Hasnae Bilil

A new multi-objective particle swarm optimization for reactive power dispatch

This paper proposes a new approach of computation using particle swarm in order to resolve multiobjective problems quickly and effectively. This approach is called accelerated multiobjective particle swarm which incorporates vector function as objective function and uses matrix computation to develop the Pareto front unlike the existing multi-objective algorithms which use an external archive. We also propose a novel method of initialization that contributes also in the acceleration of the algorithm. We apply this approach to resolve multi-objective reactive power dispatch problem. Simulations of the proposed algorithm are encouraging for very short CPU time.

Dynamic assignment of renewable energy tokens in a collaborative microgrids

Nowadays, electrical energy consumption and energy prices have increased. Thus, the microgrids (μGs) rely on renewable energy receive more attention from consumers. New neighborhoods opt for green philosophy where the most consumed energy comes from renewable generation. Indeed, each residence could be supplied from its own solar and wind generation or from centrally located power plant. However, the latters present many problems in efficiency and reliability. This paper proposes a new approach based on a dynamic assignment of renewable energy tokens (DARET) algorithm to add a smart behavior to μGs. The proposed algorithm allows residential μGs of a small geographic area to dynamically collaborate and share their individual green energy generation in order to supply their overall load. The sharing is dynamically updated in short-term by exchanging data, in terms of individual demands and supplies, between the consumers over wireless links.

Non-uniform hierarchical modulation for wireless communication in smart grid

The deregulation and liberalization of power market allows greater integration of renewable power resources at all stages of electrical networks but it makes the balance between supply and demand more difficult to reach. Thus, efficient and reliable communications between electrical network nodes must be guaranteed in order to dynamically use the power system as optimally as possible. The electrical protection decisions and real time pricing are among the categories of power system information. However, these categories have different priorities depending on their importance in the whole power system. Communication system must then take into consideration such requirements. This paper proposes the use of wireless links to transmit these information differentially protected by using non-uniform hierarchical quadrature amplitude modulation (16-QAM) where high (low) reliable symbols are assigned to critical (basic) information.

Power management for collaborative microgrids

Microgrids integrating green energy receive more attention from consumers since they present significant benefits including the ability to rely on more localized sources of power generation and keeping the environment healthy and safe. Thus, neighborhoods/cities opt for providing energy from green resources. However, the integration of renewable energies could lead to certain problems such as the destabilization and destruction of the power grids. For this reason, it is essential to build a robust management system and efficient communication between microgrids devices. This paper proposes a new approach based on a dynamic assignment of renewable energy tokens (DARET) algorithm and investigates a non-uniform hierarchical modulation as a data transmission technique over wireless channels. The proposed algorithm allows residential microgrids of a small geographic area to dynamically collaborate and share their individual green energy generation in order to supply their overall loads. The sharing is dynamically updated in short-term by exchanging data, in terms of individual demands and supplies, between the consumers over wireless links.

Dynamic Appliances Scheduling in Collaborative MicroGrids System

In this paper, a new approach that is based on a collaborative system of MicroGrids (MGs) is proposed to enable household appliance scheduling. To achieve this, appliances are categorized into flexible and non-flexible deferrable loads (DLs), according to their electrical components. We propose a dynamic scheduling algorithm where users can systematically manage the operation of their electric appliances. The main challenge is to develop a flattening function calculus (reshaping) for both flexible and non-flexible DLs. In addition, implementation of the proposed algorithm would require dynamically analyzing two successive multiobjective optimization (MOO) problems. The first one targets the activation schedule of non-flexible DLs and the second one deals with the power profiles of flexible DLs. The MOO problems are resolved by using a fast and elitist multiobjective genetic algorithm (NSGA-II). Finally, in order to show the efficiency of the proposed approach, a case study of a collaborative system that consists of 40 MGs registered in the load curve for the flattening program has been developed. The results verify that the load curve can indeed become very flat by applying the proposed scheduling approach.

Hybrid renewable energy system design using multobjective optimization

The aim of this work is to present a comparative study between different structures of hybrid renewable energy systems (HRES). Three main combinations of photovoltaique (PV), wind turbine (WT), diesel generators (DG) and battery bank (BT) are considered in order to present to the system designer all possibilities he can need as basic data to make decision. For each scenario, we investigate multi-objective genetic algorithm to optimize simultaneously the annualized system cost (ASC) and the reliability, which is represented by renewable energy fraction (REF) and the Loss-of-Load Probability (LOLP) considered as the operating time of the diesel generator. The proposed method was applied to the analysis of HRES which can supply either remote village or industrial areas. The decision variables included in the optimization process are the power to install of PV array and wind turbine, battery bank number and diesel generator rated power.