# A Geographical-Based Multi-Criteria Approach for Marine Energy Farm Planning

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Methodology Principles

- -
- Step 1.1: Generate a series of constraint maps. Each map represents a geographical area with a specific conflict source. Each constraints map is respectively divided into a number (n, m, k in Figure 3) of spatial units, u
_{i}, having an attribute, a_{i}, describing the activities. With respect to the example presented in Figure 3, these maps are C_{1}for fishing area, C_{2}for buried cables and C_{3}for military activities and have n, m and k spatial units, respectively.C_{1}: [u_{1}, …, u_{n}] → [a_{1}, …, a_{n}]C_{2}: [u_{1}, …, u_{m}] → [a_{1}, …, a_{m}]C_{3}: [u_{1}, …, u_{k}] → [a_{1}, …, a_{k}] - -
- Step 1.2: Derive the multi-criteria map. The generation of this intermediate map, C
_{m}, is obtained by the overlay of constraint maps. This intermediate map is composed of a set of p spatial units uʹ_{1}, …, uʹ_{p}. This means that each spatial unit, uʹ_{i}, is derived from the intersection of spatial units from C_{1}and C_{2}and C_{3}and is valued by three attributes a(C_{1}), a(C_{2}) and a(C_{3}).C_{m}: [uʹ_{1}, …, uʹ_{i}, …, uʹ_{p}] → [aʹ_{1}, …, aʹ_{i}, …, aʹ_{p}] with uʹ_{i}→ aʹ_{i}= {a_{i}(C_{1}), a_{i}(C_{2}), a_{i}(C_{3})} - -
- Step 1.3: Apply the MCA method. The goal of this step is to reduce the dimension of uʹ
_{i}to a single attribute, A, applying the aggregation method of Electre III. - -
- Step 1.4: Generate the decision-based map. The p spatial unit, uʹ, of this final map, C
_{d}, is the same as the multi-criteria map. However, each of these units has a single attribute. The unique value assigned to each spatial unit reflects the final social acceptance ranking of each sub-area.C_{d}: [uʹ_{1}, …, uʹ_{p}] → [A_{1}, …, A_{p}]

- -
- Step 2.1: Generate a map (as Step 1.2) derived from an overlay of the social acceptance map with geographical constraints involved in the estimation of the cost and energy. The geographical maps considered here are the bathymetry, marine currents and seafloor geological characteristics. This derived map, Cʹ
_{m}, is split into q spatial units, uʹʹ, having only one attribute: the value of social acceptability. This step generates a map of the study area segmented into homogeneous sub-areas. Values, such as the depth, are the parameters used in the appraisal of the cost.Cʹ_{m}: [uʹʹ_{1}, …, uʹʹ_{q}] → [A_{1}, … A_{q}] - -
- Step 2.2: Optimize and evaluate the possible solutions using a genetic algorithm. For each spatial unit, uʹʹ
_{i}, the optimization process searches, among all the possible configurations of the farm (number of machines, design choices, individual rating of the machines, etc.), for those that maximize the energy produced while minimizing the cost. The genetic algorithm is used to define a Pareto frontier that takes into account these two objectives. This Pareto frontier denotes the optimal solutions (i.e., solutions that minimize the cost for a set of given values of produced energy), as it exhibits a set of solutions that dominate the others (i.e., a solution, A, dominates a solution, B, if A is better than B regarding all dimensions considered). The optimal solutions exhibited by this Pareto frontier are ordered. This process provides the p best solutions for the cost-energy couple, (Cost_{j}_{=1, ... ,p}, E_{j}_{=1, ... ,p}) for each defined spatial unit (uʹʹ_{i}). - -
- Step 2.3: Project the optimization solutions to each spatial unit of the multi-criteria map, Cʹ
_{m}, as new attributes (cost and energy). Therefore, each spatial unit, Uopt, is characterized by three criteria. The social acceptance, A, and the p pairs of values related to the optimal cost and an energy corresponding to the optimal farm configurations, which have been found in the previous step.Cʹ_{m }: [uʹʹ_{1}, …, uʹʹ_{i}, …,uʹʹ_{q}] with uʹʹ_{i}= Uopt_{i}→ [A_{i}, (Cost_{j}_{=1, ... , p}, E_{j}_{=1, ... , p})_{i}]

## 3. Multi-Criteria Approach

#### 3.1. Social Acceptance

**Figure 5.**Sea activities (adapted from [16]).

#### 3.2. Energy Assessment

- -
- vertical axis turbine;
- -
- horizontal axis turbine with yaw;
- -
- horizontal axis turbine without yaw.

