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The objective of this paper is to devise a strategy for developing a flexible tool to efficiently install a marine energy farm in a suitable area. The current methodology is applied to marine tidal current, although it can be extended to other energy contexts with some adaptations. We introduce a three-step approach that searches for marine farm sites and technological solutions. The methodology applied is based on a combination of Geographic Information Systems (GIS), multi-criteria analysis (MCA) and an optimization algorithm. The integration of GIS and MCA is at the core of the search process for the best-suited marine areas, taking into account geographical constraints, such as human activity, pressure on the environment and technological opportunities. The optimization step of the approach evaluates the most appropriate technologies and farm configurations in order to maximize the quantity of energy produced while minimizing the cost of the farm. Three main criteria are applied to finally characterize a location for a marine energy farm: the global cost of the project, the quantity of energy produced and social acceptance. The social acceptance criterion is evaluated by the MCA method, Electre III, while the optimization of the energy cost is approximated by a genetic algorithm. The whole approach is illustrated by a case study applied to a maritime area in North-West France.

Marine renewable energy has recently been a subject of increasing interest, as oceans have a tremendous amount of energy available close to many population areas [

Despite rapid technological progress in the development of marine energy technologies, the implementation of successful marine farms is still a non-straightforward issue, as many parameters should be considered: technological, spatial, economic and social. In particular, the selection of an experimental test area, as well as future farm locations is a complex geographical and multi-criteria decision problem. The selection of the best implantation site is a tricky task, due to the huge amount of constraints to take into consideration. In addition to the technical and economic constraints, the sea is a regulated space where an important number of human activities take place, especially near the coasts. We believe that the combination of GIS capabilities with multi-criteria analysis can provide a flexible approach to identify several solutions and facilitate expertise between stakeholders. The objective of the research presented in this paper is to develop such a methodology in order to find an optimal location for a marine converter farm and to choose the harnessing systems in terms of technology choice, size, power rating and the number of machines. The current approach is applied to marine current energy, but the principles of the methodology can be extended to other marine energies (e.g., wind, swell).

The problem to consider can be split into two parts. The first step is to identify the best location: implementation of an offshore tidal energy farm that can be treated as a geographical problem. Geographical constraints arise from the technological/cost limits and/or from the selected marine area. The selection process should manage the conflicting nature of these constraints, as well as quantitative and qualitative ones (particularly for societal aspects). The combination of GIS and multi-criteria analysis should help users evaluate various possibilities, taking into account multiple and conflicting criteria and objectives. The second step aims to find the optimal farm configuration and the most adequate technologies. In order to find the optimal configurations among the various technologies available, a genetic algorithm has been applied to optimize the process. This optimization evaluates the system’s cost and quantity of energy produced for a given area and suggests the optimal farm configurations and the technological solution.

The methodology developed hereafter includes three criteria: the global cost of the project, the quantity of energy produced and social acceptance; but, the principles of this approach can be extended to take into account additional criteria (

The rest of the paper is organized as follows.

Multi-criteria approach: principles.

Over the past few years, several decision-making and multi-criteria approaches have been applied for renewable energy planning and to take into account different socio-economic scenarios [

Methodology principles.

The first step of the methodology is similar to the one suggested in [

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 (_{i}_{i}_{1}_{2}_{3}_{1}_{1}_{n}_{1}_{n}_{2}_{1}_{m}_{1}_{m}_{3}_{1}_{k}_{1}_{k}

Step 1.2: Derive the multi-criteria map. The generation of this intermediate map, _{m}_{1}_{p}_{i}_{1}_{2}_{3}_{1}_{2}_{3}_{m}_{1}_{i}_{p}_{1}_{i}_{p}_{i}_{i}_{i}_{1}_{i}_{2}_{i}_{3}

Step 1.3: Apply the MCA method. The goal of this step is to reduce the dimension of _{i}

Step 1.4: Generate the decision-based map. The _{d}_{d}_{1}_{p}_{1}_{p}

First step of the methodology.

The maritime area is then divided into several zones according to the social acceptance criterion. The second step of the methodology is dedicated to the evaluation and optimization of the cost and energy production; it is divided into three internal steps (

Second step of the methodology.

