

Ard is being developed as a modular tool to enable these types of research queries. This starts from our research goals, which are that Ard should be:

1. principled: fully documented, and adhering to best-practices for code development
2. modular and extensible: choose the parts you want, skip the ones you don't, build yourself the ones we don't have
3. effective: fully tested and testable at the unit and system level, and built with a derivative-forward approach

This, then, allows us to attempt to accomplish the technical goals of Ard, to:

1. allow optimization of wind farm layouts for specific wind resource profiles
2. target wholistic and complex system-level optimization objectives like LCOE and beyond-LCOE metrics
3. naturally incorporate multi-fidelity analyses to efficiently integrate physics-resolving simulation

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Released as open-source software by the National Renewable Energy Laboratory under NREL software record number SWR-25-18.

Copyright © 2024, Alliance for Sustainable Energy, LLC.