- -
- three-stage gearbox with DFIG (double-fed induction generator),
- -
- direct drive with PMSG (permanent magnet synchronous generator).

#### 3.3. Marine Farm System Cost

## 4. Case Study

^{−1}at least during 30% of the time for a year. Our study is focused on “Raz de Sein”, which is also a high-density human activity area and particularly for fishery. In order to find the place generating the fewest conflicts between sea users, the first step consists of classifying the study area in different zones according to the social acceptance criteria.

#### 4.1. Social Acceptance Evaluation Using Electre III

**P**b (a is strongly preferred to b): g(a) − g(b) > p

**Q**b (a is weakly preferred to b): q < g(a) − g(b) ≤ p

**I**b (a is indifferent to b; and b to a): |g(a) − g(b)| ≤ q

Constraints | Weight (k) | Indifference Threshold (q) | Preference Threshold (p) | Veto Threshold (v) |
---|---|---|---|---|

Trawling/dredge | 3 | 0 | 1 | 3 |

Nets | 2 | 0 | 1 | 3 |

Floating lines | 1 | 0 | 1 | 3 |

Ground lines | 2 | 0 | 1 | 3 |

#### 4.2. Cost and Energy: Evaluation Using a Genetic Algorithm

^{2}) are spatially aggregated when they have similar current characteristics. Each spatial unit is, at this step, a homogenous part of the study area according to the social acceptance criteria and the two parameters involved in the estimation of the cost and energy produced. In order to estimate these two parameters, other attributes are added to each of these spatial units: their area, distance to the harbor and distance to the electric grid. The area allows one to define the maximum number of turbines, NT

_{max}, which can be installed in the spatial unit (NT

_{max}is at least equal to one, as the installation of a single turbine does not need a large area). This number depends on a minimum distance between two devices, which also depends on the turbine radius, R. The turbines are supposed to be placed as a patchwork with a device spacing approximated to seven times its diameter, D. NT

_{max}is defined as follows:

_{su}is the area of the spatial unit.

_{max}and is evaluated as a function of the water depth in the area and surface and bottom margins:

_{max}≤ depth-margins

**Figure 12.**(

**a**) Multi-criteria map social acceptance ranking visualization. (

**b**) Multi-criteria map current resource visualization.

- -
- the turbine type (TT): VA or HA without yaw or HA + yaw
- -
- the rotor radius (R): 2.5 m to R
_{max}with a step of 0.5 m - -
- the drive train configuration (DT): Direct-drive PMSG or DFIG + gearbox
- -
- the rating power (P
_{n}) of DT: 0.1 to 3MW with a step of 0.1 MW - -
- the number of turbines (NT) one to NT
_{max}

^{2}and a mean depth of 24 m. The distance of this unit from Brest harbor and from the fictive grid connection point are, respectively, 48 km and 10 km. The marine current distribution is described by Figure 13. A surface of (7D)

^{2}is reserved for one turbine. A top margin of 5 m is suggested to allow small boat navigation and to minimize turbulence and swell effects. Moreover, a five-meter bottom clearance is recommended as a minimum distance to avoid damage by materials moving at the seabed and to minimize the hydrodynamic effects related to the boundary layer [23]; as the minimum radius considered is 2.5 m and 10 m are allocated for bottom and top clearance (this explains why depths under 15 m have been previously removed).

_{max}= 32. It can also be noticed that the cost of the energy decreases when the number of turbines increases, due to equipment sharing effects.

#### 4.3. Final Ranking

^{2}house. That means that if, for two alternatives, the difference of the energy is less than 10 MWh, these two alternatives are considered as equivalent under this criterion. When the difference lies between the indifference and preference thresholds, a linear interpolation is performed. This allows one to derive a fuzzy outranking relation that permits one to state how an action weakly outranks another one. The cost preference threshold is set to 100 k€ (this being the lowest value that can be attributed, due to the cost approximation). The 2265 possible solutions are sorted into 1376 ranks for each spatial unit. The best alternative for a given spatial unit is having the lowest rank among the alternatives belonging to that unit. Figure 14 gives the classification of the study area, according to the best alternative of the spatial unit, based on the three criteria considered.

^{−1}during at least 50% or 60% of the time, except for C, for instance. The two first spatial units considered (A and B) are located in the lowest fishery area and correspond to a low cost.