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, _{m}_{m}_{1}_{q}_{1}_{q}

Step 2.2: Optimize and evaluate the possible solutions using a genetic algorithm. For each spatial unit, _{i}_{j}_{=1, ... ,p}, _{j}_{=1, ... ,p}) for each defined spatial unit (_{i}

Step 2.3: Project the optimization solutions to each spatial unit of the multi-criteria map, _{m}_{m }_{1}_{i}_{q}_{i}_{i}_{i}_{j}_{=1, ... , p}, _{j}_{=1, ... , p})_{i}

The third step of the methodology is to apply an MCA to find the best solutions. Electre III ranks the different alternatives. The possibility offered by the Electre III method is to affect specific weights for each criterion and to reduce the choice of farm configurations proposed by the optimization algorithm. The principles of this approach are described in the next section.

The insertion of a new maritime activity should take into account socio-ecological constraints that are complex, heterogeneous, dynamic and prone to nonlinear and often abrupt changes [

Sea activities (adapted from [

The estimation of the produced electricity is based on the available resource, the performance of the extracting system and the running time. In the approach developed, the performance basically depends on the following components: turbine, gearbox, generator, power converter and transmission elements. The non-operational time is integrated in this estimation and is calculated using the downtime statistical rates of each component.

In the case of a marine current turbine, the resource is estimated with the tidal current velocity, which can be predicted for each hour using a model developed by the French National Hydrography and Oceanographic Service (SHOM) [

For the turbine, three solutions are considered:

vertical axis turbine;

horizontal axis turbine with yaw;

horizontal axis turbine without yaw.

The vertical axis (VA) turbine harnesses the current from all directions, but the efficiency is lower than the horizontal axis (HA) turbine. The HA turbine is characterized by a capture angle (20°). Currents whose orientation is higher than 10° of the turbine’s axis are considered not harnessed. The current is also modified inside the capture cone, modelled with a cosines law [

For the generator/converter/drive train associations, two solutions are considered:

three-stage gearbox with DFIG (double-fed induction generator),

direct drive with PMSG (permanent magnet synchronous generator).

These different associations are proposed in order to illustrate the compromise between an expensive generator/power electronics system (PMSG) and the DFIG, where the cost of the power electronics is significantly reduced, but that requires a very failure-prone gearbox with a high level of maintenance [

The global cost of the project should include the farm’s initial cost, installation/dismantling and maintenance operations’ costs. A farm is generally composed of the following components: a set of turbines, a transformer station located at sea or on land (depending on the distance to the coast and the total power of the farm), a network of submarine inter-turbines cables (buried or not, depending on the seafloor type), a network of submarine cables for transmitting the energy from the offshore substation to the shore station and a connection cable to the electric distribution grid. Accordingly, the cost of the farm depends on two main elements. The first one is that which rests on the technological characteristics (design, layout/configuration), while the second one depends on the parameters, including the geographical position of the farm. For instance, the cost of a turbine depends on the technologies and components used. Some cost components, such as the foundation of the system, also depend on the location (in relation with depth and seabed geological properties).

The installation/dismantling cost is estimated by a parameter that includes the distance to the harbor, the vessel’s characteristics and the number of turbines, as suggested in [

The case study considers a scenario of marine current turbine implantation in the Iroise Sea located in the north-west of France, where areas with high marine current velocity have been identified. Two areas are particularly well-known: the “Raz de Sein area” near Sein Island and the Fromveur pass area near Ouessant Island. ^{−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.

Study area.

Let us apply the strategy described by Step 1 of the methodology in order to evaluate the social acceptance criteria. The Electre III outranking MCA method has been applied. In this example, the demonstration is restricted to professional fishery activities, which are the principal human activity nearby the Sein area. Different kinds of fishery practices are identified in the study area. The knowledge of the location of the fishery activities comes from the regulation and seabed properties [

Floating line fishing areas.

Net fishing areas.

Electre III also incorporates the fuzzy dimension of a decision-based process. The outranking relation,

Dredge and trawling areas.

Ground line fishing areas.

The veto threshold,

Threshold values and weights.

Constraints | Weight ( |
Indifference Threshold ( |
Preference Threshold ( |
Veto Threshold ( |
---|---|---|---|---|

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

Nets | 2 | 0 | 1 | 3 |

Floating lines | 1 | 0 | 1 | 3 |

Ground lines | 2 | 0 | 1 | 3 |

Classification of the study area according to fishery activities.

