Current status of research on optimum sizing of stand-alone hybrid solar–wind power generation systems

Maximizing performance of a grid-connected photovoltaic (PV)-fuel cell hybrid system by use of a two-loop controller is discussed. One loop is a neural network controller for maximum power point tracking, which extracts maximum available solar power from PV arrays under varying conditions of insolation, temperature, and system load. A real/reactive power controller (RRPC) is the other loop. The RRPC achieves the system's requirements for real and reactive powers by controlling incoming fuel to fuel cell stacks as well as switching control signals to a power conditioning subsystem. Results of time-domain simulations prove not only the effectiveness of the proposed computer models of the two-loop controller but also its applicability for use in stability analysis of the hybrid power plant.

Two-loop controller for maximizing performance of a grid-connected photovoltaic-fuel cell hybrid power plant

This paper analyzes the minimum energy capacity ratings that an energy-storage (ES) system should accomplish in order to achieve a defined constant power production in a photovoltaic (PV) power plant. ES is a key issue for the further integration of intermittent and stochastic renewable energy sources, which are not currently dispatchable due to their dependence on real-time weather conditions, as is the case of PV technology. This paper proposes and describes an energy management strategy (EMS) for operating PV power plants with ES in the future. The goal of this EMS is to endow these power plants with a constant production that can be controlled and hence traded on electricity markets. This paper presents simulated results on the percentage of time throughout the year during which a large-scale PV+ES plant operates properly with different ES ratings and according to different configurations of this EMS. Finally, a test bench was developed, and experimental results validating this EMS were obtained.

Evaluation of Storage Energy Requirements for Constant Production in PV Power Plants

A simulation model for predicting the performance of a solar photovoltaic system with alternating current loads

Condition monitoring can reduce machine breakdown losses, increase productivity and operation safety, and therefore deliver significant benefits to many industries. The emergence of wireless sensor networks (WSNs) with smart processing ability play an ever-growing role in online condition monitoring of machines. WSNs are cost-effective networking systems for machine condition monitoring. It avoids cable usage and eases system deployment in industry, which leads to significant savings. Powering the nodes is one of the major challenges for a true WSN system, especially when positioned at inaccessible or dangerous locations and in harsh environments. Promising energy harvesting technologies have attracted the attention of engineers because they convert microwatt or milliwatt level power from the environment to implement maintenance-free machine condition monitoring systems with WSNs. The motivation of this review is to investigate the energy sources, stimulate the application of energy harvesting based WSNs, and evaluate the improvement of energy harvesting systems for mechanical condition monitoring. This paper overviews the principles of a number of energy harvesting technologies applicable to industrial machines by investigating the power consumption of WSNs and the potential energy sources in mechanical systems. Many models or prototypes with different features are reviewed, especially in the mechanical field. Energy harvesting technologies are evaluated for further development according to the comparison of their advantages and disadvantages. Finally, a discussion of the challenges and potential future research of energy harvesting systems powering WSNs for machine condition monitoring is made.

S. Deambi

Papers

Battery storage for PV power systems: An overview