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

Stand-alone solar-pv hydrogen energy systems incorporating reverse osmosis

The world’s increasing energy demand means the rate at which fossil fuels are consumed has increased resulting in greater carbon dioxide emissions. For many small (marginalised) or coastal communities, access to potable water is limited alongside good availability of renewable energy sources (solar or wind). One solution is to utilise small-scale renewably powered stand-alone energy systems to help supply power for everyday utilities and to operate desalination systems serving potable water (drinking) needs reducing diesel generator dependence. In such systems, on-site water production is essential so as to service electrolysis for hydrogen generation for Proton Exchange Membrane (PEM) fuel cells. Whilst small Reverse Osmosis (RO) units may function as a (useful) dump load, it also directly impacts the power management of stand-alone energy systems and affects operational characteristics. However, renewable energy sources are intermittent in nature, thus power generation from renewables may not be adequate to satisfy load demands. Therefore, energy storage and an effective Power Management Strategy (PMS) are vital to ensure system reliability. This thesis utilises a combination of experiments and modelling to analyse the performance of renewably powered stand-alone energy systems consisting of photovoltaic panels, PEM electrolysers, PEM fuel cells, batteries, metal hydrides and Reverse Osmosis (RO) under various scenarios. Laboratory experiments have been done to resolve time-resolved characteristics for these system components and ascertain their impact on system performance. However, the main objective of the study is to ascertain the differences between applying (simplistic) predictive/optimisation techniques compared to intelligent tools in renewable energy systems. This is achieved through applying intelligent tools such as Neural Networks and Particle Swarm Optimisation for different aspects that govern system design and operation as well as solar irradiance prediction. Results indicate the importance of device level transients, temporal resolution of available solar irradiance and type of external load profile (static or time-varying) as system performance is affected differently. In this regard, minute resolved simulations are utilised to account for all component transients including predicting the key input to the system, namely available solar resource which can be affected by various climatic conditions such as rainfall. System behaviour is (generally) more accurately predicted utilising Neural Network solar irradiance prediction compared to the ASHRAE clear sky model when benchmarked against measured irradiance data. Allowing Particle Swarm Optimisation (PSO) to further adjust specific control set-points within the systems PMS results in improvements in system operational characteristics compared to using simplistic rule-based design methods. In such systems, increasing energy storage capacities generally allow for more renewable energy penetration yet only affect the operational characteristics up to a threshold capacity. Additionally, simultaneously optimising system size and PMS to satisfy a multi-objective function, consisting of total Net Present Cost and CO2 emissions, yielded lower costs and carbon emissions compared to HOMER, a widely adopted sizing software tool. Further development of this thesis will allow further improvements in the development of renewably powered energy systems providing clean, reliable, cost-effective energy. All simulations are performed on a desktop PC having an Intel i3 processor using either MATLAB/Simulink or HOMER

Training of neural network by using ABC algorithm, PSO and FPA for prediction of gold price

Altın fiyatının tahmini için kullanılan yapay zekâ tekniklerinden biri yapay sinir ağları (YSA)’dır. YSA ile başarılı modeller oluşturmak için başarılı bir eğitim süreci şarttır. Başarılı bir eğitim süreci için başarılı bir eğitim algoritması gereklidir. Bu çalışmada YSA eğitimi için popüler meta-sezgisel algoritmalar olan yapay arı kolonisi (ABC) algoritması, parçacık sürü optimizasyonu (PSO) ve çiçek tozlaşma algoritması (FPA) kullanılmıştır. Ocak 2022 ile Haziran 2022 arasındaki 6 aylık altın fiyatları kullanılmaktadır. Altın verisinin zaman serisi 2 girdiden oluşan veri setlerine dönüştürülmüştür. Altın fiyatının günlük tahmini için ilgili meta sezgisel algoritmalar kullanılarak bu veri seti üzerinde YSA eğitimi gerçekleştirilmiştir. Verilerin %80'i eğitim sürecinde kullanılmıştır. Kalan veriler test sürecine tahsis edilmiştir. Hata ölçüsü olarak ortalama karesel hata (MSE) kullanıldı. Altın fiyatını etkin bir şekilde tahmin edebilmek için farklı ağ yapıları denenmiştir. Altın fiyatının tahmini için ABC algoritması, PSO ve FPA’nın performansları karşılaştırılmıştır. Çalışmanın sınırlılıkları dahilinde ABC algoritmasının performansının PSO ve FPA'ya göre daha etkili olduğu görülmüştür.One of the artificial intelligence techniques used for prediction of gold price is artificial neural networks (ANNs). A successful training process is essential in order to create successful models with an ANN. A successful training algorithm is required for a successful training process. In this study, artificial bee colony (ABC) algorithm, particle swarm optimization (PSO) and flower pollination algorithm (FPA), which are popular meta-heuristic algorithms, are used for ANN training. 6 months gold prices between January 2022 and June 2022 are utilized. The time series of gold data was transformed into data sets consisting of 2 inputs.ANN training was performed on these this dataset by using related meta-heuristic algorithms for daily forecast of gold price. 80% of the data was used in the training process. The remaining data was allocated to the testing process. The mean squared error (MSE) was used as the error metric. Different network structures were tried to predict the gold price effectively. The performances of ABC algorithm, PSO and FPA are compared for prediction of gold price. Within the limitations of the study, it was seen that the performance of ABC algorithm was more effective than PSO and FPA

