Aymen Chaouachi

Multiobjective Intelligent Energy Management for a Microgrid

In this paper, a generalized formulation for intelligent energy management of a microgrid is proposed using artificial intelligence techniques jointly with linear-programming-based multiobjective optimization. The proposed multiobjective intelligent energy management aims to minimize the operation cost and the environmental impact of a microgrid, taking into account its preoperational variables as future availability of renewable energies and load demand (LD). An artificial neural network ensemble is developed to predict 24-h-ahead photovoltaic generation and 1-h-ahead wind power generation and LD. The proposed machine learning is characterized by enhanced learning model and generalization capability. The efficiency of the microgrid operation strongly depends on the battery scheduling process, which cannot be achieved through conventional optimization formulation. In this paper, a fuzzy logic expert system is used for battery scheduling. The proposed approach can handle uncertainties regarding to the fuzzy environment of the overall microgrid operation and the uncertainty related to the forecasted parameters. The results show considerable minimization on operation cost and emission level compared to literature microgrid energy management approaches based on opportunity charging and Heuristic Flowchart (HF) battery management.

Three Control Strategies to Improve the Microgrid Transient Dynamic Response During Isolated Mode: A Comparative Study

The necessity to solve global warming problems by reducing CO2 emission in the electricity generation field had led to increasing interest in microgrids (MGs), particularly those containing the renewable sources such as solar and wind generation. Wind speed fluctuations cause high variations in the output power of a wind turbine which cause fluctuations in frequency and voltage of the MG during islanding mode and originate stability problems. In this paper, three techniques are proposed for solving and reducing the consequences of this problem. In the first technique, we develop a new fuzzy logic pitch angle controller. In the second technique, we design an energy-storage ultracapacitor which directly smoothes the output power of the wind turbine and enhances the performance of the MG during the islanding mode. In the third technique, storage batteries are used to support the MG in the islanding mode.

Neural Network Ensemble-Based Solar Power Generation Short-Term Forecasting

This paper presents the applicability of artificial neural networks for 24 hour ahead solar power generation forecasting of a 20 kW photovoltaic system, the developed forecasting is suitable for a reliable Microgrid energy management. In total four neural networks were proposed, namely: multi-layred perceptron, radial basis function, recurrent and a neural network ensemble consisting in ensemble of bagged networks. Forecasting reliability of the proposed neural networks was carried out in terms forecasting error performance basing on statistical and graphical methods. The experimental results showed that all the proposed networks achieved an acceptable forecasting accuracy. In term of comparison the neural network ensemble gives the highest precision forecasting comparing to the conventional networks. In fact, each network of the ensemble over-fits to some extent and leads to a diversity which enhances the noise tolerance and the forecasting generalization performance comparing to the conventional networks. Keywords—Neural network ensemble, Solar power generation, 24 hour forecasting, Comparative study.

Microgrid efficiency enhancement based on neuro-fuzzy MPPT control for Photovoltaic generator

In terms of optimal Microgrid (MG) control, the output power of a non-dispatchable Distributed Generation (DG) as a Photovoltaic (PV) system need to be controlled based on the optimal operating condition of its primary energy source by the mean of a Maximum Power Point Tracking (MPPT) to extract the potential maximum power which is nonlinearly depending on the weather conditions. In this work we presented a new methodology for this purpose using an approach based on a neuro-fuzzy generalized method to estimate the reference voltage (V*pv) that guaranties an optimal power transfer between the DG and the microgrid. The neuro-fuzzy network is composed of a fuzzy rule-based classifier and three Radial Basis Functions Neural Networks (RBFNN). Inputs of the network (irradiance and temperature) are classified before they are fed into the appropriated network for either training or estimation process. Simulation results under several rapid irradiance variations proved that the proposed MPPT control for the PV generator achieved high energy conversion efficiency comparing to a Normal Operating Power (NOP) (when the PV generator is directly coupled to the inverter, without MPPT control).

Micro-grid Dynamic Response Enhancement Using New Proportional Integral Wind Turbine Pitch Controller and Neuro-fuzzy Photovoltaic Maximum Power Point Tracking Controller

Abstract Power deregulation and shortage of transmission capacities have led to increased interest in distributed generators, especially renewable sources. In this study, a complete model is developed that can simulate in detail the transient dynamic performance of the micro-grid during and subsequent to the islanding process. Wind speed fluctuations cause high fluctuations in output power of a wind turbine, resulting in fluctuations in frequency and voltages of the micro-grid during islanding mode. In this study, new proportional integral pitch controller is proposed to smooth the output power of a wind turbine to reduce frequency and voltage fluctuations. The proposed proportional integral controller is compared with the conventional proportional integral controller that is used for wind turbine power control. The obtained results proved that the proposed controller is effective for the improvement of micro-grid performances. In addition, a neuro-fuzzy controller is also proposed for obtaining maximum power point tracking of the photovoltaic panels installed in the micro-grid. All models and controllers are developed on a Matlab® Simulink® environment.

Micro-grid transient dynamic response enhancement during and subsequent to huge and multiple disturbances by connecting it with nearby micro-grids

The necessity for solving global warming problems by reducing CO2 emissions in the electricity generation field has led to an increase in interest in micro-grids (MGs), especially one which included renewable sources. This article deals with the connection of two nearby MGs during emergency situations in order to enhance their transient dynamic performance during and subsequent to two huge disturbances under different load conditions of the two MGs. The first disturbance is the two MGs islanding from the main grid followed by failure of a dominant micro source in the first MG. Also, this study introduces a novel neuro-fuzzy maximum power point tracking (MPPT) controller applied to all photovoltaic panels installed in the two MGs. All components and micro sources installed inside the two MGs are dynamically modelled in detail. All micro source models and controllers are built in the Matlab® Simulink® environment.