Page 247 - Fundamentals of Ocean Renewable Energy Generating Electricity From The Sea

Chapter 9

Optimization

To make the best use of an ocean renewable energy resource, for example,
to maximize electricity generation whilst minimizing environmental impacts,
it is necessary to perform optimization. Optimization theory has wide-ranging
applications, but here we consider applications to offshore wind, tidal energy,
and wave energy. Many intraarray optimization topics are common across the
majority of ocean renewable energy resources, for example, optimizing device
spacing and minimizing variability. We also consider interarray optimization
within the context of tidal energy plants—a predictable resource with strong
potential for phasing of power stations along a tidal wave, hence minimizing
variability in aggregated power output. Finally, we introduce tools available for
optimizing wind farms (HOMER) and tidal energy arrays (OpenTidalFarm).

9.1 INTRODUCTION TO OPTIMIZATION THEORY
Optimization is closely related to decision making; hence, we face many
optimization problems in our daily life as an individual, company, organization,
or government. When faced with several alternatives, we are always interested
in the best alternative or decision. For instance, if you are planning a trip, you
can choose amongst several alternative forms of transport, including flights,
trains, rental cars, or bus. The best mode of transport would depend on your
criteria, which is technically called the objective function. If minimizing the
cost is the criteria, the best option may be transport by bus. If the objective is to
minimize travel time, flights might be the best option. Any optimization problem
needs an objective function. Usually, the objective function will be either
minimized or maximized. Depending on the application, other names such as
cost function (minimize in economy), profit function (maximize in marketing),
fitness (maximize in genetic programming through natural selection), energy
function (minimize in physics), or error (minimize in numerical modelling) are
used. As an ocean science example, model parameter estimation (in calibration,
inverse modelling, or data assimilation) is an optimization problem. Given a
set of observed data, the best model parameters (e.g. bottom drag coefficient,
wind drag coefficient, boundary conditions, etc.) are selected such that the error
between model simulations and observations is minimized.

Fundamentals of Ocean Renewable Energy. https://doi.org/10.1016/B978-0-12-810448-4.00009-4
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