Simple Simulation of Perturb and Observe MPPT Algorithm on Synchronous Buck Converter

The efficiency of the PV system can be improved by operating the solar panel on its Maximum Power Point (MPP). However, ariations in irradiance and temperature will lead to the shifting of solar panel MPP. To continuously operate the solar panel near its MPP, a tracking algorithm is needed. In this research, a model consisting of a synchronous buck converter and a Maximum Power Point Tracking (MPPT) algorithm will be designed as aMATLAB/Simulink model. Perturb and Observe technique will be used to implement the algorithm into the synchronous buck converter, which will control a 10 W solar panel load so it will operate near its MPP. Results show that the PV system model can track the Solar Panel MPP in various simulated irradiance

Design of a Load Frequency Controller Based on an Optimal Neural Network

Data Availability Statement: Not applicable.A load frequency controller (LFC) is a crucial part in the distribution of a power system network (PSN) to restore its frequency response when the load demand is changed rapidly. In this paper, an artificial neural network (ANN) technique is utilised to design the optimal LFC. However, the training of the optimal ANN model for a multi-area PSN is a major challenge due to its variations in the load demand. To address this challenge, a particle swarm optimization is used to distribute the nodes of a hidden layer and to optimise the initial neurons of the ANN model, resulting in obtaining the lower mean square error of the ANN model. Hence, the mean square error and the number of epochs of the ANN model are minimised to about 9.3886 × 10−8 and 25, respectively. To assess this proposal, a MATLAB/Simulink model of the PSN is developed for the single-area PSN and multi-area PSN. The results show that the LFC based on the optimal ANN is more effective for adjusting the frequency level and improves the power delivery of the multi-area PSN comparison with the single-area PSN. Moreover, it is the most reliable for avoiding the fault condition whilst achieving the lowest time multiplied absolute error about 3.45 s when compared with the conventional ANN and PID methods.Funding: This research received no external funding

Maximum Power Point Tracking Techniques for Photovoltaic Panel: A Review and Experimental Applications

This article contains a review of essential control techniques for maximum power point tracking (MPPT) to be applied in photovoltaic (PV) panel systems. These devices are distinguished by their capability to transform solar energy into electricity without emissions. Nevertheless, the efficiency can be enhanced provided that a suitable MPPT algorithm is well designed to obtain the maximum performance. From the analyzed MPPT algorithms, four different types were chosen for an experimental evaluation over a commercial PV system linked to a boost converter. As the reference that corresponds to the maximum power is depended on the irradiation and temperature, an artificial neural network (ANN) was used as a reference generator where a high accuracy was achieved based on real data. This was used as a tool for the implementation of sliding mode controller (SMC), fuzzy logic controller (FLC) and model predictive control (MPC). The outcomes allowed different conclusions where each controller has different advantages and disadvantages depending on the various factors related to hardware and software.This research was funded by the Basque Government through the project EKOHEGAZ (ELKARTEK KK-2021/00092), by the Diputación Foral de Álava (DFA), through the project CONAVANTER, and by the UPV/EHU, through the project GIU20/063

Modelling and optimisation of integrated urban energy systems for both heating and power

