Ocean energy holds out great potential for supplying remote maritime areas with their energy requirements, where the grid size is often small and unconnected to a continental grid. Thanks to their high maturity and competitive price, solar and wind energies are currently the most used to provide electrical energy. However, their intermittency and variability limit the power supply reliability. To solve this drawback, storage systems and Diesel generators are often used. Otherwise, among all marine renewable energies, tidal and wave energies are reaching an interesting technical level of maturity. The better predictability of these sources makes them more reliable than other alternatives. Thus, combining different renewable energy sources would reduce the intermittency and variability of the total production and so diminish the storage and genset requirements. To foster marine energy integration and new multisource system development, an up-to-date review of projects already carried out in this field is proposed. This article first presents the main characteristics of the different sources which can provide electrical energy in remote maritime areas: solar, wind, tidal, and wave energies. Then, a review of multi-source systems based on marine energies is presented, concerning not only industrial projects but also concepts and research work. Finally, the main advantages and limits are discussed.

This paper presents an original approach aiming to obtain the optimum configuration of an offshore wind farm (OWF). Thanks to cost models, we take into account the costs off all the parts of the electrical network. The optimization platform, based on a genetic algorithm, also allows us to evaluate the reliability of an offshore wind farm (OWF). This approach is used to compare the topologies obtained in different cases on a real OWF, the 'Banc de Guerande'. The selected design of the OWF is compared to results obtained using different methods: cost optimization, or both cost and reliability optimization. The optimization results show that the ring topologies give interesting results if the total cost including the expected energy not supplied (EENS) during the lifetime of the offshore wind farm are taken into account. The number of offshore substations and their positions are also considered in the optimization: we show that the introduction of more substations in order to obtain better performances is possible at a reasonable cost. The presented optimization tool can help to design such farms taking into account several constraints.

Intermittent power interruptions and blackouts with long outage durations are very common, especially on weak distribution grids such as in developing countries. This paper proposes a hybrid photovoltaic (PV)-battery-system sizing optimization through a genetic algorithm to address the reliability in fragile grids measured by the loss of power supply probability (LPSP) index. Recorded historical outage data from a real stochastic grid in Ethiopia and measured customer load is used. The resulting hybrid-system Pareto solutions give the flexibility for customers/power utilities to choose appropriate sizes based on the required reliability level. To evaluate the sizing solutions’ robustness, this work considers and compares grid outage modeling through two different approaches. The first is a Markov model, developed to be minimally implemented with limited outage data available. The second is a Weibull model, commonly used to describe extreme phenomena and failure analysis. It is more faithful in reproducing the dispersion of outage events. Using these models, the effectiveness and performance of the PV-battery system is verified on a large number of simulated outage scenarios, to estimate the real performance of the optimized design. It leads to a more accurate evaluation of the behavior of a renewable power system to a weak and unreliable electrical grid.

The objective of this paper is to present a new criterion for robust sizing of (Photovoltaic (PV) - Fuel cell (FC) - Batteries (Bat)) installation, with respect to the variability of the resource. After a presentation and modeling of the studied system, a multi-objective sizing using a genetic algorithm NSGA-II have been introduced. The study takes into account the aging of batteries and fuel cell for a total lifetime of 25 years for all the system. The sizing is based on an energy management by simulation of the system operation using 5 years of measurements from 2009 to 2013. A new criterion has been introduced for the choice of the reference solar radiation profile which considered here as the energy ressource of the system. This profile has been selected based on the analysis of the system sizing's robustness.

In this paper, a design methodology for synchronous reluctance machines (SynRM) working with variable torque and speed profiles was presented. Unlike conventional solutions which size the machine considering a reduced number of working points in order to reduce the computation time, the solution proposed in this paper takes into account all the points which allow for better management of the constraints along the cycle to avoid an oversizing of the machine. To solve this problem with a reduced computation time, the geometry of the motor as well as the control strategy were optimized in two steps. In the first step, the d-q axis stator currents were analytically expressed. In the second step, the geometry was optimized with the use of a genetic algorithm. As an application of this method, the case of a small and low-cost electric vehicle (EV) was chosen with the objective of minimizing both the mass and the energy lost for the standardized urban dynamometer driving schedule (UDDS). The method was based on the use of a 1-D analytical model which was validated by a 2D finite element analysis (FEA).