In recent years, the investment in the wind energy sector has increased in the context of producing green electricity and saving the environment. The installation of small wind turbines (SWTs) represents an actual strategy for meeting energy needs for off-grid systems and certain specialized applications. SWTs are more expensive per kilowatt installed as compared to large-scale wind turbines. Therefore, the main objective of this study is to produce an economical technology for the wind power market offering low-cost SWTs. The idea consists of considering a simple structure of the wind turbine using direct-drive permanent magnet synchronous generator (DDPMSG). DDPMSGs are the most useful machines in the wind energy field thanks to several advantages, such as elimination of noise and maintenance cost due to suppression of the gearbox and absence of the rotor circuit excitation barriers by the presence of the permanent magnets (PMs). Their major downside is the high cost of active materials, especially the PMs. Thus, the improvement of the generator design is treated as being the main component of the considered chain to assure active materials’ mass and cost reduction. The methodology studied aims to explain the approach of the design integrated by optimization of the considered system. It is based on the elaboration of analytical models to find a feasible structure for the system, taking into account the multi-disciplinary analysis. The relevance of these models is validated by the finite element method using 2D MATLAB-FEMM simulation. The models are integrated to elaborate the optimization problem based on a genetic algorithm to improve the cost of the proposed generator by minimizing the mass of its active constructive materials. As an outcome, an optimal solution is offered for the wind generators market, providing a 16% cost reduction.

Due to the stochastic characteristics of wind power generation and following varying demands for load consumption over a planning period, the optimal reconfiguration (OR) of the radial distribution network (RDN) represents a complex problem of a combinatorial nature. This paper evaluates two types of optimal reconfiguration searching for an optimal solution and considering time-varying changes. The first one is a static reconfiguration of RDN (SRRDN) made at a fixed load consumption point and during constant generated renewable power integration. The second one is a dynamic reconfiguration of RDN (DRRDN) made following a stochastic integration of wind energy (WTDG) and/or variation in load demand characteristics. In total, five scenarios are investigated in order to evaluate optimal reconfiguration of RDN (ORRDN) with the aim of reducing total active power losses (TAPL), improving the voltage profile (VP), and minimizing switches’ change costs (SCC). To deal with this, a hybrid optimization technique (SAMPSO) combining the simulated annealing algorithm (SA) with a modified particle swarm optimization (MPSO) is undertaken. This hybrid method coupled with the MATPOWER toolbox is tested on the standard IEEE 69-bus RDN through both SRRDN and DRRDN. The results show the effectiveness of this improved reconfiguration procedure for enhancing the test system performance. A comparison between the proposed optimization method and previous findings’ methods is undertaken in this work. The obtained results proved the superiority and effectiveness of the SAMPSO method in solving the SRRDN and DRRDN problems.

Advances in PV technology have given rise to the increasing integration of PV-based distributed generation (PVDG) systems into distribution systems to mitigate the dependence on one power source and alleviate the global warming caused by traditional power plants. However, high power output coming from intermittent PVDG can create reverse power flow, which can cause an increase in system power losses and a distortion in the voltage profile. Therefore, the appropriate placement and sizing of a PVDG coupled with an energy storage system (ESS) to stock power during off-peak hours and to inject it during peak hours are necessary. Within this context, a new methodology based on an optimal power flow management strategy for the optimal allocation and sizing of PVDG systems coupled with battery energy storage (PVDG-BES) systems is proposed in this paper. To do this, this problem is formulated as an optimization problem where total real power losses are considered as the objective function. Thereafter, a new optimization technique combining a genetic algorithm with various chaotic maps is used to find the optimal PVDG-BES placement and size. To test the robustness and applicability of the proposed methodology, various benchmark functions and the IEEE 14-bus distribution network under fixed and intermittent load profiles are used. The simulation results prove that obtaining the optimal size and placement of the PVDG-BES system based on an optimal energy management strategy (EMS) presents better performance in terms of power losses reduction and voltage profile amelioration. In fact, the total system losses are reduced by 20.14% when EMS is applied compared with the case before integrating PVDG-BES.