Taking into account the rapid increase of renewable energy power generation in the UK, the electrified heating represents an attractive solution for decarbonisation of heat in the long term. However, this will significantly increase the peak power demand in winter and bring further challenges to the grid. Therefore, this work aims to model and optimise a district-level multi-vector integrated energy system for both heating and power through technical and market analysis of using a variety of local renewable energy resources for electricity and heat. In this thesis, the integrated urban energy system is modelled and optimised in multi processes. As a target system, the heating and electricity demand of the University of Glasgow is used as a case study. In order to model the heating and electricity demand under different weather profiles, the heat demand of the buildings is modelled in an engineering model and a statistical model respectively to predict the hourly heat demand according to weather conditions; while the electricity demand is modelled considering both the building baseload and occupancy rate. In heat demand modelling, in order to distinguish the heat demand of each building from the data of whole campus provided by the Energy Center when the detailed building parameters are unknown, this work uses a bottom-up building energy model, which uses physical process of heat transfer to simulate the space heating of buildings, and proposes a Bayesian-based calibration method to calibrate the building parameters in the model. The results show that the Bayesian approach-based emulator performs better with fewer calibration times to find the optimal point, which is relable and efficient to calibrate the thermal parameters in building energy models. Due to the complexity of building a bottom-up building energy model, it is not easy to expand the model to larger areas or add more building samples in the model. Therefore, this work also builds a more general statistical model that can predict the heat demand of different types of buildings simply by giving weather conditions and building characteristics. This work uses artificial neural networks (ANN) technology to simulate the nonlinear relationship between weather conditions, building characteristics and heat demand. In order to improve the training efficiency of ANN, a new sensitivity analysis method is proposed to analyse the correlation between input variables and detect and remove the variables with low importance and the variables that have high importance but contain duplicated features. The result shows the proposed method can re duce training time by around 20% while achieving the same training and prediction performance compared with the traditional sensitivity analysis method. In the electricity demand modelling, the impact of randomness of occupants’ activity on power demand forecasting for buildings has been a difficult problem. In order to solve this problem, this work proposes two approaches for fitting and predicting the electricity demand of office buildings by splitting the time horizon for different occupancy rates. The first proposed approach splits the electricity demand data into fixed time periods and using linear regression approach to fit the building baseload and occupancy rate. The second proposed approach uses the ANN and fuzzy logic techniques to fit the building baseload, peak load, and occupancy rate with multi-variables of weather variables. The result shows that the proposed methods reduce the prediction error of electricity demand by 30% and 55% compared with the conventional ANN approach. To study the impact of electrified heating on buildings and the grid, an Integrated Energy Network (IEN) is established that includes the heat and electric demands of buildings, as well as the generation of local renewable resources and energy storage techniques. In order to rationally plan this new type of IEN based on electric heat pump (HP), this work studies and develops a particle swarm optimisation (PSO) algorithm-based optimisation size method to maximize the decarbonisation on building heating under limited investment cost. According to different source of electric driven, the IEN can be designed as a grid powered HP based heating system and a grid independent renewable heating system (RHS). For the grid powered IEN, this work formulates an operating scheme based on different electricity tariffs to reduce the operational cost of grid power. For the grid independent RHS, this work uses the PSO algorithm to optimise the size of local renewable resources, heat pumps and storage equipment based on the annual investment cost to minimise the total CO2 emission and reduce the operational cost of natural gas. This work provides a feasible solution for how to invest in RHS to replace the existing gas boiler/CHP based heating system. In summary, the significance of this study is to use of local renewable energy sources in electric heating taking into account the local weather conditions and the demand of heat and electricity to reduce carbon emissions in heating and electricity supply

Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications

This book is a Special Issue Reprint edited by Prof. Massimo Vitelli and Dr. Luigi Costanzo. It contains original research articles covering, but not limited to, the following topics: maximum power point tracking techniques; forecasting techniques; sizing and optimization of PV components and systems; PV modeling; reconfiguration algorithms; fault diagnosis; mismatching detection; decision processes for grid operators

Modelling and controlling of integrated photovoltaic-module and converter systems for partial shading operation using artificial intelligence

The thesis has three main themes: analysis and optimal design of Cuk DC-DC converters; integration of Cuk DC-DC converters with photovoltaic (PV) modules to improve operation during partial shading; and an artificial intelligence model for the PV module, permitting an accurate maximum power point (MPP) tracking in the integrated system. The major contribution of the thesis is the control of an integrated photovoltaic module and DC-DC converter configuration for obtaining maximum power generation under non-uniform solar illumination. In place of bypass diodes, the proposed scheme embeds bidirectional Cuk DC-DC converters within the serially connected PV modules. A novel control scheme for the converters has been developed to adjust their duty ratios, enabling all the PV modules to operate at the MPPs corresponding to individual lighting conditions. A detailed analysis of a step-down Cuk converter has been carried out leading to four transfer functions of the converter in two modes, namely variable input - constant output voltage, and variable output - constant input voltage. The response to switch duty ratio variation is shown to exhibit a non-minimum phase feature. A novel scheme for selecting the circuit components is developed using the criteria of suppressing input current and output voltage ripple percentages at a steady state, and minimising the time integral of squared transient response errors. The designed converter has been tested in simulation and in practice, and has been shown to exhibit improved responses in both operating modes. A Neuro-Fuzzy network has been applied in modelling the characteristics of a PV module. Particle-Swarm-Optimisation (PSO) has been employed for the first time as the training algorithm, with which the tuning speed has been improved. The resulting model has optimum compactness and interpretability and can predict the MPPs of individual PV modules in real time. Experimental data have confirmed its improved accuracy. The tuned Neuro-Fuzzy model has been applied to a practical PV power generation system for MPP control. The results have shown an average error of 1.35% compared with the maximum extractable power of the panel used. The errors obtained, on average, are also about four times less than those using the genetic-algorithm-based model proposed in a previous research. All the techniques have been incorporated in a complete simulation system consisting of three PV panels, one boost and two bidirectional Cuk DC-DC converters. This has been compared under the same weather conditions as the conventional approach using bypass diodes. The results have shown that the new system can generate 32% more power