**Table 2.**Results of the genetic algorithm optimization. PMSG, permanent magnet synchronous generator; TT, turbine type; NT, number of turbines; DT, direct-drive turbine.

Alternatives | Energy (MWh/year) | Cost (M€) | P_{n} (MW) | R (m) | TT | NT | DT | €/MWh (20 years) |
---|---|---|---|---|---|---|---|---|

1 | 1068 | 5.7 | 0.3 | 7 | HA + yaw | 1 | PMSG | 267 |

2 | 1210 | 5.8 | 0.4 | 7 | HA + yaw | 1 | PMSG | 240 |

3 | 1469 | 6.2 | 1 | 7 | HA + yaw | 1 | PMSG | 211 |

4 | 1478 | 6.3 | 1.1 | 7 | HA + yaw | 1 | PMSG | 213 |

5 | 1493 | 6.4 | 1.4 | 7 | HA + yaw | 1 | PMSG | 214 |

6 | 1496 | 6.9 | 2.1 | 7 | HA + yaw | 1 | PMSG | 231 |

7 | 2137 | 8.3 | 0.3 | 7 | HA + yaw | 2 | PMSG | 194 |

8 | 2420 | 8.4 | 0.4 | 7 | HA + yaw | 2 | PMSG | 174 |

9 | 2956 | 9.3 | 1.1 | 7 | HA + yaw | 2 | PMSG | 157 |

10 | 2985 | 9.7 | 1.4 | 7 | HA + yaw | 2 | PMSG | 162 |

11 | 2993 | 10.2 | 1.8 | 7 | HA + yaw | 2 | PMSG | 170 |

12 | 3205 | 10.8 | 0.3 | 7 | HA + yaw | 3 | PMSG | 168 |

13 | 3630 | 11 | 0.4 | 7 | HA + yaw | 3 | PMSG | 152 |

14 | 4407 | 12.2 | 1 | 7 | HA + yaw | 3 | PMSG | 138 |

15 | 4433 | 12.4 | 1.1 | 7 | HA + yaw | 3 | PMSG | 140 |

16 | 4478 | 13 | 1.4 | 7 | HA + yaw | 3 | PMSG | 145 |

17 | 4489 | 13.8 | 1.8 | 7 | HA + yaw | 3 | PMSG | 154 |

18 | 8867 | 21.6 | 1.1 | 7 | HA + yaw | 6 | PMSG | 122 |

Constraints | Weight (k) | Indifference Threshold (q) | Preference Threshold (p) | Veto Threshold (v) |
---|---|---|---|---|

Energy | 1 | 10 (MWh) | 300 (MWh) | 3000 (MWh) |

Cost | 3 | 0 | 0.1 (M€) | 1 (M€) |

Social acceptance | 1 | 0 | 1 | 3 |

Spatial Unit | Rank | Energy (MWh/year) | Cost (M€) | P_{n} (MW) | R (m) | TT | NT | NT_{max} | DT | €/MWh (20 years) |
---|---|---|---|---|---|---|---|---|---|---|

A | 1 | 2710 | 6.3 | 0.5 | 11 | HA | 1 | 5 | PMSG | 116.2 |

B | 3 | 2860 | 6.5 | 1.1 | 11 | HA + yaw | 1 | 4 | PMSG | 113. 6 |

C | 6 | 7426 | 11.9 | 0.6 | 12 | HA | 3 | 10 | PMSG | 80.1 |

D | 7 | 3221 | 7.3 | 2.1 | 11 | HA + yaw | 1 | 7 | PMSG | 113.3 |

## 5. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

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Maslov, N.; Brosset, D.; Claramunt, C.; Charpentier, J.-F.
A Geographical-Based Multi-Criteria Approach for Marine Energy Farm Planning. *ISPRS Int. J. Geo-Inf.* **2014**, *3*, 781-799.
https://doi.org/10.3390/ijgi3020781

**AMA Style**

Maslov N, Brosset D, Claramunt C, Charpentier J-F.
A Geographical-Based Multi-Criteria Approach for Marine Energy Farm Planning. *ISPRS International Journal of Geo-Information*. 2014; 3(2):781-799.
https://doi.org/10.3390/ijgi3020781

**Chicago/Turabian Style**

Maslov, Nicolas, David Brosset, Christophe Claramunt, and Jean-Frédéric Charpentier.
2014. "A Geographical-Based Multi-Criteria Approach for Marine Energy Farm Planning" *ISPRS International Journal of Geo-Information* 3, no. 2: 781-799.
https://doi.org/10.3390/ijgi3020781