Page 255 - Fundamentals of Ocean Renewable Energy Generating Electricity From The Sea
P. 255

Optimization Chapter | 9 245


                                              C t
                                    minimize                            (9.6)
                                              E p
                                   subject to  constraints
             where C t can be chosen as the total cost of the project, and E p is the total energy
             production during the lifetime of the project. Alternatively, the investment cost
             divided by AEP can be minimized, which does not take into account the interest
             rate, operation and maintenance cost, and the time value of money.


             Constraints
             Micro-siting is subject to several constraints. The most common constraint is
             the available area/plot for the farm (i.e. all turbines should be located inside the
             array). The minimum distance between turbines can also be a constraint due
             to technical issues or standards/regulations. Another constraint is the maximum
             available investment. Although a large project can produce reasonable and lower
             LCOE, the resources for financing a project are usually limited. The layout of a
             wind farm may be subjected to some constraints to reduce the visual impact or
             improve aesthetic design (e.g. Fig. 9.3B). Finally, forbidden areas in a farm can
             limit the search space. These areas may be excluded due to foundation issues,
             dedicated to electrical cable routes, etc.

             Solution Techniques
             Due to the complexity of the objective function and constraints of the wind
             farm optimization problem, classical optimization techniques that are often
             more suited to convex and continuous problems are less popular. Therefore,
             metaheuristic methods are more effective and common in this area. Metaheuris-
             tic approaches provide satisfactory solutions, but they do not necessarily find
             the theoretical optimum. Genetic algorithm, particle swarm optimization, the
             greedy algorithm, evolutionary algorithm, and ant colony are metaheuristic
             approaches applied in this area [5,9].


             9.2.2 TEC Array Optimization
             Interdevice Spacing
             The 2009 EMEC guide the assessment of tidal energy resources [10] recom-
             mends that the lateral spacing between devices (the distance between axes)
             should be two-and-a-half times the rotor diameter (2.5D), and the downstream
             spacing should be 10D—both based on the assumption of horizontal axis
             turbines. Further, the EMEC guide states that devices should be positioned in
             an alternating downstream (i.e. staggered) arrangement (Fig. 9.5). Although
             the exact details of device spacings is device-specific and can be debated (and
             indeed no justification is given for the values 2.5D and 10D, although we can
             assume that such guidance has propagated through from the wind industry),
   250   251   252   253   254   255   256   257   258   259   260