New battery model considering thermal transport and partial charge stationary effects in photovoltaic off-grid applications

The optimization of the battery pack in an off-grid Photovoltaic application must consider the minimum sizing that assures the availability of the system under the worst environmental conditions. Thus, it is necessary to predict the evolution of the state of charge of the battery under incomplete daily charging and discharging processes and fluctuating temperatures over day-night cycles. Much of previous development work has been carried out in order to model the short term evolution of battery variables. Many works focus on the on-line parameter estimation of available charge, using standard or advanced estimators, but they are not focused on the development of a model with predictive capabilities. Moreover, normally stable environmental conditions and standard charge-discharge patterns are considered. As the actual cycle-patterns differ from the manufacturer's tests, batteries fail to perform as expected. This paper proposes a novel methodology to model these issues, with predictive capabilities to estimate the remaining charge in a battery after several solar cycles. A new non-linear state space model is proposed as a basis, and the methodology to feed and train the model is introduced. The new methodology is validated using experimental data, providing only 5% of error at higher temperatures than the nominal one

POWER CONDITIONING UNIT FOR SMALL SCALE HYBRID PV-WIND GENERATION SYSTEM

Small-scale renewable energy systems are becoming increasingly popular due to soaring fuel prices and due to technological advancements which reduce the cost of manufacturing. Solar and wind energies, among other renewable energy sources, are the most available ones globally. The hybrid photovoltaic (PV) and wind power system has a higher capability to deliver continuous power with reduced energy storage requirements and therefore results in better utilization of power conversion and control equipment than either of the individual sources. Power conditioning units (p.c.u.) for such small-scale hybrid PV-wind generation systems have been proposed in this study. The system was connected to the grid, but it could also operate in standalone mode if the grid was unavailable. The system contains a local controller for every energy source and the grid inverter. Besides, it contains the supervisory controller. For the wind generator side, small-scale vertical axis wind turbines (VAWTs) are attractive due to their ability to capture wind from different directions without using a yaw. One difficulty with VAWTs is to prevent over-speeding and component over-loading at excessive wind velocities. The proposed local controller for the wind generator is based on the current and voltage measured on the dc side of the rectifier connected to the permanent magnet synchronous generator (PMSG). Maximum power point tracking (MPPT) control is provided in normal operation under the rated speed using a dc/dc boost converter. For high wind velocities, the suggested local controller controls the electric power in order to operate the turbine in the stall region. This high wind velocity control strategy attenuates the stress in the system while it smoothes the power generated. It is shown that the controller is able to stabilize the nonlinear system using an adaptive current feedback loop. Simulation and experimental results are presented. The PV generator side controller is designed to work in systems with multiple energy sources, such as those studied in this thesis. One of the most widely used methods to maximize the output PV power is the hill climbing technique. This study gives guidelines for designing both the perturbation magnitude and the time interval between consecutive perturbations for such a technique. These guidelines would improve the maximum power point tracking efficiency. According to these guidelines, a variable step MPPT algorithm with reduced power mode is designed and applied to the system. The algorithm is validated by simulation and experimental results. A single phase H-bridge inverter is proposed to supply the load and to connect the grid. Generally, a current controller injects active power with a controlled power factor and constant dc link voltage in the grid connected mode. However, in the standalone mode, it injects active power with constant ac output voltage and a power factor which depends on the load. The current controller for both modes is based on a newly developed peak current control (p.c.c.) with selective harmonic elimination. A design procedure has been proposed for the controller. Then, the method was demonstrated by simulation. The problem of the dc current injection to the grid has been investigated for such inverters. The causes of dc current injection are analyzed, and a measurement circuit is then proposed to control the inverter for dc current injection elimination. Characteristics of the proposed method are demonstrated, using simulation and experimental results. At the final stage of the study, a supervisory controller is demonstrated, which manages the different operating states of the system during starting, grid-connected and standalone modes. The operating states, designed for every mode, have been defined in such a hybrid model to allow stability and smooth transition between these states. The supervisory controller switches the system between the different modes and states according to the availability of the utility grid, renewable energy generators, the state of charge (SOC) of energy storage batteries, and the load. The p.c.u. including the supervisory controller has been verified in the different modes and states by simulation

Optimisation of stand-alone hydrogen-based renewable energy systems using intelligent techniques

Wind and solar irradiance are promising renewable alternatives to fossil fuels due to their availability and topological advantages for local power generation. However, their intermittent and unpredictable nature limits their integration into energy markets. Fortunately, these disadvantages can be partially overcome by using them in combination with energy storage and back-up units. However, the increased complexity of such systems relative to single energy systems makes an optimal sizing method and appropriate Power Management Strategy (PMS) research priorities. This thesis contributes to the design and integration of stand-alone hybrid renewable energy systems by proposing methodologies to optimise the sizing and operation of hydrogen-based systems. These include using intelligent techniques such as Genetic Algorithm (GA), Particle Swarm Optimisation (PSO) and Neural Networks (NNs). Three design aspects: component sizing, renewables forecasting, and operation coordination, have been investigated. The thesis includes a series of four journal articles. The first article introduced a multi-objective sizing methodology to optimise standalone, hydrogen-based systems using GA. The sizing method was developed to calculate the optimum capacities of system components that underpin appropriate compromise between investment, renewables penetration and environmental footprint. The system reliability was assessed using the Loss of Power Supply Probability (LPSP) for which a novel modification was introduced to account for load losses during transient start-up times for the back-ups. The second article investigated the factors that may influence the accuracy of NNs when applied to forecasting short-term renewable energy. That study involved two NNs: Feedforward, and Radial Basis Function in an investigation of the effect of the type, span and resolution of training data, and the length of training pattern, on shortterm wind speed prediction accuracy. The impact of forecasting error on estimating the available wind power was also evaluated for a commercially available wind turbine. The third article experimentally validated the concept of a NN-based (predictive) PMS. A lab-scale (stand-alone) hybrid energy system, which consisted of: an emulated renewable power source, battery bank, and hydrogen fuel cell coupled with metal hydride storage, satisfied the dynamic load demand. The overall power flow of the constructed system was controlled by a NN-based PMS which was implemented using MATLAB and LabVIEW software. The effects of several control parameters, which are either hardware dependent or affect the predictive algorithm, on system performance was investigated under the predictive PMS, this was benchmarked against a rulebased (non-intelligent) strategy. The fourth article investigated the potential impact of NN-based PMS on the economic and operational characteristics of such hybrid systems. That study benchmarked a rule-based PMS to its (predictive) counterpart. In addition, the effect of real-time fuel cell optimisation using PSO, when applied in the context of predictive PMS was also investigated. The comparative analysis was based on deriving the cost of energy, life cycle emissions, renewables penetration, and duty cycles of fuel cell and electrolyser units. The effects of other parameters such the LPSP level, prediction accuracy were also investigated. The developed techniques outperformed traditional approaches by drawing upon complex artificial intelligence models. The research could underpin cost-effective, reliable power supplies to remote communities as well as reducing the dependence on fossil fuels and the associated environmental footprint