Dhaker Abbes

Original article: Life cycle cost, embodied energy and loss of power supply probability for the optimal design of hybrid power systems

Stand-alone hybrid renewable energy systems are more reliable than one-energy source systems. However, their design is crucial. For this reason, a new methodology with the aim to design an autonomous hybrid PV-wind-battery system is proposed here. Based on a triple multi-objective optimization (MOP), this methodology combines life cycle cost (LCC), embodied energy (EE) and loss of power supply probability (LPSP). For a location, meteorological and load data have been collected and assessed. Then, components of the system and optimization objectives have been modelled. Finally, an optimal configuration has been carried out using a dynamic model and applying a controlled elitist genetic algorithm for multi-objective optimization. This methodology has been applied successfully for the sizing of a PV-wind-battery system to supply at least 95% of yearly total electric demand of a residential house. Results indicate that such a method, through its multitude Pareto front solutions, will help designers to take into consideration both economic and environmental aspects.

Lifetime estimation tool of lead–acid batteries for hybrid power sources design

Abstract Generally, battery lifespan depends on the number of cycles and depth of discharge (DOD). Nevertheless, in a renewable hybrid power system, charge and discharge cycles are random and not regular. Therefore, it is important to develop an aging model suitable to this case. Thus, in this paper, a pertinent way for aging lead–acid batteries connected to a stand-alone multi-source renewable system has been developed. It is based on the Rain Flow method for counting cycles and considers instantaneous DOD and average temperature. In fact, for each functioning year, a classification of the number of cycles according to the DOD is done. Then, based on these data, the battery degradation rate is estimated so that it is possible to draw conclusions about battery lifespan. The method has been successfully applied to a multi-source power system simulated dynamically under Matlab/Simulink. This last takes into account with good accuracy several inputs and elements such as sun irradiation, wind speed, load profile, photovoltaic generator, wind turbine, and diesel generator. Results show the influence of the DOD and the batteries nominal capacity on their lifespan. A mean of eight years’ life is detected. Finally, a reasonable over-sizing may favor battery longevity.

Real time supervision for a hybrid renewable power system emulator

Abstract This paper is focused on the design and the implementation of a hybrid PV-wind power system with batteries. It aims to emulate the behavior of a hybrid power system in order to face load consumption variations. Final system includes relevant contributions such as quality of emulator (a large number of parameters are considered); capacity to study various impacts simultaneously, a fast dynamic and a set of experimental tests that have been achieved and validated with a test bench. Moreover, a relevant supervision strategy based on currents control and batteries State Of Charge (SOC) estimation has been successfully performed despite simplicity of converter controls.

Estimation of wind turbine and solar photovoltaic energy using variant sampling intervals

In this paper, we estimate renewable energy from wind and solar for different sites. Two methods are applied in case of wind energy: Weibull distribution and direct integration methodology. In additional, influence of the data sampling interval is studied. Results show that the computational method based on the integration of the power is more accurate. Also, it is shown that the hourly time resolution provides satisfactory accuracy in wind and solar resource estimation, in comparison with one minutes resolution. Results allow better design for hybrid systems (wind and photovoltaic energy). Moreover, Data acquisitions are minimized. A shorter time of treatment and less expensive measurement equipments are required.

Operating power reserve quantification through PV generation uncertainty analysis of a microgrid

Due to renewable energy sources (RES) variable nature and their wide integration into power systems, setting an adequate operating power reserve is important to compensate unpredictable imbalance between generation and consumption. However, this power reserve should be ideally minimized to reduce system cost with a satisfying security level. Although many forecasting methodologies have been developed for forecasting energy generation and load demand, management tools for decision making of operating reserve are still needed. This paper deals with power reserve quantification through uncertainty analysis with a photovoltaic (PV) generator. Indeed, using an artificial neural network based predictor (ANNs), PV power and load have been forecasted 24 hours ahead, and also forecasting errors have been predicted. Through forecasting uncertainty analysis, the power reserve quantification is calculated according to various risk indexes.