As for the previous steps and before applying the optimization algorithm, a multi-criteria map has to be built by taking into account the geographical constraints involved in the estimation of the cost and energy. These constraints modify the suitability resources map. The new map is generated by an overlay of bathymetry, current resource and social acceptance. ^{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, _{max}_{max}_{max}_{su}

The maximum radius allowed for a specific spatial unit is denoted as _{max}_{max} ≤

Next, the installation and maintenance costs depend on the harbor distance. For the considered case study, this harbor is that of Brest, which is planned to house a complete set of specific marine energy maintenance and installation devices.

(

In fact, the design of a marine current turbine cannot be treated by a classic and deterministic optimization method. Due to the non-linearity of the variables considered and the high number of possible combinations, stochastic methods, such as inductive learning, neural networks and genetic algorithms, should be preferred [

the turbine type (TT): VA or HA without yaw or HA + yaw

the rotor radius (R): 2.5 m to _{max}

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 _{max}

In the case of a VA turbine, the rotor radius corresponds to half the turbine’s height. The width of the VA turbine is calculated in such a way that its area is the same as that of an HA turbine with an equivalent radius.

In order to illustrate the solution exhibited by the application of the optimization algorithm, let us consider a spatial unit (green contour in ^{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 ^{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 [

Distribution of current.

For the spatial unit considered, the current is characterized by a dissymmetric distribution. _{max}

The genetic algorithm, applied with the same parameters, is performed for all spatial units (^{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.

In

The results show that a majority of the most suitable spatial units have currents, whose velocities are superior to 1 ms^{−1} during at least 50% or 60% of the time, except for

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 |

Threshold values and weights.

Constraints | Weight (k) | Indifference Threshold ( |
Preference Threshold ( |
Veto Threshold ( |
---|---|---|---|---|

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

Cost | 3 | 0 | 0.1 (M€) | 1 (M€) |

Social acceptance | 1 | 0 | 1 | 3 |

Areas ranked considering energy, cost and acceptance.

In the case study, no information about the budget allocated for the marine current turbine has been considered so far. As the cost is a minimized criterion with the highest weight, the alternatives generated as best solutions tend to be low-cost projects (

Spatial unit ranking and turbine characteristics.

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 |

The research presented in this paper introduces an approach whose objective is to find the most suitable sites and marine farm preliminary design. The method developed integrates and combines GIS, multi-criteria analysis and an optimization algorithm. A first spatial structuring and ranking of the study area according to some social acceptance criteria has been applied using Electre III. For each part of the study area, several types of marine current turbines have been evaluated using a genetic-based optimization process based on cost and energy. A final ranking has been applied re-using Electre III for the three criteria, that is, social acceptance, cost and energy produced. A classification of the areas and the turbine characteristics for each spatial subdivision has been performed. The whole approach is illustrated in a context in which the project manager has to give priority to a low-cost project rather than a project based on social or energy criteria, but the principles behind the decision-based process can be adapted to different scenarios and criteria depending on user choices.

Three criteria have been taken into consideration by our modelling approach. The social acceptance has been limited in this work to fishery areas, but additional geographical constraints can be taken into account with some minor adaptations of our approach. The methodology can also be enriched by some seasonal characteristics of the different activities considered. Cost estimation has been essentially based on the extrapolation of offshore wind turbine current technological knowledge; the parameters used in the model proposed can also be modified as soon as additional feedback on marine current turbines is available.

In further work, a more precise subdivision of the study area will be explored, also taking into account other activities, such as water-based activities or ship corridors and refining the granularity of the temporal dimension. Indeed, the integration of additional spatial criteria is likely to increase the number of spatial subdivisions and decrease the area of the resulting spatial units in the study area, thus potentially reducing the number of turbines that can be installed. We plan to apply some spatial clustering and aggregation techniques, as well as to explore the idea of sharing different technologies at neighboring places. A more precise location of the turbines into the respective spatial units is also a track to explore to increase the efficiency of the marine farm. Other criteria, such as environmental constraints, will be also considered in further work.

The authors thank the reviewers for their useful and constructive comments and suggestions. Nicolas Maslov’s research has been founded by the Brittany Region. The authors also thank the SHOM for providing the marine current data.

Nicolas Maslov is the main contributor of this paper which presents some of the main results of his doctoral research that has been developed under the join supervision of David Brosset, Christophe Claramunt and Jean-Frédéric Charpentier. Jean-Frédéric Charpentier has been particularly involved in the supervision of the marine energy components, while David Brosset and Christophe Claramunt have been guiding Nicolas Maslov for the GIS and genetic algorithm developments. All the computed results and data processing presented in this paper are Nicolas Maslov’s own work.

The authors declare no conflict of interest.