Introduction

Dig in to 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.

Wind farms are complicated, multi-disciplinary systems. They are aerodynamic machines (composed of complicated control systems, power electronic devices, etc.), social and political objects, generators of electrical power and consumers of electrical demand, and the core value generator (and cost) of complicated financial instruments. Moreover, the design of any one of these aspects affects all the rest!

Ard is a platform for wind farm layout optimization that seeks to enable plant-level design choices that can incorporate these different aspects and their interactions to make wind energy projects more successful. In brief, we are designing Ard to be: principled, modular, extensible, and effective, to allow resource-specific wind farm layout optimization with realistic, well-posed constraints, holistic and complex objectives, and natural incorporation of multiple fidelities and disciplines.

Documentation

The documentation is organized as follows:

• Getting Started: helpful information to get you started using Ard, including installation instructions

• Reference: dry, but hopefully useful, reference material including the API reference manual

• Explanation: discursive content on the whys, hows, and the wherefores of Ard for those that are interested

Design philosophy

The design of Ard was inspired by two use cases in particular:

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 a change in one discipline impacts the entire value chain down to production, cost, value, and/or societal outcomes of energy or even optimize with respect to these, and

2. private industry researchers who run business cases and may want to drop in proprietary analysis modules for specific disciplines while preserving some of the open-source modules of Ard.

Ard is being developed as a modular tool to enable these types of research queries. The goals during the development of Ard are to be:

1. principled:

   • robustly documented

   • adhering to best-practices for code development

2. modular and extensible:

   • choose the analysis components you want

   • skip the ones you don't

   • build yourself the ones we don't have

3. effective

   • robustly tested and testable at both unit and system levels These principles guide us to implement, using OpenMDAO as a backbone, a multi-disciplinary design, analysis, and optimization (MDAO) model of the wind farm layout problem, a toolset to accomplish the capability goals of Ard, to:

4. allow optimization of wind farm layouts for specific wind resource profiles

5. enable the incorporation of realistic but well-posed constraints

6. target holistic and complex system-level optimization objectives like LCOE and beyond-LCOE metrics

7. naturally incorporate analyses across fidelities to efficiently integrate advanced simulation

Current capabilities

For the beta pre-release of Ard, we concentrate on optimization problems for wind plants, starting from structured layouts to minimize LCOE. This capability is demonstrated for a land-based (LB) wind farm in examples/01_onshore and tested in an abridged form in test/system/ard/api/test_LCOE_LB_stack.py. In this example, the wind farm layout is parametrized with two angles, named orientation and skew, and turbine distancing for rows and columns. Additionally, we have offshore examples adjacent to the onshore example in the examples subdirectory. In the beta pre-release stage, the constituent subcomponents of these problems are known to work and have full testing coverage.

These cases start from a four parameter farm layout, compute land use area, make FLORIS estimates of annual energy production (AEP), compute turbine capital costs, balance-of-station (BOS), and operational costs elements of NREL's turbine systems engineering tool WISDEM, 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.

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