Solar radiation forecasting using artificial neural network for local power reserve

Renewable energy sources have a variable nature and are greatly depending on weather conditions. The load is also uncertain. Hence, it is necessary to use power reserve equipment to compensate unforeseen imbalances between production and load. However, this power reserve must be ideally minimized in order to reduce the system cost with a satisfying security level. The quantification of power reserve could be calculated through analysis of forecasting uncertainty errors of both generation and load. Therefore, in this paper, a back propagation artificial neural network approaches is derived to forecast solar radiations. Predictions have been analyzed according to weather classification. Some error indexes have been introduced to evaluate forecasting models performances and calculate the prediction accuracy. Forecasting results can be used for decision making of power reserve for renewable energy sources system with some probability or possibility methods.

Design and operation optimization of a hybrid railway power substation

Railway traffic increases and electricity market liberalization constrain the railway actors to consider new solutions to handle the energy consumption. Hence, a technology change in the railway electrical systems is considered through the integration of renewable energy sources and storage units. In this context, a relevant methodology is proposed here for optimal design and operation analysis of railway hybrid power substations. This method is useful for the analysis and improvement of future railway power network efficiency.

Classical vector, first-order sliding-mode and high-order sliding-mode control for a grid-connected variable-speed wind energy conversion system: A comparative study

This article proposes a comparative study between different control strategies of a wind energy conversion system. It aims to guarantee a robust control strategy which gives a good performance despite the external disturbances. Studied system comprises a wind turbine, a permanent magnet synchronous generator, and two converters linked by a DC bus. The whole is connected to the grid through a resistor–inductor filter. A classical vector control based on proportional–integral controller is applied to our system. Owing to the sensitivity of this control against external disturbances, a control strategy using first-order sliding mode has been proposed. This strategy provides good performance, such as insensitivity to non-linearity of system. Yet, the theory of first sliding mode has faced the problem of chattering, which proved to be a major drawback. To overcome this problem, a control strategy using sliding mode of higher order was implemented on the basis of the super-twisting algorithm.

Day-ahead optimal operational and reserve power dispatching in a PV-based urban microgrid

Accurate sizing of power reserve (PR) due to inaccurate forecast of both renewable energy sources (RES) and load demand in a microgrid can provide substantial cost reductions. This paper proposes two strategies to dispatch the PR into different power generators. The first one uses only micro gas turbine (MGT). The second one uses MGTs plus PV based active generators (AGs). The latter enables the RES to cover the system load demand and PR for some period during a day. To implement those methods, firstly an urban microgrid with PV power generators is introduced. Then, day-ahead optimal planning with dynamic programming (DP) for unit commitment problem (UCP) is applied under several non-linear constraints. Finally, a case study application has been completed to verify the proposed methods.

Multi-criteria fuzzy-logic optimized supervision for hybrid railway power substations

Renewable energy sources and storage units’ integration in the railway power substations is an alternative solution to handle the energy consumption, due to railway traffic increase and electricity market liberalization. To integrate this technology change in the railway network, an adapted energy management system has to be established. However, when considering only energy efficiency aspects on the energy management strategy, an economical viable solution cannot be ensured. This paper proposes a supervision strategy based on multi-criteria approach including energetic, environmental and economic constraints. The energy management objectives such as reducing the network power demand, favoring local renewable consumption and ensuring storage availability are treated in different time levels. Economic aspects are first integrated in predictive mode based on forecast data. Then a supervision strategy is developed based on fuzzy logic approach and graphical tool to build it. An optimization study of the supervision strategy is proposed in order to conclude on system performance. Simulation results are discussed for different scenarios cases and the reaction of the hybrid railway power substation is detailed. Results show that this methodology can be successfully applied for hybrid systems energy management in order to improve their energy efficiency.