Search results

The purpose of this study is toward a better understanding of the interplay between the design and the operation under uncertainties of a simple distributed energy system (DES), by analyzing the sensitivity of the operation strategy over the size of the assets.

A two-step framework is developed in this work: first, the equipment sizes are obtained solving an integrated design approach where an operation strategy F^d is embedded in a design loop; then, once the sizes have been fixed, the DES is evaluated with an operation strategy F^a (which can be the same as F^d). The operation strategies F^d and F^a are not necessarily the same, so the objective of the paper is to study the interplay between the design and the operation by varying the optimality level of the operation strategies in both phases.

The results show that the design of DES cannot be approached without considering its close relationship with the operation strategy. Indeed, the design method needs to be chosen according to the performance of the operation policy finally used in real life: no matter if the operation strategies are the same in both phases but they must lead to a similar level of optimality in terms of operating performance.

The originality of this work is to shed light on the importance of the operation strategy in the design procedure as it seems rarely addressed, to the best of the authors’ knowledge, in the literature. Indeed, most of the paper dealing with stochastic design of DES solves single large two-stage problems without discussing the way power flows are finally controlled in real life. The optimal design and operation of DES is rarely addressed conjointly, this study aims at bridging the gap between these two isolated scientific communities.

The effectiveness of full passive wind turbine (WT) systems has been recently demonstrated. Such low cost and reliable structures without active control and with a minimum number of sensors can be efficient only if the system design parameters are mutually adapted through an integrated optimal design approach. The purpose of this paper is to analyze the effectiveness of a passive WT with regard to the variations of PMSG electrical parameters.

This work is more specifically devoted to the sensitivity analysis of a passive WT system according to the electrical variable variations of a Permanent Magnet Synchronous Generator (PMSG). It also investigates the interest of a robust design approach for reducing the sensitivity of the WT efficiency to the most influencing variables.

It is shown that efficiency of the passive WT system is rather sensitive to the variation of the stator flux and DC voltage at the system output.

A robust design approach is investigated in order to reduce the passive WT sensitivity to the stator flux and DC voltage variations.

A wide number of applications requires classifying or grouping data into a set of categories or clusters. The most popular clustering techniques to achieve this objective are K‐means clustering and hierarchical clustering. However, both of these methods necessitate the a priori setting of the cluster number. The purpose of this paper is to present a clustering method based on the use of a niching genetic algorithm to overcome this problem.

The proposed approach aims at finding the best compromise between the inter‐cluster distance maximization and the intra‐cluster distance minimization through the silhouette index optimization. It is capable of investigating in parallel multiple cluster configurations without requiring any assumption about the cluster number.

The effectiveness of the proposed approach is demonstrated on 2D benchmarks with non‐overlapping and overlapping clusters.

The proposed approach is also applied to the clustering analysis of railway driving profiles in the context of hybrid supply design. Such a method can help designers to identify different system configurations in compliance with the corresponding clusters: it may guide suppliers towards “market segmentation”, not only fulfilling economic constraints but also technical design objectives.

The paper presents the optimization of the power flows inside a microgrid with renewable sources and two kinds of storage. The considered microgrid consists in commercial buildings with maximum daily peak value of 50 kW, photovoltaic arrays with total capacity of 175 kW, a 50 kW/50 kWh high speed flywheel storage and a 50 kW/50 kWh set of Li-ion accumulators.

The power flows in the microgrid are optimized the day ahead at one hour discretization in order to minimize the electric bill. Several scheduling strategies are proposed for solving the corresponding optimization problem including standard deterministic methods, stochastic algorithms and hybrid heuristics.

All scheduling strategies investigated in the paper are compared with regard to their accuracy and computational time.

Beyond the comparison of different algorithms devoted to the power flow optimization problem, our approach also addresses the integration of battery ageing in the scheduling strategy.